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

在识别与一系列疾病和特征相关的数百个遗传变异方面取得了巨大成功，但这些变异中很少有人了解它们如何影响特征。此外，检测到的变异不一定会对性状产生影响，因为它可能与导致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(例如胆固醇水平)有影响的基因变异的基本生物学很感兴趣，因为有证据表明，这可能导致更好的疾病治疗和预防。我特别感兴趣的是提高我们对心脏代谢性疾病的了解，因为它们对社会以及全球都有很大的影响。2013年，心血管疾病(CVD)导致的死亡占全球死亡人数的近三分之一，2016年占欧洲国家所有死亡人数的45%，而在发展中国家，心脏代谢性疾病预计会比传染病(如艾滋病毒/艾滋病)造成更大的负担。最近的技术进步使获得数百项与新陈代谢有关的测量成为可能，有证据表明，了解基因对人类新陈代谢的影响可以提高我们对心脏代谢性疾病的理解，并为修改现有药物以治疗其他疾病提供参考。然而，许多性状的遗传分析往往是通过对单个性状的逐一分析来处理的，而不考虑它们之间的任何相关性。取而代之的是，我将开发一种方法，确定许多特征与许多遗传变异之间的关联。这种方法对任何一组大的特征都有广泛的适用性，因此除了我将在本文中分析的那些之外，对疾病和特征的影响潜力很大。我还将开发将来自多个特征的信息结合在一起的方法，以创建一组遗传变异，这些遗传变异将以一定的概率包含真正的因果变异。对多个性状的联合分析已被证明可以产生更精细的潜在因果变量集，但当研究之间存在重叠的个体时，这种方法还不存在，这是一种常见的情况；这是我打算填补的方法空白。这些方法将被应用于几个独特的数据集，例如来自欧洲和非洲祖先人口的数百个代谢组学测量以及心脏代谢、人体测量和血液相关测量。当将来自不同祖先的信息放在一起考虑时，发现遗传变异和特征之间的关联以及构建潜在因果变异的更精细分辨率集的可能性往往更高。然而，大多数联合分析不同祖先的方法在结合种群间的信息以检测相关变异和失去种群特有效应之间的平衡方面遇到了困难。相反，我将开发一种适应性分析方法，有望实现这种平衡，并将联合考虑多种特征。目前，还没有方法来构建多个性状和多个种族的潜在因果变异集；众所周知，考虑多个性状可以带来改进，多个种族也是如此，但这两者尚未结合起来。这是我计划填补的方法学工具箱中的另一个空白。所有方法都将在用户友好的软件中免费在线使用，我还将创建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论到疾病特异性的各种研究人员。

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.