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Bioinformatics 2009 25(12):i21-i29; doi:10.1093/bioinformatics/btp226
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A Classifier-based approach to identify genetic similarities between diseases

Marc A. Schaub 1, Irene M. Kaplow 2, Marina Sirota 3, Chuong B. Do 1, Atul J. Butte 3,4,5 and Serafim Batzoglou 1,*

1Department of Computer Science, Stanford University, Stanford, CA 94305, 2Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, 3Stanford Center for Biomedical Informatics Research, 251 Campus Dr., Stanford, CA 94305, 4Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305 and 5Lucile Packard Children's Hospital, 725 Welch Road, Palo Alto, CA 94304, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Genome-wide association studies are commonly used to identify possible associations between genetic variations and diseases. These studies mainly focus on identifying individual single nucleotide polymorphisms (SNPs) potentially linked with one disease of interest. In this work, we introduce a novel methodology that identifies similarities between diseases using information from a large number of SNPs. We separate the diseases for which we have individual genotype data into one reference disease and several query diseases. We train a classifier that distinguishes between individuals that have the reference disease and a set of control individuals. This classifier is then used to classify the individuals that have the query diseases. We can then rank query diseases according to the average classification of the individuals in each disease set, and identify which of the query diseases are more similar to the reference disease. We repeat these classification and comparison steps so that each disease is used once as reference disease.

Results: We apply this approach using a decision tree classifier to the genotype data of seven common diseases and two shared control sets provided by the Wellcome Trust Case Control Consortium. We show that this approach identifies the known genetic similarity between type 1 diabetes and rheumatoid arthritis, and identifies a new putative similarity between bipolar disease and hypertension.

Contact: serafim{at}cs.stanford.edu


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