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Semiparametric Bayes conditional graphical models for imaging genetics applications

dc.contributor.authorKundu, Suprateek
dc.contributor.authorKang, Jian
dc.date.accessioned2017-01-10T19:03:47Z
dc.date.available2017-03-01T14:41:59Zen
dc.date.issued2016
dc.identifier.citationKundu, Suprateek; Kang, Jian (2016). "Semiparametric Bayes conditional graphical models for imaging genetics applications." Stat 5(1): 322-337.
dc.identifier.issn2049-1573
dc.identifier.issn2049-1573
dc.identifier.urihttps://hdl.handle.net/2027.42/135205
dc.publisherJohn Wiley & Sons, Ltd
dc.subject.otherimaging genetics
dc.subject.othermodularity
dc.subject.othersemiparametric Bayes
dc.subject.othervariable selection
dc.subject.otherconditional graphical model
dc.subject.otherbrain functional network
dc.titleSemiparametric Bayes conditional graphical models for imaging genetics applications
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMathematics
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/135205/1/sta4119_am.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/135205/2/sta4119.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/135205/3/sta4119-sup-0002-Supplementary2.pdf
dc.identifier.doi10.1002/sta4.119
dc.identifier.sourceStat
dc.identifier.citedreferenceSmith SM, Beckmann CF, Andersson J, Auerbach EJ, Bijsterbosch J, Douaud G, Duff E, Feinberg DA, Griffanti L, Harms MP, Kelly M, Laumann T, Miller KL, Moeller S, Petersen S, Power J, Salimi‐Khorshidi G, Snyder AZ, Vu AT, Woolrich MW, Xu J, Yacoub E, Uurbil K, Van Essen DC & Glasser MF ( 2012 ), ‘ Resting‐state fMRI in the human connectome project ’ Neuroimage, 80, 144 – 168.
dc.identifier.citedreferenceMier D, Kirsch P & Meyer‐Lindenberg A ( 2010 ), ‘ Neural substrates of pleiotropic action of genetic variation in COMT: a meta‐analysis ’ Molecular Psychiatry, 15, 918 – 927.
dc.identifier.citedreferenceMunafo MR, Attwood AS & Flint J ( 2008 ), ‘ Bias in genetic association studies: effects of research location and resources ’ Psychological Medicine, 38, 1213 – 1214.
dc.identifier.citedreferenceMeunier D, Lambiotte R & Bullmore ET ( 2010 ), ‘ Modular and hierarchically modular organization of brain networks ’ Frontiers in Neuroscience, 4, 200. doi:10.3389/fnins.2010.00200.
dc.identifier.citedreferenceNicolini C & Bifone A ( 2016 ), ‘ Modular structure of brain functional networks: breaking the resolution limit by surprise ’ Scientific Reports, 6, 19250. doi: 10.1038/srep19250.
dc.identifier.citedreferencePan RK & Sinha S ( 2009 ), ‘ Modularity produces small‐world networks with dynamical time‐scale separation ’ Europhysics Letters Association, 85, 68006.
dc.identifier.citedreferencePark T & Casella G ( 2008 ), ‘ The Bayesian lasso ’ Journal of the American Statistical Association, 103, 681 – 686.
dc.identifier.citedreferencePiscopo P, Tosto G, Belli C, Talarico G, Galimberti D, Gasparini M, Canevelli M, Poleggi A, Crestini A, Albani D, Forloni G, Lucca U, Quadri P, Tettamanti M, Fenoglio C, Scarpini E, Bruno G, Vanacore N & Confaloni A ( 2015 ), ‘ SORL1 gene is associated with the conversion from mild cognitive impairment to Alzheimer’s disease ’ Journal of Alzheimer’s Disease, 46, 771 – 776.
