Method, device, equipment and storage medium for constructing population pedigree

Abstract

The present application discloses a method, device, equipment and storage medium for constructing a population pedigree, which obtains a chromosome map file of a population sample, wherein the population sample includes multiple individual samples, and the chromosome map file includes SNP site information of the individual samples; the segment ratios of multiple target chromosome segments between individual samples are calculated based on the chromosome map file using the principle of linkage disequilibrium, and the number and type of common ancestor segments contained in each target chromosome segment are different; the genome kinship correlation coefficients between individual samples are calculated based on the chromosome map file using the similarity between SNP sites; the kinship between individual samples is determined based on the segment ratio and the genome kinship correlation coefficient; and the population pedigree of the population sample is constructed based on the genome kinship correlation coefficient and the kinship. The reconstruction of a multi-generation and multi-variety population pedigree is achieved, filling the gap in the current method for reconstructing a real large-scale population pedigree.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, equipment and storage medium for constructing a population pedigree. Background Art

[0002] Parentage testing is required when constructing a population pedigree. The current parentage testing methods mainly include the exclusion method, likelihood method, and genotype reconstruction method. The exclusion method types a few selected short tandem repeats (STR) or single nucleotide polymorphism (SNP) sites, and then calculates the non-paternity exclusion probability between the assumed parent-offspring pairs to determine whether the assumed parent-offspring pairs are real parent-offspring pairs. The likelihood method types several to dozens of highly polymorphic STR or SNP sites, and performs a parent-offspring likelihood analysis on a small population of a single variety with a presumed parent-offspring relationship based on the typing results, determines the likelihood probability between the assumed parent-offspring pairs, and determines whether the assumed parent-offspring relationship is not a real parent-offspring relationship.

[0003] However, the exclusion method and likelihood method can only determine the parent-child relationship between parents and children, and cannot determine the other relationships such as siblings, half-siblings, grandparents and grandchildren. In addition, they can only be used for a single breed and need to assume the parent-child relationship between individuals in advance. If the sequencing information of the parents in the population to be analyzed is missing or not sequenced, as well as the mixing of multiple breeds and multiple generations, it will be impossible to analyze their kinship and thus impossible to reconstruct the pedigree.

[0004] The genotype reconstruction method mainly uses computer simulation data to analyze the genetic structure of human populations, and lacks large-scale research on real populations. The genotype reconstruction method using real data is only for rare protected varieties with a single variety, clear personal pedigree records and clear generation divisions. There is currently no pedigree reconstruction for populations with multiple varieties, multiple generations and missing pedigrees. Summary of the invention

[0005] The present application provides a method, device, equipment and storage medium for constructing a population pedigree to solve the limitation problem that the current method can only analyze parent-offspring relationships, and realize the pedigree reconstruction of large-scale real populations of multiple varieties and generations.

[0006] In order to solve the above technical problems, in the first aspect, the present application provides a method for constructing a population pedigree, comprising:

[0007] Obtaining a chromosome map file of a population sample, where the population sample includes multiple individual samples, and the chromosome map file includes SNP site information of the individual samples;

[0008] Using the principle of linkage disequilibrium, according to the chromosome map file, the fragment ratios of multiple target chromosome fragments between individual samples are calculated, each of the target chromosome fragments contains a different type of common ancestral fragments, and each of the target chromosome fragments contains a different number of common ancestral fragments, and the common ancestral fragments are gene fragments derived from a common ancestor and have the same nucleotide sequence;

[0009] Using the similarity between SNP sites, the genomic kinship correlation coefficient between individual samples is calculated according to the chromosome map file;

[0010] Determine the kinship between individual samples based on fragment ratios and genomic kinship correlation coefficients;

[0011] Based on the genomic genetic correlation coefficient and kinship relationship, a population phylogeny of the population sample is constructed.

[0012] Preferably, the chromosome map file of the population sample is obtained, including:

[0013] Perform SNP loci typing on multiple individual samples to obtain an initial map file;

[0014] The SNP sites that do not meet the preset SNP requirements in the initial map file are eliminated to obtain the chromosome map file.

[0015] Preferably, the segment ratios of multiple target chromosome segments between individual samples are calculated based on the chromosome map file using the linkage disequilibrium principle, including:

[0016] Using the principle of linkage disequilibrium, the common ancestral fragments between individual samples are determined according to the chromosome map file;

[0017] According to the common ancestral fragments, the chromosome fragments between individual samples are classified to obtain a variety of target chromosome fragments;

[0018] Calculate the proportion of each target chromosome fragment to the total number of chromosome fragments.

