SA38 GWAS-BASED MACHINE LEARNING APPROACH TO PREDICT DULOXETINE RESPONSE AND REMISSION IN MAJOR DEPRESSIVE DISORDER

Background 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. GWAS based Machine Learning (ML) models may be useful to predict treatment response, thus improving antidepressant treatment. Methods A sample of 186 MDD patients received treatment with duloxetine for up to 8 weeks were categorized as “responders” based on a Montgomery-Åsberg Depression Rating Scale (MADRS) change >50% from baseline; or “remitters” based on a MADRS score of 10 or lower at end point. The initial dataset (N=186) was randomly divided into training and test sets (70% and 30%, or 130 and 56 individuals respectively), resulting in ten different training and test set pairs. A genome-wide logistic regression was performed 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, repeated cross-validation. Results For the response phenotype, LASSO regression suggested twenty SNPs to be entered in prediction models. Calculated models for response achieved accuracy of 63.43% for CRT and 78.93% for SVM. For the remission phenotype, LASSO filtering suggested five SNPs. Best models were characterized by an accuracy of 65.45% with a sensitivity of 69.31% and specificity of 61.43%. Discussion In this study, the potential of using GWAS data to predict duloxetine response was examined using ML models. These models managed to capture a fraction of responders and remitters (i.e. moderate sensitivity), but failed to filter out non-responders and non-remitters (i.e. low specificity). Inclusion of additional non-genetic variables to create integrated models may help improve prediction results.