title: From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer's disease relevant SNPs.
abstract: BACKGROUND
  Imaging genetic studies typically focus on identifying single-nucleotide polymorphism (SNP) markers associated with imaging phenotypes. Few studies perform regression of SNP values on phenotypic measures for examining how the SNP values change when phenotypic measures are varied. This alternative approach may have a potential to help us discover important imaging genetic associations from a different perspective. In addition, the imaging markers are often measured over time, and this longitudinal profile may provide increased power for differentiating genotype groups. How to identify the longitudinal phenotypic markers associated to disease sensitive SNPs is an important and challenging research topic.
  
  
  RESULTS
  Taking into account the temporal structure of the longitudinal imaging data and the interrelatedness among the SNPs, we propose a novel 'task-correlated longitudinal sparse regression' model to study the association between the phenotypic imaging markers and the genotypes encoded by SNPs. In our new association model, we extend the widely used ℓ(2,1)-norm for matrices to tensors to jointly select imaging markers that have common effects across all the regression tasks and time points, and meanwhile impose the trace-norm regularization onto the unfolded coefficient tensor to achieve low rank such that the interrelationship among SNPs can be addressed. The effectiveness of our method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected imaging predictors relevant to disease sensitive SNPs.
  
  
  BACKGROUND
  Software is publicly available at: http://ranger.uta.edu/%7eheng/Longitudinal/
  
  
  BACKGROUND
  heng@uta.edu or shenli@inpui.edu.
authors.affiliation: Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019, USA.
authors.forename: Hua
authors.initials: H
authors.lastname: Wang
authors.forename: Feiping
authors.initials: F
authors.lastname: Nie
authors.forename: Heng
authors.initials: H
authors.lastname: Huang
authors.forename: Jingwen
authors.initials: J
authors.lastname: Yan
authors.forename: Sungeun
authors.initials: S
authors.lastname: Kim
authors.forename: Kwangsik
authors.initials: K
authors.lastname: Nho
authors.forename: Shannon L
authors.initials: SL
authors.lastname: Risacher
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*; D056726:Genetic Association Studies; D005838:Genotype; D006801:Humans; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D012044:Regression Analysis