title: A novel structure-aware sparse learning algorithm for brain imaging genetics.
abstract: Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.
authors.forename: Lei
authors.initials: L
authors.lastname: Du
authors.forename: Yan
authors.initials: Y
authors.lastname: Jingwen
authors.forename: Sungeun
authors.initials: S
authors.lastname: Kim
authors.forename: Shannon L
authors.initials: SL
authors.lastname: Risacher
authors.forename: Heng
authors.initials: H
authors.lastname: Huang
authors.forename: Mark
authors.initials: M
authors.lastname: Inlow
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Saykin
authors.forename: Li
authors.initials: L
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000235:genetics* / Q000473:pathology*; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology* / Q000503:physiopathology*; D063132:Connectome / Q000379:methods; D020022:Genetic Predisposition to Disease / Q000235:genetics; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D040641:Quantitative Trait Loci / Q000235:genetics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity