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dc.titleA set-based mixed effect model for gene-environment interaction and its application to neuroimaging phenotypes
dc.contributor.authorWang, C
dc.contributor.authorSun, J
dc.contributor.authorGuillaume, B
dc.contributor.authorGe, T
dc.contributor.authorHibar, D.P
dc.contributor.authorGreenwood, C.M.T
dc.contributor.authorQiu, A
dc.contributor.authorThe Alzheimer's Disease Neuroimaging Initiative
dc.identifier.citationWang, C, Sun, J, Guillaume, B, Ge, T, Hibar, D.P, Greenwood, C.M.T, Qiu, A, The Alzheimer's Disease Neuroimaging Initiative (2017). A set-based mixed effect model for gene-environment interaction and its application to neuroimaging phenotypes. Frontiers in Neuroscience 11 (APR) : 191. ScholarBank@NUS Repository.
dc.description.abstractImaging genetics is an emerging field for the investigation of neuro-mechanisms linked to genetic variation. Although imaging genetics has recently shown great promise in understanding biological mechanisms for brain development and psychiatric disorders, studying the link between genetic variants and neuroimaging phenotypes remains statistically challenging due to the high-dimensionality of both genetic and neuroimaging data. This becomes even more challenging when studying gene-environment interaction (G×E) on neuroimaging phenotypes. In this study, we proposed a set-based mixed effect model for gene-environment interaction (MixGE) on neuroimaging phenotypes, such as structural volumes and tensor-based morphometry (TBM). MixGE incorporates both fixed and random effects of G×E to investigate homogeneous and heterogeneous contributions of multiple genetic variants and their interaction with environmental risks to phenotypes. We discuss the construction of score statistics for the terms associated with fixed and random effects of G×E to avoid direct parameter estimation in the MixGE model, which would greatly increase computational cost. We also describe how the score statistics can be combined into a single significance value to increase statistical power. We evaluated MixGE using simulated and real Alzheimer's Disease Neuroimaging Initiative (ADNI) data, and showed statistical power superior to other burden and variance component methods. We then demonstrated the use of MixGE for exploring the voxelwise effect of G×E on TBM, made feasible by the computational efficiency of MixGE. Through this, we discovered a potential interaction effect of gene ABCA7 and cardiovascular risk on local volume change of the right superior parietal cortex, which warrants further investigation. © 2017 Wang, Sun, Guillaume, Ge, Hibar, Greenwood, Qiu and the Alzheimer's Disease Neuroimaging Initiative.
dc.sourceUnpaywall 20200831
dc.subjectABCA7 gene
dc.subjectAlzheimer disease
dc.subjectbrain size
dc.subjectcardiovascular risk
dc.subjectgenetic variability
dc.subjectgenotype environment interaction
dc.subjectkernel method
dc.subjectnuclear magnetic resonance imaging
dc.subjectparietal cortex
dc.subjectright hemisphere
dc.subjectsuperior parietal cortex
dc.subjecttensor based morphometry
dc.contributor.departmentBIOMEDICAL ENGINEERING
dc.description.sourcetitleFrontiers in Neuroscience
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