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Nat Genet. 2018 Apr;50(4):538-548. doi: 10.1038/s41588-018-0092-1. Epub 2018 Apr 9.

Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights.

Author information

1
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. alexander_gusev@dfci.harvard.edu.
2
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. alexander_gusev@dfci.harvard.edu.
3
Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. alexander_gusev@dfci.harvard.edu.
4
Department of Pathology and Lab Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
5
Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
6
Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
7
Center for Human Disease Modeling, Duke University Medical Center, Durham, NC, USA.
8
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
9
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
10
Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA.
11
Department of Computer Science, Harvard University, Cambridge, MA, USA.
12
Center for Genomic and Computational Biology, Duke University, Durham, NC, USA.
13
Department of Pediatrics, Division of Medical Genetics, Duke University Medical Center, Durham, NC, USA.
14
Department of Genetics, Harvard Medical School, Boston, MA, USA.
15
Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
16
Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
17
Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands.
18
MRC Centre for Psychiatric Genetics and Genomics, Cardiff University, Cardiff, UK.
19
Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, CA, USA.
20
Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
21
Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
22
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
23
Department of Pathology and Lab Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. pasaniuc@ucla.edu.
24
Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, CA, USA. pasaniuc@ucla.edu.
25
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. aprice@hsph.harvard.edu.
26
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. aprice@hsph.harvard.edu.

Abstract

Genome-wide association studies (GWAS) have identified over 100 risk loci for schizophrenia, but the causal mechanisms remain largely unknown. We performed a transcriptome-wide association study (TWAS) integrating a schizophrenia GWAS of 79,845 individuals from the Psychiatric Genomics Consortium with expression data from brain, blood, and adipose tissues across 3,693 primarily control individuals. We identified 157 TWAS-significant genes, of which 35 did not overlap a known GWAS locus. Of these 157 genes, 42 were associated with specific chromatin features measured in independent samples, thus highlighting potential regulatory targets for follow-up. Suppression of one identified susceptibility gene, mapk3, in zebrafish showed a significant effect on neurodevelopmental phenotypes. Expression and splicing from the brain captured most of the TWAS effect across all genes. This large-scale connection of associations to target genes, tissues, and regulatory features is an essential step in moving toward a mechanistic understanding of GWAS.

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