| Title |
Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers
|
|---|---|
| Published in |
Brain Imaging and Behavior, October 2013
|
| DOI | 10.1007/s11682-013-9262-z |
| Pubmed ID | |
| Authors |
Li Shen, Paul M. Thompson, Steven G. Potkin, Lars Bertram, Lindsay A. Farrer, Tatiana M. Foroud, Robert C. Green, Xiaolan Hu, Matthew J. Huentelman, Sungeun Kim, John S. K. Kauwe, Qingqin Li, Enchi Liu, Fabio Macciardi, Jason H. Moore, Leanne Munsie, Kwangsik Nho, Vijay K. Ramanan, Shannon L. Risacher, David J. Stone, Shanker Swaminathan, Arthur W. Toga, Michael W. Weiner, Andrew J. Saykin, for the Alzheimer’s Disease Neuroimaging Initiative |
| 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. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 13% |
| United Kingdom | 1 | 13% |
| Unknown | 6 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 63% |
| Scientists | 2 | 25% |
| Science communicators (journalists, bloggers, editors) | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 219 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | <1% |
| Germany | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Turkey | 1 | <1% |
| Denmark | 1 | <1% |
| United Kingdom | 1 | <1% |
| Unknown | 212 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 48 | 22% |
| Researcher | 37 | 17% |
| Student > Master | 22 | 10% |
| Student > Doctoral Student | 18 | 8% |
| Other | 13 | 6% |
| Other | 49 | 22% |
| Unknown | 32 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 36 | 16% |
| Medicine and Dentistry | 31 | 14% |
| Neuroscience | 30 | 14% |
| Psychology | 16 | 7% |
| Computer Science | 15 | 7% |
| Other | 44 | 20% |
| Unknown | 47 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 January 2017.
All research outputs
#4,432,092
of 22,725,280 outputs
Outputs from Brain Imaging and Behavior
#288
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Outputs of similar age
#41,188
of 207,956 outputs
Outputs of similar age from Brain Imaging and Behavior
#4
of 23 outputs
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