Genome-wide association study of treatment-resistance in depression and meta-analysis of three independent samples
Notice bibliographique
Résumé
BACKGROUND: Treatment-resistant depression (TRD) is the most problematic outcome of depression in terms of functional impairment, suicidal thoughts and decline in physical health.AimsTo investigate the genetic predictors of TRD using a genome-wide approach to contribute to the development of precision medicine. METHOD: A sample recruited by the European Group for the Study of Resistant Depression (GSRD) including 1148 patients with major depressive disorder (MDD) was characterised for the occurrence of TRD (lack of response to at least two adequate antidepressant treatments) and genotyped using the Infinium PsychArray. Three clinically relevant patient groups were considered: TRD, responders and non-responders to the first antidepressant trial, thus outcomes were based on comparisons of these groups. Genetic analyses were performed at the variant, gene and gene-set (i.e. functionally related genes) level. Additive regression models of the outcomes and relevant covariates were used in the GSRD participants and in a fixed-effect meta-analysis performed between GSRD, STAR*D (n = 1316) and GENDEP (n = 761) participants. RESULTS: No individual polymorphism or gene was associated with TRD, although some suggestive signals showed enrichment in cytoskeleton regulation, transcription modulation and calcium signalling. Two gene sets (GO:0043949 and GO:0000183) were associated with TRD versus response and TRD versus response and non-response to the first treatment in the GSRD participants and in the meta-analysis, respectively (corrected P = 0.030 and P = 0.027). CONCLUSIONS: The identified gene sets are involved in cyclic adenosine monophosphate mediated signal and chromatin silencing, two processes previously implicated in antidepressant action. They represent possible biomarkers to implement personalised antidepressant treatments and targets for new antidepressants.Declaration of interestD.S. has received grant/research support from GlaxoSmithKline and Lundbeck; has served as a consultant or on advisory boards for AstraZeneca, Bristol-Myers Squibb, Eli Lilly, Janssen and Lundbeck. S.M. has been a consultant or served on advisory boards for: AstraZeneca, Bristol-Myers Squibb, Forest, Johnson & Johnson, Leo, Lundbeck, Medelink, Neurim, Pierre Fabre, Richter. S.K. has received grant/research support from Eli Lilly, Lundbeck, Bristol-Myers Squibb, GlaxoSmithKline, Organon, Sepracor and Servier; has served as a consultant or on advisory boards for AstraZeneca, Bristol-Myers Squibb, GlaxoSmithKline, Eli Lilly, Lundbeck, Pfizer, Organon, Schwabe, Sepracor, Servier, Janssen and Novartis; and has served on speakers' bureaus for AstraZeneca, Eli Lily, Lundbeck, Schwabe, Sepracor, Servier, Pierre Fabre, Janssen and Neuraxpharm. J.Z. has received grant/research support from Lundbeck, Servier, Brainsway and Pfizer, has served as a consultant or on advisory boards for Servier, Pfizer, Abbott, Lilly, Actelion, AstraZeneca and Roche and has served on speakers' bureaus for Lundbeck, Roch, Lilly, Servier, Pfizer and Abbott. J.M. is a member of the Board of the Lundbeck International Neuroscience Foundation and of Advisory Board of Servier. A.S. is or has been consultant/speaker for: Abbott, AbbVie, Angelini, Astra Zeneca, Clinical Data, Boehringer, Bristol Myers Squibb, Eli Lilly, GlaxoSmithKline, Innovapharma, Italfarmaco, Janssen, Lundbeck, Naurex, Pfizer, Polifarma, Sanofi and Servier. C.M.L. receives research support from RGA UK Services Limited.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,005 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,000 |
| 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 ».