Chiara Fabbri

The paper identifies biologically distinct subtypes of MDD based on energy-related symptoms and finds genetic and metabolic differences between them.

This study performs a trans-ancestry GWAS of SSRI response in depression, finding suggestive associations and enrichment in serotonergic pathways but no genome-wide significant variants.

This study examines the association between CYP2C19 and CYP2D6 metabolic phenotypes and antidepressant response using genotype imputation across 13 studies, finding no significant associations after correction for multiple testing.

This paper ranks antidepressants based on their similarity to gene expression profiles of treatment responders, finding escitalopram and citalopram show high rankings in some cell lines.

This study finds that depression with atypical neurovegetative symptoms has distinct genetic links to immune-metabolic traits and alcohol consumption.

The paper develops a polygenic predictor for treatment-resistant depression using whole exome sequencing and genome-wide genotyping, finding that genetic scores combined with clinical factors improve prediction.

This study identifies genetic variants associated with sociability as a continuous trait and finds genetic correlations with schizophrenia, major depressive disorder, and autism spectrum disorders.

This paper conducts a meta-analysis showing that CYP2C19 poor metabolizers have better antidepressant efficacy but similar side effects compared to extensive metabolizers.

This study identifies two SNPs associated with antidepressant response in GENDEP and STAR*D studies but finds limited replication in independent cohorts.

This study investigates whether polygenic risk scores (PRS) can predict antidepressant response in MDD, finding no significant predictive effect.