Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to investigate the causal relationship between exposure and outcome. It has become widely popular due to its versatile applications in epidemiological research. Its rising popularity is largely driven by the ease of accessing summary-level data from large consortia, making it a cost-effective choice for researchers. In this thesis, we focus on three issues in MR that result in potential bias in causal inference. We first address the “winner’s curse” in MR, which arises from selecting genetic markers based on their significance or ranking. To mitigate this bias, we adapt the bootstrap-based BR-squared method to function with summary-level data. Our findings reveal that the correction methods can effectively reduce bias, albeit with an increase in variability. We then develop a method that accounts for the correlation caused by sample overlap while addressing potential bias from weak instruments. This proposed method yields stable causal estimates, although the standard errors of causal estimates may not be precisely estimated. Lastly, we introduce a novel approach for identifying invalid instrumental variables showing signs of horizontal pleiotropy. We recommend using the bootstrap method to account for the data-driven process of IV selection. Our results indicate that the bootstrap intervals approach the nominal level of coverage rate when the proportion of invalid IVs is less than 50%. / Dissertation / Doctor of Philosophy (PhD)

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An exciting new era has dawned for the prevention and management of CAD utilizing genetic risk variants. The recent identification of over 60 susceptibility loci for coronary artery disease (CAD) confirm not only the importance of established risk factors, but also the existence of many novel causal pathways that are expected to improve our understanding of the genetic basis of CAD and facilitate the development of new therapeutic agents over time. Concurrently, Mendelian randomization studies have provided intriguing insights on the causal relationship between CAD-related traits, and highlight the potential benefits of long-term modifications of risk factors. Lastly, genetic risk scores of CAD may serve not only as prognostic, but also as predictive markers and carry the potential to considerably improve the delivery of established prevention strategies. This review will summarize the evolution and discovery of genetic risk variants for CAD and their current and future clinical applications.

Although complete understanding of the mechanisms of rare genetic variants in disease continues to elude us, Next Generation Sequencing (NGS) has facilitated significant gene discoveries across the disease spectrum. However, the cost of NGS hinders its use for identifying rare variants in common diseases that require large samples. To circumvent the need for larger samples, designing efficient sampling studies is crucial in order to detect potential associations. This research therefore evaluates sampling designs for rare variant - quantitative trait association studies and assesses the effect on power that freely available public cohort data can have in the design. Performing simulations and evaluating common and unconventional sampling schemes results in several noteworthy findings. Specifically, the extreme-trait design is the most powerful design for analyzing quantitative traits. This research also shows that sampling more individuals from the extreme of clinical interest does not increase power. Variant filtering has served as a "proof-of-concept" approach for the discovery of disease-causing genes in Mendelian traits and formal statistical methods have been lacking in this area. However, combining variant filtering schemes with existing rare variant association tests is a practical alternative. Thus, this thesis also compares the robustness of six burden-based rare variant association tests for Mendelian traits after a variant filtering step in the presence of genetic heterogeneity and genotyping errors. This research shows that with low locus heterogeneity, these tests are powerful for testing association. With the exception of the weighted sum statistic (WSS), the remaining tests were very conservative in preserving the type I error when the number of affected and unaffected individuals was unequal. The WSS, on the other hand, had inflated type I error as the number of unaffected individuals increased. The framework presented can serve as a catalyst to improve sampling design and to develop robust statistical methods for association testing.

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.

Indiana University-Purdue University Indianapolis (IUPUI) / Alternative RNA splicing is an important means of genetic control and transcriptome diversity. Alternative splicing events are frequently studied independently, and coordinated splicing controlled by common factors is often overlooked: The molecular mechanisms by which splicing regulators promote or repress specific pre-mRNA processing are still not yet well understood. It is well known that splicing factors can regulate splicing in a context-dependent manner, and the search for modulation of splicing factor activity via direct or indirect mechanisms is a worthwhile pursuit towards explaining context-dependent activity. We hypothesized that the combined analysis of hundreds of consortium RNA-seq datasets could identify trans-acting “modulators” whose expression is correlated with differential effects of a splicing factor on its target splice events in mRNAs. We first tested a genome-wide approach to identify relationships between RNA-binding proteins and their inferred modulators in kidney cancer. We then applied a more targeted approach to identify novel modulators of splicing factor SRSF1 function over dozens of its intron retention splicing targets in a neurological context using hundreds of dorsolateral prefrontal cortex samples. Our hypothesized model was further strengthened with the incorporation of genetic variants to impute gene expression in a Mendelian randomization-based approach. The modulators of intron retention splicing we identified may be associated with risk variants linked to Alzheimer’s Disease, among other neurological disorders, to explain disease-causing splicing mechanisms. Our strategy can be widely used to identify modulators of RNA-binding proteins involved in tissue-specific alternative splicing.

