Voxelwise gene-wide association study (vGeneWAS): Multivariate gene-based association testing in 731 elderly subjects

Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-1769, USA.
NeuroImage (Impact Factor: 6.36). 06/2011; 56(4):1875-91. DOI: 10.1016/j.neuroimage.2011.03.077
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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.

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    • ", ( denotes the number of alleles shared IBS by subjects j and k at the SNPs, and takes values 0, 1, or 2. Here, we assume 1   IBS if one individual has missing genotype and emphasise that it does not affect the results if picking other values. The healthy control and schizophrenia subjects were mixed into this model to increase the statistic power, which is a routine in imaging genetic association study towards illness (Hibar et al., 2011a, Hibar et al., 2011b, Ge et al., 2012), see (Hibar et al., 2011a) for a review and (Hibar et al., 2011b, Ge et al., 2012, Vounou et al., 2012) for examples. "
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    Human Brain Mapping 11/2014; 35(11). DOI:10.1002/hbm.22560 · 5.97 Impact Factor
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    • "Voxel intensity and cluster size methods have been used for genome-wide association studies (GWAS) (Stein et al., 2010), but the multiple comparisons problem most often does not permit to find significant results, despite efforts to estimate the effective number of tests (Gao et al., 2010) or by paying the cost of a permutation test (Da Mota et al., 2012). Working at the genes level instead of SNPs (Hibar et al., 2011; Ge et al., 2012) is "
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    • "One of the goals of the ADNI study is to perform genome-wide association tests on the entire genome, and identify the genetic variants that influence the voxel-level differences. Several work have been published to investigate this goal, i.e., Stein et al. (2010b), Stein et al. (2010a), Shen et al. (2010), Vounou et al. (2010), and Hibar et al. (2011). In the ADNI study, the phenotype is presented by brain structural magnetic resonance imaging (MRI) scans (31,662 brain voxels), each containing a value that represents the volumetric difference of such voxel from a healthy reference brain -a tensor based morphometry is used to compute the 3D map of regional brain volume differences compared to an average template image based on healthy elderly subjects. "
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