Genome-wide Association Studies And Genomic Prediction For Residual Feed Intake And Their Component Traits In Beef Cattle

Part Ⅰ:Feed efficiency is of particular interest to the beef industry as feed is the largest variable cost in production while fatty acid composition is emerging as an important trait both economically and socially,due to the potential implications of dietary fatty acids on human health.Quantifying correlations between feed efficiency and fatty acid composition will contribute to construction of optimal multiple trait selection indexes to maximize beef production profitability.In the present study,we estimated phenotypic and genetic correlations of feed efficiency measures including residual feed intake(RFI),RFI adjusted for final ultrasound backfat thickness(RFIf),their component traits average daily gain(ADG),dry matter intake(DMI),metabolic body weight(MWT)and final ultrasound backfat thickness(FUFAT)with 25 major fatty acids in the subcutaneous adipose tissues of 1366 finishing steers and heifers using bivariate animal models.The phenotypic correlations of RFI and RFIf with the 25 individual and grouped fatty acid traits were generally low(<0.25 in magnitude).However,relatively stronger genetic correlation coefficients of RFI and RFIf with polyunsaturated fatty acid traits includng g n-6/n-3 ratio(0.52±0.29 and 0.45±0.31),18:2n-6(0.45±0.18 and 0.40±0.19),n-6(0.43±0.18 and 0.38±0.19),PUFA(0.42±0.18 and 0.36±0.20),and 9c-16:1(-0.43±0.20 and-0.33±0.22)were observed.Hence selection of low RFI or more efficient beef cattle will improve fatty acid profiles by lowering the content of n-6 PUFA,thus reducing the ratio of n-6/n-3,along with increasing the amount of 9c-16:1.Moderate to moderately high genetic correlations were also observed for DMI with 9c-14:1(-0.32±0.17)and sumCLA(-0.45±0.21),suggesting that selection of beef cattle with lower DMI will lead to an increase amount of 9c-14:1 and sumCLA in the subcutaneous adipose tissue.However,unfavorable genetic correlations were detected for ADG with 11t-18:1(-0.38±0.23)and sumCLA(-0.73±0.26),implying that selection of beef cattle with a better growth rate will decrease the contents of healthy fatty acids 11t-18:1 and sumCLA.Therefore,it is recommended that a multiple trait selection index be used when genetic improvements of fatty acid composition,feed efficiency,feed intake and growth are important in the breeding objective.Part 2:To explore genetic architectures underlying RFI,DMI,ADG and MWT for better genetic improvement on the traits via maker assisted or genomic selection in beef cattle,a genome-wide association study(GWAS)was performed based on whole genome imputed sequences including 12 million single nucleotide polymorphisms(SNPs)in 7573 beef cattle across 6 diffenent populations.In total 6,285,628 and 1388 SNPs that were significantly associated with RFI,DMI,ADG and MWT,respectively.Five SNPs affecting ADG and four SNPs affecting MWT were overlapped with the cattle public QTL database.In addition,we predicted the biologic annotation of 1463 unique SNPs from the 2307 significant SNPs detected above and classified them into three categories according to the order of annotation for less to high importance.There were 1311,84 and 6 in the first(CHIP),the second(REG)and the third(NSC)class,respectively.The GWAS for RFI showed that 1 and 5 significant loci were identified on chromosome 5 and 12,respectively.With the annotation of gene function,DCP1B and SUPT20H genes were perceived as potential candidate genes.Moreover,we found that most of the SNPs were pleiotropic,with 602 of 1463 SNPs strongly associated with at least two traits of DMI,ADG and MWT.Most of pleiotropic SNPs(136)were located in the region 37.9-39.0 Mb on BTA 6,and thus DCAF16,NCAPG and LCORL genes were considered as the most compelling candidate genes.The region of 24.89-25.07 Mb on BTA 14 harbored many pleiotropic SNPs as well,and the potential candidate genes were MOS,PLAG1 and LYN genes.Furthermore,the pleiotropic and missense mutation(rs42661323)located in STC2 gene on BAT 20 was in high linkage disequilibrium(r2=0.96)with the most significant SNP associated with MWT and ADG.Therefore,rs42661323 is most likely to be a causative variant.The current study represents the first GWAS paper for RFI,DMI,ADG and MWT using whole genome imputed sequence with the largest sample size of beef cattle to date.These results would provide insights into the genetic architectures and molecular mechanisms for feed efficiency,feed intake and growth traits in beef cattle.Part 3:Genomic selection(GS)offers great a promise to select animal based on genetic merit for traits that are difficult or costly to measure such as feed efficiency in beef cattle.The key factor of successful genomic selection is high accuracy of genomic prediction.To maximize the genetic improvement rate and profitability for RFI,DMI and ADG in beef cattle,we investigated how various statistical methods(GBLUP,BayesB and BayesR),density of genetic markers(50K and 777K)and combined data of different breed/populations affect prediction accuracy.A total of 7573 individuals with 50K and imputed 777K genotype were included in this study.A five fold cross-validation method was conducted to quantify the accuracy of genomic prediction,where the accuracy was computed as the correlation between the estimated genomic breeding values and the adjusted phenotypic values divided by the square root of the trait heritability.We found the highest prediction accuracy was 0.66±0.04 when 777K genotype,multi-breed reference population under BayesR were used to predict DMI.The average accuracies among different stratages under BayesR for RFI,DMI and ADG were 0.37,0.43 and 0.39,respectively,which were slightly larger than that under BayesB model(0.35,0.38 and 0.36)and under GBLUP model(0.35,0.38 and 0.36).Moreover,combining multiple breeds/population as reference population outperformed than single breed reference population,increasing the accuracy by 0.06 on average.Increasing the genotype density led to a relatively small improvement of 0.02 on average.Therefore,a high density SNP genotype of a large reference population using BayesR model was recommended for feed efficiency,feed intake and average daily gain in beef cattle genetic evaluation.