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NOVEL LOCI FOR ANTIDEPRESSANT RESPONSE IDENTIFIED FROM GENOME-WIDE ASSOCIATION STUDIES AND MULTI-TRAIT META-ANALYSES
Ist Teil von
European neuropsychopharmacology, 2023-10, Vol.75, p.S53-S53
Ort / Verlag
Elsevier B.V
Erscheinungsjahr
2023
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
Antidepressants exhibit a large variation in efficacy in treated individuals, and increasing evidence suggests that individual genetics contribute to antidepressant treatment response. However, genome-wide association studies (GWAS) of antidepressant response are limited by insufficient sample sizes as well as variability in defining treatment-related phenotypes, and no robustly replicated genome-wide associations have been detected to date. In this study, we both conducted GWAS and GWAS meta-analyses, to increase statistical power to detect genomic loci associated with antidepressant response.
Utilizing questionnaire data of treatment efficacy, we conducted a GWAS on antidepressant response in the Estonian Biobank, separately for selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs). GWAS results were first meta-analyzed with SSRI response GWAS and SNRI response GWAS from Li et al 2020, based on similar questionnaires of treatment efficacy, and then meta-analyzed with GWAS on remission after antidepressant treatment measured using rating scales for depressive symptoms (Pain et al. 2022) and GWAS on treatment resistant depression based on number of prescribed antidepressants (Wigmore et al. 2020), using Multi-Trait Analysis of GWAS (MTAG). In addition, we conducted gene-set enrichment analyses, leave-one-out polygenic prediction of antidepressant response, and genetic correlation analyses.
The observed single nucleotide polymorphism (SNP) based heritability (h2) for SSRI non-response was 0.107 (SE=0.063), and 0.029 for SNRI non-response (SE=0.019). The SSRI non-response meta-analysis (22,649 non-responders and 25,057 responders) identified three genome-wide significant associations (lead SNPs: rs1106260, beta=0.0525, p=7.722e-09; rs4884091, beta=0.064, p=3.692e-8; and rs113266071, beta=-0.1236, p=1.284e-08), of which two (lead SNPs: rs1106260, and rs113266071) have not been identified in any of the individual GWAS. In the SNRI non-response meta-analysis (3,328 non-responders and 4,819 responders), one novel genome-wide significant locus (lead SNP: rs10104815, beta=-0.091, p=8.451e-9) and one previously identified locus (lead SNP: rs4955665, beta=-0.084, p=3.737e-8) was identified. A meta-analysis combining SSRIs and SNRIs did not identify any additional associations. Leave-one-out validation of antidepressant response in the Estonian Biobank demonstrated significant prediction of antidepressant response. Individuals in the top 2.5% highest polygenic score, indicative for high genetic liability for non-response to antidepressants, had a 1.52-fold (CI 1.04-2.21) higher risk of antidepressant non-response compared to individuals with average genetic risk (percentiles 40-60%). Non-response to antidepressants had strongest significant positive genetic correlation with insomnia, and strongest significant negative correlation with educational attainment and cognition. Gene set enrichment analyses implicate significant enrichment of genes involved in ubiquinone metabolic process.
In the present study, three novel loci associated with non-response to SSRIs or SNRIs were identified. These results suggest that multi-trait meta-analyses of GWAS utilizing different measures of treatment response can increase sample sizes to improve the discovery of variants associated with non-response to antidepressants.