[0019] Preferably, the common ancestral fragments between individual samples are determined based on the chromosome map file using the principle of linkage disequilibrium, including:

[0020] Linkage disequilibrium analysis was performed on the SNP loci of each individual sample;

[0021] According to the chromosome map file, determine the physical positions between SNP sites that do not meet the population linkage disequilibrium threshold;

[0022] Multiple adjacent gene sequences that are physically continuous and have allelic nucleotide sequences with other individual samples are connected to obtain a common ancestral fragment.

[0023] Preferably, the similarity between SNP sites is used to calculate the genomic kinship coefficient between individual samples according to the chromosome map file, including:

[0024] Based on the preset similarity formula between the first SNP sites and the chromosome map file, the genomic kinship correlation coefficient between individual samples in the same family is calculated;

[0025] Based on the preset similarity formula between the second SNP sites and the chromosome map file, the genomic kinship correlation coefficient between individual samples of different families was calculated.

[0026] Preferably, determining the kinship between individual samples based on the fragment ratio and the genome kinship correlation coefficient comprises:

[0027] Based on the preset correspondence between the fragment ratio, the genomic kinship correlation coefficient and the preset kinship relationship, the kinship relationship corresponding to the fragment ratio and the genomic kinship correlation coefficient of the individual sample is determined.

[0028] Preferably, constructing a population pedigree of a population sample based on the genome genetic correlation coefficient and kinship relationship includes:

[0029] Based on the genome kinship correlation coefficient, the kinship relationships are clustered to obtain a kinship family clustering diagram;

[0030] Based on kinship and kinship family cluster diagrams, the population phylogeny of the population sample is reconstructed.

[0031] In a second aspect, the present application also provides a method for constructing a population pedigree, comprising:

[0032] An acquisition module is used to acquire a chromosome map file of a population sample, where the population sample includes multiple individual samples, and the chromosome map file includes SNP site information of the individual samples;

[0033] The first calculation module is used to calculate the segment ratios of multiple target chromosome segments between individual samples according to the chromosome map file using the linkage disequilibrium principle, wherein each target chromosome segment contains a different number of common ancestor segments, and the common ancestor segments are gene segments derived from a common ancestor and having the same nucleotide sequence;

[0034] The second calculation module is used to calculate the genome kinship correlation coefficient between individual samples according to the chromosome map file by using the similarity between SNP sites;

[0035] A determination module, for determining the kinship between individual samples based on fragment ratios and genome kinship correlation coefficients;

[0036] A construction module is used to construct a population phylogeny of a population sample based on genomic kinship correlation coefficients and kinship relationships.

[0037] In a third aspect, the present application further provides a computer device, including a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the method for constructing a population pedigree as in the first aspect is implemented.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for constructing a population pedigree as in the first aspect.

[0039] Compared with the prior art, this application has the following beneficial effects:

[0040] By obtaining a chromosome map file including SNP site information of individual samples, and using the principle of linkage disequilibrium, according to the chromosome map file, the fragment ratios of multiple target chromosome fragments between individual samples are calculated, so as to realize the generation relationship analysis between individual samples according to the differences in fragment ratios of multiple target chromosome fragments at different levels of kinship, so as to make the pedigree coverage wider and the generation depth deeper; using the similarity between SNP sites, according to the chromosome map file, the genomic kinship correlation coefficient between individual samples is calculated; and based on the fragment ratio and the genomic kinship correlation coefficient, the kinship between individual samples is determined to analyze the breed relationship between individual samples, and it is possible to analyze individuals of multiple breeds; finally, based on the genomic kinship correlation coefficient and kinship, the population pedigree of population samples is constructed, thereby realizing the reconstruction of multi-generation and multi-breed population pedigrees, filling the current gap in the reconstruction method of real large-scale population pedigrees. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of a process for constructing a population phylogeny according to an embodiment of the present application;

[0042] Figure 2 This is a schematic diagram of the SNP position distribution shown in the embodiment of the present application;

[0043] Figure 3 This is a schematic diagram of IBD distribution shown in an embodiment of the present application;

