TY - JOUR T1 - Transcriptome-wide association studies: opportunities and challenges JF - bioRxiv DO - 10.1101/206961 SP - 206961 AU - Michael Wainberg AU - Nasa Sinnott-Armstrong AU - Nicholas Mancuso AU - Alvaro N. Barbeira AU - David A Knowles AU - David Golan AU - Raili Ermel AU - Arno Ruusalepp AU - Thomas Quertermous AU - Ke Hao AU - Johan LM Björkegren AU - Hae Kyung Im AU - Bogdan Pasaniuc AU - Manuel A Rivas AU - Anshul Kundaje Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/10/14/206961.abstract N2 - Transcriptome-wide association studies (TWAS) integrate GWAS and gene expression datasets to find gene-trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes, using simulations and case studies of literature-curated candidate causal genes for schizophrenia, LDL cholesterol and Crohn’s disease. We explore risk loci where TWAS accurately prioritizes the likely causal gene, as well as loci where TWAS prioritizes multiple genes, some of which are unlikely to be causal, because they share the same variants as eQTLs. We illustrate that TWAS is especially prone to spurious prioritization when using expression data from tissues or cell types that are less related to the trait, due to substantial variation in both expression levels and eQTL strengths across cell types. Nonetheless, TWAS prioritizes candidate causal genes at GWAS loci more accurately than simple baselines based on proximity to lead GWAS variant and expression in trait-related tissue. We discuss current strategies and future opportunities for improving the performance of TWAS for causal gene prioritization. Our results showcase the strengths and limitations of using expression variation across individuals to determine causal genes at GWAS loci and provide guidelines and best practices when using TWAS to prioritize candidate causal genes. ER -