Molecular analysis using long free fragments in pregnant women

Abstract

Method and system described herein relate to use long free DNA fragments to analyze biological samples from pregnant individuals. DNA fragments of biological samples are often analyzed using the state of methylated CpG sites and single nucleotide polymorphisms (SNPs). CpG sites and SNPs are usually spaced hundreds or thousands of base pairs from the nearest CpG sites or SNPs. It is unlikely or impossible to find two or more continuous CpG sites or SNPs on most of the free DNA fragments. Free DNA fragments longer than 600bp can include multiple CpG sites and / or SNPs. Compared with independent short free DNA fragments, there are multiple CpG sites and / or SNPs on long free DNA fragments that can allow analysis. Long free DNA fragments can be used to identify tissue of origin and / or for providing information about fetuses in pregnant women.

Description

[0001] This application is a divisional application based on Chinese patent application No. 202180013180.5.

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This application claims priority to U.S. Provisional Application No. 62 / 970,634, filed on February 5, 2020, and U.S. Provisional Application No. 63 / 135,486, filed on January 8, 2021, the entire contents of both cases are incorporated herein for all purposes. Background Art

[0004] The modal size of circulating free DNA in pregnant women has been reported to be about 166 bp (Lo et al., Sci Transl Med. 2010; 2: 61ra91). There are very few published data on fragments greater than 600 bp. An example is the work of Amicucci et al., which reports the amplification of an 8 kb fragment of the basic protein Y2 gene (BPY2) of the Y chromosome from maternal plasma using PCR (Amicucci et al., Clin Chem 2000; 40: 301-2). It is not known whether the data can be used universally across the entire genome. In fact, there are many challenges in using large-scale parallel short read sequencing technology, such as using the Illumina platform to detect long DNA fragments greater than 600 bp (Lo et al., Sci Transl Med. 2010; 2: 61ra91; Fan et al., Clin Chem 2010; 56: 1278-86). These challenges include: (1) The recommended size range of the Illumina sequencing platform typically spans 100-300 bp (De Maio et al. MicrobGenom. 2019; 5(9)); (2) DNA amplification should involve sequencing library preparation (via PCR) or sequencing cluster generation (via bridge amplification) on the flow cell. Such an amplification process can facilitate the amplification of shorter DNA fragments, which is partly due to the fact that long DNA templates (e.g., >600 bp) should require a relatively long time to complete daughter strand synthesis compared to short DNA templates (e.g., <200 bp). Therefore, within the fixed time frame of these PCR processes before or during sequencing on the Illumina platform, those long DNA molecules whose daughter strands cannot be completely generated during the PCR process will not be available in downstream analysis; (3) long DNA molecules will have a greater probability of forming secondary structures that hinder amplification; (4) using Illumina sequencing technology, long DNA molecules will be more likely to produce clusters containing more than one clonal DNA molecule than short DNA molecules because the library is denatured, diluted and spread on a two-dimensional surface followed by bridge amplification (Head et al. Biotechniques. 2014;56:61-4). Summary of the invention

[0005] Method and system described herein relate to the use of long free DNA fragments to analyze biological samples. The use of these long free DNA fragments allows for analysis that is not considered or that is impossible to perform with shorter free DNA fragments. The DNA fragments of biological samples are often analyzed using the state of methylated CpG sites and single nucleotide polymorphisms (SNPs). CpG sites and SNPs are usually spaced hundreds or thousands of base pairs from the nearest CpG sites or SNPs. The length of most of the free DNA fragments in biological samples is usually less than 200bp. Therefore, it is unlikely or impossible to find two or more continuous CpG sites or SNPs on most of the free DNA fragments. Free DNA fragments longer than 200bp comprising free DNA fragments longer than 600bp or 1kb can include multiple CpG sites and / or SNPs. Compared with independent short free DNA fragments, the presence of multiple CpG sites and / or SNPs on long free DNA fragments can allow for more efficient and / or more accurate analysis. Long free DNA fragments can be used to identify tissue of origin and / or for providing information about fetuses in pregnant women. In addition, it was unexpected that samples from pregnant women were accurately analyzed using long cell-free DNA fragments, since we expected that the long cell-free DNA fragments were primarily of maternal origin. We did not expect that long cell-free DNA fragments of fetal origin would be present in amounts sufficient to provide information about the fetus.

[0006] The long free DNA fragments in which SNPs are present can be used to determine the haplotype of fetal inheritance. The long free DNA fragments can have a methylation pattern indicating the tissue of origin by having multiple CpG sites. In addition, trinucleotide repeats and other repetitive sequences can be present on the long free DNA fragments. These repetitive sequences can be used to determine the possibility of the genetic disorder of the fetus or the fetal affinity. The amount of the long free DNA fragments can be used to determine the gestational age. Similarly, the motifs at the ends of the long free DNA fragments can also be used to determine the gestational age. Long free DNA fragments (comprising, for example, the amount of the fragments, length distribution, genomic location, methylation status, etc.) can be used to determine pregnancy-related disorders.

[0007] These and other embodiments of the present disclosure are described in detail below.For example, other embodiments relate to systems, devices, and computer-readable media related to the methods described herein.

[0008] A better understanding of the nature and advantages of embodiments of the present disclosure may be obtained by reference to the following detailed description and accompanying drawings.

[0009] As non-limiting examples, the present application provides the following embodiments:

[0010] Embodiment 1. A method for analyzing a biological sample obtained from a female pregnant with a fetus, the female having a first haplotype and a second haplotype in a first chromosome region, the biological sample comprising a plurality of free DNA molecules from the fetus and the female, the method comprising:

[0011] receiving reads corresponding to the plurality of free DNA molecules;

[0012] measuring the sizes of the plurality of free DNA molecules;

[0013] identifying a first group of cell-free DNA molecules from the plurality of cell-free DNA molecules as having a size greater than or equal to a cutoff value;

[0014] Determining the sequence of the first haplotype and the sequence of the second haplotype from the reads corresponding to the first group of free DNA molecules;

[0015] aligning a second group of cell-free DNA molecules from the plurality of cell-free DNA molecules to the sequence of the first haplotype, the second group of cell-free DNA molecules having a size less than the cutoff value;

[0016] aligning a third group of cell-free DNA molecules from the plurality of cell-free DNA molecules with the sequence of the second haplotype, the third group of cell-free DNA molecules having a size less than the cutoff value;

[0017] measuring a first value of a parameter using the second set of free DNA molecules;

[0018] measuring a second value of the parameter using the third set of free DNA molecules;

[0019] comparing the first value to the second value; and

[0020] A likelihood that the fetus inherited the first haplotype is determined based on the comparison of the first value and the second value.

[0021] Embodiment 2. A method according to embodiment 1, wherein the cutoff value is 600 nt.

[0022] Embodiment 3. A method according to embodiment 1, wherein the cutoff value is 1 knt.

[0023] Embodiment 4. The method according to any one of embodiments 1 to 3, wherein determining the sequence of the first haplotype and the sequence of the second haplotype from the reads corresponding to the first group of free DNA molecules comprises:

[0024] The reads corresponding to the first set of free DNA molecules are aligned to a reference genome.

[0025] Embodiment 5. The method of embodiment 1, wherein determining the sequence of the first haplotype and the sequence of the second haplotype from the reads corresponding to the first group of free DNA molecules comprises:

[0026] aligning the first subset of reads to the second subset of reads to identify different alleles at loci in the reads,

[0027] determining that the first subset of the reads has a first allele at the locus,

[0028] determining that the second subset of the reads has a second allele at the locus,

[0029] determining that the first subset of the reads corresponds to the first haplotype, and

[0030] It is determined that the second subset of the reads corresponds to the second haplotype.

[0031] Embodiment 6. A method according to any one of embodiments 1 to 5, wherein the parameter is the free DNA molecule count, the free DNA molecule size profile or the free DNA molecule methylation degree.

[0032] Embodiment 7. The method according to embodiment 6, wherein:

[0033] The parameter is the free DNA molecule count, and

[0034] The method further comprises:

[0035] When the first value is greater than the second value, it is determined that the possibility that the fetus inherits the first haplotype is higher than the possibility that the fetus inherits the second haplotype.

[0036] Embodiment 8. The method according to embodiment 6, wherein:

[0037] The parameter is the free DNA molecule size profile, and

[0038] The method further comprises:

[0039] When the first value is less than the second value, it is determined that the fetus is more likely to inherit the first haplotype than the second haplotype, indicating that the second group of free DNA molecules is characterized by a size profile that is smaller than the third group of free DNA molecules.

[0040] Embodiment 9. The method according to embodiment 6, wherein:

[0041] The parameter is the degree of methylation of the free DNA molecule, and

[0042] The method further comprises:

[0043] When the first value is less than the second value, it is determined that the possibility that the fetus inherits the first haplotype is higher than the possibility that the fetus inherits the second haplotype.

[0044] Embodiment 10. The method according to any one of embodiments 1 to 9, further comprising:

[0045] calculating a separation value using the first value and the second value;

[0046] comparing the separation value to a cutoff value; and

[0047] A likelihood of fetal aneuploidy is determined based on the comparison of the separation value to the cutoff value.

[0048] Embodiment 11. The method according to embodiment 10, wherein:

[0049] determining said cut-off value from a reference sample from a pregnant female carrying a euploid fetus,

[0050] The cut-off value is determined from a reference sample from a pregnant female carrying an aneuploid fetus, or

[0051] The cut-off value was calculated assuming an aneuploid fetus.

[0052] Embodiment 12. The method according to any one of embodiments 1 to 11, further comprising:

[0053] identifying a number of repetitive sequences of a subsequence in a read of the reads corresponding to the first set of free DNA molecules,

[0054] in:

[0055] Determining the sequence of the first haplotype includes determining the number of repeats of the subsequence.

[0056] Embodiment 13. The method according to embodiment 12, wherein:

[0057] The repetitive sequence of the subsequence is associated with a repetitive sequence-associated disease, and

[0058] The method further includes determining the likelihood that the fetus will inherit the repetitive sequence-associated disease.

[0059] Embodiment 14. A method of analyzing a biological sample obtained from a female pregnant with a fetus, the biological sample comprising a plurality of free DNA molecules from the fetus and the female, the method comprising:

[0060] receiving sequence reads corresponding to the plurality of free DNA molecules;

[0061] measuring the sizes of the plurality of free DNA molecules;

[0062] identifying a group of free DNA molecules from the plurality of free DNA molecules as having a size greater than or equal to a cutoff value; and

[0063] For one free DNA molecule in the group of free DNA molecules:

[0064] Determine the methylation status at each site in multiple loci,

[0065] Determine the methylation pattern, where:

[0066] The methylation pattern indicates the methylation status at each site in the plurality of sites using one or more sequence reads corresponding to the free DNA molecule,

[0067] comparing the methylation pattern to one or more reference patterns, wherein each of the one or more reference patterns is determined for a specific tissue type; and

[0068] The methylation pattern is used to determine the tissue of origin of the cell-free DNA molecule.

[0069] Embodiment 15. A method according to embodiment 14, wherein the cutoff value is 600nt.

[0070] Embodiment 16. A method according to embodiment 14, wherein the cutoff value is 1 knt.

[0071] Embodiment 17. The method according to any one of embodiments 14 to 16, further comprising determining the tissue of origin of each free DNA molecule in the group of free DNA molecules by:

[0072] determining the methylation status at each site in a plurality of corresponding sites, wherein the plurality of corresponding sites correspond to the free DNA molecules,

[0073] determining the methylation pattern, and

[0074] The methylation pattern is compared to at least one of the one or more reference patterns.

[0075] Embodiment 18. The method according to embodiment 17, further comprising:

[0076] Determine the amount of free DNA molecules corresponding to each tissue of origin, and

[0077] The contribution of each tissue of origin in the biological sample is determined using the measurement of free DNA molecules corresponding to the tissue of origin.

[0078] Embodiment 19. The method of any one of Embodiments 14 to 18, wherein measuring the size of the plurality of free DNA molecules comprises:

[0079] The sequence reads are aligned to a reference genome.

[0080] Embodiment 20. The method of any one of embodiments 14 to 18, wherein measuring the size of the plurality of free DNA molecules comprises:

[0081] performing full-length sequencing on the plurality of free DNA molecules, and

[0082] The number of nucleotides in each free DNA molecule in the plurality of free DNA molecules is counted.

[0083] Embodiment 21. The method of embodiment 14 or 17, wherein measuring the size of the plurality of free DNA molecules comprises:

[0084] The plurality of free DNA molecules from the biological sample are physically separated from other free DNA molecules in the biological sample, wherein the other free DNA molecules have a size smaller than the cutoff value.

[0085] Embodiment 22. The method of any one of Embodiments 14 to 21, wherein a reference pattern in the one or more reference patterns is determined by:

[0086] measuring a methylation density at each reference site in a plurality of reference sites using DNA molecules from a reference tissue, comparing the methylation density at each reference site in the plurality of reference sites to one or more threshold methylation densities, and

[0087] Each reference site in the plurality of reference sites is identified as methylated, unmethylated, or non-informative based on comparing the methylation density to the one or more threshold methylation densities, wherein the plurality of sites are the plurality of reference sites identified as methylated or unmethylated.

[0088] Embodiment 23. The method of any one of Embodiments 14 to 22, wherein the tissue of origin is placenta.

[0089] Embodiment 24. A method according to any one of Embodiments 14 to 22, wherein the tissue of origin is fetal or maternal.

[0090] Embodiment 25. The method of embodiment 24, wherein:

[0091] The tissue of origin is fetal,

[0092] The method further comprises:

[0093] aligning a sequence read of the sequence reads to a first region of a reference genome, the first region comprising a plurality of sites corresponding to an allele, the plurality of sites comprising a threshold number of sites,

[0094] determining a first haplotype using the corresponding alleles present at each of the plurality of loci,

[0095] comparing the first haplotype to a second haplotype corresponding to a male individual, and

[0096] The comparison is used to determine a classification of the likelihood that the male individual is the father of the fetus.

[0097] Embodiment 26. The method of embodiment 24, wherein:

[0098] The tissue of origin is fetal,

[0099] The method further comprises:

[0100] aligning a sequence read of the sequence reads to a first region of a reference genome, the first region comprising a first plurality of sites corresponding to an allele, the plurality of sites comprising a threshold number of sites,

[0101] comparing the alleles at each of the plurality of loci with the alleles at the corresponding loci in the genome of the male individual, and

[0102] The comparison is used to determine a classification of the likelihood that the male individual is the father of the fetus.

[0103] Embodiment 27. The method according to embodiment 24, further comprising:

[0104] For each free DNA molecule in the set of free DNA molecules:

[0105] Comparing the sequence reads corresponding to the free DNA molecules with the reference genome,

[0106] identifying the sequence read as corresponding to a haplotype present in the female,

[0107] using the methylation pattern to determine that the tissue of origin is fetal, and

[0108] The haplotype is determined to be the maternally inherited fetal haplotype.

[0109] Embodiment 28. The method according to embodiment 27, further comprising:

[0110] identifying the haplotype as carrying a disease-causing genetic mutation or variation, and

[0111] The fetus is classified as likely to suffer from a disease caused by the genetic mutation or variation.

[0112] Embodiment 29. The method of embodiment 28, wherein identifying the haplotype as carrying the causative genetic mutation comprises:

[0113] identifying the genetic mutation or variation in the first sequence reads,

[0114] measuring a first degree of methylation in a second sequence read corresponding to a first genomic location within a first distance of the first sequence read, and

[0115] measuring a second degree of methylation in a third sequence read corresponding to a second genomic position within a second distance of the first sequence read, wherein:

[0116] The first methylation level and the second methylation level are associated with the genetic mutation.

[0117] Embodiment 30. The method according to embodiment 24, further comprising:

[0118] For each free DNA molecule in the set of free DNA molecules:

[0119] Comparing the sequence reads corresponding to the free DNA molecules with the reference genome,

[0120] The sequence reads are identified as corresponding to a region, wherein the region is determined by:

[0121] receiving a plurality of fetal sequence reads corresponding to a plurality of fetal DNA molecules from fetal tissue,

[0122] receiving a plurality of maternal sequence reads corresponding to a plurality of maternal DNA molecules,

[0123] determining, for each fetal sequence read in the plurality of fetal sequence reads, a fetal methylation state at each methylation site in a plurality of methylation sites within the region,

[0124] determining, for each maternal sequence read in the plurality of maternal sequence reads, a maternal methylation state at each methylation site in the plurality of methylation sites,

[0125] determining a value of a parameter characterizing the amount of sites where the fetal methylation state differs from the maternal methylation state,

[0126] comparing said value of said parameter to a threshold value, and

[0127] It is determined that the value of the parameter exceeds the threshold.

[0128] Embodiment 31. A method according to any one of Embodiments 14 to 28, wherein the cutoff value is at least 500 nt.

[0129] Embodiment 32. The method according to any one of embodiments 14 to 31, wherein determining the tissue of origin of the free DNA molecule comprises inputting the methylation pattern into a machine learning model, the model being trained by:

[0130] receiving a plurality of training methylation patterns, each training methylation pattern having a methylation state at one or more sites among the plurality of sites, each training methylation pattern being determined by a DNA molecule from a known tissue,

[0131] storing a plurality of training samples, each training sample comprising one of the plurality of training methylation patterns and a label indicating the known tissue corresponding to the training methylation pattern, and

[0132] The parameters of the model are optimized using the plurality of training samples based on a plurality of outputs of the model that match or do not match corresponding labels when the plurality of training methylation patterns are input into the model, wherein an output of the model indicates a tissue corresponding to the input methylation pattern.

[0133] Embodiment 33. A method according to embodiment 32, wherein the machine learning model includes a convolutional neural network (CNN), a linear regression, a logistic regression, a deep recurrent neural network, a Bayesian classifier, a hidden Markov model (HMM), a linear discriminant analysis (LDA), k-means clustering, density-based spatial clustering of applications with noise (DBSCAN), a random forest algorithm or a support vector machine (SVM).

[0134] Embodiment 34. A method according to embodiment 32, wherein each DNA molecule from the known tissue is cellular DNA.

[0135] Embodiment 35. A method according to embodiment 32 or 34, wherein the parameters of the model include a first parameter indicating whether one of the multiple sites has the same methylation state as another site in the multiple sites.

[0136] Embodiment 36. A method according to any one of Embodiments 32 to 35, wherein the parameters of the model include a second parameter indicating the distance between each site in the plurality of sites.

[0137] Embodiment 37. A method according to any one of Embodiments 14 to 31, wherein one of the one or more reference patterns corresponds to a reference tissue,

[0138] The method further includes determining the tissue of origin to be the reference tissue when the methylation pattern matches the reference pattern.

[0139] Embodiment 38. A method according to any one of Embodiments 14 to 37, wherein the plurality of sites comprises at least 5 CpG sites.

[0140] Embodiment 39. The method of any one of embodiments 14 to 31, wherein determining the tissue of origin using the methylation pattern comprises:

[0141] determining a similarity score by comparing the methylation pattern to a first reference methylation pattern from a first reference tissue of a plurality of reference tissues;

[0142] comparing the similarity score to a threshold; and

[0143] When the similarity score exceeds the threshold, the origin tissue is determined to be the first reference tissue.

[0144] Embodiment 40. The method of embodiment 39, wherein:

[0145] The similarity score is a first similarity score,

[0146] The method further comprises:

[0147] The threshold is calculated by:

[0148] A second similarity score is determined by comparing the methylation pattern with a second reference methylation pattern from a second reference tissue of the plurality of reference tissues, the first reference tissue and the second reference tissue being different tissues, and the threshold is the second similarity score.

[0149] Embodiment 41. The method of embodiment 39 or 40, wherein:

[0150] The first reference methylation pattern includes a first subset of sites having at least a first methylation probability for the first reference tissue,

[0151] The first reference methylation pattern includes a second subset of sites having at most a second methylation probability for the first reference tissue, and

[0152] Determining the similarity score includes:

[0153] increasing the similarity score when a site in the plurality of sites is methylated and the site in the plurality of sites is in the first subset of sites, and

[0154] The similarity score is reduced when a site in the plurality of sites is methylated and the site in the plurality of sites is in the second subset of sites.

[0155] Embodiment 42. The method of embodiment 39 or 40, wherein:

[0156] The first reference methylation pattern includes the plurality of sites, wherein each site in the plurality of sites is characterized by a probability of methylation and a probability of unmethylation for the first reference tissue,

[0157] The similarity score is determined by:

[0158] For each site in the plurality of sites:

[0159] determining the probability in the reference tissue corresponding to the methylation state of the site in the free DNA molecule,

[0160] The product of multiple probabilities is calculated, and the product is the similarity score.

[0161] Embodiment 43. A method according to embodiment 42, wherein the probability is determined using a Beta (β) distribution.

[0162] Embodiment 44. The method according to any one of embodiments 14 to 43, further comprising:

[0163] sequencing the plurality of free DNA molecules to obtain sequence reads, and

[0164] The methylation status of a site is determined by measuring the characteristics of the nucleotide corresponding to the site and the nucleotides adjacent to the site.

[0165] Embodiment 45. A method according to any one of Embodiments 14 to 44, wherein the size of the plurality of free DNA molecules includes the number of CpG sites.

[0166] Embodiment 46. The method of any one of Embodiments 14 to 45, wherein at least one of the plurality of sites is methylated.

[0167] Embodiment 47. A method according to any one of Embodiments 14 to 46, wherein two sites of the plurality of sites are separated by at least 160 nt.

[0168] Embodiment 48. A method of analyzing a biological sample obtained from a female pregnant with a fetus, the biological sample comprising cell-free DNA molecules from the fetus and the female, the method comprising:

[0169] receiving a first sequence read corresponding to one of the cell-free DNA molecules;

[0170] aligning the first sequence read to a region of a reference genome, the region being known to potentially contain a repeat sequence of a subsequence;

[0171] identifying the number of repeating sequences of the subsequence in the first sequence reads corresponding to the free DNA molecule;

[0172] comparing the number of repeats of the subsequence to a threshold number; and

[0173] A classification of the likelihood that the fetus has a genetic condition is determined using the comparison of the number of repeat sequences to the threshold number.

[0174] Embodiment 49. The method of Embodiment 48, wherein determining the classification of the likelihood that the fetus has the genetic condition comprises:

[0175] When the number of the repeated sequences exceeds the threshold number, it is determined that the fetus is likely to suffer from the genetic disorder.

[0176] Embodiment 50. A method according to embodiment 48 or 49, wherein the threshold number is 55 or more.

[0177] Embodiment 51. The method according to any one of embodiments 48 to 50, wherein the genetic disorder is fragile X syndrome.

[0178] Embodiment 52. A method according to any one of embodiments 48 to 51, wherein the subsequence is a trinucleotide sequence.

[0179] Embodiment 53. A method according to any one of embodiments 48 to 52, wherein the free DNA molecules have a length greater than a cutoff value.

[0180] Embodiment 54. A method according to embodiment 53, wherein the cutoff value is 600nt.

[0181] Embodiment 55. A method according to embodiment 53, wherein the cutoff value is 1 knt.

[0182] Embodiment 56. The method of any one of Embodiments 48 to 54, further comprising determining that the free DNA molecule is of fetal origin.

[0183] Embodiment 57. The method of embodiment 56, wherein:

[0184] The number of repeated sequences of the subsequence in the first sequence of reads is a first number of repeated sequences of the subsequence,

[0185] Determining that the cell-free DNA molecule is of fetal origin includes:

[0186] receiving a second sequence read corresponding to a cell-free DNA molecule of maternal origin obtained from a buffy coat or sample of the female prior to pregnancy,

[0187] aligning the second sequence read to the region of the reference genome,

[0188] identifying a second number of repeats of the subsequence in the second sequence of reads, and

[0189] It is determined that the second number of repeated sequences is less than the first number of repeated sequences.

[0190] Embodiment 58. The method of embodiment 56, wherein:

[0191] Determining that the cell-free DNA molecule is of fetal origin includes:

[0192] Determining the degree of methylation of the free DNA molecule using the methylated sites and unmethylated sites of the free DNA molecule, and

[0193] The methylation level is compared to a reference level.

[0194] Embodiment 59. A method according to embodiment 58, further comprising determining that the methylation level exceeds the reference level.

[0195] Embodiment 60. The method of embodiment 56, wherein:

[0196] Determining that the cell-free DNA molecule is of fetal origin includes:

[0197] determining the methylation pattern of multiple sites of the free molecule,

[0198] A similarity score is determined by comparing the methylation pattern to a reference pattern from maternal or fetal tissue, and comparing the similarity score to one or more threshold values.

[0199] Embodiment 61. The method of embodiment 48, further comprising:

[0200] receiving a plurality of sequence reads corresponding to the free DNA molecules,

[0201] aligning the plurality of sequence reads to a plurality of regions of the reference genome, wherein the plurality of regions are known to potentially contain repetitive sequences of subsequences,

[0202] identifying a number of repeating sequences of the subsequence in the plurality of sequence reads;

[0203] comparing the number of repeats of the subsequence to a plurality of threshold numbers; and

[0204] For each of a plurality of genetic conditions, the comparison to a threshold number of the plurality of threshold numbers is used to determine a classification of the likelihood that the fetus has the corresponding genetic condition.

[0205] Embodiment 62. A method of analyzing a biological sample obtained from a female pregnant with a fetus, the biological sample comprising cell-free DNA molecules from the fetus and the female, the method comprising:

[0206] receiving a first sequence read corresponding to one of the cell-free DNA molecules;

[0207] aligning the first sequence read to a first region of a reference genome;

[0208] identifying a first number of repeats of a first subsequence in the first sequence reads corresponding to the free DNA molecule;

[0209] analyzing the sequence data obtained from the male individual to determine whether a second number of repeats of the first subsequence is present in the first region; and

[0210] The determination of the presence or absence of the second number of repeats of the first subsequence is used to determine a classification of the likelihood that the male individual is the father of the fetus.

[0211] Embodiment 63. The method according to embodiment 62, further comprising:

[0212] The cell-free DNA molecules were determined to be of fetal origin.

[0213] Embodiment 64. A method according to embodiment 62 or 63, wherein the first subsequence comprises an allele.

[0214] Embodiment 65. The method of any one of Embodiments 62 to 64, wherein:

[0215] The classification is that when it is determined that the second number of repeat sequences of the first subsequence exists, the male individual may be the father, or

[0216] The classification is that the male individual is probably not the father when it is determined that the second number of repeat sequences of the first subsequence is not present.

[0217] Embodiment 66. The method according to any one of embodiments 62 to 65, further comprising:

[0218] comparing the first number of repeated sequences with the second number of repeated sequences,

[0219] in:

[0220] The classification for determining the likelihood that the male individual is the father includes:

[0221] using said comparison of said first number of repeats and said second number of repeats, and

[0222] The classification is that the male individual is likely to be the father when the first number of repeat sequences is within a threshold of the second number of repeat sequences.

[0223] Embodiment 67. The method of any one of Embodiments 62 to 66, wherein:

[0224] The free DNA molecule is a first free DNA molecule;

[0225] The method further comprises:

[0226] receiving a second sequence read corresponding to a second one of the cell-free DNA molecules;

[0227] aligning the second sequence read to a second region of the reference genome;

[0228] identifying a first number of repeats of a second subsequence in the second sequence read corresponding to the second free DNA molecule;

[0229] analyzing the sequence data obtained from the male individual to determine whether a second number of repeats of the second subsequence is present in the second region;

[0230] in:

[0231] Determining the classification of the likelihood that the male individual is the father of the fetus further includes using the determination of whether the second number of repeats of the second subsequence is present in the second region.

[0232] Embodiment 68. A method according to any one of Embodiments 62 to 67, wherein the free DNA molecules have a size greater than a cutoff value.

[0233] Embodiment 69. A method according to embodiment 68, wherein the free DNA molecule has a size greater than 600 nt.

[0234] Embodiment 70. A method according to embodiment 68, wherein the free DNA molecule has a size greater than 1 knt.

[0235] Embodiment 71. A method of analyzing a biological sample obtained from a female pregnant with a fetus, the biological sample comprising a plurality of free DNA molecules from the fetus and the female, the method comprising:

[0236] measuring the sizes of the plurality of free DNA molecules;

[0237] measuring a first amount of free DNA molecules having a size greater than a cutoff value;

[0238] generating a value of a normalization parameter using the first quantity;

[0239] comparing the value of the normalization parameter to one or more calibration data points, wherein each calibration data point indicates a gestational age corresponding to a calibration value of the normalization parameter, and wherein the one or more calibration data points are determined from a plurality of calibration samples having a known gestational age and comprising free DNA molecules having a size greater than the cutoff value; and

[0240] The comparison is used to determine gestational age.

[0241] Embodiment 72. The method according to embodiment 71, further comprising:

[0242] A reference gestational age of the fetus is determined using ultrasound or the date of the woman's last menstrual period,

[0243] comparing the gestational age to the reference gestational age, and

[0244] The comparison of the gestational age to the reference gestational age is used to determine a classification of the likelihood of a pregnancy-related condition.

[0245] Embodiment 73. The method according to embodiment 71, further comprising:

[0246] determining a first subsequence corresponding to at least one end of said free DNA molecule having a size greater than said cutoff value,

[0247] in:

[0248] The first amount pertains to free DNA molecules having a size greater than the cutoff value and having the first subsequence at one or more ends of the corresponding free DNA molecules.

[0249] Embodiment 74. A method according to embodiment 73, wherein the first subsequence is 1, 2, 3 or 4 nucleotides.

[0250] Embodiment 75. The method of embodiment 73 or 74, wherein generating the value of the normalization parameter comprises:

[0251] (a) normalizing the first amount by the total amount of free DNA molecules having a size greater than the cutoff value;

[0252] (b) normalizing the first amount by a second amount of free DNA molecules having a size greater than the cutoff value and terminating on a second subsequence, the second subsequence being different from the first subsequence, or

[0253] (c) normalizing the first amount by a third amount of free DNA molecules having a size smaller than the cutoff value.

[0254] Embodiment 76. A method according to any one of Embodiments 71 to 75, further comprising receiving sequence reads corresponding to the plurality of free DNA molecules.

[0255] Embodiment 77. A method of analyzing a biological sample obtained from a female pregnant with a fetus, the biological sample comprising a plurality of free DNA molecules from the fetus and the female, the method comprising:

[0256] measuring the sizes of the plurality of free DNA molecules;

[0257] measuring a first amount of free DNA molecules having a size greater than a cutoff value;

[0258] generating a first value of a normalization parameter using the first quantity;

[0259] obtaining a second value corresponding to an expected value of the standardized parameter for a healthy pregnancy, wherein the second value is dependent on the gestational age of the fetus;

[0260] determining a deviation between the first value of the normalized parameter and the second value of the normalized parameter; and

[0261] The deviation is used to determine a classification of the likelihood of a pregnancy-related condition.

[0262] Embodiment 78. The method of embodiment 77, wherein obtaining the second value comprises:

[0263] The second value is obtained from a calibration table of measurement results on pregnant women having calibration values ​​for the standardization parameters, wherein the calibration table is generated by:

[0264] obtaining a first table with measurements of pregnant female individuals for gestational age,

[0265] obtaining a second table of gestational age with calibrated values ​​of said standardized parameters, and

[0266] The calibration table with the calibration values ​​regarding the measurement results is generated from the first table and the second table.

[0267] Embodiment 79. The method of embodiment 78, wherein the measurement result of the pregnant female individual is the time since the last menstrual period.

[0268] Embodiment 80. A method according to Embodiment 78, wherein the measurement result of the pregnant female individual is a feature of an image of the pregnant female individual.

[0269] Embodiment 81. A method according to embodiment 80, wherein the characteristics of the image include the length, size, appearance or anatomical structure of a fetus of the female individual.

[0270] Embodiment 82. The method of any one of Embodiments 72 to 81, wherein the pregnancy-related disorder comprises preeclampsia, intrauterine growth restriction, placenta accreta, preterm birth, hemolytic disease of the newborn, placental insufficiency, hydrops fetalis, fetal malformations, hemolysis, elevated liver enzymes and low platelet count (HELLP) syndrome, or systemic lupus erythematosus.

[0271] Embodiment 83. A method according to any one of Embodiments 71 to 82, wherein the cutoff value is 600 nt or greater.

[0272] Embodiment 84. A method according to any one of Embodiments 71 to 82, wherein the cutoff value is 1,000 nt or greater.

[0273] Embodiment 85. A method according to any one of Embodiments 71 to 84, wherein the first quantity is a number or a frequency.

[0274] Embodiment 86. The method of any one of Embodiments 71 to 85, wherein generating the value of the normalized parameter using the first amount comprises:

[0275] measuring a second amount comprising free DNA molecules of a size less than the cutoff value; and

[0276] A ratio of the first amount to the second amount is calculated.

[0277] Embodiment 87. The method of embodiment 86, wherein:

[0278] The cutoff value is a first cutoff value,

[0279] The second cutoff value is less than the first cutoff value, and

[0280] The second amount includes free DNA molecules having a size smaller than the second cutoff value or the second amount includes all free DNA molecules in the plurality of free DNA molecules.

[0281] Embodiment 88. A method of analyzing a biological sample obtained from a female pregnant with a fetus, the biological sample comprising a plurality of free DNA molecules from the fetus and the female, the method comprising:

[0282] measuring the sizes of the plurality of free DNA molecules;

[0283] identifying a group of free DNA molecules having a size greater than a cutoff value;

[0284] Generating a value of a terminal motif parameter using the first quantity, wherein generating the value of the terminal motif parameter comprises:

[0285] measuring the first amount of free DNA molecules in the group having a first subsequence at one or more ends of the free DNA molecules in the group;

[0286] comparing said value of said terminal motif parameter to a threshold value; and

[0287] The comparison is used to determine a classification of the likelihood of a pregnancy-related condition.

[0288] Embodiment 89. The method according to embodiment 88, further comprising:

[0289] measuring a second amount of free DNA molecules having a subsequence different from the first subsequence at one or more ends of the free DNA molecules, and

[0290] in:

[0291] Generating the value of the terminal motif parameter comprises using a ratio of the first amount to the second amount.

[0292] Embodiment 90. A method according to embodiment 88, wherein the length of the first subsequence is 1, 2, 3 or 4 nucleotides.

[0293] Embodiment 91. A method according to embodiment 90, wherein the first subsequence includes the last nucleotide at the end of the corresponding free DNA molecule.

[0294] Embodiment 92. The method of embodiment 88, wherein:

[0295] The threshold is a first threshold, and

[0296] The terminal motif parameter is the first terminal motif parameter,

[0297] The method further comprises:

[0298] measuring a second amount of free DNA molecules having a second subsequence different from the first subsequence at one or more ends of the free DNA molecules,

[0299] generating a value for the second end motif parameter using the third quantity, and

[0300] comparing said value of said second terminal motif parameter to a second threshold value;

[0301] in:

[0302] Determining the classification of the likelihood of the pregnancy-related condition uses the comparison of the value of the second terminal motif parameter to the second threshold, wherein the pregnancy-related condition is likely when the value of the first terminal motif parameter exceeds the first threshold and the value of the second terminal motif parameter exceeds the second threshold.

[0303] Embodiment 93. A method according to embodiment 88, wherein the first amount of free DNA molecules includes free DNA molecules determined to be from the tissue of origin.

[0304] Embodiment 94. The method of embodiment 88, wherein:

[0305] The threshold is a first threshold, and

[0306] The group of free DNA molecules is a first group of free DNA molecules,

[0307] The method further comprises:

[0308] identifying a second group of free DNA molecules having a size within a first size range, the first size range comprising sizes greater than the cutoff value,

[0309] generating a value for a size parameter using a second amount of free DNA molecules in the second group, and

[0310] comparing said value of said size parameter with a second threshold value,

[0311] Wherein determining the classification of the likelihood of the pregnancy-related condition comprises using the comparison of the value of the size parameter to the second threshold value.

[0312] Embodiment 95. A method according to any one of embodiments 88 to 94, wherein the cutoff value is 600nt.

[0313] Embodiment 96. A method according to any one of Embodiments 88 to 94, wherein the cutoff value is 1,000 nt.

[0314] Embodiment 97. A method of analyzing a biological sample of a pregnant organism, the biological sample comprising a plurality of free nucleic acid molecules, the method comprising:

[0315] The plurality of free nucleic acid molecules are sequenced, wherein more than 20% of the sequenced free nucleic acid molecules have a length greater than 200 nt.

[0316] Embodiment 98. A method according to embodiment 97, wherein sequencing is performed by single molecule real-time technology.

[0317] Embodiment 99. The method of embodiment 97 or 98, wherein:

[0318] More than 11% of the sequenced free nucleic acid molecules have a length greater than 400 nt,

[0319] More than 10% of the sequenced free nucleic acid molecules have a length greater than 500 nt,

[0320] More than 8% of the sequenced free nucleic acid molecules have a length greater than 600 nt,

[0321] More than 6% of the sequenced free nucleic acid molecules have a length greater than 1 knt,

[0322] More than 3% of the sequenced free nucleic acid molecules have a length greater than 2 knt,

[0323] More than 1% of the plurality of sequenced free nucleic acid molecules have a length greater than 3 knt,

[0324] At least 0.9% of the sequenced plurality of free nucleic acid molecules have a length greater than 4 knt, or

[0325] At least 0.04% of the plurality of sequenced free nucleic acid molecules have a length greater than 10 knt.

[0326] Embodiment 100. A method according to any one of Embodiments 97 to 99, wherein the plurality of free nucleic acid molecules comprises at least 100 free nucleic acid molecules.

[0327] Embodiment 101. A method according to any one of Embodiments 97 to 100, wherein the multiple free nucleic acid molecules are from multiple different genomic regions.

[0328] Embodiment 102. A method according to any one of embodiments 97 to 101, wherein the sequencing produces reads for any one of embodiments 1 to 94.

[0329] Embodiment 103. The method of any one of Embodiments 97 to 101, wherein the sequencing produces reads,

[0330] The method further comprises:

[0331] The reads are used to determine fetal aneuploidy, aberration, genetic mutation or variation, or parental haplotype inheritance.

[0332] Embodiment 104. The method of any one of Embodiments 1 to 103, wherein:

[0333] The plurality of free DNA molecules are enriched relative to the biological sample to obtain a size greater than or equal to the cutoff value, wherein more than 20% of the free nucleic acid molecules in the biological sample have a size greater than 200 nt.

[0334] Embodiment 105. The method of embodiment 104, further comprising:

[0335] The plurality of free DNA molecules is enriched using electrophoresis.

[0336] Embodiment 106. The method of embodiment 104, further comprising:

[0337] The plurality of free DNA molecules are enriched using magnetic beads for selectively binding free DNA molecules based on size.

[0338] Embodiment 107. The method of embodiment 104, further comprising:

[0339] The plurality of free DNA molecules are enriched using hybridization, immunoprecipitation, amplification or CRISPR.

[0340] Embodiment 108. A method according to any one of embodiments 105 to 107, wherein enrichment is used to obtain a size greater than 600nt, 700nt, 800nt, 900nt or 1knt.

[0341] Embodiment 109. The method according to any one of embodiments 1 to 103, wherein the plurality of free DNA molecules are enriched relative to the biological sample to obtain a methylation profile,

[0342] The method further comprises:

[0343] Immunoprecipitation is used to enrich the plurality of free DNA molecules.

[0344] Embodiment 110. A computer program product comprising instructions that, when executed, control a computing system to perform a method as described in any of the above embodiments.

[0345] Embodiment 111. A computer-readable storage medium comprising the computer program product according to embodiment 110.

[0346] Embodiment 112. A computing system comprising a computer program product according to embodiment 111. BRIEF DESCRIPTION OF THE DRAWINGS

[0347] Figure 1A and Figure 1B The size distribution of free DNA determined according to an embodiment of the present invention is shown: (A) 0-20 kb on a linear scale, (B) 0-20 kb on a logarithmic scale.

[0348] Figure 2A and Figure 2B The size distribution of free DNA determined according to an embodiment of the present invention is shown. (A) 0-5 kb on the y-axis on a linear scale. (B) 0-5 kb on the y-axis on a logarithmic scale.

[0349] Figure 3A and Figure 3B The size distribution of free DNA determined according to an embodiment of the present invention is shown. (A) 0-400 bp on the y-axis on a linear scale. (B) 0-400 bp on the y-axis on a logarithmic scale.

[0350] Figure 4A and Figure 4BThe size distribution of free DNA between fragments carrying shared alleles (shared) and fragments carrying fetal-specific alleles (fetal-specific) determined according to an embodiment of the present invention is shown. (A) 0-20 kb bp on a linear scale of the y-axis. (B) 0-20 kb on a logarithmic scale of the y-axis. The blue line indicates fragments carrying shared alleles (mainly maternal origin) and the red line indicates fragments carrying fetal-specific alleles (are placental origin).

[0351] Figure 5A and Figure 5B The size distribution of free DNA between fragments carrying shared alleles (shared) and fragments carrying fetal-specific alleles (fetal-specific) determined according to an embodiment of the present invention is shown. (A) 0-5 kb bp on a linear scale of the y-axis. (B) 0-5 kb on a logarithmic scale of the y-axis. The blue line indicates fragments carrying shared alleles (mainly maternal origin) and the red line indicates fragments carrying fetal-specific alleles (placental origin).

[0352] Fig. 6A and Figure 6B The size distribution of free DNA between fragments carrying shared alleles (shared) and fragments carrying fetal-specific alleles (fetal-specific) determined according to an embodiment of the present invention is shown. (A) 0-1 kb on the linear scale of the y-axis. (B) 0-1 kb on the logarithmic scale of the y-axis. The blue line indicates fragments carrying shared alleles (mainly maternal origin) and the red line indicates fragments carrying fetal-specific alleles (are placental origin).

[0353] Fig. 7A and Figure 7B The size distribution of free DNA between fragments carrying shared alleles (shared) and fragments carrying fetal-specific alleles (fetal-specific) determined according to an embodiment of the present invention is shown. (A) 0-400 bp on a linear scale of the y-axis. (B) 0-400 bp on a logarithmic scale of the y-axis. The blue line indicates fragments carrying shared alleles (mainly maternal origin) and the red line indicates fragments carrying fetal-specific alleles (placental origin).

[0354] Figure 8 The single-molecule double-stranded DNA methylation degree between the fragment carrying the maternal-specific allele and the fragment carrying the fetal-specific allele according to an embodiment of the present invention is shown.

[0355] Fig. 9A and Fig. 9BShown are (A) the fitted distribution of single-molecule double-stranded DNA methylation levels between fragments carrying maternal-specific alleles and fragments carrying fetal-specific alleles according to an embodiment of the present invention and (B) receiver operating characteristic (ROC) analysis using the single-molecule double-stranded DNA methylation levels.

[0356] Fig. 10A and Fig. 10B The correlation between the degree of single-molecule double-stranded DNA methylation and the fragment size of plasma DNA according to an embodiment of the present invention is shown. (A) Size range of 0-20 kb. (B) Size range of 0-1 kb.

[0357] Fig.11A and Fig. 11B Examples of identified long fetal-specific DNA molecules in maternal plasma DNA of pregnant women according to embodiments of the present invention are shown. (A) Black bars indicate long fetal-specific DNA molecules aligned with regions in chromosome 10 of the human reference genome. (B) Detailed illustration of genetic and epigenetic determined using PacBio sequencing disclosed herein. Bases highlighted in yellow (marked by arrows) may be attributed to sequence errors, which may be corrected in some embodiments.

[0358] Fig. 12A and Fig. 12B Examples of identified long maternal DNA molecules carrying shared alleles in maternal plasma DNA of pregnant women according to embodiments of the present invention are shown. (A) Black bars indicate long maternal-specific DNA molecules aligned to regions in chromosome 6 of a human reference. (B) Detailed illustration of genetic and epigenetic information determined using PacBio sequencing according to embodiments of the present invention.

[0359] Fig.13 The frequency distribution of DNA from the placenta (red) and DNA from maternal blood cells (blue) at different resolutions of 1 kb to 20 kb according to the degree of methylation according to an embodiment of the present invention is shown.

[0360] Fig.14A and Fig. 14B Shown is the frequency distribution of DNA from the placenta (red) and DNA from maternal blood cells (blue) according to the degree of methylation within 16-kb and 24-kb windows according to an embodiment of the present invention.

[0361] Fig.15A and Fig. 15BExamples of identified long maternal-specific DNA molecules in maternal plasma DNA of pregnant women according to embodiments of the present invention are shown. (A) Black bars indicate long maternal-specific DNA molecules aligned to regions in chromosome 8 of a human reference. (B) Detailed illustration of genetic and epigenetic determinations using PacBio sequencing according to embodiments of the present invention.

[0362] Fig.16 A maternal genetic diagram of the inferred fetus is shown according to an embodiment of the present invention.

[0363] Fig.17 Depicted is the determination of genetic / epigenetic disorders in plasma DNA molecules using maternal origin and fetal origin information according to an embodiment of the present invention.

[0364] Fig.18 FIG. 4 illustrates the identification of abnormal fetal segments according to an embodiment of the present invention.

[0365] Figures 19A-19G A graphical representation of error correction for cell-free DNA genotyping using PacBio sequencing according to an embodiment of the present invention is shown. '.' indicates a base identical to a reference base in the Watson strand. ',' indicates a base identical to a reference base in the Crick strand. 'Letters' indicate alternative alleles that differ from the reference allele. '*' indicates an insertion. '^' indicates a deletion.

[0366] Fig. 20 A method of analyzing a biological sample obtained from a female pregnant with a fetus according to an embodiment of the present invention is shown.

[0367] Fig.21 A method of analyzing a biological sample obtained from a female pregnant with a fetus in order to determine haplotype inheritance according to an embodiment of the present invention is shown.

[0368] Fig. 22 Shown is a methylation pattern for determining the tissue of origin of long DNA molecules in plasma according to an embodiment of the present invention.

[0369] Fig.23 Receiver operating characteristic (ROC) curves for determining fetal origin and maternal origin according to an embodiment of the present invention are shown.

[0370] Fig.24 Shown are pairwise methylation patterns according to an embodiment of the present invention.

[0371] Fig.25 4 is a distribution table of selected marker regions among different chromosomes according to an embodiment of the present invention.

[0372] Fig.26A classification table of plasma DNA molecules based on single-molecule methylation patterns of plasma DNA molecules according to an embodiment of the present invention using different percentages of buffy coat DNA molecules with mismatch scores greater than 0.3 as selection criteria for marker regions.

[0373] Fig. 27 A flow chart of a method for determining fetal genetics using placenta-specific methylation haplotypes in a non-invasive manner according to an embodiment of the present invention is shown.

[0374] Fig.28 FIG. 2 is a diagram illustrating the principle of non-invasive prenatal detection of fragile X syndrome using long free DNA in maternal plasma according to an embodiment of the present invention.

[0375] Fig.29 The maternal inheritance of a fetus based on methylation patterns according to an embodiment of the present invention is shown.

[0376] Fig.30 A qualitative analysis of maternal inheritance of a fetus using genetic and epigenetic information of plasma DNA molecules according to an embodiment of the present invention is shown.

[0377] Fig.31 The detection rate of qualitative analysis of maternal inheritance of the fetus in a genome-wide manner using genetic and epigenetic information of plasma DNA molecules compared to relative haplotype dosage (RHDO) analysis according to an embodiment of the present invention is shown.

[0378] Fig.32 The relationship between the detection rate of paternal-specific variants in a genome-wide manner and the number of sequenced plasma DNA molecules of different sizes used for analysis is shown according to an embodiment of the present invention.

[0379] Fig.33 The workflow of non-invasive detection of Fragile X according to an embodiment of the present invention is shown.

[0380] Fig.34 Shown is the methylation pattern of plasma DNA compared to the methylation profiles of placental and buffy coat DNA according to an embodiment of the present invention.

[0381] Fig.35 is a table showing the distribution of CpG sites in 500-bp regions throughout the human genome according to an embodiment of the present invention.

[0382] Fig.36 is a table showing the distribution of CpG sites in 1-kb regions throughout the human genome according to an embodiment of the present invention.

[0383] Fig.37is a table showing the distribution of CpG sites in 3-kb regions throughout the human genome according to an embodiment of the present invention.

[0384] Fig.38 4 is a table showing the contribution ratio of different tissues to DNA molecules in maternal plasma using methylation status matching analysis according to an embodiment of the present invention.

[0385] Fig.39A and Fig.39B The relationship between placental contribution and fetal DNA fraction inferred by SNP approach according to an embodiment of the present invention is shown.

[0386] Fig.40 A method of analyzing a biological sample obtained from a female pregnant with a fetus to determine tissue of origin using methylation pattern analysis according to an embodiment of the present invention is shown.

[0387] Fig.41A and Fig.41B Shown is the size distribution of free DNA molecules from early, mid and late pregnancy maternal plasma samples according to an embodiment of the present invention.

[0388] Fig.42 4 is a table showing the ratio of long plasma DNA molecules at different gestational stages according to an embodiment of the present invention.

[0389] Fig.43A and Fig.43B Shown are the size distributions of DNA molecules covering fetal-specific alleles from early, mid, and late pregnancy maternal plasma according to an embodiment of the present invention.

[0390] Fig.44A and Fig.44B Shown are the size distributions of DNA molecules covering maternal-specific alleles from early, mid, and late pregnancy maternal plasma according to an embodiment of the present invention.

[0391] Fig.45 4 is a table showing the ratio of fetal and maternal plasma DNA molecules at different gestational stages according to an embodiment of the present invention.

[0392] Fig.46A , Fig.46B and Fig.46C A graph showing the proportions of fetal-specific plasma DNA fragments in a particular size range across different trimesters according to an embodiment of the present invention.

[0393] Fig.47A , Fig.47B and Fig.47C A graph showing the ratio of base contents at the 5' end of free DNA molecules spanning a fragment size range of 0 kb to 3 kb from maternal plasma in early, mid and late pregnancy according to an embodiment of the present invention.

[0394] Fig.48 4 is a table of terminal nucleotide base ratios among short and long free DNA molecules from maternal plasma in early, mid and late pregnancy according to an embodiment of the present invention.

[0395] Fig.49 4 is a table showing the terminal nucleotide base ratios among short and long free DNA molecules covering fetal-specific alleles from maternal plasma in early, mid and late pregnancy according to an embodiment of the present invention.

[0396] Fig.50 4 is a table of terminal nucleotide base ratios among short and long free DNA molecules covering maternal-specific alleles from maternal plasma in early, mid and late pregnancy according to an embodiment of the present invention.

[0397] Fig.51 Depicted is a hierarchical clustering analysis of short and long plasma free DNA molecules using 256 terminal motifs according to an embodiment of the present invention.

[0398] Fig.52A and Fig.52B A principal component analysis of 4-mer terminal motif profiles according to an embodiment of the present invention is shown.

[0399] Fig.53 4 is a table of 25 terminal motifs with the highest frequencies among short plasma DNA molecules from maternal plasma in early pregnancy according to an embodiment of the present invention.

[0400] Fig.54 4 is a table of 25 terminal motifs with the highest frequencies among short plasma DNA molecules from mid-pregnancy maternal plasma according to an embodiment of the present invention.

[0401] Fig.55 4 is a table of 25 terminal motifs with the highest frequencies among short plasma DNA molecules from maternal plasma in late pregnancy according to an embodiment of the present invention.

[0402] Fig.56 4 is a table of 25 terminal motifs with the highest frequencies among long plasma DNA molecules from maternal plasma in early pregnancy according to an embodiment of the present invention.

[0403] Fig.57 4 is a table of 25 terminal motifs with the highest frequencies among long plasma DNA molecules from mid-pregnancy maternal plasma according to an embodiment of the present invention.

[0404] Fig.58 4 is a table of 25 terminal motifs with the highest frequencies among long plasma DNA molecules from maternal plasma in late pregnancy according to an embodiment of the present invention.

[0405] Fig.59A , Fig.59B and Fig.59C Shown are motif frequency scatter plots of 16 NNXY motifs among short and long plasma DNA molecules in maternal plasma in (A) early pregnancy, (B) mid-pregnancy, and (C) late pregnancy according to an embodiment of the present invention.

[0406] Fig.60 A method of analyzing a biological sample obtained from a female pregnant with a fetus in order to determine gestational age is shown according to an embodiment of the present invention.

[0407] Fig.61 A method of analyzing a biological sample obtained from a female pregnant with a fetus in order to classify the likelihood of a pregnancy-related disorder according to an embodiment of the present invention is shown.

[0408] Fig.62 4 is a table showing clinical information of four pre-eclampsia cases according to an embodiment of the present invention.

[0409] Figures 63A-63D 1 is a size distribution diagram of free DNA molecules from maternal plasma samples of pre-eclampsia and normotensive late pregnancy according to an embodiment of the present invention.

[0410] Figures 64A-64D 1 is a size distribution diagram of free DNA molecules from maternal plasma samples of pre-eclampsia and normotensive late pregnancy according to an embodiment of the present invention.

[0411] Figures 65A-65D 1 is a size distribution diagram of DNA molecules covering fetal-specific alleles from maternal plasma samples of pre-eclampsia and normotensive late pregnancy according to an embodiment of the present invention.

[0412] Figures 66A-66D 1 is a size distribution diagram of DNA molecules covering fetal-specific alleles from maternal plasma samples of pre-eclampsia and normotensive late pregnancy according to an embodiment of the present invention.

[0413] Figures 67A-67D 1 is a size distribution diagram of DNA molecules covering maternal-specific alleles from maternal plasma samples of pre-eclampsia and normotensive late pregnancy according to an embodiment of the present invention.

[0414] Figures 68A-68D 1 is a size distribution diagram of DNA molecules covering maternal-specific alleles from maternal plasma samples of pre-eclampsia and normotensive late pregnancy according to an embodiment of the present invention.

[0415] Fig.69A and Fig.69BGraph showing the ratio of short DNA molecules covering fetal-specific alleles and maternal-specific alleles in maternal plasma samples of preeclampsia and normotensive women sequenced by PacBio SMRT sequencing according to an embodiment of the present invention.

[0416] Fig.70A and Fig.70B Graph showing the proportion of short DNA molecules in maternal plasma samples of preeclampsia and normotensive women sequenced by PacBio SMRT sequencing and Illumina sequencing according to an embodiment of the present invention.

[0417] Fig.71 is a size ratio graph indicating the relative proportions of short and long DNA molecules in preeclampsia and normotensive maternal plasma samples sequenced by PacBio SMRT sequencing according to an embodiment of the present invention.

[0418] Figures 72A-72D The ratios of different ends of plasma DNA molecules in maternal plasma samples with preeclampsia and normotensive maternal plasma sequenced by PacBio SMRT sequencing according to an embodiment of the present invention are shown.

[0419] Fig.73 Hierarchical cluster analysis of maternal plasma DNA samples in late pregnancy with preeclampsia and normotensive pregnancy using the frequencies of plasma DNA molecules having each of the four types of fragment ends (the first nucleotide at the 5' end of each strand), namely, the C-terminus, the G-terminus, the T-terminus, and the A-terminus, according to an embodiment of the present invention is shown.

[0420] Fig.74 Hierarchical cluster analysis of maternal plasma DNA samples in late pregnancy with preeclampsia and normotensive pregnancy using 16 dinucleotide motifs XYNN (a dinucleotide sequence of the first nucleotide and the second nucleotide from the 5' end) according to an embodiment of the present invention is shown.

[0421] Fig.75 Hierarchical cluster analysis of maternal plasma DNA samples in late pregnancy with preeclampsia and normotensive pregnancy using 16 dinucleotide motifs NNXY (a dinucleotide sequence of the third and fourth nucleotides from the 5' end) according to an embodiment of the present invention is shown.

[0422] Fig.76 Hierarchical cluster analysis of maternal plasma DNA samples in late pregnancy with preeclampsia and normotensive pregnancy using 256 tetranucleotide motifs (a dinucleotide sequence from the first nucleotide to the fourth nucleotide at the 5′ end) according to an embodiment of the present invention is shown.

[0423] Figures 77A-77DThe contribution of T cells among four types of fragment ends in maternal plasma DNA samples of preeclampsia and normotensive mothers according to an embodiment of the present invention is shown.

[0424] Fig.78 A method of analyzing a biological sample obtained from a female pregnant with a fetus to determine the likelihood of a pregnancy-related disorder is shown according to an embodiment of the present invention.

[0425] Fig.79 A graphical representation showing inferred maternal inheritance of a repeat sequence-associated disease in a fetus according to an embodiment of the present invention.

[0426] Fig.80 A diagram showing inferred paternal inheritance of a repeat sequence-associated disease in a fetus according to an embodiment of the present invention.

[0427] Fig.81 , Fig.82 and Fig.83 A table showing examples of repeat expansion diseases.

[0428] Fig.84 A table showing examples of repeat expansion detection and repeat-associated methylation determination in a fetus according to an embodiment of the present invention.

[0429] Fig.85 A method of analyzing a biological sample obtained from a female pregnant with a fetus in order to determine the likelihood of a genetic disorder in the fetus is shown according to an embodiment of the present invention.

[0430] Fig.86 A method of analyzing a biological sample obtained from a female pregnant with a fetus to determine kinship according to an embodiment of the present invention is shown.

[0431] Fig.87 Shown are the methylation patterns of two representative plasma DNA molecules after size selection.

[0432] Fig.88 This is a sequencing information table of size-selected samples and non-size-selected samples according to an embodiment of the present invention.

[0433] Fig.89A and Fig.89B Graph showing plasma DNA size profiles for samples subjected to bead-based size selection and samples without bead-based size selection according to embodiments of the present invention.

[0434] Fig.90A and Fig.90B Shown is the size profile between fetal and maternal DNA molecules in a size-selected sample according to an embodiment of the present invention.

[0435] Fig.91Statistical table of the number of plasma DNA molecules carrying informative SNPs between size-selected samples and non-size-selected samples according to an embodiment of the present invention.

[0436] Fig.92 4 is a table showing the methylation levels in size-selected plasma DNA samples and non-size-selected plasma DNA samples according to an embodiment of the present invention.

[0437] Fig.93 4 is a table of methylation levels in maternal or fetal specific free DNA molecules according to an embodiment of the present invention.

[0438] Fig.94 Detailed Description of the Invention Table of the top 10 terminal motifs in size-selected and non-size-selected samples according to an embodiment of the present invention.

[0439] Fig.95 Receiver operating characteristic (ROC) diagram showing the efficacy analysis of long plasma DNA molecules enhancing tissue of origin according to an embodiment of the present invention.

[0440] Fig.96 The diagram illustrates the principle of airport sequencing for plasma DNA molecules according to an embodiment of the present invention.

[0441] Fig.97 4 is a table showing the percentage of plasma DNA molecules within a specific size range and their corresponding methylation levels according to an embodiment of the present invention.

[0442] Fig.98 is a graph of size distribution and methylation patterns across different sizes according to an embodiment of the present invention.

[0443] Fig.99 is a table of fetal DNA fractions determined using nanopore sequencing according to an embodiment of the present invention.

[0444] Fig.100 4 is a table showing the degree of methylation between fetal-specific DNA molecules and maternal-specific DNA molecules according to an embodiment of the present invention.

[0445] Fig.101 4 is a table showing the percentage of plasma DNA molecules within a specific size range and the corresponding methylation levels of fetal DNA molecules and maternal DNA molecules according to an embodiment of the present invention.

[0446] Fig.102A and Fig.102B Graph showing the size distribution of fetal DNA molecules and maternal DNA molecules determined by nanopore sequencing according to an embodiment of the present invention.

[0447] Fig.103It is a diagram showing the difference in methylation levels between fetal DNA molecules and maternal DNA molecules based on a single informative SNP and two informative SNPs according to an embodiment of the present invention.

[0448] Fig.104 4 is a table showing the difference in methylation levels between fetal DNA molecules and maternal DNA molecules according to an embodiment of the present invention.

[0449] Fig.105 A measurement system according to an embodiment of the present invention is shown.

[0450] Fig.106 A computer system according to an embodiment of the present invention is shown.

[0451] the term

[0452] "Tissue" corresponds to a group of cells in a pregnant individual or its fetus that are grouped into a functional unit. More than one type of cell may be found in a single tissue. Different types of tissue may consist of different types of cells (e.g., liver cells, alveolar cells, or blood cells), but may also correspond to tissues from different organisms (mother and fetus; tissue in a pregnant individual that has received a transplant; tissue of a pregnant organism or its fetus infected with a microorganism or virus). "Reference tissue" may correspond to a tissue used to determine the degree of tissue-specific methylation. Multiple samples of the same tissue type from different pregnant individuals or their fetuses may be used to determine the degree of tissue-specific methylation of the tissue type.

[0453] A "biological sample" refers to any sample taken from a pregnant individual (e.g., a human (or other animal), such as a pregnant woman, a sick person or a pregnant person suspected of being sick, a pregnant organ transplant recipient, or a pregnant individual suspected of having a disease process involving an organ (e.g., the heart in myocardial infarction or the brain in stroke or the hematopoietic system in anemia)) and containing one or more nucleic acid molecules of interest. The biological sample can be a body fluid, such as blood, plasma, serum, urine, vaginal fluid, vaginal washings, pleural fluid, ascites, cerebrospinal fluid, saliva, sweat, tears, sputum, bronchoalveolar lavage fluid, nipple discharge, aspirates from different parts of the body (e.g., thyroid, breast), intraocular fluid (e.g., humoral fluid), etc. Stool samples can also be used. In various embodiments, a majority of the DNA in a biological sample that has been enriched for free DNA (e.g., a plasma sample obtained via a centrifugation protocol) can be free, for example, more than 50%, 60%, 70%, 80%, 90%, 95%, or 99% of the DNA can be free. The centrifugation protocol may include, for example, 3,000 g x 10 minutes to obtain a fluid portion, and centrifugation at, for example, 30,000 g for 10 minutes to remove residual cells. As part of the biological sample analysis, a statistically significant number of free DNA molecules of the biological sample may be analyzed (e.g., to provide accurate measurements). In some embodiments, at least 1,000 free DNA molecules are analyzed. In other embodiments, at least 10,000 or 50,000 or 100,000 or 500,000 or 1,000,000 or 5,000,000 or more free DNA molecules may be analyzed. At least the same number of sequence reads may be analyzed.

[0454] "Sequence read" refers to a string of nucleotides sequenced from any part or all of a nucleic acid molecule. For example, a sequence read can be a short string of nucleotides (e.g., 20-150 nucleotides) sequenced from a nucleic acid fragment, a short string of nucleotides at one or both ends of a nucleic acid fragment, or the sequencing of an entire nucleic acid fragment present in a biological sample. Sequence reads can be obtained in various ways, such as using sequencing techniques or using probes, such as in a hybridization array or a capture probe such as can be used in a microarray; or amplification techniques, such as polymerase chain reaction (PCR) or linear amplification or isothermal amplification using a single primer. As part of the analysis of a biological sample, a statistically significant number of sequence reads can be analyzed, for example, at least 1,000 sequence reads can be analyzed. As other examples, at least 10,000 or 50,000 or 100,000 or 500,000 or 1,000,000 or 5,000,000 or more sequence reads can be analyzed.

[0455] A "site" (also referred to as a "genomic site") corresponds to a single site, which may be a single base position or a group of related base positions, such as a CpG site or a larger group of related base positions. A "locus" may correspond to a region comprising multiple sites. A locus may contain only one site, in which case the locus is equivalent to one site in such circumstances.

[0456] "Methylation status" refers to the methylation status at a given site. For example, a site may be methylated, unmethylated, or in some cases undetermined.

[0457] The "methylation index" of each genomic site (e.g., a CpG site) may refer to the ratio of DNA fragments showing methylation at the site (e.g., as determined by sequence reads or probes) relative to the total number of reads covering the site. A "read" may correspond to information obtained from a DNA fragment (e.g., the methylation state at a site). The read may be obtained using reagents (e.g., primers or probes) that preferentially hybridize to DNA fragments with a specific methylation state at one or more sites. Typically, the reagent is applied after treatment with a method that differentially modifies or differentially identifies DNA molecules depending on the methylation state of the DNA molecule, such as bisulfite conversion, or methylation-sensitive restriction enzymes, or methylation-binding proteins, or anti-methylcytosine antibodies, or single-molecule sequencing techniques that recognize methylcytosine and hydroxymethylcytosine (e.g., single-molecule real-time sequencing and nanopore sequencing (e.g., from Oxford Nanopore Technologies)).

