RT Journal Article
SR Electronic
T1 Functionally-informed fine-mapping and polygenic localization of complex trait heritability
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 807792
DO 10.1101/807792
A1 Omer Weissbrod
A1 Farhad Hormozdiari
A1 Christian Benner
A1 Ran Cui
A1 Jacob Ulirsch
A1 Steven Gazal
A1 Armin P. Schoech
A1 Bryce van de Geijn
A1 Yakir Reshef
A1 Carla Márquez-Luna
A1 Luke O’Connor
A1 Matti Pirinen
A1 Hilary K. Finucane
A1 Alkes L. Price
YR 2019
UL http://biorxiv.org/content/early/2019/10/29/807792.abstract
AB 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.