Method and system for kinship assessment of missing persons and disaster / conflicting victims

Abstract

In certain aspects, the present disclosure relates to performing DNA-based kinship analyses that involve analyzing between 2,000 to 50,000 SNPs, including sample preparation and sequencing techniques and methods that can be used to calculate the degree of relationship of a DNA profile from a person of interest, such as a missing person or a victim of a conflict or disaster, to one or more reference DNA profiles in a set of reference DNA profiles, and to determine the degree of relationship between the DNA profiles and one or more reference DNA profiles in a set of reference DNA profiles. The reference DNA profile set includes at least one DNA profile from a relative of the person of interest.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Application No. 63 / 398,512, filed on August 16, 2022, entitled “METHODS AND SYSTEMS FOR KINSHIP VALUATION FOR MISSING PERSONS AND DISASTER / CONFLICT VICTIMS,” and U.S. Provisional Application No. 63 / 445,541, filed on February 14, 2023, entitled “METHODS AND SYSTEMS FOR KINSHIP EVALUATION FOR MISSING PERSONS AND DISASTER / CONFLICT VICTIMS,” the contents of which are incorporated herein by reference in their entirety. Technical Field

[0003] The present disclosure relates in some aspects to methods and systems for DNA-based kinship assessment of persons of interest, such as missing persons and victims of conflict and disaster. Background Art

[0004] Current methods for generating DNA profiles for genetic database comparison include genotyping using dense SNP microarrays and whole genome sequencing (WGS), followed by associating the evidence sample with distant relatives in the database, which requires large and high-quality DNA samples and is not suitable for familial searching or forensic purposes. Forensic case samples are often small in number and low in quality, and often require querying publicly accessible genetic databases to identify familial relationships. However, in the case of missing persons or victims of disasters or conflicts, family members may be hesitant to provide genetic samples that would help identify such persons if their genetic samples were uploaded to publicly accessible databases. Therefore, a new and improved method is needed to generate DNA-based profiles that allow identification of familial relationships of persons of interest, such as missing persons or victims of disasters or conflicts, without the use of publicly accessible genetic databases. Summary of the invention

[0005] Provided herein is a method for performing DNA-based kinship analysis, comprising: providing a nucleic acid sample from a person of interest, amplifying the nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences, wherein the plurality of target sequences collectively include a plurality of single nucleotide polymorphisms (SNPs) of at least between 2,000 and 50,000 or about between 2,000 and 50,000, thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions, generating a nucleic acid library from the amplification products, sequencing the nucleic acid library generated from the amplification products, analyzing the sequence of the amplification products, determining the genotypes of a plurality of SNPs, thereby generating a DNA profile, and calculating the degree of relationship of the DNA profile to one or more reference DNA profiles, wherein the one or more reference DNA profiles are contained in a reference DNA profile set, wherein the reference DNA profile set includes one or more reference DNA profiles from relatives of the person of interest.

[0006] Also provided herein is a method for performing DNA-based kinship analysis, comprising: providing a nucleic acid sample from a person of interest, amplifying the nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences, wherein the plurality of target sequences collectively include a plurality of single nucleotide polymorphisms (SNPs) of at least between 2,000 and 50,000 or approximately between 2,000 and 50,000, thereby generating an amplification product, wherein the amplification is performed in one or more multiplex PCR reactions, generating a nucleic acid library from the amplification product, sequencing the nucleic acid library generated from the amplification product, determining the genotypes of a plurality of SNPs, thereby generating a DNA profile, and calculating the degree of relationship of the DNA profile to one or more reference DNA profiles, wherein the one or more reference DNA profiles are contained in a reference DNA profile set, wherein the reference DNA profile set includes one or more reference DNA profiles from relatives of the person of interest.

[0007] In any of these embodiments, sequencing is performed using massively parallel sequencing (MPS). In any of these embodiments, sequencing does not include whole genome sequencing (WGS).

[0008] In any some of these embodiments, the method further comprises generating a family tree comprising DNA profiles related to the one or more DNA profiles.

[0009] Also provided herein is a method for constructing a nucleic acid library of a person of interest, comprising: providing a nucleic acid sample from a person of interest, amplifying the nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences, wherein the plurality of target sequences collectively comprise a plurality of single nucleotide polymorphisms (SNPs) at least between 2,000 and 50,000 or about 2,000 and 50,000, thereby generating a nucleic acid library comprising amplified products, wherein the amplification is performed in one or more multiplex PCR reactions. In some embodiments, the method further comprises a step of sequencing the amplified products to generate a DNA profile of the person of interest.

[0010] The present invention also provides a method for constructing a nucleic acid library of reference DNA samples, comprising: providing a nucleic acid sample from a relative of a person of interest, amplifying the nucleic acid sample using multiple primers that specifically hybridize to multiple target sequences, wherein the multiple target sequences collectively include multiple single nucleotide polymorphisms (SNPs) of at least between 2,000 and 50,000 or approximately between 2,000 and 50,000, thereby generating a nucleic acid library including amplification products, wherein the amplification is performed in one or more multiplex PCR reactions.

[0011] In some embodiments, the relative is a first, second, third, fourth, or fifth degree relative of the person of interest. In any of these embodiments, the relative is a first, second, or third degree relative of the person of interest.

[0012] In any of these embodiments, the nucleic acid sample comprises genomic DNA. In any of these embodiments, the nucleic acid sample comprises one or more enzyme inhibitors. In any of these embodiments, the one or more enzyme inhibitors comprise one or more inhibitors selected from the group consisting of heme, heme, humic acid, indigo, tannic acid, collagen, calcium and hydroxyapatite. In any of these embodiments, the nucleic acid sample comprises low-quality nucleic acid molecules and / or low-quantity nucleic acid molecules. In any of these embodiments, the low-quality nucleic acid molecules are degraded genomic DNA and / or fragmented genomic DNA. In any of these embodiments, the low-quality nucleic acid molecules have a molecular weight of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200. In some embodiments, the nucleic acid sample comprises a degradation index (DI) of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200. In some of these embodiments, the low-quality nucleic acid molecules have a DI of at least 1 and up to or less than 158.3. In some of these embodiments, the nucleic acid sample comprises high-quality nucleic acid molecules. In some of these embodiments, the high-quality nucleic acid molecules have a DI of less than 1.

[0013] In any some of these embodiments, the person of interest is a missing person. In any some of these embodiments, the person of interest is a victim of a disaster or conflict.

[0014] In any of these embodiments, the nucleic acid sample is derived from saliva, blood, semen, hair, teeth, bone, or skin. In any of these embodiments, the nucleic acid sample is derived from saliva, blood, or semen. In any of these embodiments, the nucleic acid sample is derived from bone or hair. In any of these embodiments, the nucleic acid sample is derived from a buccal swab, paper, fabric, or other matrix or object impregnated with saliva, blood, semen, or other body fluids, or containing hair or skin cells.

[0015] In any such embodiments, the nucleic acid sample includes between or about 3 pg to 100 ng of genomic DNA. In any such embodiments, the nucleic acid sample includes between or about 100 pg to 5 ng of genomic DNA, between or about 50 pg to 5 ng of genomic DNA, or between or about 3 pg to 5 ng of genomic DNA.

[0016] In any of these embodiments, the plurality of SNPs include kinship SNPs (kiSNPs). In any of these embodiments, the plurality of SNPs include Y-chromosome SNPs (Y-SNPs). In any of these embodiments, the plurality of SNPs include kiSNPs and Y-SNPs. In any of these embodiments, the plurality of SNPs include kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotypic SNPs (piSNPs), X-chromosome SNPs (X-SNPs), and Y-chromosome SNPs (Y-SNPs). In some of these embodiments, the plurality of SNPs include one or more SNPs selected from the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs. In some such embodiments, at least or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the plurality of SNPs are kinship SNPs.

[0017] In any of these embodiments, the reference DNA profile set includes up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles. In any of these embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest. In any of these embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest, and wherein each of at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference DNA profile set is a first degree relative, a second degree relative, a third degree relative, a fourth degree relative, or a fifth degree relative. In any of these embodiments, at least 50% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest.

[0018] In any of these embodiments, each relative of the person of interest in the reference DNA profile is a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative, or a fifth-degree relative of the person of interest, respectively. In any of these embodiments, each relative of the person of interest in the reference DNA profile is a first-degree relative, a second-degree relative, or a third-degree relative of the person of interest, respectively. In any of these embodiments, the identity of each relative of the person of interest in the reference DNA profile is known. In any of these embodiments, the identity of each of the one or more reference DNA profiles in the reference DNA profile is known. In any of these embodiments, the reference DNA profile is in a database. In some embodiments, the database is not publicly accessible.

[0019] In any of these embodiments, sequencing comprises a sequencing complexity of up to 40 plexes. In any of these embodiments, sequencing comprises a sequencing complexity of up to 32 plexes. In any of these embodiments, sequencing comprises a sequencing complexity of up to 12 plexes to 32 plexes. In any of these embodiments, sequencing comprises a sequencing complexity of 24 plexes to 32 plexes. In any some of such embodiments, the sequencing comprises a sequencing complexity of or about 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, or 35-plex. In any of these embodiments, the sequencing comprises a sequencing complexity of 8 to 16-plex or about 8 to 16-plex for postmortem samples, and / or the sequencing comprises a sequencing complexity of 24 to 40-plex or about 24 to 40-plex for antemortem samples. For postmortem samples, sequencing comprises a sequencing complexity of 8 to 16-plex or about 8 to 16-plex, and / or for antemortem samples, sequencing comprises a sequencing complexity of 24 to 40-plex or about 24 to 40-plex. In any of these embodiments, the sequencing comprises a sequencing complexity of 12-plex or about 12-plex for postmortem samples, and / or the sequencing comprises a sequencing complexity of 32-plex or about 32-plex for antemortem samples. For postmortem samples, sequencing comprises a sequencing complexity of 12-plex or about 12-plex, and / or for antemortem samples, sequencing comprises a sequencing complexity of 32-plex or about 32-plex. In any of these embodiments, the sequencing comprises a sequencing complexity of or about 30-plex, 31-plex, or 32-plex. The sequencing comprises a sequencing complexity of or about 30-plex, 31-plex, or 32-plex.

[0020] In any some of these embodiments, the method further includes identifying a person of interest.

[0021] Also provided herein is a method for calculating a degree of association, comprising: obtaining a DNA profile comprising genotypes of at least between 2,000 and 50,000 SNPs or between about 2,000 and 50,000 SNPs, wherein the DNA profile is from a person of interest; and calculating the degree of relationship of the DNA profile to one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference DNA profile set, wherein the reference DNA profile set includes one or more reference DNA profiles from relatives of the person of interest.

[0022] Also provided herein is a method for calculating a degree of association, comprising: generating a DNA profile comprising genotypes of at least between 2,000 and 50,000 SNPs or between about 2,000 and 50,000 SNPs, wherein the DNA profile is from a person of interest; and calculating the degree of relationship of the DNA profile to one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference DNA profile set, wherein the reference DNA profile set includes one or more reference DNA profiles from relatives of the person of interest.

[0023] In any such embodiments, a kinship model is used to calculate the degree of relationship. In any such embodiments, a kinship model trained using a PCA method is used to calculate the degree of relationship. In some embodiments, the PCA method used to train the kinship model is PCA or involves PCA. In any such embodiments, the PCA method is PC-AiR. In some embodiments, PC-AiR includes the following steps: (1) estimating kinship coefficients between all pairs of samples in a training database - optionally training DNA profiles - where pairs with kinship coefficients > 0.025 are identified as related and pairs with kinship coefficients < -0.025 are identified as ancestrally differentiated; (2) initializing an unrelated sample set including all samples; and (3) iteratively: (i) identifying the set of samples in the unrelated sample set that has the most related samples in the unrelated sample set, designated as X, (ii) identifying the set of samples in X that has the fewest ancestrally differentiated pairs compared to the samples in the unrelated sample set, designated as Y; and (iii) terminating the process if Y has zero samples, or, if Y has at least one sample, randomly selecting a sample from Y to remove from U and repeating starting at step (3)(i).

[0024] In any of these embodiments, the PCA method is an improved PC-Air. In any of these embodiments, the improved PC-AiR includes the following steps: (1) estimating kinship coefficients between all pairs of samples of the training database (optionally training DNA profiles), wherein pairs with kinship coefficients > 0.01 are identified as related, and pairs with kinship coefficients < -0.025 are identified as ancestrally differentiated; (2) removing all DNA profiles with ≥ 5% missing data; and (3) ranking all DNA profiles by identifying each DNA profile with a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles ranked from least to most in the complete database, and ties are broken by the number of ancestrally differentiated DNA profiles ranked from most to least in the complete database. In some embodiments, step (3) involves iteratively traversing the sorted DNA profiles, and for each DNA profile: (i) if the DNA profile does not yet appear in the related sample set, adding it to the unrelated sample set and adding all related DNA profiles to the related sample set, and (ii) if the DNA profile is already in the related sample set, skipping to the next DNA profile and repeating starting at any such (3)(i).

[0025] In any of these embodiments, calculating the degree of relationship includes using PC-Relate to calculate a kinship coefficient. In any of these embodiments, the degree of relationship is calculated by using a DNA profile of the person of interest as an input to PC-Relate. In any of these embodiments, the degree of relationship is calculated by using a kinship model and a DNA profile of the person of interest as input to PC-Relate. In any of these embodiments, one or more reference DNA profiles are also provided as input to PC-Relate.

[0026] In any some of these embodiments, calculating the degree of relatedness comprises calculating a kinship coefficient using a genome-wide kinship algorithm as follows:

[0027]

[0028] wherein the person of interest and the reference DNA profiles in the one or more reference DNA profiles are i and j, is the kinship coefficient, is the estimated allele frequency, s is the SNP among the SNPs typed in both individuals, and g is and g js are the numbers of reference alleles in i and j at SNP s, respectively, and and are the expected allele frequencies calculated from PC-AiR for i and j at SNP s, respectively.

[0029] In any of these embodiments, calculating the degree of relationship includes calculating a likelihood ratio. In some embodiments, calculating the likelihood ratio includes comparing a plurality of SNPs between the DNA profile and one or more reference DNA profiles. In some embodiments, calculating the likelihood ratio includes comparing a set of SNPs between the DNA profile and one or more reference DNA profiles, the set of SNPs including kinship SNPs from within the plurality of SNPs.

[0030] In any some of these embodiments, calculating the likelihood ratio comprises dividing a probability that the DNA profile is associated with a reference DNA profile of the one or more reference DNA profiles by a probability that the DNA profile is not associated with the reference DNA profile based on the genotypes of the plurality of SNPs.

[0031] In any such embodiments, the likelihood ratio (LR) is calculated as follows:

[0032]

[0033] Wherein, D represents the genotype, H r represents the assumption that individuals are correlated, and H u The assumption that individuals are unrelated.

[0034] In any such embodiments, LR is calculated as follows:

[0035]

[0036] Wherein, 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2.

[0037] In any of these embodiments, the person of interest is biologically male, and the method further comprises calculating the likelihood ratio of a shared Y chromosome between the DNA profile and one or more reference DNA profiles. In some embodiments, calculating the likelihood ratio of a shared Y chromosome comprises comparing a SNP set between the DNA profile and one or more reference DNA profiles, the SNP set comprising one or more Y-SNPs.

[0038] In some such embodiments, one or more Y-SNPs are included in a plurality of SNPs. In some embodiments, one or more Y-SNPs include at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84 or 85 Y-SNPs. In some such embodiments, one or more Y-SNPs include 85 Y-SNPs.

[0039] In any some of such embodiments, calculating the likelihood ratio of sharing a Y chromosome includes dividing the probability that the DNA profile shares a Y chromosome with a reference DNA profile in one or more reference DNA profiles by the probability that the DNA profile does not share a Y chromosome with the reference DNA profile based on the genotype of one or more Y-SNPs.

[0040] In any of these embodiments, at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the DNA profiles in the reference DNA profile set are from relatives of missing persons or victims of disasters or conflicts. In any of these embodiments, each DNA profile in the reference DNA profile set is from a relative of a missing person or a victim of a disaster or conflict.

[0041] In any such embodiments, the set of reference DNA profiles includes up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles. In any such embodiments, the set of reference DNA profiles includes up to 100 reference DNA profiles.

[0042] In any such embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest. In any such embodiments, at least 50% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest.

[0043] In any some of these embodiments, each relative of the person of interest in the reference DNA profile is a first degree relative, a second degree relative, a third degree relative, a fourth degree relative, or a fifth degree relative of the person of interest.

[0044] In any of these embodiments, the identity of each relative of the person of interest in the reference DNA profile set is known. In any of these embodiments, the identity of each relative of the person of interest in the DNA reference profile set is known. In any of these embodiments, the identity of each of the one or more reference DNA profiles in the reference DNA profile set is known.

[0045] In any of these embodiments, the reference DNA profile set is in a database. In some embodiments, the database is not publicly accessible. In any of these embodiments, the database is not accessible by a third-party genetics service.

[0046] Also provided herein are nucleic acid libraries constructed using any of the methods described herein.

[0047] Also provided herein are multiple primers that specifically hybridize to multiple target sequences, wherein the multiple target sequences include at least between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) or about 2,000 to 50,000 of the SNPs in a nucleic acid sample from a person of interest, wherein the multiple primers are used in one or more multiplex PCR reactions to amplify the nucleic acid sample to produce amplification products.

[0048] Also provided herein are multiple primers that specifically hybridize to multiple target sequences, wherein the multiple target sequences include at least between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) or about 2,000 to 50,000 of the SNPs in a nucleic acid sample from a person of interest and a nucleic acid sample from one or more reference samples, wherein the one or more reference samples include samples from relatives of the person of interest, and wherein the multiple primers are used in one or more multiplex PCR reactions to amplify the nucleic acid sample from the person of interest and the nucleic acid sample from the one or more reference samples to produce amplification products.

[0049] In any some of these embodiments, the nucleic acid sample from the person of interest includes genomic DNA.

[0050] In any such embodiments, the nucleic acid sample from the person of interest includes one or more enzyme inhibitors. In any such embodiments, the one or more enzyme inhibitors include one or more inhibitors selected from the group consisting of heme, heme, humic acid, indigo, tannic acid, collagen, calcium and hydroxyapatite. In any such embodiments, the nucleic acid sample from the person of interest includes low-quality nucleic acid molecules and / or low-quantity nucleic acid molecules. In certain embodiments, the low-quality nucleic acid molecules are degraded genomic DNA and / or fragmented genomic DNA. In any of these embodiments, the low-quality nucleic acid molecules have a molecular weight of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200. In some embodiments, the nucleic acid molecules of low quality have a degradation index (DI) of at least 1 and up to or less than 158.3. In some embodiments, the nucleic acid sample from the person of interest and / or the nucleic acid sample from one or more reference samples include high quality nucleic acid molecules. In some embodiments, the high quality nucleic acid molecules have a DI of less than 1.

[0051] In any some of these embodiments, the person of interest is a missing person. In any some of these embodiments, the person of interest is a victim of a disaster or conflict.

[0052] In any of these embodiments, the nucleic acid sample from the person of interest is derived from a buccal swab, paper, fabric, or other matrix or object impregnated with saliva, blood or other body fluids, or containing hair or skin cells.

[0053] In any such embodiments, the nucleic acid sample from the person of interest includes genomic DNA between or about 3pg to 100ng. In any such embodiments, the nucleic acid sample from the person of interest includes genomic DNA between or about 100pg to 5ng, genomic DNA between or about 50pg to 5ng, or genomic DNA between or about 3pg to 5ng. In any such embodiments, the nucleic acid sample from the person of interest includes genomic DNA at or about 1ng.

[0054] In any of these embodiments, the plurality of SNPs include kinship SNPs (kiSNPs). In any of these embodiments, the plurality of SNPs include Y chromosome SNPs (Y-SNPs). In any of these embodiments, the plurality of SNPs include kiSNPs and Y-SNPs. In any of these embodiments, the plurality of SNPs include kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotypic SNPs (piSNPs), X chromosome SNPs (X-SNPs), and Y chromosome SNPs (Y-SNPs). In any of these embodiments, the plurality of SNPs include one or more SNPs selected from the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs.

[0055] In any such embodiments, at least or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the plurality of SNPs are kinship SNPs. In any such embodiments, at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the DNA profiles in the reference DNA profile set are from relatives of missing persons or victims of disasters or conflicts.

[0056] In any of these embodiments, each of the one or more reference samples is from a relative of a missing person or a victim of a disaster or conflict. In any of these embodiments, the one or more reference samples include up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference samples. In any of these embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the one or more reference samples are from relatives of the person of interest. In any such embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest, and wherein each of at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set is a first degree relative, a second degree relative, a third degree relative, a fourth degree relative or a fifth degree relative. In any such embodiments, at least 50% of the one or more reference samples are from relatives of the person of interest. In any of these embodiments, each relative of the person of interest in one or more reference samples is a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative, or a fifth-degree relative of the person of interest. In any of these embodiments, each relative of the person of interest in one or more reference samples is a first-degree relative, a second-degree relative, or a third-degree relative of the person of interest. In any of these embodiments, the identity of each relative of the person of interest in one or more reference samples is known. In any of these embodiments, the identity of each of the one or more reference samples is known.

[0057] The present invention also provides a method for constructing a DNA profile, comprising: providing a nucleic acid sample from a person of interest, amplifying the nucleic acid sample using multiple primers that specifically hybridize to multiple target sequences, wherein the multiple target sequences collectively include multiple single nucleotide polymorphisms (SNPs) of at least between 2,000 and 50,000 or about 2,000 to 50,000, thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions, sequencing the amplification products, and determining the genotypes of multiple SNPs, thereby generating a DNA profile.

[0058] The present invention also provides a method for constructing a DNA profile, comprising: providing a nucleic acid sample from a person of interest, providing a nucleic acid sample from a relative of the person of interest, amplifying the nucleic acid sample from the person of interest and the nucleic acid sample from the relative using multiple primers that specifically hybridize to multiple target sequences, wherein the multiple target sequences collectively include multiple single nucleotide polymorphisms (SNPs) of at least between 2,000 and 50,000 or about between 2,000 and 50,000, thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions, sequencing the amplification products, and determining the genotypes of multiple SNPs, thereby generating DNA profiles of the person of interest and the relative of the person of interest.

[0059] In any such embodiments, sequencing does not include whole genome sequencing (WGS). In any such embodiments, the nucleic acid sample includes genomic DNA. In any such embodiments, the nucleic acid sample of the person of interest and / or the nucleic acid sample of a relative of the person of interest includes genomic DNA.

[0060] In any of these embodiments, the nucleic acid sample, the nucleic acid sample of the person of interest, and / or the nucleic acid sample of the relative comprises one or more enzyme inhibitors. In some embodiments, the one or more enzyme inhibitors include one or more inhibitors selected from the group consisting of heme, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite.

[0061] In any of these embodiments, the nucleic acid sample, the nucleic acid sample of the person of interest, and / or the nucleic acid sample of the relative comprises low-quality nucleic acid molecules and / or low-quantity nucleic acid molecules. In some embodiments, the low-quality nucleic acid molecules are degraded genomic DNA and / or fragmented genomic DNA. In any of these embodiments, the low-quality nucleic acid molecules have a molecular weight of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200. In some embodiments, the nucleic acid sample, the nucleic acid sample of the person of interest, and / or the nucleic acid sample of the relative comprise high quality nucleic acid molecules. In some embodiments, the high quality nucleic acid molecules have a DI of less than 1.

[0062] In any some of these embodiments, the person of interest is a missing person. In any some of these embodiments, the person of interest is a victim of a disaster or conflict.

[0063] In any some of these embodiments, the relative of the person of interest is a first, second, third, fourth, or fifth degree relative. In any some of these embodiments, the relative of the person of interest is a first, second, or third degree relative.

[0064] In any of these embodiments, the nucleic acid sample, the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative is derived from a buccal swab, paper, fabric, or other matrix or object impregnated with saliva, blood or other body fluids, or containing hair or skin cells.

[0065] In any such embodiments, the nucleic acid sample, the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative include genomic DNA between 3pg and 100ng or between about 3pg and 100ng. In any such embodiments, the nucleic acid sample, the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative include genomic DNA between 100pg and 5ng or between about 100pg and 5ng, genomic DNA between 50pg and 5ng or between about 50pg and 5ng, or genomic DNA between 3pg and 5ng or between about 3pg and 5ng. In any such embodiments, the nucleic acid sample, the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative include genomic DNA at or about 1ng.

[0066] In any of these embodiments, a plurality of SNPs include kinship SNPs. In any of these embodiments, a plurality of SNPs include Y chromosome SNPs (Y-SNPs). In any of these embodiments, a plurality of SNPs include kiSNPs and Y-SNPs. In any of these embodiments, a plurality of SNPs include kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotypic SNPs (piSNPs), X-chromosome SNPs (X-SNPs), and Y-chromosome SNPs (Y-SNPs). In some of these embodiments, a plurality of SNPs include one or more SNPs selected from the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs. In some such embodiments, at least or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the plurality of SNPs are kinship SNPs.

[0067] In any of these embodiments, the sequencing comprises a sequencing complexity of up to 40-plex. In any of these embodiments, the sequencing comprises a sequencing complexity of up to 32-plex. In any of these embodiments, the sequencing comprises a sequencing complexity of 12-plex to 32-plex. In any of these embodiments, the sequencing comprises a sequencing complexity of 24-plex to 32-plex. In any of these embodiments, the sequencing comprises a sequencing complexity of 4-plex, 5-plex, 6-plex, 7-plex, 8-plex, 9-plex, 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, 35-plex, 36-plex, 37-plex, 38-plex, 39-plex, 40-plex, 41-plex, 42-plex, 43-plex, 44-plex, 45-plex, or about a 4-plex, 5-plex, 6-plex, 7-plex, 8-plex, 9-plex, 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, 35-plex, 36-plex, 37-plex, 38-plex, 39-plex, 40-plex, 41-plex, 42-plex, 43-plex, 44-plex, or 45-plex. In any some of such embodiments, the sequencing comprises a sequencing complexity of or about 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, or 35-plex. In any such embodiments, sequencing comprises a sequencing complexity of 8 to 16-plex or about 8 to 16-plex for postmortem samples, and / or the sequencing comprises a sequencing complexity of 24 to 40-plex or about 24 to 40-plex for antemortem samples. In any such embodiments, sequencing comprises a sequencing complexity of 12-plex or about 12-plex for postmortem samples, and / or the sequencing comprises a sequencing complexity of 32-plex or about 32-plex for antemortem samples. In any such embodiments, sequencing comprises a sequencing complexity of 30-plex, 31-plex, or 32-plex, or about 30-plex, 31-plex, or 32-plex.

[0068] Also provided herein is a method for identifying genetic relatives of a DNA profile, comprising: calculating the degree of relationship between a DNA profile according to any one of claims 127 to 161 and one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference DNA profile set, the reference DNA profile set comprising one or more reference DNA profiles from relatives of a person of interest; and generating a family tree, the family tree comprising DNA profiles related to the one or more reference DNA profiles.

[0069] In some embodiments, one or more reference DNA profiles are part of a database.

[0070] In any of these embodiments, the reference DNA profile set includes up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles. In any of these embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest. In any of these embodiments, at least 50% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest. In any some of these embodiments, each relative of the person of interest is a first degree relative, a second degree relative, a third degree relative, a fourth degree relative, or a fifth degree relative of the person of interest.

[0071] In any of these embodiments, the identity of each relative of the person of interest in the reference DNA profile set is known. In any of these embodiments, the identity of each of the one or more reference DNA profiles in the reference DNA profile set is known.

[0072] In any of these embodiments, the reference DNA profile set is in a database. In some embodiments, the database is not publicly accessible. In any of these embodiments, the database is not accessible by a third party genetics service.

[0073] Also provided herein is a method for identifying the identity of a DNA profile, comprising: calculating the degree of relationship of a DNA profile comprising a genotype of at least between 2,000 and 50,000 SNPs or between about 2,000 and 50,000 SNPs to one or more reference DNA profiles, wherein the DNA profile is from a person of interest, and wherein the one or more reference DNA profiles are included in a reference DNA profile set, the reference DNA profile set comprising one or more reference DNA profiles from relatives of the person of interest; and generating a family tree, the family tree comprising the DNA profile associated with the one or more reference DNA profiles.

[0074] In some embodiments, the DNA profile is generated by any of the methods for generating a DNA profile described herein.

[0075] In any such embodiments, a kinship model is used to calculate the degree of relationship. In any such embodiments, a kinship model trained using a PCA method is used to calculate the degree of relationship. In some embodiments, the PCA method used to train the kinship model is PCA or involves PCA. In any such embodiments, the PCA method is PC-AiR. In any such embodiments, PC-AiR includes the following steps: (1) estimating kinship coefficients between all pairs of samples in a training database - optionally training DNA profiles - where pairs with kinship coefficients > 0.025 are identified as related and pairs with kinship coefficients < -0.025 are identified as ancestrally differentiated; (2) initializing an unrelated sample set including all samples; and (3) iteratively: (i) identifying the set of samples in the unrelated sample set that has the most related samples in the unrelated sample set, designated as X, (ii) identifying the set of samples in X that has the fewest ancestrally differentiated pairs compared to the samples in the unrelated sample set, designated as Y; and (iii) terminating the process if Y has zero samples, or, if Y has at least one sample, randomly selecting a sample from Y to remove from U and repeating starting at step (3)(i).

[0076] In any of these embodiments, the PCA method is an improved PC-Air. In some embodiments, the improved PC-AiR includes the following steps: (1) estimating kinship coefficients between all pairs of samples of the training database (optionally training DNA profiles), wherein pairs with kinship coefficients > 0.01 are identified as related, and pairs with kinship coefficients < -0.025 are identified as ancestrally differentiated; (2) removing all DNA profiles with ≥ 5% missing data; and (3) sorting all DNA profiles by identifying each DNA profile with a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles ranked from least to most in the complete database, and ties are broken by the number of ancestrally differentiated DNA profiles ranked from most to least in the complete database. In some embodiments, step (3) involves iteratively traversing the sorted DNA profiles, and for each DNA profile: (i) if the DNA profile does not yet appear in the related sample set, adding it to the unrelated sample set and adding all related DNA profiles to the related sample set, and (ii) if the DNA profile is already in the related sample set, jumping to the next DNA profile and repeating starting at step (3)(i).

[0077] In any of these embodiments, calculating the degree of relationship includes calculating a kinship coefficient using PC-Relate. In some embodiments, the degree of relationship is calculated by providing a DNA profile of the person of interest as an input to PC-Relate. In any of these embodiments, the degree of relationship is calculated by providing a kinship model and a DNA profile of the person of interest as input to PC-Relate.

[0078] In any some of these embodiments, one or more reference DNA profiles are also provided as input to PC-Relate.

[0079] In any some of these embodiments, calculating the degree of relatedness comprises calculating a kinship coefficient using a genome-wide kinship algorithm as follows:

[0080]

[0081] Wherein, the reference DNA profiles of the person of interest and one or more reference DNA profiles are i and j, φ ij is the kinship coefficient, is the estimated allele frequency, s is the SNP among the SNPs typed in both individuals, and g is and g js are the numbers of reference alleles in i and j at SNP s, respectively, and and are the expected allele frequencies calculated from PC-AiR for i and j at SNP s, respectively.

[0082] In any of these embodiments, calculating the degree of relationship includes calculating a likelihood ratio. In some embodiments, calculating the likelihood ratio includes comparing a plurality of SNPs between the DNA profile and the one or more reference DNA profiles. In some embodiments, calculating the likelihood ratio includes comparing a set of SNPs between the DNA profile and the one or more reference DNA profiles, the set of SNPs including kinship SNPs from within the plurality of SNPs.

[0083] In any some of these embodiments, calculating the likelihood ratio comprises dividing a probability that the DNA profile is associated with a reference DNA profile of the one or more reference DNA profiles by a probability that the DNA profile is not associated with the reference DNA profile based on the genotypes of the plurality of SNPs.

[0084] In any such embodiments, the likelihood ratio (LR) is calculated as follows:

[0085]

[0086] Wherein, D represents the genotype, H r represents the assumption that individuals are correlated, and H u The assumption that individuals are unrelated.

[0087] In any such embodiments, LR is calculated as follows:

[0088]

[0089] Wherein, 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2.

[0090] In any such embodiments, the personnel of interest is biologically male, and the method also includes calculating the likelihood ratio of sharing a Y chromosome between a DNA spectrum and one or more reference DNA spectra. In certain embodiments, calculating the likelihood ratio of sharing a Y chromosome includes comparing a DNA spectrum and a SNP set between one or more reference DNA spectra, and the SNP set includes one or more Y-SNPs. In certain embodiments, one or more Y-SNPs are included in a plurality of SNPs. In any such embodiments, one or more Y-SNPs include at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84 or 85 Y-SNPs. In any such embodiments, one or more Y-SNPs include 85 Y-SNPs.

[0091] In any some of such embodiments, calculating the likelihood ratio of sharing a Y chromosome includes dividing the probability that the DNA profile shares a Y chromosome with a reference DNA profile in one or more reference DNA profiles by the probability that the DNA profile does not share a Y chromosome with the reference DNA profile based on the genotype of one or more Y-SNPs.

[0092] In any of these embodiments, at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the DNA profiles in the reference DNA profile set are from relatives of missing persons or victims of disaster or conflict.

[0093] In any some of these embodiments, each of the one or more reference samples is from a relative of a missing person or a victim of a disaster or conflict. In any some of these embodiments, the one or more reference samples include up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference samples.

[0094] In any such embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of one or more reference samples are from relatives of the person of interest. In any such embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest, and wherein each of at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference DNA profile set is a first degree relative, a second degree relative, a third degree relative, a fourth degree relative, or a fifth degree relative. In any such embodiments, at least 50% of the one or more reference samples are from relatives of the person of interest. In any of these embodiments, each relative of the person of interest in the one or more reference samples is a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative, or a fifth-degree relative of the person of interest. In any of these embodiments, each relative of the person of interest in the one or more reference samples is a first-degree relative, a second-degree relative, or a third-degree relative of the person of interest.

[0095] In any some of these embodiments, the identity of each relative of the person of interest in the one or more reference samples is known. In any some of these embodiments, the identity of each of the one or more reference samples is known.