Mendelian Randomization (MR) methods use genetic variants as instrumental variables (IV) to infer causal relationships between an exposure and an outcome, which overcomes the inability to infer such a relationship in observational studies due to unobserved confounders. There are several MR methods, including the inverse variance weighted (IVW) method, which has been extended to deal with correlated IVs; the median method, which provides consistent causal estimates in the presence of pleiotropy when less than half of the genetic variants are invalid IVs but assumes independent IVs. In this dissertation, we propose two new methods to improve precision for MR analysis. In the first chapter, we extend the median method to correlated IVs: the quasi-boots median method, that accounts for IV correlation in the standard error estimation using a quasi-bootstrap method. Simulation studies show that this method outperforms existing median methods under the correlated IVs setting with and without the presence of pleiotropic effects. In the second chapter, to overcome the lack of an effective solution to account for sample overlap in current IVW methods, we propose a new overall causal effect estimator by exploring the distribution of the estimator for individual IVs under the independent IVs setting, which we name the IVW-GH method. In the final chapter, we extend the IVW-GH method to correlated IVs. In simulation studies, the IVW-GH method outperforms the existing IVW methods under the one-sample setting for independent IVs and shows reasonable results for other settings. We apply these proposed methods to genome-wide association results from the Framingham Heart Study Offspring Study and the Million Veteran Program to identify potential causal relationships between a number of proteins and lipids. All the proposed methods are able to identify some proteins known to be related to lipids. In addition, the quasi-boots median method is robust to pleiotropic effects in the real data application. Consequently, the newly proposed quasi-boots median method and IVW-GH method may provide additional insights for identifying causal relationships. / 2025-01-23T00:00:00Z

Background and Objectives: Pharmacogenetics has the potential to maximize drug efficacy and minimize adverse effects of cardiovascular disease (CVD) but its translation into clinical practice been slow. However, recent advancements in genotyping and statistical methodologies have now provided robust evidence in the support of personalized medicine. This thesis addresses how the advancements in pharmacogenetics may help to gain novel insights into existing drug targets, inform and guide clinical decision-making and validate potential disease target pathways. Methods: This was achieved by exploring whether the COX-2 genetic variant (rs20417) is associated with a decreased risk of CVD outcomes, assessing whether bile acid sequestrants (BAS) are associated with a reduced the risk of coronary artery disease (CAD) using the principles of Mendelian Randomization and investigating whether genetic variants associated with dysglycaemia are associated with an increased risk of CAD. Results: We demonstrated that COX-2 carrier status was associated with a decreased risk of major cardiovascular outcomes. Furthermore, we also showed that BAS appear to be associated with a reduced risk of CAD and genetic variants associated with HbA1c and diabetes were associated with an increased risk of CAD. Conclusions: The convergence of technological and statistical advancements in pharmacogenetics have led to a more high-quality and cost-effective means of assessing the effect of CVD therapeutic agents. / Thesis / Doctor of Philosophy (PhD)

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)

Two methods of crop improvement are open to the plant breeder. Pure-line selection, which might be mentioned first, deals with the natural variability in plant populations. Thru selection, isolation, and comparative yield tests superior individuals are located. Nothing, however, can be added which the plant does not already have. Here hybridization provides a new starting point. Increased variation, new combination of characters, and thus greater opportunities are provided for improvement. The present investigation has for its purpose a study of the latter phase of this subject. In certain parts of Utah the straw from dry-land wheat is used for feeding of cattle and horses. Turkey, the chief variety grown, is bearded in character. Some stockmen claim that the beards are not a disadvantage in feeding, but the majority of feeders favor straw free from this disagreeable feature. Heading and threshing outfits also make considerable complaint when handling Turkey. In fact, certain outfits have experienced difficulty in keeping men on Turkey jobs if any other work could be obtained. Yet, in spite of this fact, Turkey continues to be the predominating variety because of better quality which is reflected in the prices, and in addition higher-yielding power. So in contemplation of this situation it was deemed advisable to attempt crossing Turkey and others of the Crimean group with other winter wheats somewhat lower in quality and yielding power, yet having the desirable character of beardlessness. The bearded wheats, Turkey, Armavir, Beloglina, and Kharkov, were selected for the qualities of high yield, good milling, and drought resistance. Kofod and Gold Coin were chosen for the character of beardlessness. The aim in making the crosses was to determine the possibility of combining the desirable characters of these wheat types in single individual plants. While detailed genetic data were taken of the F1 and F2 plants, this work was only incidental to the main reason given for the crossing, except as it was of value in predicting and interpreting results.