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Current Alzheimer Research

Editor-in-Chief

ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

Review Article

Neuroimaging Genetics and Network Analysis in Alzheimer’s Disease

Author(s): Seok Woo Moon*

Volume 20, Issue 8, 2023

Published on: 10 November, 2023

Page: [526 - 538] Pages: 13

DOI: 10.2174/0115672050265188231107072215

Price: $65

Open Access Journals Promotions 2
Abstract

The issue of the genetics in brain imaging phenotypes serves as a crucial link between two distinct scientific fields: neuroimaging genetics (NG). The articles included here provide solid proof that this NG link has considerable synergy. There is a suitable collection of articles that offer a wide range of viewpoints on how genetic variations affect brain structure and function. They serve as illustrations of several study approaches used in contemporary genetics and neuroscience. Genome-wide association studies and candidate-gene association are two examples of genetic techniques. Cortical gray matter structural/volumetric measures from magnetic resonance imaging (MRI) are sources of information on brain phenotypes. Together, they show how various scientific disciplines have benefited from significant technological advances, such as the single-nucleotide polymorphism array in genetics and the development of increasingly higher-resolution MRI imaging. Moreover, we discuss NG’s contribution to expanding our knowledge about the heterogeneity within Alzheimer’s disease as well as the benefits of different network analyses.

Keywords: Neuroimaging, genetics, methodology, GWAS, Alzheimer’s disease, network analysis.

