中国农业科学2024，Vol.57Issue(11)：2243-2253,11.DOI:10.3864/j.issn.0578-1752.2024.11.015
基于靶向捕获测序的猪单倍型基因组选择效果研究
The Efficiency of Haplotype-Based Genomic Selection Using Genotyping by Target Sequencing in Pigs
摘要
Abstract
[Objective]This study explored the efficiency of haplotype-based genomic selection using genotyping by target sequencing technology in order to provide the useful experience for molecular breeding of pigs in China.[Method]The growth traits records of 1 267 Large White pigs and the reproduction trait records of 800 Large White pigs were collected and genotyped by using the porcine 50K liquid-phase SNP panel(liquid-phase 50K)based on the Genotyping by Target Sequencing(GBTS)as well as its target resequencing data.Three strategies for haplotype block partition,including fixed SNP number,fixed length,and target block,were proposed to construct haplotypes,and the corresponding haplotype-based genomic prediction were compared with single SNP genomic prediction.The target block partition strategy was mainly based on target resequencing data obtained by the GBTS,and one haplotype block contained one target site in liquid-phase 50K and its upstream and downstream SNPs(mSNP)within 400 bp from target resequencing data.In this study,after phasing haplotype using Beagle 5.1 within each haplotype block,the different haplotypes were recoded as haplotype alleles,and then haplotype allele matrix was constructed using haplotype dosage model.A single-step GBLUP model(ssGBLUP)was then used to estimate genomic breeding values for three traits of days at 100 kg body weight(AGE),backfat thickness at 100 kg(BF),and total number of bom(TNB).A two-trait animal model was implemented for two growth traits of AGE and BF,and repeatability model for TNB.Younger validation population and five-fold cross validation were carried out to assess the efficiency of genomic prediction.The correlation coefficients between estimated breeding values and genomic estimated breeding values were measured as the accuracy of genomic prediction,and the regression coefficients of genomic estimated breeding values on estimated breeding values were measured unbiasedness,respectively.[Result]The results from the younger validation population showed that the target resequencing data yielded lower accuracies of genomic selection on three traits than liquid-phase 50K SNP,even its number of SNP after genotype quality control was increased from 42 302 in liquid phase 50K to 88 105.The haplotype blocks partitioned by target block strategy contained 2.08 SNPs and 5.67 haplotype alleles on average.The accuracies of haplotype-based genomic selection based on all three haplotype block partition strategies were improved,and the target block gained the largest improvement,yielding 4.80％,1.98％and 6.04％higher accuracies on AGE,BF and TNB than liquid-phase 50K,respectively.In addition,the target block strategy generated the lowest bias in most scenarios as well.The five-fold cross-validation obtained similar results as younger validation population did,target block gained advantages over both single SNP and other haplotype block partition methods.The fixed length of 400 bp block partition strategy performed comparable with target block,while it was time demanding.Although fixed 2 and 5 SNP haplotype block partition methods performed better than single SNP method,they were worse than target block.[Conclusion]Due to the short length,the linkage disequilibrium between most of the target sites in liquid-phase 50K SNPs and their mSNPs in same blocks are strong,resulting in target block strategy obtained higher accuracy in haplotype-based genomic selection than single SNP and other haplotype block partition strategies,which could make use of the technique advantage of GBTS,and further broaden the application of liquid-phase chip.关键词
猪/液相芯片/基因组选择/单倍型/靶向捕获测序Key words
pig/liquid chip/genomic selection/haplotype/genotyping by target sequencing引用本文复制引用
刘燕玲,丁向东,邱奥,张梓鹏,王雪,都鹤鹤,罗文学,王贵江,魏霞,施文颖..基于靶向捕获测序的猪单倍型基因组选择效果研究[J].中国农业科学,2024,57(11):2243-2253,11.基金项目
财政部和农业农村部:国家现代农业产业技术体系(CARS-35)、山东省农业良种工程项目(2022LZGC003)、河北省重点研发计划(19226376D) （CARS-35）
