Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe

Cornelis Blauwendraat, Margherita Francescatto, J. Raphael Gibbs, Iris E. Jansen, Javier Simón-Sánchez, Dena G. Hernandez, Allissa A. Dillman, Andrew B. Singleton, Mark R. Cookson, Patrizia Rizzu, Peter Heutink

Research output: Contribution to journalArticleAcademicpeer-review

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 and MAPT 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.

Original languageEnglish
Article number65
JournalGenome Medicine
Volume8
Issue number1
DOIs
Publication statusPublished - 10 Jun 2016

Cite this

Blauwendraat, C., Francescatto, M., Gibbs, J. R., Jansen, I. E., Simón-Sánchez, J., Hernandez, D. G., ... Heutink, P. (2016). Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe. Genome Medicine, 8(1), [65]. https://doi.org/10.1186/s13073-016-0320-1
Blauwendraat, Cornelis ; Francescatto, Margherita ; Gibbs, J. Raphael ; Jansen, Iris E. ; Simón-Sánchez, Javier ; Hernandez, Dena G. ; Dillman, Allissa A. ; Singleton, Andrew B. ; Cookson, Mark R. ; Rizzu, Patrizia ; Heutink, Peter. / Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe. In: Genome Medicine. 2016 ; Vol. 8, No. 1.
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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 and MAPT 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.",
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Blauwendraat, C, Francescatto, M, Gibbs, JR, Jansen, IE, Simón-Sánchez, J, Hernandez, DG, Dillman, AA, Singleton, AB, Cookson, MR, Rizzu, P & Heutink, P 2016, 'Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe' Genome Medicine, vol. 8, no. 1, 65. https://doi.org/10.1186/s13073-016-0320-1

Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe. / Blauwendraat, Cornelis; Francescatto, Margherita; Gibbs, J. Raphael; Jansen, Iris E.; Simón-Sánchez, Javier; Hernandez, Dena G.; Dillman, Allissa A.; Singleton, Andrew B.; Cookson, Mark R.; Rizzu, Patrizia; Heutink, Peter.

In: Genome Medicine, Vol. 8, No. 1, 65, 10.06.2016.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe

AU - Blauwendraat, Cornelis

AU - Francescatto, Margherita

AU - Gibbs, J. Raphael

AU - Jansen, Iris E.

AU - Simón-Sánchez, Javier

AU - Hernandez, Dena G.

AU - Dillman, Allissa A.

AU - Singleton, Andrew B.

AU - Cookson, Mark R.

AU - Rizzu, Patrizia

AU - Heutink, Peter

PY - 2016/6/10

Y1 - 2016/6/10

N2 - 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 and MAPT 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.

AB - 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 and MAPT 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.

KW - Cap analysis gene expression sequencing (CAGEseq)

KW - Expression quantitative trait loci (eQTL)

KW - Frontal lobe cortex

KW - NRGN

KW - PARK16

UR - http://www.scopus.com/inward/record.url?scp=84976873473&partnerID=8YFLogxK

U2 - 10.1186/s13073-016-0320-1

DO - 10.1186/s13073-016-0320-1

M3 - Article

VL - 8

JO - Genome Medicine

JF - Genome Medicine

SN - 1756-994X

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