ArticleBMC bioinformatics2006
GPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 90 citations in OpenAlex.
- Article
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- Implications of genome-wide association studies in cancer therapeutics.British journal of clinical pharmacology · 2013Review
- Identifying dysfunctional miRNA-mRNA regulatory modules by inverse activation, cofunction, and high interconnection of target genes: a case study of glioblastoma.Neuro-oncology · 2013Article
- Identifying interacting genetic variations by fish-swarm logic regression.BioMed research international · 2013Article
- A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.BioMed research international · 2013Article
- The success of pharmacogenomics in moving genetic association studies from bench to bedside: study design and implementation of precision medicine in the post-GWAS era.Human genetics · 2012Review
- Recommendations and proposed guidelines for assessing the cumulative evidence on joint effects of genes and environments on cancer occurrence in humans.International journal of epidemiology · 2012Article
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- Statistical Optimization of Pharmacogenomics Association Studies: Key Considerations from Study Design to Analysis.Current pharmacogenomics and personalized medicine · 2011Article
- Practical and theoretical considerations in study design for detecting gene-gene interactions using MDR and GMDR approaches.PloS one · 2011Article
- Article
- A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context.BMC bioinformatics · 2010Article
- A general framework for formal tests of interaction after exhaustive search methods with applications to MDR and MDR-PDT.PloS one · 2010Article
- Prioritizing GWAS results: A review of statistical methods and recommendations for their application.American journal of human genetics · 2010Review
- Neural networks for modeling gene-gene interactions in association studies.BMC genetics · 2009Article
- Methods for optimizing statistical analyses in pharmacogenomics research.Expert review of clinical pharmacology · 2009Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.