Gene-Environment Interplay for Well-Being

Well-being is a phenotype that is increasingly recognized as both an important research topic across several disciplines (e.g., psychology, sociology, economics) as well as a public policy goal. The term well-being itself is an umbrella term that is used to describe a multitude of constructs, including (but not exclusive to) happiness, subjective well-being, and psychological well-being. Based on former research, we have learned that individual differences in well-being phenotypes are caused partly by genetic factors, and partly by environmental factors. In this thesis, I aim to unite genetic and environmental research on well-being by studying how they dynamically combine and contribute to well-being. In the first section of this thesis, I start with a re-evaluation of the (relationship between different) well-being measures and well-being genetics. Next, in the second section, I present data- driven approaches to studying environmental correlates of well-being and use the outcomes of these studies to examine potential gene-environment correlation (the phenomenon where exposure to the environment depends on one’s genotype). In section three, I study influence of genetic and environmental factors on well-being and self-rated health during an extreme environmental shift that took place during the writing of this thesis: the COVID-19 pandemic. Lastly, in section four, I move from correlational approaches to causal approaches. In this last section, we focus on socioenvironmental influences on adolescent well-being and on the potentially causal effect of longer education on several well-being and health outcomes. All in all, the work presented in this thesis presents us with novel insights on gene-environment interplay for well-being that help us understand well-being better and may be used to guide further research and policy. In the discussion of the thesis, I present interesting directions for future interdisciplinary research in this area, including the inclusion of more diverse populations and different data types.