[1]
Shen, L.; Kim, S.; Risacher, S.L.; Nho, K.; Swaminathan, S.; West, J.D.; Foroud, T.; Pankratz, N.; Moore, J.H.; Sloan, C.D.; Huentelman, M.J.; Craig, D.W.; DeChairo, B.M.; Potkin, S.G.; Jack, C.R., Jr; Weiner, M.W.; Saykin, A.J. Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. Neuroimage, 2010, 53(3), 1051-1063.
[http://dx.doi.org/10.1016/j.neuroimage.2010.01.042] [PMID: 20100581]
[2]
Shav-tal, Y. Imaging gene expression: Methods and protocols; Humana Press: New York, NY, 2013.
[http://dx.doi.org/10.1007/978-1-62703-526-2]
[3]
Jiang, W.; King, T.Z.; Turner, J.A. Imaging genetics towards a refined diagnosis of schizophrenia. Front. Psychiatry, 2019, 10, 494.
[http://dx.doi.org/10.3389/fpsyt.2019.00494] [PMID: 31354550]
[4]
Mayeux, R. Epidemiology of neurodegeneration. Annu. Rev. Neurosci., 2003, 26(1), 81-104.
[http://dx.doi.org/10.1146/annurev.neuro.26.043002.094919] [PMID: 12574495]
[5]
Mayeux, R. Alzheimer’s disease: Epidemiology. Handb. Clin. Neurol., 2008, 89, 195-205.
[http://dx.doi.org/10.1016/S0072-9752(07)01218-3] [PMID: 18631744]
[6]
Gatz, M.; Reynolds, C.A.; Fratiglioni, L.; Johansson, B.; Mortimer, J.A.; Berg, S.; Fiske, A.; Pedersen, N.L. Role of genes and environments for explaining Alzheimer disease. Arch. Gen. Psychiatry, 2006, 63(2), 168-174.
[http://dx.doi.org/10.1001/archpsyc.63.2.168] [PMID: 16461860]
[7]
Ertekin-Taner, N. Genetics of Alzheimer’s disease: A centennial review. Neurol. Clin., 2007, 25(3), 611-667, v.
[http://dx.doi.org/10.1016/j.ncl.2007.03.009] [PMID: 17659183]
[8]
Lautenschlager, N.T.; Cupples, L.A.; Rao, V.S.; Auerbach, S.A.; Becker, R.; Burke, J.; Chui, H.; Duara, R.; Foley, E.J.; Glatt, S.L.; Green, R.C.; Jones, R.; Karlinsky, H.; Kukull, W.A.; Kurz, A.; Larson, E.B.; Martelli, K.; Sadovnick, A.D.; Volicer, L.; Waring, S.C.; Growdon, J.H.; Farrer, L.A. Risk of dementia among relatives of Alzheimer’s disease patients in the MIRAGE study: What is in store for the oldest old? Neurology, 1996, 46(3), 641-650.
[http://dx.doi.org/10.1212/WNL.46.3.641] [PMID: 8618660]
[9]
Toga, A.W. Neuroimage databases: The good, the bad and the ugly. Nat. Rev. Neurosci., 2002, 3(4), 302-309.
[http://dx.doi.org/10.1038/nrn782] [PMID: 11967560]
[10]
Stein, J.L.; Hua, X.; Lee, S.; Ho, A.J.; Leow, A.D.; Toga, A.W.; Saykin, A.J.; Shen, L.; Foroud, T.; Pankratz, N.; Huentelman, M.J.; Craig, D.W.; Gerber, J.D.; Allen, A.N.; Corneveaux, J.J.; Dechairo, B.M.; Potkin, S.G.; Weiner, M.W.; Thompson, P. Voxelwise genome-wide association study (vGWAS). Neuroimage, 2010, 53(3), 1160-1174.
[http://dx.doi.org/10.1016/j.neuroimage.2010.02.032] [PMID: 20171287]
[11]
Potkin, S.G.; Guffanti, G.; Lakatos, A.; Turner, J.A.; Kruggel, F.; Fallon, J.H.; Saykin, A.J.; Orro, A.; Lupoli, S.; Salvi, E.; Weiner, M.; Macciardi, F. Hippocampal atrophy as a quantitative trait in a genome-wide association study identifying novel susceptibility genes for Alzheimer’s disease. PLoS One, 2009, 4(8), e6501.
[http://dx.doi.org/10.1371/journal.pone.0006501] [PMID: 19668339]
[12]
Glahn, D.C.; Paus, T.; Thompson, P.M. Imaging genomics: Mapping the influence of genetics on brain structure and function. Hum. Brain Mapp., 2007, 28(6), 461-463.
[http://dx.doi.org/10.1002/hbm.20416] [PMID: 17471577]
[13]
Cannon, T.D.; Thompson, P.M.; van Erp, T.G.M.; Huttunen, M.; Lonnqvist, J.; Kaprio, J.; Toga, A.W. Mapping heritability and molecular genetic associations with cortical features using probabilistic brain atlases: Methods and applications to schizophrenia. Neuroinformatics, 2006, 4(1), 5-20.
[http://dx.doi.org/10.1385/NI:4:1:5] [PMID: 16595856]
[14]
Potkin, S.G.; Turner, J.A.; Guffanti, G.; Lakatos, A.; Torri, F.; Keator, D.B.; Macciardi, F. Genome-wide strategies for discovering genetic influences on cognition and cognitive disorders: Methodological considerations. Cogn. Neuropsychiatry, 2009, 14(4-5), 391-418.
[http://dx.doi.org/10.1080/13546800903059829] [PMID: 19634037]
