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Multivariate association between single-nucleotide polymorphisms in Alzgene linkage regions and structural changes in the brain: discovery, refinement and validation

Szefer Elena, Graham Jinko (), Lu Donghuan, Beg Mirza Faisal and Nathoo Farouk
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Graham Jinko: Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Dr, Burnaby, BC V5A 1S6, Canada
Beg Mirza Faisal: School of Engineering Science, Simon Fraser University, 8888 University Dr, Burnaby, BC V5A 1S6, Canada
Nathoo Farouk: Department of Mathematics and Statistics, University of Victoria, PO Box 1700 STN CSCVictoria, BC V8W 2Y2, Canada

Statistical Applications in Genetics and Molecular Biology, 2017, vol. 16, issue 5-6, 349-365

Abstract: Using publicly-available data from the Alzheimer’s Disease Neuroimaging Initiative, we investigate the joint association between single-nucleotide polymorphisms (SNPs) in previously established linkage regions for Alzheimer’s disease (AD) and rates of decline in brain structure. In an initial, discovery stage of analysis, we applied a weighted RV test to assess the association between 75,845 SNPs in the Alzgene linkage regions and rates of change in structural MRI measurements for 56 brain regions affected by AD, in 632 subjects. After confirming association, we selected refined lists of 1694 and 22 SNPs via a bootstrap-enhanced sparse canonical correlation analysis. In a final, validation stage, we confirmed association between the refined list of 1694 SNPs and the imaging phenotypes in an independent data set. Genes corresponding to priority SNPs having the highest contribution in the validation data have previously been implicated or hypothesized to be implicated in AD, including GCLC, IDE, and STAMBP1andFAS. Though the effect sizes of the 1694 SNPs in the priority set are likely small, further investigation within this set may advance understanding of the missing heritability in AD. Our analysis addresses challenges in current imaging-genetics studies such as biased sampling designs and high-dimensional data with low association signal.

Keywords: Multivariate analysis; Linkage regions; Imaging genetics; Endophenotypes; Inverse probability weighting; Variable importance probabilities (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1515/sagmb-2016-0077

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