[0096] Also provided herein are kits comprising at least one container means, wherein the at least one container means comprises any of the plurality of primers described herein. In any such embodiments, the plurality of SNPs comprises between or about 2,000 to 11,000 SNPs, 3,000 to 11,000 SNPs, 4,000 to 11,000 SNPs, 5,000 to 11,000 SNPs, 5,500 to 11,000 SNPs, 6,000 to 11,000 SNPs, 7,000 to 15,000 SNPs, 7,000 to 14,000 SNPs, 7,000 to 13,000 SNPs, 7,000 to 12,000 SNPs, In some embodiments, the plurality of SNPs comprises 10,230 SNPs. In any of these embodiments, the plurality of SNPs includes between or about 2,000 to 11,000 SNPs, 3,000 to 11,000 SNPs, 4,000 to 11,000 SNPs, 5,000 to 11,000 SNPs, 5,500 to 11,000 SNPs, 6,000 to 11,000 SNPs, 7,000 to 15,000 SNPs, 7,000 to 14,000 SNPs, 7,000 to 13,000 SNPs, 7,000 to 12,000 SNPs, In some embodiments, the plurality of SNPs comprises 10,230 SNPs.

[0097] In any some of these embodiments, the method further comprises generating a family tree comprising DNA profiles associated with one or more DNA profiles contained in the reference DNA profile set. In some embodiments, the family tree comprises DNA profiles associated with one or more DNA profiles from relatives of the person of interest. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 An exemplary schematic diagram of a method for generating a library that can be sequenced is depicted.

[0099] Figure 2 Results are shown for the number of loci identified using different input titers of genomic DNA including 5 ng, 2.5 ng, 1 ng, 500 pg, 250 pg, 100 pg and 50 pg.

[0100] Figure 3 Shown is the percentage of loci with degenerate DNA detected using the assay described herein (call rate) compared to the microarray (GSA) call rate.

[0101] Figure 4 The number of loci detected in the presence of the inhibitors heme, humic acid, indigo, and tannic acid compared to the reference control is shown.

[0102] Figure 5 An exemplary family tree generated by the methods described herein is shown.

[0103] Figure 6 Expected and observed kinship coefficients calculated using the algorithm described in this paper are shown.

[0104] Figure 7 Shows the results of the 1:many search algorithm on an example case study.

[0105] Figure 8 Depicted is an exemplary family tree generated from the results of a 1:many search algorithm.

[0106] Fig. 9 is a table summarizing the number and types of loci detected using different input titers of genomic DNA, including 5 ng, 2.5 ng, 1 ng, 500 pg, 250 pg, 100 pg, and 50 pg.

[0107] Fig.10 is a table summarizing the number and type of loci detected using DNA in the presence of the inhibitors heme, humic acid, tannic acid, and indigo, compared to positive amplification controls in the absence of inhibitors.

[0108] Fig.11 is a table summarizing the number and type of loci detected in two DNA samples obtained 9 hours and 22 hours after simulated sexual assault. DNA was isolated from sperm fractions by differential extraction method and the input amount was 500 pg DNA.

[0109] Fig.12 Shown are the number of loci detected in saliva samples with increasing amounts of phenol, a known inhibitor of PCR amplification, by the phenol-chloroform-isoamyl alcohol (PCIA) extraction method.

[0110] Fig.13 The number of loci detected in blood samples separated by different matrices or methods commonly performed in forensic laboratories is shown, including blood with rust, blood in denim, blood on a swab, and blood from Chelex TM Blood containing varying levels of heme, a known inhibitor of PCR amplification, was extracted.

[0111] Fig.14 An exemplary schematic diagram of a method for assessing kinship of an individual of interest, e.g., a missing person or a victim of a conflict or disaster, is depicted, the method comprising analyzing kinship using DNA profiles, e.g., SNP reports, uploaded to a local server that includes at least one DNA profile from a relative.

[0112] Fig.15 The total number of SNPs detected for individual samples in a set of real postmortem samples is shown.

[0113] Fig.16 Shown are the total number of SNPs detected for individual samples within an exemplary set of relevant simulated postmortem samples, including samples artificially degraded by boiling or low input samples with ODI and less than 1 ng of input DNA.

[0114] Fig.17 Shown are the total number of SNPs detected for individual samples within an exemplary related antemortem private family sample set, which includes samples from CEPH / Utah including up to a second degree of relationship validated by Coriell, and three unrelated samples.

[0115] Fig.18 AE profiles 2,000 SNPs in first-, second-, and third-degree relatives ( Fig.18 A), 4,000 SNPs ( Fig.18 B) 6,000 SNPs ( Fig.18 C) 8,000 SNPs ( Fig.18 D) and 10,000 SNPs ( Fig.18E) Receiver operating characteristic (ROC) curve of the results.

[0116] Fig.19 AE profiles 2,000 SNPs in fourth- and fifth-degree relatives ( Fig.19 A), 4,000 SNPs ( Fig.19 B) 6,000 SNPs ( Fig.19 C) 8,000 SNPs ( Fig.19 D) and 10,000 SNPs ( Fig.19 E) ROC curve of the results.

[0117] Fig. 20 A depicts the distribution of the number of SNPs typed in the downsampled datasets of 16-plex and 30-plex sequenced libraries. Fig. 20 B and 20C depict the number of SNPs typed in sequence library samples sequenced in 3 (3plex), 12 (12plex), 16 (16plex), 24 (24plex), and 32 (32plex) sample runs for simulated antemortem (AM) samples, where libraries were generated using 1 ng of intact DNA ( Fig. 20 B) and the number of SNPs typed in sequence library samples sequenced in 3 (3plex) and 12 (12plex) sample runs for simulated postmortem (PM) samples generated from cremated, buried and incinerated skeletons, dental remains, whole blood degraded DNA samples, and low input DNA samples of 50, 100, 250 and 500 pg input DNA ( Fig. 20 C).

[0118] Fig.21A and 21B Depicted are simulated premortal (simulated AM) samples from coeval teeth, blood, taphonomicon, coeval skeleton, or low-input DNA ( Fig.21A ) and simulated postmortem (simulated PM) ( Fig. 21B ) samples' allelic identity and heterozygosity.

[0119] Fig.22A -D depicts a graphical representation of the sensitivity and specificity of the entire kinship coefficient for the anonymous GEDMatch sample, for first-, second-, third-, fourth-, and fifth-degree relationships, where the number of SNPs typed was 2,000 ( Fig.22A )、4,000( Fig. 22B )、6,000( Fig.23 C) or 8,000( Fig.22D ).

[0120] Fig.23The pedigree of the Utah / CEPH 1463 family is depicted, consisting of grandparents, parents, and siblings, thus representing first- and second-degree relationships. Samples were sequenced in 12, 16, 24, and 32 sample pools.

[0121] Fig.24 Depicted is the distribution of the number of SNPs typed among samples in the Utah / CEPH 1463 family when sequencing was performed with four different numbers of samples per run: 12 samples per run (12-plex), 16 samples per run (16-plex), 24 samples per run (24-plex), and 32 samples per run (32-plex).

[0122] Fig.25 A and 25B depict the kinship coefficients ( Fig.25 A) and the base 10 log-likelihood ratio (LogLR) ( Fig.25 B). These samples include samples of grandparents (G), parents (P), siblings (S), unrelated controls (U), unrelated grandparents (GU), unrelated parents (PU), and unrelated siblings (SU).

[0123] Fig.26 Depicts the pedigree of a private related family (RF) consisting of parents, aunts, cousins, first cousins ​​(1C1R) and second cousins.

[0124] Fig. 27 A depicts the distribution of the number of SNPs typed for a private related family (RF) using degraded / low input DNA samples in 12-plex and complete samples in 30-plex, which elicited primary, secondary, tertiary, quaternary, and quinary degrees of relationship. Fig. 27 B depicts kinship coefficients for pairs of individuals from private related families (RF), with the corresponding log-likelihood ratio indicated on the top of each bar. DETAILED DESCRIPTION

[0125] Unless otherwise indicated, the implementation of the technology described herein can employ conventional techniques and descriptions of molecular biology, cell biology, biochemistry and sequencing technology, which are within the skill of those skilled in the art. Suitable techniques can be specifically described with reference to the examples herein.

[0126] All publications (including patent documents, scientific papers and databases) mentioned in this application are incorporated herein by reference in their entirety for all purposes to the same extent as if each individual publication were individually incorporated herein by reference. If definitions described in this application are contrary to or inconsistent with definitions described in patents, applications, published applications and other publications incorporated by reference in this application, the definitions described in this application take precedence over the definitions incorporated by reference in this application.

[0127] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0128] Overview

[0129] Samples from missing persons or victims of disasters or conflicts may be highly degraded and may not be suitable for whole genome sequencing (WGS), microarrays, or short tandem repeat (STR) analysis. Mitochondrial analysis has high sensitivity and may be suitable in some cases, but only considers maternal inheritance and therefore has disadvantages for use in kinship analysis. In addition, current methods for generating DNA profiles for comparison in genetic databases involve genotyping using dense SNP microarrays and WGS, and then linking the evidence sample to distant relatives in the database, which requires large and high-quality DNA samples and is not suitable for family searches or for identifying missing persons or victims of disasters or conflicts. These samples may be low-quantity and low-quality samples, for example, including degraded DNA, and the data from current methods require extensive induction to generate results that can be uploaded to search databases. Finally, relatives of missing persons or victims of disasters or conflicts generally do not want their genetic data uploaded to public databases. The new and improved methods provided herein overcome these limitations and allow the use of low-quantity and low-quality - e.g., degraded - DNA to generate nucleic acid profiles, thereby performing genetic analysis more efficiently than alternative methods such as WGS or SNP microarrays, and without the need to upload genetic data to publicly accessible genetic databases. Additionally, the new and improved methods provided herein include improved methods for performing kinship analysis that require less computation to calculate accurate kinship relationships.

[0130] Victims of fatalities need to be identified for both humanitarian and legal reasons: to provide resolution for families searching for missing family members, and to provide justice to the bereaved families in civil and criminal cases when the deaths were not accidental. Identification of victims in mass fatality incidents (MFIs) can be challenging due to the large number of victims and the impact of the disaster on the integrity of the victims’ bodies. MFIs are caused by accidents such as disease / famine, earthquakes / tsunamis / hurricanes, plane / train / automobile collisions or fires, or by human acts such as war, terrorist attacks, or human rights violations / genocide. Recent events such as the terrorist attacks on the World Trade Center in New York on September 11, 2001 or the Christmas Day tsunami caused by the December 26, 2004 earthquake off the west coast of northern Sumatra, have caused unimaginable loss of life and have demonstrated the need for effective procedures and methods to recover and catalog the remains of the victims, store information related to the remains, and identify them.

[0131] The most common methods of identification (called disaster victim identification or DVI) are fingerprinting, dental comparison (dental examination or radiology), inventory of personal effects, autopsy for evidence of surgical scars / procedures and tattoos, and DNA analysis. Traditional methods such as fingerprinting and dental comparison are often used as the first step because of their low labor and cost and quick procedures, but these methods require antemortem records of fingerprints and dental images for identification. Personal effects can be misleading because victims may have similar jewelry or other personal items. Comparison of surgical procedures and tattoos also requires antemortem medical records or records of tattoos or other alterations. Finally, these methods require that the remains of the victim are relatively intact. Some MFIs may result in fragmentation and commingling of remains. In the case of fragmentation, DNA analysis of postmortem (PM) samples can not only help identify the missing person, but can also help assign multiple remains or body parts to a specific individual. DNA analysis requires an antemortem (AM) sample of the missing person, such as a razor, beard brush, toothbrush, or hairbrush for comparison. When an antemortem (AM) sample of the missing person is not available, a sample donated by a close family member can help with identification. DNA analysis is more time-consuming than traditional methods and has specific requirements for laboratory cleanliness and chain-of-custody tracking of samples to be analyzed, which can be challenging in a field situation.

[0132] If traditional methods are unsuccessful or the remains are incomplete, the success of DNA analysis will depend on sample collection, timing of collection, storage conditions, and the number of samples obtained (associated with decay and decomposition). DNA identification relies on noncoding DNA markers used in forensic genomics, which include short tandem repeats (STRs), such as the 20 autosomal core loci included in the Combined DNA Index System or CODIS, maternal mitochondrial DNA, or STRs on the Y chromosome (Y-STRs) on the paternal side. Autosomal markers are preferred due to the complexity of large-scale disasters where multiple family members with the same maternal or paternal lineage may be missing.

[0133] STR analysis has been used successfully for human identification in criminal, missing persons, and paternity cases for many years. The success of this type of analysis is due to the high polymorphism of these markers and the number of markers that can be analyzed simultaneously at one time. These markers have also been used successfully for DVI. In the case of MFI, software solutions have been helpful to assist with the large number of pairwise comparisons of PM (victim) and AM (self or relative) samples and the statistical calculation of the degree of relatedness. Several software packages are available for the analysis of autosomal and Y-STR as well as mitochondrial DNA data. Not all MFI samples are suitable for STR analysis. Highly degraded DNA samples will not amplify the larger STR markers in commonly used capillary electrophoresis (CE) kits, limiting the number of markers that can be typed for identification. The development of CE kits that utilize smaller amplicons has facilitated CE analysis of degraded DNA (Butler paper). Next-generation sequencing (NGS or massively parallel sequencing) STR testing allows for smaller amplicon sizes and the analysis of more markers in a single test, both of which can improve the ability to recover information from degraded DNA samples.

[0134] The use of STR data is particularly useful in cases where AM DNA samples are available for the missing person or very close family members, such as first-degree relatives (parents, children, or siblings). If only more distant family members are available for comparison, the success rate of STR analysis will be lower because of the potential for a large number of false positive identifications of unrelated individuals. This is particularly true in cases where the MFI occurred many years ago and the very close family members of the missing person have passed away. In cases where DNA of more distant relatives (second and third degree relatives, such as nieces, nephews, grandchildren, or great-grandchildren) is available for comparison, the use of a large number of markers, such as single nucleotide polymorphisms (SNPs), can aid in identification.

[0135] The method described herein was developed to query 10,230 forensically relevant SNPs for solving cold cases, including missing persons identification, among other purposes. Previous methods involved analyzing amplified DNA using NGS with up to 3 samples per sequencing run. This method was originally designed to type enough SNPs to detect up to five degrees of relationships when searching DNA microarray databases, such as GEDmatch PRO, to assist law enforcement in solving cold cases. To facilitate DVI, a cost-effective, higher throughput solution was needed, and a new, improved method was developed in which amplified DNA was sequenced at a higher complexity to type enough SNPs with significant overlap to determine third-degree relationships. As shown in Example 13, simulated PM samples were sequenced at a multiplex of 12 samples per run, and simulated AM samples were sequenced at a multiplex of 32 samples per run, generating sufficient typed loci data to use the method described herein and the kinship algorithm to identify third-degree relationships without false positive identifications.

[0136] The goals of the kinship algorithm are threefold: to identify relationships in the absence of pedigree or relationship information, which is important when trying to assign multiple remains to a single individual or when relatives of the victim are unavailable or unknown; to reduce the false positive rate of identifying relationships, which is typically increased when STRs are used to calculate likelihood ratios (Alonso et al., Croat Med J 2005, 46, 540-548, the contents of which are hereby incorporated by reference in their entirety); and, to maintain the privacy of victims of MFI, which requires knowledge of programming languages ​​such as R, local builds of likelihood ratio software such as Familias (Kling et al., Forensic Sci Int Genet 2014, 13, 121-127, doi:10.1016 / j.fsigen.2014.07.004, the contents of which are hereby incorporated by reference in their entirety), or private accounts on Bonaparte (Slooten et al., Forensic Science International: Genetics 2011, 5, 308-315, doi:10.1016 / j.fsigen.2010.06.005, the contents of which are hereby incorporated by reference in their entirety) (can only identify up to two levels of relationships). Because victims of MFI and families of victims often request confidentiality during the remains identification process, it is necessary to maintain genotype information on a private server. Therefore, in some embodiments, the kinship algorithm described herein is located on a private server to identify relationships between samples prepared as described herein. The local software does not upload the results to the law enforcement database; instead, the results are maintained on the private server for review. In addition, in some embodiments, for degraded / low input simulated PM samples sequenced at a complexity of 12 and simulated reference or AM samples sequenced at a complexity of 32, the kinship algorithm described herein can effectively identify up to and including three levels of relationships with perfect sensitivity and specificity.

[0137] Thus, disclosed herein is a method for identifying a person of interest by performing a DNA-based kinship analysis using a DNA profile from a person of interest, e.g., a missing person or a victim of a disaster or conflict, and determining the degree of relationship between the DNA profile and one or more reference DNA profiles comprising known relatives of the person of interest.

[0138] Provided herein is a method for performing DNA-based kinship analysis, comprising: providing a nucleic acid sample from a person of interest, amplifying the nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences, wherein the plurality of target sequences collectively include a plurality of single nucleotide polymorphisms (SNPs) of at least between 2,000 and 50,000 or about between 2,000 and 50,000, thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions, generating a nucleic acid library from the amplification products, sequencing the nucleic acid library generated from the amplification products, analyzing the sequence of the amplification products, determining the genotypes of a plurality of SNPs, thereby generating a DNA profile, and calculating the degree of relationship of the DNA profile to one or more reference DNA profiles, wherein the one or more reference DNA profiles are contained in a reference DNA profile set, wherein the reference DNA profile set includes one or more reference DNA profiles from relatives of the person of interest.

[0139] Also provided herein is a method for performing DNA-based kinship analysis, comprising: providing a nucleic acid sample from a person of interest, amplifying the nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences, wherein the plurality of target sequences collectively include a plurality of single nucleotide polymorphisms (SNPs) of at least between 2,000 and 50,000 or approximately between 2,000 and 50,000, thereby generating an amplification product, wherein the amplification is performed in one or more multiplex PCR reactions, generating a nucleic acid library from the amplification product, sequencing the nucleic acid library generated from the amplification product, determining the genotypes of a plurality of SNPs, thereby generating a DNA profile, and calculating the degree of relationship of the DNA profile to one or more reference DNA profiles, wherein the one or more reference DNA profiles are contained in a reference DNA profile set, wherein the reference DNA profile set includes one or more reference DNA profiles from relatives of the person of interest.

[0140] The present invention also provides a method for constructing a nucleic acid library of a person of interest, comprising: providing a nucleic acid sample from the person of interest, amplifying the nucleic acid sample using multiple primers that specifically hybridize to multiple target sequences, wherein the multiple target sequences collectively include multiple single nucleotide polymorphisms (SNPs) of at least between 2,000 and 50,000 or approximately between 2,000 and 50,000, thereby generating a nucleic acid library including amplification products, wherein the amplification is performed in one or more multiplex PCR reactions.

[0141] Also provided herein is a method for constructing a nucleic acid library of a reference DNA sample, comprising: providing a nucleic acid sample from a relative of a person of interest, amplifying the nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences, wherein the plurality of target sequences collectively comprise a plurality of single nucleotide polymorphisms (SNPs) at least between 2,000 and 50,000 or about 2,000 to 50,000, thereby generating a nucleic acid library comprising amplified products, wherein the amplification is performed in one or more multiplex PCR reactions. In some embodiments, the relative is a first-, second-, third-, fourth-, or fifth-degree relative of the person of interest. In some embodiments, the relative is a first-, second-, or third-degree relative of the person of interest.

[0142] Also provided herein is a method for calculating a degree of association, comprising: obtaining a DNA profile comprising genotypes of at least between 2,000 and 50,000 SNPs or between about 2,000 and 50,000 SNPs, wherein the DNA profile is from a person of interest; and calculating the degree of relationship of the DNA profile to one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference DNA profile set, wherein the reference DNA profile set includes one or more reference DNA profiles from relatives of the person of interest.

[0143] Also provided herein is a method for calculating a degree of association, comprising: generating a DNA profile comprising genotypes of at least between 2,000 and 50,000 SNPs or between about 2,000 and 50,000 SNPs, wherein the DNA profile is from a person of interest; and calculating the degree of relationship of the DNA profile to one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference DNA profile set, wherein the reference DNA profile set includes one or more reference DNA profiles from relatives of the person of interest.

[0144] Also provided herein are nucleic acid libraries constructed using any of the methods described herein, e.g., any method of constructing a nucleic acid library as described herein.

[0145] Also provided herein are multiple primers that specifically hybridize to multiple target sequences, wherein the multiple target sequences include at least between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) or about 2,000 to 50,000 of the SNPs in a nucleic acid sample from a person of interest, wherein the multiple primers are used in one or more multiplex PCR reactions to amplify the nucleic acid sample to produce amplification products.

[0146] Also provided herein are multiple primers that specifically hybridize to multiple target sequences, wherein the multiple target sequences include at least between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) or about 2,000 to 50,000 of the SNPs in a nucleic acid sample from a person of interest and a nucleic acid sample from one or more reference samples, wherein the one or more reference samples include samples from relatives of the person of interest, and wherein the multiple primers are used in one or more multiplex PCR reactions to amplify the nucleic acid sample from the person of interest and the nucleic acid sample from the one or more reference samples to produce amplification products.

[0147] The present invention also provides a method for constructing a DNA profile, comprising: providing a nucleic acid sample from a person of interest, amplifying the nucleic acid sample using multiple primers that specifically hybridize to multiple target sequences, wherein the multiple target sequences collectively include multiple single nucleotide polymorphisms (SNPs) of at least between 2,000 and 50,000 or about 2,000 to 50,000, thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions, sequencing the amplification products, and determining the genotypes of multiple SNPs, thereby generating a DNA profile.

[0148] The present invention also provides a method for constructing a DNA profile, comprising: providing a nucleic acid sample from a person of interest, providing a nucleic acid sample from a relative of the person of interest, amplifying the nucleic acid sample from the person of interest and the nucleic acid sample from the relative using multiple primers that specifically hybridize to multiple target sequences, wherein the multiple target sequences collectively include multiple single nucleotide polymorphisms (SNPs) of at least between 2,000 and 50,000 or about between 2,000 and 50,000, thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions, sequencing the amplification products, and determining the genotypes of multiple SNPs, thereby generating DNA profiles of the person of interest and the relative of the person of interest.

[0149] Also provided herein are DNA profiles constructed using any of the methods described herein, e.g., using any of the methods for constructing a DNA profile described herein.

[0150] Also provided herein is a method for identifying genetic relatives of a DNA profile, comprising: calculating the degree of relationship of any DNA profile described herein to one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference DNA profile set, the reference DNA profile set comprising one or more reference DNA profiles from relatives of a person of interest; and generating a family tree, the family tree comprising DNA profiles related to the one or more reference DNA profiles.

[0151] Also provided herein is a method for identifying the identity of a DNA profile, comprising: calculating the degree of relationship of a DNA profile comprising a genotype of at least between 2,000 and 50,000 SNPs or between about 2,000 and 50,000 SNPs to one or more reference DNA profiles, wherein the DNA profile is from a person of interest, and wherein the one or more reference DNA profiles are included in a reference DNA profile set, the reference DNA profile set comprising one or more reference DNA profiles from relatives of the person of interest; and generating a family tree, the family tree comprising the DNA profile associated with the one or more reference DNA profiles.

[0152] Also provided herein are kits comprising at least one container means, wherein the at least one container means comprises any one of the plurality of primers described herein.

[0153] In any such embodiments, the degree of kinship is calculated using a kinship model. In any such embodiments, the degree of kinship is calculated using a kinship model trained using a PCA method. In any such embodiments, the PCA method used to train the kinship model is PCA or involves PCA.

[0154] In any of these embodiments, the PCA method is PC-AiR. In some embodiments, PC-AiR includes the following steps: (1) estimating kinship coefficients between all pairs of samples in a training database, optionally training DNA profiles, wherein pairs with kinship coefficients > 0.025 are identified as related and pairs with kinship coefficients < -0.025 are identified as ancestrally differentiated; (2) initializing an unrelated sample set including all samples; and (3) iteratively: (i) identifying the set of samples in the unrelated sample set that has the most related samples in the unrelated sample set, designated as X, (ii) identifying the set of samples in X that has the fewest ancestrally differentiated pairs compared to the samples in the unrelated sample set, designated as Y; and (iii) terminating the process if Y has zero samples, or, if Y has at least one sample, randomly selecting a sample from Y to remove from U and repeating starting at step (3)(i).

[0155] In any of these embodiments, the PCA method is an improved PC-Air. In some embodiments, the improved PC-AiR includes the following steps: (1) estimating kinship coefficients between all pairs of samples of the training database, optionally training DNA profiles, wherein pairs with kinship coefficients > 0.01 are identified as related, and pairs with kinship coefficients < -0.025 are identified as ancestrally differentiated; (2) removing all DNA profiles with ≥ 5% missing data; and (3) sorting all DNA profiles by identifying each DNA profile with a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles ranked from least to most in the complete database, and ties are broken by the number of ancestrally differentiated DNA profiles ranked from most to least in the complete database. In some embodiments, step (3) involves iteratively traversing the sorted DNA profiles, and for each DNA profile: (i) if the DNA profile is not already in the related sample set, adding it to the unrelated sample set and adding all related DNA profiles to the related sample set, and (ii) if the DNA profile is already in the related sample set, skipping to the next DNA profile and repeating starting at step (3)(i).

[0156] In any of these embodiments, calculating the degree of relationship includes using PC-Relate to calculate the kinship coefficient. In some embodiments, the degree of relationship is calculated by providing the DNA profile of the person of interest as the input of PC-Relate. In any of these embodiments, the degree of relationship is calculated by providing the kinship model and the DNA profile of the person of interest as the input of PC-Relate. In any of these embodiments, one or more reference DNA profiles are also provided as the input of PC-Relate. In any of these embodiments, calculating the degree of relationship includes calculating the likelihood ratio. In some embodiments, calculating the likelihood ratio includes comparing a plurality of SNPs between the DNA profile and the one or more reference DNA profiles. In some embodiments, calculating the likelihood ratio includes comparing a set of SNPs between the DNA profile and the one or more reference DNA profiles, the set of SNPs including kinship SNPs from within the plurality of SNPs. In some embodiments, the person of interest is biologically male, and the method further includes calculating the likelihood ratio of a shared Y chromosome between the DNA profile and the one or more reference DNA profiles. In some embodiments, calculating the likelihood ratio of a shared Y chromosome includes comparing a set of SNPs between the DNA profile and the one or more reference DNA profiles, the set of SNPs including one or more Y-SNPs. In some embodiments, one or more Y-SNPs are contained within the plurality of SNPs. In some embodiments, the one or more Y-SNPs include at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84 or 85 Y-SNPs. In some embodiments, the one or more Y-SNPs include 85 Y-SNPs. In some embodiments, calculating the likelihood ratio of sharing a Y chromosome includes dividing the probability that the DNA profile shares a Y chromosome with a reference DNA profile in one or more reference DNA profiles by the probability that the DNA profile does not share a Y chromosome with the reference DNA profile based on the genotype of the one or more Y-SNPs.

[0157] In some embodiments, calculating the likelihood ratio comprises dividing a probability that the DNA profile is associated with a reference DNA profile in one or more reference DNA profiles by a probability that the DNA profile is not associated with the reference DNA profile based on the genotypes of the plurality of SNPs.

[0158] In any some of these embodiments, calculating the likelihood of sharing chromosome Y includes calculating a log likelihood to identify matching chromosomes by providing as input a DNA profile of the person of interest and a matching profile.

[0159] Samples and sample handling

[0160] In some aspects, the sample disclosed herein can be or include any suitable biological sample, or a sample derived therefrom. In some aspects, the sample described herein is processed and amplified using any known suitable method to supplement the method described herein. Exemplary samples, sample processing methods, and sample amplification methods are described below.

[0161] A. Nucleic acid samples

[0162] The nucleic acid samples disclosed herein can be derived from any biological sample, for example, any biological sample from a person of interest. The biological sample can be derived from blood, oral swabs, hair, teeth, bones, skin, tissues and / or semen, or any other source for obtaining the DNA of a person of interest. In some embodiments, the nucleic acid sample is derived from a biological sample, which is or includes blood, hair, teeth, bones, semen, skin or sperm. In some embodiments, the nucleic acid sample is derived from a tissue sample. In some embodiments, the biological sample is a DNA sample. In some embodiments, the nucleic acid sample includes DNA. In some embodiments, the DNA is genomic DNA (gDNA). In some embodiments, the nucleic acid sample from the person of interest includes genomic DNA and / or the nucleic acid sample from the reference DNA sample, such as a relative of the person of interest, includes genomic DNA. In some embodiments, the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative of the person of interest includes genomic DNA. The DNA from which the nucleic acid sample can be obtained may be complete or partially degraded. The DNA from which the nucleic acid sample can be obtained may be damaged, degraded or inhibited due to (but not limited to) the age of the source material, variable extraction, storage procedures or environmental exposure. In some embodiments, the DNA is damaged by calcium inhibition, cremation, incineration and preservation. In some embodiments, the methods described herein include providing a nucleic acid sample from a person of interest.

[0163] In some embodiments, the DNA from which the nucleic acid sample is obtained is a low quantity and / or low quality DNA sample. In some embodiments, the DNA from which the nucleic acid sample is obtained is a low quantity and low quality DNA sample. In some embodiments, the low quality DNA sample comprises low quality nucleic acid molecules. In some embodiments, the low quality nucleic acid molecules are degraded DNA, e.g., genomic DNA, and / or fragmented DNA, e.g., genomic DNA.

[0164] The quality of a nucleic acid (e.g., DNA) sample can be determined by calculating a degradation index (DI). The DI is calculated by dividing the concentration of the small DNA target by the concentration of the large DNA target (DI = concentration of small DNA target / concentration of large DNA target). In general, a DI value of less than 1 generally indicates that the nucleic acid (e.g., DNA) is not degraded, is not a low-quality sample, and / or is a high-quality sample; a DI value of 1 to 10 generally indicates that the nucleic acid (e.g., DNA) has a slight to moderate degree of degradation; a DI value greater than 10 generally indicates that the nucleic acid (e.g., DNA) is highly degraded.

[0165] In some embodiments, the low-quality nucleic acid molecules have a molecular weight of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200. 150, 160, 165, 170, 175, 180, 185, 190, 195, or 200 or more DI. In some embodiments, the low quality nucleic acid molecules have a DI of at least 1 and equal to or less than 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more. In some embodiments, low-quality nucleic acid molecules have a DI of at least 2 and are equal to or less than 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more. In some embodiments, the DI of low-quality nucleic acid molecules is 2 or at least 2 or more. In some embodiments, the DI of low-quality nucleic acid molecules is 5 or at least 5 or more. In some embodiments, the DI of low-quality nucleic acid molecules is 10 or at least 10 or more. In some embodiments, the DI of low-quality nucleic acid molecules is 20 or at least 20 or more. In some embodiments, the DI of low-quality nucleic acid molecules is between 1 and 200. In some embodiments, the DI of low-quality nucleic acid molecules is between 1 and 175. In some embodiments, the DI of low-quality nucleic acid molecules is at least 1 and is 158.3 or less. In some embodiments, the DI of low-quality nucleic acid molecules is between 2 and 200. In some embodiments, the DI of low-quality nucleic acid molecules is between 2 and 175.In some embodiments, the DI of low-quality nucleic acid molecules is at least 2 and equal to or less than 158.3. In some embodiments, the DI of low-quality nucleic acid molecules is between 5 and 200. In some embodiments, the DI of low-quality nucleic acid molecules is between 5 and 175. In some embodiments, the DI of low-quality nucleic acid molecules is at least 5 and equal to or less than 158.3. In some embodiments, the DI of low-quality nucleic acid molecules is between 10 and 200. In some embodiments, the DI of low-quality nucleic acid molecules is between 10 and 175. In some embodiments, the DI of low-quality nucleic acid molecules is at least 10 and equal to or less than 158.3.

[0166] In some embodiments, the low-quality nucleic acid molecules have a DI of between or about 1 to 10, between or about 1 to 50, between or about 1 to 50, between or about 1 to 100, between or about 1 to 100, between or about 1 to 200, between or about 2 to 10, between or about 2 to 50, between or about 2 to 50, between or about 2 to 100, between or about 200, between or about 200, between or about 5 to 10, between or about 5 to 50, between or about 5 to 50, between or about 5 to 100, between or about 5 to 200.

[0167] In some embodiments, the DNA obtained from the nucleic acid sample is a high-quality nucleic acid sample. In some embodiments, the DI of the high-quality nucleic acid sample is less than 1.

[0168] In some embodiments, the nucleic acid sample comprises one or more enzyme inhibitors. In some embodiments, the one or more enzyme inhibitors comprise one or more inhibitors in the group consisting of heme, humic acid (e.g., heme), humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite. In some embodiments, the one or more enzyme inhibitors comprise heme.

[0169] In some embodiments, the nucleic acid sample is from a person of interest, e.g., a missing person or a victim of a disaster or conflict. In some embodiments, the person of interest is a missing person. The missing person may be missing for any reason, and may be voluntary or involuntary. For example, in some embodiments, the missing person is involuntary and has been hijacked or kidnapped. In some embodiments, the missing person is voluntary and has fled, is evading detection, or is otherwise hiding.

[0170] In some embodiments, the nucleic acid sample is from a reference DNA sample. In some embodiments, the reference DNA sample is from a relative of the person of interest. Thus, in some embodiments, the nucleic acid sample is from a relative of the person of interest, such as a first, second, third, fourth, or fifth degree relative of the person of interest. In some embodiments, one or more of the one or more reference DNA profiles are from a reference DNA sample, for example, a reference DNA sample from a relative of the person of interest.

[0171] In some embodiments, the person of interest is a victim of a disaster or conflict. The victim of a disaster or conflict can be a victim of any type of disaster or conflict. For example, in some embodiments, the victim of a disaster or conflict is a victim of a disaster, such as a hurricane, a tornado, a storm, a fire, including wildfires / forest fires, tsunamis, earthquakes, floods, volcanic eruptions, avalanches, etc. In some embodiments, the disaster is a natural disaster. As used herein, "natural disaster" refers to any disaster caused by a natural process of the earth, such as a process associated with weather and / or geological events, for example, a hurricane, a flood, a storm, a tsunami, an earthquake, a volcanic eruption, etc. In some embodiments, the disaster is a non-natural disaster. "Non-natural disaster" as used herein refers to any disaster other than a natural disaster, including disasters caused by human factors, including disasters involving cars, airplanes, ships and trains, disasters involving the collapse of buildings, roads, mines and bridges, disasters involving burning buildings, and disasters caused by other human factors. In some embodiments, the victim of a disaster or conflict is a victim of a conflict, such as a war or other conflicts between people. As used herein, “conflict” means any conflict between different countries or states or between different groups within a country or state, such as an armed conflict, such as a war or terrorist attack, or any other conflict between groups that results in the death and / or injury of people.

[0172] In some embodiments, the person of interest is biologically female. In some embodiments, the person of interest is biologically male.

