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

一个性状的遗传力决定了我们对其潜在遗传的理解程度的上限。对于人类疾病来说，这决定了我们如何成功地预测个体的疾病风险，以及我们如何开发有效的药物和治疗方法。根据双胞胎、兄弟姐妹风险或其他以家庭为基础的研究所做的测量，许多人类疾病都是高度遗传的。然而，目前我们还不能充分利用这种遗传性。有证据表明，这是因为疾病比以前想象的要复杂得多。很少有一个单一的基因决定一个人是否发展一种疾病。相反，人们已经意识到，一个人的风险往往受到大量遗传因素的影响。这一认识意味着有必要开发分析基因数据的新方法。这些方法必须认识到，许多因素可能对任何给定的性状都很重要。我的项目概述了基于此设计的新方法，其中一种方法解释了如何根据DNA更好地预测个体是否会患上疾病。例如，假设一个人经历了癫痫发作。有50%的机会，这个人将有进一步的癫痫发作，因此将被诊断为癫痫。在这种情况下，有必要服用抗癫痫药物来治疗这种情况。然而，也有50%的机会，个人将永远不会经历另一次癫痫发作。然而，为了充分肯定这一点，个人必须被观察一年，在此期间不允许驾驶机动车。我提出了一种预测方法，这将提高我们的能力，以确定是否一个人谁的经验，癫痫发作将随后发展癫痫。这将加快所需的时间来管理药物，或加快所需的时间来接收所有清除。对于一个人被诊断患有癫痫的情况下，有必要决定什么是最合适的类型的药物提供。这一决定取决于个人患有何种癫痫亚型，因为不同的药物更适合不同的亚型。然而，通常很难确定一个人患有哪种类型的癫痫。因此，我将开发一种更好地对个体进行分类的方法，同样是基于他们的遗传数据。为了使我的方法尽可能有用，我将使它们免费提供，并设计它们供所有类型的科学家使用。

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.