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.
Components of heritability in a UK Biobank cohort
We will analyze heritability of several polygenic traits. We will use existing methods and methods under development for partitioning heritability by functional annotation (e.g. cell-type-specific enhancer regions, gene pathways, etc.) to learn about underlying trait biology. We will also examine how SNP heritability varies across LD and MAF categories. Finally, we will evaluate missing heritability using new methods to estimate heritability explained by haplotypes, narrow-sense heritability (using PSMC) and epistatic components of heritability (using Hadamard products). We plan to study a wide range of health-related phenotypes, including diseases and quantitative traits like height and BMI. The data in the UK Biobank's cohort will allow us to partition heritability at higher resolution and to evaluate missing heritability. These will inform both our understanding of trait biology and the design of future genetic studies. Both of these outcomes will benefit attempts to find actionable drug targets for human disease. Moreover, the methods we develop for partitioning heritability will be published and made open-source for use by the broader research community. We will work with annotations from Finucane et al. 2015 Nat Gen as well as gene sets and new annotations from the ENCODE and Roadmap Epigenomics Consortia and others. We will apply LD score regression [Finucane et al. 2015 Nat Genet], BOLT-REML [Loh et al. 2015 Nat Genet], and a new method under development to assess heritability enrichment of these annotations, as well as enrichment/depletion by LD and MAF, within/across traits and populations. We will also apply new methods to estimate heritability explained by haplotypes, total narrow-sense heritability and epistatic components of heritability.
Steven Gazal, Hilary K. Finucane, Po-Ru Loh, Pier Francesco Palamara, Xuanyao Liu, Armin Schoech, Brendan Bulik-Sullivan, Benjamin M Neale, Alexander Gusev, Alkes L. Price. Linkage disequilibrium dependent architecture of human complex traits reveals action of negative selection Nature Genetics volume 49, pages 1421 1427 (2017) doi:10.1038/ng.3954
|Lead investigator:||Alkes Price|
|Lead institution:||Harvard School of Public Health|