Hauschild, Anne-Christin

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Translational Psychiatry"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Marshe, Victoria S."],["dc.contributor.author","Maciukiewicz, Malgorzata"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Islam, Farhana"],["dc.contributor.author","Qin, Li"],["dc.contributor.author","Tiwari, Arun K."],["dc.contributor.author","Sibille, Etienne"],["dc.contributor.author","Blumberger, Daniel M."],["dc.contributor.author","Karp, Jordan F."],["dc.contributor.author","Flint, Alastair J."],["dc.contributor.author","Müller, Daniel J."],["dc.date.accessioned","2022-06-08T07:58:41Z"],["dc.date.available","2022-06-08T07:58:41Z"],["dc.date.issued","2021"],["dc.description.abstract","Abstract Antidepressant outcomes in older adults with depression is poor, possibly because of comorbidities such as cerebrovascular disease. Therefore, we leveraged multiple genome-wide approaches to understand the genetic architecture of antidepressant response. Our sample included 307 older adults (≥60 years) with current major depression, treated with venlafaxine extended-release for 12 weeks. A standard genome-wide association study (GWAS) was conducted for post-treatment remission status, followed by in silico biological characterization of associated genes, as well as polygenic risk scoring for depression, neurodegenerative and cerebrovascular disease. The top-associated variants for remission status and percentage symptom improvement were PIEZO1 rs12597726 ( OR  = 0.33 [0.21, 0.51], p  = 1.42 × 10 −6 ) and intergenic rs6916777 ( Beta  = 14.03 [8.47, 19.59], p  = 1.25 × 10 −6 ), respectively. Pathway analysis revealed significant contributions from genes involved in the ubiquitin-proteasome system, which regulates intracellular protein degradation with has implications for inflammation, as well as atherosclerotic cardiovascular disease ( n  = 25 of 190 genes, p  = 8.03 × 10 −6 , FDR-corrected p  = 0.01). Given the polygenicity of complex outcomes such as antidepressant response, we also explored 11 polygenic risk scores associated with risk for Alzheimer’s disease and stroke. Of the 11 scores, risk for cardioembolic stroke was the second-best predictor of non-remission, after being male (Accuracy = 0.70 [0.59, 0.79], Sensitivity = 0.72, Specificity = 0.67; p  = 2.45 × 10 −4 ). Although our findings did not reach genome-wide significance, they point to previously-implicated mechanisms and provide support for the roles of vascular and inflammatory pathways in LLD. Overall, significant enrichment of genes involved in protein degradation pathways that may be impaired, as well as the predictive capacity of risk for cardioembolic stroke, support a link between late-life depression remission and risk for vascular dysfunction."],["dc.identifier.doi","10.1038/s41398-021-01248-3"],["dc.identifier.pii","1248"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110496"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.eissn","2158-3188"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Genome-wide analysis suggests the importance of vascular processes and neuroinflammation in late-life antidepressant response"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","S228"],["dc.bibliographiccitation.journal","European Neuropsychopharmacology"],["dc.bibliographiccitation.lastpage","S229"],["dc.bibliographiccitation.volume","29"],["dc.contributor.author","Marshe, Victoria"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Maciukiewicz, Malgorzata"],["dc.contributor.author","Mueller, Daniel"],["dc.date.accessioned","2022-06-08T07:57:50Z"],["dc.date.available","2022-06-08T07:57:50Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1016/j.euroneuro.2019.08.218"],["dc.identifier.pii","S0924977X19307746"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110229"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.issn","0924-977X"],["dc.title","T19DEVELOPING AND EVALUATING A STANDARDIZED, MACHINE LEARNING WORKFLOW FOR PREDICTING PSYCHIATRIC PHENOTYPES USING GENOME-WIDE AND CLINICAL DATA"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","S327"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","Biological Psychiatry"],["dc.bibliographiccitation.lastpage","S328"],["dc.bibliographiccitation.volume","85"],["dc.contributor.author","Marshe, Victoria"],["dc.contributor.author","Maciukiewicz, Malgorzata"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Sibille, Etienne"],["dc.contributor.author","Blumberger, Daniel"],["dc.contributor.author","Karp, Jordan"],["dc.contributor.author","Lenze, Eric J."],["dc.contributor.author","Reynolds, Charles"],["dc.contributor.author","Kennedy, James L."],["dc.contributor.author","Mulsant, Benoit"],["dc.contributor.author","Mueller, Daniel J."],["dc.date.accessioned","2022-06-08T07:57:46Z"],["dc.date.available","2022-06-08T07:57:46Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1016/j.biopsych.2019.03.830"],["dc.identifier.pii","S0006322319309801"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110208"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.issn","0006-3223"],["dc.title","S79. Predicting Venlafaxine Remission in Late-Life Depression Using Genome-Wide and Clinical Data"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","62"],["dc.bibliographiccitation.journal","Journal of Psychiatric Research"],["dc.bibliographiccitation.lastpage","68"],["dc.bibliographiccitation.volume","99"],["dc.contributor.author","Maciukiewicz, Malgorzata"],["dc.contributor.author","Marshe, Victoria S."],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Foster, Jane A."],["dc.contributor.author","Rotzinger, Susan"],["dc.contributor.author","Kennedy, James L."],["dc.contributor.author","Kennedy, Sidney H."],["dc.contributor.author","Müller, Daniel J."],["dc.contributor.author","Geraci, Joseph"],["dc.date.accessioned","2021-09-17T08:40:29Z"],["dc.date.available","2021-09-17T08:40:29Z"],["dc.date.issued","2018"],["dc.description.abstract","Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and is commonly treated with antidepressant drugs. However, large variability is observed in terms of response to antidepressants. Machine learning (ML) models may be useful to predict treatment outcomes. A sample of 186 MDD patients received treatment with duloxetine for up to 8 weeks were categorized as \"responders\" based on a MADRS change >50% from baseline; or \"remitters\" based on a MADRS score ≤10 at end point. The initial dataset (N = 186) was randomly divided into training and test sets in a nested 5-fold cross-validation, where 80% was used as a training set and 20% made up five independent test sets. We performed genome-wide logistic regression to identify potentially significant variants related to duloxetine response/remission and extracted the most promising predictors using LASSO regression. Subsequently, classification-regression trees (CRT) and support vector machines (SVM) were applied to construct models, using ten-fold cross-validation. With regards to response, none of the pairs performed significantly better than chance (accuracy p > .1). For remission, SVM achieved moderate performance with an accuracy = 0.52, a sensitivity = 0.58, and a specificity = 0.46, and 0.51 for all coefficients for CRT. The best performing SVM fold was characterized by an accuracy = 0.66 (p = .071), sensitivity = 0.70 and a sensitivity = 0.61. In this study, the potential of using GWAS data to predict duloxetine outcomes was examined using ML models. The models were characterized by a promising sensitivity, but specificity remained moderate at best. The inclusion of additional non-genetic variables to create integrated models may improve prediction."],["dc.identifier.doi","10.1016/j.jpsychires.2017.12.009"],["dc.identifier.pmid","29407288"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89601"],["dc.language.iso","en"],["dc.relation.eissn","1879-1379"],["dc.relation.issn","0022-3956"],["dc.title","GWAS-based machine learning approach to predict duloxetine response in major depressive disorder"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","S1122"],["dc.bibliographiccitation.journal","European Neuropsychopharmacology"],["dc.bibliographiccitation.volume","29"],["dc.contributor.author","Hauschild, Anne-Christin"],["dc.contributor.author","Maciukiewicz, Malgorzata"],["dc.contributor.author","Tiwari, Arun"],["dc.contributor.author","Zai, Clement"],["dc.contributor.author","Liebermann, Jeffrey A."],["dc.contributor.author","Meltzer, Herbert"],["dc.contributor.author","Kennedy, James L."],["dc.contributor.author","Mueller, Daniel"],["dc.date.accessioned","2022-06-08T07:57:50Z"],["dc.date.available","2022-06-08T07:57:50Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1016/j.euroneuro.2018.08.104"],["dc.identifier.pii","S0924977X18304085"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110227"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.issn","0924-977X"],["dc.title","F24SYSTEMS BIOLOGY APPROACH TO EVALUATE GENETIC FACTORS OF ANTIPSYCHOTIC INDUCED WEIGHT GAIN IN PATIENTS WITH SCHIZOPHRENIA"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]