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Nat Genet. 2019 Apr;51(4):592-599. doi: 10.1038/s41588-019-0385-z. Epub 2019 Mar 29.

Opportunities and challenges for transcriptome-wide association studies.

Author information

1
Department of Computer Science, Stanford University, Stanford, CA, USA.
2
Department of Genetics, Stanford University, Stanford, CA, USA.
3
Department of Pathology & Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
4
Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.
5
New York Genome Center, New York, NY, USA.
6
Department of Computer Science, Columbia University, New York, NY, USA.
7
Department of Cardiac Surgery, Tartu University Hospital, Tartu, Estonia.
8
Clinical Gene Networks AB, Stockholm, Sweden.
9
Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA.
10
Department of Genetics & Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
11
Clinical Gene Networks AB, Stockholm, Sweden. johan.bjorkegren@mssm.edu.
12
Department of Genetics & Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. johan.bjorkegren@mssm.edu.
13
Department of Pathophysiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia. johan.bjorkegren@mssm.edu.
14
Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden. johan.bjorkegren@mssm.edu.
15
Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA. haky@uchicago.edu.
16
Department of Pathology & Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. pasaniuc@ucla.edu.
17
Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. pasaniuc@ucla.edu.
18
Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. pasaniuc@ucla.edu.
19
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. mrivas@stanford.edu.
20
Department of Computer Science, Stanford University, Stanford, CA, USA. akundaje@stanford.edu.
21
Department of Genetics, Stanford University, Stanford, CA, USA. akundaje@stanford.edu.

Abstract

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.

PMID:
30926968
DOI:
10.1038/s41588-019-0385-z
[Indexed for MEDLINE]

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