[0173] In some embodiments, the nucleic acid sample is derived from a buccal swab, paper, fabric, such as denim or other matrix, or an object impregnated with saliva, blood, sperm or other body fluids, or containing hair or skin cells. In some embodiments, the object impregnated with saliva, blood, sperm or other body fluids, or containing hair or skin cells is a personal item, such as a toothbrush or hairbrush. In some embodiments, the nucleic acid sample is derived from an object containing hair or skin cells, such as a hairbrush or toothbrush. In some embodiments, the nucleic acid sample is derived from a personal item, such as a toothbrush or hairbrush. In some embodiments, the nucleic acid sample is derived from a toothbrush or hairbrush. In some embodiments, a personal item is an item used by and / or associated with the person from whom the nucleic acid sample was derived, such that the person's nucleic acid is present on or in the item.

[0174] In some embodiments, the nucleic acid sample is from a crime scene, such as a homicide, an assault, such as a sexual assault, a burglary, or any other crime requiring identification of the participants. In some embodiments, the nucleic acid sample is from a sexual assault.

[0175] In some embodiments, the nucleic acid sample is obtained at or about 30 minutes, at or about 1 hour, at or about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 or more hours after the sample comprising the nucleic acid sample is stored from its source (e.g., a human subject). In some embodiments, after a sample comprising a nucleic acid sample is stored from its source (e.g., a human subject), the sample is stored for 3 hours, 9 hours, 12 hours, 15 hours, 18 hours, 21 hours, 22 hours, 24 hours, 36 hours, 48 ​​hours, 3 days, 4 days, 5 days, 6 days, 7 days, 2 weeks, 3 weeks, 4 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 1 year ... weeks, 4 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 1 year, 2 months In some embodiments, the nucleic acid sample is obtained 24 hours or less (e.g., 22 hours or less) after the sample comprising the nucleic acid sample is stored from its source (e.g., a human subject).

[0176] In some embodiments, the nucleic acid sample includes DNA between 3pg and 100ng or about 3pg and 100ng, such as genomic DNA, or DNA between 50pg and 100ng or about 50pg and 100ng, such as genomic DNA. In some embodiments, the nucleic acid sample includes DNA between 100pg and 5ng or about 100pg and 5ng, such as genomic DNA. In some embodiments, the nucleic acid sample includes 1ng of DNA or about 1ng of DNA, such as genomic DNA. In some embodiments, the nucleic acid sample includes DNA between 3pg and 100ng or about 3pg and 100ng, such as genomic DNA.

[0177] In some embodiments, the nucleic acid sample includes 3pg or about 3pg to 100ng or about 100ng of DNA, such as genomic DNA. In some embodiments, the nucleic acid sample includes 10pg or about 10pg to 100pg or about 100ng of DNA, such as genomic DNA, or includes 10pg or about 10pg to 5ng or about 5ng of DNA, such as genomic DNA. In some embodiments, the nucleic acid sample includes 10pg or about 10pg to 10ng, 10pg or about 10pg to 5ng, 25pg or about 25pg to 10ng, 25pg or about 25pg to 5ng, 50pg or about 50pg to 10ng, or 50pg or about 50pg to 5ng of DNA, such as genomic DNA.

[0178] In some embodiments, the nucleic acid sample comprises 3 pg or about 3 pg to 5 ng or about 5 ng of DNA, such as genomic DNA. In some embodiments, the nucleic acid sample comprises 50 pg or about 50 pg to 5 ng or about 5 ng of DNA, such as genomic DNA. In some embodiments, the nucleic acid sample comprises at or about 2.5 ng, 3 pg, 4 pg, 5 pg, 6 pg, 7 pg, 8 pg, 9 pg, 10 pg, 15 pg, 20 pg, 25 pg, 30 pg, 35 pg, 40 pg, 45 pg, 50 pg, 55 pg, 60 pg, 70 pg, 75 pg, 80 pg, 85 pg, 90 pg, 95 pg, 100 pg, 125 pg, 150 pg, 175 pg, 200 pg, 225 pg, 250 pg, 275 pg, 300 pg, 325 pg, 350 pg, 375 pg, 400 pg, 425 pg, 450 pg, 4 75pg, 500pg, 600pg, 700pg, 800pg, 900pg, 1ng, 1.1ng, 1.2ng, 1.3ng, 1.4ng, 1.5ng, 1.6ng, 1.7ng, 1.8ng, 1.9ng, 2ng, 2.1ng, 2.2ng, 2.3ng, 2.4ng, 2.5ng, 2.6ng, 2.7ng, 2.8ng, 2.9ng, 3ng, 3.25ng, 3.5ng, 3.75ng, 4ng, 4.25ng, 4.5ng, 4.75ng or 5ng of DNA, such as genomic DNA, or between any two of the foregoing values.In some embodiments, the nucleic acid sample comprises between or about 3 pg and 10 ng, between or about 3 pg and 5 ng, between or about 3 pg and 4 ng, between or about 3 pg and 3 ng, between or about 3 pg and 2 ng, between or about 10 pg and 10 ng, between or about 10 pg and 5 ng, between or about 10 pg and 4 ng, between or about 10 pg and 3 ng, between or about 10 pg and 2 ng, between or about 25 pg and 10 ng, between or about 25 pg and 5 ng, Between or about 25 pg and 4 ng, between or about 25 pg and 3 ng, between or about 25 pg and 2 ng, between or about 40 pg and 10 ng, between or about 40 pg and 5 ng, between or about 40 pg and 4 ng, between or about 40 pg and 3 ng, between or about 40 pg and 2 ng, between or about 50 pg and 10 ng, between or about 50 pg and 5 ng, between or about 50 pg and 4 ng, between or about 50 pg and 3 ng, between or about 50 pg and 2 ng. between or about 10 pg and 2 ng, between or about 10 pg and 2 ng, between or about 10 pg and 1.5 ng, between or about 10 pg and 1 ng, between or about 20 pg to 2 ng, between or about 20 pg and 1.5 ng, between or about 20 pg and 1 ng, between or about 25 pg and 2 ng, between or about 25 pg and 1.5 ng, between or about 25 pg and 1 ng, between or about 30 pg and 2 ng, between or about 30 pg and 1.5 ng, between or about 30 pg and 1 ng Between, between or about 35 pg and 2 ng, between or about 35 pg and 1.5 ng, between or about 35 pg and 1 ng, between or about 40 pg and 2 ng, between or about 40 pg and 1.5 ng, between or about 40 pg and 1 ng, between or about 45 pg and 2 ng, between or about 45 pg and 1.5 ng, between or about 45 pg and 1 ng, between or about 50 pg and 2 ng, between or about 50 pg and 1.5 ng, or between or about 50 pg and 1 ng.

[0179] B. Sample Processing and Amplification

[0180] In some embodiments, the methods provided herein include the step of amplifying a nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences, wherein the plurality of target sequences collectively include a plurality of single nucleotide polymorphisms (SNPs) of at least or between about 2,000 and 50,000, thereby generating amplification products, wherein the amplification is performed in one or more multiplex PCR reactions.

[0181] A variety of steps can be performed to prepare or process nucleic acid samples for assay and / or during assay. Unless otherwise indicated, the preparation or processing steps described below can generally be combined in any manner and in any order to appropriately prepare or process a particular sample disclosed herein for analysis and / or sequencing.

[0182] In some embodiments, the amount of the nucleic acid sample provided is, is about, or is less than 1 ng of genomic DNA. In some embodiments, the methods disclosed herein include the amplification of genomic DNA. In some embodiments, the amplification of genomic DNA includes one or more multiple polymerase chain reactions (PCR) including multiple primers to generate an amplified product. In some embodiments, the amplification of genomic DNA includes a single multiple PCR reaction. In some embodiments, the amplification of genomic DNA includes two multiple PCR reactions. In some embodiments, the amplification of genomic DNA includes three multiple PCR reactions. In some embodiments, the amplification of genomic DNA includes four multiple PCR reactions.

[0183] In some embodiments, one or more primers in a plurality of primers are designed according to the atypical design strategy described in WO 2015 / 126766A1, which is hereby incorporated by reference in its entirety. In some embodiments, the length of one or more primers in a plurality of primers is at least 24 nucleotides, and / or the melting temperature is less than 60 degrees Celsius, and / or is rich in AT, and the AT content is at least 60%. In some embodiments, the length of one or more primers in a plurality of primers is at least 24 nucleotides hybridized with the target sequence, and / or the melting temperature is between 50 degrees Celsius and 60 degrees Celsius, and / or is rich in AT, and the AT content is at least 60%. In some embodiments, the melting temperature of one or more primers in a plurality of primers is less than 58 degrees Celsius, or less than 54 degrees Celsius.

[0184] In some embodiments, multiple primers may be used to carry out multiple cycles of amplification of genomic DNA, the multiple primers hybridizing with multiple target sequences and / or marking multiple target sequences, the multiple target sequences collectively comprising multiple single nucleotide polymorphisms (SNPs) at least at or between about 2,000 and 50,000, or at least at or between about 5,000 and 50,000. In some embodiments, multiple primers may be used to carry out multiple cycles of amplification of genomic DNA, the multiple primers hybridizing with multiple target sequences and / or marking multiple target sequences, the multiple target sequences collectively comprising multiple SNPs at least at or between about 2,000 and 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000 or 50,000. In some embodiments, multiple primers can be used to perform multiple cycles of amplification of genomic DNA, and the multiple primers hybridize with multiple target sequences and / or label multiple target sequences, and the multiple target sequences collectively include at least or between about 5,000 to 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000 or 50,000 SNPs. In some embodiments, multiple primers can be used to perform multiple cycles of amplification of genomic DNA, and the multiple primers hybridize with multiple target sequences and / or label multiple target sequences, and the multiple target sequences collectively include at least or between about 10,000 to 11,000 SNPs. In some embodiments, genomic DNA can be subjected to multiple cycles of amplification using multiple primers that hybridize to and / or label multiple target sequences that collectively include at least between or about 2,000 and 11,000 SNPs, between 3,000 and 11,000 SNPs, between 4,000 and 11,000 SNPs, between 5,000 and 11,000 SNPs, between 5,500 and 11,000 SNPs, between 6,000 and 11,000 SNPs, between 7,000 and 15,000 SNPs, between 7,000 and 14,000 SNPs, between 7,000 and 13,000 SNPs, or between 7,000 and 14,000 SNPs. 0 SNPs, between 7,000 and 12,000 SNPs, between 7,000 and 11,000 SNPs, between 8,000 and 15,000 SNPs, between 8,000 and 14,000 SNPs, between 8,000 and 13,000 SNPs, between 8,000 and 12,000 SNPs, between 8,000 and 11,000 SNPs, between 9,000 and 15,000 SNPs, between 9,000 and 14,000 SNPs, between 9,000 and 13,000 SNPs, between 9,000 and 12,000 SNPs, or between 9,000 and 11,000 SNPs.In some embodiments, multiple primers may be used to perform multiple cycles of amplification of genomic DNA, the multiple primers hybridizing with multiple target sequences and / or labeling multiple target sequences, the multiple target sequences collectively including at least or between about 6,000 and 11,000 SNPs. In some embodiments, the multiple SNPs include 2,639 or about 2,639 SNPs. In some embodiments, multiple primers may be used to perform multiple cycles of amplification of genomic DNA, the multiple primers hybridizing with multiple target sequences and / or labeling multiple target sequences, the multiple target sequences collectively including 10,230 or about 10,230 SNPs.

[0185] In some embodiments, a plurality of SNPs include at least or between about 2,000 to 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000, or 50,000 SNPs. In some embodiments, a plurality of SNPs include at least or between about 5,000 to 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000, or 50,000 SNPs. In some embodiments, a plurality of SNPs include at least or between about 6,000 to 15,000, 20,000, 25,000, 30,000, 35,000, 40,000, 45,000, or 50,000 SNPs. In some embodiments, the plurality of SNPs comprises at least between or about 2,000 to 11,000 SNPs, 3,000 to 11,000 SNPs, 4,000 to 11,000 SNPs, 5,000 to 11,000 SNPs, 5,500 to 11,000 SNPs, 6,000 to 11,000 SNPs, 7,000 to 15,000 SNPs, 7,000 to 14,000 SNPs, 7,000 to 13,000 SNPs, 7,000 to 12,000 SNPs, In some embodiments, the plurality of SNPs comprises 2,639 or about 2,639 SNPs. In some embodiments, the plurality of SNPs comprises 10,230 or about 10,230 SNPs.In some embodiments, the plurality of SNPs comprises at least between or about 2,000 and 50,000 SNPs, between 5,000 and 50,000 SNPs, between 5,000 and 45,000 SNPs, between 5,000 and 40,000 SNPs, between 5,000 and 35,000 SNPs, between 5,000 and 30,000 SNPs, between 5,000 and 25,000 SNPs, between 5,000 and 20,000 SNPs, between 6,000 and 1 ...35,000 SNPs, between 5,000 and 30,000 SNPs, between 5,000 and 25,000 SNPs, between 5,000 and 20,000 SNPs, between 6,000 and 15,000 SNPs, between 5,000 and 25,000 SNPs, between 5,000 and 20,000 SNPs, between 5 00 to 50,000 SNPs, 6,000 to 45,000 SNPs, 6,000 to 40,000 SNPs, 6,000 to 35,000 SNPs, 6,000 to 30,000 SNPs, 6,000 to 25,000 SNPs, 6,000 to 20,000 SNPs, 7,000 to 50,000 SNPs, 7,000 to 45,000 SNPs, 7,000 to 40, 000 SNPs, 7,000 to 35,000 SNPs, 7,000 to 30,000 SNPs, 7,000 to 25,000 SNPs, 7,000 to 20,000 SNPs, 8,000 to 50,000 SNPs, 8,000 to 45,000 SNPs, 8,000 to 40,000 SNPs, 8,000 to 35,000 SNPs, 8,000 to 30,000 SNPs. 000 SNPs, between 8,000 and 25,000 SNPs, between 8,000 and 20,000 SNPs, between 9,000 and 50,000 SNPs, between 9,000 and 45,000 SNPs, between 9,000 and 40,000 SNPs, between 9,000 and 35,000 SNPs, between 9,000 and 30,000 SNPs, between 9,000 and 25,000 SNPs, or between 9,000 and 20,000 SNPs.

[0186] In some embodiments, the plurality of SNPs includes at least between or about 2,000 to 11,000 SNPs, 2,500 to 11,000 SNPs, 3,000 to 11,000 SNPs, 3,500 to 11,000 SNPs, 4,000 to 11,000 SNPs, 4,500 to 11,000 SNPs, 5,000 to 11,000 SNPs, 5,550 to 11,000 SNPs, Between 6,000 and 11,000 SNPs, between 6,500 and 11,000 SNPs, between 7,000 and 11,000 SNPs, between 7,500 and 11,000 SNPs, between 8,000 and 11,000 SNPs, between 8,500 and 11,000 SNPs, between 9,000 and 11,000 SNPs, between 9,500 and 11,000 SNPs, or between 10,000 and 11,000 SNPs.

[0187] In some embodiments, the plurality of SNPs include SNPs selected from one or more SNPs in the group consisting of kinship SNPs, ancestry SNPs, identity SNPs, phenotypic SNPs, X-SNPs, and Y-SNPs. In some embodiments, the plurality of SNPs include kinship SNPs, ancestry SNPs, identity SNPs, phenotypic SNPs, X-SNPs, and Y-SNPs. In some embodiments, the plurality of SNPs include kinship SNPs. In some embodiments, the plurality of SNPs include Y-SNPs. In some embodiments, the plurality of SNPs include kinship SNPs and Y-SNPs.

[0188] In any of these embodiments, the plurality of SNPs include one or more microhaplotypes. Thus, in some embodiments, a microhaplotype is a type of SNP contained in a plurality of SNPs. In some embodiments, each microhaplotype includes one or more SNPs that are shared on a single amplicon or close to each other on the genome. In general, a microhaplotype is a biomarker that is typically less than 300 nucleotides in length that displays multiple allele combinations, e.g., multiple SNP-based allele markers.

[0189] In some embodiments, the SNPs do not include SNPs with known medical associations, such as associations with known medical conditions, or low minor allele frequencies. By excluding SNPs with known medical associations, such as associations with known medical conditions, or low minor allele frequencies, privacy concerns are limited and genetic health data is protected.

[0190] In some embodiments, SNPs include SNPs that have been filtered with multiple genotype samples. In some embodiments, SNPs are selected from categories including ancestral SNPs, identity SNPs, relative SNPs, phenotypic SNPs, X-SNPs, and Y-SNPs. In some embodiments, ancestral SNPs are included in or between about 10-100 SNPs. In some embodiments, identity SNPs are included in or between about 10-200 SNPs. In some embodiments, kinship SNPs are included in or between about 7,000-12,000 SNPs. In some embodiments, phenotypic SNPs are included in or between about 1-50 SNPs. In some embodiments, X-SNPs are included in or between about 10-200 SNPs. In some embodiments, Y-SNPs are included in or between about 10-200 SNPs. In some embodiments, ancestral SNPs are included in or between 0-10% or about 0-10% of the total number of SNPs. In some embodiments, identity SNPs are included in or between 0-10% or about 0-10% of the total number of SNPs. In some embodiments, kinship SNPs are included in 80-100% or about 80-100% of the total number of SNPs. In some embodiments, at least or about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of a plurality of SNPs are kinship SNPs. In some embodiments, at least or about 85% of a plurality of SNPs are kinship SNPs. In some embodiments, at least or about 90% of a plurality of SNPs are kinship SNPs. In some embodiments, at least or about 95% of a plurality of SNPs are kinship SNPs. In some embodiments, at least or about 99% of a plurality of SNPs are kinship SNPs. In some embodiments, 100% of a plurality of SNPs are kinship SNPs. In some embodiments, phenotypic SNPs are included in 0-5% or about 0-5% of the total number of SNPs. In some embodiments, the X-SNPs are included in 0-5% or about 0-5% of the total number of SNPs. In some embodiments, the Y-SNPs are included in 0-5% or about 0-5% of the total number of SNPs. In some embodiments, the SNPs do not include medically informative or minor allele frequency SNPs. The tag region can be any sequence, such as a universal tag region, a capture tag region, an amplification tag region, a sequencing tag region, a UMI tag region, etc.

[0191] In some embodiments, the target sequence is purified and enriched, and a library of the original DNA sample is generated, also referred to as a nucleic acid library. In some embodiments, the purification combines purification beads with enzymes to purify the amplified target from other reaction components. In some embodiments, the purified target sequence is enriched by amplifying the DNA and adding UDI adapters and sequences required for cluster generation. The UDI adapters can label the DNA with a unique sequence combination that identifies each sample for analysis.

[0192] In some embodiments, a nucleic acid library is generated from amplification products, including amplification products produced by methods or embodiments described herein. Thus, in some embodiments, a nucleic acid library includes amplification products generated by amplifying a nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences, the target sequences collectively including a plurality of at least between or about 5,000 and 50,000 SNPs, or a plurality of at least between or about 2,000 and 50,000 SNPs.

[0193] In some embodiments, nucleic acid libraries or DNA libraries are normalized for quantification and quality inspection, and pooled by combining equal amounts of normalized libraries to create a library pool that can be sequenced together on the same flow cell. In some embodiments, quantification includes using a fluorescence method. In some embodiments, quantification includes a quantitative PCR method. After the pooled DNA library, it can be denatured and diluted using a method based on sodium hydroxide (NaOH), and sequencing controls can be added.

[0194] In some embodiments, nucleic acid libraries are quantified, normalized, denatured, and diluted according to the instructions in the Forenseq Kintelligence Kit User Guide (Verogen PN: V16000120, the contents of which are hereby incorporated by reference in their entirety).

[0195] In some embodiments, nucleic acid libraries of DNA libraries are prepared for sequencing by massively parallel sequencing using any known suitable method to supplement the methods described herein.

[0196] In some embodiments, also provided herein are nucleic acid libraries constructed using any of the methods described herein.

[0197] In some embodiments, the methods provided herein include the step of generating a nucleic acid library from the amplification products.

[0198] Sequencing and analysis

[0199] In some aspects, nucleic acid libraries or DNA libraries described herein can be sequenced using any known suitable method to supplement the methods described herein, and are not limited to any specific sequencing platform. In some aspects, samples disclosed herein can be analyzed using any known suitable method to supplement the methods described herein. Exemplary sequencing methods and method analysis are described below.

[0200] A. Sequencing

[0201] In some embodiments, the methods provided herein include the step of sequencing the nucleic acid library generated from the amplification products.

[0202] In some embodiments, techniques for sequencing a nucleic acid library or DNA library created by practicing the methods described herein include using polymerase-based synthesis sequencing, ligation based, pyrosequencing, or polymerase-based sequencing methods.

[0203] In some embodiments, the nucleic acid library is sequenced according to the instructions in the MiSeq FGx Sequencing System Reference Guide (e.g., Document #VD2018006, the contents of which are hereby incorporated by reference in their entirety). In some embodiments, the nucleic acid library sequenced according to the instructions in the MiSeq FGx Sequencing System Reference Guide (e.g., Document #VD2018006) has been denatured.

[0204] In some aspects, sequencing methods disclosed herein include using massive parallel sequencing (MPS). In some aspects, sequencing methods disclosed herein do not include using whole genome sequencing (WGS). In some aspects, sequencing methods disclosed herein do not include using microarrays.

[0205] In some embodiments, the sequencing methods disclosed herein detect the loci of 90% or about 90% of the SNPs.

[0206] In some embodiments, the sequencing methods disclosed herein generate an output report that includes sequencing results of an amplification product containing a plurality of SNPs.

[0207] In some embodiments, sequencing includes up to 40-plex sequencing complexity. In some embodiments, sequencing includes 2-plex to 40-plex sequencing complexity. In some embodiments, sequencing includes 12-plex to 40-plex sequencing complexity. In some embodiments, sequencing includes 12-plex to 32-plex sequencing complexity. In some embodiments, sequencing includes 12-plex to 30-plex sequencing complexity. In some embodiments, sequencing includes 24-plex to 40-plex sequencing complexity. In some embodiments, sequencing includes 24-plex to 32-plex sequencing complexity. In some embodiments, sequencing includes 28-plex to 32-plex sequencing complexity. In some embodiments, sequencing includes 2-plex, 3-plex, 4-plex, 5-plex, 6-plex, 7-plex, 8-plex, 9-plex, 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, or 32-plex sequencing complexity. In some embodiments, sequencing comprises a sequencing complexity of 30-plex or about 30-plex. In some embodiments, sequencing comprises a sequencing complexity of 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40-plex. In some embodiments, sequencing comprises a sequencing complexity of 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, or 45-plex. Sequencing complexity refers to the number of individual samples that are sequenced together, for example on a flow cell.

[0208] In some embodiments, sequencing comprises sequencing the postmortem sample at or at a sequencing complexity of between about 6-plex and 16-plex. In some embodiments, sequencing comprises sequencing the postmortem sample at or at a sequencing complexity of between about 8-plex and 14-plex. In some embodiments, sequencing comprises sequencing the postmortem sample at or at a sequencing complexity of between about 10-plex and 14-plex. In some embodiments, sequencing comprises sequencing the postmortem sample at or at a sequencing complexity of about 10-plex, 11-plex, 12-plex, 13-plex, or 14-plex. In some embodiments, sequencing comprises sequencing the postmortem sample at or at a sequencing complexity of about 12-plex.

[0209] In some embodiments, sequencing comprises sequencing antemortem samples at or with a sequencing complexity of between about 24- and 40-plex. In some embodiments, sequencing comprises sequencing postmortem samples at or with a sequencing complexity of between about 26- and 38-plex. In some embodiments, sequencing comprises sequencing postmortem samples at or with a sequencing complexity of between about 28- and 36-plex. In some embodiments, sequencing comprises sequencing postmortem samples at or with a sequencing complexity of between about 28-, 29-, 30-, 31-, 32-, 33-, or 34-plex. In some embodiments, sequencing comprises sequencing postmortem samples at or with a sequencing complexity of about 32-plex.

[0210] B. Analysis

[0211] In some embodiments, the methods provided herein include the step of analyzing the sequence of the amplification product.

[0212] In some aspects, the methods disclosed herein involve using an analysis module that automatically initiates analysis after sequencing of a sample (ie, an amplification product) is completed. In some embodiments, the analysis module comprises universal analysis software (UAS).

[0213] In some embodiments, the analysis methods disclosed herein generate an output report comprising sequencing results of amplification products containing a plurality of SNPs.

[0214] In some embodiments, sequencing results are analyzed using any suitable sequence analysis software available in the art.

[0215] In some embodiments, analysis is performed using Forenseq General Analysis Software, such as version 2.1 or 2.2 or higher (Verogen, San Diego, California), following the instructions outlined in the Forenseq General Analysis Software Reference Guide, such as version 2.2 or higher, and provided, for example, in Reference Guide Document #VD2019002, the contents of which are incorporated herein by reference in their entirety.

[0216] Genotype and DNA profile determination

[0217] In some embodiments, the methods provided herein include the step of determining the genotypes of a plurality of SNPs, thereby generating a DNA profile.

[0218] In some embodiments, a DNA profile is generated by determining the genotypes of a plurality of SNPs.

[0219] In some aspects, an output report comprising sequencing results of an amplification product comprising a plurality of SNPs generated by any of the methods described herein can be used to genotype a sample using any known suitable method to supplement the methods described herein. In some aspects, an output report comprising sequencing results of an amplification product comprising a plurality of SNPs generated by any of the methods described herein can be used to generate a DNA profile using any known suitable method to supplement the methods described herein.

[0220] In some embodiments, the DNA spectrum includes a genotype for each of a plurality of SNPs. In some embodiments, the DNA spectrum includes a genotype for at least or at least about 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of a plurality of SNPs. In some embodiments, the DNA spectrum includes a genotype for at least or at least about 85% of a plurality of SNPs. In some embodiments, the DNA spectrum includes a genotype for at least or at least about 90% of a plurality of SNPs. In some embodiments, the DNA spectrum includes a genotype for at least or at least about 95% of a plurality of SNPs. In some embodiments, the DNA spectrum includes a genotype for at least or at least about 99% or about 100% of a plurality of SNPs.

[0221] In some embodiments, the methods disclosed herein comprise determining hair color, eye color, and biogeographic ancestry.

[0222] Determination of relationship

[0223] In some embodiments, the methods provided herein include the step of calculating the degree of relationship of a DNA profile to one or more reference DNA profiles, wherein the one or more reference DNA profiles are contained in a set of reference DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest.

[0224] In some aspects, the degree of relationship of the DNA profiles described herein can be calculated with reference to one or more reference DNA profiles using any known suitable method to supplement the methods described herein.

[0225] In some embodiments, the one or more reference DNA profiles are contained in a reference DNA profile set that includes one or more reference DNA profiles from relatives of the person of interest.

[0226] In some embodiments, the reference DNA spectrum set includes up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles. In some embodiments, the reference DNA spectrum set includes up to 1000 reference DNA profiles. In some embodiments, the reference DNA spectrum set includes up to 500 reference DNA profiles. In some embodiments, the reference DNA spectrum set includes up to 250 reference DNA profiles. In some embodiments, the reference DNA spectrum set includes up to 150 reference DNA profiles. In some embodiments, the reference DNA spectrum set includes up to 100 reference DNA profiles. In some embodiments, the reference DNA spectrum set includes up to 75 reference DNA profiles. In some embodiments, the reference DNA spectrum set includes up to 50 reference DNA profiles. In some embodiments, the reference DNA profile set includes up to 25 reference DNA profiles. In some embodiments, the reference DNA profile set includes up to 15 reference DNA profiles. In some embodiments, the reference DNA profile set includes between 1 and 1,000 reference DNA profiles, between 1 and 500 reference DNA profiles, between 1 and 400 reference DNA profiles, between 1 and 300 reference DNA profiles, between 1 and 250 reference DNA profiles, between 1 and 200 reference DNA profiles, between 1 and 150 reference DNA profiles, between 1 and 100 reference DNA profiles, between 1 and 75 reference DNA profiles, between 1 and 50 reference DNA profiles, between 1 and 25 reference DNA profiles, between 1 and 20 reference DNA profiles, between 1 and 15 reference DNA profiles, between 1 and 10 reference DNA profiles, or between 1 and 5 reference DNA profiles.

[0227] In some embodiments, the set of reference DNA profiles includes at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, or 25 reference DNA profiles, and includes up to 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles.

[0228] In some embodiments, the set of reference DNA profiles includes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 12, 14, 15, 16, 17, 18, 19, or 20 reference DNA profiles.

[0229] In some embodiments, the reference DNA profile set includes DNA profiles from relatives of the person of interest. In some embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest. In some embodiments, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% or About 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% of the reference DNA profiles are from relatives of the person of interest. In some embodiments, 100% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest. In some embodiments, at least 50% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest.

[0230] In some embodiments, each of the one or more reference DNA profiles from the relatives of the person of interest is an antemortem sample. In some embodiments, one or more of the one or more reference DNA profiles from the relatives of the person of interest is an antemortem sample. In some embodiments, one or more of the one or more reference DNA profiles from the relatives of the person of interest is a postmortem sample. In some embodiments, the one or more reference DNA profiles from the relatives of the person of interest include postmortem samples and antemortem samples.

[0231] In some embodiments, each relative of the person of interest in the reference DNA profile set is a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative, or a fifth-degree relative of the person of interest. For example, in an embodiment comprising a reference DNA profile set, the reference DNA profile set comprises three reference DNA profiles from relatives of the person of interest, each of the three reference DNA profiles can independently be from a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative, or a fifth-degree relative, for example, the first reference DNA profile can be from a first-degree relative, the second reference DNA profile can be from a third-degree relative, and the third reference DNA profile can be from a first-degree relative.

[0232] In some embodiments, at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of one or more reference DNA profiles in the reference DNA profile set are related, wherein each of the one or more reference DNA profiles is independently derived from a first degree, second degree, third degree, fourth degree or fifth degree relative of each of the other one or more reference DNA profiles in the reference DNA profile set.

[0233] In some embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest, and each of at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set is a first degree relative, a second degree relative, a third degree relative, a fourth degree relative, or a fifth degree relative.

[0234] In some embodiments, the identity of each relative of the person of interest in the reference DNA profile set is known. In some embodiments, the identity of each of the one or more reference DNA profiles in the reference DNA profile set is known.

[0235] In some embodiments, the reference DNA profile set includes a DNA profile of a sample derived from a person of interest. For example, in some embodiments, the reference DNA profile set includes a DNA profile of a sample derived from a person of interest before the person of interest disappeared, for example, in the case where the person of interest is a missing person, or before the person of interest became a victim of a disaster or conflict. In some embodiments, the DNA profile of a sample derived from a person of interest before the person of interest disappeared, for example, in the case where the person of interest is a missing person, or before the person of interest became a victim of a disaster or conflict is used as a positive control for the person of interest because the sample was obtained while the person of interest was alive or before the disappearance or victimization, and is a sample that is confirmed to be derived from the person of interest. Therefore, in some embodiments, the reference DNA profile set includes a DNA profile of a sample derived from a person of interest before the person of interest disappeared, for example, in the case where the person of interest is a missing person, or before the person of interest became a victim of a disaster or conflict, and before being amplified and / or sequenced, it is known to be a sample derived from the person of interest.

[0236] In some embodiments, the reference DNA profile set is in a database, e.g., a genetic database. In some embodiments, the database is not publicly accessible, i.e., not accessible to the public. In some embodiments, the database is not a public database, such as a public database accessible to law enforcement agencies or third-party genetics services. In some embodiments, the database is not publicly accessible through a subscription service. In some embodiments, the database is not accessible to a third-party genetics service.

[0237] In some embodiments, calculating the degree of relationship of a DNA profile to one or more reference DNA profiles does not include accessing a publicly accessible database, e.g., a publicly accessible genetic database. In some embodiments, calculating the degree of relationship of a DNA profile to one or more reference DNA profiles does not require internet access to access a database comprising a set of reference DNA profiles. In some embodiments, calculating the degree of relationship of a DNA profile to one or more reference DNA profiles includes using a local database comprising a set of reference DNA profiles. As used herein, "local database" refers to a database that is only stored and accessible locally and is not accessible to the public, e.g., a third party, attempting to query the database.

[0238] In some embodiments, the reference DNA profile set includes DNA profiles from two or more unrelated families, for example, two or more unrelated families (i.e., families that are not related to each other), each family including one or more relatives of missing persons and / or victims of disasters or conflicts. For example, in a disaster or conflict event with multiple victims from multiple families that are unrelated to each other, one or more family members from each family can contribute reference DNA profiles in the reference DNA profile set. This local reference DNA profile set can then be used locally to identify victims of disasters or conflicts from multiple unrelated families.

[0239] In some embodiments, a reference DNA profile set or database (e.g., a genetic database or a local database) includes one or more DNA profiles from individuals of an ethnicity of interest. In some embodiments, at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from an ethnicity of interest. In some embodiments, at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from an ethnicity of interest. In some embodiments, at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from an ethnicity of interest. In some embodiments, at least 75%, 80%, 85%, 90%, or 95% of the reference DNA profiles in the reference DNA profile set are from the ethnicity of interest. In some embodiments, at least 95%, 95%, 97%, 98%, or 99% of the reference DNA profiles in the reference DNA profile set are from the ethnicity of interest. In some embodiments, 100% of the reference DNA profiles in the reference DNA profile set are from the ethnicity of interest.

[0240] In some embodiments, the person of interest has a race of interest.

[0241] In some embodiments, the race of interest can be any race, for example, any race from anywhere.

[0242] In some embodiments, the race of interest is a rare race. In some embodiments, the rare race accounts for 0.01%, 0.05%, 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 0.6%, 0.7%, 0.8%, 0.9%, 1%, 2%, 3%, 4% or 5% or less of the population of the country of interest or the world. In some embodiments, the race of interest is any race in the country of interest. In some embodiments, the race of interest is the dominant race in the country of interest. In some embodiments, the race of interest is a minority race in the country of interest. In some embodiments, the person of interest is from the country of interest.

[0243] The country of interest can be any country of interest. In some embodiments, the country of interest is selected from Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Cote d'Ivoire, Cape Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo (Congo-Brazzaville), Costa Rica, Croatia, Cuba, Cyprus, Czech Republic, Democratic Republic of the Congo, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Swaziland (formerly known as "Swaziland"), Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Holy See, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Sotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Myanmar (formerly Burma), Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Korea, North Macedonia, Norway, Oman, Pakistan, Palau, State of Palestine, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Saint Kitts and Nevis, Saint Lu Western Asia, Saint Vincent and the Grenadines, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, Somalia, South Africa, South Korea, South Sudan, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, East Timor, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, United States of America, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe. In some embodiments, the country of interest is the United States of America.

