Genome-wide association study and fine-mapping using imputed sequences to prioritize candidate genes for 30 complex traits in 50,309 Holstein bulls.

Abstract

Identifying causal genetic variants underlying economically important traits in dairy cattle is essential for understanding their genetic basis and optimizing breeding programs. The growing availability of sequenced reference genomes and individuals with both phenotypic and genotypic data notably enhances our ability to detect genetic associations and further pinpoint causal effects. This comprehensive GWAS of dairy cattle used deregressed breeding values as phenotypes and analyzed 11,292,243 quality-controlled, imputed sequence variants from 50,309 Holstein bulls. The number of bulls with available phenotypes ranged from 23,121 to 50,309 across 30 complex traits categorized into production and yield, type, and longevity and health. We performed GWAS using our SLEMM-GWA approach, which accounts for the varying reliability of deregressed breeding values across individuals and demonstrates computational efficiency for large sample sizes and sequence data. This analysis identified 381 significant association peaks, of which 126 are novel findings. Subsequent Bayesian fine-mapping provided statistical prioritization by assigning posterior conditional inclusion probabilities to individual variants and genes, yielding a list of credible candidate genes-an advancement over conventional GWAS reporting of all proximal genes. This prioritization offered direct statistical support for previously reported genes and, more importantly, identified credible candidate genes within the 126 newly discovered peaks for specific traits, including AOPEP, GC, E2F6, MGST1, VPS13B, ZNF652, ASPH, SFMBT1, and MAPRE2. These findings enhance the understanding of the genetic architecture of these complex dairy traits and provide valuable insights for the refinement of genomic selection strategies and breeding programs in Holstein cattle.