Riskoweb: Web-Based Genetic Profiling to Complex Disease Using Genome-Wide SNP Markers

  • Sergio Torres-Sánchez
  • Rosana Montes-Soldado
  • Nuria Medina-Medina
  • Andrés R. Masegosa
  • María Mar Abad-Grau
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 93)


Assessing risk susceptibility of individuals to a complex disease is becoming an interesting prevention tool specially recommended for those with ancestors or other relatives affected by the disease. As genome-wide DNA sequencing is getting more affordable, more dense genotyping is performed and accuracy is increased. Therefore, health public services may consider the results of this approach in their preventing plans and physicians be encouraged to perform these risk tests. A web-based tool has been built for risk assessing of complex diseases and its knowledge base is currently filled with multiple sclerosis risk variants and their effect on the disease. The genetic profiling is calculated by using a Naive Bayes network, which it has been shown to provide highly accurate results as long as dense genotyping, haplotype reconstruction and several markers at a time are considered.


Multiple Sclerosis Random Forest Complex Disease Risk Locus Genetic Risk Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Evans, D., Visscher, P., Wray, N.: Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk. Human Molecular Genetics 18, 3525–3531 (2009)CrossRefGoogle Scholar
  2. 2.
    Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156 (1996)Google Scholar
  3. 3.
    International Multiple Sclerosis Genetics Consortium (IMSGC). Evidence for polygenic susceptibility to multiple sclerosis - the shape of things to come. Am. J. Hum. Genet. 86, 621–625 (2010)Google Scholar
  4. 4.
    Compston, D.H., Sawcerand, A., Lander, S., Daly, E., Jager, M., de Bakker, P.D., Gabriel, P., Mirel, S., Ivinsonand, D., Pericak-Vance, A., Gregory, M., Rioux, S., McCauley, J., Haines, J., Barcellos, J., Cree, L., Oksenberg, B., Hauser, J., Risk, S.: International Multiple Sclerosis Genetics Consortium. New England Journal of Medicine 357(9), 851–862 (2007)CrossRefGoogle Scholar
  5. 5.
    Jager, P.D., Chibnik, L., Cui, J., Reischl, J., Lehr, S., Simon, K., Aubin, C., Bauer, D., Heubach, J., Sandbrink, R., Tyblova, M., Lelkova, P.: Steering committee of the BENEFIT study, committee of the BEYOND study, S., committee of the LTF study’, S., committee of the CCR1 study’, S., Havrdova, E., Pohl, C., Horakova, D., Ascherio, A., Hafler, D.A., Karlson, E.W.: Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: a weighted genetic risk score. Lancet. Neurol. 8(12), 1111–1119 (2009)Google Scholar
  6. 6.
    Quinlan, R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  7. 7.
    Schaid, D.: General score tests for associations of genetic markers with disease using cases and their parents. Genet. Epidemiol. 1996, 423–449 (1996)CrossRefGoogle Scholar
  8. 8.
    TZeng, J., Devlin, B., Wasserman, L., Roeder, K.: On the identification of disease mutations by the analysis of haplotype similarity and goodness of fit. Am. J. Hum. Genet. 72, 891–902 (2003)CrossRefGoogle Scholar
  9. 9.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1999)Google Scholar
  10. 10.
    Wray, N., Goddard, M., Visscher, P.: Prediction of individual genetic risk to disease from genome-wide association studies. Genome Research 17, 1520–1528 (2003)CrossRefGoogle Scholar
  11. 11.
    Zhang, S., Sha, Q., Chen, H., Dong, J., Jiang, R.: Transmission/Disequilibrium test based on haplotype sharing for tightly linked markers. Am. J. Hum. Genet. 73, 566–579 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sergio Torres-Sánchez
    • 1
  • Rosana Montes-Soldado
    • 1
  • Nuria Medina-Medina
    • 1
  • Andrés R. Masegosa
    • 2
  • María Mar Abad-Grau
    • 1
  1. 1.Department of Computer Languages and Systems - CITICUniversity of GranadaSpain
  2. 2.Department of Computer Science and Artificial Intelligence - CITICUniversity of GranadaSpain

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