Multi-Task Sparse Canonical Correlation Analysis with Application to Multi-Modal Brain Imaging Genetics

Lei Du, Kefei Liu, Xiaohui Yao, Shannon L. Risacher, Junwei Han, Andrew J. Saykin, Lei Guo, Li Shen

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

Brain imaging genetics studies the genetic basis of brain structures and functionalities via integrating genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). In this area, both multi-task learning (MTL) and sparse canonical correlation analysis (SCCA) methods are widely used since they are superior to those independent and pairwise univariate analysis. MTL methods generally incorporate a few of QTs and could not select features from multiple QTs; while SCCA methods typically employ one modality of QTs to study its association with SNPs. Both MTL and SCCA are computational expensive as the number of SNPs increases. In this paper, we propose a novel multi-task SCCA (MTSCCA) method to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. MTSCCA could make use of the complementary information carried by different imaging modalities. MTSCCA enforces sparsity at the group level via the {\mathrm G}_{2,1}G2,1-norm, and jointly selects features across multiple tasks for SNPs and QTs via the \ell _{2,1}ℓ2,1-norm. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains better correlation coefficients and canonical weights patterns. In addition, MTSCCA runs very fast and easy-to-implement, indicating its potential power in genome-wide brain-wide imaging genetics.

Original languageEnglish
Article number8869839
Pages (from-to)227-239
Number of pages13
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume18
Issue number1
DOIs
StatePublished - 1 Jan 2021

Keywords

  • Brain imaging genetics
  • multi-task sparse canonical correlation analysis
  • sparse canonical correlation analysis

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