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An analysis of genetically regulated gene expression across multiple tissues implicates novel gene candidates in Alzheimer’s disease

INTRODUCTION: Genome-wide association studies (GWAS) have successfully identified multiple independent genetic loci that harbour variants associated with Alzheimer’s disease, but the exact causal genes and biological pathways are largely unknown. METHODS: To prioritise likely causal genes associated...

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Autores principales: Gerring, Zachary F., Lupton, Michelle K., Edey, Daniel, Gamazon, Eric R., Derks, Eske M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7164172/
https://www.ncbi.nlm.nih.gov/pubmed/32299494
http://dx.doi.org/10.1186/s13195-020-00611-8
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author Gerring, Zachary F.
Lupton, Michelle K.
Edey, Daniel
Gamazon, Eric R.
Derks, Eske M.
author_facet Gerring, Zachary F.
Lupton, Michelle K.
Edey, Daniel
Gamazon, Eric R.
Derks, Eske M.
author_sort Gerring, Zachary F.
collection PubMed
description INTRODUCTION: Genome-wide association studies (GWAS) have successfully identified multiple independent genetic loci that harbour variants associated with Alzheimer’s disease, but the exact causal genes and biological pathways are largely unknown. METHODS: To prioritise likely causal genes associated with Alzheimer’s disease, we used S-PrediXcan to integrate expression quantitative trait loci (eQTL) from the Genotype-Tissue Expression (GTEx) study and CommonMind Consortium (CMC) with Alzheimer’s disease GWAS summary statistics. We meta-analysed the GTEx results using S-MultiXcan, prioritised disease-implicated loci using a computational fine-mapping approach, and performed a biological pathway analysis on the gene-based results. RESULTS: We identified 126 tissue-specific gene-based associations across 48 GTEx tissues, targeting 50 unique genes. Meta-analysis of the tissue-specific associations identified 73 genes whose expression was associated with Alzheimer’s disease. Additional analyses in the dorsolateral prefrontal cortex from the CMC identified 12 significant associations, 8 of which also had a significant association in GTEx tissues. Fine-mapping of causal gene sets prioritised gene candidates in 10 Alzheimer’s disease loci with strong evidence for causality. Biological pathway analyses of the meta-analysed GTEx data and CMC data identified a significant enrichment of Alzheimer’s disease association signals in plasma lipoprotein clearance, in addition to multiple immune-related pathways. CONCLUSIONS: Gene expression data from brain and peripheral tissues can improve power to detect regulatory variation underlying Alzheimer’s disease. However, the associations in peripheral tissues may reflect tissue-shared regulatory variation for a gene. Therefore, future functional studies should be performed to validate the biological meaning of these associations and whether they represent new pathogenic tissues.
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spelling pubmed-71641722020-04-22 An analysis of genetically regulated gene expression across multiple tissues implicates novel gene candidates in Alzheimer’s disease Gerring, Zachary F. Lupton, Michelle K. Edey, Daniel Gamazon, Eric R. Derks, Eske M. Alzheimers Res Ther Research INTRODUCTION: Genome-wide association studies (GWAS) have successfully identified multiple independent genetic loci that harbour variants associated with Alzheimer’s disease, but the exact causal genes and biological pathways are largely unknown. METHODS: To prioritise likely causal genes associated with Alzheimer’s disease, we used S-PrediXcan to integrate expression quantitative trait loci (eQTL) from the Genotype-Tissue Expression (GTEx) study and CommonMind Consortium (CMC) with Alzheimer’s disease GWAS summary statistics. We meta-analysed the GTEx results using S-MultiXcan, prioritised disease-implicated loci using a computational fine-mapping approach, and performed a biological pathway analysis on the gene-based results. RESULTS: We identified 126 tissue-specific gene-based associations across 48 GTEx tissues, targeting 50 unique genes. Meta-analysis of the tissue-specific associations identified 73 genes whose expression was associated with Alzheimer’s disease. Additional analyses in the dorsolateral prefrontal cortex from the CMC identified 12 significant associations, 8 of which also had a significant association in GTEx tissues. Fine-mapping of causal gene sets prioritised gene candidates in 10 Alzheimer’s disease loci with strong evidence for causality. Biological pathway analyses of the meta-analysed GTEx data and CMC data identified a significant enrichment of Alzheimer’s disease association signals in plasma lipoprotein clearance, in addition to multiple immune-related pathways. CONCLUSIONS: Gene expression data from brain and peripheral tissues can improve power to detect regulatory variation underlying Alzheimer’s disease. However, the associations in peripheral tissues may reflect tissue-shared regulatory variation for a gene. Therefore, future functional studies should be performed to validate the biological meaning of these associations and whether they represent new pathogenic tissues. BioMed Central 2020-04-16 /pmc/articles/PMC7164172/ /pubmed/32299494 http://dx.doi.org/10.1186/s13195-020-00611-8 Text en © The Author(s) 2020 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/. 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 in a credit line to the data.
spellingShingle Research
Gerring, Zachary F.
Lupton, Michelle K.
Edey, Daniel
Gamazon, Eric R.
Derks, Eske M.
An analysis of genetically regulated gene expression across multiple tissues implicates novel gene candidates in Alzheimer’s disease
title An analysis of genetically regulated gene expression across multiple tissues implicates novel gene candidates in Alzheimer’s disease
title_full An analysis of genetically regulated gene expression across multiple tissues implicates novel gene candidates in Alzheimer’s disease
title_fullStr An analysis of genetically regulated gene expression across multiple tissues implicates novel gene candidates in Alzheimer’s disease
title_full_unstemmed An analysis of genetically regulated gene expression across multiple tissues implicates novel gene candidates in Alzheimer’s disease
title_short An analysis of genetically regulated gene expression across multiple tissues implicates novel gene candidates in Alzheimer’s disease
title_sort analysis of genetically regulated gene expression across multiple tissues implicates novel gene candidates in alzheimer’s disease
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7164172/
https://www.ncbi.nlm.nih.gov/pubmed/32299494
http://dx.doi.org/10.1186/s13195-020-00611-8
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