Contributions of Rare Gene Variants to Familial and Sporadic FSGS
Notice bibliographique
Résumé
Significance Statement Despite many known monogenic causes of FSGS, single gene defects explain only 30% of cases. In this study, sequencing of 662 exomes from families with FSGS and 622 control exomes validated many known FSGS-causing genes. However, for some genes previously reported as FSGS related, they identified a number of purported “disease-causing” variants in controls at similar or higher frequencies. They also identified multiple additional candidate FSGS genes in which rare variants were more common among cases. Network analysis showed that their top-ranked genes were located significantly closer to known FSGS genes compared with a random gene set. These findings imply that genetic diagnosis in patients with FSGS is complicated by the nontrivial rate of variants in known FSGS genes among people without kidney disease. Background Over the past two decades, the importance of genetic factors in the development of FSGS has become increasingly clear. However, despite many known monogenic causes of FSGS, single gene defects explain only 30% of cases. Methods To investigate mutations underlying FSGS, we sequenced 662 whole exomes from individuals with sporadic or familial FSGS. After quality control, we analyzed the exome data from 363 unrelated family units with sporadic or familial FSGS and compared this to data from 363 ancestry-matched controls. We used rare variant burden tests to evaluate known disease-associated genes and potential new genes. Results We validated several FSGS-associated genes that show a marked enrichment of deleterious rare variants among the cases. However, for some genes previously reported as FSGS related, we identified rare variants at similar or higher frequencies in controls. After excluding such genes, 122 of 363 cases (33.6%) had rare variants in known disease-associated genes, but 30 of 363 controls (8.3%) also harbored rare variants that would be classified as “causal” if detected in cases; applying American College of Medical Genetics filtering guidelines (to reduce the rate of false-positive claims that a variant is disease related) yielded rates of 24.2% in cases and 5.5% in controls. Highly ranked new genes include SCAF1 , SETD2 , and LY9 . Network analysis showed that top-ranked new genes were located closer than a random set of genes to known FSGS genes. Conclusions Although our analysis validated many known FSGS-causing genes, we detected a nontrivial number of purported “disease-causing” variants in controls, implying that filtering is inadequate to allow clinical diagnosis and decision making. Genetic diagnosis in patients with FSGS is complicated by the nontrivial rate of variants in known FSGS genes among people without kidney disease.
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 | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».