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A polygenic predictor of treatment-resistant depression using whole exome sequencing and genome-wide genotyping

By Chiara Fabbri, Siegfried Kasper, Alexander Kautzky, Joseph Zohar, Daniel Souery, Stuart Montgomery, Diego Albani, Gianluigi Forloni, Panagiotis Ferentinos, Dan Rujescu, Julien Mendlewicz, Rudolf Uher, Cathryn Lewis, Alessandro Serretti

Posted 20 Sep 2019
medRxiv DOI: 10.1101/19007161

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 performed in 1320 MDD patients. Response to the first pharmacological treatment 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, considering rare variants only and rare + common variants. Gene- 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. After quality control 1209 subjects were included. 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. Significant prediction of TRD vs. response was observed in the testing sample which was improved by the addition of clinical factors. Some models were replicated, with a weaker prediction, in STAR*D and GENDEP when considering also clinical factors and in the extremes of the genetic score distribution. These results suggested relevant biological mechanisms implicated in TRD and a new methodological approach to the prediction of TRD.

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