[15]
Schreiber, M.; Bird, T.D.; Tsuang, D.W. Alzheimer’s disease genetics. Curr. Behav. Neurosci. Rep., 2014, 1(4), 191-196.
[http://dx.doi.org/10.1007/s40473-014-0026-x]
[16]
Leduc, V.; De Beaumont, L.; Théroux, L.; Dea, D.; Aisen, P.; Petersen, R. HMGCR is a genetic modifier for risk, age of onset and MCI conversion to Alzheimer’s disease in a three cohorts study. Mol. Psychiatry, 2014, 20(7), 867-73.
[PMID: 25023145]
[17]
Sherva, R.; Tripodis, Y.; Bennett, D.A.; Chibnik, L.B.; Crane, P.K.; de Jager, P.L.; Farrer, L.A.; Saykin, A.J.; Shulman, J.M.; Naj, A.; Green, R.C. Genome-wide association study of the rate of cognitive decline in Alzheimer’s disease. Alzheimers Dement., 2014, 10(1), 45-52.
[http://dx.doi.org/10.1016/j.jalz.2013.01.008] [PMID: 23535033]
[18]
Colhoun, H.M.; McKeigue, P.M.; Smith, G.D. Problems of reporting genetic associations with complex outcomes. Lancet, 2003, 361(9360), 865-872.
[http://dx.doi.org/10.1016/S0140-6736(03)12715-8] [PMID: 12642066]
[19]
Robinson, M.R.; Wray, N.R.; Visscher, P.M. Explaining additional genetic variation in complex traits. Trends Genet., 2014, 30(4), 124-132.
[http://dx.doi.org/10.1016/j.tig.2014.02.003] [PMID: 24629526]
[20]
Rosenthal, S.L.; Barmada, M.M.; Wang, X.; Demirci, F.Y.; Kamboh, M.I. Connecting the dots: Potential of data integration to identify regulatory SNPs in late-onset Alzheimer’s disease GWAS findings. PLoS One, 2014, 9(4), e95152.
[http://dx.doi.org/10.1371/journal.pone.0095152] [PMID: 24743338]
[21]
Karch, C.M.; Cruchaga, C.; Goate, A.M. Alzheimer’s disease genetics: From the bench to the clinic. Neuron, 2014, 83(1), 11-26.
[http://dx.doi.org/10.1016/j.neuron.2014.05.041] [PMID: 24991952]
[22]
Guerreiro, R.; Hardy, J. Genetics of Alzheimer’s disease. Neurotherapeutics, 2014, 11(4), 732-737.
[http://dx.doi.org/10.1007/s13311-014-0295-9] [PMID: 25113539]
[23]
Lord, J.; Cruchaga, C. The epigenetic landscape of Alzheimer’s disease. Nat. Neurosci., 2014, 17(9), 1138-1140.
[http://dx.doi.org/10.1038/nn.3792] [PMID: 25157507]
[24]
Biffi, A.; Anderson, C.D.; Desikan, R.S.; Sabuncu, M.; Cortellini, L.; Schmansky, N.; Salat, D.; Rosand, J. Genetic variation and neuroimaging measures in Alzheimer disease. Arch. Neurol., 2010, 67(6), 677-685.
[http://dx.doi.org/10.1001/archneurol.2010.108] [PMID: 20558387]
[25]
Dalca, A.V.; Sridharan, R.; Sabuncu, M.R.; Golland, P. Predictive modeling of anatomy with genetic and clinical data. Med. Image Comput. Comput. Assist. Interv., 2015, 9351, 519-526.
[http://dx.doi.org/10.1007/978-3-319-24574-4_62]
[26]
Weiner, M.W.; Veitch, D.P.; Aisen, P.S.; Beckett, L.A.; Cairns, N.J.; Green, R.C.; Harvey, D.; Jack, C.R.; Jagust, W.; Liu, E.; Morris, J.C.; Petersen, R.C.; Saykin, A.J.; Schmidt, M.E.; Shaw, L.; Siuciak, J.A.; Soares, H.; Toga, A.W.; Trojanowski, J.Q. The Alzheimer’s disease neuroimaging initiative: A review of papers published since its inception. Alzheimers Dement., 2012, 8(1S), S1-S68.
[http://dx.doi.org/10.1016/j.jalz.2011.09.172] [PMID: 22047634]
[27]
Bogdan, R.; Salmeron, B.J.; Carey, C.E.; Agrawal, A.; Calhoun, V.D.; Garavan, H.; Hariri, A.R.; Heinz, A.; Hill, M.N.; Holmes, A.; Kalin, N.H.; Goldman, D. Imaging genetics and genomics in psychiatry: A critical review of progress and potential. Biol. Psychiatry, 2017, 82(3), 165-175.
[http://dx.doi.org/10.1016/j.biopsych.2016.12.030] [PMID: 28283186]
[28]
Liu, J.; Calhoun, V.D. A review of multivariate analyses in imaging genetics. Front. Neuroinform., 2014, 8, 29.
[http://dx.doi.org/10.3389/fninf.2014.00029] [PMID: 24723883]
[29]
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.; Stone, D.J.; Swaminathan, S.; Toga, A.W.; Weiner, M.W.; Saykin, A.J. Genetic analysis of quantitative phenotypes in AD and MCI: Imaging, cognition and biomarkers. Brain Imaging Behav., 2014, 8(2), 183-207.
[http://dx.doi.org/10.1007/s11682-013-9262-z] [PMID: 24092460]
