A direct test of the diathesis-stress model for depression

Abstract

The diathesis-stress theory for depression states that the effects of stress on the depression risk are dependent on the diathesis or vulnerability, implying multiplicative interactive effects on the liability scale. We used polygenic risk scores for major depressive disorder (MDD) calculated from the results of the most recent analysis from the Psychiatric Genomics Consortium as a direct measure of the vulnerability for depression in a sample of 5221 individuals from 3083 families. In the same we also had measures of stressful life events and social support and a depression symptom score, as well as DSM-IV MDD diagnoses for most individuals. In order to estimate the variance in depression explained by the genetic vulnerability, the stressors and their interactions, we fitted linear mixed models controlling for relatedness for the whole sample as well as stratified by sex. We show a significant interaction of the polygenic risk scores with personal life events (0.12% of variance explained, P-value=0.0076) contributing positively to the risk of depression. Additionally, our results suggest possible differences in the aetiology of depression between women and men. In conclusion, our findings point to an extra risk for individuals with combined vulnerability and high number of reported personal life events beyond what would be expected from the additive contributions of these factors to the liability for depression, supporting the multiplicative diathesis-stress model for this disease.

Figure 1

(a) Increased odds of DSM IV MDD diagnosis per decile of depression IRT score assessed 4–7 years previously. (b) Association between MDD-PRS and depression scores (main effects, one-sided tests, results expressed in % of variance explained). Full sample analyses using the two versions of the PRS were run in the same target dataset with the exact same covariates. Red bars indicate positive correlation with the depression score. PRS were calculated using different p-value thresholds from the GWAS summary statistics. The most conservative only includes independent loci with genome wide significant SNPs (p-value<5e-8), while the least conservative include the most significant SNP of each haplotype (p-value<1). (c) Association between self-reported stress (personal stressful life events (PSLE) or network stressful life events (NSLE), lack of SS) and depression IRT score (main effect, one-sided tests, results expressed in % of variance explained). Blue bars indicate negative correlations and red bars indicate positive correlations with the depression score. Dashed bars indicate sex specific effects. (d) Variance of the depression score explained by the interaction between PRS and PSLE (2-sided tests). Dashed bars indicate sex specific effects. We focused on the association with the PRS comprising all haplotypes but the other associations are also reported for completeness. Blue bars indicate negative correlations and red bars indicate positive correlations with the depression score. (e) Increase in depression score (fitted values, vertical axis) as a function of PSLE and MDD-PRS. For example, the effect of the PSLE-diathesis interaction is visible when comparing the bottom (minimal PSLE) and top (maximal PSLE) edges of the surface. The difference in slopes indicates that PSLE mediates the effect of the genetic predisposition on the depression score. From right to left, results for the whole sample, females and males. In all analyses, we accounted for familial relatedness using a kinship matrix (a) or a genetic relatedness matrix calculated from SNPs (b–d). For (a) we used R package “hglm” to estimate the odds ratios (student test to test the significance of the fixed effects). For panels (b–d) the parameters of the model were estimated using GCTA 1.26.0 (student test to test the significance of the fixed effects) . All analyses controlled for age, age², sex, age*sex and age²*sex interactions, study, array, and the first four genetic principal components in the outcome variable and predictors .