[0458] The "methylation density" of a region may refer to the number of reads at sites within a region showing methylation divided by the total number of reads covering the sites in the region. A site may have a specific feature, such as a CpG site. Thus, the "CpG methylation density" of a region may refer to the number of reads showing CpG methylation divided by the total number of reads covering a CpG site in the region (e.g., a specific CpG site, a CpG site within a CpG island, or a larger region). For example, the methylation density of each 100kb sub-bit in the human genome may be determined as the ratio of all CpG sites covered by sequence reads located in a 100kb region by the total number of unconverted cytosines (which correspond to methylated cytosines) at CpG sites after bisulfite treatment. This analysis may also be performed for other sub-bit sizes, such as 500bp, 5kb, 10kb, 50kb, or 1Mb, etc. A region may be an entire genome or a chromosome or a portion of a chromosome (e.g., a chromosome arm). The methylation index of a CpG site is the same as the methylation density of a region when the region contains only the CpG site. The "proportion of methylated cytosines" can refer to the number of cytosine sites "C's" that appear methylated (e.g., unconverted after bisulfite conversion) relative to the total number of cytosine residues analyzed, i.e., cytosines in the region other than CpG instances. The methylation index, methylation density, count of molecules methylated at one or more sites, and the proportion of molecules (e.g., cytosines) methylated at one or more sites are examples of "degree of methylation." In addition to bisulfite conversion, other methods known to those skilled in the art can be used to query the methylation state of a DNA molecule, including but not limited to enzymes sensitive to methylation state (e.g., methylation-sensitive restriction enzymes), methylation binding proteins, single molecule sequencing using a platform sensitive to methylation state (e.g., nanopore sequencing (Schreiber et al., Proc Natl Acad Sci 2013; 110: 18910-18915), and by single molecule real-time sequencing (e.g., single molecule real-time sequencing from Pacific Biosciences) (Flusberg et al., Nat Methods 2010; 7: 461-465).

[0459] A "methylome" provides a measure of the amount of DNA methylation at multiple sites or loci in a genome. A methylome may correspond to the entire genome, a substantial portion of the genome, or one or more relatively small portions of the genome.

[0460] "Methylation profile" includes information related to DNA or RNA methylation at multiple sites or regions. Information related to DNA methylation may include, but is not limited to, the methylation index of CpG sites, the methylation density of CpG sites in the region (abbreviated as MD), the distribution of CpG sites on connected regions, the methylation pattern or degree of each individual CpG site in the region containing more than one CpG site, and non-CpG methylation. In one embodiment, the methylation profile may include methylation or non-methylation patterns of more than one type of base (such as cytosine or adenine). The methylation profile of a considerable part of the genome can be considered to be equivalent to the methylation group. "DNA methylation" in mammalian genomes generally refers to the addition of methyl groups to the 5' carbon (i.e., 5-methylcytosine) of cytosine residues in the middle of CpG dinucleotides. DNA methylation may occur in cytosine in other situations such as CHG and CHH, where H is adenine, cytosine, or thymine. Cytosine methylation may also be in the form of 5-hydroxymethylcytosine. Non-cytosine methylation has also been reported, such as N 6 -Methyladenine.

[0461] "Methylation pattern" refers to the order of methylated and non-methylated bases. For example, the methylation pattern can be the order of methylated bases on a single DNA strand, a single double-stranded DNA molecule, or another type of nucleic acid molecule. As an example, three consecutive CpG sites may have any one of the following methylation patterns: UUU, MMM, UMM, UMU, UUM, MUM, MUU, or MMU, where "U" indicates an unmethylated site and "M" indicates a methylated site. When we extend this concept to base modifications including but not limited to methylation, we will use the term "modification pattern", which refers to the order of modified and non-modified bases. For example, the modification pattern can be the order of modified bases on a single DNA strand, a single double-stranded DNA molecule, or another type of nucleic acid molecule. As an example, three consecutive potentially modifiable sites may have any one of the following modification patterns: UUU, MMM, UMM, UMU, UUM, MUM, MUU, or MMU, where "U" indicates an unmodified site and "M" indicates a modified site. An example of a base modification that is not based on methylation is an oxidative change as in 8-oxo-guanine.

[0462] The terms "hypermethylation" and "hypomethylation" may refer to the methylation density of a single DNA molecule, as measured by its single molecule methylation degree, e.g., the number of methylated bases or nucleotides within a molecule divided by the total number of methylatable bases or nucleotides within the molecule. A hypermethylated molecule is a molecule in which the single molecule methylation degree is equal to or higher than a threshold value, which may be defined according to different applications. The threshold value may be 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 95%. A hypomethylated molecule is a molecule in which the single molecule methylation degree is equal to or lower than a threshold value, which may be defined according to different applications and may vary according to different applications. The threshold value may be 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 95%.

[0463] The terms "hypermethylation" and "hypomethylation" may also refer to the degree of methylation of a population of DNA molecules, as measured by the degree of multi-molecule methylation of these molecules. A population of hypermethylated molecules is a population of molecules in which the degree of multi-molecule methylation is equal to or higher than a threshold value, which may be defined and may vary according to different applications. The threshold value may be 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 95%. A population of hypomethylated molecules is a population of molecules in which the degree of multi-molecule methylation is equal to or lower than a threshold value, which may be defined according to different applications. The threshold value may be 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% and 95%. In one embodiment, a population of molecules may be compared to one or more selected genomic regions. In one embodiment, one or more selected genomic regions may be associated with diseases such as genetic disorders, memory disorders, metabolic disorders or neurological disorders. The length of the one or more selected genomic regions may be 50 nucleotides (nt), 100 nt, 200 nt, 300 nt, 500 nt, 1000 nt, 2knt, 5knt, 10knt, 20knt, 30knt, 40knt, 50knt, 60knt, 70knt, 80knt, 90knt, 100knt, 200knt, 300knt, 400knt, 500knt or 1 Mnt.

[0464] The term "sequencing depth" refers to the number of times a locus is covered by sequence reads aligned to the locus. A locus may be as small as a nucleotide, or as large as a chromosome arm, or as large as an entire genome. Sequencing depth can be expressed as 50×, 100×, etc., where "×" refers to the number of times a locus is covered by sequence reads. Sequencing depth can also be applied to multiple loci or a whole genome, in which case × can refer to the average number of times a locus or a haploid genome or a whole genome is sequenced, respectively. Ultra-deep sequencing can refer to a sequencing depth of at least 100×.

[0465] A "calibrator sample" may correspond to a biological sample whose fractional concentration of clinically relevant DNA (e.g., tissue-specific DNA fraction) is known or determined via a calibration method, for example using alleles specific to a tissue, such as in a transplant in a pregnant individual, where these alleles are present in the donor genome but not in the recipient genome that can be used as markers for the transplanted organ. As another example, a calibrator sample may correspond to a sample from which a terminal motif can be determined. A calibrator sample can be used for two purposes.

[0466] A "calibration data point" comprises a "calibration value" and a measured or known fractional concentration of clinically relevant DNA (e.g., DNA of a particular tissue type). The calibration value can be determined by a relative frequency (e.g., a set value) as determined for a calibration sample, the fractional concentration of clinically relevant DNA of which is known. The calibration data points can be defined in various ways, for example, as discrete points or as a calibration function (also referred to as a calibration curve or calibration surface). The calibration function can be derived from additional mathematical transformations of the calibration data points.

[0467] "Separation value" corresponds to a difference or ratio involving two values, such as two contribution fractions or two methylation degrees. A separation value can be a simple difference or ratio. As an example, the ratio of x / y and x / (x+y) is a separation value. A separation value can include other factors such as multiplication factors. As another example, a difference or ratio of a function of the values ​​can be used, such as the difference or ratio of the natural logarithms (ln) of two values. A separation value can include differences and ratios.

[0468] "Separation values" and "aggregate values" (e.g., belonging to relative frequencies) are two examples of parameters (also called metrics) that provide a measure of the sample's variation between different classifications (states), and can therefore be used to determine different classifications. An aggregate value can be a separation value, such as when taking the difference between a set of relative frequencies of a sample and a set of reference relative frequencies, as can be done in clustering.

[0469] As used herein, the term "classification" refers to any one or more numbers or one or more other characters associated with a particular characteristic of a sample. For example, the symbol "+" (or the word "positive") may indicate that a sample is classified as having a deletion or an amplification. Classification may be binary (e.g., positive or negative) or have more classification levels (e.g., a scale of 1 to 10 or 0 to 1).

[0470] As used herein, the term "parameter" means a value that characterizes a quantitative data set and / or a numerical relationship between quantitative data sets. For example, the ratio (or a function of the ratio) between a first amount of a first nucleic acid sequence and a second amount of a second nucleic acid sequence is a parameter.

[0471] The term "size profile" generally refers to the size of DNA fragments in a biological sample. A size profile can be a histogram that provides a distribution of a certain amount of DNA fragments of various sizes. Various statistical parameters (also referred to as size parameters or just parameters) can be used to distinguish one size profile from another size profile. One parameter is the percentage of DNA fragments of a particular size or size range relative to all DNA fragments or relative to DNA fragments of another size or range.

[0472] The terms "cutoff value" and "threshold value" refer to predetermined values ​​used in an operation. For example, a cutoff size may refer to a size that is excluded from fragments larger than the size thereof. A threshold value may be a value, above or below which a particular classification is applicable. Any of these terms may be used in any of these situations. A cutoff value or threshold value may be a "reference value" representing a particular classification or distinguishing between two or more classifications or derived from the reference value. As the skilled person will appreciate, such reference values ​​may be determined in various ways. For example, a metric may be determined for individuals of two different groups with different known classifications, and a reference value may be selected to represent a classification (e.g., an average value) or a value between the metrics of two clusters (e.g., selected to obtain desired sensitivity and specificity). As another example, a reference value may be determined based on a statistical analysis or simulation of a sample. A specific cutoff value, threshold value, reference value, etc. may be determined based on desired accuracy (e.g., sensitivity and specificity).

[0473] "Pregnancy-related disorders" include any disorder characterized by abnormal relative expression levels of genes in maternal and / or fetal tissues or abnormal clinical features in the mother and / or fetus. These disorders include, but are not limited to, maternal preeclampsia (Kaartokallio et al., Sci Rep. 2015; 5: 14107; Medina-Bastidas et al., Int J Mol Sci. 2020; 21: 3597), intrauterine growth restriction (Faxén et al., Am J Perinatol. 1998; 15: 9-13; Medina-Bastidas et al., Int J Mol Sci. 2020; 21: 3597), invasive placentation, premature birth (Enquobahrie et al., BMC Pregnancy and Childbirth 2015; 21: 3597), and intrauterine growth restriction (Faxén et al., Am J Perinatol. 1998; 15: 9-13; Medina-Bastidas et al., Int J Mol Sci. 2020; 21: 3597). Pregnancy Child birth. 2009; 9: 56), hemolytic disease of the newborn, placental insufficiency (Kelly et al. Endocrinology. 2017; 158: 743-755), fetal edema (Magor et al. Blood. 2015; 125: 2405-17), fetal malformations (Slonim et al. Proc. Natl. Acad. Sci. 2009; 106: 9425-9), HELLP syndrome (Dijk et al. J Clin Invest. 2012; 122: 4003-4011), systemic lupus erythematosus (Hong et al. J Exp Med. 2019; 216: 1154-1169) and other immune diseases.

[0474] The abbreviation "bp" refers to base pairs. In some cases, "bp" can be used to indicate the length of a DNA fragment, even though the DNA fragment may be single-stranded and contain no base pairs. In the case of single-stranded DNA, "bp" can be interpreted as providing a length in nucleotides.

[0475] Abbreviation " nt " refers to nucleotide. In some cases, " nt " can be used to represent the length of single strand DNA in base. In addition, " nt " can be used to represent relative position, such as the upstream or downstream of the analyzed locus. For double strand DNA, unless the context clearly indicates otherwise, " nt " still can refer to the total number of nucleotides in single strand length rather than two strands. In some cases about technical conceptualization, data display, processing and analysis, " nt " and " bp " can be used interchangeably.

[0476] The term "machine learning model" may include models that make predictions on test data based on the use of sample data (e.g., training data), and thus may include supervised learning. Machine learning models are often developed using a computer or processor. Machine learning models may include statistical models.

[0477] The term "data analysis framework" may include algorithms and / or models that can take data as input and then output predicted results. Examples of "data analysis frameworks" include statistical models, mathematical models, machine learning models, other artificial intelligence models, and combinations thereof.

[0478] The term "real-time sequencing" may refer to techniques involving data collection or monitoring during the progress of the reactions involved in sequencing. For example, real-time sequencing may involve optical monitoring or filming of DNA polymerase and new bases.

[0479] The term "subsequence" may refer to a string of bases that is less than the complete sequence corresponding to a nucleic acid molecule. For example, when the complete sequence of a nucleic acid molecule includes 5 or more bases, the subsequence may include 1, 2, 3 or 4 bases. In certain embodiments, a subsequence may refer to a string of bases that form a unit, wherein the unit is repeated multiple times in a series continuous manner. Examples include 3nt units or subsequences repeated at the locus associated with a trinucleotide repeat disorder, 1nt to 6nt units or subsequences repeated 5 to 50 times as microsatellites, 10nt to 60nt units or subsequences repeated 5 to 50 times as minisatellites, or Alu repeats in other genetic elements.

[0480] The term "about" can mean within an acceptable error range of a particular value as determined by a person of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, according to practice in the art, "about" can mean within 1 or more than 1 standard deviation. Alternatively, "about" can mean a range of up to 20%, up to 10%, up to 5%, or up to 1% of a given value. Alternatively, particularly with respect to biological systems or methods, the term "about" can mean within a certain order of magnitude of a value, within 5 times, and more preferably within 2 times. When a particular value is described in the present application and claims, unless otherwise stated, it should be assumed that the term "about" means within an acceptable error range of a particular value. The term "about" can have a meaning as commonly understood by a person of ordinary skill in the art. The term "about" can mean ±10%. The term "about" can mean ±5%.

[0481] In the case of providing a certain range of values, it should be understood that unless the context clearly indicates otherwise, each insertion value between the upper and lower limits of the range is also specifically disclosed, accurate to the tenth of the lower limit unit. Each smaller range between any stated value or insertion value in the stated range and any other stated value or insertion value in the stated range are all included in the embodiments of the present disclosure. The upper and lower limits of these smaller ranges can be independently included in the described range or excluded outside the described range, and any limit, infinite value or two limits are included in each range in the described smaller range and are also included in the present disclosure, subject to any limit specifically excluded in the stated range. If the stated range includes one or both of the limits, the scope excluding any one or both of those included limits is also included in the present disclosure.

[0482] Standard abbreviations may be used, eg, bp, base pair; kb, kilobase; pi, picoliter; s or sec, second; min, minute; h or hr, hour; aa, amino acid; nt, nucleotide; and the like.

[0483] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, some potential and exemplary methods and materials can now be described. DETAILED DESCRIPTION

[0484] The analysis of free DNA molecules mainly involves short free DNA fragments, which is often attributed to the limitations of analytical techniques. The limited ability to obtain sequence information from long DNA molecules using Illumina sequencing technology is confirmed in recent sequencing results of mouse free DNA (Serpas et al. Proceedings of the National Academy of Sciences of the United States 2019; 116: 641-649). Only 0.02% of the sequenced DNA molecules sequenced using Illumina in wild-type mice are within the range of 600bp and 2000bp. Even if the DNA library originally prepared for Illumina sequencing is sequenced using single molecule real-time (SMRT) technology (i.e., PacBio SMRT sequencing) from Pacific Biosciences, there are still only 0.33% of sequenced DNA molecules within the range of 600bp and 2000bp. These reported data show that the sequencing step will lose 93% of the long DNA molecules within the range of 600bp and 2000bp present in the original DNA library.

[0485] We speculate that due to the limitations of PCR in amplifying the long DNA molecules described above, the DNA library preparation step will also lose a significant proportion of long free DNA molecules. Jahr et al. used gel electrophoresis to report the presence of large-sized fragments of many kilobases, such as ~10,000 kilobases (Jahr et al. Cancer Res. 2001;61:1659-65). However, the bands shown in the gel electrophoresis images will not easily provide sequence information of these molecules in the gel, let alone provide epigenetic information.

[0486] We have previously used the Oxford Nanopore Technologies sequencing platform to study cell-free DNA extracted from maternal plasma (Cheng et al. Clin Chem. 2015; 61: 1305-6). We observed a very small proportion of long plasma DNA greater than 1 kb (0.06% to 0.3%). We hypothesize that such a low percentage may be a result of the low sequencing accuracy of this platform.

[0487] In the field of free DNA, most of the research focuses on short DNA molecules (e.g., <600bp). The characteristics of the genetic and epigenetic information of long free DNA molecules are unexplored. The present disclosure provides a systematic way to analyze long free DNA molecules, which includes decoding its genetic and epigenetic information and its clinical utility in non-invasive prenatal testing, such as but not limited to the non-invasive detection of monogenic disorders, the elucidation of the fetal genome (e.g., non-invasive whole fetal genome sequencing), the detection of de novo mutations at the whole genome level, and the detection / monitoring of pregnancy-related disorders such as preeclampsia and premature birth.

[0488] I. Free DNA Size Analysis

[0489] The free DNA samples obtained from pregnant women were sequenced, and it was found that a considerable portion of the DNA fragments were long. Accurate sequencing of long free DNA fragments was demonstrated. The size profile of these long free DNA molecules was analyzed. The amount of fetal long free DNA molecules was compared with that of maternal long free DNA molecules. Long free DNA molecules can be more accurately compared with reference genomes. Long free DNA molecules can be used to determine haploid inheritance.

[0490] A plasma DNA sample from a pregnant woman in late pregnancy was analyzed using PacBio SMRT sequencing. Double-stranded free DNA molecules were ligated with hairpin adapters and subjected to single-molecule real-time sequencing using a zero-mode waveguide and a single polymerase molecule (Eid et al. Science. 2009; 323: 133-8).

[0491] We sequenced 1.1 billion subreads, of which 659.3 million subreads could be aligned to the human reference genome (hg19). The subreads were generated by 4.6 million PacBio single molecule real-time (SMRT) sequencing wells, which contained at least one subread that could be aligned to the human reference genome. On average, each molecule in the SMRT well was sequenced 143 times. In this example, there were 4.5 million circular consensus sequences (CCS), indicating 4.5 million free DNA molecules that could be used for downstream analysis. The size of each free DNA was determined by the CCS by counting the number of bases that had been identified.

[0492] Figure 1A and Figure 1B The size distribution of free DNA from 0 kb to 20 kb is shown. The y-axis shows the frequency. The x-axis shows the linear scale ( Figure 1A ) or logarithmic scale ( Figure 1B ) is a size in base pairs from 0kb to 20kb on a DNA molecule. Because sequencing is performed by a full-length DNA molecule, the size of each DNA molecule can be directly determined by counting the number of nucleotides in the sub-read or CCS. DNA fragment size measurement can be achieved using any sequencing platform that can read through full-length DNA fragments and is not limited to the use of a single molecule sequencer. For example, a Sanger sequencer can read through 800bp. Short read sequencing such as performed by an Illumina platform can be read through 250bp. Single molecule sequencers such as Pacific Biosciences and Oxford Nanopore can read through more than 10,000bp. The size of the DNA fragment can also be determined after alignment with a reference genome such as a human reference genome. The size of the DNA fragment can be determined by bilateral sequencing followed by alignment with a reference genome. Figure 1B Showing a long-tail pattern. Among 4.5 million CCSs, there are 22.5% of free DNA larger than 200bp, 19.0% of free DNA larger than 300bp, 11.8% of free DNA larger than 400bp, 10.6% of free DNA larger than 500bp, 8.9% of free DNA larger than 600bp, 6.4% of free DNA larger than 1kb, 3.5% of free DNA larger than 2kb, 1.9% of free DNA larger than 3kb, 0.9% of free DNA larger than 4kb, and 0.04% of free DNA larger than 10kb. The longest free DNA observed in the current PacBio SMRT results is 29,804bp.

[0493] One plasma DNA of a pregnant individual was also sequenced using a PCR-based library preparation protocol on the Illumina sequencing platform (Lun et al. Clinical Chemistry 2013; 59: 1583-94). Among the 18.2 million bilateral reads, there were 5.3% of free DNA larger than 200 bp, 2.0% of free DNA larger than 300 bp, 0.3% of free DNA larger than 400 bp, 0.2% of free DNA larger than 500 bp, and 0.2% of free DNA larger than 600 bp (Table 1). As a comparison, we analyzed the size profile by aggregating single-molecule real-time sequencing data from 5 pregnant individuals (i.e., a total of 4.4 million CCS). Compared with the corresponding plasma DNA molecules larger than 600 bp (0.2%) obtained by the Illumina sequencing platform, we observed more plasma DNA molecules larger than 600 bp (28.56%). These results indicate that PacBio SMRT sequencing enables us to reach 143 times more long DNA molecules (longer than 600 bp).We can obtain 4.77% of plasma DNA molecules larger than 3 kb using single molecule real-time sequencing, but there are no reads in the Illumina sequencing platform.

[0494] In contrast to previous reports (Cheng et al. Clin Chem 2015;61:1305-6) showing a very small proportion of long plasma DNA molecules larger than 1 kb (0.06% to 0.3%) using the Oxford Nanopore Technologies sequencing platform, we were able to obtain more than 21-fold more plasma DNA larger than 1 kb (6.4%), indicating that PacBio SMRT sequencing is much more efficient in obtaining sequence information from long DNA populations.

[0495] Compared with bilateral short read sequencing such as Illumina sequencing platform, long read sequencing technology such as PacBio SMRT technology has multiple advantages in determining the characteristics (such as length) of long DNA fragments. For example, long reads will generally allow us to more accurately compare with human reference genomes (such as hg19). Long read technology will also allow us to accurately determine the length of plasma DNA molecules by directly counting the number of sequenced nucleotides. In contrast, the size assessment of plasma DNA based on bilateral short reads is an indirect method for inferring the size of plasma DNA molecules using the outermost coordinates of the aligned bilateral reads. For such indirect approaches, errors in the alignment will cause accurate size inferences. In this regard, an increase in the size span between bilateral reads will increase the probability of errors in the alignment.

[0496]

[0497] Table 1. Size distribution comparison between PacBio and Illumina sequencing of cell-free DNA.

[0498] Figure 2A and Figure 2B The size distribution of free DNA from 0 kb to 5 kb is shown. The y-axis shows the frequency. The x-axis shows the linear scale ( Figure 2A ) or logarithmic scale ( Figure 2B ) with sizes in base pairs ranging from 0kb to 5kb. There is a series of main peaks with a periodic pattern. The periodic pattern even extends to molecules in the 1kb and 2kb range. The peak with the highest frequency (2.6%) is at 166bp, which is consistent with previous findings using Illumina technology (Lo et al. Science Translational Medicine 2010;2:61ra91). Figure 2B The distance between adjacent main peaks in is about 200 bp, indicating that the generation of long free DNA will also involve nucleosome structure.

[0499] Figure 3A and Figure 3B The size distribution of free DNA from 0 bp to 400 bp is shown. The y-axis shows the frequency. The x-axis shows the linear scale ( Figure 3A ) or logarithmic scale ( Figure 3B ) with a size in base pairs of 0 bp to 400 bp. The characteristic features previously reported (Lo et al. Science Translational Medicine 2010; 2:61ra91) with the most dominant peak at 166 bp and 10 bp periodicity in molecules less than 166 bp can also be reproduced using the novel method of the present disclosure. These results show that it is reliable to determine the size of molecules by counting the number of bases sequenced from single molecules of the present disclosure.

[0500] A. Size analysis of fetal and maternal DNA

[0501] The sizes of maternal and fetal DNA fragments are analyzed and compared. As an example, a pregnant woman's buffy coat DNA and matched placental DNA are sequenced to obtain 59× and 58× haploid genome coverage, respectively. We identified a total of 822,409 informative single nucleotide polymorphisms (SNPs) in which the mother is homozygous and the fetus is heterozygous. Fetal-specific alleles are defined as those alleles present in the fetal genome but not in the maternal genome. We identified 2,652 fetal-specific fragments and 24,837 shared fragments (i.e., fragments carrying shared alleles; mainly maternal origin) in maternal plasma (M13160) via PacBio sequencing. The fetal DNA fraction is 21.8%.

[0502] Figure 4A and Figure 4BThe size distribution of free DNA between fragments carrying shared alleles (Shared) and fragments carrying fetal-specific alleles (Fetal-specific) is shown. The x-axis shows the linear scale ( Figure 4A ) or logarithmic scale ( Figure 4B ) on 0kb to 20kb in base pairs. Both fragments carrying shared alleles (mainly maternal origin) and fragments carrying fetal-specific alleles (placental origin) show long-tail distributions, indicating the presence of long DNA molecules derived from both fetal and maternal origins. For fragments of main maternal origin, there are 22.6% of plasma DNA molecules with a size greater than 2kb, while for fragments of fetal origin, there are 8.5% of plasma DNA molecules with a size greater than 2kb. These results show that fetal DNA molecules contain fewer long DNA molecules. The percentage of long DNA present in this SNP-based analysis of fetal and maternal origins of plasma DNA seems to be much higher than the percentage of long DNA observed in the total size analysis. The difference may be attributed to the fact that the probability of long DNA molecules covering one or more SNPs is higher than that of short DNA molecules covering one or more SNPs, and therefore long DNA will be advantageously selected for SNP-based analysis. The relative proportion of SNP-tagged long DNA molecules that deviate from the corresponding original pool of long DNA ratios will be controlled by the size of those molecules. Among those fetal-specific DNA fragments, the longest DNA fragment was 16,186 bp, while among those carrying shared alleles, the longest fragment was 24,166 bp.

[0503] Figure 5A and Figure 5B The size distribution of free DNA between fragments carrying shared alleles (Shared) and fragments carrying fetal-specific alleles (Fetal-specific) is shown. The x-axis shows the linear scale ( Figure 5A ) or logarithmic scale ( Figure 5B ) with a size in base pairs of 0kb to 5kb. For both fetal-specific DNA fragments and shared DNA fragments that are less than 2kb, there is a series of main peaks that appear in a periodic manner. The main peaks may be aligned with the nucleosome structure.

[0504] Fig. 6A and Figure 6B The size distribution of free DNA between fragments carrying shared alleles (Shared) and fragments carrying fetal-specific alleles (Fetal-specific) is shown. The x-axis shows the linear scale ( Fig. 6A ) or logarithmic scale ( Figure 6B) in base pairs from 0kb to 1kb. For both fetal-specific DNA fragments and those fragments less than 1kb of the shared DNA fragments, there is a series of main peaks that appear in a periodic manner. The main peaks may be aligned with nucleosome structures. There seems to be an observable shift of the fetal DNA size profile towards the left side of the shared DNA fragment size profile, indicating that fetal DNA will include more short DNA molecules than maternal DNA.

[0505] Fig. 7A and Figure 7B The size distribution of free DNA between fragments carrying shared alleles (Shared) and fragments carrying fetal-specific alleles (Fetal-specific) is shown. The x-axis shows the linear scale ( Fig. 7A ) or logarithmic scale ( Figure 7B ) in base pairs from 0 bp to 400 bp. The characteristic features previously reported (Lo et al. Science Translational Medicine 2010; 2:61ra91) with the most dominant peak at 166 bp and 10 bp periodicity in both fetal and maternal molecules less than 166 bp can also be reproduced using the novel method of the present disclosure. These results indicate that it is reliable to determine the size of molecules by counting the number of bases sequenced from the single molecules of the present disclosure.

[0506] B. Size and Methylation Analysis

[0507] The methylation levels of long free maternal and fetal DNA molecules were analyzed and it was found that the methylation level of fetal DNA molecules was lower than that of maternal DNA molecules.

[0508] In PacBio SMRT sequencing, DNA polymerase mediates the incorporation of fluorescently labeled nucleotides into complementary strands. The characteristics of the fluorescent pulses generated during DNA synthesis, including inter-pulse duration and pulse width, will reflect polymerase kinetics, which can be used to determine nucleotide modifications such as, but not limited to, 5-methylcytosine using the approach described in our previous publication (U.S. Application No. 16 / 995,607, filed on August 17, 2020, entitled "DETERMINATION OF BASE MODIFICATIONS OF NUCLEIC ACIDS"), the entire contents of which are incorporated herein by reference for all purposes.

[0509] In an embodiment, we identified 95,210 fragments carrying maternal specific alleles and 2,652 fragments carrying fetal specific alleles, respectively. Maternal specific alleles are defined herein as those alleles present in the maternal genome but not in the fetal genome that can be identified from the SNPs in which the mother is heterozygous and the fetus is homozygous. In this example, we identified a total of 677,375 kinds of the informative SNPs. We measure the size of each free DNA molecule. In one embodiment, when the methylation state in the genome is variable, such as the methylation degree of CpG islands is generally lower than the methylation degree of the region without CpG islands, to minimize the variability introduced by the genome situation, we can select greater than 1kb, contain at least 5 CpG sites and correspond to a CpG density less than 5% (i.e., the number of CpG sites in the molecule divided by the total length of the molecule <0.05) fragments for downstream analysis.

[0510] Figure 8 The single-molecule double-stranded DNA methylation degree between the fragment carrying the maternal-specific allele and the fragment carrying the fetal-specific allele is shown. The y-axis shows the single-molecule double-stranded DNA methylation degree in percentage. The x-axis shows both the fragment carrying the maternal-specific allele and the fragment carrying the fetal-specific allele. The single-molecule double-stranded DNA methylation degree of the fragment carrying the fetal-specific allele (mean: 62.7%; interquartile range IQR: 50.0%-77.2%) is lower than the corresponding single-molecule double-stranded DNA methylation degree of the fragment carrying the maternal-specific allele (mean: 72.7%; IQR: 60.6%-83.3%) (P<0.0001).

[0511] Fig. 9A The empirical distribution of the single-molecule double-strand DNA methylation degree of the fragments fitted by the kernel density estimation implemented in the R package (r-project.org / ) is shown. The frequency is shown on the y-axis. The x-axis shows the single-molecule double-strand DNA methylation degree in percentage. The distribution of fetal-specific long DNA fragments is to the left of the distribution of maternal-specific fragments, indicating that lower single-molecule double-strand DNA methylation degrees are present in fetal DNA molecules.

[0512] Fig. 9BReceiver operating characteristic (ROC) analysis using single molecule double strand DNA methylation levels is shown. The y-axis shows sensitivity. The x-axis shows specificity. ROC analysis was performed using single molecule double strand DNA methylation levels to study the power of distinguishing fetal DNA fragments from maternal DNA fragments using single molecule double strand DNA methylation levels, and the area under the ROC curve (AUC) was found to be 0.62, which is greater than the random guess result of 0.5. In an embodiment, we can use the spatial pattern of methylation states (such as the sequence of methylation states), the relative or absolute distance between modified bases and genomic coordinates in single molecules to further improve the fetal / maternal origin determination of fragments in plasma. In embodiments, we can combine methylation patterns with other fragmentomic metrics (i.e., parameters regarding DNA fragmentation), including but not limited to preference for ends (Chan et al. Proc. Natl. Acad. Sci. USA 2016; 113: E8159-8168), terminal motifs (Serpas et al. Proc. Natl. Acad. Sci. USA 2019; 116: 641-649), size (Lo et al. Sci. Transl. Med. 2010; 2: 61ra), orientation awareness (i.e., regarding specific elements within the genome, such as open chromatin regions, orientation of fragmentation patterns (Sun et al. Genomes Res. 2019; 29: 418-427)), topological form (e.g., linear vs. circular DNA molecules (Ma et al. Clin. Chem. 2019; 65: 1161-1170)), to improve the classification power of fragments that distinguish placental origin (fetal origin).

[0513] Fig. 10A and Fig. 10B The single-molecule double-stranded DNA methylation degree of both fetal DNA fragments and maternal DNA fragments according to the size of the fragments is shown. The y-axis shows the single-molecule double-stranded DNA methylation degree in percentage. The x-axis shows the size from 0 kb to greater than 20 kb ( Fig. 10A ) and sizes from 0kb to greater than 1kb ( Fig. 10B On the other hand, in the long range ( Fig. 10A ) and short range ( Fig. 10B ) In both, the degree of single-molecule double-stranded DNA methylation of fetal-specific DNA molecules is generally lower than that of single-molecule double-stranded DNA methylation of maternal-specific DNA molecules. For short DNA molecules, this finding is consistent with the current understanding that the degree of methylation of fetal DNA in maternal plasma is lower than that of maternal DNA (Lun et al., Clinical Chemistry 2013; 59: 1583-94).

