Fine-mapping methods for complex traits: essential adaptations for samples of related individuals

Abstract

Fine-mapping causal variants from 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-SSS 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, gene-level posterior inclusion probability (PIPgene) is proposed to enhance detection power by aggregating variant signals. Extensive simulations based on pig genotypes 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 AUPRC). PIPgene markedly improves candidate gene identification. Application to Duroc pig traits demonstrates practical utility. This work provides robust, validated methods and associated software and scripts for accurate fine-mapping in populations with complex relatedness.