biostudies-literatureMotsinger AANIA NIH HHSNHLBI NIH HHSNLM NIH HHSNIGMS NIH HHS39https://www.ebi.ac.uk/biostudies/studies/S-EPMC1388239biostudies-literatureUnknown7<h4>Background</h4>The 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.<h4>Results</h4>We 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.<h4>Conclusion</h4>These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions.BMC bioinformaticsGPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease.PMC1388239HL65962R01 AG020135T15 LM007450LM007450U01 HL065962GM62758AG20135T32 GM062758U19 HL065962Lee SLMotsinger AAMellick GRitchie MDfalseGPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease.<h4>Background</h4>The 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.<h4>Results</h4>We 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.<h4>Conclusion</h4>These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions.2006-01-01T00:00:00Z2006 Jan2024-11-20T00:34:05.297Z2021-02-20T09:34:05ZS-EPMC13882391643620410.1186/1471-2105-7-39