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We aim to advance understanding of the causes and consequences of sleep and circadian rhythms through the study of human genetic variation in large scale population health cohorts.

This has involved the development of machine-learning methods to measure sleep and circadian rhythms from wrist-worn accelerometer data in over 100,000 participants of the UK Biobank study. The code is freely available and summary variables are available to all healthcare researchers who have access to the UK Biobank resource.

Our team has also demonstrated the first large-scale integration of genomics and wearable sensor data. This supports the use of traditional observational epidemiological analysis, and genetic Mendelian Randomisation analysis methods, to investigate whether sleep and circadian rhythms are robustly associated with major disease outcomes.

We collaborate closely with the Big Data Institute (BDI) at Oxford which offers access to access to large-scale cohorts and expertise in genetic epidemiology and machine learning of wearable sensor data.

Our team