Adjustment for the uncertainty of predicted expression in transcriptome-wide association study: a fusion of measurement error theory and bootstrapping

Abstract

Transcriptome-wide association study (TWAS) is a powerful approach to identifying novel genes associated with complex phenotypes. Standard TWAS approaches build a prediction model for the genetic component of expression based on reference transcriptome data. Next, an outcome is regressed on the predicted expression in separate GWAS data. The traditional TWAS approach disregards the uncertainty of predicted expression, which can lead to unreliable inference on gene-phenotype associations. We propose a novel approach to adjust for the uncertainty of predicted expression in TWAS. We adapt techniques from measurement error theory and implement bootstrapping algorithms for penalized regression to explicitly obtain an adjustment factor that needs to be incorporated in the unadjusted TWAS. We base the framework on adaptive Lasso. We use extensive simulations to show that the traditional TWAS inflates the type I error rate, whereas the adjusted TWAS adequately controls it. At the expense of an inflated false positive rate, the unadjusted TWAS offers a limited increase in power than the adjusted TWAS, which is statistically unjustified. Our approach produces more accurate estimates of the genes effect size than a traditional approach. We demonstrate the merits of the adjusted approach by conducting TWAS for height and lipid phenotypes, LDL, HDL, and triglycerides while integrating the Geuvadis transcriptome and UK Biobank GWAS data.