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Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers
The Genetics Core of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer’s disease (AD)-related phenotypes. Here, we provide a systematic review of genetic...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3976843/ https://www.ncbi.nlm.nih.gov/pubmed/24092460 http://dx.doi.org/10.1007/s11682-013-9262-z |
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author | Shen, Li Thompson, Paul M. Potkin, Steven G. Bertram, Lars Farrer, Lindsay A. Foroud, Tatiana M. Green, Robert C. Hu, Xiaolan Huentelman, Matthew J. Kim, Sungeun Kauwe, John S. K. Li, Qingqin Liu, Enchi Macciardi, Fabio Moore, Jason H. Munsie, Leanne Nho, Kwangsik Ramanan, Vijay K. Risacher, Shannon L. Stone, David J. Swaminathan, Shanker Toga, Arthur W. Weiner, Michael W. Saykin, Andrew J. |
author_facet | Shen, Li Thompson, Paul M. Potkin, Steven G. Bertram, Lars Farrer, Lindsay A. Foroud, Tatiana M. Green, Robert C. Hu, Xiaolan Huentelman, Matthew J. Kim, Sungeun Kauwe, John S. K. Li, Qingqin Liu, Enchi Macciardi, Fabio Moore, Jason H. Munsie, Leanne Nho, Kwangsik Ramanan, Vijay K. Risacher, Shannon L. Stone, David J. Swaminathan, Shanker Toga, Arthur W. Weiner, Michael W. Saykin, Andrew J. |
author_sort | Shen, Li |
collection | PubMed |
description | The Genetics Core of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer’s disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g., APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g., FRMD6) that were later replicated on different data sets. Several other genes (e.g., APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11682-013-9262-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-3976843 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-39768432014-05-05 Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers Shen, Li Thompson, Paul M. Potkin, Steven G. Bertram, Lars Farrer, Lindsay A. Foroud, Tatiana M. Green, Robert C. Hu, Xiaolan Huentelman, Matthew J. Kim, Sungeun Kauwe, John S. K. Li, Qingqin Liu, Enchi Macciardi, Fabio Moore, Jason H. Munsie, Leanne Nho, Kwangsik Ramanan, Vijay K. Risacher, Shannon L. Stone, David J. Swaminathan, Shanker Toga, Arthur W. Weiner, Michael W. Saykin, Andrew J. Brain Imaging Behav SI: Genetic Neuroimaging in Aging and Age-Related Diseases The Genetics Core of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer’s disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g., APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g., FRMD6) that were later replicated on different data sets. Several other genes (e.g., APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11682-013-9262-z) contains supplementary material, which is available to authorized users. Springer US 2013-10-05 2014 /pmc/articles/PMC3976843/ /pubmed/24092460 http://dx.doi.org/10.1007/s11682-013-9262-z Text en © The Author(s) 2013 https://creativecommons.org/licenses/by-nc/2.0/ Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited. |
spellingShingle | SI: Genetic Neuroimaging in Aging and Age-Related Diseases Shen, Li Thompson, Paul M. Potkin, Steven G. Bertram, Lars Farrer, Lindsay A. Foroud, Tatiana M. Green, Robert C. Hu, Xiaolan Huentelman, Matthew J. Kim, Sungeun Kauwe, John S. K. Li, Qingqin Liu, Enchi Macciardi, Fabio Moore, Jason H. Munsie, Leanne Nho, Kwangsik Ramanan, Vijay K. Risacher, Shannon L. Stone, David J. Swaminathan, Shanker Toga, Arthur W. Weiner, Michael W. Saykin, Andrew J. Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers |
title | Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers |
title_full | Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers |
title_fullStr | Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers |
title_full_unstemmed | Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers |
title_short | Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers |
title_sort | genetic analysis of quantitative phenotypes in ad and mci: imaging, cognition and biomarkers |
topic | SI: Genetic Neuroimaging in Aging and Age-Related Diseases |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3976843/ https://www.ncbi.nlm.nih.gov/pubmed/24092460 http://dx.doi.org/10.1007/s11682-013-9262-z |
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