Genome-wide fine-mapping improves identification of causal variants

Fine-mapping refines genotype-phenotype association signals to identify causal variants underlying complex traits. However, current methods typically focus on individual genomic loci and do not account for the global genetic architecture. Here we demonstrate the advantages of performing genome-wide fine-mapping (GWFM) with functional annotations and develop methods to facilitate GWFM. In simulations and real data analyses, GWFM outperforms current methods across several metrics, including error control, mapping power, resolution, precision, replication rate and trans-ancestry phenotype prediction. Across 48 complex traits, we identify credible sets that collectively explain 18% of the SNP-based heritability $\left(h_{\mathrm{SNP}}^2\right)$ on average, with 30% credible sets located outside genome-wide significant loci. Leveraging the genetic architecture estimated from GWFM, we predict that fine-mapping over 50% of $h_{\mathrm{SNP}}^2$ would require an average of 2 million samples. Finally, as proof-of-principle, we highlight a known causal variant at FTO influencing body mass index and identify new missense causal variants influencing schizophrenia and Crohn's disease risk.