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AbstractA transcriptome‐wide association study (TWAS) attempts to identify disease associated genes by imputing gene expression into a genome‐wide association study (GWAS) using an expression quantitative trait loci (eQTL) data set and then testing for associations with a trait of interest. Regulatory processes may be shared across related tissues and one natural extension of TWAS is harnessing cross‐tissue correlation in gene expression to improve prediction accuracy. Here, we studied multi‐tissue extensions of lasso regression and random forests (RF), joint lasso and RF‐MTL (multi‐task learning RF), respectively. We found that, on our chosen eQTL data set, multi‐tissue methods were generally more accurate than their single‐tissue counterparts, with RF‐MTL performing the best. Simulations showed that these benefits generally translated into more associated genes identified, although highlighted that joint lasso had a tendency to erroneously identify genes in one tissue if there existed an eQTL signal for that gene in another. Applying the four methods to a type 1 diabetes GWAS, we found that multi‐tissue methods found more unique associated genes for most of the tissues considered. We conclude that multi‐tissue methods are competitive and, for some cell types, superior to single‐tissue approaches and hold much promise for TWAS studies.
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