VarCoPP2.0 is now also part of ORVAL, a web-platform for the exploration of potential oligogenic disease signatures. ORVAL incorporates automated pre-filtering options for your VCF, variant combination pathogenicity predictions, predicted pathogenic gene networks and several PPI, pathway and prediction annotations. Explore it here:


VarCoPP2.0 predicts the potential pathogenicity of variant combinations in gene pairs. It is based on digenic data present in OLIDA and it was trained against variants from the 1000 Genomes Project. VarCoPP2.0 is a Balanced Random Forest predictor consisting of 400 decision trees.
A variant combination can be either predicted as disease-causing (i.e. candidate or probably pathogenic) or neutral (i.e. probably neutral).

VarCoPP2.0 can be applied for Single Nucleotide Variants (SNVs) and small insertions/deletions from a single individual. It is highly recommended to perform beforehand an initial variant filtering procedure and generally restrict the analysis to variants from up to 150 genes.

VarCoPP and VarCoPP2.0 were developed in the Interuniversity Institute of Bioinformatics in Brussels, under the collaboration of Université libre de Bruxelles and Vrije Universiteit Brussel.

VarCoPP2.0 is a machine-learning method that makes predictions based on probabilities.
It is provided for research purposes only and the pathogenicity predictions should be subject to further research and clinical investigation.
It is not in any way intended to be used as a substitute for professional medical advice, diagnosis, treatment or care.

Submit your variants

You can use VarCoPP2.0 through the online tool ORVAL, which integrates this predictor.

Contact us at:

For any question, troubleshooting or remark we will come back to you as soon as possible.

Citing VarCoPP

If you are using VarCoPP2.0 for your analysis, you can cite the following manuscripts:

Versbraegen N., Gravel B., Nachtegael C., Renaux A., Verkinderen E., Nowé A., Lenaerts T., Papadimitriou S. (2023) Faster and more accurate pathogenic combination predictions with VarCoPP2.0. BMC Bioinformatics. 24:179 DOI:

Papadimitriou S., Gazzo A., Versbraegen N., Nachtegael C., Aerts J., Moreau Y., Van Dooren S., Nowé A., Smits G., Lenaerts T. (2019) Predicting disease-causing variant combinations. Proceedings of the National Academy of Sciences. DOI:

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