title: Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method.
abstract: Structured sparse canonical correlation analysis (SCCA) models have been used to identify imaging genetic associations. These models either use group lasso or graph-guided fused lasso to conduct feature selection and feature grouping simultaneously. The group lasso based methods require prior knowledge to define the groups, which limits the capability when prior knowledge is incomplete or unavailable. The graph-guided methods overcome this drawback by using the sample correlation to define the constraint. However, they are sensitive to the sign of the sample correlation, which could introduce undesirable bias if the sign is wrongly estimated.
  
  
  
  We introduce a novel SCCA model with a new penalty, and develop an efficient optimization algorithm. Our method has a strong upper bound for the grouping effect for both positively and negatively correlated features. We show that our method performs better than or equally to three competing SCCA models on both synthetic and real data. In particular, our method identifies stronger canonical correlations and better canonical loading patterns, showing its promise for revealing interesting imaging genetic associations.
  
  
  
  The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/angscca/
  
  
  
  shenli@iu.edu
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA.
authors.forename: Lei
authors.initials: L
authors.lastname: Du
authors.affiliation: Department of Computer Science & Engineering, The University of Texas at Arlington, Arlington, TX, USA.
authors.forename: Heng
authors.initials: H
authors.lastname: Huang
authors.affiliation: Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA.
authors.forename: Jingwen
authors.initials: J
authors.lastname: Yan
authors.affiliation: Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA.
authors.forename: Sungeun
authors.initials: S
authors.lastname: Kim
authors.affiliation: Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA.
authors.forename: Shannon L
authors.initials: SL
authors.lastname: Risacher
authors.affiliation: Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA and.
authors.forename: Mark
authors.initials: M
authors.lastname: Inlow
authors.affiliation: Institute for Biomedical Informatics, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
authors.affiliation: Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Saykin
authors.affiliation: Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA.
authors.forename: Li
authors.initials: L
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D001921:Brain; D006801:Humans; D059906:Neuroimaging / Q000379:methods*