Identifying Multimodal Intermediate Phenotypes between Genetic Risk Factors and Disease Status in Alzheimer’s Disease

dc.contributor.authorHao, Xiaoke
dc.contributor.authorYao, Xiaohui
dc.contributor.authorYan, Jingwen
dc.contributor.authorRisacher, Shannon L.
dc.contributor.authorSaykin, Andrew J.
dc.contributor.authorZhang, Daoqiang
dc.contributor.authorShen, Li
dc.contributor.departmentRadiology and Imaging Sciences, School of Medicineen_US
dc.date.accessioned2018-03-14T16:34:00Z
dc.date.available2018-03-14T16:34:00Z
dc.date.issued2016-10
dc.description.abstractNeuroimaging genetics has attracted growing attention and interest, which is thought to be a powerful strategy to examine the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on structures or functions of human brain. In recent studies, univariate or multivariate regression analysis methods are typically used to capture the effective associations between genetic variants and quantitative traits (QTs) such as brain imaging phenotypes. The identified imaging QTs, although associated with certain genetic markers, may not be all disease specific. A useful, but underexplored, scenario could be to discover only those QTs associated with both genetic markers and disease status for revealing the chain from genotype to phenotype to symptom. In addition, multimodal brain imaging phenotypes are extracted from different perspectives and imaging markers consistently showing up in multimodalities may provide more insights for mechanistic understanding of diseases (i.e., Alzheimer’s disease (AD)). In this work, we propose a general framework to exploit multi-modal brain imaging phenotypes as intermediate traits that bridge genetic risk factors and multi-class disease status. We applied our proposed method to explore the relation between the well-known AD risk SNP APOE rs429358 and three baseline brain imaging modalities (i.e., structural magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET) and F-18 florbetapir PET scans amyloid imaging (AV45)) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The empirical results demonstrate that our proposed method not only helps improve the performances of imaging genetic associations, but also discovers robust and consistent regions of interests (ROIs) across multi-modalities to guide the disease-induced interpretation.en_US
dc.eprint.versionAuthor's manuscripten_US
dc.identifier.citationHao, X., Yao, X., Yan, J., Risacher, S. L., Saykin, A. J., Zhang, D., & Shen, L. (2016). Identifying Multimodal Intermediate Phenotypes between Genetic Risk Factors and Disease Status in Alzheimer’s Disease. Neuroinformatics, 14(4), 439–452. https://doi.org/10.1007/s12021-016-9307-8en_US
dc.identifier.issn1539-2791en_US
dc.identifier.urihttps://hdl.handle.net/1805/15534
dc.language.isoen_USen_US
dc.publisherSpringeren_US
dc.relation.isversionof10.1007/s12021-016-9307-8en_US
dc.relation.journalNeuroinformaticsen_US
dc.rightsPublisher Policyen_US
dc.sourcePMCen_US
dc.subjectAlzheimer’s diseaseen_US
dc.subjectDiagnosis-guideden_US
dc.subjectMultimodal intermediate phenotypesen_US
dc.subjectSingle nucleotide polymorphisms (SNPs)en_US
dc.titleIdentifying Multimodal Intermediate Phenotypes between Genetic Risk Factors and Disease Status in Alzheimer’s Diseaseen_US
dc.typeArticleen_US
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