| Summary: | <p>Depression is a global burden. It is one of the most common mental health disorders and top ten causes of sickness worldwide. The importance of obtaining a deeper understanding of depression and its development has been raised by various scholars, policy makers, and the media. All of these factors contribute to a burgeoning body of research into different aspects of depression. The lack of a multifaceted understanding of depression is one of the core obstacles for its treatment. Depression has both biological and non-biological risk factors driven by interconnected causes. The recognition of social and biological drivers and recent advances in molecular genetics permits an unprecedented and unique opportunity to use sociogenomic tools to deepen our knowledge of the multidimensional nature of the biological and social risks of developing depression.</p>
<p>This thesis bridges a gap in our knowledge on the historical, social, and biological predictors of depression by adopting a sociogenomic approach, focusing on the highly relevant context of the UK. The scope of the thesis is unique as I aim to not only expand on empirical evidence of the complex interplay between individual- and macro-level risk factors of depression, but also raise methodological concerns that arise due to such interplay. In four empirical chapters, I contribute to the existing literature on depression and social science genomics, in four different ways: (1) a methodological assessment of selection in genetic data and its role in the polygenic prediction of depression; (2) scrutiny of moderating patterns of birth cohorts and economic recessions associated with changing polygenic penetrance of depression; (3) exploration of the link between genetic predispositions to depression and instances of worklessness that position people into higher risks to experience depression; and, (4) investigation of the ways in which endogenous selection bias leads to spurious associations and biased variance statistics in the models with polygenic scores.</p>
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