Exploration of gene–gene interaction effects using entropy-based methods

Chen, Dong; Chu, Xun; Wang, Ying; Wang, Yi; Li, Jin; Shi, Tieliu; Huang, Wei; Li, Yixue

doi:10.1038/sj.ejhg.5201921

Cited by 80 publications

(63 citation statements)

References 37 publications

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“…All algorithms were compared using both real genetic data from Crohn's disease and simulated data that were modeled on real data from rheumatoid arthritis (Miller et al 2007). AMBIENCE differs from the entropy-based approach proposed by Dong et al (2007) in that it is capable of assessing direct effects and two-locus and also higher-order interactions. The PAI metric in AMBIENCE can effectively reduce the confounding effects caused by pairwise (and higher-order) LD.…”

Section: Discussionmentioning

confidence: 99%

“…Information theory statistics employing entropy-based statistics have been proposed for genomewide data analysis to test for allelic association with a phenotype (Zhao et al 2005(Zhao et al , 2007Li et al 2007). Entropy-based methods for two-locus interactions have also been proposed recently and were found to confirm the negative epistasis between sickle cell anemia and a-thalassemia genetic variations against malaria (Dong et al 2007).…”

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confidence: 99%

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AMBIENCE: A Novel Approach and Efficient Algorithm for Identifying Informative Genetic and Environmental Associations With Complex Phenotypes

Chanda¹,

Sucheston

Zhang³

et al. 2008

Genetics

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We developed a computationally efficient algorithm AMBIENCE, for identifying the informative variables involved in gene-gene (GGI) and gene-environment interactions (GEI) that are associated with disease phenotypes. The AMBIENCE algorithm uses a novel information theoretic metric called phenotypeassociated information (PAI) to search for combinations of genetic variants and environmental variables associated with the disease phenotype. The PAI-based AMBIENCE algorithm effectively and efficiently detected GEI in simulated data sets of varying size and complexity, including the 10K simulated rheumatoid arthritis data set from Genetic Analysis Workshop 15. The method was also successfully used to detect GGI in a Crohn's disease data set. The performance of the AMBIENCE algorithm was compared to the multifactor dimensionality reduction (MDR), generalized MDR (GMDR), and pedigree disequilibrium test (PDT) methods. Furthermore, we assessed the computational speed of AMBIENCE for detecting GGI and GEI for data sets varying in size from 100 to 10 5 variables. Our results demonstrate that the AMBIENCE information theoretic algorithm is useful for analyzing a diverse range of epidemiologic data sets containing evidence for GGI and GEI.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

AMBIENCE: A Novel Approach and Efficient Algorithm for Identifying Informative Genetic and Environmental Associations With Complex Phenotypes

Chanda¹,

Sucheston

Zhang³

et al. 2008

Genetics

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“…7 Parametric methods are more powerful than nonparametric methods provided valid assumptions are made. 7 In this regard, LD 1,7 and entropy-based [15][16] methods have clearer biological interpretation and are powerful. However, all these methods share a limitation in common.…”

Section: Introductionmentioning

confidence: 99%

A gene-based method for detecting gene–gene co-association in a case–control association study

2009

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Association study (especially the genome-wide association study) now has a key function in identification and characterization of disease-predisposing genetic variant(s), which customarily involve multiple single nucleotide polymorphisms (SNPs) in a candidate region or across the genome. Case-control association design remains the most popular and a challenging issue in the statistical analysis is the optimal use of all information contained in these SNPs. Previous approaches often treated gene-gene interaction as deviation from additive genetic effects or replaced it with SNP-SNP interaction. However, these approaches are limited for their failure of consideration of gene-gene interaction or gene-gene co-association at gene level. Although the co-association of the SNPs within a candidate gene can be detected by principal component analysis-based logistic regression model, the detection of co-association between genes in genome remains uncertain. Here, we proposed a canonical correlationbased U statistic (CCU) for detecting gene-based gene-gene co-association in the case-control design. We explored its type I error rates and power through simulation and analyzed two real data sets. By treating gene as a functional unit in analysis, we found that CCU was a strong alternative to previous approaches. We discussed the performance of CCU as a gene-based gene-gene co-association statistic and the prospect of further improvement.

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“…6 The statistical definition of epistasis was first given by Fisher 7 and developed further by Cockerham 8 and Kempthorne, 9 whereby the epistasis effect is considered as a deviation from additive genetic effects. 10 Currently, popular SSIs detecting methods are based primarily on statistics, [11][12][13] data mining, [14][15][16] machine learning 17,18 and so on. 19,20 Statistical methods contain logistic regression model, 11 the information entropy model; 12,13 data mining methods contain dimensionality reduction method, 14 Bayesian method 15,16 and so on; machine learning methods are based on a tree and random forests 17,18 and so on.…”

Section: Introductionmentioning

confidence: 99%

A gene-based information gain method for detecting gene–gene interactions in case–control studies

Huang

Guo

et al. 2015

Eur J Hum Genet

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Currently, most methods for detecting gene-gene interactions (GGIs) in genome-wide association studies are divided into SNP-based methods and gene-based methods. Generally, the gene-based methods can be more powerful than SNP-based methods. Some gene-based entropy methods can only capture the linear relationship between genes. We therefore proposed a nonparametric gene-based information gain method (GBIGM) that can capture both linear relationship and nonlinear correlation between genes. Through simulation with different odds ratio, sample size and prevalence rate, GBIGM was shown to be valid and more powerful than classic KCCU method and SNP-based entropy method. In the analysis of data from 17 genes on rheumatoid arthritis, GBIGM was more effective than the other two methods as it obtains fewer significant results, which was important for biological verification. Therefore, GBIGM is a suitable and powerful tool for detecting GGIs in case-control studies.

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Exploration of gene–gene interaction effects using entropy-based methods

Cited by 80 publications

References 37 publications

AMBIENCE: A Novel Approach and Efficient Algorithm for Identifying Informative Genetic and Environmental Associations With Complex Phenotypes

AMBIENCE: A Novel Approach and Efficient Algorithm for Identifying Informative Genetic and Environmental Associations With Complex Phenotypes

A gene-based method for detecting gene–gene co-association in a case–control association study

A gene-based information gain method for detecting gene–gene interactions in case–control studies

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