A polygenic predictor of treatment-resistant depression using whole exome sequencing and genome-wide genotyping
| Autor: | Alessandro Serretti, Julien Mendlewicz, Alexander Kautzky, Gianluigi Forloni, Diego Albani, Panagiotis Ferentinos, Siegfried Kasper, Daniel Souery, Chiara Fabbri, Joseph Zohar, Dan Rujescu, Stuart Montgomery, Rudolf Uher, Cathryn M. Lewis |
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| Přispěvatelé: | Fabbri C., Kasper S., Kautzky A., Zohar J., Souery D., Montgomery S., Albani D., Forloni G., Ferentinos P., Rujescu D., Mendlewicz J., Uher R., Lewis C.M., Serretti A. |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Exome sequencing
Oncology DISORDER Genome-wide association study Antidepressant VARIANTS Genome Depressive Disorder Treatment-Resistant 0302 clinical medicine RARE Physiopsychologie et psychologie biologique [psychiatrie] IMPUTATION Exome 0303 health sciences Depression treatment-resistant depression exome sequencing genome-wide association study pathway antidepressant polygenic prediction predictive modeling ASSOCIATION 3. Good health Psychiatry and Mental health TREATMENT-RESISTANT DEPRESSION Major depressive disorder BURDEN medicine.medical_specialty Genotype Predictive markers Article lcsh:RC321-571 Pharmacological treatment Cellular and Molecular Neuroscience 03 medical and health sciences Internal medicine Genetic model Exome Sequencing medicine Humans lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Gene Genotyping Biological Psychiatry 030304 developmental biology Depressive Disorder Major business.industry Physique Astronomie medicine.disease TAR-ASTERISK-D 030227 psychiatry ANTIDEPRESSANT RESPONSE business Pharmacogenomics Treatment-resistant depression 030217 neurology & neurosurgery Imputation (genetics) Psychiatrie |
| Zdroj: | Translational Psychiatry, 10 (1 Translational Psychiatry Fabbri, C, Kasper, S, Kautzky, A, Zohar, J, Souery, D, Montgomery, S, Albani, D, Forloni, G, Ferentinos, P, Rujescu, D, Mendlewicz, J, Uher, R, Lewis, C M & Serretti, A 2020, ' A polygenic predictor of treatment-resistant depression using whole exome sequencing and genome-wide genotyping ', Translational psychiatry, vol. 10, no. 1, 50 . https://doi.org/10.1038/s41398-020-0738-5 Translational Psychiatry, Vol 10, Iss 1, Pp 1-12 (2020) |
| Popis: | Treatment-resistant depression (TRD) occurs in ~30% of patients with major depressive disorder (MDD) but the genetics of TRD was previously poorly investigated. Whole exome sequencing and genome-wide genotyping were available in 1209 MDD patients after quality control. Antidepressant response was compared to non-response to one treatment and non-response to two or more treatments (TRD). Differences in the risk of carrying damaging variants were tested. A score expressing the burden of variants in genes and pathways was calculated weighting each variant for its functional (Eigen) score and frequency. Gene-based and pathway-based scores were used to develop predictive models of TRD and non-response using gradient boosting in 70% of the sample (training) which were tested in the remaining 30% (testing), evaluating also the addition of clinical predictors. Independent replication was tested in STAR*D and GENDEP using exome array-based data. TRD and non-responders did not show higher risk to carry damaging variants compared to responders. Genes/pathways associated with TRD included those modulating cell survival and proliferation, neurodegeneration, and immune response. Genetic models showed significant prediction of TRD vs. response and they were improved by the addition of clinical predictors, but they were not significantly better than clinical predictors alone. Replication results were driven by clinical factors, except for a model developed in subjects treated with serotonergic antidepressants, which showed a clear improvement in prediction at the extremes of the genetic score distribution in STAR*D. These results suggested relevant biological mechanisms implicated in TRD and a new methodological approach to the prediction of TRD. SCOPUS: ar.j info:eu-repo/semantics/published |
| Databáze: | OpenAIRE |
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