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Nat Genet. 2016 Mar;48(3):245-52. doi: 10.1038/ng.3506. Epub 2016 Feb 8.

Integrative approaches for large-scale transcriptome-wide association studies.

Author information

1
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
2
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
3
Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA.
4
Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
5
Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California, USA.
6
Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, California, USA.
7
Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands.
8
Department of Biological Psychology, VU University, Amsterdam, the Netherlands.
9
Bioinformatics Research Center, Department of Statistics, Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, USA.
10
Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA.
11
Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA.
12
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
13
Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
14
Department of Clinical Chemistry, Fimlab Laboratories and University of Tampere School of Medicine, Tampere, Finland.
15
Department of Clinical Physiology, Pirkanmaa Hospital District and University of Tampere School of Medicine, Tampere, Finland.
16
Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.
17
Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland.
18
Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland.
19
Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.

Abstract

Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance of one or multiple proteins. Here we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated with complex traits. We leverage expression imputation from genetic data to perform a transcriptome-wide association study (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ∼ 3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 new genes significantly associated with obesity-related traits (BMI, lipids and height). Many of these genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.

PMID:
26854917
PMCID:
PMC4767558
[Available on 2017-03-01]
DOI:
10.1038/ng.3506
[Indexed for MEDLINE]
Free PMC Article

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