Benchmarking association analyses of continuous exposures with RNA-seq in observational studies. Issue 6 (20th May 2021) - Sofer, Tamar; Kurniansyah, Nuzulul; Aguet, François; Ardlie, Kristin; Durda, Peter; Nickerson, Deborah A; Smith, Joshua D; Liu, Yongmei; Gharib, Sina A; Redline, Susan; Rich, Stephen S; Rotter, Jerome I; et al.
Abstract: Large datasets of hundreds to thousands of individuals measuring RNA-seq in observational studies are becoming available. Many popular software packages for analysis of RNA-seq data were constructed to study differences in expression signatures in an experimental design with well-defined conditions (exposures). In contrast, observational studies may have varying levels of confounding transcript-exposure associations; further, exposure measures may vary from discrete (exposed, yes/no) to continuous (levels of exposure), with non-normal distributions of exposure. We compare popular software for gene expression—DESeq2, edgeR and limma—as well as linear regression-based analyses for studying the association of continuous exposures with RNA-seq. We developed a computation pipeline that includes transformation, filtering and generation of empirical null distribution of association P -values, and we apply the pipeline to compute empirical P -values with multiple testing correction. We employ a resampling approach that allows for assessment of false positive detection across methods, power comparison and the computation of quantile empirical P -values. The results suggest that linear regression methods are substantially faster with better control of false detections than other methods, even with the resampling method to compute empirical P -values. We provide the proposed pipeline with fast algorithms in an R package Olivia, and implemented it to study the associations of measures of sleep disordered breathing with RNA-seq in peripheral blood mononuclear cells in participants from the Multi-Ethnic Study of Atherosclerosis.