GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human diseaseReportar como inadecuado




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BMC Bioinformatics

, 7:39

First Online: 25 January 2006Received: 14 July 2005Accepted: 25 January 2006

Abstract

BackgroundThe identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network GPNN as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson-s disease.

ResultsWe show that GPNN has high power to detect even relatively small genetic effects 2–3% heritability in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability <1% or when interactions involved more than three loci. We tested GPNN on a real dataset comprised of Parkinson-s disease cases and controls and found a two locus interaction between the DLST gene and sex.

ConclusionThese results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-7-39 contains supplementary material, which is available to authorized users.

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Autor: Alison A Motsinger - Stephen L Lee - George Mellick - Marylyn D Ritchie

Fuente: https://link.springer.com/



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