Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers
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Author
Shen, Li
Thompson, Paul M.
Potkin, Steven G.
Bertram, Lars
Farrer, Lindsay A.
Foroud, Tatiana M.
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.
Note: Order does not necessarily reflect citation order of authors.
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https://doi.org/10.1007/s11682-013-9262-zMetadata
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Shen, L., P. M. Thompson, S. G. Potkin, L. Bertram, L. A. Farrer, T. M. Foroud, R. C. Green, et al. 2013. “Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers.” Brain Imaging and Behavior 8 (1): 183-207. doi:10.1007/s11682-013-9262-z. http://dx.doi.org/10.1007/s11682-013-9262-z.Abstract
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.Other Sources
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3976843/pdf/Terms of Use
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