VarCoPP is now also part of ORVAL, a newly published 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:


VarCoPP predicts the potential pathogenicity of variant combinations in gene pairs. It is based on digenic data present in DIDA and it was trained against variants from the 1000 Genomes Project. VarCoPP consists of an ensemble of 500 individual Random Forest predictors.
A variant combination can be either predicted as disease-causing (i.e. candidate or probably pathogenic) or neutral (i.e. probably neutral).

VarCoPP can be applied for Single Nucleotide Variants (SNVs) and small insertions/deletions from a single individual using the genome version GRCh37/hg19. It is highly recommended to perform beforehand an initial variant filtering procedure and generally restrict the analysis to variants from up to 150 genes.

For more information about the method and its usage, you can consult our About page. VarCoPP was developed in the Interuniversity Institute of Bioinformatics in Brussels, under the collaboration of Université libre de Bruxelles and Vrije Universiteit Brussel.

VarCoPP 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

We are currently working on a standalone version of VarCoPP. In the mean time we recommend using the online tool ORVAL , which integrates this predictor.

Contact us at:

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

Citing VarCoPP

If you are using VarCoPP for your analysis, you can cite the following manuscript:

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

ULB_equalsize.001 kopie 2  (IB)2  vub.001 
© 2018-2020 The Interuniversity Institute of Bioinformatics in Brussels | Contact: