Gene expression imputation across multiple brain regions provides insights into schizophrenia risk.
Laura M HuckinsAmanda DobbynDouglas M RuderferGabriel E HoffmanWeiqing WangAntonio F PardinasVeera Manikandan RajagopalThomas Damm AlsHoang T NguyenKiran GirdharJames BoocockPanagiotis RoussosMenachem FromerRobin KramerEnrico DomeniciEric R GamazonShaun M Purcellnull nullnull nullnull nullDitte DemontisAnders Dupont BørglumJames T R WaltersMichael C O'DonovanPatrick F SullivanMichael J OwenBernie DevlinSolveig K SiebertsNancy J CoxHae Kyung ImPamela SklarEli A StahlPublished in: Nature genetics (2019)
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.
Keyphrases
- gene expression
- genome wide association study
- bipolar disorder
- genome wide
- dna methylation
- resting state
- white matter
- poor prognosis
- prefrontal cortex
- functional connectivity
- preterm infants
- cerebral ischemia
- long non coding rna
- single cell
- minimally invasive
- bioinformatics analysis
- genome wide identification
- transcription factor
- blood brain barrier
- machine learning
- subarachnoid hemorrhage