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

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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.