Abstract
Fine-mapping causal variants from genome-wide association studies (GWAS) loci is challenging in populations with substantial relatedness, such as livestock, as standard methods often assume unrelatedness, leading to poor fine-mapping accuracy. Here, we introduce a comprehensive Bayesian framework to address this. Our approach features BFMAP-Shotgun Stochastic Search for individual-level data, which uses a linear mixed model (LMM) and shotgun stochastic search with simulated annealing. For summary statistics, we develop FINEMAP-adj and SuSiE-adj, novel strategies that directly use standard FINEMAP and SuSiE for samples of related individuals by employing LMM-derived inputs (particularly a relatedness-adjusted linkage disequilibrium matrix). Furthermore, genomic-feature posterior inclusion probability (PIP), implemented here as gene-level PIP (PIPgene), is proposed to enhance detection power by aggregating variant signals. Extensive simulations based on pig genotypes across diverse heritability levels and population structures (pure-breed and multi-breed) show our methods substantially outperform existing tools (FINEMAP, SuSiE, FINEMAP-inf, SuSiE-inf, and GCTA-COJO) in samples of related individuals, achieving notable improvements in fine-mapping accuracy (e.g. up to several-fold increases in the area under the precision-recall curve). Multi-breed populations greatly enhance fine-mapping accuracy compared to single-breed populations. Additionally, PIPgene markedly improves candidate gene identification. Application to Duroc pig traits demonstrates practical utility, with functional enrichment analysis confirming our methods' superior identification of biologically relevant variants. This work provides robust, validated methods and associated software for accurate fine-mapping in populations with complex relatedness.