Imaging traits provide a powerful and biologically relevant substrate to examine the influence of genetics on the brain. Interest in genome-wide, brain-wide search for influential genetic variants is growing, but has mainly focused on univariate, SNP-based association tests. Moving to gene-based multivariate statistics, we can test the combined effect of multiple genetic variants in a single test statistic. Multivariate models can reduce the number of statistical tests in gene-wide or genome-wide scans and may discover gene effects undetectable with SNP-based methods. Here we present a gene-based method for associating the joint effect of single nucleotide polymorphisms (SNPs) in 18,044 genes across 31,662 voxels of the whole brain in 731 elderly subjects (mean age: 75.56 ± 6.82SD years; 430 males) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. Using the voxel-level volume difference values as the phenotype, we selected the most significantly associated gene (out of 18,044) at each voxel across the brain. No genes identified were significant after correction for multiple comparisons, but several known candidates were re-identified, as were other genes highly relevant to brain function. GAB2, which has been previously associated with late-onset AD, was identified as the top gene in this study, suggesting the validity of the approach. This multivariate, gene-based voxelwise association study offers a novel framework to detect genetic influences on the brain.
Bibliographical noteFunding Information:
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) ( National Institutes of Health Grant U01 AG024904 , 3U01AG024904-03S5 ). ADNI is funded by the National Institute on Aging , the National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following: Abbott , AstraZeneca AB , Bayer Schering Pharma AG , Bristol-Myers Squibb , Eisai Global Clinical Development , Elan Corporation , Genentech , GE Healthcare , GlaxoSmithKline , Innogenetics , Johnson and Johnson , Eli Lilly and Co. , Medpace, Inc. , Merck and Co., Inc. , Novartis AG , Pfizer Inc , F. Hoffman-La Roche , Schering-Plough , Synarc, Inc. , as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation , with participation from the U.S. Food and Drug Administration . Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health ( http://www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129 , K01 AG030514 , and the Dana Foundation . We also thank the many contributors to ADNI-1 genotyping sample curation at NCRAD (Kelley Faber), performing BeadChip assays at TGen (David Craig), and bioinformatics problem solving (Indiana U: Kwangsik Nho; UC Irvine: Anita Lakatos, Guia Guffanti; Pfizer: Bryan DeChairo). Additional support for algorithm development was provided by R01 EB008281 , R01 HD050735 , RC2 AG036535 , and R01 AG020098 .
Copyright 2011 Elsevier B.V., All rights reserved.
- Principal components regression