JBiSE> Vol.1 No.1, May 2008

A Combinatorial Analysis of Genetic Data for Crohn's Disease


The Both environmental and genetic factors have roles in the development of some diseases. Complex diseases, such as Crohn's disease or Type II diabetes, are caused by a combination of environmental factors and mutations in multiple genes. Patients who have been diagnosed with such diseases cannot easily be treated. However, many diseases can be avoided if people at high risk change their living style, one example being their diet. But how can we tell their susceptibility to diseases before symptoms are found and help them make informed decisions about their health? With the development of DNA microarray technique, it is possible to access the human genetic information related to specific diseases. This paper uses a combinatorial method to analyze the genetic data for Crohn's disease and search disease-associated factors for given case/control samples. An optimum random forest based method has been applied to publicly available genotype data on Crohn's disease for association study and achieved a promising result.


Cite this paper

Mao, W. and Lee, J. (2008) A Combinatorial Analysis of Genetic Data for Crohn's Disease. Journal of Biomedical Science and Engineering, 1, 52-58. doi: 10.4236/jbise.2008.11008.


[1] National Digestive Diseases Information Clearinghouse (NDDIC), http://digestive.niddk.nih.gov/ddiseases/pubs/crohns.
[2] Cardon, L.R., Bell, J.I., “Association Study Designs for Complex Diseases”, Nature Reviews: Genetics (2001), Vol.2, pp. 91-98.
[3] Hirschhorn, J.N.,Daly, M.J., “Genome-wide Association Studies for Common Diseases and Complex Diseases”, Nature Reviews: Genetics (2005), Vol.6, pp. 95-108.
[4] Merikangas, KR., Risch, N., “Will the Genomics Revolution Revolutionize Psychiatry”, American Journal of Psychiatry, (2003), 160: pp. 625-635.
[5] Botstein, D., Risch, N., “Discovering Genotypes Underlying Human Phenotypes: Past Successes for Mendelian Disease, Future Approaches for Complex Disease”, Nature Genetics (2003), 33: pp.228-237.
[6] Clark, A.G., Boerwinkle E., Hixson J. and Sing C.F., “Determinants of the success of whole-genome association testing”, Genome Res.(2005) 15, pp. 1463-1467.
[7] He, J. and Zelikovsky, A., “Tag SNP Selection Based on Multivariate Linear Regression”, Proc. of International Conference on Computational Science (2006), LNCS 3992, pp. 750-757.
[8] Brinza, D., He, J. and Zelikovsky, A., “Combinatorial Search Methods for Multi-SNP Disease Association”, Proc. of International Conference of the IEEE Engineering in Medicine and Biology (2006), pp. 5802-5805.
[9] Cook N.R., Zee R.Y., Ridker P.M., “Tree and Spline Based Association Analysis of gene-gene interaction models for ischemic stroke”, Stat Med (2004), 23(9), pp. 439-453.
[10] York T.P., Eaves L.J., “Common Disease Analysis using Multivariate Adaptive Regression Splines (MARS): Genetic Analysis Workshop 12 simulated sequence data.” Genetic Epidemiology (2001), 21 Suppl I: pp.649-654.
[11] Ritchie M.D., Hahn L.W., Roodi N., Bailey L.R., Dupont W.D., Parl F.F., Moore J.H., “Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer”, Am J Hum Genet. (2001), 69: pp. 138-147.
[12] Hahn L.W., Ritchie M.D., Moore J.H., “Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions”, Bioinformatics (2003), 19: pp. 376-382.
[13] Lunetta, K., Hayward, L., Segal, J., Van Eerdewegh, P., “Screening Large-scale Association Study Data: Exploiting Interactions Using Random Forests”, BMC Genetics (2004), pp. 5:32.
[14] Daly, M., Rioux, J., Schaffner, S., Hudson, T. and Lander, E., “High resolution haplotype structure in the human genome”, Nature Genetics (2001) 29, pp. 229-232.
[15] Mao, W., He, J., Brinza, D. and Zelikovsky, A., “A Combinatorial Method for Predicting Genetic Susceptibility to Complex Diseases”, Proc. International Conference of the IEEE Engineering In Medicine and Biology Society (EMBC 2005), pp.224-227.
[16] Mao, W., Brinza, D., Hundewale, N., Gremalschi, S. and Zelikovsky, A, “Genotype Susceptibility and Integrated Risk Factors for Complex Diseases”, Proc. IEEE International Conference on Granular Computing (GRC 2006), pp. 754-757.
[17] Kimmel, G. and Shamir R., “A Block-Free Hidden Markov Model for Genotypes and Its Application to Disease Association”, J. of Computational Biology (2005), Vol. 12, No. 10: pp. 1243-1260.
[18] Listgarten, J., Damaraju, S., Poulin B., Cook, L., Dufour, J., Driga, A., Mackey, J., Wishart, D., Greiner,R., and Zanke, B., “Predictive Models for Breast Cancer Susceptibility from Multiple Single Nucleotide Polymorphisms”, Clinical Cancer Research (2004), Vol. 10, pp. 2725-2737.
[19] Breiman, L. and Cutler, A. http://stat.berkeley.edu/breiman.
[20] Waddell, M., Page,D., Zhan, F., Barlogie, B., and Shaughnessy, J., “Predicting Cancer Susceptibility from SingleNucleotide Polymorphism Data: A Case Study in Multiple Myeloma”, Proc. of the 5th international workshop on Bioinformatics (2005), pp 21--28.
[21] Chang, C. and Lin, C. http://www.csie.ntu.edu.tw/libsvm.
[22] Brinza, D. and Zelikovsky, A., “2SNP: Scalable Phasing Based on 2-SNP Haplotypes”, Bioinformatics (2006), 22(3), pp. 371--373.

comments powered by Disqus

Copyright © 2014 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.