title: Correlating multiple SNPs and multiple disease phenotypes: penalized non-linear canonical correlation analysis.
abstract: BACKGROUND
  Canonical correlation analysis (CCA) can be used to capture the underlying genetic background of a complex disease, by associating two datasets containing information about a patient's phenotypical and genetic details. Often the genetic information is measured on a qualitative scale, consequently ordinary CCA cannot be applied to such data. Moreover, the size of the data in genetic studies can be enormous, thereby making the results difficult to interpret.
  
  
  RESULTS
  We developed a penalized non-linear CCA approach that can deal with qualitative data by transforming each qualitative variable into a continuous variable through optimal scaling. Additionally, sparse results were obtained by adapting soft-thresholding to this non-linear version of the CCA. By means of simulation studies, we show that our method is capable of extracting relevant variables out of high-dimensional sets. We applied our method to a genetic dataset containing 144 patients with glial cancer.
  
  
  BACKGROUND
  s.waaijenborg@amc.uva.nl.
authors.affiliation: Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1100 DD Amsterdam, The Netherlands. s.waaijenborg@amc.uva.nl
authors.forename: Sandra
authors.initials: S
authors.lastname: Waaijenborg
authors.forename: Aeilko H
authors.initials: AH
authors.lastname: Zwinderman
mesh_terms: D019295:Computational Biology / Q000379:methods*; D004194:Disease / Q000235:genetics; D006801:Humans; D015999:Multivariate Analysis; D009369:Neoplasms / Q000235:genetics; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide