Genomewide interaction and enrichment analysis on antidepressant response.

Area of Science:

• Pharmacogenomics
• Psychiatric Genetics
• Clinical Psychiatry

Background:

• Genomewide association studies (GWASs) for antidepressant efficacy have shown limited success.
• Antidepressant response is influenced by factors beyond genetics, potentially involving gene-environment interactions.
• Quality of Life (QoL) is a known factor affecting treatment outcomes.

Purpose of the Study:

• To predict antidepressant response using a GWAS model incorporating QoL as a moderator.
• To investigate the association between candidate genes from prior gene-environment (G × E) interaction studies and antidepressant response.
• To identify specific genetic variations and genes influencing treatment outcomes in depression.

Main Methods:

• Analysis of 1426 patients from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial.
• Genomewide association study (GWAS) model incorporating QoL and controlling for covariates.
• Enrichment analysis of single nucleotide polymorphisms (SNPs) within candidate genes, particularly serotonergic genes.

Main Results:

• A significant SNP (rs520210) in the NEDD4L gene was associated with antidepressant response when QoL was included as a moderator (p = 3.64 × 10⁻⁸).
• The significance of this SNP diminished in the Caucasian subsample.
• Enrichment analysis revealed a higher concentration of response-predicting SNPs within serotonergic genes compared to random genomic sets.

Conclusions:

• The study identifies potential target genes, including NEDD4L and serotonergic genes, for further validation in antidepressant response.
• Findings suggest that incorporating patient-reported outcomes like QoL can enhance GWAS models for predicting treatment efficacy.
• The results provide genomewide support for the involvement of serotonergic pathways in modulating antidepressant treatment outcomes.