HSM Polygenic score methodology in the emerging field of Polygenic Epidemiology

多基因流行病学新兴领域中的 HSM 多基因评分方法

• 批准号: MR/N015746/1
• 负责人: Paul O'Reilly
• 金额: $ 58.46万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FN015746%2F1
• 关键词: HSM; Polygenic; score; methodology; emerging

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.

在过去的十年中，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”（GWAS），这些研究已经确定了数千种与数百种疾病，精神疾病和人类特征相关的遗传变异。尽管这项努力在强调对人类疾病的人类基因组的区域方面取得了非凡的成功，但它还揭示了大多数人类疾病受到数百种遗传变异的影响，这些变异对疾病风险产生了较小的影响。这使得很难利用个人的遗传特征来预测他们患不同疾病或对药物治疗（例如药物药物）的不良反应的可能性。结果，遗传学尚未实现其启动分层医学时代的最大承诺：根据遗传特征，疾病预防和治疗是个性化的。但是，现在正在开发方法并将其应用于遗传数据，这些遗传数据特别考虑了遗传学如何影响我们疾病风险的“多基因”性质。这些方法统称为“多基因得分方法”。基于他们到目前为止的理论评估和实际应用，早期的迹象表明，它们可以证明遗传学提供分层医学的潜力的新希望是合理的。但是，在此之前，需要进行许多理论上的工作，以开发和测试这些多基因评分方法。对他们的表现有更大的了解至关重要，需要精炼以产生更准确的疾病风险预测并扩展以解决更广泛的医学遗传学问题，并且需要制定一系列策略，以便如何利用它们用于分层医学。该建议旨在实现这些目标。该提案始于对不同多基因分数方法的详细评估和比较。基于计算机模拟的这项研究的结果将提供有关这些方法的执行方式，可以做出的推论以及指导研究人员根据其科学问题和研究使用哪种方法的见解。我们将生成的进行此研究的软件工具将作为Web应用程序免费提供，研究人员将能够从中以自定义格式呈现的研究结果检查结果，模拟和下载其自己的多基因数据，并下载并扩展我们的模拟代码以执行多基因方法评估和比较。例如，虽然一种方法旨在评估两种疾病是否具有共同的遗传基础，对于指导哪种药物可以重新用于其他疾病，但另一种方法有助于确定观察到的危险因素与疾病之间的关联是否真正是因果关系，并且另一个人在这种关系中最有可能的因果关系在这种关系中可能会增加诸如alip sever sever of Al e and ear sever nek there sys：阿尔茨海默氏症的脂质水平较高？最后，该提案调查了许多使用多基因评分方法来帮助分层医学的策略。我们将概述和测试方法，用于使用多基因评分方法将异质疾病分类为更生物均质疾病的子集，这将有助于做出个人诊断，从而更具体。我们还描述了在临床试验中可以使用多基因评分来进行更多细微的参与者，从而降低其疗效，成本，并最终导致药物和治疗方法的发展，从而更加适合个人遗传特征。

项目成果

Causal Associations Between Modifiable Risk Factors and the Alzheimer's Phenome.

• DOI: 10.1002/ana.25918
• 发表时间: 2021-01
• 期刊: Annals of neurology
• 影响因子: 11.2
• 作者: Andrews SJ;Fulton-Howard B;O'Reilly P;Marcora E;Goate AM;collaborators of the Alzheimer's Disease Genetics Consortium
• 通讯作者: collaborators of the Alzheimer's Disease Genetics Consortium

Genetic correlations of psychiatric traits with body composition and glycemic traits are sex- and age-dependent

精神特征与身体成分和血糖特征的遗传相关性取决于性别和年龄

• DOI: 10.17615/fjsd-m874
• 发表时间: 2019
• 作者: Breen, G.

Association of Whole-Genome and NETRIN1 Signaling Pathway-Derived Polygenic Risk Scores for Major Depressive Disorder and White Matter Microstructure in the UK Biobank.

• DOI: 10.1016/j.bpsc.2018.07.006
• 发表时间: 2019-01
• 期刊: Biological psychiatry. Cognitive neuroscience and neuroimaging
• 作者: Barbu MC;Zeng Y;Shen X;Cox SR;Clarke TK;Gibson J;Adams MJ;Johnstone M;Haley CS;Lawrie SM;Deary IJ;Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium;23andMe Research Team;McIntosh AM;Whalley HC
• 通讯作者: Whalley HC

Translating genome-wide association findings into new therapeutics for psychiatry.

• DOI: 10.1038/nn.4411
• 发表时间: 2016-10-26
• 期刊: Nature neuroscience
• 影响因子: 25
• 作者: Breen G;Li Q;Roth BL;O'Donnell P;Didriksen M;Dolmetsch R;O'Reilly PF;Gaspar HA;Manji H;Huebel C;Kelsoe JR;Malhotra D;Bertolino A;Posthuma D;Sklar P;Kapur S;Sullivan PF;Collier DA;Edenberg HJ
• 通讯作者: Edenberg HJ

Depression with atypical neurovegetative symptoms shares genetic predisposition with immuno-metabolic traits and alcohol consumption.

• DOI: 10.1017/s0033291720002342
• 发表时间: 2022-03
• 期刊: Psychological medicine
• 影响因子: 6.9
• 作者: Badini I;Coleman JRI;Hagenaars SP;Hotopf M;Breen G;Lewis CM;Fabbri C
• 通讯作者: Fabbri C