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DESTinT: DEtecting STastistical INteractions in Complex Traits
Model organisms indicate that heritability attributed to gene-gene interactions may be as high as 80% for certain traits. There is no reason to assume that this would not be the case for humans. The increased complexity of human biology compared to the biology of model organisms requires investing in sophisticated epistasis detection methods, creating consensus criteria for their evaluation, and bringing awareness about pros and cons of each method. Large-scale epistasis studies can give new clues to systems-level genetic mechanisms and a better understanding of the underling biology of human complex disease traits. Though many novel methods have been proposed to carry out such studies, so far only a few of them have demonstrated replicable results. Recently, we published a minimal protocol for large scale epistasis screening. This protocol is based on our knowledge to date about epistasis mapping. However, despite the efforts to improve the detection rate of genetic interactions, to integrate (prior) omics-based information into the analysis protocol, and despite attempts to reconcile statistical epistasis with biological epistasis, several problems remain unresolved in a satisfactory way. This project aims to tackle some of these problems: the problem of confounding factors such as those arising from shared genetic ancestry, and the problem of model-dependent meta-analysis strategies used in epistasis research. In addition, this project aims to develop a gene-centric approach to epistasis analysis, which will increase interpretability and replicability. Only when epistasis detection becomes routine practice, we will be able to show the impact of epistasis on personalized medicine, disease risk prediction, and evolutionary genetics.

K. Van Steen's project as part of a FRFS WELBIO (2015) call.