Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases

Alexander Gusev, S Hong Lee, Gosia Trynka, Hilary Finucane, Bjarni J Vilhjálmsson, Han Xu, Chongzhi Zang, Stephan Ripke, Brendan Bulik-Sullivan, Eli Stahl, Anna K Kähler, Christina M Hultman, Shaun M Purcell, Steven A McCarroll, Mark Daly, Bogdan Pasaniuc, Patrick F Sullivan, Benjamin M Neale, Naomi R Wray, Soumya RaychaudhuriAlkes L Price, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Andrew McIntosh

Research output: Contribution to journalArticlepeer-review

Abstract

Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (hg(2)) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of hg(2) from imputed SNPs (5.1× enrichment; p = 3.7 × 10(-17)) and 38% (SE = 4%) of hg(2) from genotyped SNPs (1.6× enrichment, p = 1.0 × 10(-4)). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained <10% of hg(2) despite having the highest enrichment. We replicated these findings but found no significant contribution from rare coding variants in independent schizophrenia cohorts genotyped on GWAS and exome chips. Our results highlight the value of analyzing components of heritability to unravel the functional architecture of common disease.

Original languageEnglish
Pages (from-to)535-52
Number of pages18
JournalAmerican Journal of Human Genetics
Volume95
Issue number5
DOIs
Publication statusPublished - 6 Nov 2014

Keywords

  • Computer Simulation
  • Genetic Diseases, Inborn
  • Genetic Variation
  • Genome-Wide Association Study
  • Humans
  • Inheritance Patterns
  • Models, Genetic
  • Open Reading Frames
  • Regulatory Elements, Transcriptional

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