Title: Discovery of phenotypic networks from genotypic association studies with application to obesity

Authors: Christine W. Duarte; Yann C. Klimentidis; Jacqueline J. Harris; Michelle Cardel; José R. Fernández

Addresses: Center for Outcomes Research and Evaluation (CORE), Maine Medical Center Research Institute (MMCRI), Portland, ME 04101, USA ' Mel and Enid Zuckerman College of Public Health, Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, 85724, USA ' Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL 32611, USA ' Department of Pediatrics, University of Colorado Denver Anschutz Medical Campus, Aurora, CO 80045, USA ' Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL 35294, USA

Abstract: Genome-wide Association Studies (GWAS) have resulted in many discovered risk variants for several obesity-related traits. However, before clinical relevance of these discoveries can be achieved, molecular or physiological mechanisms of these risk variants needs to be discovered. One strategy is to perform data mining of phenotypically-rich data sources such as those present in dbGAP (database of Genotypes and Phenotypes) for hypothesis generation. Here we propose a technique that combines the power of existing Bayesian Network (BN) learning algorithms with the statistical rigour of Structural Equation Modelling (SEM) to produce an overall phenotypic network discovery system with optimal properties. We illustrate our method using the analysis of a candidate SNP data set from the AMERICO sample, a multi-ethnic cross-sectional cohort of roughly 300 children with detailed obesity-related phenotypes. We demonstrate our approach by showing genetic mechanisms for three obesity-related SNPs.

Keywords: genetic networks; GWAS; Bayesian networks; structural equation modelling; SEM; obesity; phenotypic networks; genotypic association studies; risk variants; SNP data; single nucleotide polymorphism; children; obesity-related phenotypes; bioinformatics.

DOI: 10.1504/IJDMB.2015.069414

International Journal of Data Mining and Bioinformatics, 2015 Vol.12 No.2, pp.129 - 143

Received: 13 Mar 2012
Accepted: 15 Mar 2012
Published online: 15 May 2015 *

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