Modeling prediction error improves power of transcriptome-wide association studies

Citations Over Time

Abstract

1 Abstract Transcriptome-wide association studies (TWAS) test for associations between imputed gene expression levels and phenotypes in GWAS cohorts using models of transcriptional regulation learned from reference transcriptomes. However, current methods for TWAS only use point estimates of imputed expression and ignore uncertainty in the prediction. We develop a novel two-stage Bayesian regression method which incorporates uncertainty in imputed gene expression and achieves higher power to detect TWAS genes than existing TWAS methods as well as standard methods based on missing value and measurement error theory. We apply our method to GTEx whole blood transcriptomes and GWAS cohorts for seven diseases from the Wellcome Trust Case Control Consortium and find 45 TWAS genes, of which 17 do not overlap previously reported case-control GWAS or differential expression associations. Surprisingly, we replicate only 2 of 40 previously reported TWAS genes after accounting for uncertainty in the prediction.

Related Papers

• → Gene and pathway-based second-wave analysis of genome-wide association studies(2009)240 cited
• → The combination of a genome-wide association study of lymphocyte count and analysis of gene expression data reveals novel asthma candidate genes(2012)52 cited
• → Genome-wide association studies in aging-related processes such as diabetes mellitus, atherosclerosis and cancer(2007)43 cited
• → A genome-wide association study links small-vessel ischemic stroke to autophagy(2017)19 cited
• → GWAS summary-based pathway analysis correcting for the genetic confounding impact of environmental exposures(2017)4 cited