[0514] In an embodiment, because the methylation degree of fetal DNA molecules is relatively lower than that of maternal DNA molecules, we will select molecules with a single molecule double-stranded DNA methylation degree less than a specific threshold such as but not limited to 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10% and 5% to enrich free DNA molecules of fetal origin in the plasma DNA pool. For example, for fragments>1kb, the fetal DNA fraction is 2.6%. If we select fragments with a single molecule double-stranded methylation degree <50% (>1kb), the fetal DNA fraction of those further selected>1kb fragments will increase to 5.6% (i.e., 115.4% increase). In another example, for fragments<200bp, the fetal DNA fraction is 26.2%. If we select fragments with single-molecule double-stranded methylation levels <50% (<200 bp), the fetal DNA fraction of those further selected fragments >200 bp will increase to 41.6% (i.e., 58.8%). Therefore, in some cases, the use of threshold single-molecule double-stranded DNA methylation levels for enriching fetal DNA will be more effective for long DNA molecules.

[0515] C. Haplotype and long-range free DNA methylation

[0516] In an embodiment, we can use the method described in the present disclosure to obtain the base composition, size and base modification of each single DNA molecule. The SNP and methylation information of long free DNA molecules can be used for haplotype analysis. The use of long DNA molecules present in the free DNA pool disclosed in the present disclosure will allow the use of haplotype information present in each common sequence to phase the variants in the genome according to, but not limited to, the published method (Edge et al. Genome Research 2017; 27: 801-812; Wenger et al. Nature-Biotechnology (Nat Biotechnol.) 2019; 37: 1155-1162). The embodiment of determining haplotypes according to the sequence information of free DNA is different from previous studies that must rely on long DNA prepared from tissue DNA. Haplotypes within a genomic region are sometimes referred to as haplotype blocks. Haplotype blocks can be regarded as a group of alleles that have been phased on a chromosome. In some embodiments, based on a set of sequence information supporting two alleles that are physically linked on a chromosome and allele overlap information between different sequences, the haplotype blocks will be extended as long as possible.

[0517] Fig.11A and Fig. 11BExamples of identified long fetal-specific DNA molecules in maternal plasma DNA of pregnant women are shown. Among those fetal-specific DNA fragments, we hereby illustrate that embodiments of our invention use one that aligns with a region in chromosome 10 (chr10:56282981-56299166) of the human reference genome ( Fig.11A ) and carries 7 fetal-specific alleles ( Fig. 11B ) of 16,186 bp. Six of the seven fetal-specific alleles were consistent with the allele information inferred from deep sequencing of the maternal and fetal genomes (using the Illumina platform). Fig. 11B According to the method described in the present disclosure, the degree of methylation was determined to be 27.1% ( Fig. 11B ), which is much lower than the average level of maternal-specific fragments (72.7%). These results suggest that single-molecule double-stranded DNA methylation patterns will serve as markers to distinguish free DNA molecules of fetal from maternal origin.

[0518] Fig. 12A and Fig. 12B An example of long maternal DNA molecules carrying shared alleles identified in maternal plasma DNA of pregnant women is shown. Among those fragments carrying shared alleles, the region aligned with the human reference chromosome 6 (chr6: 111074371-111098536) ( Fig. 12A ) and carry 18 shared alleles ( Fig. 12B ) with a maximum fragment length of 24,166 bp. All of those shared alleles were consistent with the allele information inferred from deep sequencing of the maternal and fetal genomes (using the Illumina platform) ( Fig. 12B According to the method described in the present disclosure, the degree of methylation was determined to be 66.9% ( Fig. 12B ). The genetic and epigenetic information of free DNA molecules, which are approximately kilobases in length, cannot be easily identified by short-read sequencing using, for example, bisulfite sequencing (Illumina).

[0519] Here, we describe a method for determining the relative likelihood that a molecule originates from a pregnant woman or a fetus. In pregnant women, DNA molecules carrying the fetal genotype actually originate from the placenta, while most DNA molecules carrying the maternal genotype originate from maternal blood cells. In this method, we first construct a frequency distribution curve for DNA molecules based on the degree of methylation of DNA molecules for both the placenta and maternal blood cells. To achieve this, we divide the human genome into different sized bins.

[0520] Fig.13The frequency distribution of DNA from the placenta (red) and DNA from maternal blood cells (blue) is shown at different resolutions of 1 kb to 20 kb according to the degree of methylation. The frequency is shown on the y-axis. The degree of methylation is shown on the x-axis. Examples of sub-bit sizes include but are not limited to 1 kb, 2 kb, 5 kb, 10 kb, 15 kb, and 20 kb. The degree of methylation of each sub-bit is determined based on the number of methylated CpG sites divided by the total number of CpG sites. After determining the degree of methylation of all sub-bits, frequency distribution curves for each of the placental genome and the maternal blood cell genome can be constructed for different sub-bit sizes.

[0521] Based on the degree of methylation of a long DNA molecule, its likelihood of originating from the placenta or maternal blood cells can be determined by the relative abundance of the two types of DNA molecules at that degree of methylation and the fractional concentration of fetal DNA in the sample.

[0522] Let x and y be the frequencies of DNA molecules derived from placenta and maternal blood cells, respectively, at a particular degree of methylation, and f be the fractional concentration of fetal DNA in the sample.

[0523] The probability (P) that a DNA molecule originated from the fetus can be calculated as:

[0524]

[0525] Following the previous example, consider a plasma DNA molecule of 16 kb and 27.1% methylation level.

[0526] Fig.14A and Fig. 14B According to the methylation degree, the 16kb ( Fig.14A ) and 24kb( Fig. 14B ) window of DNA from the placenta (red) and DNA from maternal blood cells (blue). The frequency is shown on the y-axis. The degree of methylation is shown on the x-axis. Frequency distribution diagram based on 16kb fragments ( Fig.14A ), the frequencies of DNA molecules derived from the placenta and maternal blood cells were 0.6% and 0.08% respectively. When the fetal DNA fraction was 21.8%, the probability that this DNA fragment originated from the placenta was 64%, indicating that the possibility of placental origin increased.

[0527] The probability that the DNA molecules of 24 kb and 66.9% methylation degree of plasma DNA molecules originated from fetal tissue can also be calculated. Based on the frequency distribution diagram of the 24 kb fragment, the frequencies of DNA molecules derived from placenta and maternal blood cells are 0.05% and 0.16%, respectively ( Fig. 14B). The probability that this DNA fragment is derived from the placenta is 0.8%, indicating that its placental origin is extremely unlikely. In other words, there is a high probability that the molecule is of maternal origin.

[0528] This calculation can further take into account the size of the DNA molecules by referring to the size distribution curves of fetal DNA and maternal DNA. The analysis can be performed, for example but not limited to, using Bayes' theorem, logistic regression, multiple regression and support vector machines, random forest analysis, classification and regression trees (CART), K nearest neighbor algorithms.

[0529] Fig.15A and Fig. 15B The region in chromosome 8 (chr8:108694010-108712904) of the human reference is shown ( Fig.15A ) and carries 7 maternal-specific alleles ( Fig. 15B ) in plasma was 18,896 bp in size. All those maternal-specific alleles were consistent with the allele information inferred from deep sequencing of maternal and fetal genomes (Illumina technology) ( Fig. 15B According to the method described in the present disclosure, the degree of methylation was determined to be 72.6% ( Fig. 15B ), showing a comparable degree of methylation to the maternal-specific fragments (72.7%). Therefore, such molecules would be more likely to be classified as fragments of maternal origin. The genetic and epigenetic information of free DNA molecules, which are about kilobases in length, cannot be easily identified by short-read sequencing using, for example, bisulfite sequencing (Illumina).

[0530] The probability that this molecule is derived from the placenta can be calculated using the method described above. Based on the frequency distribution diagram of the 19kb fragment, the frequencies of DNA molecules derived from the placenta and maternal blood cells are 0.65% and 0.23%, respectively. The probability that this DNA fragment is derived from the placenta is 43%, indicating that it is more likely to be of maternal origin.

[0531] D. Clinical application of haplotype analysis

[0532] In an embodiment, the ability to analyze both short and long DNA molecules in maternal plasma DNA will allow us to perform relative haplotype dosage (RHDO) analysis (Lo et al. Science Translational Medicine 2010; 2: 61ra91; Hui et al. Clinical Chemistry 2017; 63: 513-524) without requiring paternal or maternal or fetal genotype information previously obtained from tissue. This capability will be more cost-effective and clinically applicable than previously possible.

[0533] Fig.16This principle of how we can use free DNA in pregnant women for RHDO analysis is illustrated. Free DNA is isolated from pregnant women and subjected to SMRT sequencing at stage 1605. The size, allele information, and methylation status of each molecule, including long DNA molecules and short DNA molecules, can be determined according to the methods described in the present disclosure. At stage 1610, based on the size information, we can divide the sequenced molecules into two categories, namely long DNA molecules and short DNA molecules. The cutoff values ​​for determining the long DNA category and the short DNA category may include but are not limited to 150bp, 180bp, 200bp, 250bp, 300bp, 350bp, 400bp, 450bp, 500bp, 550bp, 600bp, 650bp, 700bp, 750bp, 800bp, 850bp, 900bp, 950bp, 1kb, 1.1kb, 1.2kb, 1.3kb, 1.4kb, 1.5kb, 1.6kb, 1.7kb, 1.8kb, 1.9kb, 2kb, 2.5kb, 3kb, 4kb, 5kb, 6kb, 7kb, 8kb, 9kb, 10kb, 15kb, 20kb, 30kb, 40kb, 50kb, 60kb, 70kb, 80kb, 90kb, 100kb, 200kb, 300kb, 400kb, 500kb or 1Mb. In an embodiment, at stage 1615, the allele information present in the long DNA molecule can be used to construct the maternal haplotype, i.e. Hap I and Hap II. The short DNA molecule can be compared with the maternal haplotype according to the allele information. Therefore, the number of free DNA molecules (e.g., short DNA) derived from maternal Hap I and Hap II can be determined.

[0534] In stage 1620, the imbalance of haplotypes can be analyzed. The imbalance can be molecule count, molecule size, or molecule methylation status. In stage 1625, the maternal inheritance of the fetus can be inferred. If the dosage of Hap I in the maternal plasma DNA is over-represented, the fetus will likely inherit maternal Hap I. Otherwise, the fetus will likely inherit maternal Hap II. Different statistical approaches including but not limited to successive probability ratio tests (SPRTs), binomial tests, chi-squared tests, Student's t-tests, nonparametric tests (such as Wilcoxon tests), and hidden Markov models will be used to determine which maternal haplotype is over-represented.

[0535] In an embodiment, in addition to counting analysis, the methylation and size of short DNA molecules are also determined and assigned to maternal haplotypes. The methylation imbalance between two haplotypes (i.e., Hap I and Hap II) can be used to determine the maternal haplotype of fetal inheritance. If the fetus has inherited Hap I, then compared to the fragments carrying the alleles of Hap II, there are more fragments carrying the alleles of Hap I in maternal plasma. The hypomethylation of the DNA fragments derived from the fetus can make the methylation degree of Hap I lower than the methylation degree of Hap II. In other words, if the methylation of Hap I shows a methylation degree lower than that of Hap II, the fetus will be more likely to inherit maternal Hap I. Otherwise, the fetus will be more likely to inherit maternal Hap II. In another embodiment, the probability that a single fragment is derived from a fetus or mother can be calculated as described above. For all fragments compared with HapI, the collective probability that these fragments are derived from a fetus can be determined based on Bayesian theorem. Similarly, the collective probability that these fragments are derived from a fetus can be calculated for Hap II. The likelihood of the fetus inheriting Hap I or Hap II can then be inferred based on the two pooled probabilities.

[0536] In an embodiment, the size extension or shortening between the two haplotypes (i.e., Hap I and Hap II) can be used to determine the maternal haplotype inherited by the fetus. If the fetus has inherited Hap I, there are more fragments carrying the allele of Hap I in the maternal plasma compared to the fragments carrying the allele of Hap II. The DNA fragments derived from the fetus will be relatively shorter than the DNA fragments derived from Hap II. In other words, if the molecule derived from Hap I contains more short DNA than Hap II, the fetus will be more likely to inherit maternal Hap I. Otherwise, the fetus will be more likely to inherit maternal Hap II.

[0537] In some embodiments, we can perform a combined analysis of count, size, and methylation between maternal Hap I and Hap II to infer the maternal inheritance of the fetus. For example, we can use logistic regression to combine those three metrics including count, size, and methylation status.

[0538] In clinical practice, haplotype-based analysis of count, size, and methylation status will allow determination of whether an unborn fetus has inherited a maternal haplotype associated with a genetic disorder, such as, but not limited to, single gene disorders including Fragile X, muscular dystrophy, Huntington disease, or beta-thalassemia. Detection of disorders involving repetitive sequences of DNA sequences in long free reads is described separately in this disclosure.

[0539] E. Targeted Sequencing of Long Free DNA Molecules

[0540] The method described in the present disclosure can also be applied to analyze one or more selected long DNA fragments. In an embodiment, the one or more long DNA fragments of interest can be first enriched by a hybridization method that allows DNA molecules from one or more regions of interest to hybridize with synthetic oligonucleotides having complementary sequences. In order to decode all size, genetic and epigenetic information into one using the method described in the present disclosure, the target DNA molecule is preferably not amplified by PCR before undergoing sequencing, because the base modification information in the original DNA molecule will not be transferred to the PCR product.

[0541] Several methods have been developed for enriching these target regions without performing PCR amplification. In another embodiment, one or more target long DNA molecules can be enriched using the clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR associated protein 9 (Cas9) system (Stevens et al. PLOS One 2019; 14(4): e0215441; Watson et al. Lab Invest 2020; 100: 135-146). Even though the CRISPR-Cas9-mediated cleavage will change the size of the original long DNA molecule, its genetic and epigenetic information is still preserved and can be obtained using the methods described in the present disclosure, including but not limited to base content, haplotype (i.e., phase) information, de novo mutations, base modifications (e.g., 4mC (N4-methylcytosine), 5hmC (5-hydroxymethylcytosine), 5fC (5-formylcytosine), 5caC (5-carboxycytosine), 1mA (N1-methyladenine), 3mA (N3-methyladenine), 7mA (N7-methyladenine), 3mC (N3-methylcytosine), 2mG (N2-methylguanine), 6mG (O6-methylguanine), 7mG (N7-methylguanine), 3mT (N3-methylthymine), 4mT (O4-methylthymine), 5mC (N4-methylcytosine), 5mC (N5-methylcytosine), 5mC (N6-methylcytosine), 5mC (N7 ... Pyrimidine) and 8oxoG (8-oxoguanine). In an embodiment, the ends of the DNA molecules in the DNA sample are first dephosphorylated to make them less likely to be directly attached to the sequencing adapter. Subsequently, the Cas9 protein and the guide RNA (crRNA) guide the long DNA molecule of interest to produce a double-stranded cut. Then, the long DNA molecule of interest flanked by double-stranded cuts on both sides is joined to the sequencing adapter specified by the selected sequencing platform. In another embodiment, the DNA can be treated with a nuclease to degrade the DNA molecules to which the Cas9 protein is not bound (Stevens et al. "Public Library of Science Comprehensive" 2019; 14 (4): e0215441). Since these methods do not involve PCR amplification, the original DNA molecules with base modifications can be sequenced and the base modifications can be determined.

[0542] In an embodiment, these methods can be used to design guide RNAs, such as long interspersed nuclear element (LINE) repeats, by reference to a reference genome such as the human reference genome (hg19) to target a large number of long DNA molecules that share homologous sequences. In one example, such analysis can be used to analyze circulating free DNA in maternal plasma to detect fetal aneuploidy (Kinde et al. "Public Library of Science Comprehensive" 2012; 7 (7): e41162). In an embodiment, deactivated or 'dead' Cas9 (dCas9) and its associated single guide RNA (sgRNA) can be used to enrich for targeted long DNA without cutting double-stranded DNA molecules. For example, the 3' end of the sgRNA can be designed to carry an additional universal short sequence. We can use single-stranded oligonucleotides labeled with biotin complementary to the universal short sequence to capture those targeted long DNA molecules bound by dCas9. In another embodiment, we can use biotin-labeled dCas9 protein or sgRNA or both to promote enrichment.

[0543] In embodiments, we can perform size selection to enrich long DNA fragments using approaches including but not limited to chemical, physical, enzymatic, gel-based, and magnetic bead-based approaches or approaches that incorporate more than the aforementioned approaches without restriction to one or more specific genomic regions of interest. In other embodiments, immunoprecipitation can be used to enrich DNA fragments with a specific methylation profile, such as mediated by the use of anti-methylcytosine antibodies and methyl-binding proteins. The methylation profile of the bound or captured DNA can be determined using non-methylation-aware sequencing.

[0544] F. General Concepts of Fetal Genetic Analysis Based on Long Plasma DNA Molecules

[0545] Fig.17 Determination of genetic / epigenetic disorders in plasma DNA molecules with information of maternal and fetal origin is depicted. Long plasma DNA molecules in pregnant women can be determined to be fetal or maternal in origin based on the genetic and / or epigenetic profiles of CpG sites in the entire or partial [i.e., region (a)] of the molecule. Genetic information may be, but is not limited to, sequence information, single nucleotide polymorphisms, insertions, deletions, tandem repeats, satellite DNA, microsatellites, minisatellites, inversions, and the like. Epigenetic information may be the methylation state of one or more CpG sites and their relative order in the plasma DNA molecule. In another embodiment, epigenetic information may be a modification of any one of A, C, G, or T. Long plasma DNA with tissue origin information can be used for non-invasive prenatal testing by determining the presence of genetic and / or epigenetic disorders in such long plasma DNA molecules [i.e., region (b)].

[0546] Fig.18Identification of fetal abnormal fragments is depicted. As an example, a long DNA fragment is identified as fetal in origin based on the methylation pattern of region (a) of the present disclosure. We can determine the likelihood that the fetus is affected by a genetic or epigenetic disorder based on such molecules of fetal origin. Genetic disorders may involve single nucleotide variants, insertions, deletions, tandem repeats, satellite DNA, microsatellites, minisatellites, inversions, and the like. Examples of genetic disorders include, but are not limited to: β-thalassemia, α-thalassemia, sickle cell anemia, cystic fibrosis, sex-linked genetic disorders (e.g., hemophilia, Duchenne muscular dystrophy), spinal muscular atrophy, congenital adrenal hyperplasia, and the like. Epigenetic disorders may be, for example, abnormal DNA methylation levels with increased (i.e., hypermethylation) or loss (hypomethylation). Examples of epigenetic disorders include, but are not limited to, Fragile X, Angelman's syndrome, Prader-Willi syndrome, facioscapulohumeral muscular dystrophy (FSHD), immunodeficiency disorders, centromere instability and facial anomaly (ICF) syndrome, etc. Genetic or epigenetic disorders may be found to be present in region (b).

[0547] G. Improve sequencing accuracy

[0548] Sequencing accuracy can be improved using sequence reads of long free DNA fragments. Fig. 11B Among the 7 alleles in the long fetal-specific DNA molecule, 1 allele appeared to be discordant between PacBio and Illumina sequencing.

[0549] Figures 19A-19G Diagram showing error correction for cell-free DNA genotyping using PacBio sequencing. Fig. 11B The first column indicates the genomic coordinates; the second column is the reference sequence. The third and subsequent columns indicate the aligned subreads. For example, Fig.19A In the , there are 8 subreads that pass through the region. '.' indicates the same as the reference base in the Watson strand. ',' indicates the same as the reference base in the Crick strand. 'Letters' indicate alternative alleles. '*' indicates insertions and / or deletions. We can see that Fig.19F The main base of the inconsistent site shown in is called 'T' in the consensus sequence. However, among the 9 subreads in the site ( Fig.19F ), only 5 of the 9 sub-reads (i.e., 56% major allele fraction (MAF)) were determined to be 'T', while the other sub-reads were determined to be 'C'. The major allele fraction (MAF) of this site Fig.19F) is lower than the major allele fraction of other loci ( Fig.19A -E and Figure 19G ) (MAF range: 67%-89%). Therefore, if we set a strict criterion for determining the base composition of each site in the common sequence, for example, using a MAF of at least 60%, this error site will be excluded from downstream interpretation. On the other hand, such an error site happens to fall into a homopolymer (i.e., a series of consecutive identical bases 'TTTTTTT'). In an embodiment, we can set a criterion that makes variants within a homopolymer flagged as QC failures and temporarily not used for downstream analysis. In an embodiment, we can apply different positioning qualities and base qualities to correct or filter low-quality bases or sub-reads to improve base composition analysis.

[0550] Where the sequencing accuracy of nanopore sequencing is further improved, embodiments of the present invention may also be used with such improved sequencing platforms and thereby produce improved accuracy.

[0551] H. Instance methods

[0552] Long free DNA fragments from biological samples with free DNA fragments obtained from pregnant women can be sequenced. These long free DNA fragments can be used to determine the haploid inheritance of the fetus.

[0553] 1. Sequencing long free DNA fragments

[0554] Fig. 20 A method 2000 of analyzing a biological sample of a pregnant organism is shown. The biological sample may contain a plurality of free nucleic acid molecules. The biological sample may be any biological sample described herein. Greater than 20% of the free nucleic acid molecules in the biological sample are greater than 200 nt (nucleotides) in size.

[0555] At block 2010, multiple free nucleic acid molecules are sequenced. Sequencing can be performed by single molecule real-time technology. In some embodiments, sequencing can be performed using a nanopore.

[0556] More than 20% of the sequenced free nucleic acid molecules may have a length greater than 200 nt. In some embodiments, 15%-20%, 20%-25%, 25%-30%, 30%-35% or more than 35% of the sequenced free nucleic acid molecules may have a length greater than 200 nt.

[0557] In some embodiments, more than 11% of the sequenced free nucleic acid molecules may have a length greater than 400nt. In embodiments, 5%-10%, 10%-15%, 15%-20%, 20%-25%, or more than 25% of the sequenced free nucleic acid molecules may have a length greater than 400nt.

[0558] In some embodiments, more than 10% of the sequenced free nucleic acid molecules may have a length greater than 500 nt. In embodiments, 5%-10%, 10%-15%, 15%-20%, 20%-25%, or more than 25% of the sequenced free nucleic acid molecules may have a length greater than 500 nt.

[0559] In embodiments, more than 8% of the sequenced free nucleic acid molecules may have a length greater than 600 nt. In embodiments, 5%-10%, 10%-15%, 15%-20%, 20%-25%, or more than 25% of the sequenced free nucleic acid molecules may have a length greater than 600 nt.

[0560] In some embodiments, more than 6% of the sequenced free nucleic acid molecules may have a length greater than 1 knt. In embodiments, 3%-5%, 5%-10%, 10%-15%, 15%-20%, 20%-25%, or more than 25% of the sequenced free nucleic acid molecules may have a length greater than 1 knt.

[0561] In embodiments, more than 3% of the plurality of free nucleic acid molecules sequenced may have a length greater than 2 knt. In embodiments, 1%-5%, 5%-10%, 10%-15%, 15%-20%, 20%-25% or more than 25% of the plurality of free nucleic acid molecules sequenced may have a length greater than 2 knt.

[0562] In embodiments, more than 1% of the plurality of free nucleic acid molecules sequenced may have a length greater than 3 knt. In embodiments, 1%-5%, 5%-10%, 10%-15%, 15%-20%, 20%-25% or more than 25% of the plurality of free nucleic acid molecules sequenced may have a length greater than 3 knt.

[0563] In some embodiments, at least 0.9% of the sequenced free nucleic acid molecules may have a length greater than 4 knt. In embodiments, 0.5%-1%, 1%-5%, 5%-10%, 10%-15%, 15%-20% or more than 20% of the sequenced free nucleic acid molecules may have a length greater than 4 knt.

[0564] In some embodiments, at least 0.04% of the plurality of free nucleic acid molecules sequenced may have a length greater than 10 knt. In embodiments, 0.01% to 0.1%, 0.1% to 0.5%, 0.5%-1%, 1%-5%, 5%-10%, 10%-15% or more than 15% of the plurality of free nucleic acid molecules sequenced may have a length greater than 4 knt.

[0565] A plurality of free nucleic acid molecules may comprise at least 10, 50, 100, 150 or 200 free nucleic acid molecules. A plurality of free nucleic acid molecules may be from a plurality of different genomic regions. For example, a plurality of chromosome arms or chromosomes may be covered by free nucleic acid molecules. At least two of a plurality of free nucleic acid molecules may correspond to non-overlapping regions.

[0566] The method of sequencing long free DNA fragments can be used by any method described herein. The reads from sequencing can be used to determine fetal aneuploidy, aberrations (e.g., copy number aberrations), genetic mutations or variations, or parental haplotype inheritance. The amount of sequence reads can represent the amount of free DNA fragments.

[0567] 2. Haploid inheritance

[0568] Fig.21 A method 2100 of analyzing a biological sample obtained from a female pregnant with a fetus is shown. The female may have a first haplotype and a second haplotype in a first chromosome region. The biological sample may include a plurality of free DNA molecules from the fetus and the female. The biological sample may be any biological sample described herein.

[0569] At block 2105, reads corresponding to a plurality of free DNA molecules may be received. The reads may be sequence reads. In some embodiments, the method may include performing sequencing.

[0570] At block 2110, the size of multiple free DNA molecules can be measured. The size can be measured by comparing one or more sequence reads corresponding to the ends of the DNA molecules with a reference genome. The size can be measured by sequencing the DNA molecules in their entirety and then counting the number of nucleotides in the entirety. The genomic coordinates at the outermost nucleotide can be used to determine the length of the DNA molecule.

[0571] At block 2115, a first group of free DNA molecules from the plurality of free DNA molecules may be identified as having a size greater than or equal to a cutoff value. The cutoff value may be any cutoff value associated with long DNA. For example, the cutoff value may comprise 150bp, 180bp, 200bp, 250bp, 300bp, 350bp, 400bp, 450bp, 500bp, 550bp, 600bp, 650bp, 700bp, 750bp, 800bp, 850bp, 900bp, 950bp, 1 kb, 1.5 kb, 2 kb, 2.5 kb, 3 kb, 4 kb, 5 kb, 6 kb, 7 kb, 8 kb, 9 kb, 10 kb, 15 kb, 20 kb, 30 kb, 40 kb, 50 kb, 60 kb, 70 kb, 80 kb, 90 kb, 100 kb, 200 kb, 300 kb, 400 kb, 500 kb, or 1 Mb.

[0572] At block 2120, a sequence of a first haplotype and a sequence of a second haplotype may be determined from the reads corresponding to the first set of free DNA molecules. Determining the sequence of the first haplotype and the sequence of the second haplotype may include aligning the reads corresponding to the first set of free DNA molecules to a reference genome.

[0573] In some embodiments, determining the sequence of the first haplotype and the sequence of the second haplotype may not include a reference genome. Determining the sequence may include aligning a first subset of reads with a second subset of reads to identify different alleles at the loci in the reads. The method may include determining that the first subset of reads has a first allele at the locus. The method may also include determining that the second subset of reads has a second allele at the locus. The method may further include determining that the first subset of reads corresponds to a first haplotype. Additionally, the method may include determining that the second subset of reads corresponds to a second haplotype. The alignment may be performed using Fig.16 The alignment described is similar.

[0574] At block 2125, a second group of free DNA molecules from the plurality of free DNA molecules may be aligned with the sequence of the first haplotype. The second group of free DNA molecules may have a size less than a cutoff value. The second group of free DNA molecules may be short DNA molecules of the first haplotype.

[0575] At block 2130, a third group of free DNA molecules from the plurality of free DNA molecules may be aligned with the sequence of the second haplotype. The third group of free DNA molecules may have a size less than the cutoff value. The third group of free DNA molecules may be short DNA molecules of the second haplotype.

[0576] At block 2135, a first value of a parameter may be measured using a second set of free DNA molecules. The parameter may be a count of free DNA molecules, a size profile of free DNA molecules, or a degree of methylation of free DNA molecules. The value may be a raw value or a statistical value (e.g., mean, median, mode, percentile, minimum, maximum). In some embodiments, the value may be normalized to the value of the parameter of a reference sample, another region, two haplotypes, or other size range.

[0577] At block 2140, a second value of the parameter may be measured using the third set of free DNA molecules. The parameter is the same parameter as the parameter of the second set of free DNA molecules.

[0578] At block 2145, the first value can be compared to the second value. The comparison can use a separation value. The separation value can be calculated using the first value and the second value. The separation value can be compared with a cutoff value. The separation value can be any separation value described herein. The cutoff value can be determined from a reference sample from a pregnant woman with a euploid fetus. In other embodiments, the cutoff value can be determined from a reference sample from a pregnant woman with an aneuploid fetus. In some embodiments, assuming an aneuploid fetus, the cutoff value can be determined. For example, the data from the reference sample of a pregnant woman with a euploid fetus can be adjusted to explain the increase or decrease in the number of copies of the chromosome region of the aneuploid. The cutoff value can be determined by adjusting the data.

[0579] At 2150, a likelihood that the fetus inherited the first haplotype may be determined based on a comparison of the first value to the second value. The likelihood may be determined based on a comparison of the separation value to the cutoff value. When the parameter is a size profile of free DNA molecules, the method may include determining that the fetus has a higher likelihood of inheriting the first haplotype than inheriting the second haplotype when the first value is less than the second value, indicating that the second group of free DNA molecules is characterized by a smaller size profile than the third group of free DNA molecules. When the parameter is a degree of methylation of free DNA molecules, the method may include determining that the fetus has a higher likelihood of inheriting the first haplotype than inheriting the second haplotype when the first value is less than the second value.

[0580] In some embodiments, the method may include identifying the number of repetitive sequences of a subsequence in a read of the reads corresponding to the first set of free DNA molecules. Determining the sequence of the first haplotype may include determining the number of repetitive sequences of the subsequence included in the sequence. The first haplotype may include a repetitive sequence-related disease, which may be any repetitive sequence-related disease described herein. The likelihood of a fetus inheriting a repetitive sequence-related disease may be determined. The likelihood of a fetus inheriting a repetitive sequence-related disease may be comparable or similar to the likelihood of a fetus inheriting the first haplotype. Identifying the repetitive sequence of a sequence is described later in the present disclosure, including using Fig.16 Describe it.

[0581] II. Using Methylation to Profiling Tissue of Origin

[0582] Long free DNA molecules may have several methylation sites. As discussed in the present disclosure, the degree of methylation of long free DNA molecules in pregnant women can be used to determine the tissue of origin. In addition, the methylation pattern present on long free DNA molecules can be used to determine the tissue of origin.

[0583] Compared to white blood cells and cells from tissues such as, but not limited to, liver, lung, esophagus, heart, pancreas, colon, small intestine, adipose tissue, adrenal glands, brain, etc., cells from placental tissue have a unique methylomic pattern (Sun et al. Proc Natl Acad Sci USA 2015; 112: E5503-12). The methylation profile of circulating fetal DNA in pregnant mothers' blood may be similar to that of circulating fetal DNA in the placenta, thus providing the possibility of exploring means for generating non-invasive fetal-specific biomarkers that are not dependent on fetal sex or genotype. However, bisulfite sequencing of maternal plasma DNA from pregnant women (e.g., using the Illumina sequencing platform) may lack the ability to distinguish molecules of fetal origin from those of maternal origin due to the following limitations: (1) plasma DNA may be degraded during bisulfite treatment, and long DNA molecules will generally be broken into shorter molecules; (2) DNA molecules larger than 500 bp may not be efficiently sequenced by the Illumina sequencing platform for downstream analysis (Tan et al., Sci Rep. 2019;9:2856).

[0584] For methylation-based analysis of tissue of origin, we can focus on a few differentially methylated regions (DMRs) and use the collective methylation signals from multiple molecules associated with the DMRs (Sun et al. Proc. Natl. Acad. Sci. USA 2015; 112: E5503-12) instead of single-molecule methylation patterns. Several studies have attempted to use methylation-sensitive restriction enzyme-based approaches (Chan et al. Clinical Chem. 2006; 52: 2211-8) or methylation-specific PCR-based approaches (Lo et al. Am J Hum Genet. 1998; 62: 768-75) to assess the contribution of the placenta to the plasma DNA pool. However, those studies are only suitable for analyzing one or a few markers and may be challenging for analyzing molecules on a genome-wide scale. However, those reads are inferred from amplified signals (i.e., PCR-based amplification during DNA library preparation in the flow cell and bridge amplification during sequencing cluster generation). The amplification step can potentially cause a preference for short DNA molecules, resulting in information loss associated with long DNA molecules. In addition, Li et al. only analyzed those reads associated with previously developed DMRs (Li et al., Nucleic Acids Res. 2018; 46: e89).

