Proteins are the primary functional units of biology and the direct targets of most drugs, yet there is limited knowledge of the genetic factors determining inter-individual variation in protein levels (protein quantitative trait loci (pQTLs)). Limitations in high-throughput proteomic measurement technology have meant well-powered genome-wide association studies for large number of proteins so far have lagged behind many of the other "omic" studies such as transcriptomics and metabolomics. This is made more challenging by the complexity of human plasma, characterised by high dynamic range spanning several magnitudes of concentrations and a large number of low abundance proteins. By using an expanded high-throughput multiplex aptamer-based proteomic assay with more than twice the proteome coverage of previous studies, I am able to greatly expand on existing knowledge on genetic determinants of human plasma proteins through testing 10.6 million DNA variants against levels of 2,994 proteins in 3,301 individuals. I identify 1,927 genetic associations with 1,478 proteins, replicating many previous associations as well as gaining novel insights into the genetic architecture of the human plasma proteome. I use several approaches to highlight the application of pQTLs to biology and disease. I show several examples linking distant pQTLs to biologically plausible genes and demonstrate the mediation of distant pQTL by local protein levels, highlighting the role of protein-protein interactions. In addition, I find epistatic effects of genetically determined phenotypes (blood group and secretor status) on protein levels. Through linking previous disease associations, I show that disease associated variants are enriched for pQTLs and I provide insights into possible mechanisms underpinning some of the disease loci. Finally, I identify causal roles for protein biomarkers in disease through multivariable Mendelian randomisation (MR) analysis, leveraging on the simultaneous measurement of multiple functionally related proteins in a locus to account for potential pleiotropic effects. Whereas MR studies of plasma proteins have been constrained by availability of few suitable genetic instruments, the data generated here remedy this bottleneck by furnishing an extensive toolkit. Overall, the work within this thesis foreshadows major advances in post-genomic science through increasing application of novel bioassay technologies to major population biobanks.

The application of multivariable Mendelian randomisation (MVMR) to analyse time-varying data with multiple measurements of both an exposure and an outcome is unclear. The purpose of this thesis is to develop and examine the properties of a potential model to extend MVMR to handle two measurements of both an outcome and an exposure. The exposure effect at Time 1 is estimated using univariable Mendelian randomisation (MR), while the exposure effects at Time 2 are estimated using MVMR by using a set of single nucleotide polymorphisms (SNPs) exclusive to the first outcome measurement. Simulations examining the properties of the causal effect estimates in the model under different scenarios were undertaken. The scenarios included different sampling schemes (1, 2, or 4 samples) for summary statistics. Confidence intervals were too wide, over-coverage was present when following the one-sample scheme, while slight under-coverage in both the two-sample and four-sample schemes was observed. Parameter estimators appeared to be mainly unaffected by increasing instrument strength. Increasing the number of SNPs pertaining to each exposure led to increased biases for the parameters affecting the second outcome measurement. Lastly, parameter estimates maintained acceptable coverage and small biases for different scenarios of overlapping SNPs. The inclusion of SNPs pertaining to the first outcome measurement in a time-varying MVMR model with two exposure and two outcome measurements allows for the estimation of exposure effects at both time points. However, the apparent drop in performance when the number of SNPs increases is of concern. / Thesis / Master of Science (MSc)

Background: Coronary heart disease (CHD) is one of the leading causes of death worldwide, and global mortality rates are expected to continue to rise over the coming decades. In Pakistan in particular, chronic diseases are responsible for 50% of the total disease burden. Circulating lipids are strongly and linearly associated with risk of CHD; however, despite considerable efforts to demonstrate causality, available evidence is conflicting and insufficient. Study of the underlying metabolic pathways implicated in the association between lipids and CHD would help to disentangle and elucidate these complex relationships. Objectives: The primary objectives of this dissertation were to (1) identify the genetic determinants of lipid metabolites and (2) advance understanding of the effect of perturbations in lipid metabolite levels on CHD and its risk factors. Methods: Direct infusion high-resolution mass spectrometry was performed on 5662 participants from the Pakistan Risk of Myocardial Infarction Study to obtain signals for 444 known lipid metabolites. Correlations and associations of the lipids with smoking, physical activity, circulating biomarkers, and other CHD risk factors were assessed. Genome-wide analyses were conducted to analyse the association of each lipid with over 6.7 million imputed single nucleotide polymorphisms. Functional annotation and Gaussian Graphical Modelling were used to link the variants associated with each lipid to the most likely mediating gene, discern the underlying metabolic pathways, and provide a visual representation of the genetic determinants of human metabolism. Mendelian randomisation was also implemented to examine the causal effect of lipids on risk of CHD. Results: The lipids were highly correlated with each other and with levels of major circulating lipids, and they exhibited significant associations with several CHD risk factors. There were 254 lipids that had significant associations with one or more genetic variants and 355 associations between lipids and variants, with a total of 89 sentinel variants from 23 independent loci. The analyses described in this dissertation resulted in the discovery of four novel loci, identified novel relationships between genetic variants and lipids, and revealed new biological insights into lipid metabolism. Conclusion: Analyses of lipid metabolites in large epidemiological studies can contribute to enhanced understanding of mechanisms for CHD development and identification of novel causal pathways and new therapeutic targets.

