HSM:Development of methodology and computationally efficient software for analysis of PGx exome sequencing studies of complex "time-to-event" outcomes

个性化医疗是一种治疗患者的方法，其中使用人口统计学，临床和遗传信息等个人信息来决定如何治疗他们-例如给予哪种药物或多少药物。最终目标是使治疗的益处最大化，危害最小化。为了确定用于指导治疗的遗传信息，使用药物遗传学（PGx）研究，其中涉及分析患者的DNA并寻找其遗传信息与药物反应之间的相关性。最常见的研究设计是全基因组关联研究（GWAS），该研究涉及对患者进行数十万种称为单核苷酸多态性（SNP）的遗传变异测试，并测量他们的药物反应。然而，用于分析GWAS的统计方法仅适用于调查与常见SNP的相关性，其中次要等位基因频率（MAF）-SNP的最不常见版本的频率-大于5%。当常见的SNP与治疗反应的结果相关时，它们的影响通常很小，因此GWAS在识别遗传变异以指导治疗方面的成功非常有限。其中一个例子是抗癫痫药物反应的PGx研究，已经进行了GWAS，但对其遗传预测因子知之甚少。全外显子组测序是GWAS的替代方法，具有许多优势，并且比全基因组测序便宜。在这种方法中，从具有显著比例的遗传变异的基因组区域（外显子组区域）收集遗传信息，所述遗传变异极有可能对分子功能产生影响，因此作为治疗反应的预测因子在生物学上是可行的。该方法还允许研究罕见的变体（MAF <5%），并且可以将它们分组到被认为具有相似的分子和生物学效应的集合中，然后使用所谓的“基于基因”的分析一起进行分析。这些分析通常比单变量方法更好地识别与结果的相关性。分析全外显子组测序数据需要不同的统计方法来分析GWAS数据，虽然已经开发了适当的方法，但这些方法仅适用于二元或连续结果的研究。PGx研究通常对“至事件发生时间”结果感兴趣，例如至疾病缓解时间或至停药时间，因此我们在识别与PGx结果相关的遗传变异方面遇到了分析瓶颈。在这个项目中，我们的目标是通过开发新的统计方法和适当的软件来解决这一瓶颈，这些方法和软件可以处理外显子组序列数据的规模和复杂性。为了实现这些目标，我们设定了以下目标：1。为具有复杂事件发生时间结局的PGx研究开发新的统计方法; 2.开发用户友好，计算效率高，免费的软件实现的方法，以处理外显子组序列数据集的规模和复杂性。3.通过在各种不同的假设下模拟外显子组序列数据，证明新方法与现有分析方法相比的效率。4.将新的方法和软件应用于外显子组序列数据集，以识别抗癫痫（AED）反应的生物标志物。通过实践研讨会提供新方法和软件的培训。我们还将把我们的方法应用于英国生物库的全外显子序列数据，以识别与心血管疾病和2型糖尿病发病时间相关的遗传变异。我们提出的方法和软件将允许对这些疾病的发病年龄进行更强大的分析，指出导致疾病在生命早期发生的基因，其中环境风险因素的影响不太重要，治疗的影响更大。这些基因可以用于药物开发。

Personalised medicine is an approach to treating patients where individual information like demographics, clinical and genetic information is used in deciding how to treat them - for example which drug or how much of a drug to give. The ultimate goal is to maximise benefit and minimise harm from treatment. To identify genetic information to be used in guiding treatment, pharmacogenetics (PGx) studies are used, which involve analysing DNA of patients and looking for correlation between their genetic information and drug response. The most common study design is the genome-wide association study (GWAS), which involves testing patients at hundreds of thousands of genetic variants known as single-nucleotide polymorphisms (SNPs) and measuring their drug response. However, the statistical methods used to analyse GWAS are suitable only for investigating correlation with common SNPs, where the minor allele frequency (MAF)-the frequency of the least common version of SNP-is greater than 5%. When common SNPs have been associated with outcomes of treatment response, their effect has typically been small, so GWAS have only had very limited success in identifying genetic variants to guide treatment. An example of this is PGx research into anti-epileptic drug response, for which GWAS have been conducted, yet little is still known about its genetic predictors.Whole-exome sequencing is an alternative approach to GWAS with many advantages and is cheaper than sequencing the whole genome. In this approach, genetic information is collected from areas of the genome (exomic regions) with a significant proportion of genetic variants which are highly likely to have an effect on molecular function, so are biologically feasible as predictors of treatment response. The approach also allows for rare variants (MAF<5%) to be investigated and they can be grouped together into sets believed to have similar molecular and biological effect, then analysed together using what is known as 'gene-based' analyses. These analyses are generally better at identifying correlations with outcome than single-variant approaches.Analysing whole-exome sequencing data requires different statistical methods to analysing GWAS data, and whilst appropriate methods have been developed, these are only for studies with binary or continuous outcomes. PGx studies are often interested in 'time-to-event' outcomes, for example time to disease remission or time to drug withdrawal, so we are experiencing an analytical bottleneck for identifying genetic variants associated with PGx outcomes. In this project we aim to address this bottleneck by developing novel statistical methods and appropriate software for time to event outcomes that can cope with the scale and complexity of exome sequence data. To achieve these aims, we have set the following objectives:1. Develop new statistical methods for PGx studies with complex time to event outcomes;2. Develop user-friendly, computationally efficient, free software implementing the methods, to deal with the scale and complexity of exome sequence datasets. 3. Demonstrate efficiency of the new methods compared to existing analysis approaches by simulating exome-sequence data under a variety of different assumptions. 4. Apply the new methods and software to an exome-sequence dataset to identify biomarkers of anti-epileptic (AED) response.5. Offer training on the new methods and software through practical workshops.We will also apply our methods to whole exome sequence data from UK Biobank, to identify genetic variants associated with time to onset of cardiovascular disease and type 2 diabetes. Our proposed methodology and software will allow for more powerful analysis of age of onset of these diseases, pointing to genes that lead to disease occurring earlier in life, where the effect of environmental risk factors are less important, and the impact of treatment is greater. These genes could then be used in drug development.