Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease

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Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease

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Title: Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease
Author: Hao, Xiaoke; Li, Chanxiu; Du, Lei; Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon L.; Saykin, Andrew J.; Shen, Li; Zhang, Daoqiang; Weiner, Michael W.; Aisen, Paul; Petersen, Ronald; Jack, Clifford R.; Mason, Sara S.; Albers, Colleen S.; Knopman, David; Johnson, Kris; Jagust, William; Trojanowki, John Q.; Toga, Arthur W.; Beckett, Laurel; Green, Robert C.; Farlow, Martin R.; Marie Hake, Ann; Matthews, Brandy R.; Brosch, Jared R.; Herring, Scott; Hunt, Cynthia; Shaw, Leslie M.; Ances, Beau; Morris, John C.; Carroll, Maria; Creech, Mary L.; Franklin, Erin; Mintun, Mark A.; Schneider, Stacy; Oliver, Angela; Kaye, Jeffrey; Quinn, Joseph; Silbert, Lisa; Lind, Betty; Carter, Raina; Dolen, Sara; Schneider, Lon S.; Pawluczyk, Sonia; Beccera, Mauricio; Teodoro, Liberty; Spann, Bryan M.; Brewer, James; Vanderswag, Helen; Fleisher, Adam; Tariot, Pierre; Burke, Anna; Trncic, Nadira; Reeder, Stephanie; Heidebrink, Judith L.; Lord, Joanne L.; Doody, Rachelle S.; Villanueva-Meyer, Javier; Chowdhury, Munir; Rountree, Susan; Dang, Mimi; Stern, Yaakov; Honig, Lawrence S.; Bell, Karen L.; Marson, Daniel; Griffith, Randall; Clark, David; Geldmacher, David; Brockington, John; Roberson, Erik; Love, Marissa Natelson; Grossman, Hillel; Mitsis, Effie; Shah, Raj C.; deToledo-Morrell, Leyla; Duara, Ranjan; Varon, Daniel; Greig, Maria T.; Roberts, Peggy; Albert, Marilyn; Onyike, Chiadi; D’Agostino, Daniel; Kielb, Stephanie; Galvin, James E.; Cerbone, Brittany; Michel, Christina A.; Pogorelec, Dana M.; Rusinek, Henry; de Leon, Mony J.; Glodzik, Lidia; De Santi, Susan; Doraiswamy, P. Murali; Petrella, Jeffrey R.; Borges-Neto, Salvador; Wong, Terence Z.; Coleman, Edward; Smith, Charles D.; Jicha, Greg; Hardy, Peter; Sinha, Partha; Oates, Elizabeth; Conrad, Gary; Porsteinsson, Anton P.; Goldstein, Bonnie S.; Martin, Kim; Makino, Kelly M.; Ismail, M. Saleem; Brand, Connie; Mulnard, Ruth A.; Thai, Gaby; Mc-Adams-Ortiz, Catherine; Womack, Kyle; Mathews, Dana; Quiceno, Mary; Levey, Allan I.; Lah, James J.; Cellar, Janet S.; Burns, Jeffrey M.; Swerdlow, Russell H.; Brooks, William M.; Apostolova, Liana; Tingus, Kathleen; Woo, Ellen; Silverman, Daniel H. S.; Lu, Po H.; Bartzokis, George; Graff-Radford, Neill R.; Parfitt, Francine; Kendall, Tracy; Johnson, Heather; van Dyck, Christopher H.; Carson, Richard E.; MacAvoy, Martha G.; Varma, Pradeep; Chertkow, Howard; Bergman, Howard; Hosein, Chris; Black, Sandra; Stefanovic, Bojana; Caldwell, Curtis; Hsiung, Ging-Yuek Robin; Feldman, Howard; Mudge, Benita; Assaly, Michele; Finger, Elizabeth; Pasternack, Stephen; Rachisky, Irina; Trost, Dick; Kertesz, Andrew; Bernick, Charles; Munic, Donna; Mesulam, Marek-Marsel; Lipowski, Kristine; Weintraub, Sandra; Bonakdarpour, Borna; Kerwin, Diana; Wu, Chuang-Kuo; Johnson, Nancy; Sadowsky, Carl; Villena, Teresa; Turner, Raymond Scott; Johnson, Kathleen; Reynolds, Brigid; Sperling, Reisa A.; Johnson, Keith A.; Marshall, Gad; Yesavage, Jerome; Taylor, Joy L.; Lane, Barton; Rosen, Allyson; Tinklenberg, Jared; Sabbagh, Marwan N.; Belden, Christine M.; Jacobson, Sandra A.; Sirrel, Sherye A.; Kowall, Neil; Killiany, Ronald; Budson, Andrew E.; Norbash, Alexander; Johnson, Patricia Lynn; Obisesan, Thomas O.; Wolday, Saba; Allard, Joanne; Lerner, Alan; Ogrocki, Paula; Tatsuoka, Curtis; Fatica, Parianne; Fletcher, Evan; Maillard, Pauline; Olichney, John; DeCarli, Charles; Carmichael, Owen; Kittur, Smita; Borrie, Michael; Lee, T.-Y.; Bartha, Rob; Johnson, Sterling; Asthana, Sanjay; Carlsson, Cynthia M.; Potkin, Steven G.; Preda, Adrian; Nguyen, Dana; Bates, Vernice; Capote, Horacio; Rainka, Michelle; Scharre, Douglas W.; Kataki, Maria; Adeli, Anahita; Zimmerman, Earl A.; Celmins, Dzintra; Brown, Alice D.; Pearlson, Godfrey D.; Blank, Karen; Anderson, Karen; Flashman, Laura A.; Seltzer, Marc; Hynes, Mary L.; Santulli, Robert B.; Sink, Kaycee M.; Gordineer, Leslie; Williamson, Jeff D.; Garg, Pradeep; Watkins, Franklin; Ott, Brian R.; Querfurth, Henry; Tremont, Geoffrey; Salloway, Stephen; Malloy, Paul; Correia, Stephen; Rosen, Howard J.; Miller, Bruce L.; Perry, David; Mintzer, Jacobo; Spicer, Kenneth; Bachman, David; Pomara, Nunzio; Hernando, Raymundo; Sarrael, Antero; Relkin, Norman; Chaing, Gloria; Lin, Michael; Ravdin, Lisa; Smith, Amanda; Raj, Balebail Ashok; Fargher, Kristin

