RT Journal Article
SR Electronic
T1 Integrative approaches for large-scale transcriptome-wide association studies
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 024083
DO 10.1101/024083
A1 Alexander Gusev
A1 Arthur Ko
A1 Huwenbo Shi
A1 Gaurav Bhatia
A1 Wonil Chung
A1 Brenda W J H Penninx
A1 Rick Jansen
A1 Eco JC de Geus
A1 Dorret I Boomsma
A1 Fred A Wright
A1 Patrick F Sullivan
A1 Elina Nikkola
A1 Marcus Alvarez
A1 Mete Civelek
A1 Aldonis J Lusis
A1 Terho Lehtimäki
A1 Emma Raitoharju
A1 Mika Kähönen
A1 Ilkka Seppälä
A1 Olli T. Raitakari
A1 Johanna Kuusisto
A1 Markku Laakso
A1 Alkes L. Price
A1 Päivi Pajukanta
A1 Bogdan Pasaniuc
YR 2015
UL http://biorxiv.org/content/early/2015/08/07/024083.1.abstract
AB Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance levels of one or multiple proteins. In this work we introduce a powerful strategy that integrates gene expression measurements with large-scale genome-wide association data to identify genes whose cis-regulated expression is associated to complex traits. We use a relatively small reference panel of individuals for which both genetic variation and gene expression have been measured to impute gene expression into large cohorts of individuals and identify expression-trait associations. We extend our methods to allow for indirect imputation of the expression-trait association from summary association statistics of large-scale GWAS1-3. We applied our approaches to expression data from blood and adipose tissue measured in ∼3,000 individuals overall. We then imputed gene expression into GWAS data from over 900,000 phenotype measurements4-6 to identify 69 novel genes significantly associated to obesity-related traits (BMI, lipids, and height). Many of the novel genes were associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Overall our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.