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Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method
Alzheimer disease (AD) is the fourth major cause of death in the elderly following cancer, heart disease and cerebrovascular disease. Finding candidate causal genes can help in the design of Gene targeted drugs and effectively reduce the risk of the disease. Complex diseases such as AD are usually c...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6355707/ https://www.ncbi.nlm.nih.gov/pubmed/30740125 http://dx.doi.org/10.3389/fgene.2018.00703 |
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author | Hu, Yang Zhao, Tianyi Zang, Tianyi Zhang, Ying Cheng, Liang |
author_facet | Hu, Yang Zhao, Tianyi Zang, Tianyi Zhang, Ying Cheng, Liang |
author_sort | Hu, Yang |
collection | PubMed |
description | Alzheimer disease (AD) is the fourth major cause of death in the elderly following cancer, heart disease and cerebrovascular disease. Finding candidate causal genes can help in the design of Gene targeted drugs and effectively reduce the risk of the disease. Complex diseases such as AD are usually caused by multiple genes. The Genome-wide association study (GWAS), has identified the potential genetic variants for most diseases. However, because of linkage disequilibrium (LD), it is difficult to identify the causative mutations that directly cause diseases. In this study, we combined expression quantitative trait locus (eQTL) studies with the GWAS, to comprehensively define the genes that cause Alzheimer disease. The method used was the Summary Mendelian randomization (SMR), which is a novel method to integrate summarized data. Two GWAS studies and five eQTL studies were referenced in this paper. We found several candidate SNPs that have a strong relationship with AD. Most of these SNPs overlap in different data sets, providing relatively strong reliability. We also explain the function of the novel AD-related genes we have discovered. |
format | Online Article Text |
id | pubmed-6355707 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-63557072019-02-08 Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method Hu, Yang Zhao, Tianyi Zang, Tianyi Zhang, Ying Cheng, Liang Front Genet Genetics Alzheimer disease (AD) is the fourth major cause of death in the elderly following cancer, heart disease and cerebrovascular disease. Finding candidate causal genes can help in the design of Gene targeted drugs and effectively reduce the risk of the disease. Complex diseases such as AD are usually caused by multiple genes. The Genome-wide association study (GWAS), has identified the potential genetic variants for most diseases. However, because of linkage disequilibrium (LD), it is difficult to identify the causative mutations that directly cause diseases. In this study, we combined expression quantitative trait locus (eQTL) studies with the GWAS, to comprehensively define the genes that cause Alzheimer disease. The method used was the Summary Mendelian randomization (SMR), which is a novel method to integrate summarized data. Two GWAS studies and five eQTL studies were referenced in this paper. We found several candidate SNPs that have a strong relationship with AD. Most of these SNPs overlap in different data sets, providing relatively strong reliability. We also explain the function of the novel AD-related genes we have discovered. Frontiers Media S.A. 2019-01-25 /pmc/articles/PMC6355707/ /pubmed/30740125 http://dx.doi.org/10.3389/fgene.2018.00703 Text en Copyright © 2019 Hu, Zhao, Zang, Zhang and Cheng. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Hu, Yang Zhao, Tianyi Zang, Tianyi Zhang, Ying Cheng, Liang Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method |
title | Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method |
title_full | Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method |
title_fullStr | Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method |
title_full_unstemmed | Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method |
title_short | Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method |
title_sort | identification of alzheimer's disease-related genes based on data integration method |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6355707/ https://www.ncbi.nlm.nih.gov/pubmed/30740125 http://dx.doi.org/10.3389/fgene.2018.00703 |
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