[0244] In some embodiments, the DNA-based kinship analysis described herein includes the use of a local database. In some embodiments, the DNA-based kinship analysis described herein allows for the generation of reports with minimal user input. In some embodiments, the DNA-based kinship analysis described herein includes the use of an algorithm to calculate a kinship coefficient. In some embodiments, the kinship coefficient determines the relationship status of a sample or DNA profile to a reference DNA profile in a database. For example, in some embodiments, the kinship coefficient indicates whether each of one or more identified genetic relatives may be a great-great-grandmother, great-great-grandfather, great-grandfather, great-grandmother, grandmother, grandfather, cousin, first cousin, or second cousin based on the relative value of the kinship coefficient. In some embodiments, the reference DNA profile is part of a genetics database.

[0245] In some embodiments, the DNA-based kinship analysis described herein includes identifying genetic relatives who are at or about the 1st, 2nd, 3rd, 4th, or 5th degree. In some embodiments, the DNA-based kinship analysis described herein includes identifying genetic relatives of more than 1st, 2nd, 3rd, 4th, or 5th degree. In some embodiments, the DNA-based kinship analysis described herein includes identifying the degree of relationship between the person of interest and one or more reference DNA profiles in the reference DNA profile set. For example, in some embodiments, the method includes identifying that the person of interest is independently a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative, or a fifth-degree relative of one or more of the one or more reference DNA profiles. A person's first-degree relative is a parent (e.g., father or mother), a sibling (e.g., sister or brother), or a child (e.g., son or daughter) of the person. A person's second-degree relative is a person who shares about 25% of the person's genes, such as the person's grandparents, aunt, uncle, niece, nephew, grandchild, or half-sibling. A person's third-degree relatives are people who share about 12.5% ​​of the person's genes, such as great-grandparents, first cousins, and great-grandchildren. Fourth-degree relatives include, for example, first cousins, half-great uncles, half-great aunts, half-great-nieces, half-great-nephews, and half-cousins. Fifth-degree relatives include, for example, second cousins, half-first cousins, and second cousins.

[0246] In some embodiments, the DNA-based kinship analysis described herein includes generating a family tree comprising DNA profiles related to one or more DNA profiles.A family tree can be generated using any available means or methods.

[0247] In some embodiments, the DNA-based kinship analysis described herein includes identifying suspects by common ancestry.

[0248] In some embodiments, calculating the degree of relationship includes calculating the degree of relationship between a DNA profile, i.e., a DNA profile from a person of interest, and one or more reference DNA profiles contained in a reference DNA profile set, e.g., a reference DNA profile set including one or more reference DNA profiles from relatives of the person of interest.

[0249] In some embodiments, calculating the degree of relationship includes calculating the degree of relationship between a DNA profile (i.e., a DNA profile from a person of interest) and one or more reference DNA profiles contained in a reference DNA profile set (e.g., a reference DNA profile set including one or more reference DNA profiles from relatives of the person of interest) by comparing a set of SNPs, which is or includes one or more Y-SNPs. In some embodiments, the one or more Y-SNPs include at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84, or 85 Y-SNPs. In some embodiments, the one or more Y-SNPs are or include 85 Y-SNPs. By comparing a set of SNPs including a DNA profile from a biological male and one or more reference DNA profiles from a biological male, a likelihood ratio of male lineage can be determined.

[0250] Likelihood ratio (LR) and kinship value can be calculated using any method or algorithm known in the art. In some embodiments, the likelihood ratio is calculated using algorithms pedprobr (Brustad et al., Int. J. Legal Med., 2021, 135: 117-129, the contents of which are hereby incorporated by reference in their entirety) and dvir (Vigeland et al., Scientific Reports, 2021, 11: 13661, the contents of which are hereby incorporated by reference in their entirety). In some embodiments, the average value of the population frequency from the genome aggregation database (gnoMAD) (Karczewski et al., Nature, 2020, 581: 434-443, the contents of which are hereby incorporated by reference in their entirety) v3.0 is used in the LR calculation. In some embodiments, a mutation model is not used, and when the SNP selected for analysis has low linkage disequilibrium, theta is set to 0 (Karczewski et al., supra, the contents of which are hereby incorporated by reference in their entirety). In some embodiments, LR is calculated as follows:

[0251]

[0252] Wherein, D represents the genotype, H r represents the assumption that individuals are correlated, and H uIndicates that the individual is unrelated to the hypothesis. The related hypothesis is represented by a pedigree, in which the unidentified individual is tested as a relative. The unrelated hypothesis is represented by the Hardy-Weinberg equilibrium calculation. In some embodiments, the LR value of each locus is calculated, and then multiplied by the locus, so as to obtain the final LR of the relationship. In order to improve the efficiency of calculation, each locus LR can be converted to a logarithm, and the locus LR is added. However, due to the high complexity of the platform, and in the case of mass mortality events (MFI), the degradation of PM samples is usually very high and / or the input amount is very low, and the random effect during PCR can cause allele dropout (dropout), and may cause the situation that the allele combination of two individuals at a certain locus seems impossible. This usually occurs in parent-child relationships, in which each individual in the relationship is genotyped as a homozygote of different alleles, but one or two of them are actually heterozygotes (for example, when the parents or parents and children are actually AB, the genotype of the parents is AA and the child is genotype BB). Genotyping errors and de novo mutations are also possible causes of this situation. This situation results in an LR of 0, and may result in an overall LR of 0, since the final LR is the product of the locus LRs. To ensure that the locus LR is not 0, pedprobr is modified in some embodiments. Therefore, in some embodiments, the likelihood ratio of the locus in these cases is calculated as follows:

[0253]

[0254] Wherein, 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2 (Galván-Femenía et al., Heredity, 2021, 126:537-547, the contents of which are hereby incorporated by reference in their entirety). Allele 1 and allele 2, also referred to as the first allele and the second allele or locus, can be any two alleles of interest at a particular locus.

[0255] In some embodiments, LR is calculated as described in Galván-Femenía et al., Heredity, 2021, 126:537-547, the contents of which are hereby incorporated by reference in their entirety.

[0256] In some embodiments, calculating the degree of relationship includes calculating the likelihood ratio of sharing a Y chromosome between a DNA profile (i.e., a DNA profile from a person of interest) and one or more reference DNA profiles included in a reference DNA profile set (e.g., a reference DNA profile set including one or more reference DNA profiles from relatives of the person of interest). In some embodiments, calculating the likelihood ratio of sharing a Y chromosome includes comparing a SNP set, which is one or more Y-SNPs or includes one or more Y-SNPs. In some embodiments, one or more Y-SNPs include 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84 or 85 Y-SNPs or at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84 or 85 Y-SNPs. In some embodiments, the one or more Y-SNPs are or include 85 Y-SNPs.

[0257] In some embodiments, calculating the likelihood ratio of a shared Y chromosome includes calculating a kinship coefficient based on one or more Y-SNPs from a plurality of SNPs. In some embodiments, calculating the likelihood ratio of a shared Y chromosome includes calculating a log likelihood ratio by providing a DNA profile and one or more reference DNA profiles contained in a reference DNA profile set as input (e.g., as input to PC-Relate). In some embodiments, calculating the likelihood ratio of a shared Y chromosome can allow identification of a matching Y chromosome between a DNA profile (e.g., a DNA profile of a person of interest) and one or more of the reference DNA profiles, which can then be used to determine the likelihood ratio of the male lineage of the person of interest.

[0258] In some embodiments, this includes using a kinship coefficient that calculates the relationship measurement between two samples (e.g., between a DNA profile and one or more reference DNA profiles). In some embodiments, the methods provided herein involve using a kinship model constructed from, for example, a public genetics database (e.g., using PC-AiR or an improved PC-AiR method) to calculate a kinship coefficient, and determining the kinship of a local target sample set (e.g., using PC-Relate), rather than using a large number of publicly accessible sample sets to determine kinship on a public database. In some embodiments, this also includes calculating the likelihood ratio (LR) for each comparison. For example, the likelihood ratio (LR) is a standard measure of association in the forensic field.

[0259] In some embodiments, PC-Relate (Conomos et al., American Journal of Human Genetics, 2016, 98: 127-148, the contents of which are hereby incorporated by reference in their entirety) and PC-AiR (Conomos et al., Genetic Epidemiology, 2015, 39: 276-293, the contents of which are hereby incorporated by reference in their entirety) are used to calculate genome-wide kinship coefficients, shared cMs, and longest fragment cMs, such as previously described in Snedecor et al., Forensic Sci. Int. Genet., 2022, 61: 102769, the contents of which are hereby incorporated by reference in their entirety. This method is useful when the relationship between two individuals is unknown, so a pedigree is not required, which is suitable for situations such as with missing persons or victims of conflict.

[0260] In some embodiments, the PC-AiR method first obtains a set of genotyped individuals and divides them into two non-overlapping subsets: one set contains unrelated individuals representing all individual ancestors (unrelated subsets), and the other set contains individuals with at least one relative in the first subset (related subsets). In order to construct unrelated subsets, the original PC-AiR method was modified to improve the computational efficiency of model construction. Samples with no relatives or the least relatives are added to the unrelated subset, while samples with more relatives are excluded from the unrelated subset. This is performed by calculating each pair of kinship values, as described by Conomos et al. in Genetic Epidemiology, 2015, 39: 276-293, the contents of which are hereby incorporated by reference in their entirety, and each individual is classified as having relatives or not having relatives based on a strict threshold: if the kinship value is greater than 0.01, it is considered to be a relative, and if the kinship value is less than -0.025, it is considered to be unrelated. Samples with less than 5% missing SNP data are excluded. Next, principal component analysis (PCA) is performed on the unrelated subset, and then the variation component values ​​of all individuals in the related subset are predicted based on their genetic similarity to individuals in the unrelated subset. The resulting components represent a model that can be used to identify matches in a set of unknown individuals instead of static population frequencies.

[0261] In some embodiments, the PC-Relate method uses the principal components from PC-AiR and separates genetic relatedness into two components: one component for sharing the same alleles passed down from a recent common ancestor, and another component for sharing alleles due to a more distant common ancestor. The components from PC-AiR are used to estimate allele frequencies based on the ancestral background of the individuals, using linear regression rather than static population frequencies (such as those from gnoMAD). For two individuals i and j, the estimated allele frequencies from the PC-AiR model are then used To calculate the kinship coefficient As shown below

[0262]

[0263] Where s is a SNP among the SNPs that were typed in both individuals, and g is and g js are the numbers of reference alleles in i and j at SNP s, respectively, and and are the expected allele frequencies calculated from the PC-AiR for i and j at SNP s, respectively. The algorithm is called "genome-wide kinship" because it considers the entire genome as a fragment of relatedness, which is represented by the genome-wide kinship coefficient. When the genome-wide kinship algorithm is cited, the genome-wide kinship coefficient is used to identify relationships. When the number of SNPs shared between two individuals is less than 6000, a genome-wide kinship coefficient of more than 0.031 between two individuals is required in this study to be considered relatives.

[0264] Thus, in some embodiments, calculating the degree of relationship includes calculating a kinship coefficient using a genome-wide kinship algorithm as follows:

[0265]

[0266] where the person of interest and the reference DNA profile are i and j, is the kinship coefficient, is the estimated allele frequency, s is the SNP among the SNPs typed in both individuals, and g is and g js are the numbers of reference alleles in i and j at SNP s, respectively, and and are the expected allele frequencies calculated from PC-AiR for i and j at SNP s, respectively.

[0267] In some embodiments, calculating the degree of relationship includes using a "window kinship" method to calculate the kinship coefficient. See Snedecor et al., Forensic Sci Int Genet 2022, 61, 102769, doi: 10.1016 / j.fsigen.2022.102769, the contents of which are hereby incorporated by reference in their entirety. Window kinship involves calculating windows of kinship across genomes to find shared kinship fragments. This is performed by enumerating all possible windows within each chromosome and calculating the kinship coefficients of all windows. These windows are then filtered by the minimum kinship coefficient threshold and included in the shared cMs calculation. The filtered fragments are then iterated, and the SNP extensions that share at least one allele and two alleles are classified respectively. The total shared cM is then calculated across all fragments. When referring to the window kinship algorithm, the total shared cM and the longest cM fragment are used to identify the relationship. When the number of SNPs shared between two individuals is between 6000 and 8000, the shared cM value must be higher than 180 and the longest cM fragment must be higher than 30 to be considered as having a relationship. When the number of SNPs shared between two individuals is between 8000 and 9000, the shared cM value must be higher than 150 and the longest cM fragment must be higher than 30 to be considered as having a relationship. When the number of SNPs shared between two individuals is 9000 or more, the shared cM value must be higher than 140 and the longest cM fragment must be higher than 30 to be considered as having a relationship. The whole genome kinship coefficient can be used to filter any number of shared SNPs. However, Snedecor et al. (above) observed that when the SNP overlap is greater than 6000, filtering for shared cM and longest fragment cM (e.g., using window kinship) has higher specificity, especially for higher degrees of relationship.

[0268] More simply, the number of SNPs typed between two individuals (SNP overlap) can be used to decide when to use a whole genome kinship algorithm (<6000 SNP overlap) and when to use a window kinship algorithm (>6000 SNP overlap). And, when deciding an algorithm based on this SNP overlap, a value or set of values ​​will be used to filter the data to identify relationships, depending on the algorithm selected. The cutoff values ​​for both whole genome kinship and window kinship are selected to ensure high sensitivity, but more importantly, high specificity, as shown in Snedecor et al. (supra). Lowering these thresholds may capture more relationships (i.e., increase sensitivity), but is expected to introduce more false positive results, especially for more distant relationships (e.g., fourth and fifth degree).

[0269] In some embodiments, calculating the degree of relationship includes calculating a kinship coefficient between a DNA profile (e.g., a DNA profile from a person of interest) and one of the one or more reference DNA profiles. In some embodiments, the degree of relationship (e.g., kinship coefficient) between the DNA profile and each of the one or more reference DNA profiles is calculated. In some embodiments, based on the genotypes observed in the two samples, a likelihood ratio is calculated by dividing the probability that the query (e.g., the DNA profile of the person of interest) and the target (e.g., one of the one or more reference DNA profiles) are related by the probability that the query and the target are not related. The results can then be filtered based on the kinship coefficient and the LR to identify the most likely relationship and eliminate false matches from the one or more reference DNA profiles contained in the reference DNA profile set.

[0270] In some embodiments, calculating the likelihood ratio includes comparing multiple SNPs between a DNA spectrum and one or more reference DNA spectrums. In some embodiments, calculating the degree of relationship includes calculating the kinship coefficient based on the kinship SNPs from multiple SNPs. In some embodiments, calculating the degree of relationship includes calculating the kinship coefficient based on the kinship SNPs from multiple SNPs and calculating the kinship coefficient based on the Y-SNPs from multiple SNPs. In some embodiments, calculating the degree of relationship includes calculating the kinship coefficient based on the Y-SNPs from multiple SNPs. In some embodiments, calculating the likelihood ratio includes comparing a SNP set, which includes the kinship SNPs from multiple SNPs between a DNA spectrum and one or more reference DNA spectrums.

[0271] In certain embodiments, the personnel of interest is biologically male, and the method also includes the likelihood ratio of sharing a Y chromosome between calculating a DNA spectrum and one or more reference DNA spectrums. In certain embodiments, the likelihood ratio of calculating a shared Y chromosome includes a SNP set between a comparative DNA spectrum and one or more reference DNA spectrums, and the SNP set includes one or more Y-SNPs. In certain embodiments, one or more Y-SNPs are included in a plurality of SNPs. In certain embodiments, one or more Y-SNPs include at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84 or 85 Y-SNPs. In some such embodiments, one or more Y-SNPs include 85 Y-SNPs. In certain embodiments, the likelihood ratio of calculating a shared Y chromosome includes the genotype based on one or more Y-SNPs, the probability of a DNA spectrum sharing a Y chromosome with a reference DNA spectrum in one or more reference DNA spectrums divided by the probability that a DNA spectrum does not share a Y chromosome with a reference DNA spectrum.

[0272] In some embodiments, calculating the degree of relationship, such as the kinship coefficient, includes using a principal component analysis (PCA) method. In some embodiments, the degree of relationship is calculated using a kinship model. In some embodiments, the degree of relationship is calculated using a kinship model trained using a PCA method. In some embodiments, the PCA method for training the kinship model is PCA. In some embodiments, the PCA method for training the kinship model involves PCA. In some embodiments, the PCA method for training the kinship model can account for sample associations, such as known or implicit associations that can be caused by family structure across samples. In some embodiments, the PCA method is PC-AiR, which can allow for ancestry determination in the presence of known or implicit associations. See, for example, Conomos et al., Robust population structure inference for ancestry prediction and correction for stratification in the presence of associations, Genet Epidemiol., 2015, 39(4): 276-293, the contents of which are incorporated herein by reference. In some embodiments, the PCA method is an improved PC-AiR method, such as described herein.

[0273] In some embodiments, a training database is used to construct a kinship model. In some embodiments, the training database is a genetic database. In some embodiments, the training database is a genetics database. In some embodiments, the training database is a publicly accessible database. In some embodiments, the training database includes 1 to 10 million or more training DNA profiles. In some embodiments, the training database includes or is about or is at least about 1, 5, 25, 50, 75, 100, 500, 1,000, 1,500, 2,000, 3,000, 4,000, 5,000, 10,000, 20,000, 30,000, 40,000, 50,000, 75,000, 100,000, 125,000, 150,000, 175,000, 200,000, 225,000, 250,000 or 10,000,000 training DNA profiles, or a range between any two of the foregoing values. In some embodiments, the training database includes up to or about 100, 500, 1,000, 1,500, 2,000, 3,000, 4,000, 5,000, 10,000, 20,000, 30,000, 40,000, 50,000, 75,000, 100,000, 125,000, 150,000, 175,000, 200,000, 225,000, 250,000, In some embodiments, the number of training DNA profiles can be 275,000, 300,000, 400,000, 500,000, 600,000, 700,000, 800,000, 900,000, 1,000,000, 1,250,000, 1,500,000, 1,750,000, 2,000,000, 3,000,000, 4,000,000, 5,000,000, or 10,000,000 training DNA profiles.In some embodiments, the training database comprises between 5,000 and 500,000, or between 10,000 and 500,000, or between 15,000 and 500,000, or between 20,000 and 500,000, or between 25,000 and 500,000, or between 25,000 and 400,000, or between 25,000 and 300,000, or between 25,000 and 250,000, or between 50,000 and 500,000, or between 50,000 and 400,000, or between 50,000 and 300,000, or between 50,000 and 250,000 training DNA profiles.

[0274] In some embodiments, the PCA method is PC-AiR and the training database comprises at least 1 and up to 100, 200, 300, 400, 500, 600, 700, 800, 900, 1,000, 1,100, 1,200, 1,300, 1,400, 1,500, 1,600, 1,700, 1,800, 1,900, 2,000, 2,100, 2,200, 2,300, 2,400, 2,500, 2,600, 2,700, 2,800, 2,900, 3,000, 3,500, 4,000, 4,500, or 5,000 training DNA profiles, or a range between any two of the foregoing values.

[0275] In some embodiments, the PCA method is a modified PC-Air method and the training database includes at or about or at least at or about 3,000, 4,000, 5,000, 10,000, 20,000, 30,000, 40,000, 50,000, 75,000, 100,000, 125,000, 150,000, 175,000, 200,000, 225,000, 250,000, 275,000, or 10,000,000 training DNA profiles, or a range between any two of the foregoing values.

[0276] In some embodiments, accessing the training database does not require internet access. In some embodiments, training the kinship model does not require internet access. In some embodiments, the training database is accessible locally.

[0277] In some embodiments, the kinship model is trained by applying the PCA method to a training database. In some embodiments, the training DNA profile includes genotypes of multiple SNPs. In some embodiments, the kinship model includes principal components (PCs) obtained for the training database using the PCA method.

[0278] In some aspects, given a training database of training DNA profiles, both PC-AiR and improved PC-AiR methods are able to identify from the training database a sufficiently acceptable set of unrelated samples of training DNA profiles that are as close as possible to all ancestral contexts present in the training database while also being well sampled from all ancestral contexts present in the training database. In some embodiments, both PC-AiR and improved PC-AiR methods are able to identify a set of unrelated samples, such as training DNA profiles, in the training database. In some embodiments, the set of unrelated samples is a set of samples that samples all or nearly all of the ancestral contexts present in the training database.

[0279] In some embodiments, both the PC-AiR and improved PC-AiR methods include an initial step of estimating the kinship between all pairs of samples in the training database. In some embodiments, the kinship coefficient is estimated. In some embodiments, a simplified kinship estimation method called "KING-Robust" is used to estimate the kinship coefficient.

[0280] In some embodiments, PC-AiR then proceeds to the following steps, including: (1) initializing a set "U" using all samples from the training database; (2) for each sample, scanning the set to calculate how many samples in U the sample is related to (called "R"), and how many samples in U the sample is "ancestrally differentiated" from (called "D"); (3) selecting the sample with the highest R, or if there are multiple samples with the highest R, selecting the sample with the highest R and the lowest D; (4) removing the selected sample from U; and (5) repeating from step (2). For example, using PC-AiR, if there are 50,000 samples, the process will look at 50,000 samples in the first iteration. 2 data points, and in the second iteration we look at 49,999 2 data points, and so on, until there are no more relevant samples in the set, which may continue down to, say, 20,000 2 or 10,000 2 In some embodiments, this process continues until U contains only irrelevant samples.

[0281] In some embodiments, PC-AiR considers samples as related samples based on estimated kinship. In some embodiments, samples with an estimated kinship coefficient ≥ 0.025 are considered related samples.

[0282] In some embodiments, PC-AiR considers samples as ancestral differentiation samples based on estimated kinship. In some embodiments, samples with estimated kinship coefficients < 0.025 are considered ancestral differentiation samples.

[0283] In some embodiments, PC-AiR includes the following steps: (1) estimating kinship coefficients between all pairs of samples in a training database (e.g., training DNA profiles), wherein pairs with kinship coefficients > 0.025 are identified as related and pairs with kinship coefficients < -0.025 are identified as ancestrally differentiated; (2) initializing an unrelated sample set including all samples; and (3) iteratively: (i) identifying the set of samples in the unrelated sample set that has the most related samples in the unrelated sample set, designated as X, (ii) identifying the set of samples in X that has the fewest ancestrally differentiated pairs compared to the samples in the unrelated sample set, designated as Y; and (iii) terminating the process if Y has zero samples, or, if Y has at least one sample, randomly selecting a sample from Y to remove from U and repeating starting at step (3)(i).

[0284] In some embodiments, the improved PC-AiR method includes one or more adjustments compared to PC-AiR. In some embodiments, the improved PC-AiR method has a stricter definition of whether the samples are related. In some embodiments, if the estimated kinship coefficient is ≥ 0.01, the improved PC-AiR method considers the samples to be related.

[0285] In some embodiments, the improved PC-AiR method considers samples as ancestrally differentiated based on the estimated kinship coefficient. In some embodiments, samples with an estimated kinship coefficient <0.025 are considered ancestrally differentiated.

[0286] In some embodiments, the improved PC-AiR method includes removing all samples with >5% missing genotypes (eg, more than 5% of SNPs in the DNA profile) to ensure that each sample has sufficient information content.

[0287] In some embodiments, the improved PC-AiR method includes the following steps: (1) for each sample, calculate: "R" is the total number of related samples in the training database, "D" is the number of ancestrally differentiated samples in the database, and "S" is the set of related samples; (2) sort all samples by R (ascending) and D (descending); (3) iteratively traverse the sorted list of samples and: (i) if the sample is not in the "related" set, add it to the irrelevant set and add all samples from S (i.e., DNA profiles associated with the sample) to the related set; or (ii) if the sample is in the "related" set, ignore the sample and move to the next sample. In some aspects, this improved PC-AiR method allows a process with largely linear complexity (i.e., the running time scales linearly with the number of samples) rather than exponential complexity.

[0288] In some embodiments, the improved PC-AiR method includes the following steps: (1) estimating kinship coefficients between all pairs of samples of the training database, such as DNA profiles, wherein pairs with kinship coefficients > 0.01 are identified as related, and pairs with kinship coefficients < -0.025 are identified as ancestrally differentiated; (2) removing all DNA profiles with ≥ 5% missing data; and (3) ranking all DNA profiles by identifying each DNA profile with a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles ranked from least to most in the complete database, and ties are broken by the number of ancestrally differentiated DNA profiles ranked from most to least in the complete database. In some embodiments, step (3) involves iteratively traversing the ranked DNA profiles, and for each DNA profile: (i) if the DNA profile is not already in the related sample set, adding it to the unrelated sample set and adding all related DNA profiles to the related sample set, and (ii) if the DNA profile is already in the related sample set, skipping to the next DNA profile and repeating starting at step (3)(i).

[0289] In some embodiments, after determining the irrelevant sample set using AiR or the improved PC-AiR method, PCA is applied to the irrelevant sample set to train the kinship model. In some embodiments, the kinship model also includes PC values ​​calculated for the relevant sample set. In some embodiments, the PC values ​​of the relevant sample set are determined based on the PCs obtained for the irrelevant sample set.

[0290] In some embodiments, PCA is applied to the entire training database to build a kinship model.

[0291] In some embodiments, provided methods involve training a kinship model.

[0292] In some embodiments, the provided methods do not involve training a kinship model. In some embodiments, a kinship model is trained before calculating a degree of relationship (eg, a kinship coefficient).

[0293] In some embodiments, access to the kinship model does not require internet access. In some embodiments, the kinship model is accessible locally.

[0294] In some embodiments, a kinship model is used to calculate the degree of relationship, for example, a kinship coefficient. In some embodiments, the PC of a kinship model is used to calculate the degree of relationship. In some embodiments, calculating the degree of relationship involves obtaining the PC value of a DNA profile, for example, a DNA profile of a person of interest. In some embodiments, calculating the degree of relationship involves obtaining the PC value of a reference DNA profile or multiple reference DNA profiles. In some embodiments, the degree of relationship is calculated using the PC value of a DNA profile. In some embodiments, the degree of relationship is calculated using the PC value of a DNA profile and a reference DNA profile or multiple reference DNA profiles.

[0295] In some embodiments, PC-Relate is used to calculate the degree of relationship (e.g., kinship coefficient). See, e.g., Conomos et al., Model-free estimation of recent genetic associations, Am. J. Hum. Genet., 98(1): 127-148 (2016), the contents of which are hereby incorporated by reference in their entirety. In some embodiments, the degree of relationship is calculated by providing a DNA profile (e.g., a DNA profile of a person of interest) as an input to PC-Relate. In some embodiments, the degree of relationship is calculated by providing a kinship model (e.g., PC) and a DNA profile as input to PC-Relate. In some embodiments, a reference DNA profile or multiple reference DNA profiles are also provided as input to PC-Relate.

[0296] In some embodiments, the degree of relationship (eg, kinship coefficient) is calculated locally. In some embodiments, calculating the degree of relationship does not require Internet access.

[0297] In some embodiments, the methods described herein further include identifying a person of interest. In some embodiments, identifying a person of interest includes identifying the person of interest by the legal name of the person of interest. In some embodiments, identifying a person of interest includes identifying the person of interest by the familial relationship of the person of interest to one or more known persons in a reference DNA profile set. For example, in some embodiments, identifying a person of interest includes identifying that the person of interest is the son or daughter of a particular known person, and / or all the siblings of a particular known person.

[0298] Reagent test kit

[0299] Provided herein are kits comprising any primer, reagent or composition described herein, which may also include instructions for methods of using the kit (such as uses described herein). Kits described herein may also include other materials desirable from a commercial and user perspective, including other buffers, diluents, filters, and package inserts with instructions for performing any method described herein.

[0300] In some embodiments, provided herein are kits comprising at least one container means, wherein the at least one container means comprises any one of a plurality of primers as described herein.

[0301] Exemplary Embodiments

[0302] Exemplary embodiments provided herein include:

[0303] 1. A method for performing DNA-based kinship analysis, comprising:

[0304] Provide nucleic acid samples from interested persons,

[0305] amplifying the nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences, the plurality of target sequences collectively comprising a plurality of at least between or about between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) to generate amplification products, wherein the amplification is performed in one or more multiplex PCR reactions,

[0306] generating a nucleic acid library from the amplification products,

[0307] sequencing the nucleic acid library generated from the amplification products,

[0308] analyzing the sequence of the amplified product,

[0309] determining the genotypes of the plurality of SNPs to generate a DNA profile, and

[0310] The degree of relationship of the DNA profile to one or more reference DNA profiles is calculated, wherein the one or more reference DNA profiles are contained in a set of reference DNA profiles, the set of reference DNA profiles including one or more reference DNA profiles from relatives of the person of interest.

[0311] 2. A method for performing DNA-based kinship analysis, comprising:

[0312] Provide nucleic acid samples from interested persons,

[0313] amplifying the nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences, the plurality of target sequences collectively comprising a plurality of at least between or about between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) to generate amplification products, wherein the amplification is performed in one or more multiplex PCR reactions,

[0314] generating a nucleic acid library from the amplification products,

[0315] sequencing the nucleic acid library generated from the amplification products,

[0316] determining the genotypes of the plurality of SNPs to generate a DNA profile, and

[0317] The degree of relationship of the DNA profile to one or more reference DNA profiles is calculated, wherein the one or more reference DNA profiles are contained in a set of reference DNA profiles, the set of reference DNA profiles including one or more reference DNA profiles from relatives of the person of interest.

[0318] 3. The method according to embodiment 1 or 2, wherein the sequencing is performed using massively parallel sequencing (MPS).

[0319] 4. The method of any one of embodiments 1 to 3, wherein the sequencing does not include whole genome sequencing (WGS).

[0320] 5. The method of any one of embodiments 1 to 4, further comprising generating a family tree comprising the DNA profile associated with one or more DNA profiles.

[0321] 6. A method for constructing a nucleic acid library of a person of interest, comprising:

[0322] Provide nucleic acid samples from interested persons,

[0323] The nucleic acid sample is amplified using multiple primers that specifically hybridize to multiple target sequences, wherein the multiple target sequences collectively include multiple single nucleotide polymorphisms (SNPs) of at least between 2,000 and 50,000 or about between 2,000 and 50,000, thereby generating a nucleic acid library including amplification products, wherein the amplification is performed in one or more multiplex PCR reactions.

[0324] 7. The method according to embodiment 6 further includes the step of sequencing the amplified product to generate a DNA profile of the person of interest.

[0325] 8. A method for constructing a nucleic acid library of a reference DNA sample, comprising:

[0326] Provide nucleic acid samples from relatives of the person of interest,

[0327] The nucleic acid sample is amplified using multiple primers that specifically hybridize to multiple target sequences, wherein the multiple target sequences collectively include multiple single nucleotide polymorphisms (SNPs) of at least between 2,000 and 50,000 or about between 2,000 and 50,000, thereby generating a nucleic acid library including amplification products, wherein the amplification is performed in one or more multiplex PCR reactions.

[0328] 9. The method of embodiment 8, wherein the relative is a first, second, third, fourth, or fifth degree relative of the person of interest.

[0329] 10. The method of embodiment 8 or 9, wherein the relative is a first, second or third degree relative of the person of interest.

[0330] 11. The method of any one of embodiments 1 to 10, wherein the nucleic acid sample comprises genomic DNA.

[0331] 12. The method of any one of embodiments 1 to 11, wherein the nucleic acid sample comprises one or more enzyme inhibitors.

[0332] 13. The method of embodiment 12, wherein the one or more enzyme inhibitors include one or more inhibitors selected from the group consisting of heme, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite.

[0333] 14. The method according to any one of embodiments 1 to 13, wherein the nucleic acid sample comprises low-quality nucleic acid molecules and / or low-quantity nucleic acid molecules.

[0334] 15. The method according to embodiment 14, wherein the low-quality nucleic acid molecules are degraded genomic DNA and / or fragmented genomic DNA.

[0335] 16. The method of embodiment 14 or embodiment 15, wherein the low-quality nucleic acid molecules have a molecular weight of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200 or a DI of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200.

[0336] 17. A method according to embodiment 14 or embodiment 15, wherein the low-quality nucleic acid molecule has a DI of at least 1 and up to or less than 158.3.

[0337] 18. The method of any one of embodiments 1 to 13, wherein the nucleic acid sample comprises high-quality nucleic acid molecules.

[0338] 19. The method of embodiment 18, wherein the high-quality nucleic acid molecules have a DI less than 1.

[0339] 20. The method of any one of embodiments 1 to 19, wherein the person of interest is a missing person.

[0340] 21. The method of any one of embodiments 1 to 19, wherein the person of interest is a victim of a disaster or conflict.

[0341] 22. The method of any one of embodiments 1 to 21, wherein the nucleic acid sample is derived from saliva, blood, semen, hair, teeth, bones, or skin.

[0342] 23. The method of embodiment 22, wherein the nucleic acid sample is derived from saliva, blood or semen.

[0343] 24. The method of embodiment 22, wherein the nucleic acid sample is derived from bone or hair.

[0344] 25. The method of any one of embodiments 1 to 21, wherein the nucleic acid sample is derived from a buccal swab, paper, fabric, or other matrix or object impregnated with saliva, blood, semen or other body fluids, or containing hair or skin cells.

[0345] 26. The method of any one of embodiments 1 to 25, wherein the nucleic acid sample comprises between or about between 3 pg and 100 ng of genomic DNA.

[0346] 27. The method of any one of embodiments 1 to 26, wherein the nucleic acid sample comprises between or about 100 pg and 5 ng of genomic DNA, between or about 50 pg and 5 ng of genomic DNA, or between or about 3 pg and 5 ng of genomic DNA.

[0347] 28. The method of embodiment 26 or 27, wherein the nucleic acid sample comprises 1 ng or about 1 ng of genomic DNA.

[0348] 29. The method of any one of embodiments 1 to 28, wherein the plurality of SNPs comprises kinship SNPs (kiSNPs).

[0349] 30. A method according to any one of embodiments 1 to 29, wherein the multiple SNPs include Y chromosome SNPs (Y-SNPs).

[0350] 31. A method according to any one of embodiments 1 to 30, wherein the plurality of SNPs include kiSNPs and Y-SNPs.

[0351] 32. The method of any one of embodiments 1 to 31, wherein the plurality of SNPs comprises kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotypic SNPs (piSNPs), X-chromosome SNPs (X-SNPs), and Y-chromosome SNPs (Y-SNPs).

[0352] 33. The method of any one of embodiments 1 to 28, wherein the plurality of SNPs comprises one or more SNPs selected from the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs.

[0353] 34. A method according to any one of embodiments 1 to 33, wherein at least or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the multiple SNPs are kinship SNPs.

