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Diet and lifestyle have changed dramatically in the last few decades, leading to an increase in prevalence of obesity, defined as a body mass index >30Kg/m2, dyslipidaemias (defined as abnormal lipid profiles) and type 2 diabetes (T2D). Together, these cardiometabolic traits and diseases, have contributed to the increased burden of cardiovascular disease, the leading cause of death in Western societies. Complex traits and diseases, such as cardiometabolic traits, arise as a result of the interaction between an individual's predisposing genetic makeup and a permissive environment. Since 2007, genome-wide association studies (GWAS) have been successfully applied to complex traits leading to the discovery of thousands of trait-associated variants. Nonetheless, much is still to be understood regarding the genetic architecture of these traits, as well as their underlying biology. This thesis aims to further explore the genetic architecture of cardiometabolic traits by using complementary approaches with greater genetic and phenotype resolution, ranging from studying clinically ascertained extreme phenotypes, deep molecular profiling, or sequence level data. In chapter 2, I investigated the genetic architecture of healthy human thinness (N=1,471) and contrasted it to that of severe early onset childhood obesity (N=1,456). I demonstrated that healthy human thinness, like severe obesity, is a heritable trait, with a polygenic component. I identified a novel BMI-associated locus at PKHD1, and found evidence of association at several loci that had only been discovered using large cohorts with >40,000 individuals demonstrating the power gains in studying clinical extreme phenotypes. In chapter 3, I coupled high-resolution nuclear magnetic resonance (NMR) measurements in healthy blood donors, with next-generation sequencing to establish the role of rare coding variation in circulating metabolic biomarker biology. In gene-based analysis, I identified ACSL1, MYCN, FBXO36 and B4GALNT3 as novel gene-trait associations (P < 2.5x10-6). I also found a novel link between loss-of-function mutations in the "regulation of the pyruvate dehydrogenase (PDH) complex" pathway and intermediate-density lipoprotein (IDL), low-density lipoprotein (LDL) and circulating cholesterol measurements. In addition, I demonstrated that rare "protective" variation in lipoprotein metabolism genes was present in the lower tails of four measurements which are CVD risk factors in this healthy population, demonstrating a role for rare coding variation and the extremes of healthy phenotypes. In chapter 4, I performed a genome-wide association study of fructosamine, a measurement of total serum protein glycation which is useful to monitor rapid changes in glycaemic levels after treatment, as it reflects average glycaemia over 2-3 weeks. In contrast to HbA1c, which reflects average glucose concentration over the life-span of the erythrocyte (~3 months), fructosamine levels are not predicted to be influenced by factors affecting the erythrocyte. Surprisingly, I found that in this dataset fructosamine had low heritability (2% vs 20% for HbA1c), and was poorly correlated with HbA1c and other glycaemic traits. Despite this, I found two loci previously associated with glycaemic or albumin traits, G6PC2 and FCGRT respectively (P < 5x10-8), associated with fructosamine suggesting shared genetic influence. Altogether my results demonstrate the utility of higher resolution genotype and phenotype data in further elucidating the genetic architecture of a range of cardiometabolic traits, and the power advantages of study designs that focus on individuals at the extremes of phenotype distribution. As large cohorts and national biobanks with sequencing and deep multi-dimensional phenotyping become more prevalent, we will be moving closer to understanding the multiple aetiological mechanisms leading to CVD, and subsequently improve diagnosis and treatment of these conditions.

Complex diseases are driven by multiple risk factors, including genetic variants, environmental exposures and interactions between the two. The advent of GWAS in 2005 and subsequent methodological advances have increased our knowledge of the genetic risk factors underpinning complex diseases. In addition, some research exploring genotype-environment interaction (GxE) effects has been conducted, revealing that multiple environments are linked to interaction effects at a single locus for a given trait. However, correlation between these identified environments renders interpretation of the results difficult. This, together with the collation of large-scale biobanks that contain a multitude of phenotypic and environmental data (facilitating an increase in the number of GxE effects detected) has generated the need for methods that jointly account for GxE at multiple environments. Such methods may also increase the power to detect interaction effects by aggregating modest or weak GxE effects across environments and in addition enable additional phenotypic variance to be explained. Thus, the aim of this thesis is to provide suitable methods to identify variants subject to GxE effects, jointly accounting for multiple environmental exposures and explore these effects across a range of phenotypes using the UK Biobank data. In Chapter 2, I describe the structured linear mixed model (StructLMM), a novel computationally efficient multivariate GxE framework. This model can be used to test for interaction or association effects. The latter accounts for possible heterogeneity in variant effects across individuals due to differences in environmental exposures, thus enabling the detection of variant effects that might otherwise be masked due to the presence of interaction effects. I show through the use of simulation experiments that StructLMM is robustly calibrated and in general, better powered than existing interaction and association tests. In Chapter 3, I present an application of StructLMM, where I identify significant interaction effects with 64 lifestyle-based factors for BMI using the UK Biobank data. In addition, I show that the StructLMM association test can be used to identify loci with genotype-environment contributions. Subsequently, I explore characteristics of loci with significant interaction effects, including the fraction of the genetic variance that is explained by GxE and the environmental profiles that increase or decrease phenotypic risk, using methods that are implemented as part of StructLMM. In Chapter 4, I apply the StructLMM interaction test to multiple cardiometabolic traits using the UK Biobank data, facilitating exploration of shared GxE architecture. Additionally, I provide preliminary estimates of the amount of phenotypic variation that can be explained by GxE effects, relative to marginal association effects. Taken together, the work in this thesis demonstrates the need and advantages of jointly modelling interaction effects at multiple environments, providing a new computationally efficient method to achieve this. Combined with the recent and ongoing generation of large biobanks, further research in this field has the potential to advance our understanding of complex traits and diseases.