The Design Of Low-Density SNP Array For Genomic Selection In Simmental Beef Cattle

Genomic selection(GS)has received widespread attention from animal and plant breeders.GS refers to a genetic evaluation method that uses phenotypic data and genotypes data of high dense genetic markers to predict genomic estimated breeding values(GEBV)of selection candidates.However,the high genotyping costs limit the application of GS in Simmental beef cattle.This study combined Illumina Bovine HD(770K)genotype data of 1346 Simmental beef cattle with 13 phenotype of growth,carcass and meat quality traits,and used 4 strategies,including genome-wide association analysis(GWAS),Bayes B analysis,setting sliding windows,and gene annotation,to select candidate SNP,aiming to form a low-density SNP panel that can achieve high accuracy of GEBV and save breeding costs.This study leads several important conclusions,as follows:1.The heritability of 13 traits ranges from 0.11 to 0.56,in which most of growth and carcass traits have high heritability estimates,and meat quality traits have moderate heritability estimates.This result can provide a reference for genetic analysis of important economic traits of Chinese Simmental beef cattle.2.A low-density SNP panel containing 30684 SNP was formed through four strategies,which included SNP that was significantly associated with traits,SNP that can account for a larger proportion of additive genetic variance,informative SNP with high minor allele frequencies(MAF)and SNP that was annotated in gene region.Overall,the mean and median SNP interval space of low-density SNP panel were 81.8 Kb and 37.7 Kb,respectively,and the mean and median MAF were 0.35 and 0.39,respectively.Meanwhile,we evaluated the linkage disequilibrium(LD)of low-density SNP panel and 770 K high-density chip,and found they have very similar LD level and decay trend.These results indicated that the low-density SNP panel can provided theoretical reference and method guidance for the design of commercialized lowdensity chip for Simmental beef cattle genomic selection.3.GBLUP,Bayes A,Bayes B and Bayes Cπ were applied to evaluate the accuracy of GEBV of lowdensity SNP panel and 770 K high-density chip.Results showed that the accuracy of GEBV of 13 traits ranged from 0.18 to 0.60 in 770 K high-density chip and ranged from 0.22 to 0.47 in low-density SNP panel.Compared to the results of 770 K high-density chip,using the low-density SNP panel improved the accuracies of GEBV of live weight,carcass weight,tenderloin,eye muscle area at the 12 th rib,eye muscle area at the 12 th rib,and marbling at the 12 th rib,and the improvements were 0.05,0.02,0.06,0.12,0.08,and 0.09,respectively.However,for other traits,the use of low-density SNP panel reduced the accuracies of GEBV,in which the accuracies of GEBV of average daily gain,live weight,striploin,spencer roll,chuck roll,dressing percentage,and lean percentage decreased by 0.03-0.07,and the accuracy of retail meat weight decreased by about 0.15.In addition,the correlation coefficients of GEBV estimated by low density SNP panel and 770 K high-density chip was ranged from 0.5 to 0.8 in 13 traits.Overall,these results indicated that the pre-selected low-density SNP dataset can achieve high or moderate accuracy for most traits and save breeding costs.