Role of common and rare genetic variants in the aetiology of trigeminal neuralgia
Notice bibliographique
Résumé
Summary Background Trigeminal neuralgia (TN) is characterized by repeated paroxysmal attacks of severe facial pain usually lasting 1-3 minutes. Lifetime prevalence is ca.3 per 1,000, more common in women, and with onset generally in middle age. Medications usually provide relief in the early stages of the disorder, but for many patients, severe drug side effects emerge and medically intractable pain returns, sometimes lasting for life. Some patients present with paroxysmal pain predominantly while others also experience substantial concomitant constant facial pain. Some patients have a history of a blood vessel compressing and damaging their trigeminal nerve (neurovascular compression, NVC). For these “classical” cases, surgery often provides complete or substantial pain relief for many years. “Idiopathic” cases without NVC or any other apparent cause also occur. NVC was previously observed to be less frequent in females who had early age of onset and these patients may constitute a unique subgroup. Our aim was to evaluate the role of inherited genetic variation in the aetiology of TN in patient subgroups based on age of onset, presence of NVC and sex. Methods To maximize aetiological homogeneity, only patients with predominantly paroxysmal pain and minimal concomitant continuous pain were included in the analysis. Conditions known to cause secondary TN such as tumors or multiple sclerosis were excluded. The GWAS analysis was based on 626 TN patients and 827 Control subjects of European ancestry recruited in Canada, the UK, and US. A Genome-Wide Association Study (GWAS) analysis was performed using Affymetrix’s Precision Medicine arrays yielding 7,781,254 biallelic DNA variants available after Quality Control (QC) and imputation. Rare damaging mutations in genes with functions relevant to the biology of TN were identified in Whole Genome Sequencing (WGS) genomic DNA of 100 patients using a novel strategy based on overlap of symptoms of TN with symptoms of known genetic disorders. Findings The GWAS analysis revealed associations at eight genome locations including near LRP1B (P-value 6.3 X 10 -15 ), a gene important for repair of myelin sheath injury that has been previously proposed as a target for the treatment of neuropathic pain. Associations were also found for the potassium channel gene KCNK10 , and for CHL1, CUX1, SGMS1 and ZNF804B genes, all genes with neural functions potentially relevant to the aetiology of TN. In addition, high-risk genotypes at the CUX1 and KCNK10 genes exhibit significant interactions with patients’ sex and the presence or absence of NVC (P-values 0.005 and 0.017, respectively). Whole genome sequencing of 100 TN patients revealed mutations in ion channel genes TRPM4 (six patients), SCN10A and SCNN1B (five patients), CACNA1F, CACNA1S and SCN5A (four patients) and CACNA1H , SCN2A and SCN9A (three patients). Female patients with onset prior to age 46 had more mutated genes with myelin-related functions (P-value 0.004) and associated with epilepsy or seizure (P-value 0.03) than older onset females and males of any onset age. Interpretation Risk of TN in patients presenting with paroxysmal pain only is associated with both common genetic variants and with rare mutations. Some high-risk genotypes have significant interactions with sex and NVC. Evidence of the condition’s heterogeneous genetic aetiology should be considered when evaluating novel therapies. Funding Grants from the William H. and Leila A. Cilker Genetics Research Program of the Facial Pain Research Foundation, The Foundation of the University of Medicine and Dentistry of New Jersey, and Rutgers School of Dental Medicine, Rutgers Health, Rutgers – The State University of New Jersey Contact Scott R Diehl, PhD, scott.diehl@rutgers.edu , 973-972-7053
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».