Opportunities and challenges for transcriptome-wide association studies

By Michael Wainberg, Nicholas A. Sinnott-Armstrong, Nicholas Mancuso, A. Barbeira, David A. Knowles, David Golan, R. Ermel, A. Ruusalepp, T. Quertermous, K. Hao and 5 more. Nature Genetics, 2019

757 citations recorded by the source corpus.

Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and gene expression datasets to identify gene–trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes at GWAS loci, by using simulations and case studies of literature-curated candidate causal genes for schizophrenia, low-density-lipoprotein 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 likely to be non-causal, owing to sharing of expression quantitative trait loci (eQTL). TWAS is especially prone to spurious prioritization with expression data from non-trait-related tissues or cell types, owing to substantial cross-cell-type variation in expression levels and eQTL strengths. Nonetheless, TWAS prioritizes candidate causal genes more accurately than simple baselines. We suggest best practices for causal-gene prioritization with TWAS and discuss future opportunities for improvement. Our results showcase the strengths and limitations of using eQTL datasets to determine causal genes at GWAS loci. Transcriptome-wide association studies (TWAS) prioritize candidate causal genes at GWAS loci. This Perspective discusses the challenges to TWAS analysis, caveats to interpretation of results and opportunities for improvements to this class of methods.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e7db05c36efd6aabb0bd793fda02e6f82d5a0d1d

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Citations in the corpus

Genome-wide enhancer maps link risk variants to disease genes (2021, result): “Our observations are consistent with a model where—although variants often affect the expression of multiple genes—only a subset of these effects are likely to be relevant to disease 34 (Supplementary Note 1).”
webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study (2021, background): “This problem is due to linkage disequilibrium (LD) between causal and non-causal variants, which masks the effects of causal variants on the phenotype of interest (4).”