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

Xiaoke Hao, Xiaohui Yao, Jingwen Yan, Shannon L. Risacher, Andrew Saykin, Daoqiang Zhang, Li Shen, The Alzheimer’S Disease Neuroimaging Initiative For The Alzheimer’S Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Neuroimaging 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.

Original languageEnglish (US)
Pages (from-to)1-14
Number of pages14
JournalNeuroinformatics
DOIs
StateAccepted/In press - Jun 9 2016

Fingerprint

Neuroimaging
Alzheimer Disease
Phenotype
Imaging techniques
Brain
Genetic Markers
Positron-Emission Tomography
Single Nucleotide Polymorphism
Multimodal Imaging
Nucleotides
Polymorphism
Inborn Genetic Diseases
Amyloid
Positron emission tomography
Multivariate Analysis
Genotype
Regression Analysis
Magnetic Resonance Imaging
Databases
Magnetic resonance

Keywords

  • Alzheimer’s disease
  • Diagnosis-guided
  • Multimodal intermediate phenotypes
  • Single nucleotide polymorphisms (SNPs)

ASJC Scopus subject areas

  • Neuroscience(all)
  • Information Systems
  • Software

Cite this

Hao, X., Yao, X., Yan, J., Risacher, S. L., Saykin, A., Zhang, D., ... For The Alzheimer’S Disease Neuroimaging Initiative, T. AS. D. N. I. (Accepted/In press). Identifying Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease Status in Alzheimer’s Disease. Neuroinformatics, 1-14. https://doi.org/10.1007/s12021-016-9307-8

Identifying Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease Status in Alzheimer’s Disease. / Hao, Xiaoke; Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon L.; Saykin, Andrew; Zhang, Daoqiang; Shen, Li; For The Alzheimer’S Disease Neuroimaging Initiative, The Alzheimer’S Disease Neuroimaging Initiative.

In: Neuroinformatics, 09.06.2016, p. 1-14.

Research output: Contribution to journalArticle

Hao, X, Yao, X, Yan, J, Risacher, SL, Saykin, A, Zhang, D, Shen, L & For The Alzheimer’S Disease Neuroimaging Initiative, TASDNI 2016, 'Identifying Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease Status in Alzheimer’s Disease', Neuroinformatics, pp. 1-14. https://doi.org/10.1007/s12021-016-9307-8
Hao, Xiaoke ; Yao, Xiaohui ; Yan, Jingwen ; Risacher, Shannon L. ; Saykin, Andrew ; Zhang, Daoqiang ; Shen, Li ; For The Alzheimer’S Disease Neuroimaging Initiative, The Alzheimer’S Disease Neuroimaging Initiative. / Identifying Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease Status in Alzheimer’s Disease. In: Neuroinformatics. 2016 ; pp. 1-14.
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