[0354] 35. The method of any one of embodiments 1 to 34, wherein the set of reference DNA profiles comprises up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles.

[0355] 36. The method of any one of embodiments 1 to 35, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest.

[0356] 37. The method of any one of embodiments 1 to 36, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest, and wherein each of at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set is a first degree relative, a second degree relative, a third degree relative, a fourth degree relative or a fifth degree relative.

[0357] 38. The method of any one of embodiments 1 to 37, wherein at least 50% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest.

[0358] 39. The method of any one of embodiments 36 to 38, wherein each relative of the person of interest in the reference DNA profile set is a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative, or a fifth-degree relative of the person of interest.

[0359] 40. The method of embodiment 39, wherein each relative of the person of interest in the reference DNA profile is a first-degree relative, a second-degree relative, or a third-degree relative of the person of interest.

[0360] 41. The method of any one of embodiments 1 to 40, wherein the identity of each relative of the person of interest in the reference DNA profile is known.

[0361] 42. The method of any one of embodiments 1 to 41, wherein the identity of each of the one or more reference DNA profiles in the reference DNA profile set is known.

[0362] 43. The method of any one of embodiments 1 to 42, wherein the reference DNA profile set is in a database.

[0363] 44. The method of embodiment 43, wherein the database is not publicly accessible.

[0364] 45. A method according to any one of embodiments 1 to 44, wherein the sequencing comprises a sequencing complexity of up to 40 multiplexes.

[0365] 46. ​​A method according to any one of embodiments 1 to 44, wherein the sequencing comprises a sequencing complexity of up to 32 multiplexes.

[0366] 47. A method according to any one of embodiments 1 to 44, wherein the sequencing comprises a sequencing complexity of 12-plex to 32-plex.

[0367] 48. A method according to any one of embodiments 1 to 44, wherein the sequencing comprises a sequencing complexity of 24 to 32 multiplexes.

[0368] 49. The method of any one of embodiments 1 to 44, wherein the sequencing comprises a sequencing complexity of or about 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, or 35-plex.

[0369] 50. A method according to any one of embodiments 1 to 49, wherein the sequencing comprises a sequencing complexity of 8 to 16-plex or about 8 to 16-plex for postmortem samples, and / or the sequencing comprises a sequencing complexity of 24 to 40-plex or about 24 to 40-plex for antemortem samples.

[0370] 51. A method according to any one of embodiments 1 to 50, wherein the sequencing comprises a sequencing complexity of 12-plex or approximately 12-plex for postmortem samples, and / or the sequencing comprises a sequencing complexity of 32-plex or approximately 32-plex for antemortem samples.

[0371] 52. The method of any one of embodiments 1 to 51, wherein the sequencing comprises a sequencing complexity of 30-plex, 31-plex, or 32-plex or a sequencing complexity of approximately 30-plex, 31-plex, or 32-plex.

[0372] 53. The method according to any one of embodiments 1 to 52 also includes identifying the person of interest.

[0373] 54. A method for calculating a degree of association, comprising:

[0374] obtaining a DNA profile comprising genotypes of at least between or about 2,000 and 50,000 SNPs, wherein the DNA profile is from the person of interest; and

[0375] A degree of relationship between the DNA profile and one or more reference DNA profiles is calculated, wherein the one or more reference DNA profiles are included in a set of reference DNA profiles, the set of reference DNA profiles including one or more reference DNA profiles from relatives of the person of interest.

[0376] 55. A method for calculating a degree of association, comprising:

[0377] generating a DNA profile comprising genotypes of at least between or about 2,000 and 50,000 SNPs, wherein the DNA profile is from the person of interest; and

[0378] A degree of relationship between the DNA profile and one or more reference DNA profiles is calculated, wherein the one or more reference DNA profiles are included in a set of reference DNA profiles, the set of reference DNA profiles including one or more reference DNA profiles from relatives of the person of interest.

[0379] 56. A method according to any one of embodiments 1 to 55, wherein the degree of relationship is calculated using a kinship model.

[0380] 57. A method according to any one of embodiments 1 to 56, wherein the degree of relationship is calculated using a kinship model trained using a PCA method.

[0381] 58. A method according to embodiment 57, wherein the PCA method used to train the kinship model is PCA or involves PCA.

[0382] 59. The method of embodiment 57 or embodiment 58, wherein the PCA method is PC-AiR.

[0383] 60. A method according to embodiment 59, wherein the PC-AiR includes the following steps: (1) estimating kinship coefficients between all pairs of samples in a training database (optionally training DNA profiles), wherein pairs with kinship coefficients > 0.025 are identified as related and pairs with kinship coefficients < -0.025 are identified as ancestrally differentiated; (2) initializing an unrelated sample set including all samples; and (3) iteratively: (i) identifying the set of samples in the unrelated sample set that has the most related samples in the unrelated sample set, designated as X, (ii) identifying the set of samples in X that has the fewest ancestrally differentiated pairs compared to the samples in the unrelated sample set, designated as Y; and (iii) terminating the process if Y has zero samples, or, if Y has at least one sample, randomly selecting a sample from Y to remove from U and repeating starting at step (3)(i).

[0384] 61. The method of embodiment 57 or embodiment 58, wherein the PCA method is a modified PC-Air.

[0385] 62. The method of embodiment 61, wherein the improved PC-AiR comprises the following steps: (1) estimating kinship coefficients between all pairs of samples of the training database (optionally training DNA profiles), wherein pairs with kinship coefficients > 0.01 are identified as related, and pairs with kinship coefficients < -0.025 are identified as ancestrally differentiated; (2) removing all DNA profiles with ≥ 5% missing data; and (3) ranking all DNA profiles by identifying each DNA profile with a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles ranked from least to most in the complete database, and ties are broken by the number of ancestrally differentiated DNA profiles ranked from most to least in the complete database. In some embodiments, step (3) involves iteratively traversing the ranked DNA profiles, and for each DNA profile: (i) if the DNA profile is not already in the related sample set, adding it to the unrelated sample set and adding all related DNA profiles to the related sample set, and (ii) if the DNA profile is already in the related sample set, skipping to the next DNA profile and repeating starting at step (3)(i).

[0386] 63. A method according to any one of embodiments 1 to 62, wherein calculating the degree of relationship includes using PC-Relate to calculate a kinship coefficient.

[0387] 64. The method of embodiment 63, wherein the degree of relationship is calculated by using the DNA profile of the person of interest as an input to PC-Relate.

[0388] 65. The method of any one of embodiments 57 to 64, wherein the degree of relationship is calculated by providing a kinship model and the DNA profile of the person of interest as input to PC-Relate.

[0389] 66. The method of any one of embodiments 63 to 65, wherein the one or more reference DNA profiles are also provided as input to PC-Relate.

[0390] 67. The method of any one of embodiments 1 to 66, wherein calculating the degree of relationship comprises calculating a kinship coefficient using a whole genome kinship algorithm as follows:

[0391]

[0392] wherein the person of interest and the reference DNA profile of the one or more reference DNA profiles are i and j, is the kinship coefficient, is the estimated allele frequency, s is the SNP among the SNPs typed in both individuals, and g is and g js are the numbers of reference alleles in i and j at SNP s, respectively, and and are the expected allele frequencies calculated from PC-AiR for i and j at SNP s, respectively.

[0393] 68. A method according to any one of embodiments 1 to 67, wherein calculating the degree of relationship includes calculating a likelihood ratio.

[0394] 69. The method of embodiment 68, wherein calculating the likelihood ratio comprises comparing the plurality of SNPs between the DNA profile and the one or more reference DNA profiles.

[0395] 70. A method according to embodiment 68, wherein calculating the likelihood ratio includes comparing a set of SNPs between the DNA profile and the one or more reference DNA profiles, and the set of SNPs includes kinship SNPs from within the plurality of SNPs.

[0396] 71. A method according to any one of embodiments 68 to 70, wherein calculating the likelihood ratio includes dividing the probability that the DNA profile is associated with a reference DNA profile in the one or more reference DNA profiles based on the genotypes of the multiple SNPs by the probability that the DNA profile is not associated with the reference DNA profile.

[0397] 72. The method of any one of embodiments 68 to 71, wherein the likelihood ratio (LR) is calculated as follows:

[0398]

[0399] Wherein, D represents the genotype, H r represents the assumption that individuals are correlated, and H u The assumption that individuals are unrelated.

[0400] 73. The method of any one of embodiments 68 to 71, wherein the LR is calculated as follows:

[0401]

[0402] Wherein, 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2.

[0403] 74. A method according to any one of embodiments 1 to 73, wherein the person of interest is biologically male, and the method further comprises calculating the likelihood ratio of sharing a Y chromosome between the DNA profile and the one or more reference DNA profiles.

[0404] 75. A method according to embodiment 74, wherein calculating the likelihood ratio of a shared Y chromosome includes comparing a set of SNPs between the DNA profile and the one or more reference DNA profiles, wherein the set of SNPs includes one or more Y-SNPs.

[0405] 76. A method according to embodiment 75, wherein the one or more Y-SNPs are contained within the multiple SNPs.

[0406] 77. A method according to embodiment 75, wherein the one or more Y-SNPs include at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84 or 85 Y-SNPs.

[0407] 78. A method according to any one of embodiments 75 to 77, wherein the one or more Y-SNPs include 85 Y-SNPs.

[0408] 79. A method according to any one of embodiments 75 to 78, wherein calculating the likelihood ratio of sharing a Y chromosome comprises dividing the probability that the DNA profile shares a Y chromosome with a reference DNA profile in the one or more reference DNA profiles by the probability that the DNA profile does not share a Y chromosome with the reference DNA profile based on the genotype of the one or more Y-SNPs.

[0409] 80. The method of any one of embodiments 1 to 79, wherein at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the DNA profiles in the reference DNA profile set are from relatives of missing persons or victims of disaster or conflict.

[0410] 81. The method of any one of embodiments 1 to 80, wherein each of said DNA profiles in said reference DNA profile set is from a relative of a missing person or a victim of a disaster or conflict.

[0411] 82. The method of any one of embodiments 1 to 81, wherein the set of reference DNA profiles comprises up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 reference DNA profiles.

[0412] 83. The method of any one of embodiments 1 to 82, wherein the set of reference DNA profiles comprises up to 100 reference DNA profiles.

[0413] 84. The method of any one of embodiments 1 to 83, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest.

[0414] 85. The method of any one of embodiments 1 to 84, wherein at least 50% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest.

[0415] 86. The method of embodiment 84 or embodiment 85, wherein each relative of the person of interest in the reference DNA profile set is a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative, or a fifth-degree relative of the person of interest.

[0416] 87. The method of any one of embodiments 1 to 86, wherein the identity of each relative of the person of interest in the reference DNA profile is known.

[0417] 88. The method of any one of embodiments 1 to 87, wherein the identity of each relative of the person of interest in the DNA reference profile is known.

[0418] 89. The method of any one of embodiments 1 to 88, wherein the identity of each of the one or more reference DNA profiles in the reference DNA profile set is known.

[0419] 90. The method of any one of embodiments 1 to 89, wherein the reference DNA profile set is in a database.

[0420] 91. The method of embodiment 90, wherein the database is not publicly accessible.

[0421] 92. A method according to embodiment 90 or embodiment 91, wherein the database cannot be accessed by a third party genetics service.

[0422] 93. A nucleic acid library constructed using the method of any one of embodiments 6 to 92.

[0423] 94. A plurality of primers that specifically hybridize to a plurality of target sequences, the plurality of target sequences comprising at least between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) or approximately between 2,000 and 50,000 of the SNPs in a nucleic acid sample from a person of interest, wherein the plurality of primers are used in one or more multiplex PCR reactions to amplify the nucleic acid sample to produce amplification products.

[0424] 95. A plurality of primers that specifically hybridize to a plurality of target sequences, the plurality of target sequences comprising at least between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) or about between 2,000 and 50,000 of said SNPs in a nucleic acid sample from a person of interest and a nucleic acid sample from one or more reference samples,

[0425] wherein the one or more reference samples include samples from relatives of the person of interest, and

[0426] The plurality of primers are used in one or more multiplex PCR reactions to amplify the nucleic acid sample from the person of interest and the nucleic acid sample from one or more reference samples to generate amplification products.

[0427] 96. A plurality of primers according to Example 94 or Example 95, wherein the nucleic acid sample from the person of interest comprises genomic DNA.

[0428] 97. A plurality of primers according to any one of embodiments 94 to 96, wherein the nucleic acid sample from the person of interest comprises one or more enzyme inhibitors.

[0429] 98. The plurality of primers of embodiment 97, wherein the one or more enzyme inhibitors comprise one or more inhibitors selected from the group consisting of heme, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite.

[0430] 99. A plurality of primers according to any one of embodiments 94 to 98, wherein the nucleic acid sample from the person of interest comprises low-quality nucleic acid molecules and / or low-quantity nucleic acid molecules.

[0431] 100. A plurality of primers according to Example 99, wherein the low-quality nucleic acid molecules are degraded genomic DNA and / or fragmented genomic DNA.

[0432] 101. A plurality of primers according to embodiment 99 or embodiment 100, wherein the low-quality nucleic acid molecules have a length of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or a degradation index (DI) of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200.

[0433] 102. A plurality of primers according to any one of embodiments 99 to 101, wherein the low-quality nucleic acid molecule has a DI of at least 1 and up to or less than 158.3.

[0434] 103. The multiple primers of any one of embodiments 94 to 102, wherein the nucleic acid sample from the person of interest and / or the nucleic acid sample from one or more reference samples comprises high-quality nucleic acid molecules.

[0435] 104. A plurality of primers according to embodiment 103, wherein the high-quality nucleic acid molecules have a DI less than 1.

[0436] 105. A plurality of primers according to any one of embodiments 94 to 104, wherein the person of interest is a missing person.

[0437] 106. A plurality of primers according to any one of embodiments 94 to 104, wherein the person of interest is a victim of a disaster or conflict.

[0438] 107. A plurality of primers according to any one of embodiments 94 to 106, wherein the nucleic acid sample from the person of interest is derived from an oral swab, paper, fabric, or other matrix or object impregnated with saliva, blood or other body fluids, or containing hair or skin cells.

[0439] 108. A plurality of primers according to any one of embodiments 94 to 107, wherein the nucleic acid sample from the person of interest comprises between 3 pg and 100 ng or about between 3 pg and 100 ng of genomic DNA.

[0440] 109. A plurality of primers according to any one of embodiments 94 to 108, wherein the nucleic acid sample from the person of interest comprises genomic DNA at or between 100 pg and 5 ng, genomic DNA at or between 50 pg and 5 ng, or genomic DNA at or between 3 pg and 5 ng.

[0441] 110. A plurality of primers according to embodiment 108 or embodiment 109, wherein the nucleic acid sample from the person of interest comprises 1 ng or about 1 ng of genomic DNA.

[0442] 111. A plurality of primers according to any one of embodiments 94 to 110, wherein the plurality of SNPs include kinship SNPs (kiSNPs).

[0443] 112. A method according to any one of embodiments 94 to 111, wherein the multiple SNPs include Y chromosome SNPs (Y-SNPs).

[0444] 113. A method according to any one of embodiments 94 to 112, wherein the multiple SNPs include kiSNPs and Y-SNPs.

[0445] 114. A plurality of primers according to any one of embodiments 94 to 113, wherein the plurality of SNPs comprises kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotypic SNPs (piSNPs), X-chromosome SNPs (X-SNPs), and Y-chromosome SNPs (Y-SNPs).

[0446] 115. A plurality of primers according to any one of embodiments 94 to 111, wherein the plurality of SNPs include one or more SNPs selected from the group consisting of kiSNP, aiSNP, iiSNP, piSNP, X-SNP and Y-SNP.

[0447] 116. A plurality of primers according to any one of embodiments 94 to 115, wherein at least or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the plurality of SNPs are kinship SNPs.

[0448] 117. A plurality of primers according to any one of embodiments 94 to 116, wherein at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the DNA profiles in the reference DNA profile set are from relatives of missing persons or victims of disasters or conflicts.

[0449] 118. A plurality of primers according to any one of embodiments 94 to 116, wherein each of the one or more reference samples is from a relative of a missing person or a victim of a disaster or conflict.

[0450] 119. A plurality of primers according to any one of embodiments 94 to 118, wherein the one or more reference samples include up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900 or 1000 reference samples.

[0451] 120. A plurality of primers according to any one of embodiments 94 to 119, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the one or more reference samples are from relatives of the person of interest.

[0452] 121. A plurality of primers according to any one of embodiments 94 to 120, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest, and wherein each of at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set is a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative or a fifth-degree relative.

[0453] 122. A plurality of primers according to any one of embodiments 94 to 121, wherein at least 50% of the one or more reference samples are from relatives of the person of interest.

[0454] 123. A plurality of primers according to any one of embodiments 120 to 122, wherein each relative of the person of interest in the one or more reference samples is a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative or a fifth-degree relative of the person of interest.

[0455] 124. A plurality of primers according to embodiment 123, wherein each relative of the person of interest in the one or more reference samples is a first-degree relative, a second-degree relative or a third-degree relative of the person of interest.

[0456] 125. A plurality of primers as described in any one of embodiments 94 to 124, wherein the identity of each relative of the person of interest in the one or more reference samples is known.

[0457] 126. A plurality of primers according to any one of embodiments 94 to 125, wherein the identity of each of the one or more reference samples is known.

[0458] 127. A method for constructing a DNA profile, comprising:

[0459] Provide nucleic acid samples from interested persons,

[0460] amplifying the nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences, the plurality of target sequences collectively comprising a plurality of at least between or about between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) to generate amplification products, wherein the amplification is performed in one or more multiplex PCR reactions,

[0461] The amplified product is sequenced.

[0462] The genotypes of the plurality of SNPs are determined, thereby generating a DNA profile.

[0463] 128. A method for constructing a DNA profile, comprising:

[0464] Provide nucleic acid samples from interested persons,

[0465] Providing nucleic acid samples from relatives of the person of interest,

[0466] amplifying the nucleic acid sample from the person of interest and the nucleic acid sample from the relative using a plurality of primers that specifically hybridize to a plurality of target sequences, the plurality of target sequences collectively comprising a plurality of at least between or about between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) to generate amplification products, wherein the amplification is performed in one or more multiplex PCR reactions,

[0467] The amplified product is sequenced.

[0468] The genotypes of the plurality of SNPs are determined, thereby generating DNA profiles of the person of interest and the relatives of the person of interest.

[0469] 129. A method according to Example 127 or Example 128, wherein the sequencing does not include whole genome sequencing (WGS).

[0470] 130. A method according to embodiment 127 or embodiment 129, wherein the nucleic acid sample includes genomic DNA.

[0471] 131. The method of Example 128 or Example 129, wherein the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative of the person of interest comprises genomic DNA.

[0472] 132. The method of any one of embodiments 127 to 131, wherein the nucleic acid sample, the nucleic acid sample of the person of interest, and / or the nucleic acid sample of the relative comprises one or more enzyme inhibitors.

[0473] 133. The method of embodiment 132, wherein the one or more enzyme inhibitors comprise one or more inhibitors selected from the group consisting of heme, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite.

[0474] 134. The method of any one of embodiments 127 to 133, wherein the nucleic acid sample, the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative comprises low-quality nucleic acid molecules and / or low-quantity nucleic acid molecules.

[0475] 135. A method according to embodiment 134, wherein the low-quality nucleic acid molecules are degraded genomic DNA and / or fragmented genomic DNA.

[0476] 136. The method of embodiment 134 or embodiment 135, wherein the low-quality nucleic acid molecules have a molecular weight of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or a degradation index (DI) of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200.

[0477] 137. A method according to any one of embodiments 134 to 136, wherein the low-quality nucleic acid molecule has a DI of at least 1 and up to or less than 158.3.

[0478] 138. The method of any one of embodiments 127 to 133, wherein the nucleic acid sample, the nucleic acid sample of the person of interest, and / or the nucleic acid sample of the relative comprises high-quality nucleic acid molecules.

[0479] 139. A method according to embodiment 138, wherein the high-quality nucleic acid molecules have a DI less than 1.

[0480] 140. The method of any one of embodiments 127 to 139, wherein the person of interest is a missing person.

[0481] 141. The method of any one of embodiments 127 to 139, wherein the person of interest is a victim of a disaster or conflict.

[0482] 142. The method of any one of embodiments 128, 129, and 131 to 141, wherein the relative of the person of interest is a first, second, third, fourth, or fifth degree relative.

[0483] 143. The method of any one of embodiments 128, 129, and 131-141, wherein the relative of the person of interest is a first, second, or third degree relative.

[0484] 144. The method of any one of embodiments 127 to 143, wherein the nucleic acid sample, the nucleic acid sample of the person of interest, and / or the nucleic acid sample of the relative is derived from an oral swab, paper, fabric, or other matrix or object impregnated with saliva, blood or other body fluids, or containing hair or skin cells.

[0485] 145. The method of any one of embodiments 127 to 144, wherein the nucleic acid sample, the nucleic acid sample of the person of interest, and / or the nucleic acid sample of the relative comprises between 3 pg and 100 ng or about 3 pg and 100 ng of genomic DNA.

[0486] 146. A method according to any one of embodiments 127 to 145, wherein the nucleic acid sample, the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative includes between 100 pg and 5 ng or about 100 pg and 5 ng of genomic DNA, between 50 pg and 5 ng or about 50 pg and 5 ng of genomic DNA, or between 3 pg and 5 ng or about 3 pg and 5 ng of genomic DNA.

[0487] 147. The method of embodiment 145 or embodiment 146, wherein the nucleic acid sample, the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative comprises 1 ng or about 1 ng of genomic DNA.

[0488] 148. A method according to any one of embodiments 127 to 147, wherein the multiple SNPs include kinship SNPs.

[0489] 149. A method according to any one of embodiments 127 to 148, wherein the multiple SNPs include Y chromosome SNPs (Y-SNPs).

[0490] 150. A method according to any one of embodiments 127 to 149, wherein the multiple SNPs include kiSNPs and Y-SNPs.

[0491] 151. The method of any one of embodiments 127 to 150, wherein the plurality of SNPs comprises kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotypic SNPs (piSNPs), X-chromosome SNPs (X-SNPs), and Y-chromosome SNPs (Y-SNPs).

[0492] 152. A method according to any one of embodiments 127 to 151, wherein the plurality of SNPs include one or more SNPs selected from the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs and Y-SNPs.

[0493] 153. A method according to any one of embodiments 127 to 152, wherein at least or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the multiple SNPs are kinship SNPs.

[0494] 154. A method according to any one of embodiments 1 to 92 and 127 to 153, wherein the sequencing comprises a sequencing complexity of up to 40 multiplexes.

[0495] 155. A method according to any one of embodiments 1 to 92 and 127 to 153, wherein the sequencing comprises a sequencing complexity of up to 32 multiplexes.

[0496] 156. A method according to any one of embodiments 1 to 92 and 127- to 53, wherein the sequencing comprises a sequencing complexity of 12-plex to 32-plex.

[0497] 157. A method according to any one of embodiments 1 to 92 and 127 to 153, wherein the sequencing comprises a sequencing complexity of 24 to 32 multiplexes.

[0498] 158. The method of any one of embodiments 1-92 and 127-153, wherein: (a) the sequencing comprises 4-plex, 5-plex, 6-plex, 7-plex, 8-plex, 9-plex, 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex , 34-plex, 35-plex, 36-plex, 37-plex, 38-plex, 39-plex, 40-plex, 41-plex, 42-plex, 43-plex, 44-plex, 45-plex or about 4-plex, 5-plex, 6-plex, 7-plex, 8-plex, 9-plex, 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex or (b) the sequencing comprises a sequencing complexity of 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, 35-plex, 36-plex, 37-plex, 38-plex, 39-plex, 40-plex, 41-plex, 42-plex, 43-plex, 44-plex, and 45-plex; or (b) the sequencing comprises a sequencing complexity of 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, or a sequencing complexity of about 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, or 35-plex.

[0499] 159. The method of any one of embodiments 1 to 92 and 127 to 158, wherein the sequencing comprises a sequencing complexity of 8 to 16-plex or about 8 to 16-plex for postmortem samples, and / or the sequencing comprises a sequencing complexity of 24 to 40-plex or about 24 to 40-plex for antemortem samples.

[0500] 160. The method of any one of embodiments 1 to 92 and 127 to 158, wherein the sequencing comprises a sequencing complexity of 12-plex or approximately 12-plex for postmortem samples, and / or the sequencing comprises a sequencing complexity of 32-plex or approximately 32-plex for antemortem samples.

[0501] 161. The method of any one of embodiments 1 to 92 and 127 to 153, wherein the sequencing comprises a sequencing complexity of 30-plex, 31-plex, or 32-plex or a sequencing complexity of approximately 30-plex, 31-plex, or 32-plex.

[0502] 162. A method for identifying genetic relatives by DNA profiles, comprising:

[0503] Calculating a degree of relationship between the DNA profile of any one of embodiments 127 to 161 and one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference DNA profile set, the reference DNA profile set including one or more reference DNA profiles from relatives of the person of interest; and

[0504] A family tree is generated, the family tree including the DNA profile in relation to the one or more reference DNA profiles.

[0505] 163. A method according to embodiment 162, wherein the one or more reference DNA profiles are part of a database.

[0506] 164. The method of embodiment 162 or embodiment 163, wherein the set of reference DNA profiles comprises up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900 or 1000 reference DNA profiles.

[0507] 165. The method of any one of embodiments 162 to 164, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest.

[0508] 166. The method of any one of embodiments 162 to 165, wherein at least 50% of said reference DNA profiles in said reference DNA profile set are from relatives of said person of interest.

[0509] 167. The method according to Embodiment 165 or Embodiment 166, wherein each relative of the person of interest is a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative or a fifth-degree relative of the person of interest.

[0510] 168. A method according to any one of embodiments 162 to 167, wherein the identity of each relative of the person of interest in the reference DNA profile is known.

[0511] 169. The method of any one of embodiments 162 to 168, wherein the identity of each of the one or more reference DNA profiles in the reference DNA profile set is known.

[0512] 170. The method of any one of embodiments 162 to 169, wherein the reference DNA profile set is in a database.

[0513] 171. The method of embodiment 170, wherein the database is not publicly accessible.

[0514] 172. The method of embodiment 170 or embodiment 171, wherein the database is not accessible to a third party genetics service.

[0515] 173. A method for identifying a DNA profile identity, comprising:

[0516] calculating a degree of relationship of a DNA profile comprising genotypes of at least between or about between 2,000 and 50,000 SNPs to one or more reference DNA profiles, wherein the DNA profile is from a person of interest, and wherein the one or more reference DNA profiles are included in a set of reference DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest; and

[0517] A family tree is generated, the family tree including the DNA profile in relation to the one or more reference DNA profiles.

[0518] 174. The method of embodiment 173, wherein the DNA profile is generated by the method of any one of embodiments 127 to 161.

[0519] 175. A method according to embodiment 173 or embodiment 174, wherein the degree of relationship is calculated using a kinship model.

[0520] 176. A method according to any one of embodiments 173 to 175, wherein the degree of relationship is calculated using a kinship model trained using a PCA method.

[0521] 177. A method according to embodiment 176, wherein the PCA method used to train the kinship model is PCA or involves PCA.

[0522] 178. A method according to embodiment 176 or embodiment 177, wherein the PCA method is PC-AiR.

[0523] 179. A method according to embodiment 178, wherein the PC-AiR includes the following steps: (1) estimating kinship coefficients between all pairs of samples in a training database - optionally training DNA profiles - wherein pairs with kinship coefficients > 0.025 are identified as related and pairs with kinship coefficients < -0.025 are identified as ancestrally differentiated; (2) initializing an unrelated sample set including all samples; and (3) iteratively: (i) identifying the set of samples in the unrelated sample set that has the most related samples in the unrelated sample set, designated as X, (ii) identifying the set of samples in X that has the fewest ancestrally differentiated pairs compared to the samples in the unrelated sample set, designated as Y; and (iii) terminating the process if Y has zero samples, or, if Y has at least one sample, randomly selecting a sample from Y to remove from U and repeating starting at step (3)(i).

[0524] 180. The method of embodiment 176 or embodiment 177, wherein the PCA method is a modified PC-Air.

[0525] 181. A method according to embodiment 180, wherein the improved PC-AiR includes the following steps: (1) estimating kinship coefficients between all pairs of samples of the training database, optionally training DNA profiles, wherein pairs with kinship coefficients > 0.01 are identified as related, and pairs with kinship coefficients < -0.025 are identified as ancestrally differentiated; (2) removing all DNA profiles with ≥ 5% missing data; and (3) sorting all DNA profiles by identifying each DNA profile with a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles ranked from least to most in the complete database, and ties are broken by the number of ancestrally differentiated DNA profiles ranked from most to least in the complete database. In some embodiments, step (3) involves iteratively traversing the sorted DNA profiles, and for each DNA profile: (i) if the DNA profile is not already in the related sample set, adding it to the unrelated sample set and adding all related DNA profiles to the related sample set, and (ii) if the DNA profile is already in the related sample set, skipping to the next DNA profile and repeating starting at step (3)(i).

[0526] 182. A method according to any one of embodiments 173 to 181, wherein calculating the degree of relationship includes using PC-Relate to calculate the kinship coefficient.

[0527] 183. A method according to embodiment 182, wherein the degree of relationship is calculated by providing the DNA profile of the person of interest as an input to PC-Relate.

[0528] 184. The method of embodiment 182 or embodiment 183, wherein the degree of relationship is calculated by providing the kinship model and the DNA profile of the person of interest as input to PC-Relate.

[0529] 185. The method of any one of embodiments 182 to 184, wherein the one or more reference DNA profiles are also provided as input to PC-Relate.

[0530] 186. The method of any one of embodiments 173 to 185, wherein calculating the degree of relationship comprises calculating a kinship coefficient using a genome-wide kinship algorithm as follows:

[0531]

[0532] wherein the person of interest and the reference DNA profile of the one or more reference DNA profiles are i and j, is the kinship coefficient, is the estimated allele frequency, s is the SNP among the SNPs typed in both individuals, and g is and g js are the numbers of reference alleles in i and j at SNP s, respectively, and and are the expected allele frequencies calculated from PC-AiR for i and j at SNP s, respectively.

[0533] 187. A method according to any one of embodiments 173 to 186, wherein calculating the degree of relationship includes calculating a likelihood ratio.

[0534] 188. A method according to embodiment 187, wherein calculating the likelihood ratio includes comparing the multiple SNPs between the DNA profile and the one or more reference DNA profiles.

[0535] 189. A method according to embodiment 187, wherein calculating the likelihood ratio includes comparing a set of SNPs between the DNA profile and the one or more reference DNA profiles, and the set of SNPs includes kinship SNPs from within the multiple SNPs.

[0536] 190. A method according to any one of embodiments 187 to 189, wherein calculating the likelihood ratio includes dividing the probability that the DNA profile is associated with a reference DNA profile in the one or more reference DNA profiles based on the genotypes of the multiple SNPs by the probability that the DNA profile is unassociated with the reference DNA profile.

[0537] 191. The method of any one of embodiments 187 to 190, wherein the likelihood ratio (LR) is calculated as follows:

[0538]

[0539] Wherein, D represents the genotype, H r represents the assumption that individuals are correlated, and H u The assumption that individuals are unrelated.

[0540] 192. The method of any one of embodiments 187 to 190, wherein the LR is calculated as follows:

[0541]

[0542] Wherein, 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2.

[0543] 193. A method according to any one of embodiments 173 to 192, wherein the person of interest is biologically male, and the method further comprises calculating a likelihood ratio of sharing a Y chromosome between the DNA profile and the one or more reference DNA profiles.

[0544] 194. A method according to embodiment 193, wherein calculating the likelihood ratio of a shared Y chromosome includes comparing a set of SNPs between the DNA profile and the one or more reference DNA profiles, the set of SNPs including one or more Y-SNPs.

[0545] 195. A method according to embodiment 194, wherein the one or more Y-SNPs are contained within the multiple SNPs.

[0546] 196. A method according to embodiment 194 or embodiment 195, wherein the one or more Y-SNPs include at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84 or 85 Y-SNPs.

[0547] 197. A method according to any one of embodiments 194 to 196, wherein the one or more Y-SNPs include 85 Y-SNPs.

[0548] 198. A method according to any one of embodiments 193 to 197, wherein calculating the likelihood ratio of sharing a Y chromosome comprises dividing the probability that the DNA profile shares a Y chromosome with a reference DNA profile in the one or more reference DNA profiles by the probability that the DNA profile does not share a Y chromosome with the reference DNA profile based on the genotype of the one or more Y-SNPs.

[0549] 199. The method of any one of embodiments 173 to 198, wherein at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the DNA profiles in the reference DNA profile set are from relatives of missing persons or victims of disaster or conflict.

[0550] 200. The method of any one of embodiments 173 to 199, wherein each of the one or more reference samples is from a relative of a missing person or a victim of a disaster or conflict.

[0551] 201. A method according to any of embodiments 173-200, wherein the one or more reference samples include up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900 or 1000 reference samples.

[0552] 202. A method according to any one of embodiments 173 to 201, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the one or more reference samples are from relatives of the person of interest.

[0553] 203. The method of any one of embodiments 173 to 202, wherein at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest, and wherein each of at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set is a first degree relative, a second degree relative, a third degree relative, a fourth degree relative or a fifth degree relative.

[0554] 204. The method of any one of embodiments 173 to 203, wherein at least 50% of the one or more reference samples are from relatives of the person of interest.

[0555] 205. A method according to any one of embodiments 199 to 204, wherein each relative of the person of interest in the one or more reference samples is a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative or a fifth-degree relative of the person of interest.

[0556] 206. The method according to embodiment 205, wherein each relative of the person of interest in the one or more reference samples is a first-degree relative, a second-degree relative or a third-degree relative of the person of interest.

[0557] 207. A method according to any one of embodiments 173 to 206, wherein the identity of each relative of the person of interest in the one or more reference samples is known.

[0558] 208. A method according to any one of embodiments 173 to 207, wherein the identity of each of the one or more reference samples is known.

[0559] 209. A kit comprising at least one container means, wherein said at least one container means comprises a plurality of primers according to any one of embodiments 94 to 126.

