Functionally informed fine-mapping and polygenic localization of complex trait heritability

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Weissbrod , O , Hormozdiari , F , Benner , C , Cui , R , Ulirsch , J , Gazal , S , Schoech , A P , van de Geijn , B , Reshef , Y , Marquez-Luna , C , O'Connor , L , Pirinen , M , Finucane , H K & Price , A L 2020 , ' Functionally informed fine-mapping and polygenic localization of complex trait heritability ' , Nature Genetics , vol. 52 , no. 12 . https://doi.org/10.1038/s41588-020-00735-5

Title: Functionally informed fine-mapping and polygenic localization of complex trait heritability
Author: Weissbrod, Omer; Hormozdiari, Farhad; Benner, Christian; Cui, Ran; Ulirsch, Jacob; Gazal, Steven; Schoech, Armin P.; van de Geijn, Bryce; Reshef, Yakir; Marquez-Luna, Carla; O'Connor, Luke; Pirinen, Matti; Finucane, Hilary K.; Price, Alkes L.
Other contributor: University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Centre of Excellence in Complex Disease Genetics








Date: 2020-12
Language: eng
Number of pages: 23
Belongs to series: Nature Genetics
ISSN: 1061-4036
DOI: https://doi.org/10.1038/s41588-020-00735-5
URI: http://hdl.handle.net/10138/329959
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. PolyFun is a computationally scalable framework for functionally informed fine-mapping that makes full use of genome-wide data. It prioritizes more variants than previous methods when applied to 49 complex traits from UK Biobank.
Subject: CAUSAL VARIANTS
RISK PREDICTION
LOCI
ARCHITECTURE
STATISTICS
RESOURCE
CATALOG
HEALTH
SCORES
CELL
1184 Genetics, developmental biology, physiology
112 Statistics and probability
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