MétaCan
Menu
← Retour à la cohorte
Enregistrement W2922069184 · doi:10.1182/blood-2018-99-115567

Analysis of the Frequency of Spontaneous, Functionally-Significant Mutations in Genes Associated with Platelet Disorders in >120,000 Healthy Individuals

2018· article· en· W2922069184 sur OpenAlexaboutno aff
Joseph H. Oved, Michele P. Lambert, M. Anna Kowalska, Mortimer Poncz, Konrad J. Karczewski

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiquePlatelet Disorders and Treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBiologyGeneticsPhenotypePopulationGenePlatelet disorderExomeExome sequencingLoss functionContext (archaeology)PlateletImmunologyMedicine

Résumé

récupéré en direct d'OpenAlex

Abstract Inherited platelet disorders (IPD) are increasingly recognized as the cause of clinical bleeding. Advances in genomic technologies have identified a growing number of platelet-associated genes that are currently vetted with phenotypic correlates. These platelet-associated genes are a disparate group, including transcription or related nuclear factors, cytoskeletal proteins, surface receptors, intracellular proteins and granule forming proteins. At present the prevalence of each inherited platelet-associated disorder and the disorders in aggregate are not well defined. We leveraged the recent curating of 123,136 high quality exomes from a cross section of the general population in the form of the genome aggregation database (gnomAD) for analysis. We used the loss-of-function transcript effect estimator (LoFTEE) in conjunction with the gnomAD dataset to study loss of function (LoF) variants in genes of interest. With this set of predicted LoF variants, we generated a LoF frequency for each gene of interest taking into account whether the heterozygote or homozygote state is sufficient and/or necessary for clinical phenotype. These data are analyzed to determine whether each platelet-associated gene is relatively tolerant or intolerant to LoF mutations in the context of their clinical phenotypes. By this analysis, we found approximately 800 novel LoF variants in platelet-related genes in this population of >120,000 individuals. Affected genes known to cause disease phenotype in the heterozygous state (n=33) accounted for 27% of the mutations analyzed. With these data, we calculated the frequency of IPD in the general population secondary to LoF mutations and estimated the relative impact of dominant versus recessive cases of IPD. We demonstrate that the majority of manifest cases of IPD will be due to the dominantly inherited, haploinsufficient IPDs. The transcription factor gene subset (9 of the IPD associated genes) was the most intolerant to LoF variants based on ratio of observed vs. expected number of variants (pLI measurement). Interestingly, the severity of the platelet dysfunction and resultant bleeding from LoF mutations in this subset of genes is not directly related to their intolerance of these mutations. For instance, heterozygous LoF of RUNX1 result in a mild-moderate bleeding disorder; however, a pLI of 0.819 indicates this gene is moderately to very intolerant of LoF variants. These same LoF variants in RUNX1 predispose to myelodysplastic syndromes with a high risk of myeloid leukemia in the form of familial platelet disorder with predisposition to acute myeloid leukemia (FPD/AML), and likely this is driving LoF intolerance. Cytoskeletal protein encoding genes represent another subset of mostly LoF intolerant platelet-related genes. Intracellular protein encoding genes and granule protein genes have varied tolerance and platelet-associated receptor protein genes as a subgroup were most tolerant of haploinsufficiency. There were some genes with similar clinical bleeding phenotypes that had divergent tolerance to LoF. For instance, GP9 and GP1BB both cause Montreal Platelet Syndrome in the haploinsufficient state and had moderate intolerance to LoF mutations (pLI GP9 = 0.804; pLI GP1BB = 0.575). In contrast, while LoF mutations in GP1BA cause the same bleeding phenotype, this gene is much more tolerant to haploinsufficiency (pLI = 0.0002). These data indicate that perhaps there is another unidentified adverse condition associated with GP9 and GP1BB that is driving increased haploinsufficiency intolerance. In summary, we present a comprehensive analysis of known platelet-associated genes, the frequency of LoF mutations in these genes and their relative tolerance of the haploinsufficient state. These data generate an incidence of IPDs of ~0.18% in the general population. Importantly, these data also inform the driving mechanisms of LoF intolerance as there are defective genes resulting in similar bleeding phenotypes, but divergent tolerance to haploinsufficiency, indicating that further investigation is warranted for additional biology. Disclosures Lambert: CSL: Consultancy; Rigel: Consultancy; Sysmex: Consultancy; Amgen: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Summus: Consultancy; Bayer: Membership on an entity's Board of Directors or advisory committees; Shionogi: Consultancy; Educational Concepts in Medicine: Consultancy. Poncz:Incyte Corporation: Consultancy, Research Funding.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,010
Tête enseignante GPT0,244
Écart entre enseignants0,233 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueBlood→Même sujetPlatelet Disorders and Treatments→Travaux en français237 207→