GN-SCCA: GraphNet Based Sparse Canonical Correlation Analysis for Brain Imaging Genetics

Abstract

Identifying associations between genetic variants and neuroimaging quantitative traits (QTs) is a popular research topic in brain imaging genetics. Sparse canonical correlation analysis (SCCA) has been widely used to reveal complex multi-SNP-multi-QT associations. Several SCCA methods explicitly incorporate prior knowledge into the model and intend to uncover the hidden structure informed by the prior knowledge. We propose a novel structured SCCA method using Graph constrained Elastic-Net (GraphNet) regularizer to not only discover important associations, but also induce smoothness between coefficients that are adjacent in the graph. In addition, the proposed method incorporates the covariance structure information usually ignored by most SCCA methods. Experiments on simulated and real imaging genetic data show that, the proposed method not only outperforms a widely used SCCA method but also yields an easy-to-interpret biological findings.

L. Shen—This work was supported by NIH R01 LM011360, U01 AG024904 (details available at http://adni.loni.usc.edu), RC2 AG036535, R01 AG19771, P30 AG10133, and NSF IIS-1117335 at IU, by NSF CCF-0830780, CCF-0917274, DMS-0915s228, and IIS-1117965 at UTA, and by NIH R01 LM011360, R01 LM009012, and R01 LM010098 at Dartmouth.

ADNI—Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.