P509: Resolution of variants of uncertain significance by RNA sequencing
Notice bibliographique
Résumé
Introduction: The clinical implementation of next-generation sequencing has revolutionized genetic diagnostics of rare disease, yet many exome and genome analyses remain inconclusive.This is due in part to challenges with variant prioritization and interpretation, particularly for those variants within intronic regions.The accuracy of in silico tools to predict the effect of a variant on RNA expression and/or splicing outside the canonical splice site remains low and therefore these variants are almost always variants of uncertain significance (VUSs) on clinical reporting, if reported at all.Methods: Following inconclusive genetic testing, families affected with undiagnosed rare disease consented to enrollment in the Care4Rare Canada research program.RNA sequencing (RNA-Seq) analyses were performed using appropriate tissue types from affected individuals.Control samples were obtained internally as well as from The Genotype-Tissue Expression (GTEx) project.Results: RNA-Seq has been useful for resolution of VUSs in a number of projects we have analyzed.For example, in one affected individual, research re-analysis of clinical trio exome sequencing (ES) data revealed compound heterozygous intronic VUSs in TRAPPC12, which were not included in the clinical report and was in keeping with her presentation.RNA-Seq studies revealed both variants resulted in exon skipping, events which were either absent or seen rarely in the control dataset, and was able to reclassify both of these intronic variants as likely pathogenic (LP).In a second study, an affected individual with suspected short-rib thoracic dysplasia 3 with or without polydactyly (SRTD3) had clinical genetic testing which identified a likely pathogenic frameshift variant and an intronic VUS in DYNC2H1 in trans.Research RNA-Seq investigations revealed significantly decreased DYNC2H1 gene expression in the proband as well as a novel splice junction as a result of the intronic variant, which again allowed reclassification of the variant to LP.Finally, in two siblings with suspected Joubert syndrome, RNA-Seq was able to identify their second causative variant.This variant was intractable to detection by routine exome and genome sequencing analyses as it is a 57 bp deletion in a repetitive intronic region, but was detectable by RNA-Seq as it leads to novel intron inclusion in the resulting transcript.Conclusion: Our studies highlight the benefits of RNA-Seq in obtaining a diagnosis for patients with rare disease where additional data is required after inconclusive clinical testing, especially for variants with a potential mRNA splice impact.While we recognize that non-coding variants play a role in disease, we currently lack robust tools to accurately interpret and classify them and most are either reported as VUSs or not reported (depending on laboratory policy and testing scenario).RNA-Seq can assess the impact of these non-coding variants via their effect on gene expression, RNA stability and RNA processing and therefore could be considered as a follow up clinical test in the future.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».