Cargando…
Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions
The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-an...
Autores principales: | , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7550587/ https://www.ncbi.nlm.nih.gov/pubmed/33046718 http://dx.doi.org/10.1038/s41597-020-00642-8 |
_version_ | 1783592995766927360 |
---|---|
author | Sieberts, Solveig K. Perumal, Thanneer M. Carrasquillo, Minerva M. Allen, Mariet Reddy, Joseph S. Hoffman, Gabriel E. Dang, Kristen K. Calley, John Ebert, Philip J. Eddy, James Wang, Xue Greenwood, Anna K. Mostafavi, Sara Omberg, Larsson Peters, Mette A. Logsdon, Benjamin A. De Jager, Philip L. Ertekin-Taner, Nilüfer Mangravite, Lara M. |
author_facet | Sieberts, Solveig K. Perumal, Thanneer M. Carrasquillo, Minerva M. Allen, Mariet Reddy, Joseph S. Hoffman, Gabriel E. Dang, Kristen K. Calley, John Ebert, Philip J. Eddy, James Wang, Xue Greenwood, Anna K. Mostafavi, Sara Omberg, Larsson Peters, Mette A. Logsdon, Benjamin A. De Jager, Philip L. Ertekin-Taner, Nilüfer Mangravite, Lara M. |
author_sort | Sieberts, Solveig K. |
collection | PubMed |
description | The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975). |
format | Online Article Text |
id | pubmed-7550587 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75505872020-10-19 Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions Sieberts, Solveig K. Perumal, Thanneer M. Carrasquillo, Minerva M. Allen, Mariet Reddy, Joseph S. Hoffman, Gabriel E. Dang, Kristen K. Calley, John Ebert, Philip J. Eddy, James Wang, Xue Greenwood, Anna K. Mostafavi, Sara Omberg, Larsson Peters, Mette A. Logsdon, Benjamin A. De Jager, Philip L. Ertekin-Taner, Nilüfer Mangravite, Lara M. Sci Data Analysis The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975). Nature Publishing Group UK 2020-10-12 /pmc/articles/PMC7550587/ /pubmed/33046718 http://dx.doi.org/10.1038/s41597-020-00642-8 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Analysis Sieberts, Solveig K. Perumal, Thanneer M. Carrasquillo, Minerva M. Allen, Mariet Reddy, Joseph S. Hoffman, Gabriel E. Dang, Kristen K. Calley, John Ebert, Philip J. Eddy, James Wang, Xue Greenwood, Anna K. Mostafavi, Sara Omberg, Larsson Peters, Mette A. Logsdon, Benjamin A. De Jager, Philip L. Ertekin-Taner, Nilüfer Mangravite, Lara M. Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions |
title | Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions |
title_full | Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions |
title_fullStr | Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions |
title_full_unstemmed | Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions |
title_short | Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions |
title_sort | large eqtl meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions |
topic | Analysis |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7550587/ https://www.ncbi.nlm.nih.gov/pubmed/33046718 http://dx.doi.org/10.1038/s41597-020-00642-8 |
work_keys_str_mv | AT siebertssolveigk largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT perumalthanneerm largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT carrasquillominervam largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT allenmariet largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT reddyjosephs largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT hoffmangabriele largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT dangkristenk largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT calleyjohn largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT ebertphilipj largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT eddyjames largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT wangxue largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT greenwoodannak largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT mostafavisara largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT omberglarsson largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT petersmettea largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT logsdonbenjamina largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT dejagerphilipl largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT ertekintanernilufer largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions AT mangravitelaram largeeqtlmetaanalysisrevealsdifferingpatternsbetweencerebralcorticalandcerebellarbrainregions |