<p>Complex traits are influenced by genetic and environmental factors and their interactions. Most common human disorders such as cardiovascular, metabolic, autoimmune, and neurological diseases are complex. Understanding their genetic architecture and etiology is an important step to prevent, diagnose and treat these conditions. Genome Wide Association Studies (GWAS) have emerged as a powerful and widely used tool that can be used to explore and identify the genetic variants associated with complex traits. In this dissertation, we present some of the downstream applications of GWAS studies to analyze and understand the genetic risk and etiology of complex traits and provide important insights into the genetic architecture and background of several complex phenotypes. First, we examined whether prevalence of complex disorders around the world correlates to Polygenic Risk Scores (PRS). To do so, we determined the average PRS of 14 such complex disorders across 24 world populations using results of GWAS studies. We found variation in risk across populations and significant correlation was obtained between average disease risk and prevalence for seven of the studied disorders. Further exploring the power of PRS- based calculations, we performed a PRS - based phenome wide association study (PheWAS) for Tourette Syndrome (TS) and identified 57 phenotypic outcomes significantly associated with TS PRS. The strongest associations were found between TS PRS and mental health factors. Cross- disorder comparisons of phenotypic associations with genetic risk for other childhood-onset disorders (e.g.: attention deficit hyperactivity disorder [ADHD], autism spectrum disorder [ASD], and obsessive-compulsive disorder [OCD]) indicated an overlap in associations between TS and these disorders. Furthermore, we performed a sex specific PheWAS that highlighted differences in associations of complex disorders with TS PRS in males and females. Finally, we used large- scale GWAS results to identify causal associations between different biological markers (proteins, metabolites, and microbes) and subcortical brain structure volumes using Mendelian Randomization (MR) analysis. We identified eleven proteins and six metabolites to be significantly associated with subcortical brain volume structures. Enrichment analysis indicated that the associated proteins were enriched for proteolytic functions and regulation of apoptotic pathways. Overall, our work demonstrates the power of GWAS studies to help disentangle the genetic basis of complex diseases and also provides important insights into the etiology of the studied complex traits. </p>

Human ageing is accompanied by characteristic metabolic and endocrine changes, including altered hormone profiles, insulin resistance and deterioration of skeletal muscle. Obesity and diabetes may themselves drive an accelerated ageing phenotype. Untangling the causal web between ageing, obesity and diabetes is a priority in order to understand their aetiology and improve prevention and management. The role of biological ageing in determining the risk of obesity and associated conditions has often been examined using mean leukocyte telomere length (LTL), a marker of replicative fatigue and senescence. However, considering phenotypes which represent different domains of biological and functional ageing as exposures for obesity and related traits could allow the elucidation of new understudied phenotypes relevant to cardio-metabolic risk in the wider population. This PhD considers the causal role of (1) hand grip strength (HGS), a marker of overall strength and physical functioning, and (2) resting energy expenditure, an indicator of overall energy metabolism and the major component of daily energy expenditure, in cardio-metabolic risk. I also characterise a new and readily-quantifiable marker of age-related genomic instability, mosaic loss of the Y chromosome (mLOY). Observational evidence implicates each of these phenotypes in cardio-metabolic conditions and intermediate phenotypes. However, it is not possible to infer causality from these observational associations due to confounding and reverse-causality. Mendelian randomisation offers a solution to these limitations and can allow the causal nature of these relationships to be investigated. Using population-based data including UK Biobank, this thesis presents the first large-scale genetic discovery effort for each trait and provides new biological insight into their shared and separate aetiology. I used identified variants to investigate the bidirectional causal associations of each trait with cardio-metabolic outcomes, intermediate phenotypes and other related traits such as frailty and mortality. In total I identified 16 loci for hand grip strength, 19 for mLOY, and one signal for REE. I have shown that HGS is likely to be causally linked to fracture risk, and I have identified the important shared genetic architecture between mLOY, glycaemic traits and cancer. I have also demonstrated that at least one known genetic variant contributing to obesity risk acts partially via reduced REE. Overall the findings of my PhD contribute to our wider understanding of the aetiological role of ageing processes in metabolic dysfunction, and have implications for both basic science and translational applications.