A large scale multivariate parallel ICA method reveals novel imaging–genetic relationships for Alzheimer's disease in the ADNI cohort

• DOI: 10.1016/j.neuroimage.2011.12.076
• Publication Date: 2012-01-10T08:56:37Z
• Authors: Shashwath A. Meda; Balaji Narayanan; Jingyu Liu; Nora I. Perrone-B...; Michael C. Stevens; Vince D. Calhoun; David C. Glahn; Li Shen; Shannon L. Risacher; Andrew J. Saykin; Godfrey D. Pearlson
• Keywords: Male; Canada; Principal Component Analysis; Brain; Reproducibility of Results; Magnetic Resonance Imaging; Polymorphism, Single Nucleotide; Sensitivity and Specificity; United States; 3. Good health; Cohort Studies; 03 medical and health sciences; 0302 clinical medicine; Alzheimer Disease; Data Interpretation, Statistical; Multivariate Analysis; Prevalence; Humans; Female; Genetic Predisposition to Disease; Aged

The underlying genetic etiology of late onset Alzheimer's disease (LOAD) remains largely unknown, likely due to its polygenic architecture and a lack of sophisticated analytic methods to evaluate complex genotype-phenotype models. The aim of the current study was to overcome these limitations in a bi-multivariate fashion by linking intermediate magnetic resonance imaging (MRI) phenotypes with a genome-wide sample of common single nucleotide polymorphism (SNP) variants. We compared associations between 94 different brain regions of interest derived from structural MRI scans and 533,872 genome-wide SNPs using a novel multivariate statistical procedure, parallel-independent component analysis, in a large, national multi-center subject cohort. The study included 209 elderly healthy controls, 367 subjects with amnestic mild cognitive impairment and 181 with mild, early-stage LOAD, all of them Caucasian adults, from the Alzheimer's Disease Neuroimaging Initiative cohort. Imaging was performed on comparable 1.5 T scanners at over 50 sites in the USA/Canada. Four primary "genetic components" were associated significantly with a single structural network including all regions involved neuropathologically in LOAD. Pathway analysis suggested that each component included several genes already known to contribute to LOAD risk (e.g. APOE4) or involved in pathologic processes contributing to the disorder, including inflammation, diabetes, obesity and cardiovascular disease. In addition significant novel genes identified included ZNF673, VPS13, SLC9A7, ATP5G2 and SHROOM2. Unlike conventional analyses, this multivariate approach identified distinct groups of genes that are plausibly linked in physiologic pathways, perhaps epistatically. Further, the study exemplifies the value of this novel approach to explore large-scale data sets involving high-dimensional gene and endophenotype data.