[0585] In the present disclosure, we describe a new approach for distinguishing fetal DNA molecules from maternal DNA molecules in pregnant women's plasma based on the methylation pattern of a single DNA molecule in the absence of bisulfite treatment and DNA amplification. In an embodiment, one or more long plasma DNA molecules will be used for analysis (e.g., using bioinformatics and / or experimental analysis for size selection). Long DNA molecules can be defined as DNA molecules with a size of at least, but not limited to, 100 bp, 200 bp, 300 bp, 400 bp, 500 bp, 600 bp, 700 bp, 800 bp, 900 bp, 1 kb, 2 kb, 3 kb, 4 kb, 5 kb, 10 kb, 20 kb, 30 kb, 40 kb, 50 kb, 100 kb, 200 kb, etc. There is a small amount of data on the presence and methylation status of longer free DNA molecules in maternal plasma. For example, it is not known whether the methylation state of the longer free DNA molecules will reflect the methylation state of the cellular DNA of the tissue of origin, for example, long fragments have more sites where the methylation state may change after fragmentation in the body; such changes may occur when the fragments circulate in the plasma. For example, studies have shown that the methylation state of circulating DNA is related to the size of the DNA fragments (Lun et al. Clinical Chemistry 2013; 59: 1583-94). Therefore, the feasibility of inferring the tissue of origin from the longer free DNA molecules is unknown. Therefore, the approach taken to identify tissue-related methylation marks and the method taken to determine and explain the presence of the tissue-specific longer free DNA molecules are substantially different from the approaches and methods applied to short free DNA analysis.

[0586] According to embodiments of the present disclosure, we can identify short DNA molecules and long DNA molecules and determine their biological characteristics including but not limited to methylation patterns, fragment ends, size and base composition. Short DNA molecules can be defined as DNA molecules with a size less than but not limited to 50bp, 60bp, 70bp, 80bp, 90bp, 100bp, 200bp, 300bp, etc. Short DNA molecules can be DNA molecules that are not considered to be long. We describe a new approach for inferring the tissue of origin of circulating DNA molecules in pregnant women's plasma. This new approach uses methylation patterns on one or more long DNA molecules in plasma. The longer the DNA molecule, the more CpG sites it may contain. The presence of multiple CpG sites on plasma DNA molecules will provide information about the tissue of origin, even if the methylation state of any single CpG site may not provide information for determining the tissue of origin. The methylation pattern in the long DNA molecule may include the methylation state of each CpG site, the order of the methylation state, and the distance between any two CpG sites. The methylation state between two CpG sites can be determined by the distance between the two CpG sites. When CpG sites within a certain distance in a molecule (eg, CpG islands) exhibit tissue-specific patterns, the statistical model can assign more weight to those signals during tissue-of-origin analysis.

[0587] Fig. 22 This principle is schematically illustrated. Fig. 22 The methylation pattern of a DNA molecule is shown. Seven CpG sites for different tissues (placenta, liver, blood cells, colon) and six plasma DNA fragments AE are shown. Methylated CpG sites are shown in red, and unmethylated CpG sites are shown in green. As an example, we consider 7 CpG sites with various methylation states across placenta, liver, blood cells and colon tissues. We consider the following scenario: a single CpG site does not exhibit a methylation state that is specific to the placenta compared to other tissues. Therefore, the tissue of origin of those plasma DNA molecules A, B, C, D and E with variable sizes can be determined not only based on the methylation state at a single CpG site. For plasma DNA molecules A and B, because the sizes of the two molecules are relatively short, they contain only 3 and 4 CpG sites, respectively. In an embodiment, the methylation pattern in a DNA molecule containing more than one CpG site can be defined as a methylation haplotype. Such as Fig. 22As shown in , plasma DNA molecules A and B can be contributed by the placenta or liver based on their methylation haplotypes because the placenta and liver share the same methylation haplotypes in those genomic positions corresponding to molecule A (positions 1, 2, and 3) and those genomic positions corresponding to molecule B (positions 1, 2, 3, and 4). However, when we can obtain long DNA molecules such as molecules C, D, and E in plasma, we can clearly determine that those molecules C, D, and E are derived from the placenta based on the methylation haplotypes.

[0588] The reference pattern of the tissue can be based on the methylation pattern of the reference tissue. In some embodiments, the methylation pattern can be based on several reads and / or samples. The degree of methylation of each CpG site (also referred to as the methylation index MI and described below) can be used to determine whether the site is methylated.

[0589] A. Statistical Models for Methylation Patterns

[0590] In an embodiment, the likelihood that a plasma DNA molecule is derived from the placenta can be determined by comparing the methylation haplotype of a single DNA molecule with the methylation pattern in multiple reference tissues. Long plasma DNA molecules may be advantageous for the analysis. Long DNA molecules may be defined as DNA molecules having a size of at least, but not limited to, 100 bp, 200 bp, 300 bp, 400 bp, 500 bp, 600 bp, 700 bp, 800 bp, 900 bp, 1 kb, 2 kb, 3 kb, 4 kb, 5 kb, 10 kb, 20 kb, 30 kb, 40 kb, 50 kb, 100 kb, 200 kb, etc. Reference tissues may include, but are not limited to, placenta, liver, lung, esophagus, heart, pancreas, colon, small intestine, adipose tissue, adrenal glands, brain, neutrophils, lymphocytes, basophils, eosinophils, etc. In an embodiment, we can determine the possibility that the plasma DNA molecule is derived from the placenta by collaboratively analyzing the methylation haplotype of plasma DNA determined by single-molecule real-time sequencing and the methylation group data of whole-genome bisulfite sequencing based on reference tissue. As an example, whole-genome bisulfite sequencing is used to sequence the placenta and buffy coat samples to an average of 94 times and 75 times the genome coverage of the haploid genome, respectively. The methylation degree of each CpG site (also called methylation index MI) is calculated based on the number of sequenced cytosines (ie, methylated, represented by C) and the number of sequenced thymines (ie, unmethylated, represented by T):

[0591]

[0592] CpG sites were ranked into three categories based on the MI values ​​inferred from placental DNA:

[0593] 1. Category A CpG sites with MI value ≥70.

[0594] 2. Category B CpG sites with MI values ​​between 30 and 70.

[0595] 3. Category C CpG sites with MI values ​​≤30.

[0596] Similarly, CpG sites were classified into three categories using the MI values ​​at CpG sites inferred from buffy coat DNA:

[0597] 1. Category A CpG sites with MI value ≥70.

[0598] 2. Category B CpG sites with MI values ​​between 30 and 70.

[0599] 3. Category C CpG sites with MI values ​​≤30.

[0600] The categories use MI cutoffs of 30 and 70. The cutoffs may include other values, including 10, 20, 40, 50, 60, 80, or 90. In some embodiments, these categories can be used to determine a reference methylation pattern for a reference tissue (e.g., using Fig. 22 Category A sites can be considered methylated. Category C sites can be considered unmethylated. Category B sites can be considered non-informative and not included in the reference pattern.

[0601] For plasma DNA molecules with n CpG sites, the methylation status of each CpG site is determined by the approach described in our previous publication (U.S. Application No. 16 / 995,607). In some embodiments, the methylation status can be determined by bisulfite sequencing or by nanopore sequencing. In order to determine the possibility that the plasma DNA molecule is derived from the placenta or maternal background, the methylation pattern of the molecule is analyzed in combination with the methylation information in the previous placenta and maternal buffy coat DNA. In an embodiment, we use the following principle: if the CpG sites determined to be methylated (M) in the plasma DNA fragments are consistent with the higher methylation index in the placenta, such observations will indicate that this molecule is more likely to be derived from the placenta. If the CpG sites determined to be methylated (M) in the plasma DNA molecules are consistent with the lower methylation index in the placenta, such observations will indicate that this molecule is unlikely to be derived from the placenta; if the CpG sites determined to be unmethylated (U) in the plasma DNA are consistent with the lower methylation index in the placenta. Then such observations will indicate that this molecule is more likely to be derived from the placenta. If CpG sites determined to be unmethylated (U) in plasma DNA coincide with a higher methylation index in the placenta, such observations would indicate that this molecule is unlikely to be of placental origin.

[0602] We implemented the following scoring process. The initial score (S) reflecting the likelihood of fetal origin of plasma DNA fragments was set to 0. When comparing the methylation status of plasma DNA molecules with the previous methylation information of placental DNA,

[0603] a. If the CpG site on the plasma DNA molecule is determined to be 'M' and its counterpart in the placenta belongs to category A, then add 1 point to S (ie, increase the score unit by 1).

[0604] b. If the CpG site on the plasma DNA molecule is determined to be 'U' and its counterpart in the placenta belongs to category A, deduct 1 point from S (ie, reduce the score unit by 1).

[0605] c. If the CpG site on the plasma DNA molecule is determined to be 'M' and its counterpart in the placenta belongs to category B, add 0.5 points to S.

[0606] d. If the CpG site on the plasma DNA molecule is determined to be 'U' and its counterpart in the placenta belongs to category B, add 0.5 points to S.

[0607] e. If a CpG site on a plasma DNA molecule is determined to be 'M' and its counterpart in the placenta belongs to category C, deduct 1 point from S.

[0608] f. If the CpG site on the plasma DNA molecule is determined to be 'U' and its counterpart in the placenta belongs to category C, add 1 point to S.

[0609] We call the above process 'methylation status matching'.

[0610] After all CpG sites in the plasma DNA molecules are processed, the final set score S (placenta) of the plasma DNA molecules is obtained. In an embodiment, the number of CpG sites is required to be at least 30 and the length of the plasma DNA molecules is required to be at least 3 kb. Other numbers and lengths of CpG sites can be used, including but not limited to any number and length described herein.

[0611] When comparing the methylation status of plasma DNA molecules with the methylation degree of buffy coat DNA at the corresponding sites, a similar scoring process will be applied.After all CpG sites in the plasma DNA molecules are processed, the final collective score S(buffy coat) of the plasma DNA molecules is obtained.

[0612] If S(placenta)>S(buffy coat), the plasma DNA molecule is determined to be of fetal origin; otherwise, the plasma DNA molecule is determined to be of maternal origin.

[0613] There are 17 fetal-specific DNA molecules and 405 maternal-specific DNA molecules used to evaluate the efficacy of inferring the fetal-maternal origin of plasma DNA molecules. Fetal-specific molecules are plasma DNA molecules carrying fetal-specific SNP alleles, while maternal-specific DNA molecules are plasma DNA molecules carrying maternal-specific SNP alleles.

[0614] Fig.23 Receiver operating characteristic curves (ROC) for determining fetal and maternal origins are shown. The y-axis shows sensitivity and the x-axis shows specificity. The red line represents the efficacy of distinguishing molecules of fetal origin from maternal origin using the methylation state matching-based method present in the present disclosure. The blue line represents the efficacy of distinguishing molecules of fetal origin from maternal origin using the single molecule methylation degree (i.e., the proportion of CpG sites in a DNA molecule determined to be methylated). Fig.23 The area under the receiver operating characteristic curve (AUC) for the methylation status matching process (0.94) was significantly higher than the AUC based on single molecule methylation degree (0.86) (P value < 0.0001; DeLong test), indicating that methylation pattern analysis of long DNA molecules will be useful for determining fetal / maternal origin.

[0615] In an embodiment, when determining whether plasma DNA is of fetal or maternal origin, the difference (ΔS) between S (placenta) and S (buffy coat) may be considered. The absolute value of ΔS may be required to exceed a specific threshold value such as, but not limited to, 5, 10, 20, 30, 40, 50, etc. As an illustration, when we use 10 as the threshold value of ΔS, the positive predictive value (PPV) in the detection of fetal DNA molecules is increased from 14.95% to 91.67%.

[0616] In an embodiment, the methylation state of a CpG site will be affected by the methylation state of its neighboring CpG sites. The closer the nucleotide distance between any two CpG sites on a DNA molecule, the more likely it is that the two CpG sites will share the same methylation state. This phenomenon has been called co-methylation. Multiple tissue-specific CpG island methylation has been reported; therefore, in some statistical models for tissue-of-origin analysis, more weights will be assigned to densely clustered CpG sites (e.g., CpG islands) that share the same methylation state. For scenarios 'a' and 'f', if the current CpG site being explored is located within a genomic distance of no more than 100bp relative to the previous CpG site and the results of the methylation state matching process of these two consecutive CpG sites are the same, the score S of the current CpG site will be additionally added with 1 point. For scenarios 'b' and 'e', ​​if the current CpG site being searched is located within a genomic distance of no more than 100bp relative to the previous CpG site and the results of the methylation state matching process of the two consecutive CpG sites are the same, then the score S of the current CpG site will be deducted by an additional 1 point. However, if the current CpG site being searched is located within a genomic distance of no more than 100bp relative to the previous CpG site, but the results of the methylation state matching process of the two consecutive CpG sites are inconsistent, the aforementioned preset scoring process will be used. On the other hand, if the current CpG site being searched is located within a genomic distance greater than 100bp relative to the previous CpG site, the aforementioned scoring process with preset parameters will be used. Scores other than 1 and distances other than 100bp can be used, including any scores and distances described herein.

[0617] In other embodiments, CpG sites are classified into more than three categories based on the MI value inferred from placenta and buffy coat DNA. The methylation information of previous reference tissues can be inferred from single molecule real-time sequencing (i.e. nanopore sequencing and / or PacBioSMRT sequencing). Plasma DNA molecule length may be required to be at least but not limited to 100bp, 200bp, 300bp, 400bp, 500bp, 600bp, 700bp, 800bp, 900bp, 1kb, 2kb, 3kb, 4kb, 5kb, 10kb, 20kb, 30kb, 40kb, 50kb, 100kb, 200kb, etc. CpG site number may be required to be at least but not limited to 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, etc.

[0618] In an embodiment, we can use a probabilistic model for characterizing the methylation pattern of plasma DNA molecules. The methylation status of k CpG sites (k ≥ 1) on the plasma DNA molecule is represented by M = (m1, m2, ..., m k), where m at CpG site i on the plasma DNA molecule i is 0 (for the unmethylated state) or 1 (for the methylated state). In an embodiment, the M probability associated with a plasma DNA molecule derived from the placenta may depend on a reference methylation pattern in the placental tissue. The reference methylation pattern in the placental tissue for those corresponding 1, 2, ..., k CpG sites will follow a beta distribution. The beta distribution is parameterized by two positive parameters α and β represented by beta (α, β). The range of values ​​derived from the beta distribution will be from 0 to 1. Based on the deep bisulfite sequencing data of the tissue of interest, the parameters α and β are determined by the number of sequenced cytosines (methylated) and thymines (unmethylated), respectively, at each CpG site of the specific tissue. For the placenta, such a beta distribution is expressed as beta (α P , β p The probability of a plasma DNA molecule originating from the placenta, P(M|placenta), will be modeled as follows:

[0619]

[0620] Where 'i' represents the i-th CpG site; indicates the beta (β) distribution associated with the methylation pattern at the i-th CpG site in the placenta; P is the joint probability of observed plasma DNA molecules having a given methylation pattern in the k CpG sites.

[0621] The probability that a plasma DNA molecule originates from a buffy coat (ie, white blood cells), P(M|buffy coat), will be modeled as follows:

[0622]

[0623] Where 'i' represents the i-th CpG site; indicates the beta (β) distribution associated with the methylation pattern at the i-th CpG site in buffy coat DNA. P is the joint probability of observed plasma DNA molecules having a given methylation pattern in k CpG sites.

[0624] Beta and Beta It can be determined from whole-genome bisulfite sequencing of placental and buffy coat DNA, respectively.

[0625] For plasma DNA molecules, if we observe P(M|placenta)>P(M|buffy coat), then such plasma DNA molecules will likely originate from the placenta; otherwise, they will likely originate from the buffy coat. We achieved an AUC of 0.79 using this model.

[0626] B. Machine Learning Model

[0627] In yet other embodiments, we can use a machine learning algorithm for determining the fetal / maternal origin of a particular plasma DNA molecule. To test the feasibility of using a machine learning-based approach to classify fetal and maternal DNA molecules in pregnant women, we developed a methylation pattern map of plasma DNA molecules.

[0628] Fig.24 Display the definition of paired methylation patterns. Nine CpG sites are shown on the plasma DNA molecule. Methylated CpG sites are shown in red, and unmethylated CpG sites are shown in green. When two CpG sites in a pair share the same methylation state (e.g., the 1st CpG and the 5th CpG), the pair will be encoded as 1, as shown in the position 'a' indicated by the arrow. When two CpG sites in a pair have different methylation states (e.g., the 1st CpG and the 2nd CpG), the pair will be encoded as 0, as shown in the position 'b' indicated by the arrow. The coding rules are equally applicable to all pairs with any 2 CpG sites on the DNA molecule.

[0629] We use a plasma DNA molecule containing 9 CpG sites as an example. The methylation pattern of this plasma DNA molecule is determined by the approach described in our previous publication (U.S. application No. 16 / 995,607), namely UMMMUUUMM (U and M represent unmethylated CpG and methylated CpG, respectively). Pairwise comparisons of the methylation states between any two CpG sites can be applied to analysis based on machine learning or deep learning. In this example, the rules also apply to a total of 36 pairs. If there are a total of n CpG sites on the plasma DNA molecule, there will be n×(n-1) / 2 comparison pairs. Different numbers can be used, including 5, 6, 7, 8, 10, 11, 12, 13 CpG sites, etc. If the molecule contains more than the number of sites used in the machine learning model, a sliding window can be used to divide the site into an appropriate number of sites.

[0630] We obtain one or more molecules from placental and buffy coat DNA samples, respectively. The methylation patterns of those DNA molecules are determined by Pacific Bioscience (PacBio) single molecule real-time (SMRT) sequencing according to the approach described in our previous publication (U.S. Application No. 16 / 995,607). Those methylation patterns are translated into paired methylation patterns.

[0631] Paired methylation patterns associated with placental DNA and paired methylation patterns associated with buffy coat DNA are used to train convolutional neural networks (CNNs) to distinguish molecules of potential fetal origin from maternal origin. Each target output (i.e., similar to the dependent variable value) of DNA fragments from the placenta is assigned to '1', while each target output of DNA fragments from the buffy coat is assigned to '0'. Paired methylation patterns are used for training to determine the parameters (often referred to as weights) for the CNN model. When the total prediction error between the output scores calculated by the sigmoid function and the expected target output (binary value: 0 or 1) is minimized by repeatedly adjusting the model parameters, the optimal parameters of the CNN for distinguishing the fetal-maternal origin of DNA fragments are obtained. The total prediction error is measured by the S cross entropy loss function in the deep learning algorithm (https: / / keras.io / ). The model parameters learned from the training data set are used to analyze DNA molecules (such as plasma DNA molecules) to output a probability score indicating the possibility that the DNA molecule is derived from the placenta or buffy coat. If the probability score of the plasma DNA fragment exceeds a certain threshold, such plasma DNA molecules are considered to be of fetal origin. Otherwise, they will be considered to be of maternal origin. The threshold will include but is not limited to 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99, etc. In one example, we used this CNN model to achieve an AUC of 0.63 to determine whether the plasma DNA molecule is of fetal origin or maternal origin, indicating that it is possible to use a deep learning algorithm to infer the tissue of origin of DNA molecules from maternal plasma. The performance of the deep learning algorithm is further improved by obtaining more single-molecule real-time sequencing results.

[0632] In some other embodiments, the statistical model may include but is not limited to linear regression, logistic regression, deep recurrent neural network (such as long short-term memory LSTM), Bayes's classifier, hidden Markov model (HMM), linear discriminant analysis (LDA), k-means clustering, density-based spatial clustering of applications with noise (DBSCAN), random forest algorithm and support vector machine (SVM), etc. Different statistical distributions including but not limited to binomial distribution, Bernoulli distribution, gamma distribution, normal distribution, Poisson distribution, etc. will be involved.

[0633] C. Methylation haplotypes specific to the placenta

[0634] The methylation status of each CpG site on a single DNA molecule can be determined using the approach described in our previous publication (U.S. Application No. 16 / 995,607) or any of the techniques described herein. In addition to the degree of methylation of single-molecule double-stranded DNA, we can determine the single-molecule methylation pattern of each DNA molecule which can be a sequence of the methylation status of adjacent CpG sites along a single DNA molecule.

[0635] Different DNA methylation signatures can be found in different tissues and cell types. In an embodiment, we can infer the tissue of origin of individual plasma DNA molecules based on their single-molecule methylation patterns.

[0636] Genomic DNA from ten buffy coat samples and six placental tissue samples was sequenced using SMRT sequencing (PacBio). We were able to achieve 58.7-fold and 28.7-fold coverage for buffy coat DNA and placental DNA, respectively, by combining mapped high-quality circular consensus sequencing (CCS) reads from each sample type.

[0637] By using the sliding window approach, the genome is divided into about 28.2 million overlapping windows of 5 CpG sites. In other embodiments, different window sizes such as but not limited to 2, 3, 4, 5, 6, 7 and 8 CpG sites can be used. We can also use a non-overlapping window approach. Each window is considered as a potential marker area. For each potential marker area, we identify the main single molecule methylation pattern among all sequenced placental DNA molecules covering all 5 CpG sites in the marker area. Comparison will be made between the CpG sites of the plasma DNA molecules and the corresponding CpG sites of the individual DNA molecules of the reference tissue. Subsequently, we calculate the mismatch score of each buffy coat DNA molecule covering all CpG sites in the same marker area, which is carried out by comparing its single molecule methylation pattern with the main single molecule methylation pattern in the placenta.

[0638]

[0639] The number of mismatched CpG sites refers to the number of CpG sites in the buffy coat DNA molecule that show a different methylation state than the dominant single molecule methylation pattern in the placenta.

[0640] Higher mismatch scores indicate that the methylation pattern of the buffy coat DNA molecules is more different from the main single-molecule methylation pattern in the placenta. We selected marker regions that showed substantial single-molecule methylation pattern differences between the DNA molecule pool from the placenta and the buffy coat from 28.2 million potential marker regions using the following criteria: a) more than 50% of the placental DNA molecules have a main single-molecule methylation pattern; and b) more than 80% of the buffy coat DNA molecules have a mismatch score greater than 0.3. We selected 281,566 marker regions based on these criteria for downstream analysis.

[0641] Fig.25 The distribution table of selected marker regions among different chromosomes. The first column shows the chromosome number. The second column shows the number of marker regions in the chromosome.

[0642] We hereby illustrate our concept of tissue-of-origin classification of individual plasma DNA molecules based on single-molecule methylation patterns using plasma DNA molecules that are sequenced by SMRT sequencing and cover fetal-specific alleles or maternal-specific alleles as previously described in the present disclosure. Any plasma DNA molecule that covers a selected marker region and has a methylation pattern identical to the main single-molecule methylation pattern in the placenta will be classified as a placenta-specific (i.e., fetal-specific) DNA molecule. Conversely, if the single-molecule methylation pattern of a plasma DNA molecule is different from the main single-molecule methylation pattern in the placenta, we classify this molecule as non-placenta-specific. The correct classification in this analysis is defined by whether the placenta-specific methylation haplotype is present in the molecule in the manner that the fetal-specific DNA molecule is identified as fetal-derived (i.e., specific to the placenta) and the maternal DNA molecule is identified as non-fetal-derived (i.e., not specific to the placenta). Methylation-based methods previously used for tissue-of-origin analysis typically involve deconvolution of the percentage or contribution ratio of a range of tissue contributors to free DNA within a biological sample. The advantage of the present method over previous methods is that evidence of tissue contribution to free DNA (e.g., placental DNA in maternal plasma) in a biological sample can be determined without regard to the presence or absence of contributions from other tissues. In addition, the placental origin of any free DNA molecule can be determined using the present method without regard to the fractional contribution of the tissue to the free DNA molecule.

[0643] Of the 28 DNA molecules covering fetal-specific alleles, 17 (61%) were classified as placenta-specific and 11 (39%) were classified as non-placenta-specific. On the other hand, of the 467 DNA molecules covering maternal-specific alleles, 433 (93%) were classified as non-placenta-specific and 34 (7%) were classified as placenta-specific.

[0644] In an embodiment, we can use different percentages of buffy coat DNA molecules with mismatch scores greater than 0.3 as thresholds, including but not limited to greater than 60%, 70%, 75%, 80%, 85%, and 90%, etc. We can improve the overall classification accuracy of placental or non-placental origin of plasma DNA in pregnant individuals by adjusting the criteria used in marker region selection. The above actions are particularly important in the non-invasive prenatal testing environment when we try to determine whether a pathogenic mutation or copy number aberration is present in the fetus.

[0645] Fig.26 Classification table of plasma DNA molecules using different percentages of buffy coat DNA molecules with mismatch scores greater than 0.3 as selection criteria for marker regions for single molecule methylation patterns of plasma DNA molecules. The first column shows the percentage of buffy coat DNA molecules with mismatch scores greater than 0.3%. The second column divides DNA molecules into DNA molecules that cover fetal-specific alleles and DNA molecules that cover maternal-specific alleles. The third and fourth columns show that the DNA molecules based on single molecule methylation patterns are placenta-specific or non-placenta-specific. The fifth column shows the percentage of DNA molecules that are classified identically to the specific alleles in the second column.

[0646] Fig. 27 A method flow chart is shown for determining fetal genetics using placenta-specific methylation haplotypes in a non-invasive manner. Fig. 27 As shown in , free DNA from pregnant women's plasma is extracted for single-molecule real-time sequencing. Long plasma DNA molecules are identified according to the embodiments in the present disclosure. The methylation status at each CpG site of each long plasma DNA molecule is determined according to the embodiments in the present disclosure. The methylation haplotype of each long plasma DNA molecule is determined according to the embodiments in the present disclosure. If a long plasma DNA molecule is identified as carrying a placenta-specific methylation haplotype, the genetic and epigenetic information associated with the molecule will be considered to be inherited by the fetus. In an embodiment, if one or more long plasma DNA molecules containing the same pathogenic mutation as the pathogenic mutation carried by the pregnant woman are determined to be of fetal origin based on the methylation haplotype information according to the embodiments in the present disclosure, it indicates that the fetus has inherited the mutation from the mother.

[0647] Embodiments may be applicable to genetic diseases including, but not limited to, beta-thalassemia, sickle cell anemia, alpha-thalassemia, cystic fibrosis, hemophilia A, hemophilia B, congenital adrenal hyperplasia, Duchenne muscular dystrophy, Becker muscular dystrophy, achondroplasia, thanatophoric dysplasia, von Willebrand disease, Noonan syndrome, hereditary hearing loss and deafness, various inborn errors of metabolism (e.g., type I citrullinemia, propionic acidemia, type Ia glycogen storage disease (von Gierke disease), type Ib / c glycogen storage disease (von Gierke disease), type II glycogen storage disease (Pompe disease), disease), mucopolysaccharidosis (MPS) type I (Hurler / Hurler-Scheie / Scheie), MPS type II (Hunter syndrome), MPS type IIIA (Sanfilippo syndrome A), MPS type IIIB (Sanfilippo syndrome B), MPS type IIIC (Sanfilippo syndrome C), MPS type IIID (Sanfilippo syndrome D), MPS type IVA (Morquio syndrome A), MPS type IVB (Morquio syndrome B), MPS type VI (Maroteaux-Lamy syndrome), MPS type VII (Sly syndrome syndrome), mucolipidosis II (I-cell disease), metachromatic leukodystrophy, GM1 gangliosidosis, OTC deficiency (X-linked ornithine aminotransferase deficiency), adrenoleukodystrophy (X-linked ALD), Krabbe disease (globoid cell leukodystrophy), etc.

[0648] In other embodiments, the genetic disease of the fetus may be associated with de novo DNA methylation in the fetal genome that is not present in the parental genome. An example would be hypermethylation of the FMRP translational regulator 1 (FMR1) gene in a fetus with fragile X. Fragile X is caused by an expansion of the CGG trinucleotide repeat sequence in the 5' untranslated region of the FMR1 gene. The normal allele will contain about 5 to 44 copies of the CGG repeat sequence. The quasi-mutant allele will contain 55 to 200 copies of the CGG repeat sequence. The fully mutated allele will contain more than 200 copies of the CGG repeat sequence.

[0649] Fig.28Illustration of the principle of non-invasive prenatal testing for Fragile X in unaffected male fetuses of pregnant women carrying normal or quasi-mutant alleles. Fig.28 In , 'n' represents the number of CGG copies in the maternal genome; 'm' represents the number of CGG copies in the fetal genome. The genome of an unaffected pregnant woman will have an FMR1 gene that has a CGG repeat sequence with no more than 200 copies (i.e., n≤200) and is unmethylated. In contrast, the genome of a male fetus affected by Fragile X will have an FMR1 gene that has more than 200 copies of CGG repeats (m>200) and is methylated. We can identify multiple long DNA molecules from a genomic region of interest (e.g., the FMR1 gene) for which the number of repeats and methylation status can be determined simultaneously by performing single-molecule sequencing of maternal plasma DNA. If we identify one or more DNA molecules in the plasma of an unaffected woman that encompass the FMR1 gene, contain more than 200 copies of CGG repeats, and are methylated, it will indicate that the fetus will likely have Fragile X. In yet another embodiment, we can further determine the fetal origin of the plasma DNA molecules using placenta-specific methylation haplotypes according to embodiments in the present disclosure. If we identify one or more molecules containing one or more regions within a molecule that carries a placenta-specific methylation haplotype and the molecule encompasses the FMR1 gene, contains more than 200 copies of CGG repeats, and is methylated, then we can more confidently conclude that the fetus has Fragile X. Conversely, if we identify one or more molecules with placenta-specific methylation haplotypes and the molecule encompasses the FMR1 gene, contains less than 200 copies of CGG repeats, and is not methylated, then it would indicate that the fetus will likely be unaffected. In the case of Fragile X, the full mutation (>200 repeats) actually methylates the entire gene and shuts down gene function. Therefore, especially for Fragile X, detection of the long allele that is methylated (rather than showing a placental methylation profile) would be highly indicative that the fetus has the disease.

[0650] Detection of genetic disorders can be performed with or without knowledge of the mother's previous status. Women with quasi-mutations may not have any symptoms, but some women with quasi-mutations may have mild symptoms and are often only known afterwards. If we do not know the maternal mutation status, one approach is to detect long alleles in the plasma of women who do not seem to have the disease or to analyze the maternal buffy coat and determine that it does not show such long alleles. As another approach, we can combine the length of the repeat sequence of the cfDNA molecule with the methylation status. If the methylation status indicates a fetal pattern (methylation haplotype) and shows a long allele, the fetus may be affected. This approach is applicable to many trinucleotide disorders such as Huntington's disease.

[0651] D. Non-invasive construction of the fetal genome using long plasma DNA molecules

[0652] Methylation patterns can be used to determine haplotype inheritance. Using a qualitative approach using methylation patterns to determine haplotype inheritance can be more efficient than quantitative methods that characterize the amount of a specific fragment. Methylation patterns can be used to determine maternal and paternal inheritance of haplotypes.

[0653] 1. Maternal inheritance of the fetus

[0654] Lo et al. demonstrated the feasibility of constructing a whole genome genetic map and determining the mutational status of the fetus from maternal plasma DNA sequences using parental haplotype information (Lo et al. Science Translational Medicine 2010; 2:61ra91). This technique has been called relative haplotype dosage (RHDO) analysis and is a way to resolve maternal inheritance of the fetus. The principle is based on the fact that the maternal haplotype inherited by the fetus will be relatively overrepresented in the plasma DNA of the pregnant woman when compared to another maternal haplotype that is not transmitted to the fetus. Therefore, RHDO is a quantitative analysis method.

[0655] Embodiments present in the present disclosure utilize methylation patterns in long plasma DNA molecules to determine the tissue of origin of said plasma DNA molecules.In one embodiment, the disclosure herein will allow for qualitative analysis of the maternal inheritance of a fetus.

