Life Sciences Commons^™

Multivariate Adaptive Shrinkage Improves Cross-Population Transcriptome Prediction And Association Studies In Underrepresented Populations, Daniel Araujo, Chris Nguyen, Xiaowei Hu, Anna V. Mikhaylova, Christopher R. Gignoux, Kristin Ardlie, Kent D. Taylor, Peter Durda, Yongmei Liu, George Papanicolaou, Michael H. Cho, Stephen S. Rich, Jerome I. Rotter, Nhlbi Topmed Consortium, Hae Kyung Im, Ani Manichaikul, Heather Wheeler

Biology: Faculty Publications and Other Works

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy …