Partitioning heritability by functional category using GWAS summary statistics

Hilary K. Finucane(Harvard University), Brendan Bulik‐Sullivan(Broad Institute), Alexander Gusev(Harvard University), Gosia Trynka(Broad Institute), Yakir Reshef(Harvard University), Po‐Ru Loh(Harvard University), Verneri Anttilla(Broad Institute), Han Xu(Dana-Farber Cancer Institute), Chongzhi Zang(Dana-Farber Cancer Institute), Kyle Kai-How Farh(Broad Institute), Stephan Ripke(Broad Institute), Felix R. Day(University of Cambridge), Shaun Purcell(Brigham and Women's Hospital), Eli Stahl(Brigham and Women's Hospital), Sara Lindström(Harvard University), John R. B. Perry(Harvard University), Yukinori Okada(Tokyo Medical and Dental University), Soumya Raychaudhuri(Broad Institute), Mark Daly(Broad Institute), Nick Patterson(Broad Institute), Benjamin M. Neale(Broad Institute), Alkes L. Price(Broad Institute)
bioRxiv (Cold Spring Harbor Laboratory)
January 23, 2015
Cited by 32Open Access
Full Text

Abstract

Abstract Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here, we analyze a broad set of functional elements, including cell-type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits spanning a total of 1.3 million phenotype measurements. To enable this analysis, we introduce a new method for partitioning heritability from GWAS summary statistics while controlling for linked markers. This new method is computationally tractable at very large sample sizes, and leverages genome-wide information. Our results include a large enrichment of heritability in conserved regions across many traits; a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers; and many cell-type-specific enrichments including significant enrichment of central nervous system cell types in body mass index, age at menarche, educational attainment, and smoking behavior. These results demonstrate that GWAS can aid in understanding the biological basis of disease and provide direction for functional follow-up.


Related Papers

No related papers found

Powered by citation graph analysis