GWGGI: software for genome-wide gene-gene interaction analysis

Wei, Colin; Lu, Qing

doi:10.1186/s12863-014-0101-z

Cited by 8 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of the LRMW method also identified a four‐locus interaction among SNPs for type II diabetes, which was replicated in an independent data set (Lu et al, ). The method has been implemented in a C++ software package, referred to as genome‐wide gene‐gene interaction analyses (Wei & Lu, ). In this article, we extend the LRMW method in the context of mother‐offspring pair data, aiming to detect joint action among maternal variants, fetal variants, and maternal environmental exposures.…”

Section: Methodsmentioning

confidence: 99%

A Three-Way Interaction among Maternal and Fetal Variants Contributing to Congenital Heart Defects

Wei

et al. 2015

Annals of Human Genetics

Self Cite

View full text Add to dashboard Cite

SUMMARY Congenital heart defects (CHDs) develop through a complex interplay between genetic variants, epigenetic modifications and maternal environmental exposures. Genetic studies of CHDs have commonly tested single genetic variants for association with CHDs. Less attention has been given to complex gene-by-gene and gene-by-environment interactions. In this study, we applied a recently developed likelihood-ratio Mann-Whitney (LRMW) method to detect joint actions among maternal variants, fetal variants and maternal environmental exposures, allowing for high-order statistical interactions. All subjects are participants from the National Birth Defect Prevention Study, including 623 mother-offspring pairs with CHD-affected pregnancies and 875 mother-offspring pairs with unaffected pregnancies. Each individual has 872 single nucleotide polymorphisms encoding for critical enzymes in the homocysteine, folate, and transsulfuration pathways. By using the LRMW method, three variants (fetal rs625879, maternal rs2169650 and maternal rs8177441) were identified with a joint association to CHD risk (nominal P-value=1.13e-07). These 3 variants are located within gene BHMT2, GSTP1 and GPX3, respectively. Further examination indicated that maternal SNP rs2169650 may interact with both fetal SNP rs625879 and maternal SNP rs8177441. Our findings suggest that the risk of CHD may be influenced by both the intra-generational interaction within the maternal genome and the inter-generational interaction between maternal and fetal genomes.

show abstract

Section: Methodsmentioning

confidence: 99%

A Three-Way Interaction among Maternal and Fetal Variants Contributing to Congenital Heart Defects

Wei

et al. 2015

Annals of Human Genetics

Self Cite

View full text Add to dashboard Cite

show abstract

“…More recently, another tree assembling software program was developed: GWGGI (Wei and Lu, 2014 ). It differs from the previous methods in two points.…”

Section: Non-exhaustive Searches Enhanced By Artificial Intelligencementioning

confidence: 99%

A survey about methods dedicated to epistasis detection

et al. 2015

View full text Add to dashboard Cite

During the past decade, findings of genome-wide association studies (GWAS) improved our knowledge and understanding of disease genetics. To date, thousands of SNPs have been associated with diseases and other complex traits. Statistical analysis typically looks for association between a phenotype and a SNP taken individually via single-locus tests. However, geneticists admit this is an oversimplified approach to tackle the complexity of underlying biological mechanisms. Interaction between SNPs, namely epistasis, must be considered. Unfortunately, epistasis detection gives rise to analytic challenges since analyzing every SNP combination is at present impractical at a genome-wide scale. In this review, we will present the main strategies recently proposed to detect epistatic interactions, along with their operating principle. Some of these methods are exhaustive, such as multifactor dimensionality reduction, likelihood ratio-based tests or receiver operating characteristic curve analysis; some are non-exhaustive, such as machine learning techniques (random forests, Bayesian networks) or combinatorial optimization approaches (ant colony optimization, computational evolution system).

show abstract

“…Wang et al [ 25 ] took advantage of Markov chain Monte Carlo search and a Bayesian computational method to detect high-order interactions on each chromosome or filtered SNPs. Some tree-based approaches have been proposed to search disease-associated joint associations with the consideration of high-order interactions [ 26 ]. Lu et al [ 27 ] combined a likelihood ratio-based Mann–Whitney test and forward selection algorithm to search interactions among SNPs with moderate marginal effects.…”

Section: Introductionmentioning

confidence: 99%

HiSeeker: Detecting High-Order SNP Interactions Based on Pairwise SNP Combinations

Liu

Jiang

et al. 2017

Genes

View full text Add to dashboard Cite

Detecting single nucleotide polymorphisms’ (SNPs) interaction is one of the most popular approaches for explaining the missing heritability of common complex diseases in genome-wide association studies. Many methods have been proposed for SNP interaction detection, but most of them only focus on pairwise interactions and ignore high-order ones, which may also contribute to complex traits. Existing methods for high-order interaction detection can hardly handle genome-wide data and suffer from low detection power, due to the exponential growth of search space. In this paper, we proposed a flexible two-stage approach (called HiSeeker) to detect high-order interactions. In the screening stage, HiSeeker employs the chi-squared test and logistic regression model to efficiently obtain candidate pairwise combinations, which have intermediate or significant associations with the phenotype for interaction detection. In the search stage, two different strategies (exhaustive search and ant colony optimization-based search) are utilized to detect high-order interactions from candidate combinations. The experimental results on simulated datasets demonstrate that HiSeeker can more efficiently and effectively detect high-order interactions than related representative algorithms. On two real case-control datasets, HiSeeker also detects several significant high-order interactions, whose individual SNPs and pairwise interactions have no strong main effects or pairwise interaction effects, and these high-order interactions can hardly be identified by related algorithms.

show abstract

GWGGI: software for genome-wide gene-gene interaction analysis

Cited by 8 publications

References 8 publications

A Three-Way Interaction among Maternal and Fetal Variants Contributing to Congenital Heart Defects

A Three-Way Interaction among Maternal and Fetal Variants Contributing to Congenital Heart Defects

A survey about methods dedicated to epistasis detection

HiSeeker: Detecting High-Order SNP Interactions Based on Pairwise SNP Combinations

Contact Info

Product

Resources

About