An ICA with Reference Approach in Identification of Genetic Variation and Associated Brain Networks

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An ICA with Reference Approach in Identification of Genetic Variation and Associated Brain Networks

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dc.contributor.author Ghassemi, Mohammad M.
dc.contributor.author Michael, Andrew M.
dc.contributor.author Boutte, David
dc.contributor.author Perrone-Bizzozero, Nora
dc.contributor.author Macciardi, Fabio
dc.contributor.author Mathalon, Daniel H.
dc.contributor.author Ford, Judith M.
dc.contributor.author Potkin, Steven G.
dc.contributor.author Turner, Jessica A.
dc.contributor.author Calhoun, Vince D.
dc.contributor.author Liu, Jingyu
dc.contributor.author Wells, William Mercer
dc.date.accessioned 2012-07-30T18:09:15Z
dc.date.issued 2012
dc.identifier.citation Liu, Jingyu, Mohammad M. Ghassemi, Andrew M. Michael, David Boutte, William Wells, Nora Perrone-Bizzozero, Fabio Macciardi, et al. 2012. An ICA with reference approach in identification of genetic variation and associated brain networks. Frontiers in Human Neuroscience 6: 21. en_US
dc.identifier.issn 1662-5161 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:9312922
dc.description.abstract To address the statistical challenges associated with genome-wide association studies, we present an independent component analysis (ICA) with reference approach to target a specific genetic variation and associated brain networks. First, a small set of single nucleotide polymorphisms (SNPs) are empirically chosen to reflect a feature of interest and these SNPs are used as a reference when applying ICA to a full genomic SNP array. After extracting the genetic component maximally representing the characteristics of the reference, we test its association with brain networks in functional magnetic resonance imaging (fMRI) data. The method was evaluated on both real and simulated datasets. Simulation demonstrates that ICA with reference can extract a specific genetic factor, even when the variance accounted for by such a factor is so small that a regular ICA fails. Our real data application from 48 schizophrenia patients (SZs) and 40 healthy controls (HCs) include 300K SNPs and fMRI images in an auditory oddball task. Using SNPs with allelic frequency difference in two groups as a reference, we extracted a genetic component that maximally differentiates patients from controls \((p < 4 × 10^{−17})\), and discovered a brain functional network that was significantly associated with this genetic component \((p < 1 × 10^{−4})\). The regions in the functional network mainly locate in the thalamus, anterior and posterior cingulate gyri. The contributing SNPs in the genetic factor mainly fall into two clusters centered at chromosome 7q21 and chromosome 5q35. The findings from the schizophrenia application are in concordance with previous knowledge about brain regions and gene function. All together, the results suggest that the ICA with reference can be particularly useful to explore the whole genome to find a specific factor of interest and further study its effect on brain. en_US
dc.language.iso en_US en_US
dc.publisher Frontiers Research Foundation en_US
dc.relation.isversionof doi:10.3389/fnhum.2012.00021 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3284145/pdf/ en_US
dash.license LAA
dc.subject genome-wide association study en_US
dc.subject independent component analysis with reference en_US
dc.subject brain network en_US
dc.subject schizophrenia en_US
dc.subject single nucleotide polymorphisms en_US
dc.subject functional magnetic resonance imaging en_US
dc.title An ICA with Reference Approach in Identification of Genetic Variation and Associated Brain Networks en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal Frontiers in Human Neuroscience en_US
dash.depositing.author Wells, William Mercer
dc.date.available 2012-07-30T18:09:15Z

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