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Tytuł:
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Strategies for multivariate analyses of imaging genetics study in Alzheimer's disease.
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Autorzy:
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Sheng J; School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China. Electronic address: .
Wang L; School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China; College of Information Engineering, Hangzhou Vocational & Technical College, Hangzhou, Zhejiang 310018, China.
Cheng H; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA.
Zhang Q; Beijing Hospital, Beijing 100730, China.
Zhou R; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China; School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Mstar Technologies Inc., Hangzhou, Zhejiang 310018, China.
Shi Y; School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China.
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Źródło:
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Neuroscience letters [Neurosci Lett] 2021 Sep 25; Vol. 762, pp. 136147. Date of Electronic Publication: 2021 Jul 29.
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Typ publikacji:
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Journal Article; Research Support, Non-U.S. Gov't; Review
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Język:
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English
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Imprint Name(s):
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Publication: Limerick : Elsevier Scientific Publishers Ireland
Original Publication: Amsterdam, Elsevier/North-Holland.
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MeSH Terms:
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Genetic Association Studies*
Alzheimer Disease/*genetics
Alzheimer Disease/*pathology
Genome-Wide Association Study ; Humans ; Neuroimaging
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Contributed Indexing:
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Keywords: Alzheimer’s disease; Genotype; Imaging genetic; Phenotype
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Entry Date(s):
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Date Created: 20210731 Date Completed: 20220120 Latest Revision: 20230325
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Update Code:
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20240105
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DOI:
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10.1016/j.neulet.2021.136147
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PMID:
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34332030
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Alzheimer's disease (AD) is an incurable neurodegenerative disease primarily affecting the elderly population. Early diagnosis of AD is critical for the management of this disease. Imaging genetics examines the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on brain structure and function and many novel approaches of imaging genetics are proposed for studying AD. We review and synthesize the Alzheimer's Disease Neuroimaging Initiative (ADNI) genetic associations with quantitative disease endophenotypes including structural and functional neuroimaging, diffusion tensor imaging (DTI), positron emission tomography (PET), and fluid biomarker assays. In this review, we survey recent publications using neuroimaging and genetic data of AD, with a focus on methods capturing multivariate effects accommodating the large number variables from both imaging data and genetic data. We review methods focused on bridging the imaging and genetic data by establishing genotype-phenotype association, including sparse canonical correlation analysis, parallel independent component analysis, sparse reduced rank regression, sparse partial least squares, genome-wide association study, and so on. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future pharmaceutical therapy and biomarker development.
(Copyright © 2021 Elsevier B.V. All rights reserved.)
