Intelligent Data Analysis of Human Genetic Data

  • Paola Sebastiani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)


The last two decades have witnessed impressive developments in the technology of genotyping and sequencing. Thousands of human DNA samples have been genotyped at increasing densities or sequenced in full using next generation DNA sequencing technology. The challenge is now to equip computational scientists with the right tools to go beyond mining genetic data to discover small gold nuggets and build models that can decipher the mechanism linking genotypes to phenotypes and can be used to identify subjects at risk for disease. We will discuss different approaches to model genetic data, and emphasize the need of blending a deep understanding of study design, with statistical modeling techniques and intelligent data approaches to make analysis feasible and results interpretable and useful.


Bayesian Network Sickle Cell Anemia Genetic Data Familial Aggregation 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lander, E.: The new genomics: Global views of biology. Science 274, 536–539 (1996)CrossRefGoogle Scholar
  2. 2.
    Manolio, T.A.: Cohort studies and the genetics of complex disease. Nat. Genet. 41(1), 5–6 (2009)CrossRefGoogle Scholar
  3. 3.
    Manolio, T.A.: Genomewide association studies and assessment of the risk of disease. N. Engl. J. Med. 363(2), 166–176 (2010)CrossRefGoogle Scholar
  4. 4.
    Sebastiani, P., Timofeev, N., Dworkis, D.A., Perls, T.T., Steinberg, M.H.: Genome-wide association studies and the genetic dissection of complex traits. Am J. Hematol. 84(8), 504–515 (2009)CrossRefGoogle Scholar
  5. 5.
    Sebastiani, P., Solovieff, N., Sun, J.X.: Nave bayesian classifier and genetic risk score for genetic risk prediction of a categorical trait: Not so different after all! Front. Genet. 3, 26 (2012)Google Scholar
  6. 6.
    Wei, Z., Wang, K., Qu, H.Q., Zhang, H., Bradfield, J., Kim, C., Frackleton, E., Hou, C., Glessner, J.T., Chiavacci, R., Stanley, C., Monos, D., Grant, S.F.A., Polychronakos, C., Hakonarson, H.: From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes. PLoS Genet. 5(10), e1000678 (2009)Google Scholar
  7. 7.
    McKinney, B.A., Reif, D.M., Ritchie, M.D., Moore, J.H.: Machine learning for detecting gene-gene interactions: a review. Appl. Bioinformatics 5(2), 77–88 (2006)CrossRefGoogle Scholar
  8. 8.
    Jiang, X., Barmada, M.M., Cooper, G.F., Becich, M.J.: A bayesian method for evaluating and discovering disease loci associations. PLoS One 6(8), e22075 (2011)Google Scholar
  9. 9.
    Sebastiani, P., Ramoni, M., Nolan, V., Baldwin, C., Steinberg, M.: Genetic dissection and prognostic modeling of overt stroke in sickle cell anemia. Nature Genetics 37(4), 435–440 (2005)CrossRefGoogle Scholar
  10. 10.
    Okser, S., Lehtimäki, T., Elo, L.L., Mononen, N., Peltonen, N., Kähönen, M., Juonala, M., Fan, Y.M., Hernesniemi, J.A., Laitinen, T., Lyytikäinen, L.P., Rontu, R., Eklund, C., Hutri-Khnen, N., Taittonen, L., Hurme, M., Viikari, J.S.A., Raitakari, O.T., Aittokallio, T.: Genetic variants and their interactions in the prediction of increased pre-clinical carotid atherosclerosis: the cardiovascular risk in young finns study. PLoS Genet. 6(9) (September 2010)Google Scholar
  11. 11.
    Sebastiani, P., Solovieff, N., Dewan, A.T., Walsh, K.M., Puca, A., Hartley, S.W., Melista, E., Andersen, S., Dworkis, D.A., Wilk, J.B., Myers, R.H., Steinberg, M.H., Montano, M., Baldwin, C.T., Hoh, J., Perls, T.T.: Genetic signatures of exceptional longevity in humans. PLoS One 7(1), e29848 (2012)Google Scholar
  12. 12.
    Ott, J.: Analysis of Human Genetic Linkage. Johns Hopkins University Press, Baltimore (1999)Google Scholar
  13. 13.
    Wray, N.R., Yang, J., Goddard, M.E., Visscher, P.M.: The genetic interpretation of area under the roc curve in genomic profiling. PLoS Genet 6(2), e1000864 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Paola Sebastiani
    • 1
  1. 1.Department of BiostatisticsBoston University School of Public HealthBostonUSA

Personalised recommendations