Neural networks for modeling gene-gene interactions in association studies

Günther, Frauke; Wawro, Nina; Bammann, Karin

doi:10.1186/1471-2156-10-87

Cited by 28 publications

(22 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Currently, many researchers are proposing other methods, such as multifactor dimensionality reduction (Hahn et al, 2003), which is a powerful alternative to traditional parametric statistics such as logistic regression and may process the higher order data better (Wu et al, 2011). The neural network method has unique advantages in processing the interaction between genes and environment (Günther et al, 2009). In particular, the genome-wide association study of susceptibility genes for complex diseases is currently a hot research area, and many new breakthroughs were obtained in the area (Elbers et al, 2009;Roukos, 2009).…”

Section: Discussionmentioning

confidence: 99%

Application of Crossover Analysis-logistic Regression in the Assessment of Gene- environmental Interactions for Colorectal Cancer

Yang²,

Zhang³

et al. 2012

Asian Pacific Journal of Cancer Prevention

View full text Add to dashboard Cite

Background: Analysis of gene-gene and gene-environment interactions for complex multifactorial human disease faces challenges regarding statistical methodology. One major difficulty is partly due to the limitations of parametric-statistical methods for detection of gene effects that are dependent solely or partially on interactions with other genes or environmental exposures. Based on our previous case-control study in Chongqing of China, we have found increased risk of colorectal cancer exists in individuals carrying a novel homozygous TT at locus rs1329149 and known homozygous AA at locus rs671. Methods: In this study, we proposed statistical methodcrossover analysis in combination with logistic regression model, to further analyze our data and focus on assessing gene-environmental interactions for colorectal cancer. Results: The results of the crossover analysis showed that there are possible multiplicative interactions between loci rs671 and rs1329149 with alcohol consumption. Multifactorial logistic regression analysis also validated that loci rs671 and rs1329149 both exhibited a multiplicative interaction with alcohol consumption. Moreover, we also found additive interactions between any pair of two factors (among the four risk factors: gene loci rs671, rs1329149, age and alcohol consumption) through the crossover analysis, which was not evident on logistic regression. Conclusions: In conclusion, the method based on crossover analysis-logistic regression is successful in assessing additive and multiplicative gene-environment interactions, and in revealing synergistic effects of gene loci rs671 and rs1329149 with alcohol consumption in the pathogenesis and development of colorectal cancer.

show abstract

Section: Discussionmentioning

confidence: 99%

Application of Crossover Analysis-logistic Regression in the Assessment of Gene- environmental Interactions for Colorectal Cancer

Yang²,

Zhang³

et al. 2012

Asian Pacific Journal of Cancer Prevention

View full text Add to dashboard Cite

show abstract

“…The relationship is defined on a population level, i.e. the penetrance function F :{0,1,2} × [0,100] → [0,1] with F ( g u ) = P ( Y = 1| G = g U = u ), where Y ∈ {0,1} denotes the case-control status, G ∈ {0,1,2} the genotype, and U ∈ [0,100] the environmental factor, only holds in the corresponding underlying population and has to be converted to f ( g u ) if a case-control data set is analyzed [10]. …”

Section: Methodsmentioning

confidence: 99%

Artificial neural networks modeling gene-environment interaction

2012

Self Cite

View full text Add to dashboard Cite

BackgroundGene-environment interactions play an important role in the etiological pathway of complex diseases. An appropriate statistical method for handling a wide variety of complex situations involving interactions between variables is still lacking, especially when continuous variables are involved. The aim of this paper is to explore the ability of neural networks to model different structures of gene-environment interactions. A simulation study is set up to compare neural networks with standard logistic regression models. Eight different structures of gene-environment interactions are investigated. These structures are characterized by penetrance functions that are based on sigmoid functions or on combinations of linear and non-linear effects of a continuous environmental factor and a genetic factor with main effect or with a masking effect only.ResultsIn our simulation study, neural networks are more successful in modeling gene-environment interactions than logistic regression models. This outperfomance is especially pronounced when modeling sigmoid penetrance functions, when distinguishing between linear and nonlinear components, and when modeling masking effects of the genetic factor.ConclusionOur study shows that neural networks are a promising approach for analyzing gene-environment interactions. Especially, if no prior knowledge of the correct nature of the relationship between co-variables and response variable is present, neural networks provide a valuable alternative to regression methods that are limited to the analysis of linearly separable data.

show abstract

“…Günther et al [17] had used neural network to model various types of two-locus disease model. To achieve this, six neural networks (feed-forward multilayer perceptron) with five hidden neurons were carried out with 100 datasets that are generated for each of six two-locus disease models.…”

Section: Neural Networkmentioning

confidence: 99%

A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

Koo

Liew

Mohamad

et al. 2013

BioMed Research International

View full text Add to dashboard Cite

Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

show abstract

Neural networks for modeling gene-gene interactions in association studies

Cited by 28 publications

References 47 publications

Application of Crossover Analysis-logistic Regression in the Assessment of Gene- environmental Interactions for Colorectal Cancer

Application of Crossover Analysis-logistic Regression in the Assessment of Gene- environmental Interactions for Colorectal Cancer

Artificial neural networks modeling gene-environment interaction

A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

Contact Info

Product

Resources

About