DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders.

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Version: Author's accepted manuscript
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ID Serval
serval:BIB_5037815084FE
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders.
Périodique
American journal of human genetics
Auteur(s)
Quinodoz M., Royer-Bertrand B., Cisarova K., Di Gioia S.A., Superti-Furga A., Rivolta C.
ISSN
1537-6605 (Electronic)
ISSN-L
0002-9297
Statut éditorial
Publié
Date de publication
05/10/2017
Peer-reviewed
Oui
Volume
101
Numéro
4
Pages
623-629
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Résumé
In contrast to recessive conditions with biallelic inheritance, identification of dominant (monoallelic) mutations for Mendelian disorders is more difficult, because of the abundance of benign heterozygous variants that act as massive background noise (typically, in a 400:1 excess ratio). To reduce this overflow of false positives in next-generation sequencing (NGS) screens, we developed DOMINO, a tool assessing the likelihood for a gene to harbor dominant changes. Unlike commonly-used predictors of pathogenicity, DOMINO takes into consideration features that are the properties of genes, rather than of variants. It uses a machine-learning approach to extract discriminant information from a broad array of features (N = 432), including: genomic data, intra-, and interspecies conservation, gene expression, protein-protein interactions, protein structure, etc. DOMINO's iterative architecture includes a training process on 985 genes with well-established inheritance patterns for Mendelian conditions, and repeated cross-validation that optimizes its discriminant power. When validated on 99 newly-discovered genes with pathogenic mutations, the algorithm displays an excellent final performance, with an area under the curve (AUC) of 0.92. Furthermore, unsupervised analysis by DOMINO of real sets of NGS data from individuals with intellectual disability or epilepsy correctly recognizes known genes and predicts 9 new candidates, with very high confidence. In summary, DOMINO is a robust and reliable tool that can infer dominance of candidate genes with high sensitivity and specificity, making it a useful complement to any NGS pipeline dealing with the analysis of the morbid human genome.

Mots-clé
Databases, Genetic, Genes, Dominant, Genetic Diseases, Inborn/genetics, Genome, Human, Genomics, High-Throughput Nucleotide Sequencing/methods, Humans, Machine Learning, Mutation, Software
Pubmed
Web of science
Création de la notice
10/10/2017 15:32
Dernière modification de la notice
20/08/2019 15:06
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