March 2026 talk of the monthly meeting of the #animal-genomics special interest group.

It is a pleasure to announce the forthcoming talk by Junjian Wang (North Carolina State University, USA). Dr. Wang is a postdoc in the group of Dr. Christian Maltecca and Dr. Jicai Jiang at North Carolina State University’s Department of Animal Science.

Fine-mapping complex traits in farm animals: methods for related individuals and large-scale application

Fine-mapping causal variants from GWAS loci remains challenging in livestock populations, where strong relatedness and complex population structure violate assumptions of standard methods. We introduce a comprehensive Bayesian framework specifically designed for related individuals. For individual-level data, BFMAP-SSS employs a linear mixed model combined with shotgun stochastic search. For summary statistics, FINEMAP-adj and SuSiE-adj adapt existing tools by incorporating relatedness-adjusted LD matrices derived from LMM. We further propose genomic-feature posterior inclusion probabilities (PIPgene) to improve candidate gene identification by aggregating variant-level signals. Simulations in pig genotypes across diverse heritability levels and population structures demonstrate substantial improvements over existing tools in fine-mapping accuracy. We then apply this framework to a large-scale Holstein cattle GWAS, analyzing 11.3 million imputed sequence variants across 50,309 bulls and 30 complex traits. GWAS using SLEMM-GWA identified 381 significant association peaks, including 126 novel findings. Subsequent Bayesian fine-mapping statistically prioritized candidate genes within novel peaks, going beyond conventional proximal-gene reporting. Together, these contributions provide robust, validated methods and demonstrate their practical utility for dissecting the genetic architecture of complex traits in livestock.