Original Research ARTICLE

Front. Genet., 29 February 2012 | doi: 10.3389/fgene.2012.00026

Naïve Bayesian classifier and genetic risk score for genetic risk prediction of a categorical trait: not so different after all!

Paola Sebastiani1*, Nadia Solovieff2,3,4 and Jenny X. Sun1
  • 1 Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
  • 2 Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetics Research, Massachusetts General Hospital, Boston, MA, USA
  • 3 Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
  • 4 Department of Psychiatry, Harvard Medical School, Boston, MA, USA

One of the most popular modeling approaches to genetic risk prediction is to use a summary of risk alleles in the form of an unweighted or a weighted genetic risk score, with weights that relate to the odds for the phenotype in carriers of the individual alleles. Recent contributions have proposed the use of Bayesian classification rules using Naïve Bayes classifiers. We examine the relation between the two approaches for genetic risk prediction and show that the methods are mathematically related. In addition, we study the properties of the two approaches and describe how they can be generalized to include various models of inheritance.

Keywords: genetic risk prediction, genetic score, Naïve Bayes classifier, classification score, classification rule

Citation: Sebastiani P, Solovieff N and Sun JX (2012) Naïve Bayesian classifier and genetic risk score for genetic risk prediction of a categorical trait: not so different after all! Front. Gene. 3:26. doi: 10.3389/fgene.2012.00026

Received: 24 December 2011; Accepted: 12 February 2012;
Published online: 29 February 2012.

Edited by:

Albert Tenesa, University of Edinburgh, UK

Reviewed by:

Lei Zhang, University of Shanghai for Science and Technology, China
John Whittaker, GlaxoSmithKline, UK

Copyright: © 2012 Sebastiani, Solovieff and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.

*Correspondence: Paola Sebastiani, Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA 02118, USA. e-mail: sebas@bu.edu

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