[0044] Figure 4 This is a schematic diagram of the distribution of the ratio of IBD1 and IBD2 fragments shown in the examples of the present application;

[0045] Figure 5 This is a schematic diagram of an evolutionary tree clustering method according to an embodiment of the present application;

[0046] Figure 6 A schematic diagram of kinship family clustering shown in an embodiment of the present application;

[0047] Figure 7 This is a schematic diagram of the structure of a device for constructing a population phylogeny according to an embodiment of the present application;

[0048] Figure 8 A schematic diagram of the structure of a computer device shown in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] Please refer to Figure 1 , Figure 1 The flowchart of a method for constructing a group genealogy provided in an embodiment of the present application is shown in FIG. The method for constructing a group genealogy in an embodiment of the present application can be applied to computer devices, including but not limited to smart phones, laptops, tablet computers, desktop computers, physical servers, and cloud servers. Figure 1 As shown, the method for constructing a population pedigree in this embodiment includes steps S101 to S105, which are described in detail as follows:

[0051] Step S101, obtaining a chromosome map file of a population sample, wherein the population sample includes a plurality of individual samples, and the chromosome map file includes SNP site information of the individual samples.

[0052] In this step, the chromosome map file is a subfile in the SNP sequencing file. The SNP sequencing file adopts the PLINK format, which includes but is not limited to map files and ped files; the map file is a chromosome map file, which includes but is not limited to the SNP site information such as the name of the SNP site, the chromosome where it is located, and the base pair position coordinates; the ped file is an individual sequencing information file, which includes but is not limited to individual information and the SNP typing results corresponding to the individual.

[0053] It is understandable that the chromosome map file can be obtained by performing SNP site typing on individual samples in advance through other computer devices and then transplanted to this computer device; it can also be obtained by controlling the SNP site typing device in real time through this computer device to perform SNP site typing on individual samples.

[0054] Optionally, the step S101 includes: performing SNP loci typing on the plurality of individual samples to obtain an initial map file; and removing SNP sites in the initial map file that do not meet preset SNP requirements to obtain the chromosome map file.

[0055] In this optional embodiment, ear tissue, blood, semen or hair samples of the pedigree population to be reconstructed are collected, fixed with a fixative and stored at low temperature as subsequent sequencing samples. A medium- to high-density liquid or solid-phase chip (such as the Illumina GeneSeek GGP Porcine 50K solid-phase chip) is used to perform SNP site typing on the sequencing samples to obtain a sequencing file. SNP sites with a detection rate of less than 0.9, a minor allele frequency of less than 0.05, or located on sex chromosomes or with unknown chromosomal positions in the sequencing file are then eliminated, and individuals with an individual SNP detection rate of less than 0.9 are eliminated to obtain the final sequencing file.

[0056] Step S102, using the principle of linkage disequilibrium, according to the chromosome map file, calculate the fragment ratios of the multiple target chromosome fragments between the individual samples, each of the target chromosome fragments contains a different type of common ancestral fragments, each of the target chromosome fragments contains a different number of common ancestral fragments, and the common ancestral fragments are gene fragments derived from a common ancestor and have the same nucleotide sequence.

[0057] In this step, the common ancestor fragment is a homologous (Identity By Descent, IBD) fragment, that is, a fragment with the same descent, indicating that the gene fragment with the same nucleotide sequence between individuals comes from a common ancestor. Linkage disequilibrium (LD) indicates the non-random association between different SNP sites, which will cause the frequency of simultaneous inheritance of two genes to be significantly higher than the expected random frequency.

[0058] Optionally, the target chromosome segment includes IBD0, IBD1 and IBD2 segments, which respectively indicate that 0, 1 or 2 of the 2 alleles at each SNP site between two individuals on the chromosome segment are identical in blood relationship.

[0059] Step S103, using the similarity between SNP sites and according to the chromosome map file, the genomic kinship correlation coefficient between the individual samples is calculated.

[0060] In this step, the genomic kinship correlation coefficient is used to characterize the degree of kinship between multiple breed populations. Optionally, the genomic kinship correlation coefficient between individuals in the same family or different families is calculated based on the number of heterozygous SNPs and the number of homozygous SNPs between individuals.

[0061] Step S104, determining the kinship relationship between the individual samples based on the fragment ratio and the genome kinship correlation coefficient.

