GWAS & Selection signatures

Genome-wide fine-mapping improves identification of causal variants

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Abstract

Genome-wide fine-mapping (GWFM) refines association signals to identify causal variants underlying complex traits, overcoming limitations of region-specific approaches that focus only on genome-wide significant loci. We present a comprehensive evaluation of GWFM using SBayesRC, a genome-wide Bayesian mixture model that jointly analyzes all common SNPs and integrates functional annotations. Through extensive simulations under diverse genetic architectures and analyses of UK Biobank and disease GWAS data, GWFM consistently outperformed existing fine-mapping methods across multiple metrics, including posterior inclusion probability calibration, mapping power, resolution, precision, replication rate, and trans-ancestry prediction accuracy.Across 48 well-powered traits, GWFM identified 19,863 local credible sets (median size = 5 SNPs), explaining on average 18% of SNP-based heritability (h²_SNP), with ~30% located outside genome-wide significant loci. These findings highlight the importance of genome-wide analysis for capturing signals missed by conventional GWAS thresholds. Leveraging genetic architecture estimates from SBayesRC, we developed a framework to predict fine-mapping power and variance explained for prospective studies. For example, achieving 50% h²_SNP coverage would require ~2 million samples on average, with substantial variation across trait categories. We also demonstrate that incorporating functional annotations improves prioritization of biologically relevant variants, exemplified by fine-mapping known causal variants at FTO for body mass index and novel missense variants for schizophrenia and Crohn's disease.Our results establish GWFM as a robust and scalable approach for causal variant discovery, offering improved accuracy and interpretability compared to region-specific methods. By integrating functional genomic information and enabling power prediction for future studies, GWFM provides a unified framework for genetic discovery and fine-mapping, paving the way for more comprehensive characterization of complex trait architecture.

Keywords: 2026

How to Cite:

Wu, Y., Zheng, Z., Thibaut, L., Lin, T., Feng, Q., Cheng, H., Yengo, L., Goddard, M., Wray, N., Visscher, P. & Zeng, J., (2026) “Genome-wide fine-mapping improves identification of causal variants”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2286036. doi: https://doi.org/10.31274/wcgalp.23842

Rights: 1