Chromosome segregation and recombination in human meiosis: Clinical applications and insight into disjunction errors

Chromosome copy number errors (or aneuploidy) of gametes and embryos occurs in humans more frequently than in any other studied species, with a spectrum of manifestations from implantation failure to affected live births. It is predominantly problem arising in maternal meiosis with at least 20% of oocytes being aneuploid, a proportion that increases dramatically with advancing maternal age. Currently the only intervention to reduce the chances of transmitting aneuploidy is by invasive embryo biopsy procedures in high-risk groups (mainly patients with advanced maternal age) undergoing in-vitro fertilisation. Despite the severity of this problem, aneuploidy of the human preimplantation embryo is relatively poorly understood. With this in mind the purpose of this thesis is to explore the premise underpinning the use of preimplantation genetic screening (PGS) in human embryos and investigate its clinical applications and current methodologies. A series of published works demonstrate what I believe to be a significant contribution to the development of applications for studying human preimplantation aneuploidy, also providing insight into its origins and mechanisms at the earliest stages of human development. Specifically, I present a novel standard set of protocols as a general reference work from practitioners in the fields of embryo biopsy and array comparative genomic hybridisation (CGH - the current ‘gold standard’ for preimplantation aneuploidy screening). I present a summary of work encapsulated in three published clinical papers using a linkage based analysis of Single Nucleotide Polymorphism (SNP) karyotypes (Karyomapping). Karyomapping was designed as a near-universal approach for the simultaneous detection of chromosomal and monogenic disorders in a PGS setting and these results demonstrate the utility of the technique in three separate scenarios. In order to study the underlying mechanisms of female meiosis I present my findings on the use of a calcium ionophore to activate human oocytes artificially. An algorithm based on Karyomapping (termed MeioMapping) is demonstrated for the first time specifically to investigate human female meiosis. By recovering all three products of human female meiosis (oocyte, and both polar biopsies – herein termed “Trios”) using calcium ionophore, I present a novel protocol (commissioned by Nature Protocols) to allow exploration of the full extent of meiotic chromosome recombination and segregation that occurs in the female germline. Finally I present a published set of experiments using this protocol to provide new insight into meiotic segregation patterns and recombination in human oocytes. This work uncovers a previously undescribed pattern of meiotic segregation (termed Reverse Segregation), providing an association between recombination rates and chromosome mis-segregation (aneuploidy). This work demonstrates that there is selection for higher recombination rates in the female germline and that there is a role for meiotic drive for recombinant chromatids at meiosis II in human female meiosis. The work presented in this thesis provides deeper understanding of meiotically derived maternal aneuploidy and recombination. More importantly it provides a vehicle within an ethical framework to continue to expand our knowledge and uncover new insights into the basis of meiotic errors that may aid future reproductive therapies

Study designs and statistical methods for pharmacogenomics and drug interaction studies

Indiana University-Purdue University Indianapolis (IUPUI)Adverse drug events (ADEs) are injuries resulting from drug-related medical interventions. ADEs can be either induced by a single drug or a drug-drug interaction (DDI). In order to prevent unnecessary ADEs, many regulatory agencies in public health maintain pharmacovigilance databases for detecting novel drug-ADE associations. However, pharmacovigilance databases usually contain a significant portion of false associations due to their nature structure (i.e. false drug-ADE associations caused by co-medications). Besides pharmacovigilance studies, the risks of ADEs can be minimized by understating their mechanisms, which include abnormal pharmacokinetics/pharmacodynamics due to genetic factors and synergistic effects between drugs. During the past decade, pharmacogenomics studies have successfully identified several predictive markers to reduce ADE risks. While, pharmacogenomics studies are usually limited by the sample size and budget. In this dissertation, we develop statistical methods for pharmacovigilance and pharmacogenomics studies. Firstly, we propose an empirical Bayes mixture model to identify significant drug-ADE associations. The proposed approach can be used for both signal generation and ranking. Following this approach, the portion of false associations from the detected signals can be well controlled. Secondly, we propose a mixture dose response model to investigate the functional relationship between increased dimensionality of drug combinations and the ADE risks. Moreover, this approach can be used to identify high-dimensional drug combinations that are associated with escalated ADE risks at a significantly low local false discovery rates. Finally, we proposed a cost-efficient design for pharmacogenomics studies. In order to pursue a further cost-efficiency, the proposed design involves both DNA pooling and two-stage design approach. Compared to traditional design, the cost under the proposed design will be reduced dramatically with an acceptable compromise on statistical power. The proposed methods are examined by extensive simulation studies. Furthermore, the proposed methods to analyze pharmacovigilance databases are applied to the FDA’s Adverse Reporting System database and a local electronic medical record (EMR) database. For different scenarios of pharmacogenomics study, optimized designs to detect a functioning rare allele are given as well

