Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 70,086 bioRxiv papers from 306,114 authors.
Gene expression imputation across multiple brain regions reveals schizophrenia risk throughout development.
Laura M. Huckins,
Douglas M Ruderfer,
Gabriel E. Hoffman,
Antonio F. Pardiñas,
Veera M. Rajagopal,
Thomas Damm Als,
CommonMind Consortium, the Schizophrenia Working Group of the Psychiatric Genomics Consortium, iPSYCH-GEMS Schizophrenia Working Group, Ditte Demontis,
Anders D. Børglum,
Patrick F Sullivan,
Solveig K. Sieberts,
Hae Kyung Im,
Eli A Stahl
Posted 21 Nov 2017
bioRxiv DOI: 10.1101/222596
Posted 21 Nov 2017
Transcriptomic imputation approaches offer an opportunity to test associations between disease and gene expression in otherwise inaccessible tissues, such as brain, by combining eQTL reference panels with large-scale genotype data. These genic associations could elucidate signals in complex GWAS loci and may disentangle the role of different tissues in disease development. Here, we use the largest eQTL reference panel for the dorso-lateral pre-frontal cortex (DLPFC), collected by the CommonMind Consortium, to create a set of gene expression predictors and demonstrate their utility. We applied these predictors to 40,299 schizophrenia cases and 65,264 matched controls, constituting the largest transcriptomic imputation study of schizophrenia to date. We also computed predicted gene expression levels for 12 additional brain regions, using publicly available predictor models from GTEx. We identified 413 genic associations across 13 brain regions. Stepwise conditioning across the genes and tissues identified 71 associated genes (67 outside the MHC), with the majority of associations found in the DLPFC, and of which 14/67 genes did not fall within previously genome-wide significant loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple pathways associated with porphyric disorders. We investigated developmental expression patterns for all 67 non-MHC associated genes using BRAINSPAN, and identified groups of genes expressed specifically pre-natally or post-natally.
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