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Genomics and Causal Inference

Workstream 1

This workstream will significantly improve our understanding of the causal relationships between a range of metabolic health markers and SMIs, within both European and non-European ancestries. Identifying causal relationships will ultimately inform the design and stratification of future diagnostic, monitoring and intervention approaches for SMI.

Using genetics to improve our understanding of the relationship between metabolic disorders and SMI, this workstream will assess the strength of causal inferences between clinical, quantitative and molecular indices of altered metabolism and SMI, paving the way for more targeted and effective treatments.

We will expand our current programme of work on obesity, T2D and major depression to include schizophrenia and bipolar disorder within both European and non- European ancestries. This will make use of existing genomic datasets on millions of individuals and will assess bi-directional causal associations between SMI and a) quantitative clinical metrics (obesity), b) molecular biomarkers (HbA1c, fasting glucose) and c) disease-like traits (T2D, hypercholesterolaemia).

Power in Numbers







Team Members 

Olivia Walker

Editor in Chief

Dan Mitchell

Assistant Manager

Noah Patterson

Programming Editor

Tess Anderson

Art Director

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