[0062] In this step, kinship includes but is not limited to first-degree kinship, second-degree kinship, third-degree kinship and fourth-degree kinship. Since there are differences in the fragment ratios of different target chromosome fragments in different degrees of kinship, the present application uses this difference to realize kinship analysis between individuals, so as to analyze the kinship to which the individuals belong, and then analyze the generational relationship between individuals; and for multi-variety groups, the present application uses genomic kinship correlation coefficients to analyze family relationships between individuals, such as same-sibling relationships and same-sibling relationships. The present application solves the limitations of current technology that it can only analyze parent-child relationships and can only target a single variety, and realizes kinship analysis of multi-variety and multi-generation groups, thereby making the population pedigree more comprehensive and the generational depth deeper.

[0063] Step S105, constructing a population pedigree of the population sample based on the genome kinship correlation coefficient and the kinship relationship.

[0064] In this step, the genome kinship correlation coefficients and kinship relationships are clustered, and a population phylogeny of the population sample is constructed based on the clustering results.

[0065] In one embodiment, Figure 1 Based on the illustrated embodiment, step S102 includes:

[0066] Using the linkage disequilibrium principle, according to the chromosome map file, determining the common ancestral fragments between the individual samples;

[0067] Classifying the chromosome segments between the individual samples according to the common ancestral segments to obtain a plurality of target chromosome segments;

[0068] The fragment ratio of each target chromosome fragment to the total number of chromosome fragments is calculated.

[0069] In this embodiment, in the genetic process without recombination, the offspring inherits a set of homologous chromosomes from the father and mother respectively through gametes. In the actual genetic process, due to the recombination of non-sister chromatids in homologous chromosomes, the single chromosome of the offspring is not completely consistent with that of the parents, so multiple continuous identical allele sequences from the same ancestor are connected into a common ancestor segment according to linkage disequilibrium. Chromosomes are divided into IBD0, IBD1 and IBD2 segments according to the shared alleles of chromosome segments between individual pairs.

[0070] For example, according to the chromosome map file in the sequencing file, the length of each chromosome and the distribution position of each base on the corresponding chromosome are obtained (such as Figure 2As shown in Figure 1 ); Since the base pair position is the base pair number of the SNP base from the beginning to the end on the chromosome, the distribution position map of the IBD fragment on the chromosome is drawn according to the base pair position corresponding to the start and end SNP bases of each IBD fragment (as shown in Figure 1 ). Figure 3 ), and calculate the fragment ratios of IBD0, IBD1, and IBD2, as well as the proportion of common ancestral fragments shared by the genomes of the two individuals (as shown in Figure 4 shown).

[0071] Optionally, the process of determining the common ancestral fragment comprises:

[0072] Performing linkage disequilibrium analysis on the SNP loci of each individual sample;

[0073] According to the chromosome map file, determining the physical positions between SNP sites that do not meet the population linkage disequilibrium threshold;

[0074] The plurality of adjacent gene sequences that are continuous in physical position and have allelic nucleotide sequences with other individual samples are connected to obtain the common ancestral fragment.

[0075] In this embodiment, the linkage disequilibrium principle is used to calculate the genotype frequency based on the allele frequencies of all individuals in the population. When the genotype frequency does not meet the expected random frequency (i.e., the population linkage disequilibrium threshold), it is considered to be inconsistent with linkage disequilibrium.

[0076] Since the genetic information of offspring is inherited from their parents, in the absence of mutations, half of the genetic information (chromosomes) of the offspring will be consistent with the genetic information of their father (or mother). Therefore, after calculating whether each SNP site of each individual conforms to linkage disequilibrium, the physical position between the SNP sites is determined according to the map file, and the continuous, non-linkage disequilibrium, and identical nucleotide sequences are regarded as allele sequences from the same common ancestor. Multiple adjacent allele sequences are connected to obtain the common ancestor fragment.

[0077] In one embodiment, Figure 1 Based on the illustrated embodiment, step S103 includes:

[0078] Based on the preset first SNP site similarity formula, according to the chromosome map file, the genomic kinship correlation coefficient between individual samples in the same family is calculated;

[0079] Based on the preset second SNP site similarity formula and according to the chromosome map file, the genome kinship correlation coefficient between individual samples of different families is calculated.

