bioRxivGenomicsPreprint
Mental Health, Schooling Attainment and Polygenic Scores: Are There Significant Gene-Environment Associations?
Vikesh AminiD, Jere Richard BehrmaniD, Jason M. FletcheriD, Carlos Alberto FloresiD, Alfonso Flores‐LagunesiD, Hans‐Peter KohleriD
Abstract
It is well-established that (1) there is a large genetic component to mental health, and (2) higher schooling attainment is associated with better mental health. Given these two observations, we test the hypothesis that schooling may attenuate the genetic predisposition to poor mental health. Specifically, we estimate associations between a polygenic score (PGS) for depressive symptoms, schooling attainment and gene-environment (GxE) interactions with mental health (depressive symptoms and depression), in two distinct United States datasets at different adult ages-29 years old in the National Longitudinal Study of Adolescent Health (Add Health) and 54 years old in the Wisconsin Longitudinal Study (WLS). OLS results indicate that the association of the PGS with mental health is similar in Add Health and the WLS, but the association of schooling attainment is much larger in Add Health than in the WLS. There is some suggestive evidence that the association of the PGS with mental health is lower for more-schooled older individuals in the WLS, but there is no evidence of any significant GxE associations in Add Health. Quantile regression estimates also show that in the WLS the GxE associations are statistically significant only in the upper parts of the conditional depressive symptoms score distribution. We assess the robustness of the OLS results to omitted variable bias by using the siblings samples in both datasets to estimate sibling fixed-effect regressions. The sibling fixed-effect results must be qualified, in part due to low statistical power. However, the sibling fixed-effect estimates show that college education is associated with fewer depressive symptoms in both datasets.
- organism: human
- assay: GWAS summary statistics
- research focus: benchmarking
- approach: statistical model
- Citations
- 2
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