Genotype interpolation method for data of SNP chip, low coverage rate genome sequencing, transcriptome sequencing and like with high effect

Abstract

The invention discloses a genotype interpolation method capable of being efficiently used for data of an SNP chip, low-coverage-rate genome sequencing, transcriptome sequencing and the like. The method comprises the following steps: constructing an interpolation data platform, capturing sequencing data, processing and SNP, and carrying out chip data processing and RNA-Seq data processing; determining an interpolation accuracy index, constructing a phasing and interpolation software combination, and carrying out whole genome association analysis; and outputting an analysis result. According to the method, the unique characteristics of different data types and the genetic relationship of animals are considered. Through the method, three data types can be interpolated more accurately, so that a more reliable scientific basis is provided for scientific research and breeding.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing and transcriptome sequencing. Background Art

[0002] Whole genome sequencing (WGS) data of livestock will improve the resolution of identifying causal loci for traits of interest. Advances in next-generation sequencing (NGS) technology and the rapid decline in sequencing costs have made WGS available for livestock species. Nevertheless, sequencing large numbers of animals is not yet economically feasible. At the same time, with the development of recent technologies in high-throughput biology, large-scale multi-omics datasets have been generated in genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, and phenomics. Multi-omics integrated analysis can reveal the associations between different biological factors, but the appearance of missing values ​​is an inevitable problem in multi-omics comprehensive studies due to various reasons, including budget constraints, insufficient sample availability, or experimental limitations.

[0003] Therefore, imputation of missing values ​​prior to integrated analysis of multi-omics data is a cost-effective and necessary step to discover and explain biological complexity. Imputation is the process of inferring unknown genotypes for animals genotyped at a lower density using pedigree information and / or a set of reference animals genotyped at a higher density. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing and transcriptome sequencing, which can solve the problem that the traditional free surface correlated multiple wave suppression method cannot adapt to seismic data collected by seabed node observation.

[0006] In order to solve the above technical problems, the present invention provides the following technical solution, a genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing and transcriptome sequencing, comprising:

[0007] Build an interpolation data platform, capture sequencing data processing and SNPs, perform chip data processing and RNA-Seq data processing; determine interpolation accuracy indicators, build a phasing and interpolation software combination, and perform whole-genome association analysis; output analysis results.

[0008] As a preferred scheme of the genotype interpolation method described in the present invention that can be efficiently applied to SNP chips, low-coverage genome sequencing, transcriptome sequencing and other data, the capture sequencing data processing and SNP include performing 1X genome low-depth sequencing on the collected data. After the sequencing is completed, fastp is used to filter reads below the preset quality standard under default parameters, and the reads are mapped to the cattle reference genome ARS-UCD1.2 through BWA-MEM.

[0009] As a preferred scheme of the genotype interpolation method described in the present invention that can be efficiently applied to SNP chips, low-coverage genome sequencing, transcriptome sequencing and other data, the capture sequencing data processing and SNP include using SAMtools to convert and sort bam files, PICARD v3.2.0MarkDuplicates block to exclude duplicate reads, and executing GATKv.4.4.0.0HaplotypeCaller to capture SNPs. After variation capture, the variation quality score of GATK is recalibrated to filter out SNPs below the preset quality standard to obtain a SNP database.

[0010] As a preferred scheme of the genotype interpolation method described in the present invention that can be efficiently applied to SNP chips, low-coverage genome sequencing, transcriptome sequencing and other data, wherein: the chip data processing includes using GGPBovine100K SNP Chips to perform genotyping detection, wherein the bovine ARS-UCD 1.2 assembled genome is used as a reference genome.

[0011] As a preferred embodiment of the genotype interpolation method of the present invention which can be efficiently applied to SNP chips, low-coverage genome sequencing and transcriptome sequencing data, the RNA-Seq data processing includes extracting total RNA from blood samples using TRIzol reagent, purifying RNA samples and using Spectrophotometer to check quality and assess RNA quality;

[0012] If the concentration range of the RNA sample is within the preset range and the RNA integrity value RIN is greater than the preset standard, the gene expression analysis standard is met, and cDNA library preparation and RNA sequencing are required to generate double-end reads.

