Complex statistical interactions in biology

Although each individual carries dozens of deleterious mutations, each of which should have a dramatic impact on our health, life carries on due to extensive genetic buffering. Therefore, interpreting genetic information requires the understanding of not only the impact of individual mutations but also their interactions. Genetic interactions occur when the combined impact of two mutations results in an unexpected phenotype, for example a positive interaction in the case of genetic buffering. Negative interactions are even more striking, such as synthetic lethality, where two individually mild mutations lead to cell death. Understanding and predicting (even higher order) genetic interactions is a key to better understand complex traits, missing heritability and genetic buffering in humans. In the talk we will overview the major sources of data and recently developed statistical methods to analyze and predict it. Our results enable us to better understand the emergence of biological function under both healthy and pathological conditions and directly contribute to improved disease module identification, drug target prediction, and drug combination design.