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

 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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