NDLI: A hybrid Bayesian Network/Structural Equation Modeling (BN/SEM) approach for detecting physiological networks for obesity-related genetic variants

Content Provider	IEEE Xplore Digital Library
Author	Duarte, C.W. Klimentidis, Y.C. Harris, J.J. Cardel, M. Fernandez, J.R.
Copyright Year	2011
Description	Author affiliation: Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham (Duarte, C.W.; Klimentidis, Y.C.; Harris, J.J.) \|\| Department of Nutrition Sciences, University of Alabama at Birmingham (Cardel, M.; Fernandez, J.R.)
Abstract	GWAS studies have been successful in finding genetic determinants of obesity. To translate discovered genetic variants into new therapies or prevention strategies, molecular or physiological mechanisms need to be discovered. One strategy is to perform data mining of data sets with detailed phenotypic data, such as those present in dbGAP (database of Genotypes and Phenotypes) for hypothesis generation. We propose a novel technique that combines the power and computational efficiency of existing Bayesian Network (BN) learning algorithms with the statistical rigor of Structural Equation Modeling (SEM) to produce an overall system that searches the space of potential networks and evaluates promising candidates using standard SEM model selection criteria. 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 three hundred children with detailed obesity-related phenotypes. We demonstrate our approach by showing genetic mechanisms for three obesity-related SNPs.
Starting Page	696
Ending Page	702
File Size	694228
Page Count	7
File Format	PDF
ISBN	9781457716126
e-ISBN	9781457716133
DOI	10.1109/BIBMW.2011.6112455
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Obesity Numerical analysis Bayesian methods Genetics Data models Blood pressure Mathematical model
Content Type	Text
Resource Type	Article

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