[0013] As a preferred scheme of the genotype interpolation method described in the present invention that can be efficiently applied to SNP chips, low-coverage genome sequencing, transcriptome sequencing and other data, the RNA-Seq data processing includes: using fastp to filter low-quality reads under default parameters, and aligning the paired-end RNA-Seq data to the reference genome, using annotation information to generate alignment results that meet preset quality standards, converting and sorting bam files by chromosome coordinates through SAMtools, using GATK's AddOrReplaceReadGroups module to add or replace read group labels for each read in the bam file, and using the MarkDuplicates module to find and mark duplicate reads in the bam file, further processing the reads whose CIGAR string in the bam file contains a long deletion N through the SplitNCigarReads module, and performing base recalibration through the BaseRecalibrator and ApplyBQSR modules to correct sequencing errors and system deviations.

[0014] As a preferred embodiment of the genotype interpolation method of the present invention which can be efficiently applied to SNP chips, low-coverage genome sequencing and transcriptome sequencing data, the interpolation accuracy index includes measuring the consistency rate and r based on each SNP. 2 , and averaged across SNPs in MAF bins or across the entire genome; where r 2 is the square of the Pearson correlation coefficient between the observed genotype and the imputed genotype, and the consistency rate is defined as the proportion of individuals whose imputed genotype is consistent with the observed genotype;

[0015] The software combination for constructing phasing and interpolation includes, for low coverage sequencing data, using GLIMPSE2 for phasing and GLIMPSE2 for interpolation, and testing the interpolation effect of GLIMPSE2 on RNA-Seq data.

[0016] As a preferred embodiment of the genotype interpolation method of the present invention that can be efficiently applied to SNP chips, low-coverage genome sequencing and transcriptome sequencing data, the whole genome association analysis includes using a linear mixed model analysis implemented in the DMU package, and SNPs are fitted into the model:

[0017] y=1μ+bx+Za+e

[0018] Where y is the vector of pseudo-phenotypes of lactation traits, 1 is the unit vector, μ is the population mean, b is the SNP additive allele substitution effect, x is the gene dosage vector of randomly selected SNP alleles, Z is the association matrix linking additive polygenic effects to corresponding pseudo-phenotypes, a is the vector of additive polygenic effects, and e is the vector of random residuals;

[0019] Assume that a follows a normal distribution Where A is the additive genetic relationship matrix between individuals created using pedigree information, is the polygenic variance; for the random residuals, assume in is the residual variance, W is the variance including w i The diagonal matrix of elements, w i is the weight of the ith pseudo-phenotype; the weight of the pseudo-phenotype of the ith animal is calculated as in is the reliability of the ith pseudo-phenotype and is higher than 0.98. Set to 0.98;

[0020] Estimate variance components in each run and Sampling variance of allele substitution effect is obtained through the mixed model equation, and at the same time, the Wald chi-square statistic with df = 1 is constructed To check whether the SNP is associated with the trait.

[0021] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of a genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing and transcriptome sequencing.

[0022] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing, and transcriptome sequencing are implemented.

[0023] Beneficial effects of the invention: In this patent, the proposed genotype interpolation method effectively improves the accuracy and efficiency of interpolation by comprehensively utilizing comprehensive genetic data and high-density reference information. The data used in the interpolation process include SNP chip data, low-coverage whole genome sequencing data, and transcriptome sequencing data. The reliability and practicality of this method have been verified through large-scale actual data analysis, and the results show that the interpolation method of this patent not only has a clear data hierarchy, but also has extremely high reliability of results.

[0024] Compared with the traditional simple interpolation technology based on low-density genetic data, the method of this patent can more comprehensively reflect the genetic background and biological characteristics of animals. Simple interpolation methods often ignore complex genetic effects, which may lead to insufficient interpolation accuracy, thus affecting subsequent genetic research and applications. The method of this patent takes into account the unique characteristics of different data types and the relationship between animals. Through this method, the three data types can be interpolated more accurately, thus providing a more reliable scientific basis for scientific research and breeding. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0026] Figure 1 A schematic flow chart of a genotype interpolation method for data efficiently applied to SNP chips, low-coverage genome sequencing, and transcriptome sequencing is provided as an embodiment of the present invention.

