Dear Colleagues,

With the rapid development of genotyping technologies and exponential increase in computational power, we are now able to leverage the wealth of genetic data to test millions of genetic variations for their associations with complex traits and diseases. In the past decade, we have identified hundreds of genetic variations associated with a variety of human traits and diseases, however, with very limited increments in disease risks. Such a problem has led researchers to search for the explanations for the ``missing heritability''.  Many agree that the limited heritability found by initial studies is very likely a result of the overly simplified assumption on the genetic architecture of complex human traits and diseases and the constraints of most commonly used one-gene-at-a-time methodology.

Most genetic association analyses adopt univariate methods, where individual genetic factors are evaluated on the trait/disease association separately. However, many human traits and diseases, such as cancers and diabetes, are more plausibly due to the interactions among multiple genetic factors, i.e., epistasis. Such an interaction effect has not yet been fully taken into account in current research.

The research field calls for innovative and sophisticated computational methodologies that embrace the complexity of the genetic architecture of complex traits and diseases rather than ignoring it. These new methodologies should disrupt the common and simple assumptions on complex traits and diseases, and use intelligent heuristic search or modeling strategies to address the high dimensional gene-gene interactions.

In this special issue, we would like to feature a series of novel computational methods, especially in machine learning and complex network modeling, that are capable of detecting and quantifying the multi-variant gene-gene interactions associated with complex traits and diseases. We welcome any original articles relating to, but not limited to, the topics described herein.

Ting Hu, PhD
Guest Editor

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• Gene-gene interaction
• Epistasis
• Machine learning
• Network modeling
• Genetic association studies