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

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 p

SUBMITTER: Wang J 

PROVIDER: S-EPMC12636493 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature