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Approved Research

Developing and applying advanced statistical methods to dissect genetic and genomic heterogeneity in diverse complex traits

Principal Investigator: Dr Andrew Dahl
Approved Research ID: 89052
Approval date: December 14th 2022

Lay summary

Common disorders cause the lion's share of human illness. Such disorders--including diabetes, heart disease, major depression, endometriosis, and asthma--have many known causes and potential therapies. All these disorders run in families, suggesting they have genetic causes. In genetics, we seek to understand these causes to find clues to curing disease.

However, we still have a poor understanding of how, exactly, genetics cause these diseases. One major roadblock to is that common diseases are highly diverse, or heterogeneous. In other words, a single disease will have very different features in different people. For example, high BMI is a well-known risk factor for type 2 diabetes, yet there are many diabetic individuals who have low BMI. Likewise, some people with heart disease have low levels of "bad" cholesterol, and some people with depression have no history of severe life traumas. Common diseases also have heterogeneous responses to treatment: For example, lifestyle modifications can essentially cure depression in some cases, but in other cases depression persists for a lifetime despite all our best efforts.

This diversity within disease has led to the well-known idea of precision treatment. That is, we should treat each person in the optimal way, which often means giving different treatments to people with the same disease. For example, cholesterol-reducing drugs like statins may not be as effective in reducing heart disease when someone already has low cholesterol. As another example, anti-allergy medicine is less likely to help people with non-allergic forms of asthma.

Our project aims to use genetics to advance precision treatment. Rather than simply asking which genetic variants cause a disease, we will ask which genetic variants cause differences within a disease. In other words, we will ask which genetic variants cause disease for different people. To do this, we will develop and apply statistical genetic tools to dissect disease heterogeneity. This will give us more accurate genetic predictions for disease risk, and also genetic predictions for which treatments to give to which people. For example, our results may predict who best responds to more exercise, or less alcohol intake, or using cholesterol-lowering medications. All of these questions can be phrased as genetic studies of disease heterogeneity. Our three-year, rolling project  will take a big-picture view across diverse disorders available in the UK Biobank. This will ensure our methodologies are robust, and will also maximize our chances to make discoveries that advance human health.