[0080] In this embodiment, after eliminating SNP sites with low detection rates, missing sites, and sites whose chromosomal locations cannot be determined, the genomic kinship correlation coefficients between individuals are estimated based on the similarity between sites for the SNP sites on the remaining autosomes. For individuals in the same family, the genomic kinship correlation coefficients between individuals are estimated using the first SNP site similarity formula, which is:

[0081]

[0082] For individuals from different families, the individuals from different families have too much heterozygosity between individual pairs due to their distant relationship, which will lead to a high estimated genome kinship correlation coefficient between individual pairs. Therefore, the similarity formula between the second SNP sites is used to estimate the genome kinship correlation coefficient between individuals from different families. The similarity formula between the second SNP sites is:

[0083]

[0084] in, represents the genome kinship coefficient, N Aa,Aa represents the number of SNPs for which both individuals are heterozygous, N AA,aa represents the number of SNPs for which the two individuals are homozygous. and Represent the total number of heterozygotes of individual i and individual j, respectively. Represents the total number of SNPs of individual i after quality control (including homozygous and heterozygous SNP sites), Represents the total number of SNPs (including homozygous and heterozygous SNP sites) of individual j after quality control. Optionally, when calculating each pair of individuals, the SNPs with missing genotypes between the pair of individuals are excluded in advance.

[0085] In one embodiment, Figure 1 Based on the illustrated embodiment, step S104 includes:

[0086] Based on the preset corresponding relationship between the fragment ratio, the genome kinship correlation coefficient and the preset kinship relationship, the kinship relationship corresponding to the fragment ratio of the individual sample and the genome kinship correlation coefficient is determined.

[0087] In this embodiment, the preset corresponding relationship can be expressed as a preset relationship table in the computer, or it can express the preset judgment logic. Specifically, since the sperm and egg cells provide a set of haploid chromosomes to the offspring when the offspring fertilized egg is formed, the two sites on each pair of alleles of the offspring should come from the father and the mother respectively when no mutation occurs. The parents of the full siblings are the same, but there are 4 combinations of father and mother for each gene. Due to the random exchange and recombination of chromosomes, the single chromosome of the offspring is not completely consistent with the parents, resulting in the simultaneous existence of IBD0, IBD1 and IBD2 fragments among the full siblings. There are almost no IBD2 fragments between parent-child and third-degree kinship pairs, that is, there is almost no situation where the two sites of the same pair of alleles are homologous, so IBD2 is close to 0; almost all sites of repeated sequencing individuals or identical twins are homologous, that is, almost all sites are the same, so IBD2 is close to 1 and IBD1 is close to 0; full siblings or half-siblings with a kinship relationship between parents are between the two, with both IBD2 fragments and IBD1 fragments. Therefore, the fragment ratio of IBD0, IBD1 and IBD2 fragments is used to analyze the generation relationship between individuals. Since the value range of the genomic kinship coefficient is different between the same father and the same mother and the same father and the same mother, the genomic kinship coefficient can be used to determine the breed relationship between individuals.

[0088] It can be understood that the present application can simultaneously infer the distribution and proportion of common ancestral fragments between pairs of individuals in a real production population of multiple generations and mixed varieties based on IBD fragments, thereby judging the relationship between individuals and further inferring the family structure, so that the reconstructed population pedigree can more realistically and clearly reflect the relationship between individuals in the family.

[0089] In one embodiment, Figure 1 Based on the illustrated embodiment, step S105 includes:

[0090] Based on the genome kinship correlation coefficient, clustering the kinship relationship to obtain a kinship family clustering diagram;

[0091] Based on the kinship relationship and the kinship family cluster diagram, the population pedigree of the population sample is reconstructed.

[0092] In this embodiment, the neighbor-joining method (NJ) is used to cluster individuals according to the genome kinship correlation coefficient to form a kinship evolutionary tree cluster diagram (such as Figure 5 As shown). Then according to Figure 5 The genome kinship relationship is clustered to form a kinship family cluster diagram (such as Figure 6 Finally, according to the kinship relationship between individuals and Figure 6’s clustering diagram and reconstruct the population spectrum.

[0093] It should be noted that this application has the following beneficial effects:

[0094] 1. Compared with the existing methods, which require the advance screening of highly polymorphic sites, each new population needs to be constructed separately, and the generalization is poor, this method uses hundreds to thousands of times the number of sites as the existing technology, with better generalization and faster speed.

