Improved methodology for understanding the genetics of complex traits, with particular application to epilepsy.

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The heritability of a trait determines an upper bound on how well we can understand its underlying genetics. For the case of a human disease, this determines how successfully we can predict an individual's disease risk, and how well we can develop effective drugs and treatments. Many human diseases are known to be highly heritable, based on measurements made in twin, sibling-risk, or other family-based studies. However, at present we are have not been able to fully make use of this heritability. Evidence suggests that this is because disease are more complicated than once thought. It is rarely the case that a single gene determines whether an individual develops a condition. Instead, it has been realised that more often an individual's risk is affected by a large number of genetic factors. This realisation means it is necessary to develop new methods for analysing genetic data. These methods must appreciate that many factors are likely to be important for any given trait. My project outlines new methodologies designed with this in mind.One of these methods explains how to better predict whether an individual will develop a disease based on their DNA. For example, suppose that an individual experiences an epileptic seizure. There is a 50% chance that this individual will have further seizures and will therefore be diagnosed with epilepsy. In this case, it would be necessary to administer anti-epileptic drugs to treat the condition. However, there is also a 50% chance that the individual will never experience another seizure. However, to be sufficiently certain that this is the case, the individually will have to be observed for a year, and would not be allowed to drive a motor vehicle during that time. I propose a prediction method which will improve our ability to determine whether an individual who experiences a seizure will subsequently develop epilepsy. This will either speed-up the time taken to administer drugs, or speed-up the time taken to receive the all-clear.For the case that an individual is diagnosed with epilepsy, it is necessary to decide what is the most appropriate type of drugs to provide. This decision depends on what subtype of epilepsy the individual has, as different medications are more suitable for different subtypes. However, it is often difficult to determine what type of epilepsy an individual has. Therefore, I will develop a method for better classifying individuals, again based on their genetic data.So that my methods as useful as possible, I will make them freely-available, and design them to be used by all types of scientists.