Original Article

Symbolic discriminant analysis of microarray data in autoimmune disease

  1. Jason H. Moore1,2,*,
  2. Joel S. Parker1,2,
  3. Nancy J. Olsen3,4,
  4. Thomas M. Aune3,4

Article first published online: 24 JUN 2002

DOI: 10.1002/gepi.1117

Issue

Genetic Epidemiology

Genetic Epidemiology

Special Issue: Genetic Epidemiology and Microarrays

Volume 23, Issue 1, pages 57–69, June 2002

Additional Information

How to Cite

Moore, J. H., Parker, J. S., Olsen, N. J. and Aune, T. M. (2002), Symbolic discriminant analysis of microarray data in autoimmune disease. Genet. Epidemiol., 23: 57–69. doi: 10.1002/gepi.1117

Author Information

  1. 1

    Program in Human Genetics, Vanderbilt University Medical School, Nashville, Tennessee

  2. 2

    Department of Molecular Physiology and Biophysics, Vanderbilt University Medical School, Nashville, Tennessee

  3. 3

    Department of Medicine, Vanderbilt University Medical School, Nashville, Tennessee

  4. 4

    Department of Microbiology and Immunology, Vanderbilt University Medical School, Nashville, Tennessee

*Program in Human Genetics, Department of Molecular Physiology and Biophysics, 519 Light Hall, Vanderbilt University Medical School, Nashville, TN 37232-0700

Publication History

  1. Issue published online: 24 JUN 2002
  2. Article first published online: 24 JUN 2002
  3. Manuscript Accepted: 14 MAR 2002
  4. Manuscript Received: 30 NOV 2001

Funded by

  • Vanderbilt-Ingram Cancer Center, the Arthritis Foundation
  • National Institutes of Health. Grant Numbers: AR02027, AR41943, HL68744, DK58749, CA90949

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Keywords:

  • symbolic discriminant analysis;
  • DNA microarrays;
  • rheumatoid arthritis;
  • systemic lupus erythematosus

Abstract

New laboratory technologies such as DNA microarrays have made it possible to measure the expression levels of thousands of genes simultaneously in a particular cell or tissue. The challenge for genetic epidemiologists will be to develop statistical and computational methods that are able to identify subsets of gene expression variables that classify and predict clinical endpoints. Linear discriminant analysis is a popular multivariate statistical approach for classification of observations into groups. This is because the theory is well described and the method is easy to implement and interpret. However, an important limitation is that linear discriminant functions need to be prespecified. To address this limitation and the limitation of linearity, we have developed symbolic discriminant analysis (SDA) for the automatic selection of gene expression variables and discriminant functions that can take any form. In the present study, we demonstrate that SDA is capable of identifying combinations of gene expression variables that are able to classify and predict autoimmune diseases. Genet. Epidemiol. 23:57–69, 2002. © 2002 Wiley-Liss, Inc.

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