Funmap: integrating high-dimensional functional annotations to improve fine-mapping

Abstract

Fine-mapping aims to prioritize causal variants underlying complex traits by accounting for the linkage disequilibrium of GWAS risk locus. The expanding resources of functional annotations serve as auxiliary evidence to improve the power of fine-mapping. However, existing fine-mapping methods tend to generate many false positive results when integrating a large number of annotations. In this study, we propose a unified method to integrate high-dimensional functional annotations with fine-mapping (Funmap). Funmap can effectively improve the power of fine-mapping by borrowing information from hundreds of functional annotations. Meanwhile, it relates the annotation to the causal probability with a random effects model that avoids the over-fitting issue, thereby producing a well-controlled false positive rate. Paired with a fast algorithm, Funmap enables scalable integration of a large number of annotations to facilitate prioritizing multiple causal SNPs. Our simulations demonstrate that Funmap is the only method that produces well-calibrated FDR under the setting of high-dimensional annotations while achieving better or comparable power gains as compared to existing methods. By integrating GWASs of 4 lipid traits with 187 functional annotations, Funmap consistently identified more variants that can be replicated in an independent cohort, achieving 15.5% - 26.2% improvement over the runner-up in terms of replication rate.