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Open Access Publications from the University of California

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

  • Author(s): Huckins, Laura M
  • Dobbyn, Amanda
  • Ruderfer, Douglas M
  • Hoffman, Gabriel
  • Wang, Weiqing
  • Pardiñas, Antonio F
  • Rajagopal, Veera M
  • Als, Thomas D
  • T Nguyen, Hoang
  • Girdhar, Kiran
  • Boocock, James
  • Roussos, Panos
  • Fromer, Menachem
  • Kramer, Robin
  • Domenici, Enrico
  • Gamazon, Eric R
  • Purcell, Shaun
  • CommonMind Consortium
  • Schizophrenia Working Group of the Psychiatric Genomics Consortium
  • iPSYCH-GEMS Schizophrenia Working Group
  • Demontis, Ditte
  • Børglum, Anders D
  • Walters, James TR
  • O'Donovan, Michael C
  • Sullivan, Patrick
  • Owen, Michael J
  • Devlin, Bernie
  • Sieberts, Solveig K
  • Cox, Nancy J
  • Im, Hae Kyung
  • Sklar, Pamela
  • Stahl, Eli A
  • et al.

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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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