Integrative approaches for large-scale transcriptome-wide association studies

Alexander Gusev, Arthur Ko, Huwenbo Shi, Gaurav Bhatia, Wonil Chung, Brenda W. J. H. Penninx, Rick Jansen, Eco J. C. de Geus, Dorret I. Boomsma, Fred A. Wright, Patrick F. Sullivan, Elina Nikkola, Marcus Alvarez, Mete Civelek, Aldons J. Lusis, Terho Lehtimaki, Emma Raitoharju, Mika Kahonen, Ilkka Seppala, Olli T. RaitakariJohanna Kuusisto, Markku Laakso, Alkes L. Price, Paivi Pajukanta, Bogdan Pasaniuc, T. Lehtimäki, M. Kähönen, I. Seppälä

Research output: Contribution to journalArticleAcademicpeer-review

1100 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)245-252
JournalNature Genetics
Volume48
Issue number3
DOIs
Publication statusPublished - Mar 2016

Cohort Studies

  • Netherlands Twin Register (NTR)

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