Weissbrod, OmerHormozdiari, FarhadBenner, ChristianCui, RanUlirsch, JacobGazal, StevenSchoech, Arminvan de Geijn, BryceReshef, YakirMárquez-Luna, CarlaO’Connor, LukePirinen, MattiFinucane, HilaryPrice, Alkes2022-06-062020-11-16Weissbrod, Omer, Farhad Hormozdiari, Christian Benner, Ran Cui, Jacob Ulirsch, Steven Gazal, Armin P. Schoech, et al. “Functionally Informed Fine-Mapping and Polygenic Localization of Complex Trait Heritability.” Nature Genetics 52, no. 12 (December 2020): 1355–63. https://doi.org/10.1038/s41588-020-00735-5.1061-40361546-1718https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37371886Fine-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.en-USResearch Subject Categories::NATURAL SCIENCES::Biology::Cell and molecular biology::GeneticsJournal Article2022-06-0610.1038/s41588-020-00735-5