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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk

  • CommonMind Consortium
  • , The Schizophrenia Working Group of the PsyUniversity of Copenhagenchiatric Genomics Consortium
  • , iPSYCH-GEMS Schizophrenia Working Group
  • Icahn School of Medicine at Mount Sinai
  • Vanderbilt University
  • Cardiff University
  • Aarhus University
  • The Lundbeck Foundation Initiative for Integrative Psychiatric Research
  • University of California at Los Angeles
  • Massachusetts General Hospital
  • Broad Institute
  • National Institutes of Health
  • University of Trento
  • F. Hoffmann-La Roche AG
  • University of Cambridge
  • University of Pittsburgh
  • Sage Bionetworks
  • Takeda Pharmaceutical Company Limited
  • University of Pennsylvania
  • JJ Peters Virginia Medical Center
  • Trinity College Dublin
  • Eli Lilly
  • King's College London
  • Technical University of Denmark
  • Boston Children's Hospital
  • Karolinska Institutet
  • Diakonhjemmet Hospital
  • University of Oslo

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)659-674
Number of pages16
JournalNature Genetics
Volume51
Issue number4
DOIs
Publication statusPublished - 1 Apr 2019

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