Correlating multiple SNPs and multiple disease phenotypes: Penalized nonlinear canonical correlation analysis

Sandra Waaijenborg, Aeilko H. Zwinderman

Research output: Contribution to JournalArticleAcademicpeer-review

11 Citations (Scopus)


MOTIVATION: 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 can not 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 nonlinear canonical correlation analysis approach that can deal with qualitative data by transforming each qualitative variable into a continuous variable via optimal scaling. Additionally sparse results were obtained by adapting softthresholding to this nonlinear 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. CONTACT:
Original languageEnglish
Pages (from-to)2764-2771
JournalBioinformatics (Oxford, England)
Issue number21
Publication statusPublished - 2009

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