[30]
Batmanghelich, N.K.; Dalca, A.; Quon, G.; Sabuncu, M.; Golland, P. Probabilistic modeling of imaging, genetics and diagnosis. IEEE Trans. Med. Imaging, 2016, 35(7), 1765-1779.
[http://dx.doi.org/10.1109/TMI.2016.2527784] [PMID: 26886973]
[31]
Vounou, M.; Nichols, T.E.; Montana, G. Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach. Neuroimage, 2010, 53(3), 1147-1159.
[http://dx.doi.org/10.1016/j.neuroimage.2010.07.002] [PMID: 20624472]
[32]
Roussotte, F.F.; Gutman, B.A.; Madsen, S.K.; Colby, J.B.; Thompson, P.M. Combined effects of Alzheimer risk variants in the CLU and ApoE genes on ventricular expansion patterns in the elderly. J. Neurosci., 2014, 34(19), 6537-6545.
[http://dx.doi.org/10.1523/JNEUROSCI.5236-13.2014] [PMID: 24806679]
[33]
Wyman, B.T.; Harvey, D.J.; Crawford, K.; Bernstein, M.A.; Carmichael, O.; Cole, P.E.; Crane, P.K.; DeCarli, C.; Fox, N.C.; Gunter, J.L.; Hill, D.; Killiany, R.J.; Pachai, C.; Schwarz, A.J.; Schuff, N.; Senjem, M.L.; Suhy, J.; Thompson, P.M.; Weiner, M.; Jack, C.R., Jr Standardization of analysis sets for reporting results from ADNI MRI data. Alzheimers Dement., 2013, 9(3), 332-337.
[http://dx.doi.org/10.1016/j.jalz.2012.06.004] [PMID: 23110865]
[34]
Fischl, B. FreeSurfer. Neuroimage, 2012, 62(2), 774-781.
[http://dx.doi.org/10.1016/j.neuroimage.2012.01.021] [PMID: 22248573]
[35]
Reuter, M.; Rosas, H.D.; Fischl, B. Highly accurate inverse consistent registration: A robust approach. Neuroimage, 2010, 53(4), 1181-1196.
[http://dx.doi.org/10.1016/j.neuroimage.2010.07.020] [PMID: 20637289]
[36]
Ségonne, F.; Dale, A.M.; Busa, E.; Glessner, M.; Salat, D.; Hahn, H.K.; Fischl, B. A hybrid approach to the skull stripping problem in MRI. Neuroimage, 2004, 22(3), 1060-1075.
[http://dx.doi.org/10.1016/j.neuroimage.2004.03.032] [PMID: 15219578]
[37]
Fischl, B.; Salat, D.H.; Busa, E.; Albert, M.; Dieterich, M.; Haselgrove, C.; van der Kouwe, A.; Killiany, R.; Kennedy, D.; Klaveness, S.; Montillo, A.; Makris, N.; Rosen, B.; Dale, A.M. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron, 2002, 33(3), 341-355.
[http://dx.doi.org/10.1016/S0896-6273(02)00569-X] [PMID: 11832223]
[38]
Fischl, B.; Salat, D.H.; van der Kouwe, A.J.W.; Makris, N.; Ségonne, F.; Quinn, B.T.; Dale, A.M. Sequence-independent segmentation of magnetic resonance images. Neuroimage, 2004, 23(Suppl. 1), S69-S84.
[http://dx.doi.org/10.1016/j.neuroimage.2004.07.016] [PMID: 15501102]
[39]
Ségonne, F.; Pacheco, J.; Fischl, B. Geometrically accurate topology-correction of cortical surfaces using nonseparating loops. IEEE Trans. Med. Imaging, 2007, 26(4), 518-529.
[http://dx.doi.org/10.1109/TMI.2006.887364] [PMID: 17427739]
[40]
Fischl, B.; Dale, A.M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci., 2000, 97(20), 11050-11055.
[http://dx.doi.org/10.1073/pnas.200033797] [PMID: 10984517]
[41]
Fischl, B.; Sereno, M.I.; Tootell, R.B.H.; Dale, A.M. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp., 1999, 8(4), 272-284.
[http://dx.doi.org/10.1002/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4] [PMID: 10619420]
[42]
Hibar, D.P.; Adams, H.H.H.; Jahanshad, N.; Chauhan, G.; Stein, J.L.; Hofer, E.; Renteria, M.E.; Bis, J.C.; Arias-Vasquez, A.; Ikram, M.K.; Desrivières, S.; Vernooij, M.W.; Abramovic, L.; Alhusaini, S.; Amin, N.; Andersson, M.; Arfanakis, K.; Aribisala, B.S.; Armstrong, N.J.; Athanasiu, L.; Axelsson, T.; Beecham, A.H.; Beiser, A.; Bernard, M.; Blanton, S.H.; Bohlken, M.M.; Boks, M.P.; Bralten, J.; Brickman, A.M.; Carmichael, O.; Chakravarty, M.M.; Chen, Q.; Ching, C.R.K.; Chouraki, V.; Cuellar-Partida, G.; Crivello, F.; Den Braber, A.; Doan, N.T.; Ehrlich, S.; Giddaluru, S.; Goldman, A.L.; Gottesman, R.F.; Grimm, O.; Griswold, M.E.; Guadalupe, T.; Gutman, B.A.; Hass, J.; Haukvik, U.K.; Hoehn, D.; Holmes, A.J.; Hoogman, M.; Janowitz, D.; Jia, T.; Jørgensen, K.N.; Karbalai, N.; Kasperaviciute, D.; Kim, S.; Klein, M.; Kraemer, B.; Lee, P.H.; Liewald, D.C.M.; Lopez, L.M.; Luciano, M.; Macare, C.; Marquand, A.F.; Matarin, M.; Mather, K.A.; Mattheisen, M.; McKay, D.R.; Milaneschi, Y.; Muñoz Maniega, S.; Nho, K.; Nugent, A.C.; Nyquist, P.; Loohuis, L.M.O.; Oosterlaan, J.; Papmeyer, M.; Pirpamer, L.; Pütz, B.; Ramasamy, A.; Richards, J.S.; Risacher, S.L.; Roiz-Santiañez, R.; Rommelse, N.; Ropele, S.; Rose, E.J.; Royle, N.A.; Rundek, T.; Sämann, P.G.; Saremi, A.; Satizabal, C.L.; Schmaal, L.; Schork, A.J.; Shen, L.; Shin, J.; Shumskaya, E.; Smith, A.V.; Sprooten, E.; Strike, L.T.; Teumer, A.; Tordesillas-Gutierrez, D.; Toro, R.; Trabzuni, D.; Trompet, S.; Vaidya, D.; Van der Grond, J.; Van der Lee, S.J.; Van der Meer, D.; Van Donkelaar, M.M.J.; Van Eijk, K.R.; Van Erp, T.G.M.; Van Rooij, D.; Walton, E.; Westlye, L.T.; Whelan, C.D.; Windham, B.G.; Winkler, A.M.; Wittfeld, K.; Woldehawariat, G.; Wolf, C.; Wolfers, T.; Yanek, L.R.; Yang, J.; Zijdenbos, A.; Zwiers, M.P.; Agartz, I.; Almasy, L.; Ames, D.; Amouyel, P.; Andreassen, O.A.; Arepalli, S.; Assareh, A.A.; Barral, S.; Bastin, M.E.; Becker, D.M.; Becker, J.T.; Bennett, D.A.; Blangero, J.; van Bokhoven, H.; Boomsma, D.I.; Brodaty, H.; Brouwer, R.M.; Brunner, H.G.; Buckner, R.L.; Buitelaar, J.K.; Bulayeva, K.B.; Cahn, W.; Calhoun, V.D.; Cannon, D.M.; Cavalleri, G.L.; Cheng, C.Y.; Cichon, S.; Cookson, M.R.; Corvin, A.; Crespo-Facorro, B.; Curran, J.E.; Czisch, M.; Dale, A.M.; Davies, G.E.; De Craen, A.J.M.; De Geus, E.J.C.; De Jager, P.L.; De Zubicaray, G.I.; Deary, I.J.; Debette, S.; DeCarli, C.; Delanty, N.; Depondt, C.; DeStefano, A.; Dillman, A.; Djurovic, S.; Donohoe, G.; Drevets, W.C.; Duggirala, R.; Dyer, T.D.; Enzinger, C.; Erk, S.; Espeseth, T.; Fedko, I.O.; Fernández, G.; Ferrucci, L.; Fisher, S.E.; Fleischman, D.A.; Ford, I.; Fornage, M.; Foroud, T.M.; Fox, P.T.; Francks, C.; Fukunaga, M.; Gibbs, J.R.; Glahn, D.C.; Gollub, R.L.; Göring, H.H.H.; Green, R.C.; Gruber, O.; Gudnason, V.; Guelfi, S.; Håberg, A.K.; Hansell, N.K.; Hardy, J.; Hartman, C.A.; Hashimoto, R.; Hegenscheid, K.; Heinz, A.; Le Hellard, S.; Hernandez, D.G.; Heslenfeld, D.J.; Ho, B.C.; Hoekstra, P.J.; Hoffmann, W.; Hofman, A.; Holsboer, F.; Homuth, G.; Hosten, N.; Hottenga, J.J.; Huentelman, M.; Hulshoff Pol, H.E.; Ikeda, M.; Jack, C.R., Jr; Jenkinson, M.; Johnson, R.; Jönsson, E.G.; Jukema, J.W.; Kahn, R.S.; Kanai, R.; Kloszewska, I.; Knopman, D.S.; Kochunov, P.; Kwok, J.B.; Lawrie, S.M.; Lemaître, H.; Liu, X.; Longo, D.L.; Lopez, O.L.; Lovestone, S.; Martinez, O.; Martinot, J.L.; Mattay, V.S.; McDonald, C.; McIntosh, A.M.; McMahon, F.J.; McMahon, K.L.; Mecocci, P.; Melle, I.; Meyer-Lindenberg, A.; Mohnke, S.; Montgomery, G.W.; Morris, D.W.; Mosley, T.H.; Mühleisen, T.W.; Müller-Myhsok, B.; Nalls, M.A.; Nauck, M.; Nichols, T.E.; Niessen, W.J.; Nöthen, M.M.; Nyberg, L.; Ohi, K.; Olvera, R.L.; Ophoff, R.A.; Pandolfo, M.; Paus, T.; Pausova, Z.; Penninx, B.W.J.H.; Pike, G.B.; Potkin, S.G.; Psaty, B.M.; Reppermund, S.; Rietschel, M.; Roffman, J.L.; Romanczuk-Seiferth, N.; Rotter, J.I.; Ryten, M.; Sacco, R.L.; Sachdev, P.S.; Saykin, A.J.; Schmidt, R.; Schmidt, H.; Schofield, P.R.; Sigursson, S.; Simmons, A.; Singleton, A.; Sisodiya, S.M.; Smith, C.; Smoller, J.W.; Soininen, H.; Steen, V.M.; Stott, D.J.; Sussmann, J.E.; Thalamuthu, A.; Toga, A.W.; Traynor, B.J.; Troncoso, J.; Tsolaki, M.; Tzourio, C.; Uitterlinden, A.G.; Hernández, M.C.V.; Van der Brug, M.; van der Lugt, A.; van der Wee, N.J.A.; Van Haren, N.E.M.; van ’t Ent, D.; Van Tol, M.J.; Vardarajan, B.N.; Vellas, B.; Veltman, D.J.; Völzke, H.; Walter, H.; Wardlaw, J.M.; Wassink, T.H.; Weale, M.E.; Weinberger, D.R.; Weiner, M.W.; Wen, W.; Westman, E.; White, T.; Wong, T.Y.; Wright, C.B.; Zielke, R.H.; Zonderman, A.B.; Martin, N.G.; Van Duijn, C.M.; Wright, M.J.; Longstreth, W.T.; Schumann, G.; Grabe, H.J.; Franke, B.; Launer, L.J.; Medland, S.E.; Seshadri, S.; Thompson, P.M.; Ikram, M.A. Novel genetic loci associated with hippocampal volume. Nat. Commun., 2017, 8(1), 13624.