[0656] Fig.29 Show the example of determining the maternal inheritance of the fetus. The genomic position P in the maternal genome (A / G) is heterozygous. The filled circles indicate methylation sites, and the hollow circles indicate unmethylated sites. The methylation pattern in the placenta is "-MUMM-", where "M" represents the methylated cytosine at the CpG site and "U" represents the unmethylated cytosine at the CpG site. In one embodiment, the methylation pattern in the placenta and the related reference tissues can be obtained from the data previously generated by sequencing (such as single molecule real-time sequencing and / or bisulfite sequencing). In plasma DNA, it is found that a non-father plasma DNA (represented by Z) carrying allele A at the specific genomic locus shows a methylation pattern ("-MUMM-") that is compatible with the methylation pattern in the placenta relative to the methylation pattern of other tissues. The molecules carrying allele G that are not found to show a methylation pattern compatible with the methylation pattern in the placenta. Therefore, the fetal inheritance maternal allele A can be determined based on the presence of allele A and "-MUMM-" methylation pattern.

[0657] Fig.30 Qualitative analysis of maternal inheritance of the fetus using genetic and epigenetic information from plasma DNA molecules. Fig.30 As shown in the top branch of , according to embodiments in the present disclosure, plasma DNA is extracted, followed by size selection for long DNA. The size-selected plasma DNA molecules are subjected to single molecule real-time sequencing (e.g., using a system manufactured by Pacific Biosciences). Genetic and epigenetic information is determined according to embodiments in the present disclosure. For illustrative purposes, a molecule (X) is aligned to human chromosome 1 containing allele G at chromosome position a (chr1:a) and allele A at chromosome position e (chr1:e). Molecule X has allele C at chromosome position d.

[0658] The CpG methylation state of this molecule X is determined to be "-MUMM-", where "M" represents a methylated cytosine at a CpG site and "U" represents an unmethylated cytosine at a CpG site. Filled circles indicate methylated sites, and open circles indicate unmethylated sites. As a result of reference sample analysis, it is known that placental DNA has a methylation pattern of "-MUMM-" in the region between positions a and e. According to an embodiment in the present disclosure, based on the methylation pattern of molecule X matching the methylation pattern of placental DNA, molecule X is determined to be of placental origin.

[0659] like Fig.30 As shown in the lower branch of , DNA from maternal white blood cells is subjected to single molecule real-time sequencing. The epigenetic and genetic information of maternal white blood cells is obtained according to the embodiments in the present disclosure. Genetic alleles are phased into two haplotypes, namely maternal haplotype I (Hap I) and maternal haplotype II (Hap II) using methods including but not limited to WhatsHap (Patterson et al. Journal of Computational Biology (J Comput Biol.) 2015; 22: 498-509), HapCUT (Bansal et al. Bioinformatics (Bioinformatics.) 2008; 24: i153-9), HapCHAT (Beretta et al. BMC Bioinformatics (BMC bioinformatics.) 2018; 19: 252). Here, we obtain two haplotypes in the maternal genome, namely "-ACGT-" (Hap I) and "-GTAC-" (Hap II). Hap I is associated with one or more wild-type variants, while Hap II is associated with one or more disease-associated variants. One or more disease-associated variants may include, but are not limited to, single nucleotide variants, insertions, deletions, translocations, inversions, repeat sequence expansions, and / or other genetic structural variations.

[0660] For genomic position e, the maternal genotype was determined to be AA and the paternal genotype was determined to be GG. Due to the methylation pattern, the plasma DNA molecule X was determined to be of placental origin. Due to the presence of the maternal-specific allele A, but the absence of the paternal-specific allele G, molecule X was inferred to be inherited from one of the maternal haplotypes.

[0661] To further determine which maternal haplotype is passed to the fetus, we compare the allele information at genomic positions other than position chrl:e of this placental-derived molecule X with the maternal haplotype. As an example, molecule X has allele G at position a and allele C at position d. The presence of any of these alleles in molecule X indicates that molecule X should be assigned to maternal Hap II, which contains the same allele.

[0662] Therefore, we can conclude that maternal haplotype II associated with one or more disease-associated variants is transmitted to the fetus. The unborn fetus is determined to be at risk of being affected by the disease.

[0663] Compared to RHDO, which is a quantitative analysis-based approach, qualitative analysis of maternal inheritance of the fetus based on methylation patterns may require fewer plasma DNA molecules to draw conclusions about which maternal haplotype the fetus has inherited. We performed in silico analysis in a genome-wide manner with different numbers of plasma DNA molecules for analysis to assess the detection rate of maternal inheritance of the fetus.

[0664] For the RHDO simulation analysis, N plasma DNA molecules were collectively aligned to M heterozygous SNPs in the haplotype block of the maternal genome. The fetal DNA fraction was f. The paternal genotypes of those corresponding SNPs were homozygous and identical to the maternal Hap I transmitted to the fetus. Among the N plasma DNA molecules, the average of plasma DNA molecules aligned to maternal Hap I was N×(0.5+f / 2), while the average of plasma DNA molecules aligned to maternal Hap II was N×(0.5-f / 2). We assume that the plasma DNA molecules sampled from the haplotype follow a binomial distribution.

[0665] The number of plasma DNA molecules was assigned to Hap I (ie, X) following the following distribution:

[0666] X~Bin(N,0.5+f / 2)(1),

[0667] Where "Bin" represents binomial distribution.

[0668] The number of plasma DNA molecules was assigned to Hap II (ie, Y) following the following distribution:

[0669] Y~Bin(N,0.5-f / 2)(2).

[0670] Therefore, plasma DNA molecules assigned to maternal Hap I will be relatively over-represented in maternal plasma compared to maternal Hap II. To determine whether the over-representation is statistically significant, we compared the difference in plasma DNA counts between the two maternal haplotypes under the null hypothesis that both haplotypes (represented by X' and Y') are equally represented in plasma.

[0671] X'~Bin(N,0.5)(3),

[0672] Y'~Bin(N,0.5)(4).

[0673] We further define the relative dosage difference between two haplotypes as follows:

[0674] D=(XY) / N(5),

[0675] D' = (X' - Y') / N (6).

[0676] In one example, the statistic D reflecting relative haplotype dosages is compared to the D' mean (M), normalized by the D' standard deviation (SD) as follows (ie, z-score):

[0677] z-score = (D–M) / SD (7).

[0678] A z-score of >3 indicates that Hap 1 is passed to the fetus.

[0679] For RHDO analysis, based on formulas (1) to (7), we simulated 30,000 haplotype blocks in the entire genome in which Hap I was transmitted to the fetus. The average length of the haplotype block was 100kb. Each haplotype block contained an average of 100 SNPs, of which 10 SNPs would provide information in terms of contributing to haplotype imbalance. In one example, the fetal DNA fraction was 10% and the median fragment size was 150bp. We calculated the haplotype block percentage with a z-score of >3 by changing the number of plasma DNA molecules used for RHDO analysis in the range of 1 million to 300 million, which is referred to herein as the detection rate. The number of plasma DNA molecules herein is adjusted by the probability of plasma DNA covering informative SNP sites according to the Pashon distribution.

[0680] For the computer simulations related to the qualitative analysis of maternal inheritance based on methylation patterns to the fetus, we made the following assumptions for illustrative purposes:

[0681] 1) There are N plasma DNA molecules covering the haplotype block in the maternal genome for analysis.

[0682] 2) The probability of plasma DNA fragments of at least 3 kb in length for tissue of origin analysis is represented by a.

[0683] 3) The probability of a plasma DNA molecule carrying more than 10 CpG sites is represented by b.

[0684] 4) The fetal DNA fraction of those fragments >3 kb is represented by f.

[0685] As shown in one embodiment of the present disclosure, we can achieve accurate inference of the tissue of origin of plasma DNA molecules larger than 3 kb with at least 10 CpG sites. It is assumed that the number of plasma DNA molecules (Z) that meet the above criteria follows a Pashon distribution with a mean of λ (i.e., N×a×b×f).

[0686] Z~Passon (λ)(8).

[0687] In one example, based on formula (8), we simulated 30,000 haplotype blocks in which Hap I was transmitted to the fetus. The average length of each haplotype block was 100 kb. Each haplotype block contained an average of 100 SNPs, of which 20 heterozygous SNPs would be phased into two maternal haplotypes. The fetal DNA fraction was 1%. After size selection, 40% of plasma DNA molecules of size>3 kb were present. There were 87.1% of plasma DNA molecules of size>3 kb with at least 10 CpG sites. The percentage of haplotype blocks with a Z value of ≥1 indicated the detection rate. We repeated the computer simulation operation multiple times by using the number of plasma DNA molecules (N) for tissue of origin analysis with methylation pattern changes ranging from 1 million to 300 million. The number of plasma DNA molecules herein was further adjusted according to the Passon distribution by the probability of plasma DNA covering heterozygous SNPs.

[0688] Fig.31 The detection rate of the qualitative analysis of maternal inheritance of the fetus using the genetic and epigenetic information of plasma DNA molecules in a genome-wide manner is shown compared to the relative haplotype dosage (RHDO) analysis. The number of molecules used for analysis is shown on the x-axis. The detection rate of maternal inheritance of the fetus in percentage form is shown on the y-axis. Compared with RHDO, the detection rate of maternal inheritance of the fetus is higher using the approach based on methylation patterns. For example, using 100 million fragments, the detection rate based on methylation patterns is 100%, while the detection rate based on RHDO is only 55%. These results indicate that the inference of maternal inheritance of the fetus using the method based on methylation patterns will be superior to the inference of maternal inheritance of the fetus using the method based on RHDO.

[0689] 2. The fetus's paternal inheritance

[0690] The ability to obtain long plasma DNA molecules for analysis can be applied to improve the detection rate of paternal-specific variants in maternal plasma DNA, because the use of the same number of long DNA molecules will increase the total genome coverage compared to the use of short DNA molecules. We further performed computer simulations based on the following assumptions:

[0691] 1) Depending on the length of plasma DNA L, the fetal DNA fraction is f. It is rewritten as f L , where the subscript L indicates that a plasma DNA molecule with bp length L was used for analysis.

[0692] 2) The number of paternal-specific variants that need to be identified in maternal plasma DNA is V.

[0693] 3) The number of plasma DNA molecules used for analysis is N.

[0694] 4) The number of plasma DNA molecules originating from a specific genomic locus or region follows a Pashon distribution.

[0695] In one example, the fetal DNA fractions of plasma DNA molecules with sizes of 150 bp, 1 kb, and 3 kb were 10% (f 150bp =0.1), 2%(f 1kb =0.02) and 1%(f 3kb =0.01). The number of paternal-specific variants in the genome is 250,000 (V=250,000). The number of plasma DNA molecules (N) used for analysis ranges from 50 million to 500 million.

[0696] Fig.32 The relationship between the detection rate of paternal specific variants performed in a genome-wide manner and the number of sequenced plasma DNA molecules of different sizes used for analysis is shown. The number of sequenced molecules used for analysis in millions is shown on the x-axis. The percentage of paternal specific variants detected is shown on the y-axis. Different curves show DNA fragments of different sizes used for analysis, with 3kb at the top, 1kb in the middle and 150bp at the bottom. The longer the plasma DNA molecules used for analysis, the higher the detection rate of paternal specific variants that can be achieved. For example, using 400 million plasma DNA molecules, when focusing on molecules of size 150bp, 1kb and 3kb, the detection rates were 86%, 93% and 98%, respectively.

[0697] In other embodiments, other distributions may be used including but not limited to Bernoulli distribution, Beta (β) normal distribution, normal distribution, Conway-Maxwell-Poisson distribution, geometric distribution, etc. In some embodiments, Gibbs sampling and Bayes' theorem will be used for maternal and paternal genetic analysis.

[0698] 3. Analysis of X chromosome fragmentation

[0699] In an embodiment, the determination of the maternal inheritance of a fetus based on methylation patterns can facilitate non-invasive detection of fragile X using single molecule real-time sequencing of maternal plasma DNA. Fragile X is a genetic disorder usually caused by an expansion of the CGG trinucleotide repeat sequence within the FMR1 (fragile X-mental disability 1) gene on the X chromosome. Fragile X and other disorders caused by repeat sequence expansions are described elsewhere in this application. The method for detecting fragile X in a fetus can also be applied to any other repeat sequence expansion disclosed herein.

[0700] Female individuals with a quasi-mutation, defined as having 55 to 200 copies of CGG repeats in the FMR1 gene, are at risk of having a child with fragile X. The likelihood of having a fetus with fragile X depends on the number of CGG repeats present in the FMR1 gene. The greater the number of repeats in the mother, the higher the risk of expansion from a quasi-mutation to a full mutation when transmitted to the fetus. Maternal plasma samples were collected at 12 weeks of gestational age from women who were previously confirmed to carry a fragile X quasi-mutation allele of 115±2 CGG repeats and had a son (primary patient) diagnosed with fragile X. Subsequently, the maternal plasma was subjected to single-molecule real-time sequencing. In one example, we used single-molecule real-time sequencing to obtain 3.3 million circular common sequences (CCS) aligned to the human reference genome, with a median subread depth of 75 times / CCS (interquartile range: 14-237 times). The genetic and epigenetic information of each sequenced plasma DNA can be determined according to embodiments of the present disclosure. To obtain the two maternal haplotypes of chromosome X, we used the Infinium Omni2.5Exome-8 Beadchip as a microarray technology on the iScan system (Illumina) to genotype 2,000 SNPs on chromosome X for two DNAs extracted from the maternal buffy coat and the buccal swab of the primary patient. Two maternal haplotypes, Hap I and Hap II, can be inferred based on the genotype information of the maternal and primary patient genomes.

[0701] Fig.33A workflow for non-invasive detection of fragile X chromosomes is shown. In the heterozygous SNP sites of maternal buffy coat DNA, the alleles identical to the genotype of the primary patient are used to define the haplotype associated with the quasi-mutation allele (i.e., Hap I), which is a potential precursor to the complete mutation in the offspring. On the other hand, the alleles different from the genotype of the primary patient are used to define the haplotype associated with the corresponding wild-type allele (Hap II). Maternal plasma DNA from the mother of the primary patient with a fetus is subjected to single-molecule real-time sequencing. Depending on whether the genetic information obtained is identical to the alleles of Hap I or Hap II in those genomic loci being studied, the sequencing reads are assigned to maternal Hap I and Hap II. According to an embodiment in the present disclosure, the methylation pattern of the plasma DNA molecules is used to determine the tissue of origin of those plasma DNA molecules containing a specific number of CpG sites (i.e., DNA molecules identified as placental origin based on methylation pattern analysis will be determined to be derived from the fetus).

[0702] In scenario A, if fetal (i.e., placental) DNA molecules can be detected from those plasma DNA molecules assigned to maternal Hap I, but not those plasma DNA molecules assigned to maternal Hap II, then Hap I will be determined to be transmitted to the unborn fetus. The fetus will be determined to be at high risk of being affected by Fragile X. The placental origin of the plasma DNA molecules will be based on the methylation status of the molecules as discussed below.

[0703] Under scenario B, if fetal DNA molecules can be detected from those plasma DNA molecules assigned to maternal Hap II, but not those assigned to maternal Hap I, then Hap II will be determined to be transmitted to the unborn fetus. The fetus will be determined to be unaffected by Fragile X.

[0704] In an embodiment, the definition of "detectable" and "undetectable" of fetal DNA molecules can be determined based on the cutoff value of the percentage of plasma DNA molecules identified as fetal (i.e., placental) origin. The cutoff value for "detectable" may include, but is not limited to, greater than 1%, 2%, 3%, 4%, 5%, 10%, 15%, 20%, 30%, 40%, 50%, etc. The cutoff value for "undetectable" may include, but is not limited to, less than 1%, 2%, 3%, 4%, 5%, 10%, 15%, 20%, 30%, 40%, 50%, etc. In some embodiments, the difference in the percentage of plasma DNA molecules determined to be of fetal origin between Hap I and Hap II may be required to be greater than, but not limited to, 1%, 2%, 3%, 4%, 5%, 10%, 15%, 20%, 30%, 40%, 50%, etc. In some other embodiments, haplotype information can be obtained from long read sequencing technologies (e.g., PacBio or nanopore sequencing) (Edge et al. Nat Commun. 2019; 10:4660), synthetic long reads (e.g., using a platform from 10X Genomics) (Hui et al. Clin Chem 2017; 63:513-14), targeted locus amplification (TLA)-based phasing (Vermeulen et al. Am J Hum Genet 2017; 101:326-39), and statistical phasing (e.g., Shape-IT) (Delaneau et al. Nat Methods 2011; 9:179-81).

[0705] In an embodiment, we can determine the maternal and fetal origin of those plasma DNA molecules that are at least 200 bp and contain at least 5 CpG sites (or any other cutoff for long DNA molecules) according to the methylation state matching approach disclosed in this application. We identified a plasma DNA molecule located at genomic position chrX:143,782,245-143,782,786 (3.2Mb from the FMR1 gene) where the allele (position: chrX:143782434; SNP deposit number: rs6626483; allele genotype: C) is the same as the corresponding allele on maternal Hap II, but different from the corresponding allele on maternal Hap I.

[0706] Fig.34The methylation pattern of plasma DNA compared to the methylation profiles of placenta and buffy coat DNA is shown. The plasma DNA molecule contains 5 CpG sites. The methylation pattern is determined to be "MUUUU". According to the methylation state matching approach described in the present disclosure, this methylation pattern obtained from single molecule real-time sequencing is compared with the reference methylation profiles of placental tissue and buffy coat DNA samples obtained from bisulfite sequencing. The score of this molecule derived from the placenta [i.e., S (placenta)] is 2, which is greater than the score of the molecule derived from the buffy coat [i.e., S (buffy coat)] -3. Therefore, this type of plasma DNA molecule (chrX: 143, 782, 245-143, 782, 786) is determined to be of fetal origin. However, we did not observe any plasma DNA molecules carrying alleles of maternal Hap I from fetal origin. Therefore, we conclude that the fetus inherits maternal Hap II and is not affected by X chromosome fragmentation.

[0707] We hypothesize that the efficacy of the pathway described herein may not be significantly affected by X chromosome inactivation due to the following factors:

[0708] 1) X inactivation is not complete in humans. Up to 1 / 3 of the genes on the X chromosome show variable escape from X inactivation (Cotton et al. Hum Mol Genet. 2015; 25: 1528-1539). CpG sites outside of CpG islands (i.e., most CpG sites) are methylated to a similar extent in both sexes, suggesting that the methylation status of most CpG sites in the X chromosome may not be affected by X inactivation (Yasukochi et al. Proc Natl Acad Sci U S A 2010; 107: 3704-9).

[0709] 2) We used the methylation profile of sex-matched placental tissue for unborn fetuses. This strategy would be useful for detecting maternal inheritance of the fetus using the plasma DNA methylation pattern of women carrying male fetuses, since placental tissue involving male fetuses that is presumably not affected by X inactivation would have a unique methylation pattern different from other maternal tissues that are more or less involved in X inactivation of specific regions.

[0710] We further sequenced DNA extracted from maternal buffy coat samples using single-molecule real-time sequencing. We obtained 2.3 million CCSs with a median subread depth of 5x / CCS. The results confirmed that maternal Hap I carried a quasi-mutant allele with 124 CGG repeats and maternal Hap II carried a wild-type allele with 43 CGG repeats. In addition, we further sequenced DNA extracted from chorionic villus sampling of unborn fetuses using single-molecule real-time sequencing. We obtained 1.1 million CCSs with a median subread depth of 4x / CCS. The results confirmed that the unborn fetus carried the wild-type allele.

[0711] E. Distribution of CpG sites in the human genome

[0712] Longer DNA fragments give rise to the probability that larger fragments have multiple CpG sites. These multiple CpG sites can be used for methylation pattern or other analysis.

[0713] Fig.35 Shows the distribution of CpG sites in 500bp regions throughout the human genome. The first column shows the number of CpG sites. The second column shows the number of 500bp regions and the number of CpG sites. The third column shows the proportion of all regions represented by regions with a specific number of CpG sites. For example, 86.14% of 500bp regions will have at least 1 CpG site. In addition, 11.08% of 500bp regions will have at least 10 CpG sites.

[0714] Fig.36 Shows the distribution of CpG sites in 1 kb regions throughout the human genome. The first column shows the number of CpG sites. The second column shows the number of 1 kb regions and the number of CpG sites. The third column shows the proportion of all regions represented by regions with a specific number of CpG sites. For example, 91.67% of 500 bp regions will have at least 1 CpG site. In addition, 32.91% of 500 bp regions will have at least 10 CpG sites.

[0715] Fig.37 Shows the distribution of CpG sites in 3kb regions throughout the human genome. The first column shows the number of CpG sites. The second column shows the number of 3kb regions and the number of CpG sites. The third column shows the proportion of all regions represented by regions with a specific number of CpG sites. For example, 92.45% of 3kb regions will have at least 1 CpG site. In addition, 87.09% of 3kb regions will have at least 10 CpG sites.

[0716] In some embodiments, different numbers and different size cutoffs of CpG sites will be used to maximize the sensitivity and specificity of placenta-specific marker identification and tissue of origin analysis. In general, the frequency of occurrence of CpG sites is higher than that of SNPs. DNA fragments of a given size may have more CpG sites than SNPs. The table shown above can show a lower proportion of regions with the same number of SNPs as CpG sites, because there are fewer SNPs than CpG sites in the same size region. Therefore, using CpG sites allows the use of more fragments and provides better statistics than using only SNPs.

[0717] F. Example of tissue of origin analysis

[0718] In an embodiment, we can extend the analysis of tissues of origin in maternal plasma to more than two organs / tissues including T cells, B cells, neutrophils, liver and placenta. We use single molecule real-time sequencing to sequence 9 maternal DNA samples. We use plasma DNA methylation patterns to infer the contribution of placenta to maternal plasma DNA according to the methylation state matching approach described in the present disclosure. In one embodiment, for this methylation state matching analysis, the methylation pattern of each of the DNA molecules with a length of at least 500bp and containing at least 5 CpG sites in the maternal plasma DNA sample is compared with the reference tissue methylation profile obtained from bisulfite sequencing. Five tissues including neutrophils, T cells, B cells, liver and placenta are used as reference tissues. Plasma DNA molecules will be assigned to the tissue corresponding to the maximum methylation state matching score of the plasma DNA molecules. The percentage of plasma DNA molecules assigned to a tissue relative to other tissues will be regarded as the contribution ratio of the tissue to the maternal plasma DNA of the sample. In embodiments, the sum of the contribution of neutrophils, T cells, and B cells in maternal plasma provides a surrogate representation of the contribution of hematopoietic cells.

[0719] Fig.38 Shows the contribution ratio of DNA molecules from different tissues in maternal plasma analyzed using methylation status matching. The first column shows the sample identification. The second column shows the hematopoietic cell contribution in percentage. The third column shows the liver contribution in percentage. The fourth column shows the placental contribution in percentage. Fig.38 The major contributors of maternal plasma DNA were shown to be hematopoietic cells (median: 55.9%), which is consistent with previous reports (Sun et al. Proc Natl Acad Sci USA 2015;112:E5503-12; Zheng et al. Clin Chem 2012;58:549-58).

[0720] Fig.39A and Fig.39BThe relationship between placental contribution and fetal DNA fraction inferred by SNP approach is shown. The X-axis shows the fetal fraction determined by SNP approach. The Y-axis shows the placental contribution in maternal plasma as a percentage determined by using methylation status matching analysis. Fig.39A A good correlation was shown between the placental contribution determined by methylation status matching analysis and the fetal DNA fraction inferred by SNPs (Pearson's r = 0.95; P value < 0.0001). We further performed tissue deconvolution analysis of maternal plasma DNA by comparing plasma DNA methylation density determined by single-molecule real-time sequencing with various reference tissue methylation profiles obtained from bisulfite sequencing according to quadratic programming (Sun et al. Proc. Natl. Acad. Sci. USA 2015; 112: E5503-12). Fig.39B Using a methylation density-based approach showed a reduced correlation between placental contribution (Sun et al. PNAS 2015; 112:E5503-12) and fetal DNA fraction compared to using a methylation status-matched analysis (Pearson correlation coefficient = 0.65; P value = 0.059).

[0721] These data indicate that it is feasible to infer the proportion of DNA molecules contributed by different tissues in a maternal plasma DNA sample. In another embodiment, this method can also be used to measure DNA molecules from different cell types or tissues in a sample obtained after an invasive solid tissue biopsy or from solid tissues obtained after surgery. In some embodiments, the use of methylation patterns at the level of a single DNA molecule to infer the contribution ratio of different tissues to maternal plasma DNA will be superior to the use of approaches based on the collective methylation density of all sequenced plasma DNA molecules from the entire genome.

[0722] G. Instance methods

[0723] Fig.40 A method 4000 of analyzing a biological sample obtained from a female pregnant with a fetus is shown. The biological sample may contain a plurality of free DNA molecules from the fetus and the female.

[0724] At block 4010, sequence reads corresponding to a plurality of cell-free DNA molecules may be received. In some embodiments, method 4000 may include performing sequencing of the cell-free DNA molecules.

[0725] At block 4020, the size of a plurality of free DNA molecules may be measured. The measurement may include aligning the sequence reads with a reference genome. In some embodiments, the measurement may include full-length sequencing of nucleotides in the full-length sequence and counting their number. In some embodiments, the measurement may include physically separating a plurality of free DNA molecules from a biological sample from other free DNA molecules in the biological sample, wherein the other free DNA molecules have a size less than a cutoff value. Physical separation may include any of the techniques described herein, including the use of beads.

[0726] At block 4030, a group of free DNA molecules from a plurality of free DNA molecules can be identified as having a size greater than or equal to a cutoff value. The cutoff value can be greater than or equal to 200nt. The cutoff value can be at least 500nt, including 600nt, 700nt, 800nt, 900nt, 1knt, 1.1knt, 1.2knt, 1.3knt, 1.4knt, 1.5knt, 1.6knt, 1.7knt, 1.8knt, 1.9knt or 2knt. The cutoff value can be any cutoff value described herein for long free DNA molecules. The size can be the number of CpG sites rather than the length of the molecule. For example, the cutoff value can be 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or more CpG sites.

[0727] At block 4040, for one free DNA molecule in the group of free DNA molecules, the methylation state at each site in the plurality of sites can be determined. The plurality of sites can include at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or more CpG sites. At least one of the plurality of sites can be methylated. Two sites in the plurality of sites can be spaced at least 160nt, 170nt, 180nt, 190nt, 200nt, 250nt or 500nt. The method can include sequencing the plurality of free DNA molecules to obtain sequence reads, and determining the methylation state of the site by measuring the characteristics of the nucleotides corresponding to the site and the nucleotides of the adjacent sites. For example, methylation can be generally determined as in U.S. Application No. 16 / 995,607.

[0728] At block 4050, a methylation pattern may be determined. The methylation pattern may indicate the methylation status at each site in the plurality of sites.

[0729] At block 4060, the methylation pattern may be compared to one or more reference patterns. Each of the one or more reference patterns may be determined for a particular tissue type. In some embodiments, the comparison may include determining the number of sites that match the reference pattern.

[0730] The reference pattern in one or more reference patterns can be determined by measuring the methylation density at each reference site in a plurality of reference sites using a DNA molecule from a reference tissue. The methylation density at each reference site in a plurality of reference sites can be compared with one or more threshold methylation densities. Each reference site in a plurality of reference sites can be identified as methylated, unmethylated or non-informative based on comparing methylation density with one or more threshold methylation densities, wherein a plurality of sites are identified as a plurality of reference sites that are methylated or unmethylated. Non-informative sites may include sites with a methylation density between two threshold methylation densities. For example, the methylation index of a non-informative site may be between 30 and 70 or in any other range as described herein.

[0731] At step 4070, the methylation pattern may be used to determine the tissue of origin of the free DNA molecule. The tissue of origin may be the placenta. The tissue of origin may be fetal or maternal. The method may include determining the tissue of origin as the reference tissue when the methylation pattern matches the reference pattern, and using Fig. 22 The descriptions are similar. A match may refer to an exact match. In some embodiments, determining that the tissue of origin is a reference tissue may occur when the methylation pattern matches a reference pattern at a particular percentage of sites. For example, the methylation pattern may match a reference pattern at least 60%, 70%, 80%, 85%, 90%, 95%, 97% or more of the sites.

[0732] The method may include determining the tissue of origin by determining a similarity score using a comparison of the methylation pattern with a first reference methylation pattern from a first reference tissue in a plurality of reference tissues. The similarity score may be calculated using the methylation state matching method described herein or the Beta (β) distribution probability model. The similarity score may be compared to a threshold. The tissue of origin may be determined to be the first reference tissue when the similarity score exceeds the threshold. The similarity score may be a first similarity score. The method may further include calculating a threshold by determining a second similarity score using a comparison of the methylation pattern with a second reference methylation pattern from a second reference tissue in a plurality of reference tissues. The first reference tissue and the second reference tissue may be different tissues. The threshold may be a second similarity score. The first reference tissue may have the highest similarity score compared to all other reference tissues.

[0733] The first reference methylation pattern may include a first subset of sites having at least a first methylation probability for a first reference tissue. For example, the first subset of sites may be sites that are considered methylated or generally considered methylated. The first reference methylation pattern may include a second subset of sites that have at most a second methylation probability for a first reference tissue. For example, the second subset of sites may be sites that are considered unmethylated or generally considered unmethylated. Determining a similarity score may include increasing a similarity score when one of the multiple sites is methylated and the site in the multiple sites is in the first subset of sites, and reducing a similarity score when one of the multiple sites is methylated and the site in the multiple sites is in the second subset of sites. The similarity score may be determined to be similar to the methylation state matching approach described herein.

[0734] The first reference methylation pattern includes multiple sites, wherein each site in the multiple sites is characterized by a methylation probability and an unmethylated probability for a first reference tissue. For each site in the multiple sites, a similarity score can be determined by determining the probability in the reference tissue corresponding to the methylation state of the site in the free DNA molecule. The similarity score can be determined by calculating the product of multiple probabilities. The product can be a similarity score. The probability can be determined by beta (β) distribution, similar to the approach described herein.

[0735] Method 4000 may further include determining the tissue of origin of each free DNA molecule in the set of free DNA molecules. This determination may include determining the methylation state at each site in a plurality of corresponding sites, wherein the plurality of corresponding sites correspond to free DNA molecules. Determining the tissue of origin may further include determining a methylation pattern. Additionally, determining the tissue of origin may also include comparing the methylation pattern to at least one of the one or more reference patterns. In some embodiments, the comparison of the methylation pattern may be compared to Fig. 22 Similar to the accompanying description. Fig. 22 In , placenta, liver, blood cells, and colon are examples of reference tissues with illustrated reference patterns. Fig.38 Hematopoietic cells are shown as another example of reference tissue.

[0736] In some embodiments, the amount of free DNA molecules corresponding to each tissue of origin can be determined. Each tissue of origin may include each reference tissue in a plurality of reference tissues. The contribution fraction of the tissue of origin can be determined using the measurement of free DNA molecules corresponding to each tissue of origin. For example, the tissue of origin may be placenta. Other tissues of origin may include hematopoietic cells and liver. For example, the contribution fraction of the placenta can be determined by dividing the amount of free DNA molecules by the total free DNA molecules corresponding to all tissues of origin. In some embodiments, the fraction calculated by dividing the amount of free DNA molecules by the total free DNA molecules can be related to the contribution fraction via a function or a set of calibration data points. Both the function and the set of calibration data points can be determined by multiple calibration samples with known contribution fractions of tissues of origin. Each calibration data point can specify the contribution fraction corresponding to the calibration value of the fraction. The function can represent a linear or nonlinear fit of the calibration data points and can relate the contribution fraction to the fraction of the tissue of origin or other parameters related to the tissue of origin. The embodiment of determining the contribution fraction can be used in conjunction with the already used Fig.39A and Fig.39B The described embodiments are similar.

[0737] A machine learning model can be used to determine tissue of origin. The model can be trained by receiving a plurality of training methylation patterns, each training methylation pattern having a methylation state at one or more of a plurality of sites, each training methylation pattern being determined by a DNA molecule from a known tissue. Each molecule from a known tissue can be cellular DNA. Training can include storing a plurality of training samples, each training sample including one of a plurality of training methylation patterns and a label indicating a known tissue corresponding to the training methylation pattern. Training can include using the plurality of training samples to optimize parameters of the model based on an output of the model that matches or does not match corresponding labels when the plurality of training methylation patterns are input into the model. The parameters can include a first parameter indicating whether one of a plurality of sites has the same methylation state as another of a plurality of sites. For example, the model can be used with Fig.24 The parameters may include a second parameter indicating the distance between each of the plurality of sites. In some embodiments, the machine learning model may not require alignment of the methylation sites to a reference genome. The output of the model may specify an organization corresponding to the input methylation pattern.

