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

Authors: WANG, JUNJIAN - North Carolina State University; GAO, YAHUI - University Of Maryland; Toghiani, Sajjad; COLE, JOHN - Council On Dairy Cattle Breeding; MALTECCA, CHRISTIAN - North Carolina State University; MA, LI - University Of Maryland; JIANG, JICAI - North Carolina State University

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/12/2025
Publication Date: 11/1/2025
Citation: Wang, J., Gao, Y., Toghiani, S., Cole, J.B., Maltecca, C., Ma, L., Jiang, J. 2025. Genome-wide association study and fine-mapping using imputed sequences to prioritize candidate genes for 30 complex traits in 50,309 Holstein bulls. Journal of Dairy Science. 108(11):12506–12518. https://doi.org/10.3168/jds.2025-27058.
DOI: https://doi.org/10.3168/jds.2025-27058

Interpretive Summary: For dairy farmers, identifying the best animals for breeding—those that yield more milk or resist disease better—has always been a challenging task. Now, groundbreaking research is providing them with a significant new genetic advantage. Scientists conducted a large-scale study, analyzing over 11 million unique genetic markers from more than 50,000 Holstein bulls across 30 traits. Using innovative and efficient computational methods, they successfully pinpointed 381 key genetic regions linked to traits categorized into production and yield, type, and longevity and health, including 126 entirely new discoveries. Crucially, the most likely specific genes within these regions were statistically identified, providing a clearer and more direct understanding of the genetic basis for valuable dairy traits. This breakthrough significantly enhances the understanding of dairy cattle genetics, enabling breeders to make faster and more accurate decisions. The direct impact is the rapid development of healthier, more productive herds, which translates into increased efficiency and profitability for dairy farmers, improved well-being for the animals, a more robust agricultural economy, and a valuable advancement for genetic science. This research effectively leverages taxpayer investments to deliver tangible benefits for the dairy industry and contribute to a more sustainable food supply.

Technical 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 genome-wide association study of dairy cattle utilized de-regressed breeding values as phenotypes and analyzed 11,292,243 quality-controlled, imputed DNA 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 de-regressed breeding values across individuals and demonstrates computational efficiency for large sample sizes and sequence data. This analysis identified 381 significant association peaks (P < 5×10^-8), 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, including DGAT1, ABCG2, GPIHBP1, ZNF623, ZC3H3, PLEC, HSF1, ABCC9, ARRDC3, CCND2, TMTC2, IGF2, and BTBD9, and, more importantly, identified credible candidate genes within the 126 newly discovered peaks, 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.