Use of Ultra-High-Density Snp Data for Genetic Evaluation of Predicted Feed Intake, Feed Efficiency, Carcass, and Growth Traits in Hereford Cattle

Abstract

Keeping input costs low and producing the best quality product are two important goals of beef cattle producers. One way to reach these goals is to select for feed efficient animals to lower input costs and to select for the animals with the highest carcass quality traits to increase revenue. This study used ultra-high-density single nucleotide polymorphism (SNP) data to evaluate genetic merit for these traits in beef cattle. To find a more economical method for producers to select for feed efficiency independent of average daily gain (ADG), this study analyzed whether predicted dry matter intake (pDMI) phenotypes could be utilized as an indicator trait for efficiency. A genome-wide association study (GWAS) was performed to identify genomic regions that are important to predicting genetic merit in feed efficiency traits. A subset of the largest effect SNPs for each trait were compared to large effect SNP regions of other phenotypes to identify regions of the genome that impact feed efficiency but are independent of gain. The largest effect SNPs were analyzed to identify genes and biological pathways that could be directly linked to these regions. Carcass traits were analyzed using the same procedure. The direct genomic value (DGV) accuracies for ADG, dry matter intake (DMI), pDMI, and residual feed intake (RFI) ranged from 0.27 to 0.51. There were seven QTL regions in common between pDMI and DMI that were independent of ADG QTL regions. A gene clustering tool, Partial Correlation coefficient with Information Theory (PCIT), identified various genes in these QTL regions that are linked to obesity and weight loss in mice. In addition, the accuracies for carcass phenotypes varied from 0.47 to 0.60.