Systematic Approach for Compound Angus Populations Revealing Positional Candidate Genes and Improving Prediction...

Carcass traits, which reflect growth performance and muscle development, are economically important in beef cattle, yet their genetic determinants remain poorly characterized. Both single-population Genome-wide association studies (GWAS) methods, such as BLINK, and cross-population meta-analysis approaches are widely used to identify genetic variants, yet their comparative performance in genomic prediction for complex traits in structured populations remains underexplored. Few studies have directly compared these methods in genomic prediction. To address this gap, this study aims to (i) identify positional candidate genes associated with carcass traits and (ii) evaluate the context-dependent advantages of Covariate Adjustment (CA) and meta in genomic prediction. In this study, we analyzed carcass weight (CW), live weight (LW), and dressing percentage (DP) in 279 crossbred Angus cattle genotyped with the PHR0105_Bt140K_v1.0 SNP chip. GWAS was performed on the full population using BLINK, and results from three subpopulations were combined via meta-analysis, with significance thresholds for both approaches determined by a shuffle-based method. Candidate genes located within ±10 kb of significant SNPs were associated with different carcass traits, including STRIT1, SEL1L3, NOC4L and ANK1 for DP; SNCA and DNAH5 for CW; and GYPC, GPR158, and GUCY1A1 for LW. Prediction accuracy under MAS and MABLUP showed meta slightly outperformed BLINK in MAS, while BLINK was better with covariate adjustment; after incorporating kinship in MABLUP, meta achieved higher accuracy and population partitioning was negligible. Overall, MABLUP yielded the highest accuracy (0.52-0.79) versus MAS (0.37-0.54) in all traits. These findings provide a methodological basis for selecting appropriate GWAS strategies in structured populations and highlight candidate genes.