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 |
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Pages (from-to) | 659-674 |
Number of pages | 16 |
Journal | Nature Genetics |
Volume | 51 |
Issue number | 4 |
DOIs | |
Publication status | Published - Apr 2019 |
Other keywords
- Brain/physiopathology
- Case-Control Studies
- Gene Expression/genetics
- Genetic Predisposition to Disease
- Genome-Wide Association Study/methods
- Genotype
- Humans
- Polymorphism, Single Nucleotide/genetics
- Quantitative Trait Loci/genetics
- Risk
- Schizophrenia/genetics
- Transcriptome/genetics