title: Correspondence between fMRI and SNP data by group sparse canonical correlation analysis.
abstract: Both genetic variants and brain region abnormalities are recognized as important factors for complex diseases (e.g., schizophrenia). In this paper, we investigated the correspondence between single nucleotide polymorphism (SNP) and brain activity measured by functional magnetic resonance imaging (fMRI) to understand how genetic variation influences the brain activity. A group sparse canonical correlation analysis method (group sparse CCA) was developed to explore the correlation between these two datasets which are high dimensional-the number of SNPs/voxels is far greater than the number of samples. Different from the existing sparse CCA methods (sCCA), our approach can exploit structural information in the correlation analysis by introducing group constraints. A simulation study demonstrates that it outperforms the existing sCCA. We applied this method to the real data analysis and identified two pairs of significant canonical variates with average correlations of 0.4527 and 0.4292 respectively, which were used to identify genes and voxels associated with schizophrenia. The selected genes are mostly from 5 schizophrenia (SZ)-related signalling pathways. The brain mappings of the selected voxles also indicate the abnormal brain regions susceptible to schizophrenia. A gene and brain region of interest (ROI) correlation analysis was further performed to confirm the significant correlations between genes and ROIs.
authors.affiliation: Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA; Center of Genomics and Bioinformatics, Tulane University, New Orleans, LA 70118, USA. Electronic address: dlin5@tulane.edu.
authors.forename: Dongdong
authors.initials: D
authors.lastname: Lin
authors.affiliation: The Mind Research Network, Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: vcalhoun@unm.edu.
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
authors.affiliation: Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA; Center of Genomics and Bioinformatics, Tulane University, New Orleans, LA 70118, USA; Center for Systems Biomedicine, Shanghai University for Science and Technology, Shanghai, China. Electronic address: wyp@tulane.edu.
authors.forename: Yu-Ping
authors.initials: YP
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D059906:Neuroimaging / Q000379:methods*; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D012559:Schizophrenia / Q000235:genetics*; D012680:Sensitivity and Specificity
keywords: Feature selection; Group sparse CCA; Imaging genetics; SNP; fMRI