[0738] The machine learning model may be a convolutional neural network (CNN) or any model described herein. Models may include, but are not limited to, linear regression, logistic regression, deep recurrent neural networks (e.g., long short-term memory, LSTM), Bayesian classifiers, hidden Markov models (HMM), linear discriminant analysis (LDA), k-means clustering, density-based spatial clustering of applications with noise (DBSCAN), random forest algorithms, and support vector machines (SVM).

[0739] Affinity can be determined by method 4000. The tissue of origin can be a fetus. The method can further include aligning one of the sequence reads with a first region of a reference genome, the first region including a plurality of sites corresponding to alleles, the plurality of sites including a threshold number of sites, determining a first haplotype using the corresponding alleles present at each site in the plurality of sites, comparing the first haplotype with a second haplotype corresponding to the male individual, and using the comparison to determine a classification of the likelihood that the male individual is the father of the fetus. If the haplotypes match, the male individual may be considered the father, or if the haplotypes do not match, the male individual may not be considered the father. In some embodiments, the first haplotype can be compared to two haplotypes of the male individual.

[0740] In an embodiment, when the tissue of origin is a fetus, affinity can be tested by comparing a sequence read in the sequence read with the first district of the reference genome. The first district may include a plurality of sites corresponding to the first allele. A plurality of sites may include a threshold number of sites. The threshold number of sites may be 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or more sites. The alleles at each site in the plurality of sites may be compared with the alleles at the corresponding sites in the genome of the male individual. The classification of the possibility of the male individual being the father of the fetus can be used to compare. If the alleles of a specific number or percentage match, the male individual may be considered as the father, and if the alleles less than the number or percentage match, the male individual may not be considered as the father. The cut-off percentage may be 100%, 90%, 80% or 70%.

[0741] In some embodiments, haplotypes can be determined. The method may include comparing the sequence reads corresponding to the free DNA molecules with the reference genome for each free DNA molecule in the group of free DNA molecules. The sequence reads can be identified as corresponding to the haplotypes present in women. The haplotypes present in women can be known from genotyping women. In some embodiments, the haplotypes of women can be known by analyzing the DNA fragment concentrations of the haplotypes in the biological samples from women. The tissue of origin can be determined to be fetal using the methylation pattern. The haplotype can be determined to be a maternally inherited fetal haplotype.

[0742] Inheritance of haplotypes can be determined using methylation of reference tissues rather than using a known methylation profile such as the methylation profile associated with an inscribed locus. A match or similarity score of a methylation pattern to a reference pattern can exclude knowledge of whether a given allele or locus is methylated based on the parent from which it was inherited.

[0743] Haplotypes may be identified as carrying pathogenic genetic mutations or variations. Identifying haplotypes as carrying pathogenic genetic mutations may include identifying genetic mutations or variations in a first sequence read. Genetic variations may include single nucleotide differences, deletions, or insertions. A first methylation degree in a second sequence read corresponding to a first genomic position within a first distance of a first sequence read may be measured. A second methylation degree in a third sequence read corresponding to a second genomic position within a second distance of a first sequence read may also be measured. The first distance may be 100nt, 200nt, 300nt, 400nt, 500nt, 600nt, 700nt, 800nt, 900nt, 1knt, 2knt, 5knt, or 10knt. The second sequence read and the third sequence read may be on the same chromosome arm as the first sequence read. The first methylation degree and the second methylation degree may be associated with a genetic mutation or variation. The first methylation degree and the second methylation degree may be greater than one or two threshold degrees associated with a genetic mutation or variation. The threshold degree may be determined using individuals known to have or not have a genetic mutation or variation. The method may comprise classifying a fetus as likely to have a disease caused by a genetic mutation or variation.

[0744] The fetal-specific methylation pattern may be determined. The method may include, for each free DNA molecule in the group of free DNA molecules, comparing the sequence reads corresponding to the free DNA molecule with the reference genome. The method may include identifying the sequence read as corresponding to a region. The region may be determined by receiving a plurality of fetal sequence reads corresponding to a plurality of fetal DNA molecules from fetal tissue. The method may include receiving a plurality of maternal sequence reads corresponding to a plurality of maternal DNA molecules. The method may include determining the fetal methylation state at each methylation site in a plurality of methylation sites within the region for each fetal sequence read in a plurality of fetal sequence reads. The method may include determining the maternal methylation state at each methylation site in a plurality of methylation sites for each maternal sequence read in a plurality of maternal sequence reads.

[0745] A method for determining a fetal-specific methylation pattern may include determining the value of a parameter that characterizes the amount of sites where the fetal methylation state is different from the maternal methylation state. The method may include comparing the value of the parameter to a threshold value. The parameter may be the ratio of sites that differ between fetal DNA molecules and maternal DNA molecules. The ratio may be a mismatch score as described herein. The threshold may indicate the lowest level of mismatch scores and may be 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or greater. In some embodiments, the threshold may represent the average mismatch score of maternal or fetal DNA molecules. The method may include determining that the value of the parameter exceeds the threshold value. In some embodiments, a certain percentage of the value of the parameter of the maternal or fetal DNA molecules may be required to exceed the threshold value. For example, the percentage may be 50%, 60%, 70%, 80%, 90% or greater. In some embodiments, a certain percentage of the fetal DNA molecules corresponding to the region may be required to have a fetal-specific methylation pattern. For example, the percentage may be 40%, 50%, 60%, 70%, 80% or greater. This method may be used with Fig.25 The method described is similar.

[0746] The method may comprise enriching the biological sample from the tissue of origin to obtain free DNA molecules. Enriching the biological sample may comprise selecting and amplifying the group of free DNA molecules. Enrichment may comprise size-based selection as described herein. In some embodiments, enrichment may comprise selection based on methylation patterns. For example, capture and sequencing based on methyl-CpG binding domains (MBDs) may be used. Free DNA may be incubated with a tagged MBD protein that can bind to methylated cytosine. Subsequently, the protein-DNA complex may be precipitated using antibodies coupled to magnetic beads. DNA molecules with more methylated CpG sites may be preferentially enriched for downstream analysis.

[0747] III. Changes of long free DNA fragments with gestational age

[0748] The amount of long free DNA fragments can vary with gestational age. Long free DNA fragments can be used to determine gestational age. In addition, compared with shorter free DNA fragments, long free DNA fragments can be more abundant in specific terminal motifs, and the relative amount of specific terminal motifs can vary with gestational age. The amount of terminal motifs can also be used to determine gestational age. The deviation of the gestational age determined using long free DNA fragments and the gestational age determined via other clinical techniques can indicate pregnancy-related disorders. In certain embodiments, long free DNA fragments can be used to determine the possibility of pregnancy-related disorders without having to determine gestational age.

[0749] A. Size analysis of fetal and maternal DNA

[0750] Plasma DNA from two pregnant women in early pregnancy (gestational age: 13 weeks), two pregnant women in mid-pregnancy (gestational age: 21-22 weeks), and five pregnant women in late pregnancy (gestational age: 38 weeks) were sequenced using single-molecule real-time (SMRT) sequencing (PacBio). For each case, a median of 176 million (range: 49 million-685 million) subreads were obtained, of which 128 million (range: 35 million-507 million) subreads could be aligned to the human reference genome (hg19). Each molecule in the SMRT well was sequenced an average of 107 times. A median of 965,308 (range: 251,686-2,871,525) high-quality circular common sequencing (CCS) reads were defined as CCS reads with at least 3 subreads and could be used for downstream analysis.

[0751] All sequenced molecules from samples obtained from each trimester were combined for size analysis. A total of 1.94 million, 5.09 million, and 4.45 million free DNA molecules were present in maternal plasma samples from early, mid, and late pregnancy, respectively.

[0752] Fig.41A and Fig.41B The size distribution of free DNA molecules in the size range of 0 kb to 5 kb from maternal plasma samples from early pregnancy, mid-pregnancy, and late pregnancy is shown. The x-axis shows the size. The y-axis shows the frequency. Fig.41A , plotting the size distribution on the y-axis in the range of 0 kb to 5 kb on a linear scale, and for Fig.41B The size distribution of the y-axis is plotted on a logarithmic scale in the range of 0 kb to 5 kb. Plasma DNA from all three trimesters was Fig.41A The expected main peak at 166 bp is shown in Fig.41B Shown in FIG. 1 is a series of major peaks appearing in a periodic pattern extending to molecules in the 1 kb and 2 kb range.

[0753] Fig.42Table showing the proportion of long plasma DNA molecules in different gestational periods. The first column shows the gestational age associated with the plasma sample. The second column shows the proportion of DNA molecules longer than 500bp. The third column shows the proportion of DNA molecules longer than 1kb. Compared with early pregnancy and mid-pregnancy, the frequency of plasma DNA molecules of 500bp or greater than 500bp in late pregnancy increases. The proportion of long plasma DNA molecules greater than 500bp in early pregnancy, mid-pregnancy and late pregnancy is 15.8%, 16.1% and 32.3%, respectively. The proportion of long plasma DNA molecules greater than 1kb in early pregnancy, mid-pregnancy and late pregnancy is 11.3%, 10.6% and 21.4%, respectively. When the long free DNA molecules in maternal plasma in early pregnancy and mid-pregnancy show similar proportions, the proportion of long DNA molecules in maternal plasma in late pregnancy is about twice the aforementioned long DNA molecule proportion.

[0754] For all maternal plasma DNA samples analyzed in this public case, DNA extracted from paired maternal buffy coat and fetal samples was genotyped on the Infinium Omni2.5Exome-8 Beadchip on the iScan system (Illumina) as an array hybridization-based genotyping method. Depending on whether the case is from early pregnancy, mid-pregnancy or late pregnancy, fetal samples are obtained by chorionic villus sampling, amniocentesis or placental sampling, respectively. For each case, a median of 203,647 informative single nucleotide polymorphisms (SNPs) were identified, in which the mother was homozygous and the fetus was heterozygous. When the sequenced DNA molecules from each pregnancy period for all cases were combined together, we identified a total of 1,362, 2,984 and 6,082 DNA molecules covering fetal-specific alleles in early pregnancy, mid-pregnancy and late pregnancy, respectively. On the other hand, for each case, a median of 210,820 informative SNPs were identified where the mother was heterozygous and the fetus was homozygous. We identified a total of 30,574, 65,258, and 78,346 DNA molecules covering maternal-specific alleles in early, mid, and late pregnancy, respectively. Among all maternal plasma samples, the median fetal DNA fraction determined from sequencing data of DNA molecules ≤600 bp was 15.6% (range 7.6%-26.7%).

[0755] Fig.43A and Fig.43B The size distribution of DNA molecules covering fetal-specific alleles from maternal plasma in early pregnancy, mid-pregnancy, and late pregnancy is shown. The x-axis shows size. The y-axis shows frequency. Fig.43A , plotting the size distribution on the y-axis in the range of 0 kb to 3 kb on a linear scale, and for Fig.43B, plotting the size distribution on the y-axis ranging from 0 kb to 3 kb on a logarithmic scale.

[0756] Fig.44A and Fig.44B The size distribution of DNA molecules covering maternal-specific alleles from maternal plasma in early pregnancy, mid-pregnancy, and late pregnancy is shown. The x-axis shows size. The y-axis shows frequency. Fig.44A , plotting the size distribution on the y-axis in the range of 0 kb to 3 kb on a linear scale, and for Fig.44B , plotting the size distribution on the y-axis ranging from 0 kb to 3 kb on a logarithmic scale.

[0757] like FIG. 43A to FIG. 44B As shown in , plasma DNA molecules covering fetal-specific alleles and plasma DNA molecules covering maternal-specific alleles from all three trimesters exhibit a long-tail distribution, indicating that long DNA molecules derived from both fetal and maternal sources are present in all three trimesters.

[0758] Fig.45 The table shows the proportion of long fetal and maternal plasma DNA molecules at different gestational stages. The first column shows the gestational age associated with the plasma sample. The second column shows the proportion of fetal DNA molecules longer than 500bp. The third column shows the proportion of maternal DNA molecules longer than 500bp. The fourth column shows the proportion of fetal DNA molecules longer than 1kb. The fifth column shows the proportion of maternal DNA molecules longer than 1kb. Among the pool of DNA molecules in maternal plasma, DNA molecules covering fetal-specific alleles (of placental origin) have a smaller proportion of long DNA molecules than DNA molecules covering maternal-specific alleles. The proportion of long plasma DNA molecules covering fetal-specific alleles with a size greater than 500bp in early pregnancy, mid-pregnancy, and late pregnancy were 19.8%, 23.2%, and 31.7%, respectively. The proportion of long plasma DNA molecules covering fetal-specific alleles with a size greater than 1kb in early pregnancy, mid-pregnancy, and late pregnancy were 15.2%, 16.5%, and 19.9%, respectively.

[0759] Regardless of the fact that there is a smaller proportion of long plasma DNA molecules in early and mid-pregnancy maternal plasma compared to late pregnancy maternal plasma, and that fetal DNA molecules contain fewer long DNA molecules in all three trimesters, the methods described in our previous publications and this publication allow us to analyze a significant proportion of long plasma DNA molecules that were previously impossible to analyze using short-read sequencing technologies. In addition, we can use different size selection strategies including but not limited to electrophoresis, chromatography, and bead-based methods to enrich long DNA fragments in plasma samples.

[0760] Fig.46A , Fig.46B and Fig.46C Graph showing the proportion of fetal-specific plasma DNA fragments in specific size ranges at different gestational ages. Gestational age of the pregnancies evaluated was confirmed by dating ultrasound. Fig.46A Results are shown for DNA fragments less than or equal to 150 bp. Fig.46B Results for DNA fragments ranging from 150 to 600 bp are shown. Fig.46C Results are shown for DNA fragments greater than or equal to 600 bp. The graph has the fetal-specific fragment ratio on the y-axis and gestational age on the x-axis. As shown in the graph, the fetal-specific fragment ratio ( Fig.46B ) compared with the fetal specific fragments shorter than 150 bp ( Fig.46A ) and the proportion of fetal-specific fragments longer than 600 bp ( Fig.46C ) will achieve a specific discrimination power to distinguish late pregnancy samples from early pregnancy and mid-pregnancy samples. The ratio of fetal-specific fragments longer than 600bp can provide the best discrimination power. This conclusion is demonstrated by the following facts: when the ratio of fetal-specific fragments shorter than 150bp is used, the absolute minimum distance between the combined group of late pregnancy and early pregnancy and mid-pregnancy is 0.38, and when the ratio of fetal-specific fragments greater than 600bp is used, the corresponding value is 3.76. These results show that the use of long DNA molecules reflects pathophysiological states, which will be better than the use of short DNA molecules.

[0761] B. Plasma DNA end analysis

[0762] In addition to size, we determined the first nucleotide at the 5' end of both the Watson strand and the Crick strand for each sequenced DNA molecule. This analysis consisted of 4 types of ends, namely A-end, C-end, G-end and T-end. The percentage of plasma DNA molecules with specific ends from maternal plasma samples obtained from each pregnancy was calculated. The percentage of A-end, C-end, G-end and T-end at each fragment size was further analyzed.

[0763] Fig.47A , Fig.47B and Fig.47C Graph showing the ratio of base content at the 5' end of free DNA molecules from early, mid and late pregnancy maternal plasma across the fragment size range of 0 kb to 3 kb. Fig.47A Shows maternal plasma in early pregnancy. Fig.47B Shows mid-pregnancy maternal plasma. Fig.47CShow maternal plasma in late pregnancy. Base content in percentage form is shown on the y-axis. Fragment size in base pairs is shown on the x-axis. As seen in the diagram, the C-terminals across many size ranges (mostly less than 1kb) are presented and vary according to different size ranges used for early pregnancy, mid-pregnancy and late pregnancy samples. The plasma DNA end pattern of late pregnancy samples seems to be different from that of early pregnancy and mid-pregnancy samples. For example, the T-end and G-end curves under sizes within the range of 105bp to 172bp are mixed together, but they diverge in early pregnancy and mid-pregnancy samples. For longer fragments (e.g., greater than about 1kb), the C-end fragment is not the most abundant fragment. At about 1kb, the G-end fragment exceeds the C-end fragment, and then at about 2kb, the A-end fragment becomes more abundant than the G-end fragment.

[0764] Fig.48 It is a table of the terminal nucleotide base ratios among short free DNA molecules and long free DNA molecules from maternal plasma in early pregnancy, mid-pregnancy and late pregnancy. The first column shows the bases at the ends of the molecules. The second column shows the expected ratio points and species. The third column shows the terminal species ratio among fragments less than or equal to 500bp in maternal plasma in early pregnancy. The fourth column shows the terminal species ratio among fragments greater than 500bp in maternal plasma in early pregnancy. The fifth and sixth columns are similar to the third and fourth columns, respectively, except that mid-pregnancy maternal plasma replaces early pregnancy maternal plasma. The seventh and eighth columns are similar to the third and fourth columns, respectively, except that late pregnancy maternal plasma replaces early pregnancy maternal plasma.

[0765] If the free DNA fragmentation is completely random, the terminal nucleotide base ratio should reflect the composition of the human genome, which is 29.5% A, 29.5% T, 20.5% C and 20.5% G, as shown in Figure 2. Fig.48 As shown in the second column of . In contrast to random fragmentation, the 5' ends of short free DNA molecules ≤ 500 bp showed considerable overrepresentation of the C terminus (30.4%, 30.4% and 31.3% for maternal plasma in early, mid and late pregnancy, respectively), slight overrepresentation of the G terminus (27.4%, 26.9% and 25.3% for early, mid and late pregnancy, respectively), underrepresentation of the A terminus (19.8%, 19.4% and 19.3% for early, mid and late pregnancy, respectively), and underrepresentation of the T terminus (22.4%, 23.3% and 24.1% for early, mid and late pregnancy, respectively).

[0766] However, when compared to short free DNA molecules, long free DNA molecules of >500 bp showed a considerable increase in the proportion of A-termini (29.6%, 26.0% and 26.7% for maternal plasma in early, mid and late pregnancy, respectively), a slight increase in the proportion of G-termini (31.0%, 29.5% and 29.9% for early, mid and late pregnancy, respectively), a considerable decrease in the proportion of T-termini (13.9%, 16.9% and 16.4% for early, mid and late pregnancy, respectively) and a slight decrease in the proportion of C-termini (25.5%, 27.5% and 27.1% for early, mid and late pregnancy, respectively).

[0767] Fig.49 The table shows the terminal nucleotide base ratios of short and long free DNA molecules covering fetal-specific alleles from maternal plasma in early, mid and late pregnancy. Fig.50 It is a table of the terminal nucleotide base ratios among short free DNA molecules and long free DNA molecules covering maternal specific alleles from maternal plasma in early pregnancy, mid-pregnancy and late pregnancy. The first column shows the bases at the ends of the molecules. The second column shows the expected ratio points and species. The third column shows the terminal species ratio among fragments less than or equal to 500bp of maternal plasma in early pregnancy. The fourth column shows the terminal species ratio among fragments greater than 500bp of maternal plasma in early pregnancy. The fifth and sixth columns are similar to the third and fourth columns, respectively, except that mid-pregnancy maternal plasma replaces early pregnancy maternal plasma. The seventh and eighth columns are similar to the third and fourth columns, respectively, except that late pregnancy maternal plasma replaces early pregnancy maternal plasma. Fig.49 and Fig.50 It was shown that the difference in the terminal nucleotide base ratio among short free DNA molecules and long free DNA molecules remained unchanged even when we examined the DNA molecules covering the fetal-specific allele and the DNA molecules covering the maternal-specific allele separately.

[0768] Fig.51 A hierarchical cluster analysis of short plasma free DNA molecules and long plasma free DNA molecules using 256 4-mer terminal motifs is shown. Each column indicates the sample used to analyze the terminal motif frequency based on short fragments (represented by cyan in the first column) and long fragments (represented by yellow in the first column), respectively. Starting from the second column, each column indicates the type of terminal motif. The terminal motif frequency is presented in a series of color gradients according to the column standardized frequency (z-score) (i.e., the standard deviation value below or above the average frequency in the entire sample). The redder the color, the higher the terminal motif frequency, while the bluer the color, the lower the terminal motif frequency.

[0769] exist Fig.51 In, we characterize short free DNA molecules and long free DNA molecules by analyzing their 4-mer terminal motif profiles. We determine the first 4 nucleotide sequences (4-mer motifs) at the 5' ends of Watson strands and Crick strands for each sequenced DNA molecule. For each maternal plasma sample, the frequency of each plasma DNA terminal motif of short plasma DNA molecules (≤500bp) and long plasma DNA molecules (>500bp) is calculated respectively. The hierarchical cluster analysis based on 256 4-mer terminal motif frequencies shows that the terminal motif profiles of the long DNA molecules in the entire different maternal plasma samples form clusters different from the terminal motif profiles of the short DNA molecules. These results show that long DNA and short DNA have different fragmentation characteristics. In an embodiment, we will use the relative disturbance of these terminal motifs between long DNA molecules and short DNA molecules to indicate the free DNA contribution derived from, such as but not limited to, apoptosis and necrosis of cell death pathways. The activity through improvement from these cell death pathways may be related to pregnancy-related disorders and other disorders.

[0770] Fig.52A and Fig.52B Shown is a principal component analysis (PCA) performed using the 4-mer terminal motif profiles for classification analysis. Fig.52A Short free DNA molecules (≤500 bp) from different trimesters are shown. Fig.52B Long free DNA molecules (>500 bp) from maternal plasma samples from different trimesters are shown. The percentages in brackets on the x-axis and y-axis represent the amount of variability described by the corresponding component. Each blue dot represents a maternal plasma sample from early pregnancy. Each yellow dot represents a maternal plasma sample from mid-pregnancy. Each red dot represents a maternal plasma sample from late pregnancy. The ellipse represents the 95% confidence level for grouping data points from a specific trimester. Fig.52A ) (also described in U.S. Application No. 15 / 787,050), long free DNA molecules ( Fig.52B ) yields clearer spacing between early, mid, and late pregnancy maternal plasma samples. In embodiments, we can utilize the terminal motif profiles of long plasma DNA molecules alone or in combination with other maternal plasma DNA features including but not limited to methylation level and size for molecular gestational age assessment.

[0771] For example, we used a neural network to train a model for predicting gestational age based on 256 terminal motifs, total methylation levels, and the proportion of fragments with a size of ≥600bp. The output variables are 1, 2, and 3, representing early pregnancy, mid-pregnancy, and late pregnancy. The input variables include 256 terminal motifs, total methylation levels, and the proportion of fragments with a size of ≥600bp. We used the leave-one-out method to evaluate the performance of predicting gestational age. For a data set consisting of 9 samples, the leave-one-out method was performed in a manner where one sample was selected as a test sample and the remaining 8 samples were used to train a model based on a neural network. Based on the established model, such test samples were determined to be 1, 2, or 3. Subsequently, we repeated this method for other samples that had not yet been tested. For such training and testing processes, we repeated a total of 9 times. By comparing those test results with clinical information about gestational age, 8 of the 9 samples (89%) were appropriately predicted in terms of gestational age. In another embodiment, the analysis may be performed using, for example but not limited to, Bayesian theorem, logistic regression, multiple regression and support vector machines, random forest analysis, classification and regression trees (CART), K nearest neighbor algorithms.

[0772] Next, all sequenced molecules from samples obtained from each trimester were combined for downstream terminal motif analysis. The 256 terminal motifs were ranked according to their frequency among short and long plasma DNA molecules.

[0773] Figure 53 to Figure 58 Table of the 25 terminal motifs with the highest frequency for DNA fragments of specific lengths (shorter or longer than 500 bp) and at different gestational ages. Fig.53 , Fig.54 and Fig.55 This is a table of terminal motifs in short fragments (<500bp) sorted by terminal motif level. Figure 53 to Figure 55 In the figure, the first column shows the terminal motifs. The second column shows the motif frequency rank in the short fragments. The third column shows the motif frequency rank in the long fragments. The fourth column shows the motif frequency in the short fragments. The fifth column shows the motif frequency in the long fragments. The sixth column shows the fold change (the motif frequency in the short fragments divided by the motif frequency in the long fragments).

[0774] Fig.56 , Fig.57 and Fig.58 This is a table of terminal motifs in long fragments (>500bp) sorted by terminal motif level. Figure 56 to Figure 58 In the figure, the first column shows the terminal motifs. The second column shows the motif frequency rank in the long fragments. The third column shows the motif frequency rank in the short fragments. The fourth column shows the motif frequency in the long fragments. The fifth column shows the motif frequency in the short fragments. The sixth column shows the fold change (the motif frequency in the long fragment divided by the motif frequency in the short fragment).

[0775] Fig.53 and Fig.56 From samples taken early in pregnancy. Fig.54 and Fig.57 From a second trimester sample. Fig.55 and Fig.58 From late pregnancy samples.

[0776] Among the top 25 most frequent terminal motifs in short plasma DNA molecules, 11 of them started with a CC dinucleotide. Terminal motifs starting with CC together accounted for 14.66%, 14.66%, and 15.13% of short plasma DNA terminal motifs in early, mid, and late pregnancy maternal plasma, respectively. Among the top 25 most frequent terminal motifs in long plasma DNA molecules, 4-mer motifs ending with a TT dinucleotide accounted for 9 of them in mid and late pregnancy maternal plasma and 10 of them in early pregnancy maternal plasma.

[0777] We determined the dinucleotide sequence of the third nucleotide (X) and the fourth nucleotide (Y) at the 5' end of both the Watson strand and the Crick strand for each sequenced DNA molecule. X and Y can be one of the four nucleotide bases in DNA. There are 16 possible NNXY motifs, namely NNAA, NNAT, NNAG, NNAC, NNTA, NNTT, NNTG, NNTC, NNGA, NNGT, NNGG, NNGC, NNCA, NNCT, NNCG and NNCC.

[0778] Fig.59A , Fig.59B and Fig.59C Motif frequency scatter plot showing 16 NNXY motifs among short and long plasma DNA molecules. Fig.59A Displays early pregnancy results. Fig.59B Displays second trimester results. Fig.59C Show the results of late pregnancy. The motif frequency of long fragments is shown on the y-axis. The motif frequency of short fragments is shown on the x-axis. Each circle represents one of 16 NNXY motifs. The dotted line in each scatter plot refers to the 1.5-fold increase (upper line) and reduction (lower line) of the motif frequency in long plasma DNA molecules (>500bp) compared to short plasma DNA molecules (≤500bp). The circle outside the shaded area represents the motif with a change multiple>1.5.

[0779] In all three trimesters (Figure 11), while the ends of short plasma DNA molecules showed a high frequency of 4-mer motifs starting with CC dinucleotides (CCNN) (Jiang et al. Cancer Discov 2020;10(5):664-673; Chan et al. Am J Human Genetics 2020;107(5):882-894), the ends of long plasma DNA molecules showed a >1.5-fold increase in the frequency of 4-mer motifs ending with TT (NNTT). In early, mid, and late pregnancy maternal plasma, the NNTT motif accounted for 18.94%, 15.22%, and 15.30% of the long plasma DNA terminal motifs, respectively. In contrast, in early, mid, and late pregnancy maternal plasma, the NNTT motif accounted for only 9.53%, 9.29%, and 8.91% of the short plasma DNA terminal motifs, respectively.

[0780] As previously reported by Han et al., free DNA newly released from dead cells into plasma is enriched for A-terminal fragments of >150bp. It was found that DNA fragmentation factor β (DFFB), which is the main intracellular nuclease involved in DNA fragmentation during apoptosis, is responsible for generating the fragments (Han et al. American Journal of Human Genetics 2020; 106: 202-214). In the present disclosure, we have shown that long free DNA molecules of >500bp are also enriched for A-terminal fragments, indicating that DFFB may also be responsible for generating these fragments. In normal pregnancy, trophoblast apoptosis increases with the development of pregnancy (Sharp et al. American Journal of Reproductive Immunology (Am J Reprod Immuno) 2010; 64 (3): 159-69). In fact, our finding that the proportion of long DNA molecules covering fetal-specific alleles increases with the development of pregnancy may reflect that trophoblast apoptosis increases with the development of pregnancy.

[0781] In an embodiment, we can use the methods described herein to analyze long free DNA molecules in maternal plasma for prediction, screening, and development monitoring of placenta-related pregnancy complications, including but not limited to preeclampsia, intrauterine growth restriction (IUGR), premature birth, and gestational trophoblastic disease. Increased levels of trophoblastic apoptosis have been reported in placenta-related pregnancy complications, such as preeclampsia (Leung et al. Am J Obstet Gynecol 2001; 184: 1249-1250), IUGR (Smith et al. Am J Obstet Gynecol 1997; 177: 1395-1401; Levy et al. Am J Obstet Gynecol 2002; 186: 1056-1061) and gestational trophoblastic disease. In addition, elevated levels of fetal DNA in maternal plasma have been reported in preeclampsia (Lo et al. Clinical Chem 1999; 45(2): 184-8; Smid et al. Ann NY Acad Sci 2001; 945: 132-7), IUGR (Sekizawa et al. Am J Obstet Gynecol 2003; 188: 480-4), and preterm birth (Leung et al. Lancet 1998; 352(9144): 1904-5). We hypothesize that in placenta-related pregnancy complications, the proportion of long free DNA molecules of placental origin in maternal plasma samples is increased due to increased placental cell apoptosis. Therefore, long free DNA molecules of placental origin themselves and long DNA markers including but not limited to A-terminal fragments and NNTT motifs may serve as biomarkers of placental cell apoptosis.

[0782] Although single nucleotide motifs and 4 nucleotide motifs were used in the above analysis, in other embodiments motifs of other lengths, such as 2, 3, 5, 6, 7, 8, 9, 10 or longer, may be used.

[0783] C. Instance methods

[0784] Long free DNA fragments can be used to determine the gestational age of women with a fetus. The amount of long free DNA fragments varies with gestational age and can be used to determine gestational age. The terminal motifs of free DNA fragments also vary with gestational age and can be used to determine gestational age. When the gestational age determined using long free DNA fragments significantly deviates from the gestational age determined via other clinical techniques, the pregnant woman and / or fetus can be considered to suffer from pregnancy-related disorders. In certain embodiments, it may not be necessary to determine the gestational age to determine the possibility of pregnancy-related disorders.

[0785] 1. Gestational age

[0786] Fig.60A method 6000 of analyzing a biological sample obtained from a female pregnant with a fetus is shown. The gestational age can be determined and can be used to classify the likelihood of a pregnancy-related disorder. The biological sample can contain a plurality of free DNA molecules from the fetus and the female.

[0787] Sequence reads corresponding to multiple free DNA molecules may be accepted.In some embodiments, sequencing to obtain sequence reads may be performed.

[0788] At block 6020, the size of the plurality of free DNA molecules may be measured. The size may be compared with Fig.21 Size can be measured using sequence reads.

[0789] At block 6030, a first amount of free DNA molecules having a size greater than a cutoff value may be measured. The amount may be the number, total length, or mass of the free DNA molecules.

[0790] At block 6040, the value of the normalization parameter may be generated using the first quantity. The value of the normalization parameter may be a first quantity normalized by the total number of free DNA molecules, by the number of free DNA molecules from the fetus or the mother, or by the number of DNA molecules from a particular region. Figure 46A-C As described, the normalization parameter can be the fetal-specific fragment ratio.

[0791] At block 6050, the value of the normalization parameter may be compared to one or more calibration data points. Each calibration data point may specify a gestational age corresponding to the calibration value of the normalization parameter. For example, a gestational age at a particular gestational stage or a particular number of weeks may correspond to the calibration value of the normalization parameter. One or more calibration data points may be determined from a plurality of calibration samples having a known gestational age and comprising free DNA molecules having a size greater than a cutoff value. In some embodiments, the calibration data points are determined from functionally correlated gestational ages having values ​​of the normalization parameter.

[0792] At block 6060, the gestational age may be determined using the comparison. The gestational age may be considered the age corresponding to the calibration value that is closest to the value of the standardized parameter. In some embodiments, the gestational age may be considered the highest age corresponding to the calibration value that is lower than the value of the standardized parameter.

[0793] The method may further include determining a reference gestational age of the fetus using ultrasound or the date of the woman's last menstrual period. The method may also include comparing the gestational age to the reference gestational age. The method may further include determining a classification of the likelihood of a pregnancy-related condition using the comparison of the gestational age to the reference gestational age. For example, a difference between the gestational age and the reference gestational age may indicate a pregnancy-related condition. The difference may be a gestational age difference of a different gestational period or a minimum number of weeks (e.g., 1, 2, 3, 4, 5, 6, 7, or more weeks).

