Common genetic variants associated with characteristics and clinical outcomes of atrial fibrillation: from a large population based registry (FinnGen)
Notice bibliographique
Résumé
Abstract Background Atrial Fibrillation (AF) in younger patients without coexisting structural heart disease is believed to be mediated by genetic causes; however data on genetic associations from large registries are limited. Methods We obtained de-identified data from the FinnGen registry, which is a national census-based registry with available data on genetic variants from participants in Finland. Imputed genetic variants from microarray data including common genetic variants across cases and controls were analyzed by GWAS, and further fine-mapping of causal SNPs to gene-level, miRNA-level, and pathway-level association was performed. The diagnosis of AF was derived from the International Classification of Disease-10 (ICD) codes. Results The total sample size in Finn-Gen registry was 500,348 participants, amongst those 56.4% were females. The median age of AF-onset in females was 71.8 vs. 68.7 years in males. Upon analysis of participants with available genetic data, there were a total of 21,323,076 imputed genetic variants across 63,532 cases with AF and 252,810 controls. There were a total of 7,619 significant SNPs in GWAS which mapped to 183 enriched unique loci across 142 genes, including most strongly to loci in chromosomes 16 (near ZFHX3, 989 SNPs), 1 (KCND3, KCCN3, 759 SNPs), 7 (CAV1, 119 SNPs), and chromosome 4 (PITX2, LINC01438, 111 SNPs) (Figure 1). Additional gene-level analysis implicated 313 genes including both previously characterized and novel markers such as SCN5A, TBX5, TTN, and ELOVL6. Pathway-level association of significant genes were consistent with AF-related biological processes such as cardiac conduction within His-Purkinje cells, action potential generation, repolarization, actin-mediated cell contraction, potassium channel activity, and hyperaldosteronism. Two micro-RNA hsa-miR-204 and hsa-miR-211 were identified as potential key regulators of AF-associated genes from the study. After excluding the participants who had structural heart disease, the highest degree of genetic colocalization (as assessed by "Colocalization hits") was evident for the coexisting diagnosis of hypertension (HTN), cardioembolic stroke. During the follow-up of 21 years, between 01/1998 and 12/2019; AF diagnosis was associated with an increased risk of mortality in both men and women. The effect size of AF diagnosis and overall mortality was stronger in men (adjusted HR of 2.01, 95% CI: 1.84-2.19, p < 0.001) in comparison with women (adjusted HR of 1.87, 95% CI: 1.72-2.02, p < 0.001). Conclusions In a large epidemiological database (FinnGen), the diagnosis of atrial fibrillation was associated with a polygenic pattern of association across several genes including ZFHX3, KCND3, CAV1, PITX2, SCN5A, and TTN and biological processes. Over a relatively longer period of follow up of 21 years, the diagnosis of AF was associated with an increased risk of mortality in both men and women.Manhattan plot of AF-GWAS
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».