ORVAL : a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations

Alexandre Renaux, Sofia Papadimitriou, Nassim Versbraegen, Charlotte Nachtegael, Simon Boutry, Ann Nowé, Guillaume Smits, Tom Lenaerts

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)
56 Downloads (Pure)


A tremendous amount of DNA sequencing data is being produced around the world with the ambition to capture in more detail the mechanisms underlying human diseases. While numerous bioinformatics tools exist that allow the discovery of causal variants in Mendelian diseases, little to no support is provided to do the same for variant combinations, an essential task for the discovery of the causes of oligogenic diseases. ORVAL (the Oligogenic Resource for Variant AnaLysis), which is presented here, provides an answer to this problem by focusing on generating networks of candidate pathogenic variant combinations in gene pairs, as opposed to isolated variants in unique genes. This online platform integrates innovative machine learning methods for combinatorial variant pathogenicity prediction with visualization techniques, offering several interactive and exploratory tools, such as pathogenic gene and protein interaction networks, a ranking of pathogenic gene pairs, as well as visual mappings of the cellular location and pathway information. ORVAL is the first web-based exploration platform dedicated to identifying networks of candidate pathogenic variant combinations with the sole ambition to help in uncovering oligogenic causes for patients that cannot rely on the classical disease analysis tools. ORVAL is available at https://orval.ibsquare.be.

Original languageEnglish
Article numbergkz437
Pages (from-to)W93–W98
Number of pages6
JournalNucleic Acids Research
Issue numberW1
Publication statusPublished - 31 May 2019

Bibliographical note

© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.


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