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

Nature Genetics
Omer WeissbrodAlkes L Price

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.

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Citations

Jul 2, 2021·Current Cardiology Reports·Evan D MuseAli Torkamani
Jul 31, 2021·Nature Genetics·Luke J O'Connor
Aug 24, 2021·Frontiers in Genetics·Evan K Irving-PeaseFernando Racimo
Sep 24, 2021·Science·Tuuli Lappalainen, Daniel G MacArthur
Oct 5, 2021·Seminars in Immunopathology·Kazuyoshi Ishigaki
Oct 8, 2021·Human Molecular Genetics·Brian M Schilder, Towfique Raj

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Methods Mentioned

BETA
genotyping

Software Mentioned

fastPAINTOR−
HESS
SuSiE
BOLT
mathop
COJO
PolyFun
fastPAINTOR
LDSC
GCTA

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