Gene expression imputation across multiple brain regions provides insights into schizophrenia risk
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Accepted Version
Date
2019-03-25
Authors
Huckins, Laura M.
Dobbyn, Amanda
Ruderfer, Douglas M.
Hoffman, Gabriel
Wang, Weiqing
Pardiñas, Antonio F.
Rajagopal, Veera M.
Als, Thomas D.
Nguyen, Hoang T.
Girdhar, Kiran
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Volume Title
Publisher
Springer Nature Ltd.
Published Version
Abstract
Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.
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Keywords
Schizophrenia , Gene expression , Multiple brain regions
Citation
Huckins, L. M., Dobbyn, A., Ruderfer, D. M. et al. (2019) 'Gene expression imputation across multiple brain regions provides insights into schizophrenia risk', Nature Genetics, 51, pp. 659–674. https://doi.org/10.1038/s41588-019-0364-4
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© 2019, the Authors, under exclusive licence to Springer Nature America, Inc. This version of the paper has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1038/s41588-019-0364-4