212 Genome-Wide Association Study Investigating the Genomic Components of Efficiency in Beef Cows

Abstract

Abstract Efficiency of mature beef cows consuming high-forage diets is not comparable with standard definitions of beef cattle efficiency such as feed conversion or residual feed intake in the feedlot setting. This study aimed to conduct a genome-wide association study (GWAS) to identify regions of the bovine genome associated with efficiency in mature beef cows. Ninety-eight black Angus cows were managed under extensive feeding programs over a two-year period. Using rump fat at calving, calving date, and calf weaning weight as a percentage of the body weight of the dam, a weighted percentile scoring system was used to rank efficiency. Eighty-three of the cows were retained for the GWAS. The 20 most and 20 least efficient cows (HD dataset, n=40) were genotyped with the Illumina BovineHD BeadChip (777,000 SNPs) while the remaining cows were genotyped with the Neogen GGP Bovine 100K chip (LD dataset, n = 43). The LD dataset was imputed to the HD SNP array density using Beagle5.4 (FULL dataset, n = 83). Three separate GWAS were conducted using GAPIT (version3). The first GWAS was performed on the HD dataset using a quantitative phenotype determined by the ranking system. The second GWAS was performed with the HD dataset using a qualitative phenotype (efficient vs non-efficient cows). For the final GWAS, the FULL dataset with ranked phenotypes was used. Five models were evaluated for each GWAS, including the general linear model (GLM), mixed linear model (MLM), multiple loci mixed model (MLMM), fixed and random model circulating probability unification (FarmCPU), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK). The model that best represented each dataset as determined by quantile-quantile plots was used for downstream analysis. Although results of the GWAS were not significant (Bonferroni threshold), associations were identified on BTA10, 16, 17, 25, 27 and 29. Positional candidate genes in the associated regions include: FOXN3, UPB1, SNX29, DDX54, LOC112444612, RITA1, and TACC1. Further functional analyses are required to confirm these findings; however, this work provides a foundation for identifying genes and gene mutations influencing this newly described phenotype.