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Identifying genes associated with brain volumetric differences through tissue specific transcriptomic inference from GWAS summary data

BACKGROUND: Brain volume has been widely studied in the neuroimaging field, since it is an important and heritable trait associated with brain development, aging and various neurological and psychiatric disorders. Genome-wide association studies (GWAS) have successfully identified numerous associati...

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Autores principales: Mai, Hung, Bao, Jingxuan, Thompson, Paul M., Kim, Dokyoon, Shen, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520794/
https://www.ncbi.nlm.nih.gov/pubmed/36171548
http://dx.doi.org/10.1186/s12859-022-04947-w
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author Mai, Hung
Bao, Jingxuan
Thompson, Paul M.
Kim, Dokyoon
Shen, Li
author_facet Mai, Hung
Bao, Jingxuan
Thompson, Paul M.
Kim, Dokyoon
Shen, Li
author_sort Mai, Hung
collection PubMed
description BACKGROUND: Brain volume has been widely studied in the neuroimaging field, since it is an important and heritable trait associated with brain development, aging and various neurological and psychiatric disorders. Genome-wide association studies (GWAS) have successfully identified numerous associations between genetic variants such as single nucleotide polymorphisms and complex traits like brain volume. However, it is unclear how these genetic variations influence regional gene expression levels, which may subsequently lead to phenotypic changes. S-PrediXcan is a tissue-specific transcriptomic data analysis method that can be applied to bridge this gap. In this work, we perform an S-PrediXcan analysis on GWAS summary data from two large imaging genetics initiatives, the UK Biobank and Enhancing Neuroimaging Genetics through Meta Analysis, to identify tissue-specific transcriptomic effects on two closely related brain volume measures: total brain volume (TBV) and intracranial volume (ICV). RESULTS: As a result of the analysis, we identified 10 genes that are highly associated with both TBV and ICV. Nine out of 10 genes were found to be associated with TBV in another study using a different gene-based association analysis. Moreover, most of our discovered genes were also found to be correlated with multiple cognitive and behavioral traits. Further analyses revealed the protein–protein interactions, associated molecular pathways and biological functions that offer insight into how these genes function and interact with others. CONCLUSIONS: These results confirm that S-PrediXcan can identify genes with tissue-specific transcriptomic effects on complex traits. The analysis also suggested novel genes whose expression levels are related to brain volumetric traits. This provides important insights into the genetic mechanisms of the human brain. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04947-w.
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spelling pubmed-95207942022-09-30 Identifying genes associated with brain volumetric differences through tissue specific transcriptomic inference from GWAS summary data Mai, Hung Bao, Jingxuan Thompson, Paul M. Kim, Dokyoon Shen, Li BMC Bioinformatics Research BACKGROUND: Brain volume has been widely studied in the neuroimaging field, since it is an important and heritable trait associated with brain development, aging and various neurological and psychiatric disorders. Genome-wide association studies (GWAS) have successfully identified numerous associations between genetic variants such as single nucleotide polymorphisms and complex traits like brain volume. However, it is unclear how these genetic variations influence regional gene expression levels, which may subsequently lead to phenotypic changes. S-PrediXcan is a tissue-specific transcriptomic data analysis method that can be applied to bridge this gap. In this work, we perform an S-PrediXcan analysis on GWAS summary data from two large imaging genetics initiatives, the UK Biobank and Enhancing Neuroimaging Genetics through Meta Analysis, to identify tissue-specific transcriptomic effects on two closely related brain volume measures: total brain volume (TBV) and intracranial volume (ICV). RESULTS: As a result of the analysis, we identified 10 genes that are highly associated with both TBV and ICV. Nine out of 10 genes were found to be associated with TBV in another study using a different gene-based association analysis. Moreover, most of our discovered genes were also found to be correlated with multiple cognitive and behavioral traits. Further analyses revealed the protein–protein interactions, associated molecular pathways and biological functions that offer insight into how these genes function and interact with others. CONCLUSIONS: These results confirm that S-PrediXcan can identify genes with tissue-specific transcriptomic effects on complex traits. The analysis also suggested novel genes whose expression levels are related to brain volumetric traits. This provides important insights into the genetic mechanisms of the human brain. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04947-w. BioMed Central 2022-09-28 /pmc/articles/PMC9520794/ /pubmed/36171548 http://dx.doi.org/10.1186/s12859-022-04947-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Mai, Hung
Bao, Jingxuan
Thompson, Paul M.
Kim, Dokyoon
Shen, Li
Identifying genes associated with brain volumetric differences through tissue specific transcriptomic inference from GWAS summary data
title Identifying genes associated with brain volumetric differences through tissue specific transcriptomic inference from GWAS summary data
title_full Identifying genes associated with brain volumetric differences through tissue specific transcriptomic inference from GWAS summary data
title_fullStr Identifying genes associated with brain volumetric differences through tissue specific transcriptomic inference from GWAS summary data
title_full_unstemmed Identifying genes associated with brain volumetric differences through tissue specific transcriptomic inference from GWAS summary data
title_short Identifying genes associated with brain volumetric differences through tissue specific transcriptomic inference from GWAS summary data
title_sort identifying genes associated with brain volumetric differences through tissue specific transcriptomic inference from gwas summary data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520794/
https://www.ncbi.nlm.nih.gov/pubmed/36171548
http://dx.doi.org/10.1186/s12859-022-04947-w
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