par Gravel, Barbara 
;Renaux, Alexandre ;Papadimitriou, Sofia ;Smits, Guillaume ;Nowe, Ann ;Lenaerts, Tom
Référence Bioinformatics, 40, 4, btae184
Publication Publié, 2024-04
;Renaux, Alexandre ;Papadimitriou, Sofia ;Smits, Guillaume ;Nowe, Ann ;Lenaerts, Tom Référence Bioinformatics, 40, 4, btae184
Publication Publié, 2024-04
Article révisé par les pairs
| Résumé : | Motivation: Whole exome sequencing (WES) has emerged as a powerful tool for genetic research, enabling the collection of a tremendous amount of data about human genetic variation. However, properly identifying which variants are causative of a genetic disease remains an important challenge, often due to the number of variants that need to be screened. Expanding the screening to combinations of variants in two or more genes, as would be required under the oligogenic inheritance model, simply blows this problem out of proportion. Results: We present here the High-throughput oligogenic prioritizer (Hop), a novel prioritization method that uses direct oligogenic information at the variant, gene and gene pair level to detect digenic variant combinations in WES data. This method leverages information from a knowledge graph, together with specialized pathogenicity predictions in order to effectively rank variant combinations based on how likely they are to explain the patient’s phenotype. The performance of Hop is evaluated in cross-validation on 36 120 synthetic exomes for training and 14 280 additional synthetic exomes for independent testing. Whereas the known pathogenic variant combinations are found in the top 20 in approximately 60% of the cross-validation exomes, 71% are found in the same ranking range when considering the independent set. These results provide a significant improvement over alternative approaches that depend simply on a monogenic assessment of pathogenicity, including early attempts for digenic ranking using monogenic pathogenicity scores. |
