Trait-associated SNPs are more likely to be eQTLs: Annotation to enhance discovery from GWAS

Dan L. Nicolae, Eric Gamazon, Wei Zhang, Shiwei Duan, M. Eileen Dolan, Nancy J. Cox

Research output: Contribution to journalArticlepeer-review

805 Scopus citations

Abstract

Although genome-wide association studies (GWAS) of complex traits have yielded more reproducible associations than had been discovered using any other approach, the loci characterized to date do not account for much of the heritability to such traits and, in general, have not led to improved understanding of the biology underlying complex phenotypes. Using a web site we developed to serve results of expression quantitative trait locus (eQTL) studies in lymphoblastoid cell lines from HapMap samples (http://www.scandb.org), we show that single nucleotide polymorphisms (SNPs) associated with complex traits (from http://www.genome.gov/gwastudies/) are significantly more likely to be eQTLs than minor-allele-frequency- matched SNPs chosen from high-throughput GWAS platforms. These findings are robust across a range of thresholds for establishing eQTLs (p-values from 10-4-10-8), and a broad spectrum of human complex traits. Analyses of GWAS data from the Wellcome Trust studies confirm that annotating SNPs with a score reflecting the strength of the evidence that the SNP is an eQTL can improve the ability to discover true associations and clarify the nature of the mechanism driving the associations. Our results showing that trait-associated SNPs are more likely to be eQTLs and that application of this information can enhance discovery of trait-associated SNPs for complex phenotypes raise the possibility that we can utilize this information both to increase the heritability explained by identifiable genetic factors and to gain a better understanding of the biology underlying complex traits.

Original languageEnglish (US)
JournalPLoS genetics
Volume6
Issue number4
DOIs
StatePublished - Apr 2010

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics
  • Genetics(clinical)
  • Cancer Research

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