Association of the Polygenic Scores for Personality Traits and Response to Selective Serotonin Reuptake Inhibitors in Patients with Major Depressive Disorder

Abstract

Studies reported a strong genetic correlation between the Big Five personality traits and major depressive disorder (MDD). Moreover, personality traits are thought to be associated with response to antidepressants treatment that might partly be mediated by genetic factors. In this study, we examined whether polygenic scores (PGSs) derived from the Big Five personality traits predict treatment response and remission in patients with MDD who were prescribed selective serotonin reuptake inhibitors (SSRIs). In addition, we performed meta-analyses of genome-wide association studies (GWASs) on these traits to identify genetic variants underpinning the cross-trait polygenic association. The PGS analysis was performed using data from two cohorts: the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS, n = 529) and the International SSRI Pharmacogenomics Consortium (ISPC, n = 865). The cross-trait GWAS meta-analyses were conducted by combining GWAS summary statistics on SSRIs treatment outcome and on the personality traits. The results showed that the PGS for openness and neuroticism were associated with SSRIs treatment outcomes at p < 0.05 across PT thresholds in both cohorts. A significant association was also found between the PGS for conscientiousness and SSRIs treatment response in the PGRN-AMPS sample. In the cross-trait GWAS meta-analyses, we identified eight loci associated with (a) SSRIs response and conscientiousness near YEATS4 gene and (b) SSRI remission and neuroticism eight loci near PRAG1, MSRA, XKR6, ELAVL2, PLXNC1, PLEKHM1, and BRUNOL4 genes. An assessment of a polygenic load for personality traits may assist in conjunction with clinical data to predict whether MDD patients might respond favorably to SSRIs.

Predicting rehospitalization within 2 years of initial patient admission for a major depressive episode: a multimodal machine learning approach

“…In addition, unimodal PGRS discrimination was still low at these thresholds (AUC = 54.83 and 54.17 respectively, Supplementary Information 3.2). PGRS scores have received strong interest in psychiatric research, hoping that the aggregation of multiple single-nucleotide polymorphisms may illuminate genetic differences in psychiatric traits as well as parse the heterogeneity of outcomes such as medication response 47,48 . Significant associations with outcomes of interest have been consistently demonstrated, however, commonly explain less than 1–2% of variance, limiting their current clinical use.…”

Genome‐wide association analysis of autism identified multiple loci that have been reported as strong signals for neuropsychiatric disorders

“…This locus is significant in Intelligence [Hill et al, ];Response to SSRI in MDD or openness [Amare et al, ]; Depression [Howard et al, ]; Schizophrenia [Amare et al, ; Howard et al, ];Cognitive performance [Lee et al, ]; Educational attainment (MTAG) [Lee et al, ]; General cognitive ability [Davies et al, ; Lee et al, ] in GWAS study.…”

Predicting Rehospitalization within 2 Years of Initial Patient Admission for a Major Depressive Episode: A Multimodal Machine Learning Approach

“…In addition, unimodal PGRS discrimination was still low at these thresholds (AUC = 54.83 and 54.17 respectively, Supplementary Information 3.2). PGRS scores have received strong interest in psychiatric research, hoping that the aggregation of multiple singlenucleotide polymorphisms may illuminate genetic differences in psychiatric traits as well as parse the heterogeneity of outcomes such as medication response 47,48 . Significant associations with outcomes of interest have been consistently demonstrated, however, commonly explain less than 1-2% of variance, limiting their current clinical use.…”