HSM Polygenic score methodology in the emerging field of Polygenic Epidemiology

在过去的十年里，医学遗传学领域的研究人员进行了大量的‘全基因组关联研究’，这些研究已经确定了与数百种疾病、精神障碍和人类特征相关的数千种遗传变异。虽然这一努力在突出人类基因组中导致人类疾病的区域方面取得了异常成功，但它也揭示了大多数人类疾病受到数百种基因变异的影响，每种基因变异对疾病风险的影响都很小。这使得利用个人的基因特征来预测他们感染不同疾病或对药物等医疗治疗产生不良反应的可能性非常困难。因此，遗传学尚未实现开启分层医学时代的最大希望：疾病预防和治疗根据基因图谱进行个性化。然而，现在正在开发方法，并将其应用于基因数据，这些方法特别考虑到遗传学如何影响我们的疾病风险的“多基因”性质。这些方法统称为“多基因计分方法”。根据到目前为止的理论评估和实际应用，早期迹象表明，它们可能证明对遗传学提供分层医学的潜力重新抱有希望是合理的。然而，在这之前，需要进行大量的理论工作来开发和测试这些多基因评分方法。更好地了解它们的表现是至关重要的，需要对它们进行改进，以产生更准确的疾病风险预测，并加以推广，以解决医学遗传学中的更广泛问题，并需要为如何利用它们进行分层医学制定一套策略。本提案旨在实现上述每个目标。本提案首先对不同多基因评分方法的性能进行了详细的评估和比较。这项基于计算机模拟的研究结果将提供关于这些方法如何执行的见解，它们可以做出什么推断，并根据他们的科学问题和研究指导研究人员使用哪种方法。我们将生产的用于执行这项研究的软件工具将作为网络应用程序免费提供，研究人员将能够：检查以可定制格式呈现的研究结果，模拟和下载他们自己的多基因数据，并下载和扩展我们的模拟代码，以执行多基因方法评估和他们自己的比较。接下来，该提案介绍了一套新的多基因评分方法，每种方法都是为回答特定的科学问题而量身定做的。例如，虽然一种方法旨在评估两种疾病是否有共同的遗传基础，这有助于指导哪些药物可以用于其他疾病，另一种方法有助于确定观察到的风险因素和疾病之间的关联是否真的是因果关系，还有另一种方法推断这种关系中最可能的因果关系方向，这可能会回答关键的医学问题，例如：高血脂水平是否会增加阿尔茨海默氏症的风险，或者阿尔茨海默氏症的发病是否会导致更高的血脂水平？最后，该提案调查了使用多基因评分方法来辅助分层医学的一些策略。我们将概述和测试使用多基因评分方法将异质性疾病分层为更具生物同质性的疾病的子集的方法，这将有助于使个体诊断和后续治疗更具特异性。我们还描述了在临床试验中如何使用个人的多基因评分来更细微地选择参与者，降低他们的疗效和成本，并最终导致更适合个人基因特征的药物和治疗的开发。

Over the last decade, researchers in the field of medical genetics have conducted a huge number of 'genome-wide association studies' (GWAS), which have identified thousands of genetic variants associated with hundreds of diseases, psychiatric disorders and human traits. While this endeavour has been exceptionally successful in highlighting regions of the human genome that contribute to human disease, it has also revealed that most human diseases are influenced by hundreds of genetic variants that each have a small impact on disease risk. This makes it extremely difficult to exploit an individual's genetic profile to predict how likely they are to contract different diseases or suffer adverse reactions to medical treatments, such as pharmaceutical drugs. As a result, genetics has yet to fulfil its greatest promise of initiating an era of stratified medicine: where disease preventions and treatments are individualised according to genetic profile. However, methods are now being developed and applied to genetic data that take special account of the 'polygenic' nature of how genetics influences our disease risk. These methods are known collectively as 'polygenic score methods'. Based on their theoretical evaluation and practical application so far, early signs indicate that they may justify renewed hope in the potential of genetics to deliver stratified medicine. However, before this is possible, much theoretical work needs to go in to the development and testing of these polygenic score methods. A greater understanding of their performance is crucial, they need to be refined to produce more accurate disease risk prediction and extended to solve a wider variety of problems in medical genetics, and a set of strategies needs to be developed for how they can be exploited for stratified medicine. This proposal aims to achieve each of these goals.This proposal begins with a detailed evaluation and comparison of the performance of different polygenic score methods. The results from this study, based on computer simulation, will offer insights into how these methods perform, what inferences they can make, and guide researchers on which method to use depending on their scientific question and study. The software tool that we will produce to perform this study will be made freely available as a web application, from which researchers will be able to: inspect results from our study presented in a customisable format, simulate and download their own polygenic data, and download and extend our simulation code to perform polygenic method evaluations and comparisons of their own.Next the proposal introduces a set of new polygenic score methods, each one tailored to answer a specific scientific question. For example, while one method is designed to assess whether two diseases share a common genetic basis, useful for guiding which pharmaceutical drugs could be repurposed for other diseases, another helps determine whether an observed association between a risk factor and a disease is truly causal, and yet another infers the most likely direction of causality in such a relationship, which could answer key medical such as: Do high lipid levels increase the risk of Alzheimer's or does the onset of Alzheimer's result in higher lipid levels? Finally, the proposal investigates a number of strategies for using polygenic score methods to aid in stratified medicine. We will outline and test approaches for using polygenic score methods to stratify heterogenous disorders into sub-sets of more biologically homogenous disorders, which will help make individual diagnosis, and thus subsequent treatment, more specific. We also describe ways in which polygenic scores on individuals can be used for more nuanced selection of participants in clinical trials, reducing their efficacy, cost, and ultimately leading to the development of drugs and treatments more tailored to individual genetic profile.