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Methods to improve genetic understanding of cardiometabolic traits through multiple traits and diverse population studies

通过多种性状和多样化人群研究提高对心脏代谢性状的遗传理解的方法

基本信息

批准号：
MR/R021368/1
负责人：
Jennifer Asimit
金额：
$ 95.84万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FR021368%2F1
关键词：
Methods improve genetic understanding cardiometabolic

项目摘要

There has been great success in identifying hundreds of genetic variants associated with a large spectrum of diseases and traits, but very few of these variants have an understood role in how they impact the trait. Moreover, a detected variant does not necessarily contribute to effects in the trait, since it may instead have a high correlation with the variant that causes the effect. There is substantial interest in understanding the underlying biology of genetic variants that have an impact on disease or disease-relevant measurements (e.g. cholesterol levels), since there is evidence that this could lead to better disease treatment and prevention. I am particularly interested in improving our knowledge of cardiometabolic diseases due to their high impact on society, as well as globally. Cardiovascular disease (CVD) caused almost one third of deaths worldwide in 2013 and accounted for 45% of all deaths in European countries in 2016, while cardiometabolic disorders are expected to have a greater burden than infectious diseases (e.g. HIV/AIDS) in developing countries.Recent technological advances have made it possible to obtain hundreds of measurements related to metabolism and there is evidence that understanding the genetic influences on human metabolism could improve our understanding of cardiometabolic diseases, as well as inform strategies for modifying existing drugs to treat additional diseases. However, the genetic analysis of many traits is often tackled by one-by-one analyses of individual traits without considering any correlations between them. Instead I will develop a method that identifies associations between many traits with many genetic variants. There is a broad applicability of this method to any large set of traits so there is high potential for impact on diseases and traits beyond those that I will analyse in this fellowship. I will also develop methods that combine information from multiple traits to create sets of genetic variants that will contain the true causal variants with a certain probability. Joint analyses of multiple traits have been shown to result in more refined sets of potential causal variants, but such methods do not yet exist when there are overlapping individuals between the studies, a common situation; this is a gap in methods that I intend to fill. These methods will be applied to several unique datasets, such as hundreds of metabolomics measurements and cardiometabolic, anthropometric and blood-related measurements from both European and African ancestry populations.Gains in the probability to detect associations between genetic variants and traits, as well as the construction of finer resolution sets of potential causal variants, are often likely when information from different ancestries are considered together. However, most methods for jointly analysing diverse ancestries encounter difficulties in the balance between combining the information across the populations to detect associated variants and losing population-specific effects. Instead, I will develop an adaptive analysis approach that is expected to achieve this balance and will also jointly consider multiple traits. At the moment, no methods exist to construct sets of potential causal variants for multiple traits and multiple ethnicities; considering multiple traits is known to give improvements, as does multiple ethnicities, but the two have not yet been combined. This is another void in the methodological toolbox that I plan to fill.All methods will be freely available on-line in user-friendly software and I will also produce an on-line reference database of relationships that are found between the many metabolomics measurements. These are expected to be of wide-spread use to a wide spectrum of researchers from methodological to disease-specific.

在识别与大量疾病和性状相关的数百种遗传变异方面已经取得了巨大的成功，但这些变异中很少有人知道它们如何影响性状。此外，检测到的变异并不一定对性状的影响有贡献，因为它可能与导致这种影响的变异有很高的相关性。人们对了解对疾病或与疾病相关的测量（如胆固醇水平）有影响的遗传变异的潜在生物学非常感兴趣，因为有证据表明，这可能导致更好的疾病治疗和预防。我对提高我们对心脏代谢疾病的认识特别感兴趣，因为它们对社会和全球都有很大的影响。2013年，心血管疾病（CVD）造成了全球近三分之一的死亡，2016年占欧洲国家所有死亡人数的45%，而在发展中国家，心脏代谢疾病的负担预计将超过传染病（如艾滋病毒/艾滋病）。最近的技术进步使人们有可能获得数百种与代谢有关的测量，有证据表明，了解遗传对人类代谢的影响可以提高我们对心脏代谢疾病的理解，并为修改现有药物以治疗其他疾病提供信息。然而，许多性状的遗传分析往往是通过单个性状的逐一分析来解决的，而不考虑它们之间的任何相关性。相反，我将开发一种方法来识别许多性状与许多遗传变异之间的联系。这种方法对任何大的性状集都有广泛的适用性，因此对疾病和性状的影响潜力很大，超出了我将在本奖学金中分析的那些。我还将开发方法，将来自多个特征的信息结合起来，创建一组遗传变异，这些变异将以一定的概率包含真正的因果变异。对多个特征的联合分析已经显示出更精确的潜在因果变异集，但当研究之间存在重叠的个体时，这种方法尚不存在，这是一种常见的情况；这是我打算填补的方法上的空白。这些方法将应用于几个独特的数据集，例如来自欧洲和非洲祖先人群的数百个代谢组学测量和心脏代谢、人体测量和血液相关测量。当把来自不同祖先的信息放在一起考虑时，在检测遗传变异和性状之间的关联以及构建潜在因果变异的更精细的分辨率集的可能性方面的收益往往是可能的。然而，大多数联合分析不同祖先的方法在结合整个种群的信息以检测相关变异和失去种群特异性影响之间的平衡方面遇到困难。相反，我将开发一种适应性分析方法，该方法有望实现这种平衡，并将共同考虑多种特征。目前，还没有方法构建多性状和多种族的潜在因果变异集；考虑多种特征是已知的，多种族也是如此，但这两者还没有结合起来。这是我计划填补的方法论工具箱中的另一个空白。所有的方法都将在用户友好的软件中免费在线提供，我还将制作一个在线参考数据库，其中包含许多代谢组学测量之间的关系。预计这些方法将广泛应用于从方法学到疾病特异性的广泛研究人员。