dc.identifier.citedreferenceReiman EM, Webster JA, Myers AJ, Hardy J, Dunckley T, Zismann VL, Joshipura KD, Pearson JV, Hu‐Lince D, Huentelman MJ, Craig DW, Coon KD, Liang WS, Herbert RH, Beach T, Rohrer KC, Zhao AS, Leung D, Bryden L, Marlowe L, Kaleem M, Mastroeni D, Grover A, Heward CB, Ravid R, Rogers J, Hutton ML, Melquist S, Petersen RC, Alexander GE, Caselli RJ, Kukull W, Papassotiropoulos A & Stephan DA ( 2007 ), ‘ GAB2 alleles modify Alzheimer’s risk in APOE ε 4 Carriers ’ Neuron, 54, 713 – 720.
dc.identifier.citedreferenceSethuraman J ( 1994 ), ‘ A constructive definition of Dirichlet priors ’ Statistica Sinica, 4, 639 – 650.
dc.identifier.citedreferenceSporns O ( 2010. Networks of the Brain, MIT Press, Cambridge MA.
dc.identifier.citedreferenceStam CJ, Jones BF, Nolte G, Breakspear M & Scheltens P ( 2007 ), ‘ Small‐world networks and functional connectivity in Alzheimer’s disease ’ Cerebral Cortex, 17, 92 – 99.
dc.identifier.citedreferenceStein JL, Hua X, Lee S, Ho AJ, Leow AD, Toga AW, Saykin AJ, Shen L, Foroud T, Pankratz N, Huentelman MJ, Craig DW, Gerber JD, Allen AN, Corneveaux JJ, Dechairo BM, Potkin SG, Weiner MW & Thompson P ( 2010 ), ‘ Voxelwise genome‐wide association study (vGWAS) ’ Neuroimage, 53, 1160 – 1174.
dc.identifier.citedreferenceStingo FC, Guindani M, Vannucci M & Calhoun VD ( 2013 ), ‘ An integrative Bayesian modeling approach to imaging genetics ’ Journal of the American Statistical Association, 108, 875 – 891.
dc.identifier.citedreferenceSupekar K, Menon V, Rubin D, Musen M & Greicius MD ( 2008 ), ‘ Network analysis of intrinsic functional brain connectivity in Alzheimer’s Disease ’ PLoS Computional Biology, 4, 1 – 11.
dc.identifier.citedreferenceWang Y, Bi L, Wang H, Li Y, Di Q, Xu W & Qian Y ( 2012 ), ‘ NEDD9 rs760678 polymorphism and the risk of Alzheimer’s disease: a meta‐analysis ’ Neuroscience Letters, 527, 121 – 125.
dc.identifier.citedreferenceWang H, Jin X, Zhang Y & Wang J ( 2016 ), ‘ Single‐subject morphological brain networks: connectivity mapping, topological characterization and testretest reliability ’ Brain and Behavior, 6, e00448. doi:10.1002/brb3.448.
dc.identifier.citedreferenceWollmer MA ( 2010 ), ‘ Cholesterol‐related genes in Alzheimer’s disease ’ Biochim Biophysica Acta, 1808, 762 – 773.
dc.identifier.citedreferenceYin J & Li H ( 2011 ), ‘ A sparse conditional graphical model for analysis of genetical genomics data ’ Annals of Applied Statistics, 5, 2630 – 2650.
dc.identifier.citedreferenceZhu H, Khondker Z, Lu Z & Ibrahim JG ( 2014 ), ‘ Bayesian generalized low rank regression models for neuroimaging phenotypes and genetic markers ’ Journal of the American Statistical Association, 109, 977 – 990.
dc.identifier.citedreferenceBenjamini Y & Hochberg Y ( 1995 ), ‘ Controlling the false discovery rate: a practical and powerful approach to multiple testing ’ Journal of the Royal Statistical Society, Series B (Statistical Methodology), 57, 289 – 300.
dc.identifier.citedreferenceBiswal B, Zerrin Yetkin F, Haughton VM & Hyde JS ( 1995 ), ‘ Functional connectivity in the motor cortex of resting human brain using echo‐planar mri ’ Magnetic Resonance in Medicine, 34, 537541.
dc.identifier.citedreferenceBhadra A & Mallick BK ( 2013 ), ‘ Joint high‐dimensional Bayesian variable and covariance selection with an application to eQTL analysis ’ Biometrics, 69, 447 – 457.
