bioRxivGeneticsPreprint
Cognitive ability, Socioeconomic Status, and Depressive Symptoms: A Gene-Environment-Trait Correlation
Abstract
PurposeDepression is genetically influenced, but the mechanisms that underlie these influences are largely unknown. Recently, shared genetic influences were found between depression and both cognitive ability and educational attainment (EA). Although genetic influences are often thought to represent direct biological pathways, they can also reflect indirect pathways, including modifiable environmental mediations (gene-environment-trait correlations). Here, I tested whether the genetic correlation between cognitive ability and depressive symptoms partly reflects an environmental mediation involving socioeconomic status (SES). MethodsAs previously done to increase statistical power, and due to their high phenotypic and genetic correlation, EA was used as a proxy for cognitive ability. Summary statistics from a recent genome-wide association study of EA were used to calculate EA polygenic scores. Two independent samples were used: 522 non-Hispanic Caucasian university students from the Duke Neurogenetics Study (277 women, mean age 19.78{+/-}1.24 years) and 5,243 white British volunteers (2,669 women, mean age 62.30{+/-}7.41 years) from the UK biobank. ResultsMediation analyses in the two samples indicated that higher proxy-cognitive ability polygenic scores predicted higher SES, which in turn predicted lower depressive symptoms. ConclusionCurrent findings suggest that some of the genetic correlates of depressive symptoms depend on an environmental mediation and consequently that modifying the environment, specifically through social and economic policies, can affect the genetic influences on depression. Additionally, these results suggest that findings from genetic association studies of depression may be context-contingent and reflect social, cultural, and economic processes in the examined population.
- organism: human
- assay: GWAS summary statistics
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 5
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