Predicting disease-causing variant combinations

Sofia Papadimitriou, Andrea Gazzo, Nassim Versbraegen, Charlotte Nachtegael, Jan Aerts, Yves Moreau, Sonia Van Dooren, Ann Nowé, Guillaume Smits, Tom Lenaerts

Onderzoeksoutput: Articlepeer review

51 Citaten (Scopus)
64 Downloads (Pure)


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.

Originele taal-2English
Pagina's (van-tot)11878-11887
Aantal pagina's10
TijdschriftProceedings of the National Academy of Sciences of the United States of America
Nummer van het tijdschrift24
StatusPublished - 11 jun 2019

Bibliografische nota

Copyright © 2019 the Author(s). Published by PNAS.


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