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
Source: PubMed

ABSTRACT 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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Available from: Andrew J Saykin, Sep 25, 2015
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    • "As demonstrated in Stein et al. (2010), it took 300 high performance CPU nodes running approximately 27 h to perform VGWAS analysis based on simple linear models with only a few covariates to process an imaging genetic dataset with 448,293 SNPs and 31,622 voxels in the brain of each of 740 subjects. As demonstrated in Hibar et al. (2011), it took 80 high performance CPU nodes running approximately 13 days to perform VGWAS analysis based on simple linear models with only a few covariates to process an imaging genetic dataset with 18,044 genes and 31,622 voxels in the brain of each of 740 subjects. One can imagine the computational challenges associated with VGWAS when the imaging genetics is advanced to the use of both ultra-high-resolution imaging (N V ~ 10 7 ) and whole-genome sequencing (N V ~ 10 8 ). "
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    ABSTRACT: More and more large-scale imaging genetic studies are being widely conducted to collect a rich set of imaging, genetic, and clinical data to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. Several major big-data challenges arise from testing genome-wide (NC>12 million known variants) associations with signals at millions of locations (NV~10(6)) in the brain from thousands of subjects (n~10(3)). The aim of this paper is to develop a Fast Voxelwise Genome Wide Association analysiS (FVGWAS) framework to efficiently carry out whole-genome analyses of whole-brain data. FVGWAS consists of three components including a heteroscedastic linear model, a global sure independence screening (GSIS) procedure, and a detection procedure based on wild bootstrap methods. Specifically, for standard linear association, the computational complexity is O(nNVNC) for voxelwise genome wide association analysis (VGWAS) method compared with O((NC+NV)n(2)) for FVGWAS. Simulation studies show that FVGWAS is an efficient method of searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. Finally, we have successfully applied FVGWAS to a large-scale imaging genetic data analysis of ADNI data with 708 subjects, 193,275voxels in RAVENS maps, and 501,584 SNPs, and the total processing time was 203,645seconds for a single CPU. Our FVGWAS may be a valuable statistical toolbox for large-scale imaging genetic analysis as the field is rapidly advancing with ultra-high-resolution imaging and whole-genome sequencing. Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 05/2015; 118. DOI:10.1016/j.neuroimage.2015.05.043 · 6.36 Impact Factor
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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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    ABSTRACT: The Disrupted in Schizophrenia Gene 1 (DISC1) plays a role in both neural signaling and development and is associated with schizophrenia, although its links to altered brain structure and function in this disorder are not fully established. Here we have used structural and functional MRI to investigate links with six DISC1 single nucleotide polymorphisms (SNPs). We employed a brain-wide association analysis (BWAS) together with a Jacknife internal validation approach in 46 schizophrenia patients and 24 matched healthy control subjects. Results from structural MRI showed significant associations between all six DISC1 variants and gray matter volume in the precuneus, post-central gyrus and middle cingulate gyrus. Associations with specific SNPs were found for rs2738880 in the left precuneus and right post-central gyrus, and rs1535530 in the right precuneus and middle cingulate gyrus. Using regions showing structural associations as seeds a resting-state functional connectivity analysis revealed significant associations between all 6 SNPS and connectivity between the right precuneus and inferior frontal gyrus. The connection between the right precuneus and inferior frontal gyrus was also specifically associated with rs821617. Importantly schizophrenia patients showed positive correlations between the six DISC-1 SNPs associated gray matter volume in the left precuneus and right post-central gyrus and negative symptom severity. No correlations with illness duration were found. Our results provide the first evidence suggesting a key role for structural and functional connectivity associations between DISC1 polymorphisms and the precuneus in schizophrenia. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
    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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    ABSTRACT: Brain imaging is a natural intermediate phenotype to understand the link between genetic information and behavior or brain pathologies risk factors. Massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such data is carried out with increasingly sophisticated techniques and represents a great computational challenge. Fortunately, increasing computational power in distributed architectures can be harnessed, if new neuroinformatics infrastructures are designed and training to use these new tools is provided. Combining a MapReduce framework (TomusBLOB) with machine learning algorithms (Scikit-learn library), we design a scalable analysis tool that can deal with non-parametric statistics on high-dimensional data. End-users describe the statistical procedure to perform and can then test the model on their own computers before running the very same code in the cloud at a larger scale. We illustrate the potential of our approach on real data with an experiment showing how the functional signal in subcortical brain regions can be significantly fit with genome-wide genotypes. This experiment demonstrates the scalability and the reliability of our framework in the cloud with a 2 weeks deployment on hundreds of virtual machines.
    Frontiers in Neuroinformatics 04/2014; 8:31. DOI:10.3389/fninf.2014.00031 · 3.26 Impact Factor
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