Integrative approaches for large-scale transcriptome-wide association studies.

Nat Genet
Authors
Keywords
Abstract

Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance of one or multiple proteins. Here we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated with complex traits. We leverage expression imputation from genetic data to perform a transcriptome-wide association study (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ∼ 3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 new genes significantly associated with obesity-related traits (BMI, lipids and height). Many of these genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.

Year of Publication
2016
Journal
Nat Genet
Volume
48
Issue
3
Pages
245-52
Date Published
2016 Mar
ISSN
1546-1718
URL
DOI
10.1038/ng.3506
PubMed ID
26854917
PubMed Central ID
PMC4767558
Links
Grant list
F31 HL127921 / HL / NHLBI NIH HHS / United States
F32 GM106584 / GM / NIGMS NIH HHS / United States
T32 HG002536 / HG / NHGRI NIH HHS / United States
P01 HL028481 / HL / NHLBI NIH HHS / United States
R25 GM055052 / GM / NIGMS NIH HHS / United States
T32HG002536 / HG / NHGRI NIH HHS / United States
HL-095056 / HL / NHLBI NIH HHS / United States
R01 GM053725 / GM / NIGMS NIH HHS / United States
HL-28481 / HL / NHLBI NIH HHS / United States
R01 GM105857 / GM / NIGMS NIH HHS / United States
R01 HL095056 / HL / NHLBI NIH HHS / United States