[0794] The method may further comprise the use of a terminal motif. For example, the method may comprise determining a first subsequence corresponding to at least one end of a free DNA molecule having a size greater than a cutoff value. The first amount may belong to free DNA molecules having a size greater than the cutoff value and having a first subsequence at one or more ends of the corresponding free DNA molecule. The first subsequence may be or comprise 1, 2, 3, 4, 5, or 6 nucleotides. For example, using Fig.52A and Fig.52B As described, terminal motifs can be used to determine gestational age via PCA analysis. Calibration samples with different terminal motifs and known gestational age and subjected to PCA analysis can be used. Other classification and regression algorithms such as linear discriminant analysis, logistic regression, support vector machines, linear regression, nonlinear regression, etc. can be used on the terminal motifs. Classification and regression algorithms can correlate gestational age with specific terminal motifs and / or specific size fragments.

[0795] The terminal motif can be used as shown in Figures 47-59 or Fig.94 Any motif discussed. The terminal motif rank or frequency can be compared with the terminal motif rank or frequency in a calibration sample from an individual with a known gestational age. Subsequently, the terminal motif rank or frequency can be used to determine the gestational age. The presence of a terminal motif at a rank or frequency that deviates from the rank or frequency determined from a reference sample with the same gestational age can indicate a pregnancy-related disorder.

[0796] Generating a value for a normalization parameter may include (a) normalizing a first amount by a total amount of free DNA molecules having a size greater than a cutoff value; (b) normalizing the first amount by a second amount of free DNA molecules having a size greater than the cutoff value and ending with a second subsequence, the second subsequence being different from the first subsequence, or (c) normalizing the first amount by a third amount of free DNA molecules having a size less than the cutoff value.

[0797] 2. Pregnancy-related conditions

[0798] Fig.61 A method 6100 of analyzing a biological sample obtained from a female pregnant with a fetus is shown. An embodiment may include the possibility of classifying pregnancy-related conditions without having to determine gestational age. The biological sample may include a plurality of free DNA molecules from the fetus and the female.

[0799] Sequence reads corresponding to multiple free DNA molecules may be accepted.In some embodiments, sequencing to obtain sequence reads may be performed.

[0800] At block 6120, the size of the plurality of free DNA molecules may be measured. The size may be compared with Fig.21 The size can be measured using the accepted sequence reads.

[0801] At block 6130, a first amount of free DNA molecules having a size greater than a cutoff value may be measured. The cutoff value may be greater than or equal to 200 nt. The cutoff value may be at least 500 nt, including 600 nt, 700 nt, 800 nt, 900 nt, 1 knt, 1.1 knt, 1.2 knt, 1.3 knt, 1.4 knt, 1.5 knt, 1.6 knt, 1.7 knt, 1.8 knt, 1.9 knt, or 2 knt. The cutoff value may be any cutoff value described herein for long free DNA molecules. The first amount may be a number or a frequency.

[0802] At block 6140, a first value of a normalization parameter may be generated using the first amount. Generating the value of the normalization parameter may include measuring a second amount of free DNA molecules having a size less than a cutoff value; and calculating a ratio of the first amount to the second amount. The cutoff value may be a first cutoff value. The second cutoff value may be less than the first cutoff value. The second amount may include free DNA molecules having a size less than the second cutoff value or the second amount may include all free DNA molecules in a plurality of free DNA molecules. The normalization parameter may be a measure of the frequency of long free DNA molecules.

[0803] At block 6150, a second value corresponding to an expected value of a standardized parameter for a healthy pregnancy may be obtained. The second value may depend on the gestational age of the fetus. The second value may be an expected value. In some embodiments, the second value may be a cutoff value that is distinguished from an abnormal value.

[0804] Obtaining the second value may include obtaining the second value from a calibration table of measurements of pregnant women with calibration values ​​of standardized parameters. The calibration table may be generated by obtaining a first table of gestational age with measurements of individual pregnant women. A second table of gestational age with calibration values ​​of standardized parameters may be obtained. The data in the first table and the second table may be from the same individual or different individuals. A calibration table of measurements with calibration values ​​may be generated from the first table and the second table. The calibration table may include a function of the calibration values ​​of the measurements.

[0805] The measurement result of the pregnant female individual can be the time since the last menstrual period or the image (e.g., ultrasound) feature of the pregnant female individual. The measurement result of the pregnant female individual can be the image feature of the pregnant female individual. For example, the image feature can include the length, size, appearance, or anatomical structure of the fetus of the female individual. The feature can include biometric measurements, such as top-hip length or femur length. The appearance of specific organs can be used, including the appearance of a four-chambered heart or the vertebrae on the spinal cord. Gestational age can be determined by a medical practitioner from an ultrasound image (e.g., Committee on Obstetric Practice et al., "Methods for estimating the due date", Committee Opinion, No. 700, May 2017).

[0806] In some embodiments, the machine learning model may associate one or more calibration data points with image features. The model may be trained by receiving a plurality of training images. Each training image may be from a female individual known to be free of pregnancy-related conditions or known not to suffer from pregnancy-related conditions. The female individual may have a range of gestational ages. Training may include storing a plurality of training samples from the female individual. Each training sample may include a known value of a standardized parameter associated with the training image. The model may be trained by optimizing the parameters of the model based on the output of the model of a matching or non-matching image with a known value of the standardized parameter using the plurality of training samples. The output of the model may specify a value of the standardized parameter corresponding to the image. A second value of the standardized parameter may be generated by inputting an image of a female into the machine learning model.

[0807] At block 6160, a deviation between the first value of the normalization parameter and the second value of the normalization parameter may be determined. The deviation may be a separation value.

[0808] At block 6170, the deviation may be used to determine a classification of the likelihood of a pregnancy-related condition. When the deviation exceeds a threshold, the pregnancy-related condition may be likely. The threshold may indicate a statistically significant difference. The threshold may indicate a difference of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100%.

[0809] Pregnancy-related disorders may include preeclampsia, intrauterine growth restriction, placenta accreta, preterm labor, hemolytic disease of the newborn, placental insufficiency, hydrops fetalis, fetal malformations, hemolysis, elevated liver enzymes and low platelet count (HELLP) syndrome, or systemic lupus erythematosus.

[0810] IV. Dimensional and terminal analysis for pregnancy-related disorders

[0811] The size and / or end analysis of long DNA molecules is used to determine the possibility of pre-eclampsia. The method can also be applied to other pregnancy-related conditions. DNA extracted from maternal plasma samples of four pregnant women diagnosed with pre-eclampsia was subjected to single molecule real-time (SMRT) sequencing (PacBio).

[0812] Fig.62 Table showing clinical information for four cases of preeclampsia. The first column shows the case number. The second column shows the gestational age in weeks when blood sampling was stopped. The third column shows the sex of the fetus. The fourth column shows clinical information about preeclampsia (PET).

[0813] M12804 is a case of severe preeclampsia (PET) with pre-existing IgA nephropathy. M12873 is a case of chronic hypertension with superimposed mild PET. M12876 is a case of severe late-onset PET. M12903 is a case of severe late-onset PET with intrauterine growth restriction (IUGR). Five normotensive late pregnancy maternal plasma samples were used as controls for subsequent analyses in the present disclosure.

[0814] For the four preeclampsia cases and five normotensive third trimester maternal plasma DNA samples analyzed in this publication, DNA extracted from paired maternal buffy coat and placenta samples were genotyped using the Infinium Omni2.5 Exome-8 Beadchip on the iScan system (Illumina).

[0815] The plasma DNA concentration of each sample was quantified by Qubit dsDNA high sensitivity assay performed using a Qubit fluorometer (Thermo Fisher Scientific). The mean plasma DNA concentrations for pre-eclampsia cases and late pregnancy cases were 95.4 ng / mL (range 52.1-153.8 ng / mL) plasma and 10.7 ng / mL (range 6.4-19.1 ng / mL) plasma, respectively. The mean plasma DNA concentration for pre-eclampsia cases was approximately 9 times higher than that for late pregnancy cases.

[0816] For preeclampsia and normotensive late pregnancy maternal plasma samples, the average fetal DNA fraction determined from sequencing data of DNA molecules ≤600 bp covering informative single nucleotide polymorphisms (SNPs) in which the mother is homozygous and the fetus is heterozygous was 22.6% (range 16.6%-25.7%) and 20.0% (range 15.6%-26.7%), respectively.

[0817] A. Dimensional Analysis

[0818] Size analysis was performed according to embodiments in the present disclosure on pre-eclampsia and normotensive late pregnancy maternal plasma samples. Figures 63A-63D and Figures 64A-64D The size distribution of plasma DNA molecules from preeclampsia and normotensive late pregnancy cases is shown. The x-axis shows size. The y-axis shows frequency. Figures 63A-63D , plot the size distribution on the x-axis in the range of 0 kb to 1 kb on a linear scale, and for Figures 64A-64D , plotting the size distribution on the x-axis ranging from 0 kb to 5 kb on a logarithmic scale. Fig.63A and Fig.64A Showing sample M12804. Fig.63B and Fig.64B Showing sample M12873. Fig.63C and Fig.64C Showing sample M12876. Fig.63D and Fig.64D Showing sample M12903.

[0819] The blue line represents the size distribution of all sequenced plasma DNA molecules from five normotensive late pregnancy cases combined. The red line represents the size distribution of sequenced plasma DNA molecules from a single preeclampsia case. Figures 63A-63D In the figure, the blue line is the line of the shorter peak at 200 bp and the line of the taller peak between 300 bp and 400 bp. Figures 64A-64D In the figure, the blue line corresponds to the higher peak at 1 kb.

[0820] In general, the plasma DNA size profile of patients with preeclampsia is shorter than that of normotensive pregnant women in late pregnancy, with an increased height of the 166-bp peak and an increased proportion of DNA molecules shorter than 166 bp ( Figures 63A-63D ). These changes were more pronounced in two severe preeclampsia cases, M12876 and M12903. The changes were even more dramatic in preeclampsia case M12903, which was associated with intrauterine growth restriction (IUGR).

[0821] Three of the four preeclampsia plasma samples showed a reduced proportion of long plasma DNA molecules with a size range of 200-5000 bp ( Figures 64B-64D). The proportions of long plasma DNA molecules >500 bp in M12873, M12876, and M12903 were 11.7%, 8.9%, and 4.5%, respectively, while the proportion of long plasma DNA molecules in the combined sequencing data from five normotensive late pregnancy cases was 32.3%. Compared with the combined sequencing data from the five normotensive late pregnancy cases, plasma samples from a case of severe preeclampsia (PET) with pre-existing IgA nephropathy (M12804) showed a reduced proportion of shorter DNA molecules less than 2000 bp, but an increased proportion of longer DNA molecules greater than 2000 bp ( Figure 2A ). The proportion of long plasma DNA molecules in M12804 was 34.9%.

[0822] Figures 65A-65D and Figures 66A-66D Shown are the size distributions of DNA molecules covering fetal-specific alleles from maternal plasma samples of preeclampsia and normotensive late pregnancy. Each of panels A to D shows a different preeclampsia sample. The x-axis shows size. Figures 65A-65D The y-axis shows the frequency and Figures 66A-66D The y-axis in shows the cumulative frequency. Figures 66A-66D The size is 0kb to 35kb.

[0823] The blue line in each figure represents the size distribution of all sequenced plasma DNA molecules covering fetal-specific alleles from five normotensive late pregnancy cases combined. The red line in each figure represents the size distribution of sequenced plasma DNA molecules covering fetal-specific alleles from a single preeclampsia case. Figures 65A-65D In the figure, the blue line is the line of the shorter peak at 200 bp and the line of the taller peak between 300 bp and 400 bp. Figures 66A-66D In the figure, the blue line corresponds to the line of the lower peak between 100 bp and 1000 bp.

[0824] Figures 67A-67D and Figures 68A-68D Shown are the size distributions of DNA molecules covering fetal-specific alleles from maternal plasma samples of preeclampsia and normotensive late pregnancy. Each of panels A to D shows a different preeclampsia sample. The x-axis shows size. Figures 67A-67D The y-axis shows the frequency and Figures 68A-68D The y-axis in shows the cumulative frequency. Figures 68A-68D The size is 0kb to 35kb.

[0825] The blue line in each figure represents the size distribution of all sequenced plasma DNA molecules covering maternal-specific alleles from five normotensive late pregnancy cases combined. The red line in each figure represents the size distribution of sequenced plasma DNA molecules covering maternal-specific alleles from a single preeclampsia case. Fig.67A In the figure, the blue line is the line of the higher peak at 200 bp and the line of the higher peak between 300 bp and 400 bp. Figures 67B-67D In the figure, the blue line is the line of the shorter peak at 200 bp. Fig.68A In the figure, the blue line corresponds to the higher peak between 1000 bp and 10000 bp. Figures 68B-68D In the figure, the blue line corresponds to the line of the lower peak between 100 bp and 1000 bp.

[0826] When compared with normotensive late pregnancy maternal plasma samples, plasma DNA shortening was observed in three of four preeclampsia plasma samples for DNA molecules containing fetal-specific alleles ( Figures 65B-65D and Figures 66B-66D ) and a DNA molecule encompassing the maternal-specific allele ( Figures 67B-67D and Figures 68B-68D The exception was case M12804, a severe PET case with pre-existing IgA nephropathy, which showed an increased proportion of shorter DNA molecules less than 1 kb and a decreased proportion of longer DNA molecules greater than 1 kb among those plasma DNA molecules encompassing the fetal-specific allele ( Fig.65A and Fig.66A Indeed, the plasma DNA molecules covering the maternal-specific allele in case M12804 showed an elongated size profile ( Fig.67A and Fig.68A ).

[0827] Fig.69A and Fig.69B Figure 2 shows the ratio of short DNA molecules covering (A) fetal-specific alleles and (B) maternal-specific alleles in maternal plasma samples from preeclampsia and normotensive mothers sequenced by PacBio SMRT sequencing. The y-axis shows the ratio of short DNA fragments <150 bp. The x-axis shows normal samples and PET samples.

[0828] In an embodiment, the ratio of short DNA molecules is defined as the percentage of maternal plasma DNA molecules less than 150bp in size. M12804 was excluded from this analysis because this case had pre-existing IgA nephropathy, but other samples did not have pre-existing IgA nephropathy. When compared with the normotensive control plasma sample group, the preeclampsia plasma sample group showed a significantly increased ratio of short DNA molecules covering fetal-specific alleles (P=0.036, Wilcoxon rank sum test) and a ratio of short DNA molecules covering maternal-specific alleles (P=0.036, Wilcoxon rank sum test).

[0829] Fig.70A and Fig.70B Figure 2 shows the proportion of short DNA molecules sequenced by (A) PacBio SMRT sequencing and (B) Illumina sequencing in maternal plasma samples from preeclampsia and normotensive women. The y-axis shows the proportion of short DNA fragments <150 bp.

[0830] In an embodiment, the proportion of short DNA molecules is defined as the percentage of maternal plasma DNA molecules with a size less than 150bp. M12804 was removed from this analysis because this case may show a different size profile compared to other preeclampsia cases in this group due to pre-existing IgA nephropathy in this case. When compared with the normotensive control plasma sample group (median: 12.1%; range: 8.5%-15.8%), the preeclampsia plasma sample group showed a significantly increased proportion of short DNA molecules (median: 28.0%; range: 25.8%-35.1%) (P=0.036, Wilcoxon rank-sum test). On the contrary, in the four preeclampsia and four gestational age-matched normotensive maternal plasma DNA sample groups previously subjected to bisulfite conversion and Illumina sequencing, the proportion of short DNA molecules in preeclampsia plasma and control plasma samples was not significantly different (P=0.340, Wilcoxon rank-sum test) ( Fig.70B ).

[0831] In some embodiments, we can use the 20% cutoff value of the short DNA molecule ratio in the maternal plasma sample sequenced by PacBio SMRT sequencing to determine whether the pregnant woman is at a high risk or low risk of suffering from preeclampsia. The maternal plasma sample with a short DNA molecule of a ratio higher than 20% will be determined to be at a high risk of suffering from preeclampsia, and the maternal plasma sample with a short DNA molecule of a ratio lower than 20% will be determined to be at a low risk of suffering from preeclampsia. In the case of using this cutoff value, both sensitivity and specificity are 100%. In some other embodiments, the cutoff value of the ratio of the short DNA molecule used may include but is not limited to 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, etc. In another embodiment, the short DNA molecule ratio in the maternal plasma sample will be used to monitor and assess the severity of preeclampsia during pregnancy.

[0832] In the Examples, the size ratio indicating the relative proportion of short DNA molecules to long DNA molecules was calculated for each sample using the following equation.

[0833]

[0834] Wherein P(50-150) indicates the proportion of sequenced plasma DNA molecules with a size range of 50 bp to 150 bp; and P(200-1000) indicates the proportion of sequenced plasma DNA molecules with a size range of 200 bp to 1000 bp.

[0835] Fig.71 Size ratio graph indicating the relative proportion of short DNA molecules to long DNA molecules in preeclampsia and normotensive maternal plasma samples sequenced by PacBio SMRT sequencing. The y-axis shows the size ratio. The x-axis shows the normal sample and the PET sample. The preeclampsia plasma sample group showed a significantly higher size ratio when compared to the normotensive control plasma sample group (P=0.016, Wilcoxon rank sum test).

[0836] In an embodiment, we can utilize the size overview generated by the long read sequencing platform including but not limited to PacBio SMRT sequencing and Oxford Nanopore sequencing to predict the development and severity of preeclampsia in pregnant women. In certain embodiments, we can monitor the development of preeclampsia and the development of severe preeclampsia characteristics including but not limited to liver and kidney injury by analyzing the size overview of plasma DNA molecules. In certain embodiments, the size parameter used in the analysis may include but not limited to the size ratio of short DNA molecules or long DNA molecule ratio and the relative ratio of indicating short DNA molecules and long DNA molecules. The cutoff value for determining short DNA and long DNA categories may include but not limited to 150bp, 180bp, 200bp, 250bp, 300bp, 350bp, 400bp, 450bp, 500bp, 550bp, 600bp, 650bp, 700bp, 750bp, 800bp, 850bp, 900bp, 950bp, 1kb etc. The size range used to determine the size ratio of short molecules to long molecules may include, but is not limited to, 50-150 bp, 50-166 bp, 50-200 bp, 200-400 bp, 200-1000 bp, 200-5000 bp, or other combinations.

[0837] Size end analysis can include the use of Fig.61 The method described in method 6100.

[0838] B. Fragment end analysis

[0839] Fragment end analysis was performed on pre-eclampsia and normotensive late pregnancy maternal plasma samples according to the embodiments in the present disclosure. The first nucleotide at the 5' end of both the Watson strand and the Crick strand was determined for each sequenced plasma DNA molecule. The ratio of T-terminal, C-terminal, A-terminal and G-terminal fragments was determined for each plasma DNA sample.

[0840] Figures 72A-72D Shown are the different end ratios of plasma DNA molecules in preeclampsia and normotensive maternal plasma samples sequenced by PacBio SMRT sequencing. The x-axis shows normal late pregnancy samples and PET samples. The y-axis shows the given end ratio. Fig.72A Displays the T-end ratio. Fig.72B Displays the C-terminal ratio. Fig.72C Displays the A-side ratio. Fig.72D G-terminal ratio is shown. When compared with the normotensive control plasma sample group, the preeclampsia plasma sample group showed a significantly increased T-terminal plasma DNA molecule ratio (P=0.016, Wilcoxon rank-sum test) and a significantly decreased G-terminal plasma DNA molecule ratio (P=0.016, Wilcoxon rank-sum test).

[0841] Fig.73 Hierarchical cluster analysis of pre-eclampsia and normotensive late pregnancy maternal plasma DNA samples using four types of fragment ends (the first nucleotide at the 5' end of each strand), i.e., C-end, G-end, T-end, and A-end, is shown. Each column indicates a plasma DNA sample. The first column indicates which group each sample belongs to, with cyan indicating normotensive late pregnancy maternal plasma DNA samples and orange indicating pre-eclampsia plasma DNA samples. Cyan covers the first five columns. Orange covers the last four columns.

[0842] Starting from the second column, each column indicates the type of fragment end. According to the column standardized frequency (z-score) (i.e., the standard deviation value of the mean frequency lower or higher than the whole sample), the terminal motif frequency is presented with a series of color gradients. The redder the color, the higher the terminal motif frequency is indicated, and the bluer the color, the lower the terminal motif frequency is indicated. The hierarchical cluster analysis based on 4 types of fragment end frequencies shows that the fragment end profiles of preeclampsia plasma DNA samples form a cluster different from the cluster of normotensive late pregnancy plasma DNA samples.

[0843] In an embodiment, we can determine the dinucleotide sequence of the first nucleotide (X) and the second nucleotide (Y) at the 5' end of both Watson strand and Crick strand for each sequenced DNA molecule. X and Y can be one of the four nucleotide bases in DNA. There are 16 possible dinucleotide terminal motifs XYNN, namely AANN, ATNN, AGNN, ACNN, TANN, TTNN, TGNN, TCNN, GANN, GTNN, GGNN, GCNN, CANN, CTNN, CGNN and CCNN. We can determine the dinucleotide sequence of the third nucleotide (X) and the fourth nucleotide (Y) at the 5' end of both Watson strand and Crick strand for each sequenced DNA molecule according to the embodiments in the present disclosure. There are 16 possible dinucleotide NNXY motifs. We can also determine the first tetranucleotide sequence (4-mer motif) at the 5' end of both Watson strand and Crick strand for each sequenced DNA molecule.

[0844] Fig.74 Hierarchical cluster analysis of maternal plasma DNA samples in preeclampsia and normotensive late pregnancy using 16 dinucleotide motifs XYNN (a dinucleotide sequence of the first and second nucleotides at the 5' end) is shown. Fig.75 Hierarchical cluster analysis of maternal plasma DNA samples from preeclampsia and normotensive late pregnancy using 16 dinucleotide m...

Claims

1. A method for non-diagnostic purposes of analyzing a biological sample obtained from a female pregnant with a fetus, the female having a first haplotype and a second haplotype in a first chromosome region, the biological sample comprising a plurality of free DNA molecules from the fetus and the female, the method comprising: receiving reads corresponding to the plurality of free DNA molecules; measuring the sizes of the plurality of free DNA molecules; identifying a first group of free DNA molecules from the plurality of free DNA molecules as having a size greater than or equal to a cutoff value, wherein the cutoff value is at least 500 nt; Determining the sequence of the first haplotype and the sequence of the second haplotype using the reads corresponding to the first group of free DNA molecules; aligning a second group of cell-free DNA molecules from the plurality of cell-free DNA molecules to the sequence of the first haplotype, the second group of cell-free DNA molecules having a size less than the cutoff value; aligning a third group of cell-free DNA molecules from the plurality of cell-free DNA molecules with the sequence of the second haplotype, the third group of cell-free DNA molecules having a size less than the cutoff value; using the second set of free DNA molecules to measure a first value of a parameter, wherein the parameter is a free DNA molecule count, a free DNA molecule size profile, or a free DNA molecule methylation degree; measuring a second value of the parameter using the third set of free DNA molecules; comparing the first value to the second value; and A likelihood that the fetus inherited the first haplotype is determined based on the comparison of the first value and the second value.

2. The method according to claim 1, wherein the cut-off value is 600 nt. The method according to claim 1 , wherein the cut-off value is 1 knt.

4. The method of claim 1, wherein determining the sequence of the first haplotype and the sequence of the second haplotype from the reads corresponding to the first set of free DNA molecules comprises: The reads corresponding to the first set of free DNA molecules are aligned to a reference genome.

5. The method of claim 1, wherein determining the sequence of the first haplotype and the sequence of the second haplotype from the reads corresponding to the first set of free DNA molecules comprises: aligning the first subset of reads to the second subset of reads to identify different alleles at loci in the reads, determining that the first subset of the reads has a first allele at the locus, determining that the second subset of the reads has a second allele at the locus, determining that the first subset of the reads corresponds to the first haplotype, and It is determined that the second subset of the reads corresponds to the second haplotype.

6. The method according to claim 1, wherein: The parameter is the free DNA molecule count, and The method further comprises: When the first value is greater than the second value, it is determined that the possibility that the fetus inherits the first haplotype is higher than the possibility that the fetus inherits the second haplotype.

7. The method according to claim 1, wherein: The parameter is the free DNA molecule size profile, and The method further comprises: When the first value is less than the second value, it is determined that the fetus is more likely to inherit the first haplotype than the second haplotype, indicating that the second group of free DNA molecules is characterized by a size profile that is smaller than the third group of free DNA molecules.

8. The method according to claim 1, wherein: The parameter is the degree of methylation of the free DNA molecule, and The method further comprises: When the first value is less than the second value, it is determined that the possibility that the fetus inherits the first haplotype is higher than the possibility that the fetus inherits the second haplotype.

9. The method according to claim 1, further comprising: identifying a number of repetitive sequences of a subsequence in a read of the reads corresponding to the first set of free DNA molecules, in: Determining the sequence of the first haplotype includes determining the number of repeats of the subsequence.

10. The method of claim 1, wherein the plurality of free DNA molecules are enriched relative to the biological sample to obtain a size greater than or equal to the cutoff value, wherein more than 20% of the free DNA molecules in the biological sample have a size greater than 200 nt.

11. The method according to claim 10, further comprising: The plurality of free DNA molecules is enriched using electrophoresis.

12. The method according to claim 10, further comprising: The plurality of free DNA molecules are enriched using magnetic beads for selectively binding free DNA molecules based on size.

13. The method according to claim 10, further comprising: The plurality of free DNA molecules are enriched using hybridization, immunoprecipitation, amplification or CRISPR.

14. The method of claim 10, wherein enrichment is used to obtain a size greater than 600 nt.

15. The method of claim 1, wherein the plurality of free DNA molecules are enriched relative to the biological sample to obtain a methylation profile, The method further comprises: Immunoprecipitation is used to enrich the plurality of free DNA molecules.

16. The method according to claim 1, wherein the reads corresponding to the plurality of free DNA molecules are obtained by single molecule sequencing.

17. The method of claim 16, wherein the single molecule sequencing comprises optically monitoring a DNA polymerase incorporating new bases into complementary strands of free DNA molecules in the plurality of free DNA molecules.

18. The method of claim 10, wherein enrichment is used to obtain a size greater than 700 nt.

19. The method of claim 10, wherein enrichment is used to obtain a size greater than 800 nt.

20. The method of claim 10, wherein enrichment is used to obtain a size greater than 900 nt.

21. The method of claim 10, wherein enrichment is used to obtain sizes greater than 1 knt.

22. A computer product comprising a non-transitory computer-readable medium storing instructions that, when executed, control a computing system to perform a method of analyzing a biological sample obtained from a female pregnant with a fetus, the female having a first haplotype and a second haplotype in a first chromosome region, the biological sample comprising a plurality of free DNA molecules from the fetus and the female, the method comprising: receiving reads corresponding to the plurality of free DNA molecules; measuring the sizes of the plurality of free DNA molecules; identifying a first group of free DNA molecules from the plurality of free DNA molecules as having a size greater than or equal to a cutoff value, wherein the cutoff value is at least 500 nt; Determining the sequence of the first haplotype and the sequence of the second haplotype using the reads corresponding to the first group of free DNA molecules; aligning a second group of cell-free DNA molecules from the plurality of cell-free DNA molecules to the sequence of the first haplotype, the second group of cell-free DNA molecules having a size less than the cutoff value; aligning a third group of cell-free DNA molecules from the plurality of cell-free DNA molecules with the sequence of the second haplotype, the third group of cell-free DNA molecules having a size less than the cutoff value; using the second set of free DNA molecules to measure a first value of a parameter, wherein the parameter is a free DNA molecule count, a free DNA molecule size profile, or a free DNA molecule methylation degree; measuring a second value of the parameter using the third set of free DNA molecules; comparing the first value to the second value; and A likelihood that the fetus inherited the first haplotype is determined based on the comparison of the first value and the second value.

23. The computer program product of claim 22, wherein the cutoff value is 600 nt.

24. The computer program product of claim 22, wherein the cutoff value is 1 knt.

25. The computer product of claim 22, wherein determining the sequence of the first haplotype and the sequence of the second haplotype from the reads corresponding to the first set of free DNA molecules comprises: The reads corresponding to the first set of free DNA molecules are aligned to a reference genome.

26. The computer product of claim 22, wherein determining the sequence of the first haplotype and the sequence of the second haplotype from the reads corresponding to the first set of free DNA molecules comprises: aligning the first subset of reads to the second subset of reads to identify different alleles at loci in the reads, determining that the first subset of the reads has a first allele at the locus, determining that the second subset of the reads has a second allele at the locus, determining that the first subset of the reads corresponds to the first haplotype, and It is determined that the second subset of the reads corresponds to the second haplotype.

27. The computer product of claim 22, wherein: The parameter is the free DNA molecule count, and The method further comprises: When the first value is greater than the second value, it is determined that the possibility that the fetus inherits the first haplotype is higher than the possibility that the fetus inherits the second haplotype.

28. The computer product of claim 22, wherein: The parameter is the free DNA molecule size profile, and The method further comprises: When the first value is less than the second value, it is determined that the fetus is more likely to inherit the first haplotype than the second haplotype, indicating that the second group of free DNA molecules is characterized by a size profile that is smaller than the third group of free DNA molecules.

29. The computer product of claim 22, wherein: The parameter is the degree of methylation of the free DNA molecule, and The method further comprises: When the first value is less than the second value, it is determined that the possibility that the fetus inherits the first haplotype is higher than the possibility that the fetus inherits the second haplotype.

30. The computer program product of claim 22, wherein the cutoff value is a first cutoff value, The method further comprises: calculating a separation value using the first value and the second value; comparing the separation value to a second cutoff value; and A likelihood of fetal aneuploidy is determined based on the comparison of the segregation value to the second cutoff value.

31. The computer product of claim 30, wherein: determining said second cutoff value from a reference sample from a pregnant female carrying a euploid fetus, determining said second cut-off value from a reference sample from a pregnant female carrying an aneuploid fetus, or The second cutoff value was calculated assuming an aneuploid fetus.

32. The computer product of claim 22, further comprising: identifying a number of repetitive sequences of a subsequence in a read of the reads corresponding to the first set of free DNA molecules, in: Determining the sequence of the first haplotype includes determining the number of repeats of the subsequence.

33. The computer product of claim 32, wherein: The number of the repeat sequences in the subsequence is associated with a repeat sequence-associated disease, and The method further includes determining the likelihood that the fetus will inherit the repetitive sequence-associated disease.

34. The computer product of claim 22, wherein the plurality of free DNA molecules are enriched relative to the biological sample to obtain a size greater than or equal to the cutoff value, wherein more than 20% of the free DNA molecules in the biological sample have a size greater than 200 nt.

35. The computer product of claim 34, further comprising: The plurality of free DNA molecules is enriched using electrophoresis.

36. The computer product of claim 34, further comprising: The plurality of free DNA molecules are enriched using magnetic beads for selectively binding free DNA molecules based on size.

37. The computer product of claim 34, further comprising: The plurality of free DNA molecules are enriched using hybridization, immunoprecipitation, amplification or CRISPR.

38. The computer program product of claim 37, wherein enrichment is used to obtain sizes greater than 600 nt.

39. The computer product of claim 22, wherein the plurality of free DNA molecules are enriched relative to the biological sample to obtain a methylation profile, The method further comprises: Immunoprecipitation is used to enrich the plurality of free DNA molecules.

40. The computer program product of claim 22, wherein the reads corresponding to the plurality of free DNA molecules are obtained by single molecule sequencing.

41. The computer product of claim 40, wherein the single molecule sequencing comprises optically monitoring a DNA polymerase incorporating new bases into complementary strands of free DNA molecules in the plurality of free DNA molecules.

42. The computer program product of claim 37, wherein enrichment is used to obtain sizes greater than 700 nt.

43. The computer program product of claim 37, wherein enrichment is used to obtain sizes greater than 800 nt.

44. The computer program product of claim 37, wherein enrichment is used to obtain sizes greater than 900 nt.

45. The computer program product of claim 37, wherein enrichment is used to obtain sizes greater than 1 knt.

46. ​​A system comprising: The computer product of any one of claims 22 to 45; and One or more processors configured to execute instructions stored on the computer-readable medium.

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