The identification of genes that influence the risk of common, complex disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions. We have previously introduced a genetic programming neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of genetic and gene-environment combinations associated with disease risk. Previous empirical studies suggest GPNN has excellent power for identifying gene-gene and gene-environment interactions. The goal of this study was to compare the power of GPNN to stepwise logistic regression (SLR) and classification and regression trees (CART) for identifying gene-gene and gene-environment interactions. SLR and CART are standard methods of analysis for genetic association studies. Using simulated data, we show that GPNN has higher power to identify gene-gene and gene-environment interactions than SLR and CART. These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions in studies of human disease.
GPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease.BMC Bioinformatics. 2006 Jan 25;7:39. doi: 10.1186/1471-2105-7-39. BMC Bioinformatics. 2006. PMID: 16436204 Free PMC article.
Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity.Genet Epidemiol. 2003 Feb;24(2):150-7. doi: 10.1002/gepi.10218. Genet Epidemiol. 2003. PMID: 12548676
Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases.BMC Bioinformatics. 2003 Jul 7;4:28. doi: 10.1186/1471-2105-4-28. Epub 2003 Jul 7. BMC Bioinformatics. 2003. PMID: 12846935 Free PMC article.
Engineering Aspects of Olfaction.In: Persaud KC, Marco S, Gutiérrez-Gálvez A, editors. Neuromorphic Olfaction. Boca Raton (FL): CRC Press/Taylor & Francis; 2013. Chapter 1. Neuromorphic Olfaction. 2013. PMID: 26042329 Free Books & Documents. Review.
Multifactor dimensionality reduction for detecting gene-gene and gene-environment interactions in pharmacogenomics studies.Pharmacogenomics. 2005 Dec;6(8):823-34. doi: 10.2217/14622422.214.171.1243. Pharmacogenomics. 2005. PMID: 16296945 Review.
Cited by 12 articles
Gene-gene interaction: the curse of dimensionality.Ann Transl Med. 2019 Dec;7(24):813. doi: 10.21037/atm.2019.12.87. Ann Transl Med. 2019. PMID: 32042829 Free PMC article. Review.
Robust genetic interaction analysis.Brief Bioinform. 2019 Mar 25;20(2):624-637. doi: 10.1093/bib/bby033. Brief Bioinform. 2019. PMID: 29897421 Free PMC article. Review.
Evolutionary computation: the next major transition of artificial intelligence?BioData Min. 2017 Jul 29;10:26. doi: 10.1186/s13040-017-0147-3. eCollection 2017. BioData Min. 2017. PMID: 28775766 Free PMC article. No abstract available.
Statistical and Computational Methods for Genetic Diseases: An Overview.Comput Math Methods Med. 2015;2015:954598. doi: 10.1155/2015/954598. Epub 2015 May 28. Comput Math Methods Med. 2015. PMID: 26106440 Free PMC article. Review.
Binning somatic mutations based on biological knowledge for predicting survival: an application in renal cell carcinoma.Pac Symp Biocomput. 2015:96-107. Pac Symp Biocomput. 2015. PMID: 25592572 Free PMC article.
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