PSXIV-22 Identification of genetic markers for carcass quality traits within the American Simmental Association Carcass Merit Program.

摘要

Beef product quality is a leading influence on consumer decisions to consume beef. Insufficient marbling and subsequent lower quality grades, lack of a uniform product, reduced or insufficient tenderness, high carcass weight or high yield grade can negatively impact beef quality and consumer acceptability. Beef product attributes can be improved by making genetic selection decisions based on accurate predictions of genetic merit for carcass and meat quality in candidate breeding animals. In an effort to advance their ability to accurately predict the breeding value of potential sires for carcass traits, the American Simmental Association (ASA) launched the Carcass Merit Program (CMP) in 1997 as a means to collect progeny sire group carcass information. This program has allowed the ASA to accumulate significant genetic performance information that can/has been reliably conveyed to producers who utilize Simmental genetics. This project aimed to utilize the accumulated progeny data and sire genotypes to further enhance the predictive accuracy by identifying DNA markers for carcass attributes. All records were extracted that contained birth weight, calving ease scores, weaning weights, back fat, ribeye area, marbling, hot carcass weights (HCW), and kidney, pelvic, heart fat (KPH), as well as sire EPD, performance, and genotype data. Progeny data was organized by sire family and progeny phenotypes were constructed. Sire genotypes were filtered and a single-locus mixed linear and multi-loci mixed linear model was used to perform an association analysis on the genotype data, accounting for relatedness and pedigree structure. Significant markers and suggestive QTL were identified utilizing an adjusted p-value < 0.01. On average significant markers explained approximately 20% of the genetic variance in the traits. For example, for the trait carcass weight, the single-loci identified 591 significant markers and the mixed-loci model identified 558 markers (p < 0.01) which explained 19.86% of the genetic variance.