Fine-mapping aims to identify causal variants impacting complex traits. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy by leveraging functional annotations across the entire genome—not just genome-wide-significant loci—to specify prior probabilities for fine-mapping methods such as SuSiE or FINEMAP. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well calibrated and identified >20% more variants with a posterior causal probability >0.95 than identified in their nonfunctionally informed counterparts. In analyses of 49 UK Biobank traits (average n = 318,000), PolyFun + SuSiE identified 3,025 fine-mapped variant–trait pairs with posterior causal probability >0.95, a >32% improvement versus SuSiE. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.
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PolyFun fine-mapping results generated in the present study are available for public download at http://data.broadinstitute.org/alkesgroup/polyfun_results. Summary LD information generated in the present study is available for public download at https://data.broadinstitute.org/alkesgroup/UKBB_LD. Baseline-LF v2.2.UKB annotations and LD scores for UK Biobank SNPs are available at https://data.broadinstitute.org/alkesgroup/LDSCORE/baselineLF_v2.2.UKB.tar.gz. Access to the UK Biobank resource is available via application (http://www.ukbiobank.ac.uk).
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We thank B. Pasaniuc, G. Kichaev, M. Stephens, G. Wang, M. Kanai, B. M. Schilder and T. Raj for helpful discussions. This research was conducted using the UK Biobank Resource under application no. 16549 and was funded by National Institutes of Health grants (nos. U01 HG009379, R37 MH107649, R01 MH101244 and R01 HG006399) and the Academy of Finland grants (nos. 288509 and 312076). H.K.F. is supported by E. and W. Schmidt. Computational analyses were performed on the O2 High-Performance Compute Cluster at Harvard Medical School.
The authors declare no competing interests.
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Extended Data Fig. 1 Assessing the individual impact of step 1 of PolyFun (estimating functional enrichment) via perturbation analysis, by randomly shuffling different proportions of annotation coefficient estimates.
For each evaluated value of the proportion of shuffled annotation coefficient estimates, we report the number of experiments having each obtained FDR level >0 (left panel) and the number of experiments having each obtained power level >0 (right panel), out of 1000 experiments. FDR and power are reported with respect to identifying PIP ≥ 0.95 SNPs. Experiments with FDR = 0 (resp. power=0) are not reported in the left panel (resp. right panel) to improve clarity. Numerical reports are provided in Supplementary Table 6.
We display an ideogram of all 2,225 PIP > 0.95 fine-mapped SNPs identified by PolyFun + SuSiE across 49 UK Biobank traits. Traits are color-coded into groups (see legend and Supplementary Table 8). White circles indicate SNPs that are pleiotropic for ≥2 genetically uncorrelated traits, with circles to the right of a white circle denoting the genetically uncorrelated traits (max of 5 colored circles due to space limitations). Numerical results are reported in Supplementary Table 10.
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Weissbrod, O., Hormozdiari, F., Benner, C. et al. Functionally informed fine-mapping and polygenic localization of complex trait heritability. Nat Genet 52, 1355–1363 (2020). https://doi.org/10.1038/s41588-020-00735-5
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