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This article is part of the supplement: Research from the Eleventh International Workshop on Network Tools and Applications in Biology (NETTAB 2011)

Open Access Research

Hierarchical Naive Bayes for genetic association studies

Alberto Malovini12*, Nicola Barbarini1, Riccardo Bellazzi1 and Francesca De Michelis3

Author affiliations

1 Department of Industrial and Information Engineering, University of Pavia, Pavia, 27100, Italy

2 IRCCS Fondazione Salvatore Maugeri, Pavia, 27100, Italy

3 ICB, Weill Cornell Medical College, New York, USA

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Citation and License

BMC Bioinformatics 2012, 13(Suppl 14):S6  doi:10.1186/1471-2105-13-S14-S6

Published: 7 September 2012

Abstract

Background

Genome Wide Association Studies represent powerful approaches that aim at disentangling the genetic and molecular mechanisms underlying complex traits. The usual "one-SNP-at-the-time" testing strategy cannot capture the multi-factorial nature of this kind of disorders. We propose a Hierarchical Naïve Bayes classification model for taking into account associations in SNPs data characterized by Linkage Disequilibrium. Validation shows that our model reaches classification performances superior to those obtained by the standard Naïve Bayes classifier for simulated and real datasets.

Methods

In the Hierarchical Naïve Bayes implemented, the SNPs mapping to the same region of Linkage Disequilibrium are considered as "details" or "replicates" of the locus, each contributing to the overall effect of the region on the phenotype. A latent variable for each block, which models the "population" of correlated SNPs, can be then used to summarize the available information. The classification is thus performed relying on the latent variables conditional probability distributions and on the SNPs data available.

Results

The developed methodology has been tested on simulated datasets, each composed by 300 cases, 300 controls and a variable number of SNPs. Our approach has been also applied to two real datasets on the genetic bases of Type 1 Diabetes and Type 2 Diabetes generated by the Wellcome Trust Case Control Consortium.

Conclusions

The approach proposed in this paper, called Hierarchical Naïve Bayes, allows dealing with classification of examples for which genetic information of structurally correlated SNPs are available. It improves the Naïve Bayes performances by properly handling the within-loci variability.