GCORE-sib: An efficient gene-gene interaction tool for genome-wide association studies based on discordant sib pairs

Genome-wide association studies (GWAS) are powerful tools for identifying pathogenic genes of complex diseases and revealing genetic structure of diseases. However, due to gene-to-gene interactions, only a part of the hereditary factors can be revealed. The meta-analysis based on GWAS can integrate gene expression data at multiple levels and reveal the complex relationship between genes. Therefore, we used meta-analysis to integrate GWAS data of sarcoma to establish complex networks and discuss their significant genes. Firstly, we established gene interaction networks based on the data of different subtypes of sarcoma to analyze the node centralities of genes. Secondly, we calculated the significant score of each gene according to the Staged Significant Gene Network Algorithm (SSGNA). Then, we obtained the critical gene set HYC of sarcoma by ranking the scores, and then combined Gene Ontology enrichment analysis and protein network analysis to further screen it. Finally, the critical core gene set Hcore containing 47 genes was obtained and validated by GEPIA analysis. Our method has certain generalization performance to the study of complex diseases with prior knowledge and it is a useful supplement to genome-wide association studies.

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Incorporating biological knowledge in the search for gene × gene interaction in genome-wide association studies

BMC Proceedings ◽

10.1186/1753-6561-3-s7-s81 ◽

2009 ◽

Vol 3 (S7) ◽

Cited By ~ 3

Author(s):

Alisa K Manning ◽

Julius Suh Ngwa ◽

Audrey E Hendricks ◽

Ching-Ti Liu ◽

Andrew D Johnson ◽

...

Keyword(s):

Association Studies ◽

Gene Interaction ◽

Genome Wide Association ◽

Biological Knowledge ◽

Genome Wide Association Studies ◽

Genome Wide

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Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.801113 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yingjie Guo ◽

Chenxi Wu ◽

Zhian Yuan ◽

Yansu Wang ◽

Zhen Liang ◽

...

Keyword(s):

Association Studies ◽

Real Data ◽

Gene Interaction ◽

Genome Wide Association ◽

Superior Performance ◽

Gene Interactions ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Genome Wide ◽

The Difference

Among the myriad of statistical methods that identify gene–gene interactions in the realm of qualitative genome-wide association studies, gene-based interactions are not only powerful statistically, but also they are interpretable biologically. However, they have limited statistical detection by making assumptions on the association between traits and single nucleotide polymorphisms. Thus, a gene-based method (GGInt-XGBoost) originated from XGBoost is proposed in this article. Assuming that log odds ratio of disease traits satisfies the additive relationship if the pair of genes had no interactions, the difference in error between the XGBoost model with and without additive constraint could indicate gene–gene interaction; we then used a permutation-based statistical test to assess this difference and to provide a statistical p-value to represent the significance of the interaction. Experimental results on both simulation and real data showed that our approach had superior performance than previous experiments to detect gene–gene interactions.

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Two-Stage Procedures for the Identification of Gene × Environment and Gene × Gene Interactions in Genome-Wide Association Studies

Statistical Approaches to Gene X Environment Interactions for Complex Phenotypes ◽

10.7551/mitpress/9780262034685.003.0002 ◽

2016 ◽

Author(s):

Charles Kooperberg ◽

James Y. Dai ◽

Li Hsu

Keyword(s):

Association Studies ◽

Gene Interaction ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Two Stage ◽

Genetic Studies ◽

Gene Environment ◽

Genome Wide ◽

Sequencing Studies ◽

Generation Sequencing

Genome-wide association studies and next generation sequencing studies offer us an unprecedented opportunity to study the genetic etiology of diseases and other traits. Over the last few years, many replicated associations between SNPs and traits have been published. It is of particular interest to identify how genes may interact with environmental factors and other genes. In this chapter, we show that a two-stage approach, where in the first stage SNPs are screened for their potential to be involved in interactions, and interactions are then tested only among SNPs that pass the screening can greatly enhance power for detecting gene-environment and gene-gene interaction in large genetic studies compared to the tests without screening.

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Post Genome-Wide Association Studies of Novel Genes Associated with Type 2 Diabetes Show Gene-Gene Interaction and High Predictive Value

PLoS ONE ◽

10.1371/journal.pone.0002031 ◽

2008 ◽

Vol 3 (5) ◽

pp. e2031 ◽

Cited By ~ 107

Author(s):

Stéphane Cauchi ◽

David Meyre ◽

Emmanuelle Durand ◽

Christine Proença ◽

Michel Marre ◽

...

Keyword(s):

Type 2 Diabetes ◽

Predictive Value ◽

Association Studies ◽

Gene Interaction ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Novel Genes ◽

Genome Wide

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Tuning Hyperparameters for Gene Interaction Models in Genome-Wide Association Studies

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-319-70139-4_80 ◽

2017 ◽

pp. 791-801 ◽

Cited By ~ 2

Author(s):

Suneetha Uppu ◽

Aneesh Krishna

Keyword(s):

Association Studies ◽

Gene Interaction ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Interaction Models ◽

Genome Wide

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Novel Methods for Epistasis Detection in Genome-Wide Association Studies

10.1101/442749 ◽

2018 ◽

Cited By ~ 2

Author(s):

Lotfi Slim ◽

Clément Chatelain ◽

Chloé-Agathe Azencott ◽

Jean-Philippe Vert

Keyword(s):

Randomized Clinical Trials ◽

Association Studies ◽

Real Data ◽

Gene Interaction ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

New Approach ◽

Pairwise Interactions ◽

Genome Wide ◽

Or Gene

More and more genome-wide association studies are being designed to uncover the full genetic basis of common diseases. Nonetheless, the resulting loci are often insufficient to fully recover the observed heritability. Epistasis, or gene-gene interaction, is one of many hypotheses put forward to explain this missing heritability. In the present work, we propose epiGWAS, a new approach for epistasis detection that identifies interactions between a target SNP and the rest of the genome. This contrasts with the classical strategy of epistasis detection through exhaustive pairwise SNP testing. We draw inspiration from causal inference in randomized clinical trials, which allows us to take into account linkage disequilibrium. EpiGWAS encompasses several methods, which we compare to state-of-the-art techniques for epistasis detection on simulated and real data. The promising results demonstrate empirically the benefits of EpiGWAS to identify pairwise interactions.

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