Towards oligogenic disease prediction with ORVAL: a web-platform to uncover pathogenic variant combinations

Research output: Unpublished contribution to conferencePoster

Abstract

The vast amount of DNA sequencing data collected from large patient cohorts have helped in identifying a wide number of disease related mutations relevant for diagnosis and therapy. Although these approaches have greatly improved our understanding of Mendelian cases, many difficulties remain in identifying the causes of a large amount of human genetic diseases due to phenotypic variability, disease heterogeneity and incomplete penetrance. These difficulties indicate the presence of more intricate genetic models involving interactions between several different variants and genes. Resources and methods need to be improved to also deal with genetic models ranging from digenic to oligogenic, where a combination of causative variants is distributed among two or a small amount of genes, respectively. ORVAL (the Oligogenic Resource for Variant AnaLysis) tries to solve this problem by generating networks of pathogenic variant combinations in gene pairs, as opposed to isolated variants in unique genes. This online platform is able to annotate and filter submitted genetic variant data and applies new machine learning-based methods for combinatorial variant pathogenicity prediction and for classifying the digenic effect (as True Digenic, Monogenic with a Modifier variant or Dual Diagnosis case). It offers several analyses as a result, such as an interactive pathogenicity network, a ranking of pathogenic gene pairs and detailed information about individual predictions such as the digenic effect, pathogenicity scores, the predictor's feature contributions and multiple bioinformatics cross-references. It also proposes exploratory tools and visualisations to examine potential oligogenic signatures in the context of their pathways, protein-protein interactions and cellular locations, to highlight the relevant molecular and biological patterns underlying oligogenic diseases. ORVAL offers an innovative web-platform that integrates different resources to predict candidate disease-causing variant combinations and explore the results within their biological context. This tool provides a new essential step towards helping clinicians and researchers to improve their oligogenic investigations by formulating new hypotheses to study more complex genetic diseases. ORVAL is available at https://orval.ibsquare.be.
Original languageEnglish
DOIs
Publication statusPublished - 23 Jul 2019
Event27th conference on Intelligent Systems for Molecular Biology and 18th European Conference on Computational Biology - Basel Congres Center, Basel, Switzerland
Duration: 21 Jul 201925 Jul 2019
https://www.iscb.org/ismbeccb2019

Conference

Conference27th conference on Intelligent Systems for Molecular Biology and 18th European Conference on Computational Biology
Abbreviated titleISMB/ECCB 2019
CountrySwitzerland
CityBasel
Period21/07/1925/07/19
Internet address

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