[0095] 2. Compared with the existing methods that can only judge the authenticity of parent-child relationships, this method can infer the first, second, third, and fourth degree kinship relationships including parent-child relationships, and there is no need to assume kinship relationships in advance. In addition to the parent-child groups of the existing methods, it can also be used for multi-generational mixed groups and the same generation groups whose parents have not been sequenced, and has a wider range of applications.

[0096] 3. Compared with the existing methods that use parent-offspring relationships to reconstruct pedigrees, the coverage and depth of the pedigrees are very limited (only individuals with sequencing information are included). This method can infer pedigrees with deeper generations (sequencing individuals and three generations back) and wider coverage through additional information such as full siblings, half siblings, and second- and third-degree relatives.

[0097] 4. Compared with the existing methods that mostly use microsatellites to determine kinship, microsatellites can only be typed using the PCR method, which has a very low throughput and is very slow when inferring large-scale populations. This method uses SNP chip typing, which greatly improves the throughput and can quickly complete the pedigree reconstruction of large-scale populations.

[0098] 5. Compared with the existing methods that require the analyzed population to be of the same breed, this method can reconstruct the pedigree of a mixed population of multiple breeds, and when reconstructing the pedigree, it can naturally distinguish whether the individual is purebred and which breed it belongs to.

[0099] 6. Compared with existing methods that mostly use simulated data or reconstruct pedigrees for humans or rare conservation groups, this method fills the gap in pedigree reconstruction methods for real large-scale production groups, and can be used to prevent breeding farms from falsifying and determine the identity of breeding pigs.

[0100] In order to execute the method for constructing the population pedigree corresponding to the above method embodiment, to achieve the corresponding functions and technical effects. Figure 7 , Figure 7 The structural block diagram of a group pedigree construction device provided in an embodiment of the present application is shown. For the sake of convenience, only the parts related to the present embodiment are shown. The group pedigree construction device provided in an embodiment of the present application includes:

[0101] An acquisition module 701 is used to acquire a chromosome map file of a population sample, wherein the population sample includes a plurality of individual samples, and the chromosome map file includes SNP site information of the individual samples;

[0102] The first calculation module 702 is used to calculate the segment ratios of multiple target chromosome segments between the individual samples according to the chromosome map file by using the linkage disequilibrium principle, wherein each target chromosome segment contains a different type of common ancestor segment, and each target chromosome segment contains a different number of common ancestor segments, and the common ancestor segments are gene segments derived from a common ancestor and having the same nucleotide sequence;

[0103] The second calculation module 703 is used to calculate the genome kinship correlation coefficient between the individual samples according to the chromosome map file by using the similarity between the SNP sites;

[0104] A determination module 704 is used to determine the kinship relationship between the individual samples based on the fragment ratio and the genome kinship correlation coefficient;

[0105] The construction module 705 is used to construct a population pedigree of the population sample based on the genome genetic correlation coefficient and the kinship relationship.

[0106] In one embodiment, the acquisition module 701 is specifically used to:

[0107] Performing SNP site typing on a plurality of individual samples to obtain an initial map file;

[0108] The SNP sites that do not meet the preset SNP requirements in the initial map file are eliminated to obtain the chromosome map file.

[0109] In one embodiment, the first calculation module 702 includes:

[0110] A determination unit, used to determine the common ancestor fragments between the individual samples according to the chromosome map file by utilizing the linkage disequilibrium principle;

[0111] A classification unit, used for classifying the chromosome segments between the individual samples according to the common ancestor segments, to obtain a plurality of target chromosome segments;

[0112] A calculation unit is used to calculate the fragment ratio of each target chromosome fragment to the total number of chromosome fragments.

[0113] In one embodiment, the determining unit is specifically configured to:

[0114] Performing linkage disequilibrium analysis on the SNP loci of each individual sample;

[0115] According to the chromosome map file, determining the physical positions between SNP sites that do not meet the population linkage disequilibrium threshold;

[0116] The plurality of adjacent gene sequences that are continuous in physical position and have allelic nucleotide sequences with other individual samples are connected to obtain the common ancestral fragment.

[0117] In one embodiment, the second calculation module 703 is specifically used to:

[0118] Based on the preset first SNP site similarity formula, according to the chromosome map file, the genomic kinship correlation coefficient between individual samples in the same family is calculated;

[0119] Based on the preset second SNP site similarity formula and according to the chromosome map file, the genome kinship correlation coefficient between individual samples of different families is calculated.

[0120] In one embodiment, the determining module 704 is specifically configured to:

[0121] Based on the preset corresponding relationship between the fragment ratio, the genome kinship correlation coefficient and the preset kinship relationship, the kinship relationship corresponding to the fragment ratio of the individual sample and the genome kinship correlation coefficient is determined.

[0122] In one embodiment, the construction module 705 is specifically used to:

[0123] Based on the genome kinship correlation coefficient, clustering the kinship relationship to obtain a kinship family clustering diagram;

[0124] Based on the kinship relationship and the kinship family cluster diagram, the population pedigree of the population sample is reconstructed.

[0125] The above-mentioned group pedigree construction device can implement the group pedigree construction method of the above-mentioned method embodiment. The options in the above-mentioned method embodiment are also applicable to this embodiment and will not be described in detail here. The rest of the contents of the embodiment of this application can refer to the contents of the above-mentioned method embodiment, and will not be repeated in this embodiment.

[0126] Figure 7 This is a schematic diagram of the structure of a computer device provided in one embodiment of the present application. Figure 7 As shown, the computer device 7 of this embodiment includes: at least one processor 70 ( Figure 7 Only one is shown in the figure) a processor, a memory 71, and a computer program 72 stored in the memory 71 and executable on the at least one processor 70, and when the processor 70 executes the computer program 72, the steps in any of the above method embodiments are implemented.

[0127] The computer device 7 may be a computing device such as a smart phone, a tablet computer, a desktop computer, a cloud server, etc. The computer device may include but is not limited to a processor 70 and a memory 71. Those skilled in the art will appreciate that Figure 7 It is only an example of the computer device 7 and does not constitute a limitation on the computer device 7. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0128] The processor 70 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0129] In some embodiments, the memory 71 may be an internal storage unit of the computer device 7, such as a hard disk or memory of the computer device 7. In other embodiments, the memory 71 may also be an external storage device of the computer device 7, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the computer device 7. The memory 71 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 71 may also be used to temporarily store data that has been output or is to be output.

[0130] In addition, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0131] An embodiment of the present application provides a computer program product. When the computer program product is run on a computer device, the computer device implements the steps in the above-mentioned method embodiments when executing the computer device.

[0132] In several embodiments provided in the present application, it is understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0133] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0134] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for constructing a population phylogeny, characterized in that: include: Obtaining a chromosome map file of a population sample, wherein the population sample includes a plurality of individual samples, and the chromosome map file includes SNP site information of the individual samples; Using the principle of linkage disequilibrium, according to the chromosome map file, the fragment ratios of the multiple target chromosome fragments between the individual samples are calculated, each of the target chromosome fragments contains a different type of common ancestral fragments, each of the target chromosome fragments contains a different number of common ancestral fragments, and the common ancestral fragments are gene fragments derived from a common ancestor and have the same nucleotide sequence; Utilizing the similarity between SNP sites, and based on the chromosome map file, calculating the genome kinship correlation coefficient between the individual samples; Determining the kinship relationship between the individual samples based on the fragment ratios and the genome kinship correlation coefficients; Based on the genome kinship correlation coefficient and the kinship relationship, constructing a population pedigree of the population sample; The step of obtaining a chromosome map file of a population sample comprises: Performing SNP site typing on a plurality of individual samples to obtain an initial map file; Eliminating the SNP sites in the initial map file that do not meet the preset SNP requirements to obtain the chromosome map file; The method of calculating the segment ratios of multiple target chromosome segments between the individual samples according to the chromosome map file using the linkage disequilibrium principle includes: Using the linkage disequilibrium principle, according to the chromosome map file, determining the common ancestral fragments between the individual samples; Classifying the chromosome segments between the individual samples according to the common ancestral segments to obtain a plurality of target chromosome segments; Calculating the fragment ratio of each target chromosome fragment to the total number of chromosome fragments; The method of using the linkage disequilibrium principle to determine the common ancestral fragment between the individual samples according to the chromosome map file comprises: Performing linkage disequilibrium analysis on the SNP loci of each individual sample; According to the chromosome map file, determining the physical positions between SNP sites that do not meet the population linkage disequilibrium threshold; Connecting the plurality of adjacent gene sequences that are physically continuous and have allelic nucleotide sequences with other individual samples to obtain the common ancestral fragment; The method of calculating the genome kinship coefficient between the individual samples based on the chromosome map file by using the similarity between the SNP sites includes: Based on the preset first SNP site similarity formula, according to the chromosome map file, the genomic kinship correlation coefficient between individual samples in the same family is calculated; Based on the preset second SNP site similarity formula, according to the chromosome map file, the genomic kinship correlation coefficient between individual samples of different families is calculated; The similarity formula between the first SNP sites is: ； The similarity formula between the second SNP sites is: ； in, represents the genome kinship coefficient, represents the number of SNPs for which both individuals are heterozygous. represents the number of SNPs for which the two individuals are homozygous. and Represent the total number of heterozygotes of individual i and individual j, respectively. represents the total number of SNPs in individual i after quality control, Represents the total number of SNPs in individual j after quality control.