[0027] Figure 2 A schematic diagram of the distribution of the number of SNPs within a 1x sequencing data interpolation quality score interval of a genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing, and transcriptome sequencing, provided by an embodiment of the present invention.

[0028] Figure 3 A schematic diagram of the distribution of the number of SNPs within a 100K chip data interpolation quality score interval for a genotype interpolation method that can be efficiently applied to SNP chips, low-coverage genome sequencing, transcriptome sequencing and other data provided by an embodiment of the present invention.

[0029] Figure 4 A schematic diagram of the distribution of the number of SNPs within an interpolation quality score interval of RNA sequencing data, which is a genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing, and transcriptome sequencing, provided by one embodiment of the present invention.

[0030] Figure 5 A schematic diagram of the distribution of SNP numbers in 1x sequencing data interpolation quality score of a genotype interpolation method that can be efficiently applied to SNP chips, low-coverage genome sequencing, transcriptome sequencing and other data provided by an embodiment of the present invention.

[0031] Figure 6A schematic diagram of the distribution of SNP numbers in 100K chip data interpolation quality scores, according to a genotype interpolation method that can be efficiently applied to SNP chips, low-coverage genome sequencing, transcriptome sequencing and other data, provided by an embodiment of the present invention.

[0032] Figure 7 A distribution diagram of the number of SNPs in the interpolation quality score of RNA sequencing data, which is a genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing and transcriptome sequencing, is provided in one embodiment of the present invention.

[0033] Figure 8 A schematic diagram of the distribution of SNP numbers with interpolation quality scores for three data types of a genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing and transcriptome sequencing, provided by an embodiment of the present invention.

[0034] Fig. 9 A genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing and transcriptome sequencing is provided in one embodiment of the present invention, and a whole-genome association Manhattan plot for milk production using 1x sequencing data is provided.

[0035] Fig.10 A genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing and transcriptome sequencing is provided in one embodiment of the present invention, and a whole-genome association Manhattan plot for milk production using 100K chip data is provided.

[0036] Fig.11 A genome-wide association Manhattan plot for milk production using RNA sequencing data is provided as a genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing, and transcriptome sequencing. DETAILED DESCRIPTION

[0037] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0038] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0039] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0040] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0041] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0042] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0043] Example 1, reference Figure 1-Figure 11 , which is the first embodiment of the present invention, provides a genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing and transcriptome sequencing, including:

[0044] Building an interpolation data platform

[0045] (1) Sequencing data processing and SNP capture

[0046] Blood samples of 1,180 Jersey cattle with daily measurement records were collected from a large-scale ranch in southern Jiangsu. After being stored in cold storage, they were sent to Beijing Compson Biotechnology Co., Ltd. for 1X genome low-depth sequencing. After sequencing, fastp v0.23.2 was first used to filter low-quality reads under default parameters. Then, BWA-MEM v0.7.17 was used to map clean reads to the cattle reference genome ARS-UCD1.2. SAMtools v1.6 was used to convert and sort bam files, and PICARDv3.2.0MarkDuplicates blocks were used to exclude duplicate reads. Finally, GATKv.4.4.0.0HaplotypeCaller was executed to capture SNPs. After variant capture, the variant quality score recalibration of GATK was used to filter out low-quality SNPs to obtain the final SNP database.

[0047] (2) Chip data processing

[0048] A total of 11,418 Jersey and Holstein cattle were selected from eight large-scale ranches in southern Jiangsu Province, and hair samples with hair follicles were collected from the tail root, with no less than 50 hairs per individual. The samples were genotyped using the GGPBovine 100KSNP Chips, with the bovine ARS-UCD 1.2 assembled genome used as the reference genome.

[0049] (3) RNA-Seq data processing

[0050] Total RNA was extracted from 400 blood samples using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). Then, RNA samples were purified (AMPure XP system) and used Their quality was checked by spectrophotometer (IMPLEN, CA, USA). RNA quality was assessed on an Agilent Bioanalyzer 2100 system. The concentration of RNA samples ranged from 71.8 to 316 ng / μl, and their RNA integrity number (RIN) was greater than 6, which was sufficient for gene expression analysis. cDNA library preparation and RNA sequencing were performed at Novegene Co Ltd., generating paired-end reads of 150 bp in length (PE150) on an Illumina HiSeq 2500 platform.

