Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 116, Issue 24, Pages 11878-11887Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1815601116
Keywords
pathogenicity; bilocus combination; variants; prediction; oligogenic
Categories
Funding
- Actions de Recherche Concertees project Deciphering Oligo-and Polygenic Genetic Architecture in Brain Developmental Disorders
- European Regional Development Fund (ERDF)
- Brussels-Capital Region-Innoviris through the ERDF-2020 project ICITY-RDI.BRU [27.002.53.01.4524]
- Fonds de la Recherche Scientifique-Fonds National de la Recherche Scientifique Fund for Research Training in Industry and Agriculture
- Vrije Universiteit Brussel PhD funding
- Vrije Universiteit Brussel, Reproduction and Genetics and Regenerative Medicine Cluster, Reproduction, and Genetics Research Group
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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.
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