2. The method for constructing a population pedigree according to claim 1, characterized in that: Determining the kinship between the individual samples based on the fragment ratio and the genome kinship correlation coefficient comprises: Based on the preset corresponding relationship between the fragment ratio, the genome kinship correlation coefficient and the preset kinship relationship, the kinship relationship corresponding to the fragment ratio of the individual sample and the genome kinship correlation coefficient is determined.

3. The method for constructing a population pedigree according to claim 1, characterized in that: The step of constructing a population pedigree of the population sample based on the genome genetic correlation coefficient and the kinship relationship comprises: Based on the genome kinship correlation coefficient, clustering the kinship relationship to obtain a kinship family clustering diagram; Based on the kinship relationship and the kinship family cluster diagram, the population pedigree of the population sample is reconstructed.

4. A device for constructing a population phylogeny, characterized in that: include: An acquisition module is used to acquire a chromosome map file of a population sample, wherein the population sample includes a plurality of individual samples, and the chromosome map file includes SNP site information of the individual samples; A first calculation module is used to calculate the segment ratios of multiple target chromosome segments between the individual samples according to the chromosome map file by using the principle of linkage disequilibrium, wherein each target chromosome segment contains a different type of common ancestor segment, and each target chromosome segment contains a different number of common ancestor segments, and the common ancestor segments are gene segments derived from a common ancestor and having the same nucleotide sequence; The second calculation module is used to calculate the genome kinship correlation coefficient between the individual samples according to the chromosome map file by using the similarity between the SNP sites; A determination module, for determining the kinship between the individual samples based on the fragment ratio and the genome kinship correlation coefficient; A construction module, for constructing a population pedigree of the population sample based on the genome kinship correlation coefficient and the kinship relationship; The acquisition module is specifically used for: Performing SNP site typing on a plurality of individual samples to obtain an initial map file; Eliminating the SNP sites in the initial map file that do not meet the preset SNP requirements to obtain the chromosome map file; The first calculation module includes: A determination unit, used to determine the common ancestor fragments between the individual samples according to the chromosome map file by utilizing the linkage disequilibrium principle; A classification unit, used for classifying the chromosome segments between the individual samples according to the common ancestor segments, to obtain a plurality of target chromosome segments; A calculation unit, used for calculating the fragment ratio of each target chromosome fragment to the total number of chromosome fragments; The determining unit is specifically used for: Performing linkage disequilibrium analysis on the SNP loci of each individual sample; According to the chromosome map file, determining the physical positions between SNP sites that do not meet the population linkage disequilibrium threshold; Connecting the plurality of adjacent gene sequences that are physically continuous and have allelic nucleotide sequences with other individual samples to obtain the common ancestral fragment; The second calculation module is specifically used for: Based on the preset first SNP site similarity formula, according to the chromosome map file, the genomic kinship correlation coefficient between individual samples in the same family is calculated; Based on the preset second SNP site similarity formula, according to the chromosome map file, the genomic kinship correlation coefficient between individual samples of different families is calculated; The similarity formula between the first SNP sites is: ； The similarity formula between the second SNP sites is: ； in, represents the genome kinship coefficient, represents the number of SNPs for which both individuals are heterozygous. represents the number of SNPs for which the two individuals are homozygous. and Represent the total number of heterozygotes of individual i and individual j, respectively. represents the total number of SNPs in individual i after quality control, Represents the total number of SNPs in individual j after quality control.

5. A computer device, characterized in that: The method comprises a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the method for constructing a population pedigree according to any one of claims 1 to 3 is implemented.

6. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the method for constructing a population phylogeny as claimed in any one of claims 1 to 3.

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