Genetic analysis of quantitative phenotypes in AD and MCI: Imaging, cognition and biomarkers

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

doi:10.1007/s11682-013-9262-z

Genetic analysis of quantitative phenotypes in AD and MCI: Imaging, cognition and biomarkers

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. StoneShanker Swaminathan, Arthur W. Toga, Michael W. Weiner, Andrew J. Saykin

^*Corresponding author for this work

Institute of Neurogenetics

156 Citations (Scopus)

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.

Original language	English
Journal	Brain Imaging and Behavior
Volume	8
Issue number	2
Pages (from-to)	183-207
Number of pages	25
ISSN	1931-7557
DOIs	https://doi.org/10.1007/s11682-013-9262-z
Publication status	Published - 01.01.2014

Funding

Acknowledgments Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc; Biogen Idec Inc; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129 and K01 AG030514. This work was also supported, in part, by National Institutes of Health (NIH) R01 LM011360 and National Science Foundation (NSF) IIS-1117335 (Dr. Shen), by National Institutes of Health (NIH) R01s AG040060, MH097268, NS080655, and HD050735 and P41 EB015922 (Dr. Thompson), by National Institutes of Health (NIH) U01 AG024904, AG036535, U24RR025736, and U24 RR21992 (Dr. Potkin), by the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF) grant number #16SV5538, the Cure Alzheimer’s Fund, and the Fidelity Biosciences Research Initiative (Dr. Bertram), by National Institutes of Health (NIH) U24 AG021886, National Cell Repository for Alzheimer’s Disease (Dr. Foroud), by National Institutes of Health (NIH) R01 HG002213, K24 AG027841 and the Alzheimer’s Association (Dr. Green), by National Institutes of Health (NIH) R01 AG042611, the Alzheimer’s Association (MNIRG-11-205368), the Charleston Conference on Alzheimer’s disease Young Investigator Award and the Brigham Young University Gerontology Program (Dr. Kauwe), by National Institutes of Health (NIH) R01 LM009012 and R01 LM010098 (Dr. Moore), by National Institutes of Health (NIH) K99 LM011384 (Dr. Nho), by Alzheimer’s Disease Neuroimaging Initiative (Grant # U01 AG024904) and the Laboratory of Neuro Imaging Resource (LONIR) (Grant # 9 P41EB015922-15) at UCLA (Dr. Toga), by National Institutes of Health (NIH) U01AG024904, RC2 AG036535, R01 AG19771, P30 AG10133, at Indiana University School of Medicine (Dr. Saykin). The authors would like to thank Erika D. Tarver from the Foundation for the National Institutes of Health for her helpful comments on the manuscript. Dr. Potkin has served on Scientific Advisory Boards, or been a consultant or grant recipient, and/ or received honoraria from Amgen, Baxter, Bristol-Myers Squibb, Ceregene, Consert, Cortex, Eisai, Elan, Eli Lilly, EnVivo, Genentech, Janssen, Lundbeck, Merck, Novartis, Otsuka, Pfizer, Roche, Shire, Sunovion, and Takeda. Dr. Foroud has served on the External Advisory Board for Washington University’s Alzheimer’s Disease Research Center and Data Use Committee for University of Pennsylvania on NIA U24 award, has received consultant fee and travel support from University of Colorado on NIH award DA011015, has received honorarium to review scientific proposals for American Association for the Advancement of Science, and has received travel support from Michael J. Fox Foundation for Parkinson’s Research and Washington University in St. Louis. Dr. Hu is an employee of Bristol-Myers Squibb. Dr. Weiner has served on Scientific Advisory Boards for Pfizer and BOLT International, has served as a consultant to Pfizer, Janssen, KLJ Associates, Easton Associates, Harvard University, inThought, INC Research, Inc., University of California, Los Angeles (UCLA), Alzheimer’s Drug Discovery Foundation (ADDF), Sanofi-Aventis Groupe, Avid RadioPharmaceuticals, Eli Lilly & Co, has received funding for travel from Pfizer, the AD/PD meeting, Paul Sabatier University, Novartis, Tohoku University, MCI Group, France, Travel eDreams, Inc. Neuroscience School of Advanced Studies (NSAS), Danone Trading, BV, CTAD ANT Congres, Kenes, Intl., ADRC, UCLA, Kenes, Intl., has served on Editorial Advisory Boards for Alzheimer’s & Dementia, MRI, has received honoraria from Pfizer, Tohoku University, Danone Trading, BV, has received Research Support from Merck, Avid, DOD, and VA. Dr. Weiner is the PI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), where organizations contributing to the Foundation for NIH and thus to the NIA funded ADNI include Abbott, Alzheimer’s Association, Alzheimer’s Drug Discovery Foundation, Anonymous Foundation, AstraZeneca Bayer Healthcare BioClinica, Inc. (ADNI 2) Bristol-Myers Squibb, Cure Alzheimer’s Fund, Eisai, Elan, Gene Network Sciences, Genentech, GE Healthcare GlaxoSmithKline Innogenetics Johnson & Johnson, Eli Lilly & Company, Medpace Merck Novartis Pfizer Inc Roche, Schering Plough, Synarc, Wyeth.

