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 language | English |
|---|---|
| Pages (from-to) | 659-674 |
| Number of pages | 16 |
| Journal | Nature Genetics |
| Volume | 51 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 1 Apr 2019 |
| Externally published | Yes |
Field of Science*
- 3.2 Clinical medicine
Publication Type*
- 1.1. Scientific article indexed in Web of Science and/or Scopus database