Predicting disease-causing variant combinations

Significance Directly assessing the pathogenicity of variant combinations in multiple genes was until now difficult. Nonetheless, this type of assessment can provide important benefits in identifying the genetic causes of rare diseases. The work presented in this paper aims to resolve this problem by presenting a machine-learning method able to predict the pathogenicity of variant combinations in gene pairs, based on pathogenic data. We demonstrate the high accuracy of this method and its effective capacity to identify novel instances. The method’s decision-making process is also made explicit, a contribution that is useful for clinical interpretation. This pioneering work will lead to toolboxes for geneticists and clinicians that can aid them in counselling their patients more effectively. Notwithstanding important advances in the context of single-variant pathogenicity identification, novel breakthroughs in discerning the origins of many rare diseases require methods able to identify more complex genetic models. We present here the Variant Combinations Pathogenicity Predictor (VarCoPP), a machine-learning approach that identifies pathogenic variant combinations in gene pairs (called digenic or bilocus variant combinations). We show that the results produced by this method are highly accurate and precise, an efficacy that is endorsed when validating the method on recently published independent disease-causing data. Confidence labels of 95% and 99% are identified, representing the probability of a bilocus combination being a true pathogenic result, providing geneticists with rational markers to evaluate the most relevant pathogenic combinations and limit the search space and time. Finally, the VarCoPP has been designed to act as an interpretable method that can provide explanations on why a bilocus combination is predicted as pathogenic and which biological information is important for that prediction. This work provides an important step toward the genetic understanding of rare diseases, paving the way to clinical knowledge and improved patient care.