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Approved Research

Genetic Heterogeneity in Diseases with Complex Inheritance

Principal Investigator: Dr John Belmont
Approved Research ID: 98786
Approval date: February 16th 2023

Lay summary

The aim of this project is to develop a new statistical method to understand the pattern of genetic variation in common diseases. We will use coronary artery disease, breast cancer, and Parkinson's disease as models, because there is a huge prior experience and information about genetic factors in these diseases. This prior knowledge will help us determine whether the new mathematical methods are useful and capable of adding new insights. Specifically, this project will examine whether new statistical techniques - called Causal Inference - help to distinguish subtypes of these diseases. Some of these subtypes are already known and we hope to identify more. 

The scientific rationale for developing this new method is that it could address a major challenge in medical practice. Currently, medications and other treatments for most diseases do not benefit all patients. Randomized controlled trials often show that not all patients respond to otherwise effective treatments but it is tough to uncover the explanation. Doctors typically use trial and error to find individualized treatments. In this research, we will evaluate some new methods to see if we can define previously unrecognized subgroups of patients. We hope that by recognizing these subgroups it will be possible to tailor effective treatments to them and avoid ineffective or harmful treatments.

This project will last about 2 years. In the first year, we will develop the algorithms needed to analyze genetic and medical data. We will test the algorithms in simulated data and then we will test the algorithms in real data from the UKBB. The final months will be used to prepare a publication of the results, which will be published in a way that makes the mathematical results available to the general public without restriction.

Public health could be positively impacted if the new method successfully identifies subtypes of common diseases. Knowing the factors that cause disease is the first step needed to develop new effective treatments.