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dc.contributor.authorGhassemi, Mohammad M.
dc.contributor.authorMichael, Andrew M.
dc.contributor.authorBoutte, David
dc.contributor.authorPerrone-Bizzozero, Nora
dc.contributor.authorMacciardi, Fabio
dc.contributor.authorMathalon, Daniel H.
dc.contributor.authorFord, Judith M.
dc.contributor.authorPotkin, Steven G.
dc.contributor.authorTurner, Jessica A.
dc.contributor.authorCalhoun, Vince D.
dc.contributor.authorLiu, Jingyu
dc.contributor.authorWells, William Mercer
dc.date.accessioned2012-07-30T18:09:15Z
dc.date.issued2012
dc.identifier.citationLiu, 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.issn1662-5161en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:9312922
dc.description.abstractTo 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.isoen_USen_US
dc.publisherFrontiers Research Foundationen_US
dc.relation.isversionofdoi:10.3389/fnhum.2012.00021en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3284145/pdf/en_US
dash.licenseLAA
dc.subjectgenome-wide association studyen_US
dc.subjectindependent component analysis with referenceen_US
dc.subjectbrain networken_US
dc.subjectschizophreniaen_US
dc.subjectsingle nucleotide polymorphismsen_US
dc.subjectfunctional magnetic resonance imagingen_US
dc.titleAn ICA with Reference Approach in Identification of Genetic Variation and Associated Brain Networksen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalFrontiers in Human Neuroscienceen_US
dash.depositing.authorWells, William Mercer
dc.date.available2012-07-30T18:09:15Z
dc.identifier.doi10.3389/fnhum.2012.00021*
dash.authorsorderedfalse
dash.contributor.affiliatedWells, William


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