[0560] 210. The method of any one of embodiments 1 to 92 and 127 to 208, wherein the plurality of SNPs comprises between or about 2,000 to 11,000 SNPs, 3,000 to 11,000 SNPs, 4,000 to 11,000 SNPs, 5,000 to 11,000 SNPs, 5,500 to 11,000 SNPs, 6,000 to 11,000 SNPs, 7,000 to 15,000 SNPs, 7,000 to 14,000 SNPs, 7,000 to 13,000 SNPs, 7 ,000 to 12,000 SNPs, 7,000 to 11,000 SNPs, 8,000 to 15,000 SNPs, 8,000 to 14,000 SNPs, 8,000 to 13,000 SNPs, 8,000 to 12,000 SNPs, 8,000 to 11,000 SNPs, 9,000 to 15,000 SNPs, 9,000 to 14,000 SNPs, 9,000 to 13,000 SNPs, 9,000 to 12,000 SNPs, or 9,000 to 11,000 SNPs.

[0561] 211. A method according to any one of embodiments 1 to 92 and 127 to 208, wherein the plurality of SNPs comprises 10,230 SNPs.

[0562] 212. A plurality of primers according to any one of embodiments 94 to 126, wherein the plurality of SNPs includes between or about 2,000 to 11,000 SNPs, 3,000 to 11,000 SNPs, 4,000 to 11,000 SNPs, 5,000 to 11,000 SNPs, 5,500 to 11,000 SNPs, 6,000 to 11,000 SNPs, 7,000 to 15,000 SNPs, 7,000 to 14,000 SNPs, 7,000 to 13,000 SNPs, 7,000 to 14,000 SNPs, 7,000 to 15,000 SNPs, 7,000 to 16,000 SNPs, 7,000 to 17,000 SNPs, 7,000 to 18,000 SNPs, 7,000 to 19 .... 00 to 12,000 SNPs, 7,000 to 11,000 SNPs, 8,000 to 15,000 SNPs, 8,000 to 14,000 SNPs, 8,000 to 13,000 SNPs, 8,000 to 12,000 SNPs, 8,000 to 11,000 SNPs, 9,000 to 15,000 SNPs, 9,000 to 14,000 SNPs, 9,000 to 13,000 SNPs, 9,000 to 12,000 SNPs, or 9,000 to 11,000 SNPs.

[0563] 213. A plurality of primers according to any one of embodiments 94 to 126, wherein the plurality of SNPs comprises 10,230 SNPs.

[0564] 214. The method according to any one of embodiments 1 to 5, 11 to 93, 127 to 161 and 210 to 213, further comprising generating a family tree comprising the DNA profile related to one or more DNA profiles contained in the reference DNA profile set.

[0565] 215. The method of embodiment 214, wherein the family tree comprises the DNA profile associated with one or more DNA profiles from relatives of the person of interest.

[0566] Example

[0567] The following examples are for illustrative purposes only and are not intended to limit the scope of the present invention.

[0568] Example 1: Generation of sequence libraries and determination of sensitivity

[0569] This example describes a method for determining the sensitivity of the multiplex polymerase chain reaction described herein to generate libraries that can be sequenced. Figure 1 An exemplary schematic diagram of the method described in this example for generating a library capable of being sequenced is depicted.

[0570] A. PCR Amplification of Genomic DNA Targets

[0571] Multiple polymerase chain reaction is carried out to amplify 10,230 individual amplicons in genomic DNA sample.Each primer pair is designed to selective hybridization and promotes the amplification of the specific single nucleotide polymorphism (SNP) of genomic DNA sample.The input genomic DNA range of test is from 50ng to 50pg, more specifically, 5ng, 2.5ng, 1ng, 500pg, 250pg, 100pg and 50pg).In short, the 18.5mlPCR master mix containing enough buffer, dNTP, MgCl2, salt and PCR additive (such as glycerol) is added to the single hole of 96-well PCR plate.Also added 5 microliters of primer pool (it comprises 10,530 primer pairs), 2-4 unit DNA polymerase (such as Phusion hot start DNA polymerase (Thermo Fisher, cat#F549L or any other thermostable DNA polymerase)), also add the genomic DNA of 50ng to 50pg.

[0572] The PCR plate was sealed and loaded into a thermal cycler (Veriti 96-well thermal cycler, Thermo Fisher Scientific, 4413964) and run according to the temperature profile described below to generate amplicon libraries.

[0573] 98℃ for 3 minutes

[0574] 18 cycles:

[0575] 96°C for 45 seconds

[0576] 80℃, 10 seconds

[0577] Use the appropriate heating mode, 54℃ for 4 minutes

[0578] Use the appropriate heating mode, 66℃ for 90 seconds

[0579] 68°C for 10 minutes

[0580] Keep at 4℃

[0581] After cycling, the amplicon pools were kept at 2-8°C until proceeding to the purification steps outlined below.

[0582] B. Purification of Amplicons from Input DNA and Primers

[0583] Two rounds of cleanup were performed using MagBind Total Pure NGS beads (Omega Biotek, M1378-02) at 1.6X and 0.6X volume ratios for binding, washing, and elution, and were found to remove genomic DNA and unbound or excess primers. The amplification and purification steps outlined herein produced amplicons of approximately 150-350 bp in length. The purified amplicons were then used in a second round of PCR to add adapters for sequencing.

[0584] C. Enrichment of purified amplicons to generate libraries that can be sequenced

[0585] The second round of PCR amplification was performed by mixing 25 ml of the purified amplicons from the above step with 5 ml of the adapter provided in the Forenseq Kintelligence kit (Verogen PN: V16000120) and 20 ml of the KPCR master mix provided in the Forenseq Kintelligence kit (Verogen PN: V160000120) in a 96-well PCR plate. The PCR plate was sealed and loaded into a thermal cycler (Veriti 96-well thermal cycler, Thermo Fisher Scientific, 4413964) and run according to the temperature profile described below to generate the amplicon library.

[0586] 98°C, 30 seconds

[0587] 15 cycles:

[0588] 98℃, 20 seconds

[0589] 66°C for 30 seconds

[0590] 72°C for 30 seconds

[0591] 72°C for 1 minute

[0592] Keep at 4℃

[0593] The library was purified using MagBind Total Pure NGS beads (Omega Biotek, M1378-02) with 1X binding, washing and elution. The purified library was quantified, normalized, denatured and diluted according to the instructions in the Forenseq Kintelligence Kit User Guide (Verogen PN: V16000120, the contents of which are hereby incorporated by reference in their entirety).

[0594] The denatured library was sequenced according to the instructions in the MiSeq FGx Sequencing System Reference Guide (Document #VD2018006, the contents of which are incorporated herein by reference in their entirety). Figure 2 As shown, the number of loci detected was similar across a range of input genomic DNA titrations.

[0595] Results were analyzed using Forenseq Universal Analysis Software 2.1 (Verogen, San Diego, CA) following the instructions outlined in Forenseq Universal Analysis Software 2.1 and provided in Reference Guide Document #VD2019002, the contents of which are hereby incorporated by reference in their entirety.

[0596] Example 2: Using degraded DNA to generate a sequence library

[0597] This example describes sequencing DNA from low-amount and highly degraded samples. Degraded DNA A series of degraded blood DNA was obtained from Innogenomics (New Orleans, Louisiana). DNA samples were used to generate sequencing libraries as described in Example 1, except that primer pairs for 10,327 loci were used in this example. Figure 3 The percentage of loci with degraded DNA detected using the assay described herein (call rate) compared to the microarray (GSA) call rate is shown in . The degradation index (DI) is shown on the x-axis and the number of loci detected is shown on the y-axis. These results show that even with highly degraded DNA (DI of 158.3), the assay was able to detect 9167 loci, which is sufficient to upload to a genetics database to search for relatives. Alternative techniques such as microarrays were unable to detect any loci in samples with a high degradation index.

[0598] Example 3: Activity evaluation of inhibitors for library preparation

[0599] This example describes an evaluation of the effects of PCR inhibitors on the library preparation disclosed herein. DNA samples from crime scenes often contain co-purified impurities that inhibit PCR. PCR inhibition is the most common reason for PCR failure when sufficient copies of DNA are present. Humic compounds (a series of substances produced during decay) are thought to contaminate DNA in soil, natural water, and recent sediments. Other common inhibitors include heme (from blood), indigo (from jeans), and tannic acid.

[0600] To evaluate the impact of inhibitors commonly found in forensic samples, library preparation was performed as described in Example 1, but 200uM heme, 50ng / uL humic acid, 133uM indigo, and 16uM tannic acid were added to the "Amplify and label targets" step above, and primer pairs for 10,380 loci were used. Results are shown in Figure 4 In the experiment, a PCR reaction without any inhibitor was marked as a control.

[0601] Example 4: Determination of the degree of relationship

[0602] This example describes exemplary results for samples prepared generally as described in Example 1 above.

[0603] Illumina Global Screening Array (GSA) 2.0 was run using 200 ng of the Utah CEPH Family 1463 DNA (Coriell Institute) sample. SNP calls were uploaded to the GEDmatch database (Verogen). Figure 5 An exemplary family tree is shown in . One of the samples, NA12889 (grandfather), was run on a ForenSeq UAS2.1 module following the library preparation protocol described in Example 1. The resulting report was uploaded to the database and searched using the 1:many search relationship tool. The kinship coefficients according to the algorithm in the database were compared to the expected kinship coefficients. The expected and observed kinship coefficients are shown in Figure 6 shown.

[0604] Example 5: Kinship Coefficient Determination in an Exemplary Case Study

[0605] This example describes the results of an exemplary case study using sample SNP profiles to determine kinship coefficients. The ability of the 1:many search algorithm to detect potential relatives was tested using 10 established pedigrees (with 12-28 family members) in the GEDmatch database. The sample SNP profiles from the assay disclosed herein were considered to be Mr. X = POI (person of interest / unknown crime scene profile). Figure 7Candidate query results (hits), kinship coefficients, and kinship status are displayed.

[0606] Then use the results generated by the search algorithm to generate a family tree for Mr. X, such as Figure 8 As shown in the family tree, Mr. X's cousins ​​(1C) and great-grandfather (G GF) are returned in the first 11 candidate query results, which are third-level relationships. Mr. X's great-great-grandmother (GG GM), great-great-uncle (GG uncle), and first cousin (1C1R) are returned in the first 15 candidate query results, which are fourth-level relationships. Mr. X's second-generation cousin (2C) is a fifth-level relationship and is the 12th candidate query result.

[0607] Example 6: Generation of sequence libraries and determination of sensitivity, including assessment by locus type

[0608] This example relates to a method of determining the sensitivity of a multiplex polymerase chain reaction described herein to generate libraries that can be sequenced, and includes an assessment by locus type.

[0609] Sequence libraries (sequenced nucleic acid libraries), also called DNA profiles, were generated in the same manner as described in Example 1, except that the results were analyzed using Forenseq Universal Analysis Software Version 2.2.

[0610] like Fig. 9 As shown, the table summarizes the number of loci detected (average of three replicates) based on the amount of input DNA (ng) for each different type of locus, for example, Y chromosome SNP (Y-SNP), X chromosome SNP (X-SNP), phenotypic SNP (piSNP), kinship SNP (kiSNP), identity SNP (iiSNP), and biogeographic ancestry SNP (aiSNP), with a total of 10,230 loci analyzed. The genomic DNA input titrations tested included 5ng, 2.5ng, 1ng, 0.5ng (500pg), 0.25ng (250pg), 0.10ng (100pg), 0.05ng (50pg) of input genomic DNA. As Fig. 9 As shown, of the total SNPs detected, at least 98.9% (10,117) of the loci were detected when the amount of each input DNA was between 0.05ng and 5ng, and at least 99.5% (10,179) of the loci were detected when the input DNA amount was 0.10ng and above.

[0611] This data demonstrates that more than 10,000 loci can be detected with high efficiency and sensitivity using different types of SNPs and input DNA amounts ranging from 0.05 g (50 pg) to 5 ng.

[0612] Example 7: Evaluation of inhibitor activity against sequence library preparation, including evaluation by locus type

[0613] This example describes an evaluation of the effects of certain inhibitors on the preparation of sequence libraries (sequenced nucleic acid libraries) disclosed herein, also known as DNA profiles, including by the type of loci being detected and sequenced. DNA samples from crime scenes often contain co-purified impurities that inhibit amplification. Common inhibitors include heme, humic acid, and indigo.

[0614] To evaluate the effects of inhibitors commonly found in forensic samples, library preparation was performed as described in Example 1, but the results were analyzed using Forenseq Universal Analysis Software version 2.2. Evaluation of the effects of certain inhibitors on amplification was performed as described in Example 3, but the inhibitors tested were as follows: 200 μM heme, 100 μM heme, 50 ng / μL humic acid, 25 ng / μL humic acid, 16 μM tannic acid, 8 μM tannic acid, 133 μM indigo, and 66.5 μM indigo were included in the amplification step as described in Example 1, and primer pairs for 10230 loci were used. A positive control reaction without any inhibitors was also performed. 1 ng of input DNA was used.

[0615] The results are as follows Fig.10 As shown, it is shown that according to the methods described herein, various SNPs including kiSNPs, Y-SNPs, X-SNPs, piSNPs, iiSNPs and aiSNPs can be amplified and detected with high efficiency and high detection rate and in combination with each other, as indicated by, for example, that all or nearly all of each type of SNP can be detected even in the presence of an inhibitor. For example, the number of kiSNPs, Y-SNPs, X-SNPs, piSNPs, iiSNPs and aiSNPs detected is similar to the number detected in the positive control lacking the inhibitor ( Fig.10 ). This data demonstrates that the presence of common inhibitors in a sample does not adversely affect the ability to amplify more than 10,000 SNPs in a PCR reaction using the methods described herein.

[0616] Example 8: Evaluation of sequence library preparation using DNA samples obtained after simulated sexual assault

[0617] This example describes the use of DNA from a simulated sexual assault sample to generate a sequence library (a library of sequenced nucleic acids), also known as a DNA profile, to confirm whether a sequence library, e.g., a library of sequenced nucleic acids, can be successfully generated using a small amount of input DNA (e.g., less than the recommended amount of 1 ng, such as 500 pg).

[0618] Simulated sexual assault DNA was obtained from samples collected 9 hours and 22 hours after the simulated sexual assault. DNA was isolated from a small fraction of sperm using differential extraction, and a small fraction of sperm from both time points was collected and stored for analysis. The amount of DNA from the small fraction of sperm that could be used as input for analysis (to generate the sequence library) was only 500 pg, which is half of the recommended amount of 1 ng.

[0619] The DNA samples were used to generate sequence libraries (libraries of sequenced nucleic acids) as described in Example 1, but the results were analyzed using Forenseq Universal Analysis Software version 2.2. Fig.11 The percentage of loci detected (call rate) and the number of each SNP present in the assay are shown. The results show that even with only 500pg of input DNA, most SNPs were detected, with 99.99% of all SNPs detected at the 9-hour time point (10,229 out of 10,230 SNPs) and 99.93% of all SNPs detected at the 22-hour time point (10,223 out of 10,230 SNPs). Specifically, all aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs were detected at both the 9-hour and 22-hour time points after the simulated sexual assault. At the 9-hour time point, only one of the 9,867 kiSNPs was not detected, and at the 22-hour time point, only 7 of the 9,867 kiSNPs were not detected. The number of loci detected is sufficient to upload to a genetics database to search for relatives.

[0620] This data shows that the methods described herein can be used to detect more than 10,000 SNPs, including various kiSNPs, Y-SNPs, X-SNPs, piSNPs, iiSNPs, and aiSNPs and create sequence libraries, using only 500 pg of DNA at 9 hours and 22 hours after a simulated sexual assault, and more than 99.9% of all SNPs can be detected. Therefore, the methods described herein are suitable for creating sequence libraries using less than the recommended amount, e.g., 500 pg, of DNA after a criminal incident (including a sexual assault), or from victims of a disaster or conflict, or from samples left behind by missing persons.

[0621] Example 9: Evaluation of PCIA residues for sequence libraries generated from saliva samples

[0622] This example describes sequencing a nucleic acid library (eg, generating a DNA profile) based on DNA from a saliva sample extracted using organic extraction using the phenol-chloroform-isoamyl alcohol (PCIA) extraction method.

[0623] Saliva DNA is extracted from saliva samples where increasing amounts of the extraction reagent PCIA (e.g., no PCIA, mild PCIA, moderate PCIA, and severe PCIA) are intentionally left in the extracted DNA as a residue that simulates an imperfect extraction. PCIA (including its component phenol) is a known inhibitor of PCR amplification.

[0624] Sequence libraries (pools of sequenced nucleic acids) were generated using DNA samples with no PCIA, mild PCIA, moderate PCIA, or severe PCIA as described in Example 1, but the results were analyzed using Forenseq Universal Analysis Software version 2.2. The total number of SNPs detected for each sample was determined, as Fig.12 The results showed that even though the PCIA carryover was high and severe, the PCIA carryover did not affect the ability of the assay to detect SNPs, as more than 10,170 SNPs were detected in each sample.

[0625] Example 10: Evaluation of sequence library generation from blood samples on various matrices and the impact of heme

[0626] This example describes sequencing of nucleic acid libraries (e.g., to generate DNA profiles) from blood samples preserved in different matrices commonly found at crime scenes (including rust and denim), from blood samples from swabs in which only 420 pg of DNA was available, and from DNA samples from a CheleX PCR assay in which heme levels increase with increasing amounts of DNA. TM Extracted blood sample. Heme is a known inhibitor of PCR amplification. Denim contains indigo dye, which is a known inhibitor of PCR amplification.

[0627] Each DNA sample was used to generate a sequence library (a library of sequenced nucleic acids) as described in Example 1, but the results were analyzed using Forenseq Universal Analysis Software version 2.2, which included samples containing blood and rust, two blood samples from denim, a 420pg blood sample from a swab, blood samples with low or moderate amounts of heme residue or no heme as controls, and a positive control blood sample. The total number of SNPs detected for each sample and reference control was determined and displayed in Fig.13The results showed that blood samples retained in different matrices could still detect 10,114 or more of the 10,230 total SNPs. A blood sample with only 420pg detected 9,563 SNPs, while samples containing heme detected more than 10,000 SNPs, and the number of SNPs detected was not affected by the amount of heme present in the sample. This shows that, according to the method provided herein, DNA extracted from blood samples retained on various matrices commonly found at crime scenes can be used to detect more than 10,000 SNPs for forensic applications.

[0628] Example 11: Kinship analysis using related samples, related antemortem samples, unrelated postmortem samples, and related simulated postmortem samples

[0629] This example describes performing the kinship analysis described herein to identify up to three degrees of relationships in four different sample sets, including non-degenerate, highly degenerate, and low input samples. Specifically, the goal of this example is to determine up to three degrees of familial relationships based on degenerate samples sequenced at high complexity, while still allowing enough SNPs to accurately predict such familial relationships, with potential matches located in a local private database (rather than a publicly accessible database). Fig.14 A schematic diagram of the method involved is depicted in , which includes the following steps: (a) collating a list of forensically relevant SNP targets and selecting >10,000 SNPs, such as 10,230 SNPs; (b) preparing sequencing libraries from post-mortem and antemortem types of DNA samples by labeling and duplicating targets, enriching targets, purifying targets, and normalizing target amounts; (c) performing next generation sequencing at higher complexity (e.g., 12-plex or higher); (d) generating SNP reports (also known as DNA profiles); (e) uploading SNP reports to a local server; (f) performing pairwise comparisons; and (g) calculating kinship coefficients and likelihood ratios, and filtering out the most likely familial relationships. In some examples, the collating in step (a) is performed in a previous workflow, and the same selected SNP targets, e.g., a specific set of 10,230 SNP targets, are utilized in the present workflow.

[0630] In each of the four sample sets, a set of 10,230 SNP targets was selected for detection. The four different sample sets were sequenced to generate sequence libraries as described in Example 1. The four different sample sets include: (1) a set of related antemortem samples from CEPH / Utah including up to two levels of relationship verified by Coriell (referred to herein as "related antemortem CEPH / Utah samples"); (2) a set of related antemortem samples from private families including up to five levels of relationship (referred to herein as "related antemortem private family samples"); (3) a set of unrelated postmortem samples including skeletons (cremated, embalmed, burned, and buried), dental remains / teeth, and degraded blood at different levels of degradation index (DI) (referred to herein as "real postmortem samples"); and (4) a set of related simulated postmortem samples including the same samples from set (2), but including the following DNA: (a) artificially degraded by boiling the DNA for 24 hours (DI range 2.1-20) or (b) sequenced at low DNA input (50pg) (referred to herein as "related simulated postmortem samples").

[0631] The MiSeq FGx sequencing system was used to perform 12-plex sequencing of real postmortem samples and the results were as follows Fig.15 As shown, it shows the number of SNPs detected for individual samples in this collection of real postmortem samples. This includes real postmortem samples labeled "teeth", "degenerate blood", "burial bones", "low input samples", "other degenerate samples" and "other real postmortem samples". Fig.15 As shown, for the true postmortem samples, 3 of the 4 burial bone samples had the lowest number of SNPs detected (or called), ranging from 248 to 1319 SNPs, and the degenerate blood samples with the highest DI (DI 158 and DI 56) had the second lowest number of SNPs detected, with 4,603 and 5,069 SNPs detected, respectively. All remaining true postmortem samples had a minimum of 6,737 and a maximum of 9,903 SNPs detected ( Fig.15 ). The “total pass” count reflects the total number of SNPs detected for each sample in the full set of 10,230 SNPs, while the “count pass” count reflects the total number of SNPs detected in the subset of 2,639 SNPs that were consistently called across samples. Fig.15 As shown, there is a core set of SNPs that are consistently called (ie, detected) across samples, as the number of SNPs detected in the 2,639 SNP subset (the "count-passed" SNPs) varies less than the total number of SNPs called overall.

[0632] The simulated postmortem samples were sequenced using the MiSeq FGx sequencing system with 12-plex sequencing, and the results are shown in Fig.16 As shown. Fig.16 As shown, for related simulated postmortem samples artificially degraded by boiling, i.e., samples with a DI greater than 0, the number of SNPs detected ranged from 1,470 to 8,999, with an average of 6,462 SNPs detected for samples from related parents and daughters. Low-input DNA samples had a DI of 0 and 0.05 ng of DNA input ( Fig.16 ).

[0633] The relevant antemortem CEPH / Utah samples were sequenced with 12-, 16-, 24-, and 32-plexes using the MiSeq FGx Sequencing System to determine the highest complexity that would yield a sufficiently high number of detected SNPs (i.e., SNP call rate) for kinship analysis. The 24-plex sequencing run detected an average of 9,691 SNPs, ranging from 8,297 SNPs detected to 9,982 SNPs detected, depending on the sample; and the 32-plex sequencing run detected an average of 9,048 SNPs, ranging from 6,894 SNPs detected to 9,827 SNPs detected (data not shown). This suggests that a 30-plex run would allow a sufficiently high throughput of detected SNPs without significantly affecting the number of detected SNPs and the confidence level for kinship analysis.

[0634] The related antemortem private family sample and three unrelated samples were sequenced using the MiSeq FGx Sequencing System using a 30-plex sequencing run, and the results are as follows Fig.17 As shown. Fig.17 As shown, more than 7,000 SNPs were detected in each sample except for one replicate of the "self" sample (labeled "rep1*"), which was likely due to an error in the library preparation of that particular sample. The samples with more than 7,000 SNPs detected included the "self" sample and several relatives of the "self" individual, including samples from a first-generation cousin, daughter, sister, nephew, cousin, and husband.

[0635] The DNA profiles generated after the sequencing run were then used for kinship analysis. Related antemortem private family samples (sequenced in 30-replicate) and simulated postmortem samples (sequenced in 12-replicate) were compared, which were derived from the same original related samples, but the related simulated postmortem samples had been artificially degraded or used with low input. Using a minimum kinship coefficient value of 0.031, all expected relationships were matched up to three levels (e.g., cousins) and no false matches were obtained (e.g., no false positives), resulting in 100% specificity and 100% sensitivity (data not shown). Some but not all expected fourth-level matches (e.g., cousins ​​one generation apart) were obtained (data not shown). These data confirm that the method is able to accurately identify up to three levels of relationships while excluding all irrelevant relationships, even in the case of highly degraded and low-input samples (such as those that may be present in cases of missing persons and disaster / conflict victims).

[0636] Next, to evaluate the most appropriate kinship coefficient threshold, the related simulated postmortem samples (sequenced at 12 repetitions) were compared to the GEDMatch database, assuming that the related individuals represented by the related simulated postmortem samples did not have any close relatives in the GEDMatch database. It was determined that to achieve 100% specificity (i.e., no false positives), a kinship coefficient of 0.062 was required. This kinship coefficient value threshold (0.062) was applied to the related simulated postmortem samples (sequenced at 12 repetitions) and compared to the related antemortem private family samples (sequenced at 30 repetitions), and it was determined that this would reduce sensitivity by excluding some known third-degree relationships.

[0637] Example 12: Evaluating the minimum number of SNPs to accurately determine kinship

[0638] To identify the minimum number of detected SNPs (i.e., SNP call rate) to accurately determine kinship relationships, we compared known relationships in the GEDMatch database to simulate a range of detected SNPs (i.e., called SNPs). This included testing the call rates of 2,000 SNPs, 4,000 SNPs, 6,000 SNPs, 8,000 SNPs, and 10,000 SNPs to identify sensitivity and specificity for first-, second-, and third-order familial relationships. Results are shown in Figure 2. Fig.18 AE, which depicts 2,000 SNPs ( Fig.18 A), 4,000 SNPs ( Fig.18 B) 6,000 SNPs ( Fig.18 C) 8,000 SNPs ( Fig.18 D) and 10,000 SNPs ( Fig.18Receiver operating characteristic (ROC) curves of the results of E). The lowest SNP call rate (n = 2,000) resulted in reduced specificity for primary, secondary, and tertiary relationships ( Fig.18 A), which suggests that the SNP call rate for the 2,000 SNPs detected is the absolute bottom line for SNP call rates that accurately identify relationships.

[0639] The ability to sequence undegraded samples in 3-plex provides the potential to identify quaternary and quinary relationships. A similar analysis was performed using the GEDMatch database, but for identifying quaternary and quinary relationships. This included testing the call rates of 2,000 SNPs, 4,000 SNPs, 6,000 SNPs, 8,000 SNPs, and 10,000 SNPs for sensitivity and specificity in identifying quaternary and quinary familial relationships. The results are shown in Fig.19 AE, which depicts 2,000 SNPs ( Fig.19 A), 4,000 SNPs ( Fig.19 B) 6,000 SNPs ( Fig.19 C) 8,000 SNPs ( Fig.19 D) and 10,000 SNPs ( Fig.19 E) is the ROC curve of the results. Fig.19 As shown in AE, a higher minimum number of called SNPs (~6,000) is required to accurately identify true fourth- and fifth-level relationships.

[0640] Example 13: High-complexity SNP sequencing for kinship analysis using related samples, simulated antemortem samples, and simulated postmortem samples

[0641] This example describes performing the kinship analysis described herein to identify degrees of relationship in different sample sets, including non-degenerate, highly degenerate, and low-input post-mortem (PM) and antemortem (AM) samples. Specifically, the goal of this example is to determine up to three degrees of familial relationships based on degenerate samples sequenced at high complexity, while still allowing enough SNPs to accurately predict such familial relationships, with potential matches located in a local private database (rather than a publicly accessible database). Fig.14A schematic diagram of the method involved is depicted in , which includes the following steps: (a) collating a list of forensically relevant SNP targets and selecting >10,000 SNPs, such as 10,230 SNPs; (b) preparing sequencing libraries from post-mortem and antemortem types of DNA samples by labeling and duplicating targets, enriching targets, purifying targets, and normalizing target amounts; (c) performing next generation sequencing at higher complexity (e.g., 12-plex or higher); (d) generating SNP reports (also known as DNA profiles); (e) uploading SNP reports to a local server; (f) performing pairwise comparisons; and (g) calculating kinship coefficients and likelihood ratios, and filtering out the most likely familial relationships. In some embodiments, the collating in step (a) is performed in a previous workflow, and the same selected SNP targets, e.g., a specific set of 10,230 SNP targets, are utilized in this workflow. In some embodiments, a window kinship algorithm is used.

[0642] A. Methodology

[0643] A set of 10,230 SNP targets was selected for detection in each sample set, including simulated antemortem and simulated postmortem samples.

[0644] Libraries were generated as described in Example 1, with 1 ng of NA24385 DNA as a positive control in these studies, and analyzed automatically in ForenSeq Universal Software version 2.3 (UAS) using expected quality control metrics.

[0645] Commercially available intact DNA was purchased for these studies and used as simulated antemortem (AM) samples, including 81 DNA samples from the 1000 Genomes Project, the CEPH collection, or the Personal Genome Project DNA samples from the Coriell Institute for Medical Research (Camden, NJ, USA), and 4 DNA samples extracted from whole blood from Innogenomics Inc. (New Orleans, LA, USA). Low input DNA samples included sample NA24385, with amounts of 0.05 ng, 0.1 ng, 0.25 ng, and 0.5 ng, respectively.

[0646] The simulated postmortem (PM) DNA extracts consisted of five contemporary tooth (CT) samples (designated CT1, CT2, CT3, CT4, and CT5), seven contemporary bone samples, and one DNA extract from an ancient skeleton from Eastern Europe. Samples CB 1, CB 3, CB 4, CB 6, and CB 7 were analyzed using PrepFiler TMDNA from seven coeval bone (CB) samples was extracted using the Forensic DNA Extraction Kit (Thermo Fisher, Waltham, MA, USA) or using a demineralization protocol for bone samples CB 2 and CB 5. TM Trio DNA quantification kit (ThermoFisher, Waltham, MA, USA) was used to determine the degradation index and DNA concentration of CB bone DNA samples. The DI of CB samples were 13.6, 4.3, 5.6, 1.1, 1.8, 2.5, and 6.5 for CB1, CB2, CB3, CB4, CB5, CB6, and CB7, respectively.

[0647] In addition, intentionally degraded DNA was used as mock AM or mock PM samples. Two sets of degraded DNA were purchased from Innogenomics Inc. (New Orleans, Louisiana, USA): DNA extracted from whole blood of two different male donors using an organic method, and DNA sheared by ultrasound at 50°C for a time range of 0 to 16 h (samples 1231 and 3551). The amount and degradation state of human DNA were also determined. The DIs of sample 1231 were 26.3, 33.6, 48.6, 160.3, and 459.8, respectively, with samples 1231 designated as 7, 8, 10, 11, and 12. The DIs of sample 3551 were 56 and 158, respectively, with samples 3551 designated as 56 and 158.

[0648] Oral samples were collected from volunteers from families with known pedigrees (RF004, RF016-021), referred to herein as related families (RF), whose pedigrees are as follows Fig.26 As shown. DNA was extracted and purified from oral swabs. Two DNA samples from related families (RF004 and RF016) were artificially degraded using high temperature treatment as follows: five replicate samples of purified oral DNA from each individual were subjected to 21 cycles of heating and cooling at 98°C for 1 hour and then at 4°C for 10 minutes. DNA-grade water was added to the subsequently dried DNA to dissolve the DNA. The degradation index and DNA concentration were determined for all DNA samples from related families. The degradation index varied between replicates, with values ​​of 1, 2.1, 2.6, 5.1, and 20 for sample RF004 and 1, 1.5, 2.0, 2.2, and 2.9 for sample RF016.

[0649] DNA sequence libraries were prepared using the ForenSeq Kintelligence kit (Verogen, San Diego, CA, USA) and following the manufacturer's instructions, and the libraries were quantified using the QuantiFluor ONE dsDNA system (Promega, Madison, WI, USA). Unique dual index adapters (UDI) were utilized when sequencing libraries with higher complexity. Before library preparation, intact DNA samples were quantified for input to library preparation. Simulated PM DNA samples were quantified using qPCR methods. Unless otherwise stated, DNA was diluted to 40 pg / μL for 1 ng of total DNA added to the library preparation reaction. Positive control DNA NA24385 was serially diluted to 20, 10, 4, and 2 pg / μL for 500, 250, 100, and 50 pg of total DNA input to simulate low input PM samples. The DNA concentration of the purchased artificially degraded samples was sufficient to add 1 ng of DNA to the library preparation reaction. Not all degraded related family samples had sufficient DNA concentration to input 1 ng DNA into the library preparation reaction. For sample RF004, degraded replicate samples with DI of 2.1, 20, 5.1 and 2.6 and 600 pg, 600 pg, 700 pg and 250 pg were added to the library preparation reaction, respectively. For sample RF016, degraded replicate samples with DI of 2.0, 2.2 and 2.9 had sufficient DNA concentration to add 1 ng to the library preparation reaction. To simulate low input samples, 50 pg of RF004 (DI of 1) and 50 and 250 pg of RF016 (DI of 1 and 1.5) were added to the library preparation reaction, respectively. The ancient bone DNA concentration was estimated to be 390 pg based on mitochondrial DNA quantification of ~1400 mtDNA copies / μL. Each library preparation set included a positive amplification control of 1 ng NA24385 DNA and a negative template control (NTC).

[0650] After amplification and purification of the target, the libraries were normalized to 0.75 ng / μL. If the library yield was less than 0.75 ng / μL, the library was neatly pooled without dilution. Mock AM libraries generated from commercially available intact DNA showed library yields > 0.75 ng / μL, with one exception of 0.67 ng / μL. Yields of some libraries generated from mock PM, low DNA input, and commercially available degraded DNA samples were also > 0.75 ng / μL. Libraries were pooled at different complexities by pipetting 8 μL of each normalized or neat library into a 1.7 ml microcentrifuge tube. Libraries generated from mock AM samples were pooled at sample complexities of 3, 12, 16, 24, 30, or 32 total libraries for denaturation and sequencing. Libraries generated from mock PM DNA samples were pooled at sample complexities of 3 or 12 total libraries for denaturation and sequencing. Pooled libraries were denatured by incubation with freshly diluted NaOH (HP3) for 5 minutes at room temperature and then diluted with HT1. Human sequencing controls (HSC), a pool of 33 STRs used as a positive sequencing control, were also denatured similarly and diluted with HT1. The denatured pool was then combined with the denatured HSC and sequenced on a MiSeq FGx instrument with a MiSeq FGx kit following the manufacturer's recommendations. Where possible, sequencing runs were created in ForenSeq Universal Analysis Software v2.2. Sequencing used 151 cycle paired-end reads for all libraries. The sequencing run included two eight-cycle index reads that required demultiplexing of the libraries using the indexes present in the UDI adapters.

[0651] To generate sequencing results of mock AM libraries with very high reads for each sample in the mock study, 30 libraries from the mock AM samples were sequenced on a NextSeq 500 instrument using the NextSeq 500 / 550 High Output Reagent Kit v2.5 (300 cycles) kit (Illumina, San Diego, CA, USA) following the manufacturer's recommendations (refer to the NextSeq and Denaturation / Pooling Guide).

