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

在识别与大范围疾病和性状相关的数百种遗传变异方面取得了巨大成功，但这些变异中很少有人对它们如何影响性状有理解的作用。此外，检测到的变体不一定有助于性状中的效应，因为它可能与引起效应的变体具有高度相关性。人们对理解影响疾病或疾病相关测量（例如胆固醇水平）的遗传变异的潜在生物学有很大的兴趣，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疾病的认识，因为它们对社会和全球的影响很大。2013年，心血管疾病（CVD）造成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%，虽然预计心脏代谢疾病的负担比传染病更大，（例如艾滋病毒/艾滋病）最近的技术进步使人们有可能获得数百种与新陈代谢有关的测量数据，有证据表明，了解基因对人体代谢的影响，可以增进我们对心脏代谢疾病的了解，也可以为改良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对单个性状的逐一分析来解决的，而没有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关联。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，超出了我将在本研究中分析的那些。我还将开发方法，将来自多个性状的联合收割机信息结合起来，以创建一组遗传变异，这些变异将以一定的概率包含真正的因果变异。多个性状的联合分析已经被证明可以产生更精确的潜在因果变异集，但是当研究之间存在重叠个体时，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，如数百个代谢组学测量和心脏代谢，人体测量和血液相关的测量，从欧洲和非洲血统的人群中，在检测遗传变异和性状之间的关联的概率增益，以及潜在的因果变异的更精细的分辨率集的构建，往往是可能的，当来自不同祖先的信息被考虑在一起。然而，大多数用于联合分析不同祖先的方法在结合整个群体的信息以检测相关变异和失去群体特异性效应之间的平衡中遇到困难。相反，我将开发一种适应性分析方法，预计将实现这种平衡，并将联合考虑多种特质。目前，还没有方法可以为多个性状和多个种族构建潜在的因果变异集;考虑多个性状和多个种族都可以带来改善，但两者尚未结合起来。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提供，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。预计这些将广泛用于从方法学到疾病特异性的广泛研究人员。

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.