Genomics and Causal Inference
Workstream 1
Severe mental illnesses (SMIs) are growing health problems, with high costs to both individuals and society. Previous research has highlighted links between SMIs and metabolic health (e.g. obesity, type 2 diabetes), although these links are poorly understood, with many unanswered questions. For example, does having both severe depression and type 2 diabetes increase your risk of other adverse health outcomes (e.g. heart problems, leg ulcers etc.) further than having depression or type 2 diabetes alone?
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Our research aims to use information from large studies with genetic data and linked health care records to test how: a) SMIs and metabolic health outcomes are related to one another, b) risk factors (such as education, poverty) influence the relationship and c) the consequences of having both SMI and metabolic health problems.​
To test our hypotheses, we will use genetics to allow us to infer causality, i.e. assess whether the relationships we see are real and not caused by ‘noise’ in the data. This is possible because our genetic code is fixed before birth and does not change as a result of our diet or lifestyle factors, such as smoking, which might otherwise lead to bias. Genes are therefore useful tools for testing the true causal link between two factors. For example, if there is a direct link from schizophrenia to type 2 diabetes, genes that increase the risk of schizophrenia will also have an effect on type 2 diabetes.
Understanding the causal relationship is important from both a public health perspective, to ensure appropriate monitoring and treatment plans are in place and for patients. We will also make use of diverse global datasets to ensure our findings reflect the global population as much as possible.
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