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Enregistrement W4385185874 · doi:10.1001/jamaneurol.2023.2363

Exome Sequencing and the Identification of New Genes and Shared Mechanisms in Polymicrogyria

2023· article· en· W4385185874 sur OpenAlexafffund
Shyam K. Akula, Allen Y. Chen, Jennifer E. Neil, Diane D. Shao, Alisa Mo, Norma K. Hylton, Stephanie DiTroia, Vijay Ganesh, Richard S. Smith, Katherine O’kane, Rebecca C. Yeh, Jack H. Marciano, Samantha L. Kirkham, Connor Kenny, Janet Song, Muna Al Saffar, Francisca Millan, David J. Harris, Andrea V. Murphy, Kara C. Klemp, Stephen R. Braddock, Harrison Brand, Isaac Wong, Michael E. Talkowski, Anne O’Donnell‐Luria, Abbe Lai, Robert Hill, Ganeshwaran H. Mochida, Ryan N. Doan, A. James Barkovich, Edward Yang, Dina Amrom, Eva Andermann, Annapurna Poduri, Christopher A. Walsh, Bassam Abu‐Libdeh, Lihadh Al‐Gazali, Edith Alva Moncayo, Eva Anderman, Anna‐Kaisa Anttonen, Saunder Barnes, Sara Barnett, Todd F. Barron, Brenda J. Barry, Lina Basel‐Vanagaite, Lailá Bastaki, Luis Bello‐Espinosa, Tawfeg Ben‐Omran, Matthew P. Bernard, Carsten Bönneman, Blaise F. D. Bourgeois, S.D.M. Brown, Roberto Caraballo, Gergory Cascino, M Clarke, Monika Cohen, Yanick J. Crow, Bernard Dan, Kira A. Dies, William B. Dobyns, François Dubeau, Christelle Moufawad El Achkar, Gregory M. Enns, Laurence Faivre, Laura Flores‐Sarnat, John Gaitanis, Kuchukhidze Giorgi, Andrew Green, A. Guberman, Renzo Guerrini, Micheil Innes, R.G. Jacobsen, Samir Khalil, Joerg Klepper, Dimitri Kranic, Kalpathy Krishnamoorthy, Anna‐Elina Lehesjoki, Dorit Lev, Richard J. Leventer, Emily C. Lisi, Valerie Loik Ramey, Sally Ann Lynch, Laila Mahmoud, David Manchester, David E. Mandelbaum, Daphna Marom, Deborah Marsden, Mayra Martinez Ojeda, Amira Masri, Līvija Medne, Denis Melanson, David T. Miller, Anna Minster, Edward Neilan, Dang Khoa Nguyen, Heather E. Olson, I Pascual-Castroviejo, Philip L. Pearl, Daniela T. Pilz, Nada Quercia, Salmo Raskin, Miriam Regev, Lance H. Rodan, Cynthia M. Rooney, Michael Rutlin, Mustafa Şahin, Mustafa A. Salih, Pierre Sarda, Harvey B. Sarnat, Ingrid E. Scheffer, Joseph T.C. Shieh, Sharon E. Smith, Janet S. Soul, Siddharth Srivastava, László Sztriha, Donatella Tampieri, John Tolmie, Meral Topçu, Eugen Trinka, John C. Tsai, Jack W. Tsao, Sheila Unger, Iris Unterberger, Goekhan Uyanik, Kette D. Valente, Thomas Voit, Louise C. Wilson, Grace Yoon

Notice bibliographique

RevueJAMA Neurology · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueEpilepsy research and treatment
Établissements canadiensMcGill UniversityMontreal Neurological Institute and Hospital
Organismes subventionnairesNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesNational Human Genome Research InstituteNational Institute on AgingStanley Center for Psychiatric Research, Broad InstituteFeinberg School of MedicineEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBroad InstituteNational Institute of Mental HealthNorthwestern UniversityHospital for Special SurgeryUniversity of California, San FranciscoSaint Louis UniversityMcGill UniversityUnited Arab Emirates UniversityHoward Hughes Medical Institute
Mots-clésPolymicrogyriaExome sequencingGeneticsExomeGenetic testingProbandMassive parallel sequencingMedicineBiologyEpilepsyMutationDNA sequencingPsychiatryGene

Résumé

récupéré en direct d'OpenAlex

Importance: Polymicrogyria is the most commonly diagnosed cortical malformation and is associated with neurodevelopmental sequelae including epilepsy, motor abnormalities, and cognitive deficits. Polymicrogyria frequently co-occurs with other brain malformations or as part of syndromic diseases. Past studies of polymicrogyria have defined heterogeneous genetic and nongenetic causes but have explained only a small fraction of cases. Objective: To survey germline genetic causes of polymicrogyria in a large cohort and to consider novel polymicrogyria gene associations. Design, Setting, and Participants: This genetic association study analyzed panel sequencing and exome sequencing of accrued DNA samples from a retrospective cohort of families with members with polymicrogyria. Samples were accrued over more than 20 years (1994 to 2020), and sequencing occurred in 2 stages: panel sequencing (June 2015 to January 2016) and whole-exome sequencing (September 2019 to March 2020). Individuals seen at multiple clinical sites for neurological complaints found to have polymicrogyria on neuroimaging, then referred to the research team by evaluating clinicians, were included in the study. Targeted next-generation sequencing and/or exome sequencing were performed on probands (and available parents and siblings) from 284 families with individuals who had isolated polymicrogyria or polymicrogyria as part of a clinical syndrome and no genetic diagnosis at time of referral from clinic, with sequencing from 275 families passing quality control. Main Outcomes and Measures: The number of families in whom genetic sequencing yielded a molecular diagnosis that explained the polymicrogyria in the family. Secondarily, the relative frequency of different genetic causes of polymicrogyria and whether specific genetic causes were associated with co-occurring head size changes were also analyzed. Results: In 32.7% (90 of 275) of polymicrogyria-affected families, genetic variants were identified that provided satisfactory molecular explanations. Known genes most frequently implicated by polymicrogyria-associated variants in this cohort were PIK3R2, TUBB2B, COL4A1, and SCN3A. Six candidate novel polymicrogyria genes were identified or confirmed: de novo missense variants in PANX1, QRICH1, and SCN2A and compound heterozygous variants in TMEM161B, KIF26A, and MAN2C1, each with consistent genotype-phenotype relationships in multiple families. Conclusions and Relevance: This study's findings reveal a higher than previously recognized rate of identifiable genetic causes, specifically of channelopathies, in individuals with polymicrogyria and support the utility of exome sequencing for families affected with polymicrogyria.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,836
Score d'incertitude au seuil0,137

Scores Codex et Gemma par catégorie

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

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 Opus0,034
Tête enseignante GPT0,299
Écart entre enseignants0,265 · 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 tête enseignante, 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

Citations33
Publié2023
Routes d'admission2
Résumé présentoui

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