Correspondence between fMRI and SNP data by group sparse canonical correlation analysis

• A group sparse canonical correlation analysis (CCA) model was developed, including several sparse models as special cases.
• The model can overcome the difficulty of conventional CCA in analysing high dimensional data with group structures.
• The model is validated by studying a biologically significant problem on how genetic variations influence brain activities.

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.

In this paper, we investigated the correspondence between single nucleotide polymorphism (SNP) and brain activity measured by functional magnetic resonance imaging (fMRI). Such a study is biologically significant for understanding how genetic variation influences the brain activity (see figure). A group sparse canonical correlation analysis method (group sparse CCA) was developed to explore the correlation between these two data sets which are high dimensional-the number of SNPs/voxels is far greater than the number of samples. 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, which were then used to identify genes and voxels associated with schizophrenia. A gene and brain region of interest (ROI) correlation analysis was further performed to confirm the significant correlations between genes and ROIs.Figure optionsDownload high-quality image (62 K)Download as PowerPoint slide