[0051] Subsequently, fastp v0.23.2 was first used to filter low-quality reads under default parameters. Paired-end RNA-Seq data were then aligned to the reference genome using STARv2.7.11b, and high-quality alignment results were generated using annotation information. Next, bam files were converted and sorted by chromosome coordinates using SAMtools v1.6. The AddOrReplaceReadGroups module of GATKv4.4.0.0 was used to add or replace read group labels for each read in the bam file. The MarkDuplicates module was then used to find and mark duplicate reads in the bam file, and the SplitNCigarReads module further processed reads in the bam file whose CIGAR (Concise Idiosyncratic GappedAlignmentReport, Concise Specific Gap Alignment Report) string contained N (long deletion). Finally, the BaseRecalibrator and ApplyBQSR modules were used to perform base recalibration to correct sequencing errors and system biases.

[0052] (4) Interpolation reference panel

[0053] The WGS horizontal interpolation reference panel used in this method comes from CattleGTEx (cattle genotype-tissue expression atlas), which integrates WGS data from multiple sources, with a total of 8,886 individuals and 192 breeds, including 906 Holsteins, 694 Jerseys, 573 Angus, 326 German Black Spots, and 322 Simmentals. The construction process uses the SNP capture process of CattleGTEx and phases it using Beagle v4.0.0.

[0054] (5) Collection of phenotypic and pedigree data

[0055] The daily data of more than 2,000 Holstein and Jersey cows were collected from 8 large-scale ranches in southern Jiangsu. The data must meet the following requirements: the parity is recorded as 1-3 parities; the age of the first calving is between 22-36 months, and the number of phenotypic records for each parity is greater than 6; the number of days of lactation (DIM) is 5-305 days; the daily milk production of each record is between 5-80kg; the somatic cell score range is 0-9, and the number of records after quality control is greater than 80,000. At the same time, the pedigree data of these dairy cows for more than three generations are collected.

[0056] Analytical methods and models

[0057] (1) Interpolation accuracy index

[0058] This method takes into account the consistency rate, non-reference consistency rate and r 2 These three metrics are commonly used to measure imputation accuracy. The concordance rate is defined as the proportion of individuals whose imputed genotype is consistent with the observed genotype. The non-reference concordance rate is similar to the concordance rate, but is limited to individuals who are not homozygous for the reference allele. 2 is the square of the Pearson correlation coefficient between the observed and imputed genotypes. The concordance rate and r were measured on a per-SNP basis. 2 , and averaged across SNPs in MAF bins (size 0.01) or across the entire genome.

[0059] (2) Software combination of phasing and interpolation

[0060] In this method, a combination of Beagle 5.2 phasing and Beagle 5.2 interpolation was tested for SNP chip data. For low-coverage sequencing data, GLIMPSE2 was used for phasing and GLIMPSE2 was used for interpolation. In addition, the interpolation effect of GLIMPSE2 was tested for RNA-Seq data. All software was run under default options, and no informative linkage map was used (set to 1 cM / 1 Mb).

[0061] The principle of Beagle 5.2 is based on the Markov Chain Monte Carlo (MCMC) method. First, the MCMC algorithm is used to infer the implicit genotype, and combined with the chromosome recombination model, the genotype prediction is refined through the local estimation of the recombination rate. The interpolation process obtains the interpolation result by finding the optimal genotype combination in the Markov chain space. The interpolation formula is:

[0062]

[0063] Among them, P (Genotype i |Data i ,θ) represents the genotype probability of a given specific site i, and θ is the recombination frequency parameter.

[0064] GLIMPSE2 uses weighted average and local linkage disequilibrium (LD) structure to achieve interpolation through Bayesian inference. First, GLIMPSE2 performs probabilistic inference on the local association structure in the data based on the HMM model, then compares the target data with a high-quality reference library, and transfers the high-frequency allele information in the reference library to the target data through Bayesian inference. At each target site, GLIMPSE2 selects the most likely genotype through the maximum a posteriori probability (MAP), and the interpolation formula is:

[0065]

[0066] Among them, P(Data|Genotype) represents the probability of a given genotype in the observed data, and P(Genotype|Reference) is the probability of genotype distribution in the reference library.

