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.

Background: The yak ( Bos grunniens ) is an essential livestock species in high-altitude alpine ecosystems, serving key roles in meat, milk, and wool production as well as ecological maintenance. However, traditional yak breeding systems face several limitations, including fragmented herd data management, inefficient phenotypic collection, missing pedigree records, and high technical barriers to applying genomic technologies. These factors constrain the widespread application of modern genetic evaluation methods in yak breeding programs. Description: We developed the Yak Genomic Evaluation Platform (YGEP, https://yakbreeding.swun.edu.cn/en/ ), an integrated digital system for yak genetic selection and herd management. YGEP integrates three core analytical modules: pedigree reconstruction, genotype imputation and genomic breeding value estimation. Benchmarking on 706 yaks showed that GBLUP achieved a prediction accuracy of r = 0.96 for cannon bone circumference (CCB), and KBeagle reached 84.0% imputation accuracy with the combined reference panel. Conclusion: YGEP bridges the gap between advanced genomic analytics and practical yak breeding. By integrating pedigree reconstruction, genotype imputation, and GEBV estimation into a user-friendly cloud platform, YGEP effectively eliminates technical and data bottlenecks. Overall, it provides an accessible, scalable, and standardized solution to advance digital herd management and precision genetic improvement in alpine livestock.