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