[0652] The sequencing data were then analyzed using secondary and tertiary data analysis as follows. TM Run quality sets metrics in UAS. These metrics include cluster density, clusters passing filters, phasing, prephasing, and Q-score threshold. Cluster density is the number of clusters per square millimeter in the run (K) and this metric is set to 400-1650 K / mm 2to obtain optimal sequencing results. The cluster passes filter metric measures the quality of base calls via the percentage of clusters that pass the Illumina chastity filter (ref), and this metric is set to ≥80%. When this metric fails, the number of available reads is affected, but the quality of those that pass is not affected. The phasing metric represents the percentage of DNA strands in a cluster that lag behind the current cycle in the reads, and values ​​≤0.25% pass. Alternatively, prephasing represents the molecules in a cluster that are ahead of the current cycle in the reads, and values ​​≤0.15% pass. If phasing or prephasing does not meet specifications, the percentage of sequencing errors may be higher. It is important to determine whether HSC passes its metrics before using the run data.

[0653] All library samples prepared using the six UDI adapters provided in the ForenSeq Kintelligence kit sequenced on the MiSeq FGx were analyzed for allele and genotype calling using the ForenSeq Universal Analysis Software (UAS) v2.3 (Verogen, San Diego, CA) as previously described (Jager et al., Forensic Sci. Int. Genet., 2017, 28: 52-70, the entire contents of which are incorporated herein by reference). For library samples prepared using additional UDI adapters to achieve higher complexity, the sequencing runs were analyzed using the same bioinformatics pipeline used in UAS, but run on a separate server via command line tools. The pipeline (run within UAS or via command line tools) has the same basic algorithm for SNP genotype calling in UASv1.3 for ForenSeq DNA signature analysis (Jager ref) (Verogen, San Diego, CA, USA). First, samples were demultiplexed based on the index sequences provided on the UDI adapters by demultiplexing the binary base call (BCL) files and generating FASTQ files. Reads 1 and 2 were aligned to the primer sequences using the Smith-Waterman-Gotoh algorithm (Gotoh, O., J.Mol.Biol., 1982, 162: 705-708). Reads aligned to specific primer pairs were assigned to loci corresponding to these pairs. Alignment was then written in BAM format. At the position of each SNP, matches with reference base calls and matches with alternative base calls were calculated, filtering for a minimum base quality of 30. The number of reads for each type of call (reference or alternative) at each locus was then summed, requiring a minimum coverage greater than 10 reads. The SNP genotype was then determined by filtering the analysis (AT) and interpretation (IT) thresholds, both of which were set to 3%. The AT and IT thresholds were determined by multiplying 3% by the sum of reads counted at the locus. When low coverage occurs, a minimum of 650 reads are used to calculate the threshold. The resulting AT and IT values ​​are then compared to the total read counts of the reference and alternative alleles for each locus. If the call passes the AT and IT thresholds, the genotype of each locus is determined. The genotyping results are then written into the variant call format (VCF).

[0654] In some studies, GEDMatch data simulation was performed to test the whole genome kinship algorithm. For these studies, GEDMatch database configuration files were downloaded and analyzed, as described in Snedecor et al., Forensic Sci. Int. Genet., 2022, 61, 102769, doi: 10.1016 / j.fsigen.2022.102769 (its content is hereby incorporated by reference in its entirety). A collection of 1000 anonymous samples (referred to as "query samples") was randomly selected from GEDMatch. The relatives of these samples were then queried in the GEDMatch database, and any query result (referred to as "target sample") was selected based on the shared centimorgan (cM) value calculated by the GEDMatch one-to-many tool. This search produced 2954 target samples and matching query samples. These results include query-target pairs with zero shared cM, representing true irrelevant pairs. Therefore, due to the inclusion of relevant and irrelevant target samples, the number of target samples is greater than the number of query samples. The degree of relationship was determined by comparing the resulting shared cM values ​​generated from the one-to-many tool with the expected shared cM ranges for each degree provided by DNA Painter, which can be accessed at the following URL: https: / / dnapainter.com / tools / sharedcmv4. The loci typed by the samples included in the query and target sets were first filtered for the 10,230 SNPs in the panel and then randomly filtered to 80%, 60%, 40%, and 20% call rates, resulting in 8000, 6000, 4000, and 2000 loci called in each query-target pair, respectively. A kinship algorithm was used to calculate a genome-wide kinship coefficient for each query-target sample pair for each decreasing level of locus call rate. Pairs with a genome-wide kinship coefficient greater than 0.031 were considered related; pairs with a genome-wide kinship coefficient less than or equal to 0.031 were considered unrelated. Sensitivity and specificity were calculated by comparing these results with the one-to-many tool query results. In other words, the one-to-many query results were considered the true set, while the results generated by the kinship algorithm were considered the test set.

[0655] The likelihood ratio (LR) and kinship value are then calculated as follows. LR is calculated using algorithms pedprobr (Brustad et al., Int. J. Legal Med., 2021, 135: 117-129, the contents of which are hereby incorporated by reference in their entirety) and dvir (Vigeland et al., Scientific Reports, 2021, 11: 13661, the contents of which are hereby incorporated by reference in their entirety) . The population frequency averages from the genomic aggregation database (gnoMAD) (Karczewski et al., Nature, 2020, 581: 434-443, the contents of which are hereby incorporated by reference in their entirety) v3.0 are used in the LR calculation. No mutation model is used, and theta is set to 0 because the SNPs selected for analysis have lower linkage disequilibrium (Karczewski et al., above). LR is calculated as follows:

[0656]

[0657] Among them, D represents genotype, H r represents the assumption that individuals are correlated, and H u The assumption that individuals are unrelated is represented. The relatedness assumption is represented by a pedigree in which unidentified individuals are tested as relatives. The unrelatedness assumption is represented by a Hardy-Weinberg equilibrium calculation. The LR value is calculated for each locus and then multiplied by the locus to give the final LR for the relationship. For computational efficiency, each locus LR is converted to logarithm and the locus LRs are summed. However, due to the high complexity of the platform and in mass mortality event (MFI) cases where PM samples are often highly degraded and / or have low input, stochastic effects during PCR can cause allele dropouts and can lead to situations where two individuals have seemingly impossible allele combinations at a locus. This often occurs in parent-child relationships where each individual in the relationship is genotyped as homozygous for different alleles, but in fact one or both are heterozygous (e.g., the parent is genotyped as AA and the child is genotyped as BB, when the parent or parent and child are actually AB). Genotyping errors and de novo mutations are also possible causes of this situation. This case results in an LR of 0 and may result in an overall LR of 0 because the final LR is the product of the locus LRs. To ensure that the locus LR is not 0, pedprobr is modified. The likelihood ratios for the loci in these cases are calculated as follows:

[0658]

[0659] Wherein, 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2 (Galván-Femenía et al., Heredity, 2021, 126:537-547, the contents of which are hereby incorporated by reference in their entirety).

[0660] Whole genome kinship coefficients, shared cMs, and longest fragment cMs were calculated using PC-Relate (Conomos et al., American Journal of Human Genetics, 2016, 98: 127-148, the contents of which are hereby incorporated by reference in their entirety) and PC-AiR (Conomos et al., Genetic Epidemiology, 2015, 39: 276-293, the contents of which are hereby incorporated by reference in their entirety), as previously described in Snedecor et al., Forensic Sci. Int. Genet., 2022, 61: 102769 (the contents of which are hereby incorporated by reference in their entirety). This method is useful when the relationship between two individuals is unknown, so a pedigree is not required.

[0661] The PC-AiR method first obtains a set of genotyped individuals and divides them into two non-overlapping subsets: one set contains unrelated individuals representing the ancestors of all individuals (unrelated subset), and the other set contains individuals with at least one relative in the first subset (related subset). In order to construct unrelated subsets, the original PC-AiR method was modified to improve the computational efficiency of model construction. Samples with no relatives or the least relatives are added to the unrelated subset, while samples with more relatives are excluded from the unrelated subset. This is performed by calculating each pair of kinship values, as described by Conomos et al. in Genetic Epidemiology, 2015, 39: 276-293, the contents of which are hereby incorporated by reference in their entirety, and each individual is classified as having relatives or not having relatives based on a strict threshold: if the kinship value is greater than 0.01, it is considered to be a relative, and if the kinship value is less than -0.025, it is considered to be unrelated. Samples with less than 5% missing SNP data are excluded. Next, principal component analysis (PCA) was performed on the unrelated subset, and then the variant component values ​​of all individuals in the related subset were predicted based on their genetic similarity to individuals in the unrelated subset. The resulting components represent a model that can be used to identify matches in a set of unknown individuals instead of static population frequencies. The model used in this study was built using the GEDMatch database.

[0662] The PC-Relate method uses the principal components from PC-AiR and separates genetic relatedness into two components: one component for sharing the same alleles passed down from a recent common ancestor, and another component for sharing alleles due to a more distant common ancestor. The components from PC-AiR are used to estimate allele frequencies based on the ancestral background of the individuals, using linear regression rather than static population frequencies (such as those from gnoMAD). For two individuals i and j, the estimated allele frequencies from the PC-AiR model are then used To calculate the kinship coefficient As shown below

[0663]

[0664] Where s is a SNP among the SNPs that were typed in both individuals, and g is and g js are the numbers of reference alleles in i and j at SNP s, respectively, and and are the expected allele frequencies calculated from the PC-AiR for i and j at SNP s, respectively. The algorithm is called "genome-wide kinship" because it considers the entire genome as a fragment of relatedness, which is represented by the genome-wide kinship coefficient. When the genome-wide kinship algorithm is cited, the genome-wide kinship coefficient is used to identify relationships. When the number of SNPs shared between two individuals is less than 6000, a genome-wide kinship coefficient of more than 0.031 between two individuals is required in this study to be considered relatives.

[0665] Snedecor et al. (above) introduced an additional step, called "window kinship", to more accurately identify distant relatives. Window kinship involves calculating windows of kinship across the genome to find shared kinship fragments. This is performed by enumerating all possible windows within each chromosome and calculating the kinship coefficients of all windows. These windows are then filtered by the minimum kinship coefficient threshold and included in the shared cMs calculation. The filtered fragments are then iterated, and SNP stretches that share at least one allele and two alleles are classified separately. The total shared cM is then calculated across all fragments. When referring to the window kinship algorithm, the total shared cM and the longest cM fragment are used to identify relationships. When the number of SNPs shared between two individuals is between 6000 and 8000, the shared cM value must be higher than 180 and the longest cM fragment must be higher than 30 to be considered to have a relationship. When the number of SNPs shared between two individuals is between 8000 and 9000, the shared cM value must be higher than 150 and the longest cM fragment must be higher than 30 to be considered to have a relationship. When the number of shared SNPs between two individuals is 9000 or more, the shared cM value must be above 140 and the longest cM segment must be above 30 to be considered as a relationship. The whole genome kinship coefficient can be used to filter any number of shared SNPs. However, Snedecor et al. (supra) observed that when the SNP overlap is greater than 6000, filtering for shared cM and longest segment cM (e.g., using window kinship) has higher specificity, especially for higher degrees of relationship.

[0666] More simply, the number of SNPs typed between two individuals (SNP overlap) can be used to decide when to use a whole genome kinship algorithm (<6000 SNP overlap) and when to use a window kinship algorithm (>6000 SNP overlap). And, when deciding an algorithm based on this SNP overlap, a value or set of values ​​will be used to filter the data to identify relationships, depending on the algorithm selected. The cutoff values ​​for both whole genome kinship and window kinship are selected to ensure high sensitivity, but more importantly, high specificity, as shown in Snedecor et al. (supra). Lowering these thresholds may capture more relationships (i.e., increase sensitivity), but is expected to introduce more false positive results, especially for more distant relationships (e.g., fourth and fifth degree).

[0667] Using the above methods, a study was conducted to sequence libraries at a higher complexity, thereby reducing the total sample reads, aiming to generate a stable but smaller set of typed SNPs, thereby achieving a cost-effective method for kinship analysis.

[0668] B. Results

[0669] To show the feasibility of achieving high complexity (plexity) using sequence libraries, high complexity (>3 samples in one sequencing run) was simulated on a collection of 30 libraries generated from high-quality DNA samples in simulated AM samples. As described above, 30 simulated AM samples were prepared and sequenced together to generate sample data with a large number of reads. The BCL files were demultiplexed using a custom local build of the ForenSeqUAS secondary analysis pipeline, which differed only in the generation of FASTQ files due to different raw data generated by NextSeq. To determine the number of reads to simulate 16 sequence libraries, 25,000,000 (total estimated number of reads) was divided by 16 (number of samples) and multiplied by the average alignment percentage (96%), resulting in a total number of reads per sample of 1,500,000 at this complexity. The same calculation was then performed to simulate 30 sequence libraries, but instead of dividing by 16, it was divided by 30, resulting in 800,000 reads. Sequencing runs with fewer simulated reads are called "downsampling." Bioinformatics tools such as seqtk (https: / / github.com / lh3 / seqtk) can be used to simulate runs with fewer reads or downsample sequencing runs. Seqtk was used to downsample each sample to 1.5 million reads to simulate the complexity of 16 samples / run, or 800,000 reads to simulate the complexity of 30 samples / run. seqtk randomly selects reads from the FASTQ file and outputs a new FASTQ with the desired number of reads. The subsequent downsampled FASTQs were processed by a locally built ForenSeq UAS pipeline, which analyzed the FASTQs as described above.

[0670] The total number of reads generated for each sample ranged from 8,086,090 to 32,707,490, with an average of 23,186,251 reads. To simulate high complexity, reads were randomly selected from the FASTQ file of each sample until the required number of reads was met, which is called downsampling. Downsampling is defined as randomly selecting reads from a FASTQ file until the required number of reads is reached. The randomly selected reads are then output to a new FASTQ file. The resulting FASTQ files were analyzed using the bioinformatics algorithm as described above. Sequencing complexities of 16 and 30 were simulated by downsampling the data to 1.5M reads and 800,000 reads for each sample, respectively. Reducing the number of reads per sample results in an expected decrease in the number of typed SNPs.

[0671] In order to determine the SNP call rate across samples, the number of typing SNPs is determined for each sample at each simulation complexity (16 and 30). By downsampling the original reads in the FASTQ file to 1.5 million reads per sample, 16 sample runs (16 multiple) data are generated, and by downsampling the original reads to 800,000 reads per sample, 30 sample runs (30 multiple) data are generated. Genotyping is then performed, and the number of typing SNPs for each sample is summarized. For 16 sample runs, the minimum value is 7781, the first quartile is 8472, the median is 8630, the third quartile is 8708, and the maximum value is 8848. For 30 multiples, the minimum value is 6375, the first quartile is 7179, the median is 7299, the third quartile is 7382, and the maximum value is 7516. Fig. 20 A shows the distribution of typing SNPs for two simulated complexities of 30 libraries. The average recovery rate at complexity of 16 was 8586 SNPs (range from 7781 to 8848 SNPs), the median was 8630 SNPs, the first quartile was 8472 SNPs, and the third quartile was 8708 SNPs; the average recovery rate at complexity of 30 was 7234 SNPs (range from 6375 to 7516 SNPs), the median was 7299, the first quartile was 7179, and the third quartile was 7382 SNPs ( Fig. 20 A).

[0672] Determining relationships using the kinship algorithms and likelihood ratios described above relies heavily on the number of SNPs typed in each sample in a one-to-one comparison, also known as SNP overlap.

[0673] The more the number of typing SNPs in the two samples, the higher the credibility of the kinship and likelihood ratio values. Therefore, the average SNP overlap between samples in the simulated 16 and 30 complexity sequencing runs was calculated to see if there are enough typing SNP loci between samples that can be shared to identify the true relationship with kinship and likelihood ratio values. The average common typing SNPs across all sample combinations in the simulated 16 and 30 complexity sequencing runs are 6998 loci, with a minimum overlap of 6058 and a maximum overlap of 7322. The simulation shows that sequencing the library in a way that the total reads obtained for each sample are less will allow for a smaller stable SNP set to be typed for each sample, and there is enough overlap to determine the kinship. The number of common typing SNPs is less than 8000, which may not be enough to identify higher-order relationships (e.g., four and five levels), but may be enough to identify three-level relationships.

[0674] It is expected that sequencing libraries at increasing complexity will result in a decrease in the number of typed SNPs, which may lead to increased allele dropout and subsequent decreased heterozygosity, particularly for DNA degraded and / or low number of postmortem (PM) samples. To assess the level of loci and allele dropout and heterozygosity at higher complexity, libraries generated from simulated antemortem samples (AM) and simulated postmortem (PM) as described above were sequenced at the recommended complexity of 3 samples per sequencing run and then at four higher complexities of 12, 16, 24, and 32 samples per sequencing run. The resulting number of typed SNPs is shown in Fig. 20 As shown in B. Fig. 20 As shown in B, the minimum, first quartile, median, third quartile and maximum values ​​are as follows: 9853, 9976, 10009, 10059 and 10135 for 3-fold; 9332, 9394, 9419, 9520 and 9945 for 12-fold; 8881, 9091, 9303, 9419 and 9901 for 16-fold; 7653, 8348, 8515, 8706 and 9753 for 32-fold. Fig. 20 As shown in C, the minimum, first quartile, median, third quartile and maximum values ​​are as follows: 7215, 8677, 9724, 9923 and 9991 for 3-fold; 4603, 8261, 9360, 9664 and 9903 for 12-fold. Fig. 20 As shown in Figure 2B, for the reference samples, the number of loci with reads below the acceptable threshold (AT) increases with increasing complexity. In addition, the distribution of loci that fall below the AT widens with increasing sequencing complexity, with an average of 10,111 (minimum 9,853, maximum 10,135) typed SNPs for sequencing samples with 3 samples per run and an average of 8,528 (minimum 7,653, maximum 9,753) typed SNPs for sequencing samples with 32 samples per run. At the highest complexity of 32 samples per sequencing run tested, the minimum number of typed SNPs remained above 7,000, which is consistent with the simulation results and Fig. 20 Sequencing these libraries at high complexity yielded a higher number of typed SNP loci compared to that shown in A.

[0675] Next, the effect of higher complexity sequencing on sister allele loss of heterozygous loci was evaluated by comparing the SNP genotypes determined by sequencing the sample library with the recommended complexity of 3 samples per run with the SNP genotypes determined by the same sample library sequenced with 32 samples per run. For each sample and each locus sequenced with the recommended complexity (3-fold) and higher complexity sequencing (32-fold), if the same alleles were typed in both runs, the genotype was considered consistent, otherwise inconsistent. The average overlap between 3-fold sequencing and 32-fold sequencing was 8,610 SNPs, with a minimum of 7,808 SNPs and a maximum of 9,667 SNPs. For autosomal loci, two alleles must match to be considered consistent. The allele consistency of each sample is calculated by dividing the number of consistent alleles by the total number of alleles in the locus typed in two sequencing runs. For simulated antemortem samples, allelic discordance (alleles falling below AT) between libraries sequenced at a complexity of 3 increased by an average of 1.9%, with a minimum of 0.50% and a maximum of 2.8% ( Fig.21A , left y-axis). Heterozygosity was determined by summing the number of heterozygous loci for each sample and dividing this value by the total number of loci called. Samples sequenced using 32 samples per run (32-plex) showed the greatest difference in heterozygosity compared to the standard complexity of 3 samples per run (3-plex), with an average difference of 6.8%, a minimum difference of 2.0%, and a maximum difference of 10.2%, while 3-plex sequencing showed greater heterozygosity than these values. ( Fig.21A , right y-axis).

[0676] The nature of samples from victims of MFI is that they are often degraded and may contain low levels of genomic DNA. However, it is also advantageous in terms of cost-effectiveness and overall process to increase the number of samples per sequencing run, although this may result in a decrease in the number of typed SNPs and reduced heterozygosity, thus compounding these issues. To evaluate the impact of increasing sequencing complexity on simulated postmortem samples compared to the standard protocol of 3 samples per sequencing run for degraded and low-input DNA samples, libraries were generated from 30 simulated postmortem (PM) samples with different degrees of degradation, low-input DNA samples, dental remains, and cremated, embalmed, burned, and buried bones. These libraries were sequenced in standard 3-plex and 12-plex. There was no significant difference in the number of typed SNPs above AT per sample and per run between the two sequencing runs, although some samples sequenced at higher complexity had significantly lower numbers of typed SNPs. For example, Fig. 20C shows that the results of 3-plex sequencing for the simulated PM sample were 9,313 SNPs on average, 7,215 SNPs on minimum, and 9,991 SNPs on maximum, while the results of 12-plex sequencing for the simulated PM sample were 8,796 SNPs on average, 4,603 SNPs on minimum, and 9,903 SNPs on maximum. These results suggest that increasing the number of samples per run for degraded and / or low DNA input samples (e.g., postmortem samples) will increase the number of loci below AT, but the greatest impact on the number of typed loci is the degradation state and / or amount of DNA.

[0677] As described for the above-mentioned simulation AM sample, the simulation PM sample of the library of 3-fold or 12-fold sequencing is carried out genotype call consistency test.This includes the same period bone sample and the same period tooth sample, in addition, also purchased two groups of degenerate DNA (samples 1231 and 3551) from Innogenomics Inc. (New Orleans, Louisiana, USA).By 32-fold or 12-fold and corresponding 3-fold operation comparison and the same allele call at each locus is classified as consistent, and different allele calls are classified as inconsistent (lower than AT), calculate the percentage consistency.For autosomal locus, two alleles must match and can be regarded as consistent.Percentage is calculated by dividing the number of matching loci by the locus total number of two runs called for each sample. Fig.21A The loci called for the simulated AM samples ranged from 7808 to 9667, with a mean of 8610; and for Fig. 21B For the simulated PM samples in , the loci called ranged from 4580 to 9795, with an average of 8627. Percent heterozygosity was determined by summing the number of heterozygous loci and then dividing that value by the total number of loci called. The simulated PM samples were DNA extracted from dental remains (Tooth), blood of varying degrees of degradation (sample names beginning with 1231 and 3551), burial bones (BB1), cremated bones (CB1), embalmed bones (CB2 and CB3), charred bones (CB5, CB6, CB7), and a range of low DNA inputs of 0.05, 0.1, and 0.25 ng (50 pg, 100 pg, and 250 pg, respectively) from the Coriell sample NA24385.

[0678] The number of loci typed in both runs ranged from 4580 to 9795, with an average of 8627 SNP loci. Allelic discordance (alleles falling below AT) increased by an average of 1.2%, with a minimum of 0.1% and a maximum of 2.6% ( Fig. 21B, right axis). Dental remains and cremated and charred skeletons showed the lowest allelic discordance, with an average discordance of 0.6%, a minimum discordance of 0.1%, and a maximum discordance of 1.3%.

[0679] Heterozygosity was calculated as described above for the simulated AM samples. The level of heterozygosity varied with the degradation level and amount of input DNA ( Fig. 21B , right axis). Two degraded DNA samples with degradation indexes of 158 and 56 (3551_158 and 3551_56, respectively) and the burial bone sample (BB1) and the 0.05ng (50pg) NA24385 sample showed the highest heterozygosity differences between sequencing complexities (9.0%, 13.5%, 26.9%, and 10.8%, respectively). 1231 libraries 7-8 and 10-12 ( Fig. 21B ) were generated from the same DNA sample with increasing amounts of degradation (DIs of 26.3, 33.6, 48.6, 160.3, and 459.8 for 1231 samples designated 7, 8, 10, 11, and 12, respectively). Heterozygosity decreased with increasing DNA degradation, with sample 1231_7 having a heterozygosity of 37% at a DI of 26.3 and sample 1231_12 having a heterozygosity of 31% at a DI of 459.8 for these pools sequenced at a complexity of 12 on the MiSeq FGx. BB1 had the highest level of heterozygosity when sequenced at 3-plex. Investigation of the intralocus balance (ILB) for this sample (BB1) showed an ILB of 53% for samples sequenced at a complexity of 3 samples per run and 28% for samples sequenced at a complexity of 12 samples per run, respectively. These results suggest that the increased heterozygosity observed in BB1 samples sequenced at a complexity of 3 samples per run is not due to sequencing errors, as ILB would be expected to be very low.

[0680] Bone samples (burned, embalmed, or cremated bone DNA extracts) with CB markers showed minimal heterozygosity differences (average difference among seven samples 1.7%; minimum difference 0.1%; maximum difference 3.9%). In addition, high throughput using this method performed well in dental remains, which showed an average heterozygosity difference of 2.5%, a minimum difference of 1.8%, and a maximum difference of 4.5%.

[0681] To assess the kinship of MFI victims, AM samples were compared to PM samples to test for relatedness, allowing for a 1-to-1 comparison. The number of SNPs typed (SNP overlap) in two individuals has a significant impact on determining relationships, especially distant relationships, such as fourth and fifth order. Therefore, SNP overlap (common SNP loci) was calculated in all sample combinations between simulated PM samples sequenced at 12 repetitions and simulated AM samples sequenced at 32 repetitions. This was done by pairing each simulated PM sample with each simulated AM sample, identifying the number of SNP loci genotyped in both samples, and adding the values ​​to give the SNP overlap for each pair. The average overlap between the simulated PM sample and the simulated AM sample combination was 8,020, with a minimum of 4,071 and a maximum of 9,727. These data suggest that with an average of approximately 8,000 SNPs, sequencing these libraries using this method at a higher complexity (12 per run for PM samples and 32 per run for AM samples) would be able to identify first, second, third, most fourth order relationships, and some fifth order relationships.

[0682] We next investigated the ability of the genome-wide kinship algorithm to identify relationships when the SNP call rate was less than 9,000 common loci (2,000-8,000 SNPs typed). Data presented by Snedecor et al. (above) showed that when the SNP call rate was approximately 9,000 loci, the accuracy of identifying all degrees of relationship using the window kinship algorithm was high. Window kinship works by computing a kinship window across the genome to find shared segments between two individuals. See Snedecor et al. (above). The genome-wide kinship algorithm treats the entire genome as a segment of kinship rather than a window between two individuals. Snedecor et al. showed that for the identification of higher order relationships (e.g., fourth and fifth degree), window kinship was more accurate when the number of SNPs typed (SNP overlap) in the two corresponding samples was at least 9,000 SNPs. As Fig. 20 As shown in B and 20C, simulated AM samples and simulated PM samples sequenced at high complexity are unlikely to exhibit the > / =9000 SNP call rates required by the window kinship a...

Claims

1. A method for performing DNA-based kinship analysis, comprising: Provide nucleic acid samples from interested persons, amplifying the nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences, the plurality of target sequences collectively comprising a plurality of at least between or about between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) to generate amplification products, wherein the amplification is performed in one or more multiplex PCR reactions, generating a nucleic acid library from the amplification products, sequencing the nucleic acid library generated from the amplification products, analyzing the sequence of the amplified product, determining the genotypes of the plurality of SNPs to generate a DNA profile, and A degree of relationship of the DNA profile to one or more reference DNA profiles is calculated, wherein the one or more reference DNA profiles are contained in a set of reference DNA profiles, the set of reference DNA profiles including one or more reference DNA profiles from relatives of the person of interest.

2. A method for performing DNA-based kinship analysis, comprising: Provide nucleic acid samples from interested persons, amplifying the nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences, the plurality of target sequences collectively comprising a plurality of at least between or about between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) to generate amplification products, wherein the amplification is performed in one or more multiplex PCR reactions, generating a nucleic acid library from the amplification products, sequencing the nucleic acid library generated from the amplification products, determining the genotypes of the plurality of SNPs to generate a DNA profile, and A degree of relationship of the DNA profile to one or more reference DNA profiles is calculated, wherein the one or more reference DNA profiles are contained in a set of reference DNA profiles, the set of reference DNA profiles including one or more reference DNA profiles from relatives of the person of interest.

3. The method according to claim 1 or 2, wherein: The sequencing was performed using massively parallel sequencing (MPS).

4. The method according to any one of claims 1 to 3, wherein: The sequencing does not include whole genome sequencing (WGS).

5. The method of any one of claims 1 to 4, further comprising generating a family tree comprising the DNA profile associated with one or more DNA profiles.

6. A method for constructing a nucleic acid library of a person of interest, comprising: Provide nucleic acid samples from interested persons, The nucleic acid sample is amplified using multiple primers that specifically hybridize to multiple target sequences, wherein the multiple target sequences collectively include multiple single nucleotide polymorphisms (SNPs) of at least between 2,000 and 50,000 or about between 2,000 and 50,000, thereby generating a nucleic acid library including amplification products, wherein the amplification is performed in one or more multiplex PCR reactions.

7. The method of claim 6, further comprising the step of sequencing the amplified product to generate a DNA profile of the person of interest.

8. A method for constructing a nucleic acid library of a reference DNA sample, comprising: Provide nucleic acid samples from relatives of the person of interest, The nucleic acid sample is amplified using multiple primers that specifically hybridize to multiple target sequences, wherein the multiple target sequences collectively include multiple single nucleotide polymorphisms (SNPs) of at least between 2,000 and 50,000 or about between 2,000 and 50,000, thereby generating a nucleic acid library including amplification products, wherein the amplification is performed in one or more multiplex PCR reactions.

9. The method according to claim 8, wherein: The relative is a first, second, third, fourth or fifth degree relative of the person of interest.

10. The method according to claim 8 or 9, wherein: The relative is a first, second or third degree relative of the person of interest.

11. The method according to any one of claims 1 to 10, wherein: The nucleic acid sample includes genomic DNA.

12. The method according to any one of claims 1 to 11, wherein: The nucleic acid sample includes one or more enzyme inhibitors.

13. The method according to claim 12, wherein: The one or more enzyme inhibitors include one or more inhibitors selected from the group consisting of heme, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite.

14. The method according to any one of claims 1 to 13, wherein: The nucleic acid sample includes low-quality nucleic acid molecules and / or low-quantity nucleic acid molecules.

15. The method according to claim 14, wherein: The low-quality nucleic acid molecules are degraded genomic DNA and / or fragmented genomic DNA.

16. The method according to claim 14 or claim 15, wherein: The low quality nucleic acid molecules have a degradation index of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200. 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200.

17. The method according to claim 14 or claim 15, wherein: The low mass nucleic acid molecules have a DI of at least 1 and up to or less than 158.

3.

18. The method according to any one of claims 1 to 13, wherein: The nucleic acid sample includes high-quality nucleic acid molecules.

19. The method according to claim 18, wherein: The high quality nucleic acid molecules have a DI less than 1.

20. The method according to any one of claims 1 to 19, wherein: The person of interest is a missing person.

21. The method according to any one of claims 1 to 19, wherein: The persons of interest are victims of a disaster or conflict.

22. The method according to any one of claims 1 to 21, wherein: The nucleic acid sample is derived from saliva, blood, semen, hair, teeth, bones or skin.

23. The method according to claim 22, wherein: The nucleic acid sample is derived from saliva, blood or semen.

24. The method according to claim 22, wherein: The nucleic acid sample is derived from bone or hair.

25. The method according to any one of claims 1 to 21, wherein: The nucleic acid sample is derived from a buccal swab, paper, fabric, or other matrix or object impregnated with saliva, blood, semen or other body fluids, or containing hair or skin cells.

26. The method according to any one of claims 1 to 25, wherein: The nucleic acid sample includes between or about between 3 pg and 100 ng of genomic DNA.

27. The method according to any one of claims 1 to 26, wherein: The nucleic acid sample includes between or about 100 pg and 5 ng of genomic DNA, between or about 50 pg and 5 ng of genomic DNA, or between or about 3 pg and 5 ng of genomic DNA.

28. The method according to claim 26 or 27, wherein: The nucleic acid sample includes 1 ng or about 1 ng of genomic DNA.

29. The method according to any one of claims 1 to 28, wherein: The plurality of SNPs include kinship SNPs (kiSNPs).

30. The method according to any one of claims 1 to 29, wherein: The plurality of SNPs include Y chromosome SNPs (Y-SNPs).

31. The method according to any one of claims 1 to 30, wherein: The plurality of SNPs include kiSNPs and Y-SNPs.

32. The method according to any one of claims 1 to 31, wherein: The plurality of SNPs include kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotypic SNPs (piSNPs), X-chromosome SNPs (X-SNPs), and Y-chromosome SNPs (Y-SNPs).

33. The method according to any one of claims 1 to 28, wherein: The plurality of SNPs include one or more SNPs selected from the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs.

34. The method according to any one of claims 1 to 33, wherein: At least 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the plurality of SNPs are kinship SNPs, or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% are kinship SNPs.

35. The method according to any one of claims 1 to 34, wherein: The set of reference DNA profiles includes up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900 or 1000 reference DNA profiles.

36. The method according to any one of claims 1 to 35, wherein: At least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest.

37. The method according to any one of claims 1 to 36, wherein: At least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest, and wherein each of at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set is a first degree relative, a second degree relative, a third degree relative, a fourth degree relative or a fifth degree relative.

38. The method according to any one of claims 1 to 37, wherein: At least 50% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest.

39. The method according to any one of claims 36 to 38, wherein: Each relative of the person of interest in the reference DNA profile is a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative, or a fifth-degree relative of the person of interest.

40. The method of claim 39, wherein: Each relative of the person of interest in the reference DNA profile is a first-degree relative, a second-degree relative, or a third-degree relative of the person of interest.

41. The method according to any one of claims 1 to 40, wherein: The identity of each relative of the person of interest in the reference DNA profile is known.

42. The method according to any one of claims 1 to 41, wherein: The identity of each of the one or more reference DNA profiles in the set of reference DNA profiles is known.

43. The method according to any one of claims 1 to 42, wherein: The reference DNA profile set is in a database.

44. The method of claim 43, wherein: The database is not publicly accessible.

45. The method of any one of claims 1 to 44, wherein: The sequencing included up to 40-plex sequencing complexity.

46. ​​The method according to any one of claims 1 to 44, wherein: The sequencing included up to 32-plex sequencing complexity.

47. The method according to any one of claims 1 to 44, wherein: The sequencing includes 12-plex to 32-plex sequencing complexity.

48. The method according to any one of claims 1 to 44, wherein: The sequencing includes 24-plex to 32-plex sequencing complexity.

49. The method according to any one of claims 1 to 44, wherein: The sequencing comprises a sequencing complexity of or about 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, or 35-plex.

50. The method according to any one of claims 1 to 49, wherein: The sequencing comprises a sequencing complexity of 8 to 16-plex or about 8 to 16-plex for postmortem samples, and / or the sequencing comprises a sequencing complexity of 24 to 40-plex or about 24 to 40-plex for antemortem samples.

51. The method according to any one of claims 1 to 50, wherein: The sequencing comprises a sequencing complexity of 12-plex or about 12-plex for postmortem samples, and / or the sequencing comprises a sequencing complexity of 32-plex or about 32-plex for antemortem samples.

