| Title: | Functionally informed fine-mapping and polygenic localization of complex trait heritability |
| Journal: | Nat Genet |
| Published: | 1 Dec 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/33199916/ |
| DOI: | https://doi.org/10.1038/s41588-020-00735-5 |
Publication 3150
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Abstract
Fine-mapping aims to identify causal variants impacting complex traits. Several recent methods improve fine-mapping accuracy by prioritizing variants in enriched functional annotations. However, these methods can only use information at genome-wide significant loci (or a small number of functional annotations), severely limiting the benefit of functional data. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy using genome-wide functional data for a broad set of coding, conserved, regulatory and LD-related annotations. PolyFun prioritizes variants in enriched functional annotations by specifying prior causal probabilities for fine-mapping methods such as SuSiE or FINEMAP, employing special procedures to ensure robustness to model misspecification and winner s curse. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well-calibrated and identified >20% more variants with posterior causal probability >0.95 than their non-functionally informed counterparts (and >33% more fine-mapped variants than previous functionally-informed fine-mapping methods). In analyses of 47 UK Biobank traits (average N=317K), PolyFun + SuSiE identified 3,025 fine-mapped variant-trait pairs with posterior causal probability >0.95, a >32% improvement vs. SuSiE; 223 variants were fine-mapped for multiple genetically uncorrelated traits, indicating pervasive pleiotropy. 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 550,000 (chronotype). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.1 Author
- Weissbrod O et al.
1 Application
| Application ID | Title |
|---|---|
| 16549 | Components of heritability in a UK Biobank cohort |
Enabling scientific discoveries that improve human health