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Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer’s disease: a study of ADNI cohorts

BACKGROUND: Alzheimer’s disease (AD) is a neurodegenerative disease that causes dementia. While molecular basis of AD is not fully understood, genetic factors are expected to participate in the development and progression of the disease. Our goal was to uncover novel genetic underpinnings of Alzheim...

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Autores principales: Song, Ailin, Yan, Jingwen, Kim, Sungeun, Risacher, Shannon Leigh, Wong, Aaron K., Saykin, Andrew J., Shen, Li, Greene, Casey S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4717572/
https://www.ncbi.nlm.nih.gov/pubmed/26788126
http://dx.doi.org/10.1186/s13040-016-0082-8
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author Song, Ailin
Yan, Jingwen
Kim, Sungeun
Risacher, Shannon Leigh
Wong, Aaron K.
Saykin, Andrew J.
Shen, Li
Greene, Casey S.
author_facet Song, Ailin
Yan, Jingwen
Kim, Sungeun
Risacher, Shannon Leigh
Wong, Aaron K.
Saykin, Andrew J.
Shen, Li
Greene, Casey S.
author_sort Song, Ailin
collection PubMed
description BACKGROUND: Alzheimer’s disease (AD) is a neurodegenerative disease that causes dementia. While molecular basis of AD is not fully understood, genetic factors are expected to participate in the development and progression of the disease. Our goal was to uncover novel genetic underpinnings of Alzheimer’s disease with a bioinformatics approach that accounts for tissue specificity. FINDINGS: We performed genome-wide association studies (GWAS) for hippocampal volume in two Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohorts. We used these GWAS in a subsequent tissue-specific network-wide association study (NetWAS), which applied nominally significant associations in the initial GWAS to identify disease relevant patterns in a functional network for the hippocampus. We compared prioritized gene lists from NetWAS and GWAS with literature curated AD-associated genes from the Online Mendelian Inheritance in Man (OMIM) database. In the ADNI-1 GWAS, where we also observed an enrichment of low p-values, NetWAS prioritized disease-gene associations in accordance with OMIM annotations. This was not observed in the ADNI-2 dataset. We provide source code to replicate these analyses as well as complete results under permissive licenses. CONCLUSIONS: We performed the first analysis of hippocampal volume using NetWAS, which uses machine learning algorithms applied to tissue-specific functional interaction network to prioritize GWAS results. Our findings support the idea that tissue-specific networks may provide helpful context for understanding the etiology of common human diseases and reveal challenges that network-based approaches encounter in some datasets. Our source code and intermediate results files can facilitate the development of methods to address these challenges. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13040-016-0082-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-47175722016-01-20 Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer’s disease: a study of ADNI cohorts Song, Ailin Yan, Jingwen Kim, Sungeun Risacher, Shannon Leigh Wong, Aaron K. Saykin, Andrew J. Shen, Li Greene, Casey S. BioData Min Short Report BACKGROUND: Alzheimer’s disease (AD) is a neurodegenerative disease that causes dementia. While molecular basis of AD is not fully understood, genetic factors are expected to participate in the development and progression of the disease. Our goal was to uncover novel genetic underpinnings of Alzheimer’s disease with a bioinformatics approach that accounts for tissue specificity. FINDINGS: We performed genome-wide association studies (GWAS) for hippocampal volume in two Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohorts. We used these GWAS in a subsequent tissue-specific network-wide association study (NetWAS), which applied nominally significant associations in the initial GWAS to identify disease relevant patterns in a functional network for the hippocampus. We compared prioritized gene lists from NetWAS and GWAS with literature curated AD-associated genes from the Online Mendelian Inheritance in Man (OMIM) database. In the ADNI-1 GWAS, where we also observed an enrichment of low p-values, NetWAS prioritized disease-gene associations in accordance with OMIM annotations. This was not observed in the ADNI-2 dataset. We provide source code to replicate these analyses as well as complete results under permissive licenses. CONCLUSIONS: We performed the first analysis of hippocampal volume using NetWAS, which uses machine learning algorithms applied to tissue-specific functional interaction network to prioritize GWAS results. Our findings support the idea that tissue-specific networks may provide helpful context for understanding the etiology of common human diseases and reveal challenges that network-based approaches encounter in some datasets. Our source code and intermediate results files can facilitate the development of methods to address these challenges. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13040-016-0082-8) contains supplementary material, which is available to authorized users. BioMed Central 2016-01-19 /pmc/articles/PMC4717572/ /pubmed/26788126 http://dx.doi.org/10.1186/s13040-016-0082-8 Text en © Song et al. 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 Short Report
Song, Ailin
Yan, Jingwen
Kim, Sungeun
Risacher, Shannon Leigh
Wong, Aaron K.
Saykin, Andrew J.
Shen, Li
Greene, Casey S.
Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer’s disease: a study of ADNI cohorts
title Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer’s disease: a study of ADNI cohorts
title_full Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer’s disease: a study of ADNI cohorts
title_fullStr Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer’s disease: a study of ADNI cohorts
title_full_unstemmed Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer’s disease: a study of ADNI cohorts
title_short Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer’s disease: a study of ADNI cohorts
title_sort network-based analysis of genetic variants associated with hippocampal volume in alzheimer’s disease: a study of adni cohorts
topic Short Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4717572/
https://www.ncbi.nlm.nih.gov/pubmed/26788126
http://dx.doi.org/10.1186/s13040-016-0082-8
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