52. The method according to any one of claims 1 to 51, wherein: The sequencing comprises a sequencing complexity of 30-plex, 31-plex or 32-plex or a sequencing complexity of about 30-plex, 31-plex or 32-plex.

53. A method according to any one of claims 1 to 52, further comprising identifying the person of interest.

54. A method for calculating a degree of association, comprising: obtaining a DNA profile comprising genotypes of at least between or about 2,000 and 50,000 SNPs, wherein the DNA profile is from the person of interest; and A degree of relationship between the DNA profile and one or more reference DNA profiles is calculated, wherein the one or more reference DNA profiles are included in a set of reference DNA profiles, the set of reference DNA profiles including one or more reference DNA profiles from relatives of the person of interest.

55. A method for calculating a degree of association, comprising: generating a DNA profile comprising genotypes of at least between or about 2,000 and 50,000 SNPs, wherein the DNA profile is from the person of interest; and A degree of relationship between the DNA profile and one or more reference DNA profiles is calculated, wherein the one or more reference DNA profiles are included in a set of reference DNA profiles, the set of reference DNA profiles including one or more reference DNA profiles from relatives of the person of interest.

56. The method according to any one of claims 1 to 55, wherein: The degree of relationship is calculated using a kinship model.

57. The method according to any one of claims 1 to 56, wherein: The degree of relationship is calculated using a kinship model trained using the PCA method.

58. The method of claim 57, wherein: The PCA method used to train the kinship model is or involves PCA.

59. The method of claim 57 or claim 58, wherein: The PCA method is PC-AiR.

60. The method of claim 59, wherein: The PC-AiR comprises the following steps: (1) estimating kinship coefficients between all pairs of samples in a training database, wherein the sample pairs are optionally trained DNA profiles, wherein pairs with kinship coefficients > 0.025 are identified as related, and pairs with kinship coefficients < -0.025 are identified as ancestrally diverged; (2) Initializing an irrelevant sample set including all samples; and (3) Iteratively: (i) identify the set of samples in the unrelated sample set that has the most related samples in the unrelated sample set, designating it as X, (ii) identify the set of samples in X that has the fewest ancestral differentiation pairs compared to the samples in the unrelated sample set, designating it as Y; and (iii) if Y has zero samples, terminate the process, or, if Y has at least one sample, randomly select a sample from Y to remove from U and repeat starting at step (3)(i).

61. The method of claim 57 or claim 58, wherein: The PCA method is a modified PC-Air.

62. The method of claim 61, wherein: The improved PC-AiR comprises the following steps: (1) estimating kinship coefficients between all pairs of samples in a training database, wherein pairs with kinship coefficients > 0.01 are identified as related, and pairs with kinship coefficients < -0.025 are identified as ancestrally diverged; (2) removing all DNA profiles with ≥5% missing data; and (3) Sort all DNA profiles by identifying each DNA profile with a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles ranked from least to most in the complete database, and ties are broken by the number of ancestral differentiation DNA profiles ranked from most to least in the complete database. In some embodiments, step (3) involves iteratively traversing the sorted DNA profiles, and for each DNA profile: (i) if the DNA profile is not already in the related sample set, adding it to the unrelated sample set and adding all related DNA profiles to the related sample set, and (ii) if the DNA profile is already in the related sample set, skipping to the next DNA profile and repeating starting at step (3(i).

63. The method according to any one of claims 1 to 62, wherein: Calculating the degree of relationship includes calculating the kinship coefficient using PC-Relate.

64. The method of claim 63, wherein: The degree of relationship is calculated by taking the DNA profile of the person of interest as input to PC-Relate.

65. The method according to any one of claims 57 to 64, wherein: The degree of relationship is calculated by providing a kinship model and the DNA profile of the person of interest as input to PC-Relate.

66. The method according to any one of claims 63 to 65, wherein: The one or more reference DNA profiles are also provided as input to PC-Relate.

67. A method according to any one of claims 1 to 66, wherein Calculating the degree of relationship includes calculating the kinship coefficient using a genome-wide kinship algorithm as follows: wherein the person of interest and the reference DNA profile of the one or more reference DNA profiles are i and j, is the kinship coefficient, is the estimated allele frequency, s is the SNP among the SNPs typed in both individuals, and g is and g js are the numbers of reference alleles in i and j at SNP s, respectively, and and are the expected allele frequencies calculated from PC-AiR for i and j at SNP s, respectively.

68. The method according to any one of claims 1 to 67, wherein: Calculating the degree of relationship includes calculating a likelihood ratio.

69. The method of claim 68, wherein: Calculating the likelihood ratio includes comparing the plurality of SNPs between the DNA profile and the one or more reference DNA profiles.

70. The method of claim 68, wherein: Calculating the likelihood ratio includes comparing a set of SNPs between the DNA profile and the one or more reference DNA profiles, the set of SNPs including kinship SNPs from within the plurality of SNPs.

71. The method of any one of claims 68 to 70, wherein: Calculating the likelihood ratio includes dividing a probability that the DNA profile is associated with a reference DNA profile of the one or more reference DNA profiles by a probability that the DNA profile is not associated with the reference DNA profile based on the genotypes of the plurality of SNPs.

72. The method of any one of claims 68 to 71, wherein: The likelihood ratio (LR) is calculated as follows: Wherein, D represents the genotype, H r represents the assumption that individuals are correlated, and H u The assumption that individuals are unrelated.

73. The method of any one of claims 68 to 71, wherein: The LR is calculated as follows: Wherein, 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2.

74. The method according to any one of claims 1 to 73, wherein: The person of interest is biologically male, and the method further comprises calculating a likelihood ratio of a shared Y chromosome between the DNA profile and the one or more reference DNA profiles.

75. The method of claim 74, wherein: Calculating the likelihood ratio of a shared Y chromosome includes comparing a set of SNPs between the DNA profile and the one or more reference DNA profiles, the set of SNPs including one or more Y-SNPs.

76. The method of claim 75, wherein: The one or more Y-SNPs are contained within the plurality of SNPs.

77. The method of claim 75, wherein: The one or more Y-SNPs include at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84 or 85 Y-SNPs.

78. A method according to any one of claims 75 to 77, wherein The one or more Y-SNPs include 85 Y-SNPs.

79. The method according to any one of claims 75 to 78, wherein: Calculating the likelihood ratio of sharing a Y chromosome includes dividing the probability that the DNA profile shares a Y chromosome with a reference DNA profile in the one or more reference DNA profiles by the probability that the DNA profile does not share a Y chromosome with the reference DNA profile based on the genotype of the one or more Y-SNPs.

80. The method according to any one of claims 1 to 79, wherein: At least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the DNA profiles in the reference DNA profile set are from relatives of missing persons or victims of disaster or conflict.

81. The method according to any one of claims 1 to 80, wherein: Each of said DNA profiles in said reference DNA profile set is from a relative of a missing person or a victim of a disaster or conflict.

82. The method according to any one of claims 1 to 81, wherein The set of reference DNA profiles includes up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900 or 1000 reference DNA profiles.

83. The method according to any one of claims 1 to 82, wherein: The reference DNA profile set includes up to 100 reference DNA profiles.

84. The method according to any one of claims 1 to 83, wherein: At least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest.

85. The method according to any one of claims 1 to 84, wherein: At least 50% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest.

86. The method of claim 84 or claim 85, wherein: Each relative of the person of interest in the reference DNA profile is a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative, or a fifth-degree relative of the person of interest.

87. The method according to any one of claims 1 to 86, wherein: The identity of each relative of the person of interest in the reference DNA profile is known.

88. The method according to any one of claims 1 to 87, wherein: The identity of each relative of the person of interest in the DNA reference profile is known.

89. The method according to any one of claims 1 to 88, wherein: The identity of each of the one or more reference DNA profiles in the set of reference DNA profiles is known.

90. The method according to any one of claims 1 to 89, wherein: The reference DNA profile set is in a database.

91. The method of claim 90, wherein: The database is not publicly accessible.

92. The method of claim 90 or claim 91, wherein: The database cannot be accessed by third party genetics services.

93. A nucleic acid library constructed using the method according to any one of claims 6 to 92.

94. A plurality of primers that specifically hybridize to a plurality of target sequences, the plurality of target sequences comprising at least between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) or about between 2,000 and 50,000 of the SNPs in a nucleic acid sample from a person of interest, wherein: The plurality of primers are used to amplify the nucleic acid sample in one or more multiplex PCR reactions to generate amplification products.

95. A plurality of primers that specifically hybridize to a plurality of target sequences, the plurality of target sequences comprising at least between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) or about between 2,000 and 50,000 of said SNPs in a nucleic acid sample from a person of interest and a nucleic acid sample from one or more reference samples, in, The one or more reference samples include samples from relatives of the person of interest, and The plurality of primers are used in one or more multiplex PCR reactions to amplify the nucleic acid sample from the person of interest and the nucleic acid sample from one or more reference samples to generate amplification products.

96. A plurality of primers according to claim 94 or claim 95, wherein: The nucleic acid sample from the person of interest includes genomic DNA.

97. A plurality of primers according to any one of claims 94 to 96, wherein: The nucleic acid sample from the person of interest includes one or more enzyme inhibitors.

98. The plurality of primers according to claim 97, wherein: The one or more enzyme inhibitors include one or more inhibitors selected from the group consisting of heme, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite.

99. A plurality of primers according to any one of claims 94 to 98, wherein: The nucleic acid sample from the person of interest includes low-quality nucleic acid molecules and / or low-quantity nucleic acid molecules.

100. The plurality of primers according to claim 99, wherein: The low-quality nucleic acid molecules are degraded genomic DNA and / or fragmented genomic DNA.

101. The plurality of primers of claim 99 or claim 100, wherein: The low quality nucleic acid molecules have a degradation index (I) of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200. or a degradation index (DI) of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200.

102. A plurality of primers according to any one of claims 99 to 101, wherein: The low mass nucleic acid molecules have a DI of at least 1 and up to or less than 158.

3.

103. A plurality of primers according to any one of claims 94 to 102, wherein: The nucleic acid sample from the person of interest and / or the nucleic acid sample from one or more reference samples comprises high quality nucleic acid molecules.

104. The plurality of primers according to claim 103, wherein: The high quality nucleic acid molecules have a DI less than 1.

105. A plurality of primers according to any one of claims 94 to 104, wherein: The person of interest is a missing person.

106. A plurality of primers according to any one of claims 94 to 104, wherein: The persons of interest are victims of a disaster or conflict.

107. A plurality of primers according to any one of claims 94 to 106, wherein: The nucleic acid sample from the person of interest is derived from a buccal swab, paper, fabric, or other matrix or object impregnated with saliva, blood or other body fluids, or containing hair or skin cells.

108. A plurality of primers according to any one of claims 94 to 107, wherein: The nucleic acid sample from the person of interest includes between or about between 3 pg and 100 ng of genomic DNA.

109. A plurality of primers according to any one of claims 94 to 108, wherein: The nucleic acid sample from the person of interest includes between or about 100 pg and 5 ng of genomic DNA, between or about 50 pg and 5 ng of genomic DNA, or between or about 3 pg and 5 ng of genomic DNA.

110. The plurality of primers of claim 108 or claim 109, wherein: The nucleic acid sample from the person of interest includes 1 ng or about 1 ng of genomic DNA.

111. A plurality of primers according to any one of claims 94 to 110, wherein: The plurality of SNPs include kinship SNPs (kiSNPs).

112. The method according to any one of claims 94 to 111, wherein: The plurality of SNPs include Y chromosome SNPs (Y-SNPs).

113. The method according to any one of claims 94 to 112, wherein: The plurality of SNPs include kiSNPs and Y-SNPs.

114. A plurality of primers according to any one of claims 94 to 113, wherein: The plurality of SNPs include kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotypic SNPs (piSNPs), X-chromosome SNPs (X-SNPs), and Y-chromosome SNPs (Y-SNPs).

115. A plurality of primers according to any one of claims 94 to 111, wherein: The plurality of SNPs include one or more SNPs selected from the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs.

116. A plurality of primers according to any one of claims 94 to 115, wherein: At least 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the plurality of SNPs are kinship SNPs, or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% are kinship SNPs.

117. A plurality of primers according to any one of claims 94 to 116, wherein: At least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the DNA profiles in the reference DNA profile set are from relatives of missing persons or victims of disaster or conflict.

118. A plurality of primers according to any one of claims 94 to 116, wherein: Each of the one or more reference samples is from a relative of a missing person or a victim of a disaster or conflict.

119. A plurality of primers according to any one of claims 94 to 118, wherein: The one or more reference samples include up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900 or 1000 reference samples.

120. A plurality of primers according to any one of claims 94 to 119, wherein: At least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the one or more reference samples are from relatives of the person of interest.

121. A plurality of primers according to any one of claims 94 to 120, wherein: At least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest, and wherein each of at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set is a first degree relative, a second degree relative, a third degree relative, a fourth degree relative or a fifth degree relative.

122. A plurality of primers according to any one of claims 94 to 121, wherein: At least 50% of the one or more reference samples are from relatives of the person of interest.

123. A plurality of primers according to any one of claims 120 to 122, wherein: Each relative of the person of interest in the one or more reference samples is a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative, or a fifth-degree relative of the person of interest.

124. The plurality of primers of claim 123, wherein: Each relative of the person of interest in the one or more reference samples is a first-degree relative, a second-degree relative, or a third-degree relative of the person of interest, respectively.

125. A plurality of primers according to any one of claims 94 to 124, wherein: The identity of each relative of the person of interest in the one or more reference samples is known.

126. A plurality of primers according to any one of claims 94 to 125, wherein: The identity of each of the one or more reference samples is known.

127. A method for constructing a DNA profile, comprising: Provide nucleic acid samples from interested persons, amplifying the nucleic acid sample using a plurality of primers that specifically hybridize to a plurality of target sequences, the plurality of target sequences collectively comprising a plurality of at least between or about between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) to generate amplification products, wherein the amplification is performed in one or more multiplex PCR reactions, The amplified product is sequenced. The genotypes of the plurality of SNPs are determined, thereby generating a DNA profile.

128. A method for constructing a DNA profile, comprising: Provide nucleic acid samples from interested persons, Providing nucleic acid samples from relatives of the person of interest, amplifying the nucleic acid sample from the person of interest and the nucleic acid sample from the relative using a plurality of primers that specifically hybridize to a plurality of target sequences, the plurality of target sequences collectively comprising a plurality of at least between or about between 2,000 and 50,000 single nucleotide polymorphisms (SNPs) to generate amplification products, wherein the amplification is performed in one or more multiplex PCR reactions, The amplified product is sequenced. The genotypes of the plurality of SNPs are determined, thereby generating DNA profiles of the person of interest and the relatives of the person of interest.

129. The method of claim 127 or claim 128, wherein: The sequencing does not include whole genome sequencing (WGS).

130. The method of claim 127 or claim 129, wherein: The nucleic acid sample includes genomic DNA.

131. The method of claim 128 or claim 129, wherein: The nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative of the person of interest comprises genomic DNA.

132. The method of any one of claims 127 to 131, wherein: The nucleic acid sample, the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative comprises one or more enzyme inhibitors.

133. The method of claim 132, wherein: The one or more enzyme inhibitors include one or more inhibitors selected from the group consisting of heme, heme, humic acid, indigo, tannic acid, collagen, calcium, and hydroxyapatite.

134. The method according to any one of claims 127 to 133, wherein: The nucleic acid sample, the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative comprises low-quality nucleic acid molecules and / or low-quantity nucleic acid molecules.

135. The method of claim 134, wherein: The low-quality nucleic acid molecules are degraded genomic DNA and / or fragmented genomic DNA.

136. The method of claim 134 or claim 135, wherein: The low quality nucleic acid molecules have a degradation index (I) of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200. or a degradation index (DI) of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200.

137. The method of any one of claims 134 to 136, wherein: The low mass nucleic acid molecules have a DI of at least 1 and up to or less than 158.

3.

138. The method of any one of claims 127 to 133, wherein: The nucleic acid sample, the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative comprises high-quality nucleic acid molecules.

139. The method of claim 138, wherein: The high quality nucleic acid molecules have a DI less than 1.

140. The method of any one of claims 127 to 139, wherein: The person of interest is a missing person.

141. The method of any one of claims 127 to 139, wherein: The persons of interest are victims of a disaster or conflict.

142. The method of any one of claims 128, 129, and 131 to 141, wherein The relative of the person of interest is a first, second, third, fourth or fifth degree relative.

143. The method of any one of claims 128, 129, and 131-141, wherein: The relative of the person of interest is a first, second or third degree relative.

144. The method of any one of claims 127 to 143, wherein: The nucleic acid sample, the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative is derived from an oral swab, paper, fabric, or other matrix or object impregnated with saliva, blood or other body fluids, or containing hair or skin cells.

145. The method of any one of claims 127 to 144, wherein: The nucleic acid sample, the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative comprises between or about 3 pg and 100 ng of genomic DNA.

146. The method of any one of claims 127 to 145, wherein: The nucleic acid sample, the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative includes between 100 pg and 5 ng or about 100 pg and 5 ng of genomic DNA, between 50 pg and 5 ng or about 50 pg and 5 ng of genomic DNA, or between 3 pg and 5 ng or about 3 pg and 5 ng of genomic DNA.

147. The method of claim 145 or claim 146, wherein: The nucleic acid sample, the nucleic acid sample of the person of interest and / or the nucleic acid sample of the relative comprises 1 ng or about 1 ng of genomic DNA.

148. The method of any one of claims 127 to 147, wherein: The plurality of SNPs include kinship SNPs.

149. The method according to any one of claims 127 to 148, wherein: The plurality of SNPs include Y chromosome SNPs (Y-SNPs).

150. The method of any one of claims 127 to 149, wherein: The plurality of SNPs include kiSNPs and Y-SNPs.

151. The method of any one of claims 127 to 150, wherein: The plurality of SNPs include kiSNPs, biogeographic ancestry SNPs (aiSNPs), identity SNPs (iiSNPs), phenotypic SNPs (piSNPs), X-chromosome SNPs (X-SNPs), and Y-chromosome SNPs (Y-SNPs).

152. The method of any one of claims 127 to 151, wherein: The plurality of SNPs include one or more SNPs selected from the group consisting of kiSNPs, aiSNPs, iiSNPs, piSNPs, X-SNPs, and Y-SNPs.

153. The method of any one of claims 127 to 152, wherein: At least 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the plurality of SNPs are kinship SNPs, or at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% are kinship SNPs.

154. The method of any one of claims 1 to 92 and 127 to 153, wherein The sequencing included up to 40-plex sequencing complexity.

155. The method of any one of claims 1 to 92 and 127 to 153, wherein The sequencing included up to 32-plex sequencing complexity.

156. The method according to any one of claims 1 to 92 and 127 to 153, wherein The sequencing includes 12-plex to 32-plex sequencing complexity.

157. The method of any one of claims 1 to 92 and 127 to 153, wherein The sequencing includes 24-plex to 32-plex sequencing complexity.

158. The method of any one of claims 1-92 and 127-153, wherein: The sequencing includes 4-plex, 5-plex, 6-plex, 7-plex, 8-plex, 9-plex, 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, 35-plex, 36-plex, 37-plex, 38-plex, 39-plex, 40-plex, 41-plex, 42-plex, 43-plex, 44-plex, 45-plex sequencing complexity. , or a sequencing complexity of about 4-plex, 5-plex, 6-plex, 7-plex, 8-plex, 9-plex, 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, 35-plex, 36-plex, 37-plex, 38-plex, 39-plex, 40-plex, 41-plex, 42-plex, 43-plex, 44-plex, 45-plex; or The sequencing comprises a sequencing complexity of or about 10-plex, 11-plex, 12-plex, 13-plex, 14-plex, 15-plex, 16-plex, 17-plex, 18-plex, 19-plex, 20-plex, 21-plex, 22-plex, 23-plex, 24-plex, 25-plex, 26-plex, 27-plex, 28-plex, 29-plex, 30-plex, 31-plex, 32-plex, 33-plex, 34-plex, or 35-plex.

159. The method of any one of claims 1 to 92 and 127 to 158, wherein The sequencing comprises a sequencing complexity of 8 to 16-plex or about 8 to 16-plex for postmortem samples, and / or the sequencing comprises a sequencing complexity of 24 to 40-plex or about 24 to 40-plex for antemortem samples.

160. The method of any one of claims 1 to 92 and 127 to 158, wherein The sequencing comprises a sequencing complexity of 12-plex or about 12-plex for postmortem samples, and / or the sequencing comprises a sequencing complexity of 32-plex or about 32-plex for antemortem samples.

161. The method of any one of claims 1 to 92 and 127 to 153, wherein The sequencing comprises a sequencing complexity of 30-plex, 31-plex or 32-plex, or a sequencing complexity of about 30-plex, 31-plex or 32-plex.

162. A method for identifying genetic relatives by DNA profiles, comprising: Calculating a degree of relationship of a DNA profile according to any one of claims 127 to 161 to one or more reference DNA profiles, wherein the one or more reference DNA profiles are included in a reference DNA profile set, the reference DNA profile set including one or more reference DNA profiles from relatives of the person of interest; and A family tree is generated, the family tree including the DNA profile in relation to the one or more reference DNA profiles.

163. The method of claim 162, wherein: The one or more reference DNA profiles are part of a database.

164. The method of claim 162 or claim 163, wherein: The set of reference DNA profiles includes up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900 or 1000 reference DNA profiles.

165. The method of any one of claims 162 to 164, wherein: At least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the set of reference DNA profiles are from relatives of the person of interest.

166. The method of any one of claims 162 to 165, wherein: At least 50% of the reference DNA profiles in the set of reference DNA profiles are from relatives of the person of interest.

167. The method of claim 165 or claim 166, wherein: Each relative of the person of interest is a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative or a fifth-degree relative of the person of interest.

168. The method of any one of claims 162 to 167, wherein: The identity of each relative of the person of interest in the reference DNA profile is known.

169. The method of any one of claims 162 to 168, wherein: The identity of each of the one or more reference DNA profiles in the set of reference DNA profiles is known.

170. The method of any one of claims 162 to 169, wherein: The reference DNA profile set is in a database.

171. The method of claim 170, wherein: The database is not publicly accessible.

172. The method of claim 170 or claim 171, wherein: The database cannot be accessed by third party genetics services.

173. A method for identifying a DNA profile identity, comprising: calculating a degree of relationship of a DNA profile comprising genotypes of at least between or about between 2,000 and 50,000 SNPs to one or more reference DNA profiles, wherein the DNA profile is from a person of interest, and wherein the one or more reference DNA profiles are included in a set of reference DNA profiles that includes one or more reference DNA profiles from relatives of the person of interest; and A family tree is generated, the family tree including the DNA profile in relation to the one or more reference DNA profiles.

174. The method of claim 173, wherein: The DNA profile is generated by the method according to any one of claims 127 to 161.

175. The method of claim 173 or claim 174, wherein: The degree of relationship is calculated using a kinship model.

176. The method of any one of claims 173 to 175, wherein: The degree of relationship is calculated using a kinship model trained using the PCA method.

177. The method of claim 176, wherein: The PCA method used to train the kinship model is or involves PCA.

178. The method of claim 176 or claim 177, wherein: The PCA method is PC-AiR.

179. The method of claim 178, wherein: The PC-AiR comprises the following steps: (1) estimating kinship coefficients between all pairs of samples in a training database, wherein pairs with kinship coefficients > 0.025 are identified as related, and pairs with kinship coefficients < -0.025 are identified as ancestrally diverged; (2) Initializing an irrelevant sample set including all samples; and (3) Iteratively: (i) identify the set of samples in the unrelated sample set that has the most related samples in the unrelated sample set, designating it as X, (ii) identify the set of samples in X that has the fewest ancestral differentiation pairs compared to the samples in the unrelated sample set, designating it as Y; and (iii) if Y has zero samples, terminate the process, or, if Y has at least one sample, randomly select a sample from Y to remove from U and repeat starting at step (3)(i).

180. The method of claim 176 or claim 177, wherein: The PCA method is a modified PC-Air.

181. The method of claim 180, wherein: The improved PC-AiR comprises the following steps: (1) estimating kinship coefficients between all pairs of samples in a training database, wherein pairs with kinship coefficients > 0.01 are identified as related, and pairs with kinship coefficients < -0.025 are identified as ancestrally diverged; (2) removing all DNA profiles with ≥5% missing data; and (3) Sort all DNA profiles by identifying each DNA profile with a ranking value. In some embodiments, the ranking value is determined based on the number of related DNA profiles ranked from least to most in the complete database, and ties are broken by the number of ancestral differentiation DNA profiles ranked from most to least in the complete database. In some embodiments, step (3) involves iteratively traversing the sorted DNA profiles, and for each DNA profile: (i) if the DNA profile is not already in the related sample set, adding it to the unrelated sample set and adding all related DNA profiles to the related sample set, and (ii) if the DNA profile is already in the related sample set, skipping to the next DNA profile and repeating starting at step (3)(i).

182. The method of any one of claims 173 to 181, wherein: Calculating the degree of relationship includes calculating the kinship coefficient using PC-Relate.

183. The method of claim 182, wherein: The degree of relationship is calculated by providing the DNA profile of the person of interest as input to PC-Relate.

184. The method of claim 182 or claim 183, wherein: The degree of relationship is calculated by providing the kinship model and the DNA profile of the person of interest as input to PC-Relate.

185. The method of any one of claims 182 to 184, wherein: The one or more reference DNA profiles are also provided as input to PC-Relate.

186. The method of any one of claims 173 to 185, wherein: Calculating the degree of relationship includes calculating the kinship coefficient using a genome-wide kinship algorithm as follows: wherein the person of interest and the reference DNA profile of the one or more reference DNA profiles are i and j, is the kinship coefficient, is the estimated allele frequency, s is the SNP among the SNPs typed in both individuals, and g is and g js are the numbers of reference alleles in i and j at SNP s, respectively, and and are the expected allele frequencies calculated from PC-AiR for i and j at SNP s, respectively.

187. The method of any one of claims 173 to 186, wherein: Calculating the degree of relationship includes calculating a likelihood ratio.

188. The method of claim 187, wherein: Calculating the likelihood ratio includes comparing the plurality of SNPs between the DNA profile and the one or more reference DNA profiles.

189. The method of claim 187, wherein: Calculating the likelihood ratio includes comparing a set of SNPs between the DNA profile and the one or more reference DNA profiles, the set of SNPs including kinship SNPs from within the plurality of SNPs.

190. The method of any one of claims 187 to 189, wherein: Calculating the likelihood ratio includes dividing a probability that the DNA profile is associated with a reference DNA profile of the one or more reference DNA profiles by a probability that the DNA profile is not associated with the reference DNA profile based on the genotypes of the plurality of SNPs.

191. The method of any one of claims 187 to 190, wherein: The likelihood ratio (LR) is calculated as follows: Wherein, D represents the genotype, H r represents the assumption that individuals are correlated, and H u The assumption that individuals are unrelated.

192. The method of any one of claims 187 to 190, wherein: The LR is calculated as follows: Wherein, 0.001 represents the genotyping error rate, p is the allele frequency of allele 1, and q is the allele frequency of allele 2.

193. The method of any one of claims 173 to 192, wherein: The person of interest is biologically male, and the method further comprises calculating a likelihood ratio of a shared Y chromosome between the DNA profile and the one or more reference DNA profiles.

194. The method of claim 193, wherein: Calculating the likelihood ratio of a shared Y chromosome includes comparing a set of SNPs between the DNA profile and the one or more reference DNA profiles, the set of SNPs including one or more Y-SNPs.

195. The method of claim 194, wherein: The one or more Y-SNPs are contained within the plurality of SNPs.

196. The method of claim 194 or claim 195, wherein: The one or more Y-SNPs include at least 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 81, 82, 83, 84 or 85 Y-SNPs.

197. The method of any one of claims 194 to 196, wherein: The one or more Y-SNPs include 85 Y-SNPs.

198. The method of any one of claims 193 to 197, wherein: Calculating the likelihood ratio of sharing a Y chromosome includes dividing the probability that the DNA profile shares a Y chromosome with a reference DNA profile in the one or more reference DNA profiles by the probability that the DNA profile does not share a Y chromosome with the reference DNA profile based on the genotype of the one or more Y-SNPs.

199. The method of any one of claims 173 to 198, wherein: At least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% of the DNA profiles in the reference DNA profile set are from relatives of missing persons or victims of disaster or conflict.

200. The method of any one of claims 173 to 199, wherein: Each of the one or more reference samples is from a relative of a missing person or a victim of a disaster or conflict.

201. The method of any one of claims 173-200, wherein: The one or more reference samples include up to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 300, 400, 500, 600, 700, 800, 900 or 1000 reference samples.

202. The method of any one of claims 173 to 201, wherein: At least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the one or more reference samples are from relatives of the person of interest.

203. The method of any one of claims 173 to 202, wherein: At least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set are from relatives of the person of interest, and wherein each of at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95% of the reference DNA profiles in the reference DNA profile set is a first degree relative, a second degree relative, a third degree relative, a fourth degree relative or a fifth degree relative.

204. The method of any one of claims 173 to 203, wherein: At least 50% of the one or more reference samples are from relatives of the person of interest.

205. The method of any one of claims 199 to 204, wherein: Each relative of the person of interest in the one or more reference samples is a first-degree relative, a second-degree relative, a third-degree relative, a fourth-degree relative, or a fifth-degree relative of the person of interest.

206. The method according to claim 205, wherein: Each relative of the person of interest in the one or more reference samples is a first-degree relative, a second-degree relative, or a third-degree relative of the person of interest, respectively.

207. The method of any one of claims 173 to 206, wherein: The identity of each relative of the person of interest in the one or more reference samples is known.

208. The method of any one of claims 173 to 207, wherein: The identity of each of the one or more reference samples is known.

209. A kit comprising at least one container device, wherein: The at least one container means comprises a plurality of primers according to any one of claims 94 to 126.

210. The method of any one of claims 1 to 92 and 127 to 208, wherein The plurality of SNPs include between 2,000 and 11,000 SNPs, 3,000 and 11,000 SNPs, 4,000 and 11,000 SNPs, 5,000 and 11,000 SNPs, 5,500 and 11,000 SNPs, 6,000 and 11,000 SNPs, 7,000 and 15,000 SNPs, 7,000 and 14,000 SNPs, 7,000 and 13,000 SNPs, 7,000 and 12,000 SNPs, 7 ,000 to 11,000 SNPs, 8,000 to 15,000 SNPs, 8,000 to 14,000 SNPs, 8,000 to 13,000 SNPs, 8,000 to 12,000 SNPs, 8,000 to 11,000 SNPs, 9,000 to 15,000 SNPs, 9,000 to 14,000 SNPs, 9,000 to 13,000 SNPs, 9,000 to 12,000 SNPs, or 9,000 to 11,000 SNPs. SNPs, or between about 2,000 to 11,000 SNPs, 3,000 to 11,000 SNPs, 4,000 to 11,000 SNPs, 5,000 to 11,000 SNPs, 5,500 to 11,000 SNPs, 6,000 to 11,000 SNPs, 7,000 to 15,000 SNPs, 7,000 to 14,000 SNPs, 7,000 to 13,000 SNPs, 7,000 to 12,000 SNPs, 7,000 to 13,000 SNPs, 7,000 to 14,000 SNPs, 7,000 to 15,000 SNPs, 7,000 to 16,000 SNPs, 7,000 to 17,000 SNPs, 7,000 to 18,000 SNPs, 7,000 to 19,000 SNPs, 7,000 to 19,000 SNPs, 7,000 to 1 0 to 11,000 SNPs, 8,000 to 15,000 SNPs, 8,000 to 14,000 SNPs, 8,000 to 13,000 SNPs, 8,000 to 12,000 SNPs, 8,000 to 11,000 SNPs, 9,000 to 15,000 SNPs, 9,000 to 14,000 SNPs, 9,000 to 13,000 SNPs, 9,000 to 12,000 SNPs, or 9,000 to 11,000 SNPs.

211. The method according to any one of claims 1 to 92 and 127 to 208, wherein The plurality of SNPs includes 10,230 SNPs.

212. A plurality of primers according to any one of claims 94 to 126, wherein: The plurality of SNPs include between 2,000 and 11,000 SNPs, 3,000 and 11,000 SNPs, 4,000 and 11,000 SNPs, 5,000 and 11,000 SNPs, 5,500 and 11,000 SNPs, 6,000 and 11,000 SNPs, 7,000 and 15,000 SNPs, 7,000 and 14,000 SNPs, 7,000 and 13,000 SNPs, 7,000 and 12,000 SNPs, 7 ,000 to 11,000 SNPs, 8,000 to 15,000 SNPs, 8,000 to 14,000 SNPs, 8,000 to 13,000 SNPs, 8,000 to 12,000 SNPs, 8,000 to 11,000 SNPs, 9,000 to 15,000 SNPs, 9,000 to 14,000 SNPs, 9,000 to 13,000 SNPs, 9,000 to 12,000 SNPs, or 9,000 to 11,000 SNPs. SNPs, or between about 2,000 to 11,000 SNPs, 3,000 to 11,000 SNPs, 4,000 to 11,000 SNPs, 5,000 to 11,000 SNPs, 5,500 to 11,000 SNPs, 6,000 to 11,000 SNPs, 7,000 to 15,000 SNPs, 7,000 to 14,000 SNPs, 7,000 to 13,000 SNPs, 7,000 to 12,000 SNPs, 7,000 to 13,000 SNPs, 7,000 to 14,000 SNPs, 7,000 to 15,000 SNPs, 7,000 to 16,000 SNPs, 7,000 to 17,000 SNPs, 7,000 to 18,000 SNPs, 7,000 to 19,000 SNPs, 7,000 to 19,000 SNPs, 7,000 to 1 0 to 11,000 SNPs, 8,000 to 15,000 SNPs, 8,000 to 14,000 SNPs, 8,000 to 13,000 SNPs, 8,000 to 12,000 SNPs, 8,000 to 11,000 SNPs, 9,000 to 15,000 SNPs, 9,000 to 14,000 SNPs, 9,000 to 13,000 SNPs, 9,000 to 12,000 SNPs, or 9,000 to 11,000 SNPs.

213. A plurality of primers according to any one of claims 94 to 126, wherein: The plurality of SNPs includes 10,230 SNPs.

214. The method of any one of claims 1 to 5, 11 to 93, 127 to 161 and 210 to 213, further comprising generating a family tree comprising the DNA profile related to one or more DNA profiles contained in the reference DNA profile set.

215. The method of claim 214, wherein: The family tree includes the DNA profile in relation to one or more DNA profiles from relatives of the person of interest.

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