[http://dx.doi.org/10.1038/ncomms13624] [PMID: 28098162]
[43]
Browning, B.L.; Browning, S.R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet., 2016, 98(1), 116-126.
[http://dx.doi.org/10.1016/j.ajhg.2015.11.020] [PMID: 26748515]
[44]
Moon, S.W.; Dinov, I.D.; Hobel, S.; Zamanyan, A.; Choi, Y.C.; Shi, R.; Thompson, P.M.; Toga, A.W. Structural brain changes in early-onset alzheimer’s disease subjects using the LONI pipeline environment. J. Neuroimaging, 2015, 25(5), 728-737.
[http://dx.doi.org/10.1111/jon.12252] [PMID: 25940587]
[45]
Moon, S.W.; Dinov, I.D.; Zamanyan, A.; Shi, R.; Genco, A.; Hobel, S.; Thompson, P.M.; Toga, A.W. Gene interactions and structural brain change in early-onset Alzheimer’s disease subjects using the pipeline environment. Psychiatry Investig., 2015, 12(1), 125-135.
[http://dx.doi.org/10.4306/pi.2015.12.1.125] [PMID: 25670955]
[46]
Dinov, I.D.; Torri, F.; Macciardi, F.; Petrosyan, P.; Liu, Z.; Zamanyan, A.; Eggert, P.; Pierce, J.; Genco, A.; Knowles, J.A.; Clark, A.P.; Van Horn, J.D.; Ames, J.; Kesselman, C.; Toga, A.W. Applications of the pipeline environment for visual informatics and genomics computations. BMC Bioinformatics, 2011, 12(1), 304.
[http://dx.doi.org/10.1186/1471-2105-12-304] [PMID: 21791102]
[47]
Dinov, I.; Van Horn, J.D.; Lozev, K.M.; Magsipoc, R.; Petrosyan, P.; Liu, Z.; Mackenzie-Graham, A.; Eggert, P.; Parker, D.S.; Toga, A.W. Efficient, distributed and interactive neuroimaging data analysis using the LONI pipeline. Front. Neuroinform., 2009, 3, 22.
[http://dx.doi.org/10.3389/neuro.11.022.2009] [PMID: 19649168]
[48]
Dinov, I.; Lozev, K.; Petrosyan, P.; Liu, Z.; Eggert, P.; Pierce, J.; Zamanyan, A.; Chakrapani, S.; Van Horn, J.; Parker, D.S.; Magsipoc, R.; Leung, K.; Gutman, B.; Woods, R.; Toga, A. Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline. PLoS One, 2010, 5(9), e13070.
[http://dx.doi.org/10.1371/journal.pone.0013070] [PMID: 20927408]
[49]
Smith, S.M. Fast robust automated brain extraction. Hum. Brain Mapp., 2002, 17(3), 143-155.
[http://dx.doi.org/10.1002/hbm.10062] [PMID: 12391568]
[50]
Shattuck, D.W.; Mirza, M.; Adisetiyo, V.; Hojatkashani, C.; Salamon, G.; Narr, K.L.; Poldrack, R.A.; Bilder, R.M.; Toga, A.W. Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage, 2008, 39(3), 1064-1080.
[http://dx.doi.org/10.1016/j.neuroimage.2007.09.031] [PMID: 18037310]
[51]
Tu, Z.; Narr, K.L.; Dollar, P.; Dinov, I.; Thompson, P.M.; Toga, A.W. Brain anatomical structure segmentation by hybrid discriminative/generative models. IEEE Trans. Med. Imaging, 2008, 27(4), 495-508.
[http://dx.doi.org/10.1109/TMI.2007.908121] [PMID: 18390346]
[52]
Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.R.; Bender, D.; Maller, J.; Sklar, P.; de Bakker, P.I.W.; Daly, M.J.; Sham, P.C. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet., 2007, 81(3), 559-575.
[http://dx.doi.org/10.1086/519795] [PMID: 17701901]
[53]
Hunter, D.J.; Kraft, P.; Jacobs, K.B.; Cox, D.G.; Yeager, M.; Hankinson, S.E.; Wacholder, S.; Wang, Z.; Welch, R.; Hutchinson, A.; Wang, J.; Yu, K.; Chatterjee, N.; Orr, N.; Willett, W.C.; Colditz, G.A.; Ziegler, R.G.; Berg, C.D.; Buys, S.S.; McCarty, C.A.; Feigelson, H.S.; Calle, E.E.; Thun, M.J.; Hayes, R.B.; Tucker, M.; Gerhard, D.S.; Fraumeni, J.F., Jr; Hoover, R.N.; Thomas, G.; Chanock, S.J. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat. Genet., 2007, 39(7), 870-874.
[http://dx.doi.org/10.1038/ng2075] [PMID: 17529973]
[54]
Hibar, D.P.; Stein, J.L.; Kohannim, O.; Jahanshad, N.; Saykin, A.J.; Shen, L.; Kim, S.; Pankratz, N.; Foroud, T.; Huentelman, M.J.; Potkin, S.G.; Jack, C.R., Jr; Weiner, M.W.; Toga, A.W.; Thompson, P.M. Voxelwise gene-wide association study (vGeneWAS): Multivariate gene-based association testing in 731 elderly subjects. Neuroimage, 2011, 56(4), 1875-1891.
[http://dx.doi.org/10.1016/j.neuroimage.2011.03.077] [PMID: 21497199]
[55]
Krzywinski, M.; Schein, J.; Birol, İ.; Connors, J.; Gascoyne, R.; Horsman, D.; Jones, S.J.; Marra, M.A. Circos: An information aesthetic for comparative genomics. Genome Res., 2009, 19(9), 1639-1645.
[http://dx.doi.org/10.1101/gr.092759.109] [PMID: 19541911]
[56]
Al-Aziz, J.; Christou, N.; Dinov, I.D. SOCR motion charts: An efficient, open-source, interactive and dynamic applet for visualizing longitudinal multivariate data. J. Stat. Educ., 2010, 18(3), v18n3/dinov.
[PMID: 21479108]
[57]
Moon, S.W.; Zhao, L.; Matloff, W.; Hobel, S.; Berger, R.; Kwon, D. Brain structure and allelic associations in Alzheimer’s disease. CNS Neurosci. Ther., 2022, 29(4), 1034-1048.
[PMID: 36575854]
[58]
Zhang, B.; Gaiteri, C.; Bodea, L.G.; Wang, Z.; McElwee, J.; Podtelezhnikov, A.A.; Zhang, C.; Xie, T.; Tran, L.; Dobrin, R.; Fluder, E.; Clurman, B.; Melquist, S.; Narayanan, M.; Suver, C.; Shah, H.; Mahajan, M.; Gillis, T.; Mysore, J.; MacDonald, M.E.; Lamb, J.R.; Bennett, D.A.; Molony, C.; Stone, D.J.; Gudnason, V.; Myers, A.J.; Schadt, E.E.; Neumann, H.; Zhu, J.; Emilsson, V. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell, 2013, 153(3), 707-720.
[http://dx.doi.org/10.1016/j.cell.2013.03.030] [PMID: 23622250]
[59]
Bleazard, T.; Lamb, J.A.; Griffiths-Jones, S. Bias in microRNA functional enrichment analysis. Bioinformatics, 2015, 31(10), 1592-1598.
[http://dx.doi.org/10.1093/bioinformatics/btv023] [PMID: 25609791]
[60]
Jansen, I.E.; Savage, J.E.; Watanabe, K.; Bryois, J.; Williams, D.M.; Steinberg, S.; Sealock, J.; Karlsson, I.K.; Hägg, S.; Athanasiu, L.; Voyle, N.; Proitsi, P.; Witoelar, A.; Stringer, S.; Aarsland, D.; Almdahl, I.S.; Andersen, F.; Bergh, S.; Bettella, F.; Bjornsson, S.; Brækhus, A.; Bråthen, G.; de Leeuw, C.; Desikan, R.S.; Djurovic, S.; Dumitrescu, L.; Fladby, T.; Hohman, T.J.; Jonsson, P.V.; Kiddle, S.J.; Rongve, A.; Saltvedt, I.; Sando, S.B.; Selbæk, G.; Shoai, M.; Skene, N.G.; Snaedal, J.; Stordal, E.; Ulstein, I.D.; Wang, Y.; White, L.R.; Hardy, J.; Hjerling-Leffler, J.; Sullivan, P.F.; van der Flier, W.M.; Dobson, R.; Davis, L.K.; Stefansson, H.; Stefansson, K.; Pedersen, N.L.; Ripke, S.; Andreassen, O.A.; Posthuma, D. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet., 2019, 51(3), 404-413.
[http://dx.doi.org/10.1038/s41588-018-0311-9] [PMID: 30617256]
[61]
Srivatsan, S.; Swiecki, M.; Otero, K.; Cella, M.; Shaw, A.S. CD2-associated protein regulates plasmacytoid dendritic cell migration, but is dispensable for their development and cytokine production. J. Immunol., 2013, 191(12), 5933-5940.
[http://dx.doi.org/10.4049/jimmunol.1300454] [PMID: 24218450]
[62]
Blokland, G.A.; de Zubicaray, G.I.; McMahon, K.L.; Wright, M.J. Genetic and environmental influences on neuroimaging phenotypes: A meta-analytical perspective on twin imaging studies. Twin Res Hum Genet, 2012, 15(3), 351-71.
[http://dx.doi.org/10.1017/thg.2012.11]
[63]
Stein, J.L.; Hua, X.; Morra, J.H.; Lee, S.; Hibar, D.P.; Ho, A.J.; Leow, A.D.; Toga, A.W.; Sul, J.H.; Kang, H.M.; Eskin, E.; Saykin, A.J.; Shen, L.; Foroud, T.; Pankratz, N.; Huentelman, M.J.; Craig, D.W.; Gerber, J.D.; Allen, A.N.; Corneveaux, J.J.; Stephan, D.A.; Webster, J.; DeChairo, B.M.; Potkin, S.G.; Jack, C.R., Jr; Weiner, M.W.; Thompson, P.M. Genome-wide analysis reveals novel genes influencing temporal lobe structure with relevance to neurodegeneration in Alzheimer’s disease. Neuroimage, 2010, 51(2), 542-554.
[http://dx.doi.org/10.1016/j.neuroimage.2010.02.068] [PMID: 20197096]
[64]
Li, J.; Zhang, Q.; Chen, F.; Yan, J.; Kim, S.; Wang, L.; Feng, W.; Saykin, A.J.; Liang, H.; Shen, L. Genetic interactions explain variance in cingulate amyloid burden: An AV-45 PET genome-wide association and interaction study in the ADNI cohort. BioMed Res. Int., 2015, 2015, 1-11.
