| Title: | Functionally informed fine-mapping and polygenic localization of complex trait heritability |
| Journal: | Nature Genetics |
| Published: | 16 Nov 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/33199916/ |
| DOI: | https://doi.org/10.1038/s41588-020-00735-5 |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7710571 |
Publication 3150
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Abstract
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.</p>
14 Authors
- Omer Weissbrod
- Farhad Hormozdiari
- Christian Benner
- Ran Cui
- Jacob Ulirsch
- Steven Gazal
- Armin P. Schoech
- Bryce van de Geijn
- Yakir Reshef
- Carla Márquez-Luna
- Luke O'Connor
- Matti Pirinen
- Hilary K. Finucane
- Alkes L. Price
1 Application
| Application ID | Title |
|---|---|
| 16549 | Components of heritability in a UK Biobank cohort |
Enabling scientific discoveries that improve human health