Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Blauwendraat, Cornelis; Francescatto, Margherita; Gibbs, J.; Jansen, Iris; Simón-Sánchez, Javier; Hernandez, Dena; Dillman, Allissa; Singleton, Andrew; Cookson, Mark; Rizzu, Patrizia; Heutink, Peter
• Is Part Of: Genome medicine. Volume 8:Issue 1(2016)
• Page Start: 1
• Page End: 15
• Publication Date: 2016-12
• Subjects: Expression quantitative trait loci (eQTL) -- frontal lobe cortex -- NRGN -- Cap analysis gene expression sequencing (CAGEseq) -- PARK16; Genomics -- Periodicals; Medical genetics -- Periodicals; 616.042
• DOI: 10.1186/s13073-016-0320-1
• Languages: English
• ISSNs: 1756-994X

Abstract

Abstract Background Expression quantitative trait loci (eQTL) analysis is a powerful method to detect correlations between gene expression and genomic variants and is widely used to interpret the biological mechanism underlying identified genome wide association studies (GWAS) risk loci. Numerous eQTL studies have been performed on different cell types and tissues of which the majority has been based on microarray technology. Methods We present here an eQTL analysis based on cap analysis gene expression sequencing (CAGEseq) data created from human postmortem frontal lobe tissue combined with genotypes obtained through genotyping arrays, exome sequencing, and CAGEseq. Using CAGEseq as an expression profiling technique combined with these different genotyping techniques allows measurement of the molecular effect of variants on individual transcription start sites and increases the resolution of eQTL analysis by also including the non-annotated parts of the genome. Results We identified 2410 eQTLs and show that non-coding transcripts are more likely to contain an eQTL than coding transcripts, in particular antisense transcripts. We provide evidence for how previously identified GWAS loci for schizophrenia (NRGN ), Parkinson's disease, and Alzheimer's disease (PARK16 andMAPT loci) could increase the risk for disease at a molecular level. Furthermore, we demonstrate that CAGEseq improves eQTL analysis because variants obtained from CAGEseq are highly enriched for having a functional effect and thus are an efficient method towards the identification of causal variants. Conclusion Our data contain both coding and non-coding transcripts and has the added value that we have identified eQTLs for variants directly adjacent to TSS. Future eQTL studies would benefit from combining CAGEseq with RNA sequencing for a more complete interpretation of the transcriptome and increased understanding of eQTL signals.