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Approved research

Understanding genetic and phenotypic contributors to complex disease risk

Principal Investigator: Dr Alicia Zhou
Approved Research ID: 44098
Approval date: March 27th 2019

Lay summary

Both an individual's genetic information and the environment they live in contribute to their risk of diseases such as heart disease and cancer. Many research groups and companies are developing genetic tests that can help people better understand their risk for disease. This information can then be used to help people and their health care providers develop personalized prevention and treatment strategies. Currently, most clinical genetic testing looks for single genetic variants that cause a large change (termed monogenic risk). New research has shown that the cumulative effects of many genetic variants that each have a small change on a person's risk (termed polygenic risk) can add up to a large change in a person's disease risk. In fact, some studies have shown that this polygenic risk can be equivalent to monogenic risk, suggesting genetic testing for polygenic risk could provide valuable health information. In this project we hope to better understand polygenic risk across a large number of people. In addition, development of better risk prediction models can help to better inform clinical decision making for patients. Using UK Biobank data, we will calculate genetic risk for a number of traits and diseases. There are multiple biological factors that contribute to complex diseases. For example, increased BMI, high blood pressure, and high cholesterol all contribute to heart attack risk. We aim to understand the different risk pathways that contribute to each individual's overall risk, to help tailor preventative interventions. In addition, we are interested in finding correlations between genetic scores for one trait and other commonly measured traits. Differences in lifestyle and environment contribute to disease risk, and understanding these differences is essential to implementing genetic testing in clinical practice. Finally, genetic patterns vary across populations and this can affect the accuracy of genetic risk testing. We aim to develop our risk prediction algorithms so that they are valid across multiple populations. The UK Biobank is a unique resource for these goals because it provides a large and unique data that represents a detailed portrait of over 500,000 Britons, one of the largest studies of such kind to be undertaken. This large sample enables researchers such as us to develop more powerful prediction methods and to ensure these predictions are valid in multiple settings. Our work will improve public health through better understanding of genetic and environmental disease risk across a large number of people.