Identification of genes habouring mutations affecting eating quality traits in beef cattle using Bayesian genomic prediction methods

Methods such as Genome-Wide Association Studies (GWAS) and Bayesian genomic prediction (BayesR) are commonly employed to enhance understanding of the genetic architecture of complex traits, identify genetic variants associated with these traits, and assist in breeding decisions and market allocation. This study aims to uncover genes harbouring variants influencing eating quality traits, including tenderness, juiciness, flavour, overall liking, and meat quality score (MQ4) in a large and diverse population of Bos taurus indicus cattle from Australia, the USA, and Ireland. The analysis involved 7,380 young males and females with phenotypic data and genotypes imputed up to 709,768 SNP (Illumina HD array). The BayesR approach was applied with a chain length of 40,000 iterations and a burn-in of 5,000 iterations. Notably, the findings highlight 324 SNPs with exceptionally high posterior inclusion probabilities (PIP > 0.9999 quantile for each trait), linked to 100 candidate genes. Among these, shared genetic signals across most of the traits within or close to genes such as CAPN1, CAST, bta-mir-2407, and CCDC171 underscore their pivotal roles in meat quality across diverse populations. These insights contribute significantly to the global effort to enhance meat quality through genomics-driven cattle breeding programs.