Identifying Multimodal Intermediate Phenotypes Between Genetic Risk Factors and Disease Status in Alzheimer's Disease.
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.
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.|Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, 46202, USA.
Xiaoke
X
Hao
Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, 46202, USA.
Xiaohui
X
Yao
Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, 46202, USA.
Jingwen
J
Yan
Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, 46202, USA.
Shannon L
SL
Risacher
Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, 46202, USA.
Andrew J
AJ
Saykin
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China. dqzhang@nuaa.edu.cn.
Daoqiang
D
Zhang
Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, 46202, USA. shenli@iu.edu.
Li
L
Shen
D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000000981:diagnostic imaging* / Q000235:genetics* / Q000473:pathology; D001057:Apolipoproteins E / Q000235:genetics; D015415:Biomarkers; D001921:Brain / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D020022:Genetic Predisposition to Disease; D005838:Genotype; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008297:Male; D064847:Multimodal Imaging; D059906:Neuroimaging; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D012307:Risk Factors
C000612345:ApoE protein, human; D001057:Apolipoproteins E; D015415:Biomarkers
Alzheimer’s disease; Diagnosis-guided; Multimodal intermediate phenotypes; Single nucleotide polymorphisms (SNPs)