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Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe
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 studi...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4903003/ https://www.ncbi.nlm.nih.gov/pubmed/27287230 http://dx.doi.org/10.1186/s13073-016-0320-1 |
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author | 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 |
author_facet | 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 |
author_sort | Blauwendraat, Cornelis |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-016-0320-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4903003 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-49030032016-06-12 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 Genome Med Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-016-0320-1) contains supplementary material, which is available to authorized users. BioMed Central 2016-06-10 /pmc/articles/PMC4903003/ /pubmed/27287230 http://dx.doi.org/10.1186/s13073-016-0320-1 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research 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 |
title | Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe |
title_full | Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe |
title_fullStr | Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe |
title_full_unstemmed | Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe |
title_short | Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe |
title_sort | comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4903003/ https://www.ncbi.nlm.nih.gov/pubmed/27287230 http://dx.doi.org/10.1186/s13073-016-0320-1 |
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