[0067] (3) Genome-wide association analysis

[0068] Association analysis was performed using linear mixed model analysis (MMA) implemented in the DMU package. SNPs were fitted to the model one by one:

[0069] y=1μ+bx+Za+e

[0070] Where: y is the vector of pseudo-phenotypes for lactation traits, 1 is the unit vector, μ is the population mean, b is the SNP additive allele substitution effect, x is the gene dosage vector of randomly selected SNP alleles (coded as 0, 1, or 2), Z is the association matrix linking additive polygenic effects to corresponding pseudo-phenotypes, a is the vector of additive polygenic effects, and e is the vector of random residuals. Assume a follows a normal distribution Where A is the additive genetic relationship matrix between individuals created using pedigree information, is the polygenic variance. For the random residuals, assume in is the residual variance, W is the variance including w i The diagonal matrix of elements, w i is the weight of the ith pseudo-phenotype. The weight of the pseudo-phenotype of the ith animal is calculated as in is the reliability of the ith pseudo-phenotype and is higher than 0.98. was set to 0.98 to avoid placing too much weight on spurious phenotypes of individuals with very high reliability. Variance components were estimated in each run and Sampling variance of allele substitution effect is obtained through the mixed model equation. Construct the Wald chi-square statistic with df = 1 To check whether the SNP is associated with the trait.

[0071] The Bonferroni correction was used to correct for multiple testing in single-trait GWAS within populations. This method applied a significance threshold corresponding to a nominal 5% type I error, defined as 0.05 / N, where N is the number of SNP loci. In addition, a less stringent chromosome-wide significance threshold, i.e., suggestive significance, was also used for Jiangsu Jersey cattle in China.

[0072] This method aims to explore and improve existing methods, evaluate the utility of phasing and interpolation software in three types of data: chip data, sequencing data (whole genome, transcriptome), to determine the best phasing and interpolation combination and practicality for different data types, so as to effectively fill in missing information, improve the accuracy and completeness of genetic data, and promote various downstream analyses. Beagle and glimpse2 software were used for chip data and sequencing data (whole genome, transcriptome), respectively, and the CattleGTEx interpolation panel phased by beagle was used. By comparing the distribution of the number of SNPs in the quality score interval of SNPs after interpolation of the three data types, it was found that 1X sequencing data and 100K chip data had a higher proportion of SNPs in the high accuracy interval (≥0.8), while RNA sequencing data had a lower proportion in this interval ( Figure 2 , 3 , 4), indicating that the interpolation accuracy of RNA sequencing data is slightly lower than that of 1X sequencing data and 100K chip data. Taking into account the interpolation accuracy and the number of SNPs after interpolation, the interpolation quality score quality control standards of 1X sequencing data, 100K chip data, and RNA sequencing data are set to 0.8, 0.8, and 0.6, respectively, for the best interpolation performance ( Figure 5 , 6 , 7). To further verify the biological significance of the data, the study used GCTA software to conduct a genome-wide association analysis (GWAS) on milk production traits, and used the Bonferroni correction method to reduce false positive findings. The results showed that the SNPs after interpolation of 1X sequencing data and 100K chip data detected significantly associated SNP sites in milk production traits, while no significant association signals were detected in RNA sequencing data ( Figure 8 , 9 , 10). These results indicate that different data types perform differently in the phasing and interpolation processes, providing a valuable reference for selecting the best phasing and interpolation combination for different types of data.

[0073] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0074] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that:

[0075] If the functions are implemented in the form of software functional units 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 invention, 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 (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0076] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0077] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0079] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0080] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing, and transcriptome sequencing, characterized in that: include, Build an interpolation data platform, capture sequencing data processing and SNPs, and perform chip data processing and RNA-Seq data processing; Determine imputation accuracy metrics, build software packages for phasing and imputation, and conduct genome-wide association analyses; Output analysis results.