Note: Order does not necessarily reflect citation order of authors.

Citation: Hao, X., C. Li, L. Du, X. Yao, J. Yan, S. L. Risacher, A. J. Saykin, et al. 2017. “Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease.” Scientific Reports 7 (1): 44272. doi:10.1038/srep44272. http://dx.doi.org/10.1038/srep44272.
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Abstract: Neuroimaging genetics is an emerging field that aims to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes. In recent studies, in order to detect complex multi-SNP-multi-QT associations, bi-multivariate techniques such as various structured sparse canonical correlation analysis (SCCA) algorithms have been proposed and used in imaging genetics studies. However, associations between genetic markers and imaging QTs identified by existing bi-multivariate methods may not be all disease specific. To bridge this gap, we propose an analytical framework, based on three-way sparse canonical correlation analysis (T-SCCA), to explore the intrinsic associations among genetic markers, imaging QTs, and clinical scores of interest. We perform an empirical study using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from AD risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging scans, and cognitive and diagnostic outcomes. The proposed T-SCCA model not only outperforms the traditional SCCA method in terms of identifying strong associations, but also discovers robust outcome-relevant imaging genetic patterns, demonstrating its promise for improving disease-related mechanistic understanding.
Published Version: doi:10.1038/srep44272
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5349597/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:32071994
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