Optimization Selecting Related SNPs For Economic Traits Of Chinese Simmental Beef Cattle

Genome selection overcomes the limitations of long periodicity,poor predictability and low accuracy of traditional breeding methods.This method can significantly improve the selection efficiency of animals and plants.It had great attention in nowadays.However,when it is applied in the Chinese Simmental beef cattle population,there are problems such as high cost of genotyping,and the variation in the chip cannot completely cover the QTL of important economic traits in the breed.It is necessary to design a special breeding chip for Chinese Simmental beef cattle population for reducing the cost of genotyping and meeting that need of breeding of Chinese Simmental beef cattle.We used sequencing data of Chinese Simmental beef cattle and the phenotype of 10 traits including carcass,growth,and meat quality trait.In this study,SNPs were selected by 3 methods: GWAS,Bayes B and sliding window method.These SNPs were combined to 9 gradient SNP subsets.Finally,the GBLUP and Bayes B models were used to test the breeding value estimation accuracy of the 9 SNP subsets.The optimal SNP subset was selected according to the result.The conclusions in this study are as follows:1.Genetic parameters of 10 important economic traits of Chinese Simmental beef cattle were estimated by REML.Among them,both the growth traits and carcass traits were medium heritability traits with the range of 0.33-0.38 and 0.28-0.38.The meat quality trait was of low heritability with the heritability 0.11-0.15.2.The SNPs selected by the three methods were combined to 9 SNP subsets with different density.For 9 subsets,the accuracy of GEBV were estimated for 10 traits were 0.177-0.446 and 0.191-0.482 using GBLUP and Bayes B methods.The accuracy of Bayes B method was higher than GBLUP method.When the first 0.01% SNPs in the GWAS method and the first 0.05% SNPs in the Bayes B method were combined(Subset C),the mean GEBV estimated accuracy for each trait was the highest.The accuracy of GEBV estimation by subset C for WW,ADG,CW,DP,and EMA traits were higher than that of the770 K chip.The accuracy for LW,CRW,RW,RMW and SF traits were lower than that of 770 K chip.3.According to the accuracy results of different subsets,the optimal SNP subset contained 34,468 SNPs,with the average SNP spacing of 67.5Kb and the average MAF of 0.38.The optimal SNP subset in this study can provide a reference for designing Chinese Simmental beef cattle special chip with medium or low density.