{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Motsinger AA"],"funding":["NIA NIH HHS","NHLBI NIH HHS","NLM NIH HHS","NIGMS NIH HHS"],"pagination":["39"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC1388239"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["7"],"pubmed_abstract":["<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."],"journal":["BMC bioinformatics"],"pubmed_title":["GPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease."],"pmcid":["PMC1388239"],"funding_grant_id":["HL65962","R01 AG020135","T15 LM007450","LM007450","U01 HL065962","GM62758","AG20135","T32 GM062758","U19 HL065962"],"pubmed_authors":["Lee SL","Motsinger AA","Mellick G","Ritchie MD"],"additional_accession":[]},"is_claimable":false,"name":"GPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease.","description":"<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.","dates":{"release":"2006-01-01T00:00:00Z","publication":"2006 Jan","modification":"2024-11-20T00:34:05.297Z","creation":"2021-02-20T09:34:05Z"},"accession":"S-EPMC1388239","cross_references":{"pubmed":["16436204"],"doi":["10.1186/1471-2105-7-39"]}}