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Cite this

Shen, L., Thompson, P. M., Potkin, S. G., Bertram, L., Farrer, L. A., Foroud, T. M., Green, R. C., Hu, X., Huentelman, M. J., Kim, S., Kauwe, J. S. K., Li, Q., Liu, E., Macciardi, F., Moore, J. H., Munsie, L., Nho, K., Ramanan, V. K., Risacher, S. L., ... Saykin, A. J. (2014). Genetic analysis of quantitative phenotypes in AD and MCI: Imaging, cognition and biomarkers. Brain Imaging and Behavior, 8(2), 183-207. https://doi.org/10.1007/s11682-013-9262-z

@article{e16251908e0e44299e707ca196690eaf,

title = "Genetic analysis of quantitative phenotypes in AD and MCI: Imaging, cognition and biomarkers",

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.",

author = "Li Shen and Thompson, \{Paul M.\} and Potkin, \{Steven G.\} and Lars Bertram and Farrer, \{Lindsay A.\} and Foroud, \{Tatiana M.\} and Green, \{Robert C.\} and Xiaolan Hu and Huentelman, \{Matthew J.\} and Sungeun Kim and Kauwe, \{John S.K.\} and Qingqin Li and Enchi Liu and Fabio Macciardi and Moore, \{Jason H.\} and Leanne Munsie and Kwangsik Nho and Ramanan, \{Vijay K.\} and Risacher, \{Shannon L.\} and Stone, \{David J.\} and Shanker Swaminathan and Toga, \{Arthur W.\} and Weiner, \{Michael W.\} and Saykin, \{Andrew J.\}",

year = "2014",

month = jan,

day = "1",

doi = "10.1007/s11682-013-9262-z",

language = "English",

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journal = "Brain Imaging and Behavior",

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Shen, L, Thompson, PM, Potkin, SG, Bertram, L, Farrer, LA, Foroud, TM, Green, RC, Hu, X, Huentelman, MJ, Kim, S, Kauwe, JSK, Li, Q, Liu, E, Macciardi, F, Moore, JH, Munsie, L, Nho, K, Ramanan, VK, Risacher, SL, Stone, DJ, Swaminathan, S, Toga, AW, Weiner, MW & Saykin, AJ 2014, 'Genetic analysis of quantitative phenotypes in AD and MCI: Imaging, cognition and biomarkers', Brain Imaging and Behavior, vol. 8, no. 2, pp. 183-207. https://doi.org/10.1007/s11682-013-9262-z

TY - JOUR

T1 - Genetic analysis of quantitative phenotypes in AD and MCI: Imaging, cognition and biomarkers

AU - Shen, Li

AU - Thompson, Paul M.

AU - Potkin, Steven G.

AU - Bertram, Lars

AU - Farrer, Lindsay A.

AU - Foroud, Tatiana M.

AU - Green, Robert C.

AU - Hu, Xiaolan

AU - Huentelman, Matthew J.

AU - Kim, Sungeun

AU - Kauwe, John S.K.

AU - Li, Qingqin

AU - Liu, Enchi

AU - Macciardi, Fabio

AU - Moore, Jason H.

AU - Munsie, Leanne

AU - Nho, Kwangsik

AU - Ramanan, Vijay K.

AU - Risacher, Shannon L.

AU - Stone, David J.

AU - Swaminathan, Shanker

AU - Toga, Arthur W.

AU - Weiner, Michael W.

AU - Saykin, Andrew J.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

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DO - 10.1007/s11682-013-9262-z

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JO - Brain Imaging and Behavior

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ER -

Genetic analysis of quantitative phenotypes in AD and MCI: Imaging, cognition and biomarkers

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