The group sparse canonical correlation analysis method in the imaging genetics research

Objective To explore the correlation between imaging data and genetic data of schizophrenia using group sparse canonical correlation analysis method. Methods A group sparse canonical analysis method was proposed, group sparse constraints $\lambda_{1}||U||_{G}$ and $\lambda_{2}||V||_{G}$ were added to traditional canonical correlation analysis model to select features groups. Then, features within each group were selected by sparse constraints $\mathrm{T}_{1}||U||_{1}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{T}_{2}||V||_{1}$. The group sparse canonical correlation analysis method was used to analyze the correlation between brain regions and genes of schizophrenia, and it’s stability and ability were also verified to select biomarkers. Results Several pairs canonical brain regions and genes were identified. The left insula and gene AKTI produced the most significant correlation, r=0.6538, and the correlations between both right rectus and gene DAOA, MAGI2 were more than 0.6. The correlation coefficients of selected features were (0.6269±0.0161) with group sparse canonical correlation analysis and (0.625 5±0.018 1) with sparse canonical correlation analysis. The biomarkers selected by group sparse canonical correlation analysis using 75 related genes to schizophrenia, was higher than using non-related genes randomly. Conclusion Several pairs canonical brain regions and genes can be identified by the group sparse canonical analysis method, which provided a new way for the study of schizophrenia and other complex mental disorders.