- 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;
- 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
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.