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Bioinformatics Advance Access originally published online on August 17, 2009
Bioinformatics 2009 25(21):2764-2771; doi:10.1093/bioinformatics/btp491
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Correlating multiple SNPs and multiple disease phenotypes: penalized non-linear canonical correlation analysis

Sandra Waaijenborg * and Aeilko H. Zwinderman

Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1100 DD Amsterdam, The Netherlands

* To whom correspondence should be addressed.


  Abstract

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 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.

Contact: s.waaijenborg{at}amc.uva.nl

Associate Editor: David Rocke

Received on December 21, 2008; revised on August 13, 2009; accepted on August 13, 2009

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