[http://dx.doi.org/10.1155/2015/647389] [PMID: 26421299]
[65]
Koran, M.E.I.; Hohman, T.J.; Thornton-Wells, T.A. Genetic interactions found between calcium channel genes modulate amyloid load measured by positron emission tomography. Hum. Genet., 2014, 133(1), 85-93.
[http://dx.doi.org/10.1007/s00439-013-1354-8] [PMID: 24026422]
[66]
Vardarajan, B.N.; Ghani, M.; Kahn, A.; Sheikh, S.; Sato, C.; Barral, S.; Lee, J.H.; Cheng, R.; Reitz, C.; Lantigua, R.; Reyes-Dumeyer, D.; Medrano, M.; Jimenez-Velazquez, I.Z.; Rogaeva, E.; St George-Hyslop, P.; Mayeux, R. Rare coding mutations identified by sequencing of A lzheimer disease genome-wide association studies loci. Ann. Neurol., 2015, 78(3), 487-498.
[http://dx.doi.org/10.1002/ana.24466] [PMID: 26101835]
[67]
Lill, C.M.; Rengmark, A.; Pihlstrøm, L.; Fogh, I.; Shatunov, A.; Sleiman, P.M.; Wang, L.S.; Liu, T.; Lassen, C.F.; Meissner, E.; Alexopoulos, P.; Calvo, A.; Chio, A.; Dizdar, N.; Faltraco, F.; Forsgren, L.; Kirchheiner, J.; Kurz, A.; Larsen, J.P.; Liebsch, M.; Linder, J.; Morrison, K.E.; Nissbrandt, H.; Otto, M.; Pahnke, J.; Partch, A.; Restagno, G.; Rujescu, D.; Schnack, C.; Shaw, C.E.; Shaw, P.J.; Tumani, H.; Tysnes, O.B.; Valladares, O.; Silani, V.; Berg, L.H.; Rheenen, W.; Veldink, J.H.; Lindenberger, U.; Steinhagen-Thiessen, E.; Teipel, S.; Perneczky, R.; Hakonarson, H.; Hampel, H.; Arnim, C.A.F.; Olsen, J.H.; Van Deerlin, V.M.; Al-Chalabi, A.; Toft, M.; Ritz, B.; Bertram, L. The role of TREM2 R47H as a risk factor for Alzheimer’s disease, frontotemporal lobar degeneration, amyotrophic lateral sclerosis, and Parkinson’s disease. Alzheimers Dement., 2015, 11(12), 1407-1416.
[http://dx.doi.org/10.1016/j.jalz.2014.12.009] [PMID: 25936935]
[68]
Medway, C.W.; Abdul-Hay, S.; Mims, T.; Ma, L.; Bisceglio, G.; Zou, F.; Pankratz, S.; Sando, S.B.; Aasly, J.O.; Barcikowska, M.; Siuda, J.; Wszolek, Z.K.; Ross, O.A.; Carrasquillo, M.; Dickson, D.W.; Graff-Radford, N.; Petersen, R.C.; Ertekin-Taner, N.; Morgan, K.; Bu, G.; Younkin, S.G. ApoE variant p.V236E is associated with markedly reduced risk of Alzheimer’s disease. Mol. Neurodegener., 2014, 9(1), 11.
[http://dx.doi.org/10.1186/1750-1326-9-11] [PMID: 24607147]
[69]
Liu, Y; Maxwell, S; Feng, T; Zhu, X; Elston, RC; Koyuturk, M Gene, pathway and network frameworks to identify epistatic interactions of single nucleotide polymorphisms derived from GWAS data. BMC Syst Biol, 2012, 6(S3), S15.
[http://dx.doi.org/10.1186/1752-0509-6-S3-S15]
[70]
Swaminathan, S.; Shen, L.; Risacher, S.L.; Yoder, K.K.; West, J.D.; Kim, S.; Nho, K.; Foroud, T.; Inlow, M.; Potkin, S.G.; Huentelman, M.J.; Craig, D.W.; Jagust, W.J.; Koeppe, R.A.; Mathis, C.A.; Jack, C.R., Jr; Weiner, M.W.; Saykin, A.J. Amyloid pathway-based candidate gene analysis of [11C]PiB-PET in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Brain Imaging Behav., 2012, 6(1), 1-15.
[http://dx.doi.org/10.1007/s11682-011-9136-1] [PMID: 21901424]
[71]
Ding, B.; Xi, Y.; Gao, M.; Li, Z.; Xu, C.; Fan, S.; He, W. Gene expression profiles of entorhinal cortex in Alzheimer’s disease. Am. J. Alzheimers Dis. Other Demen., 2014, 29(6), 526-532.
[http://dx.doi.org/10.1177/1533317514523487] [PMID: 24558171]
[72]
Sun, Y.; Bresell, A.; Rantalainen, M.; Höglund, K.; Lebouvier, T.; Salter, H. An integrated bioinformatics approach for identifying genetic markers that predict cerebrospinal fluid biomarker p-tau181/Aβ1-42 ratio in ApoE4-negative mild cognitive impairment patients. J. Alzheimers Dis., 2015, 45(4), 1061-1076.
[http://dx.doi.org/10.3233/JAD-142118] [PMID: 25720397]
[73]
Yan, J.; Kim, S.; Nho, K.; Chen, R.; Risacher, S.L.; Moore, J.H.; Saykin, A.J.; Shen, L. Hippocampal transcriptome-guided genetic analysis of correlated episodic memory phenotypes in Alzheimer’s disease. Front. Genet., 2015, 6, 117.
[http://dx.doi.org/10.3389/fgene.2015.00117] [PMID: 25859259]

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