The article discusses the detection of gene-gene interactions in human diseases, emphasizing the need to go beyond single-locus analysis to uncover complex genetic mechanisms. Following the success of genome-wide association studies (GWAS), researchers are increasingly interested in identifying interactions between genetic loci that may contribute to disease. These interactions can provide insights into the biological pathways underlying diseases. However, detecting such interactions is challenging due to the complexity of genetic and environmental factors involved. The article reviews various methodological approaches and software tools used to detect gene-gene interactions. It highlights the importance of considering interactions when analyzing genetic data, as they can increase the power to detect associations and reveal biological insights. Different statistical methods, including logistic regression, case-only analysis, and Bayesian approaches, are discussed. The article also addresses the issue of biological interpretation of statistical interactions, noting that not all statistical interactions necessarily reflect true biological interactions. The article emphasizes the computational challenges of testing for higher-order interactions, which require large datasets and complex algorithms. It also discusses data-mining and machine learning approaches, such as random forests and multifactor dimensionality reduction (MDR), which can help identify interactions in large-scale genetic studies. Additionally, it covers filtering techniques like ReliefF and Bayesian model selection methods, which can help reduce the number of loci to be tested. The article concludes by noting that while many methods exist for detecting gene-gene interactions, the biological interpretation of these interactions remains a challenge. The success of GWAS in identifying single-locus associations has demonstrated the importance of large sample sizes in detecting genetic effects. However, the detection of gene-gene interactions requires careful consideration of statistical and biological factors, as well as the use of appropriate computational tools. The article calls for further research to improve the detection and interpretation of gene-gene interactions in human diseases.The article discusses the detection of gene-gene interactions in human diseases, emphasizing the need to go beyond single-locus analysis to uncover complex genetic mechanisms. Following the success of genome-wide association studies (GWAS), researchers are increasingly interested in identifying interactions between genetic loci that may contribute to disease. These interactions can provide insights into the biological pathways underlying diseases. However, detecting such interactions is challenging due to the complexity of genetic and environmental factors involved. The article reviews various methodological approaches and software tools used to detect gene-gene interactions. It highlights the importance of considering interactions when analyzing genetic data, as they can increase the power to detect associations and reveal biological insights. Different statistical methods, including logistic regression, case-only analysis, and Bayesian approaches, are discussed. The article also addresses the issue of biological interpretation of statistical interactions, noting that not all statistical interactions necessarily reflect true biological interactions. The article emphasizes the computational challenges of testing for higher-order interactions, which require large datasets and complex algorithms. It also discusses data-mining and machine learning approaches, such as random forests and multifactor dimensionality reduction (MDR), which can help identify interactions in large-scale genetic studies. Additionally, it covers filtering techniques like ReliefF and Bayesian model selection methods, which can help reduce the number of loci to be tested. The article concludes by noting that while many methods exist for detecting gene-gene interactions, the biological interpretation of these interactions remains a challenge. The success of GWAS in identifying single-locus associations has demonstrated the importance of large sample sizes in detecting genetic effects. However, the detection of gene-gene interactions requires careful consideration of statistical and biological factors, as well as the use of appropriate computational tools. The article calls for further research to improve the detection and interpretation of gene-gene interactions in human diseases.