Living in a country with a large gap between high and low earners has been linked to poor health, including depression. Less studied is gene-by-environment interplay with income inequality as the environmental exposure. Here, we examine the association between childhood exposure to inequality and individual differences in adult depressive symptoms, testing for moderation of genetic influences by inequality using polygenic indices for major depressive disorder, as well as twin models.

The research participants were 69,924 members of twin studies from four developed countries, born between 1893 and 1979, aged 22–103 years at depressive symptom assessment. Genotyping was available for 6,256 participants. Income inequality was operationalized as share of income accruing to the top 1% for each country when the participants were between age 5 and 15 years.

Childhood income inequality was associated with depressive symptom scores in adulthood, adjusting for covariates. Each 1% rise in inequality was associated with 0.295 higher depressive symptoms (scaled on T-score units). In genetic analyses, interaction effects showed that men who faced more inequality as children and had higher genetic risk for depression reported modestly higher depressive symptoms compared to other men. For women, both genetic risk and inequality mattered, with each independently associated with depressive symptoms. Twin models showed that inequality moderated genetic variance underlying depressive symptoms; heritability of depressive symptoms was higher where exposure to income inequality was higher.

Findings illustrate the long reach of childhood exposure to income inequality and suggest that advantaged environments may help protect against the effects of deleterious genes.

Social connections might be protective against depressive and anxious symptoms and dementia in later life. The extent to which social connections are heritable versus modifiable in older age remains unknown.

We aimed to investigate the heritability of social connections and their influence on mental and cognitive health over time among older adults in a longitudinal cohort.

We analysed data from the Older Australian Twins Study (333 monozygotic, 266 dizygotic twins; 65+ years) at three time-points over 6 years. We examined the factor structure and heritability of baseline social connections and their associations with mental and cognitive health longitudinally.

We found three weakly heritable social connections factors: (a) interacting with friends/neighbours/community (h2 = 0.09, 95% CI: 0.00, 0.44); (b) family interactions/childcare (h2 = 0.13, 95% CI: 0.00, 0.43); (c) involvement in religious groups/caregiving (h2 = 0.00, 95% CI: 0.00, 0.19). Strong genetic correlations were observed between depressive symptoms and factors a (r = −0.96) and b (r = −0.60). More frequent baseline interactions with friends/neighbours/community were associated with fewer depressive symptoms cross-sectionally (B = −0.14, p = .004) and longitudinally (B = −0.09, p = 0.006), but the associations between social connections and cognitive health were not significant.

Social connections were weakly heritable, suggesting large environmental determination. Connections with friends/neighbours/community were associated with better mental health cross-sectionally and over time.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.