2. A genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing, and transcriptome sequencing as claimed in claim 1, characterized in that: The capture sequencing data processing and SNP include performing 1X genome low-depth sequencing on the collected data. After sequencing, fastp is used to filter reads below the preset quality standard under default parameters, and the reads are mapped to the bovine reference genome ARS-UCD1.2 through BWA-MEM.

3. A genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing and transcriptome sequencing as claimed in claim 2, characterized in that: The capture sequencing data processing and SNP include converting and sorting bam files using SAMtools, excluding duplicate reads using the PICARD v3.2.0 MarkDuplicates block, and executing GATKv.4.4.0.0 HaplotypeCaller to capture SNPs. After variant capture, the variant quality score of GATK is recalibrated to filter out SNPs below a preset quality standard to obtain a SNP database.

4. A genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing and transcriptome sequencing as claimed in claim 3, characterized in that: The chip data processing includes using the GGPBovine 100K SNPChips chip for genotyping detection, wherein the bovine ARS-UCD 1.2 assembled genome is used as a reference genome.

5. A genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing, and transcriptome sequencing as claimed in claim 4, characterized in that: The RNA-Seq data processing includes extracting total RNA from blood samples using TRIzol reagent, purifying RNA samples and using Spectrophotometer to check quality and assess RNA quality; If the concentration range of the RNA sample is within the preset range and the RNA integrity value RIN is greater than the preset standard, the gene expression analysis standard is met, and cDNA library preparation and RNA sequencing are required to generate double-end reads.

6. A genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing, and transcriptome sequencing as claimed in claim 5, characterized in that: The RNA-Seq data processing includes filtering low-quality reads using fastp under default parameters, aligning paired-end RNA-Seq data to a reference genome, generating alignment results that meet preset quality standards using annotation information, converting bam files by SAMtools and sorting them by chromosome coordinates, adding or replacing read group labels for each read in the bam file using the AddOrReplaceReadGroups module of GATK, and finding and marking duplicate reads in the bam file using the MarkDuplicates module, further processing reads in which the CIGAR string in the bam file contains a long deletion N using the SplitNCigarReads module, and performing base recalibration using the BaseRecalibrator and ApplyBQSR modules to correct sequencing errors and system deviations.

7. A genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing, and transcriptome sequencing as claimed in claim 6, characterized in that: The determination of the imputation accuracy index includes measuring the consistency rate and r on a per SNP basis. 2 , and averaged across SNPs in MAF bins or across the entire genome; where r 2 is the square of the Pearson correlation coefficient between the observed genotype and the imputed genotype, and the consistency rate is defined as the proportion of individuals whose imputed genotype is consistent with the observed genotype; The software combination for constructing phasing and interpolation includes, for low coverage sequencing data, using GLIMPSE2 for phasing and GLIMPSE2 for interpolation, and testing the interpolation effect of GLIMPSE2 on RNA-Seq data.

8. A genotype interpolation method that can be efficiently applied to data such as SNP chips, low-coverage genome sequencing, and transcriptome sequencing as claimed in claim 7, characterized in that: The genome-wide association analysis involved using a linear mixed model analysis implemented in the DMU package, and the SNPs were fitted into the model: y=1μ+bx+Za+e Where y is the vector of pseudo-phenotypes of lactation traits, 1 is the unit vector, μ is the population mean, b is the SNP additive allele substitution effect, x is the gene dosage vector of randomly selected SNP alleles, Z is the association matrix linking additive polygenic effects to corresponding pseudo-phenotypes, a is the vector of additive polygenic effects, and e is the vector of random residuals; Assume that a follows a normal distribution Where A is the additive genetic relationship matrix between individuals created using pedigree information, is the polygenic variance; for the random residuals, assume in is the residual variance, W is the variance including w i The diagonal matrix of elements, w i is the weight of the ith pseudo-phenotype; the weight of the pseudo-phenotype of the ith animal is calculated as in is the reliability of the ith pseudo-phenotype and is higher than 0.

98. Set to 0.98; Estimate variance components in each run and Sampling variance of allele substitution effect is obtained through the mixed model equation, and at the same time, the Wald chi-square statistic with df = 1 is constructed To check whether the SNP is associated with the trait.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

Images