dc.identifier.citedreferenceBowman FD, Zhang L, Derado G & Chen S ( 2012 ), ‘ Determining Functional Connectivity using fMRI Data with Diffusion‐Based Anatomical Weighting ’ NeuroImage, 62, 1769 – 1779.
dc.identifier.citedreferenceBuckner RL, Andrews–Hanna JR & Schacter DL ( 2008 ), ‘ The brain’s default network ’ Annals of the New York Academy of Sciences, 1124, 1 – 38.
dc.identifier.citedreferenceCai T, Li H, Liu W & Xie J ( 2013 ), ‘ Covariate‐adjusted precision matrix estimation with an application in genetical genomics ’ Biometrika, 100, 139 – 156.
dc.identifier.citedreferenceCalhoun VD, Kiehl KA & Pearlson GD ( 2008 ), ‘ Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks ’ Human Brain Mapping, 29, 828 – 838.
dc.identifier.citedreferenceDahl DB ( 2006 ) ‘ Model‐Based Clustering for Expression Data via a Dirichlet Process Mixture Model ’, in Bayesian Inference for Gene Expression and Proteomics, Do K‐A, Mller P, Vannucci M (eds), Cambridge University Press.
dc.identifier.citedreferenceDawid AP & Lauritzen SL ( 1993 ), ‘ Hyper Markov laws in the statistical analysis of decomposable graphical models ’ Annals of Statistics, 21, 1272 – 1317.
dc.identifier.citedreferenceFriedman J, Hastie T & Tibshirani R ( 2008 ), ‘ Sparse inverse covariance estimation with the lasso ’ Biostatistics, 9, 432 – 441.
dc.identifier.citedreferenceGuo Y ( 2011 ), ‘ A general probabilistic model for group independent component analysis and its estimation methods ’ Biometrics, 67, 1532 – 1542.
dc.identifier.citedreferenceHamilton G, Harris SE, Davies G, Liewald DC, Tenesa A & Payton A ( 2012 ), ‘ The role of ECE1 variants in cognitive ability in old age and Alzheimer’s disease risk ’ American Journal of Medial Genetics, Part B, 159 B, 676 – 709.
dc.identifier.citedreferenceHuang S, Li J, Sun L, Ye J, Fleisher A, Wu T, Chen K, Reiman E & Alzheimer’s disease neuroimaging initiative. 2010 ), ‘ Learning brain connectivity of Alzheimer’s disease by sparse inverse covariance estimation ’ Neuroimage, 50, 935 – 949.
dc.identifier.citedreferenceKim J, Wozniak JR, Mueller BA & Pan W ( 2015 ), ‘ Testing group differences in brain functional connectivity: using correlations or partial correlations? ’ Brain Connectivity, 5, 214 – 231.
dc.identifier.citedreferenceKyung M, Gill J & Casella G ( 2010 ), ‘ Estimation in Dirichlet random effects models ’ Annals of Statistics, 38, 979 – 1009.
dc.identifier.citedreferenceLauritzen SL ( 1996. Graphical Models. Oxford University Press: United Kingdom.
dc.identifier.citedreferenceLi B, Chun H & Zhao H ( 2012 ), ‘ Sparse estimation of conditional graphical models with application to gene networks ’ Journal of the American Statistical Association, 497, 152 – 167.
dc.identifier.citedreferenceMarcello E, Saraceno C, Musardo S, Vara H, de la Fuente AG, Pelucchi S, Di Marino D, Borroni B, Tramontano A, Pérez‐Otaño I, Padovani A, Giustetto M, Gardoni F & Di Luca M ( 2013 ), ‘ Endocytosis of synaptic ADAM10 in neuronal plasticity and Alzheimer’s disease ’ Journal of Clinical Investigation, 123, 2523 – 2538.
dc.identifier.citedreferenceMcCarthy G, Puce A, Gore JC & Allison T ( 1997 ), ‘ Face‐specific processing in the human fusiform gyrus ’ Journal of Cognitive Neuroscience, 9, 605 – 610.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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