Genome analysis and DNA marker-based characterisation of pathogenic trypanosomes

The advances in genomics technologies and genome analysis methods that offer new leads for accelerating discovery of putative targets for developing overall control tools are reviewed in Chapter 1. In Chapter 2, a PCR typing method based on restriction fragment length polymorphism analysis of the internal transcribed sequence (ITS) rDNA region was used to reveal distinct fingerprinting patterns that characterise human- and animal-derived Trypanosoma brucei gambiense and T. b. brucei isolates. Although these results also highlighted doubts about the uniformity of T. brucei subspecies, the limitation of such a typing technique that is based on a single genetic locus is obvious. As a result, the studies were extended to include all T. brucei subspecies in a more global amplified fragment length polymorphism (AFLP) genotyping (Chapter 3). This approach permitted an unbiased estimate of the total genome variance and revealed closer genetic relatedness between, and higher variability within, T. b. brucei and T. b. rhodesiense subspecies, compared to T. b. gambiense strains. However, it was clear from these studies that a finer-scale genotyping tool with enhanced resolution power was required. Chapter 4 describes such an advanced tool, a multiplex-endonuclease genotyping analysis (MEGA) approach that simultaneously accesses multiple independent restriction enzyme-based polymorphisms within the genome. It offered a robust and detailed genotyping tool and was, therefore, used to study the population genetic structure of T. brucei isolates, for epidemiological and cladistic analysis (Chapter 5). The MEGA approach envisages the application of genotyping to identify genetic profiles that are associated with specific (parasite) traits. Therefore, in Chapter 6, genotypes were constructed and correlated with human serum response traits of T. brucei rhodesiense clones and strains, to further provide a general method for measuring differential phenotypes and an objective assessment of such differences. Also, such fine-scale approach can be used to rapidly enrich for identifiable polymorphisms in a set of known DNA sequences known to be associated with a phenotypic trait of interest. Using sets of four endonucleases selected on the basis of the concept defined by the MEGA approach, the role of differential DNA methylation patterns in the human serum response properties of trypanosomes was evaluated (Chapter 6), which proved to be insignificant. Furthermore, we clarified the genetic relationships between T. equiperdum and other Trypanozoon species (Chapter 7). In Chapter 8, a general discussion of the data is presented. In summary, three main applications of molecular marker systems in trypanosomes were described in this thesis. These involve genomic studies for (1) generating sensitive tools for molecular typing of strains, (2) elucidating taxonomy of Trypanozoon, and (3) the analysis of the relationships between genetic variations and their consequent functional effects that may enhance our understanding of important traits. These applications have permitted fine-scale genotypic characterisation of the parasites, and offered a template for phenotypic correlations of the genotype data

Compositions and methods for optimizing UGT2B7 substrate dosings and for predicting UGT2B7 substrate toxicity

This invention concerns UGT2B7 and its ability to glucuronidate various drugs, including epirubicin. It concerns methods and compositions for determining the level of UGT2B7 activity based on genetic composition, and consequently, allows dosing of UTG2B7-glucuronidated drugs to be improved or optimized based on a patient's level of predicted UGT2B7 activity. It further concerns methods of treatment in which UGT2B7 substrates are administered to patients as part of a treatment regimen

Mendelian randomization and its application to genome-wide association studies

Genetics aims is to study heredity: how traits are passed from one generation to the next and how genetic variations can lead to changes in phenotypes. Some phenotypes, called complex or quantitative traits, are under the control of both genetic and environmental factors. Examples of complex traits include quantitative phenotypes, such as height or cholesterol levels, as well as certain diseases, like diabetes or cardiovascular diseases. Genome-wide association studies (GWASs) are used to statistically test for the association between each genetic variant and a given phenotype. These studies confirmed that most complex traits are influenced by a large number of genetic variants, often exhibiting small effect sizes that can only be detected using large numbers of individuals. They also permitted the estimation of narrow-sense heritability, which is the proportion of phenotypic variation that can be attributed to these genetic variations. The results of such GWASs (association results for every genetic variant) are often made publicly available and they can be used to perform follow-up analyses, for example Mendelian randomization. Mendelian randomization aims at investigating causal relationships between complex traits and estimating the causal effect of one exposure on an outcome. This method mimicks randomized controlled trials and takes advantage of the fact that genetic variations are randomly distributed across the population. By using association results for genetic variants strongly associated with a given risk factor and measuring the effect of these variants on another trait or disease, Mendelian randomization can infer the existence and the strength of the causal relationship between them. Analyses helping to understand the genetics underlying complex traits and the relationships between them are key to precision medicine. Precision medicine is an approach that takes into account the genome sequence and the environmental exposures of each patient, to provide personalized prevention and treatment to each individual. During my thesis, I have been involved in several projects aiming at developing statistical methods that rely on Mendelian randomization. In the first part, I worked on a Bayesian GWAS approach (bGWAS). The goal of this approach is to increase statistical power to discover variants associated with a trait by leveraging data from correlated risk factors. The idea is to combine (1) the causal effects of the risk factors on the trait of interest (estimated using Mendelian randomization) with (2) the association results of genetic variants with these risk factors, in order to estimate the prior effect of each variant on the trait of interest. This approach has been used to study the genetics underlying lifespan, taking into account various potential risk factors, such as body mass index, cholesterol levels, and several diseases for example. In the second part, I worked on developing Mendelian randomization extensions (MRlap and LHC-MR) that aim at tackling some of the most common sources of bias. These extensions allow for more robust causal effect estimates, when some of the Mendelian randomization assumptions are violated, as well as for an extension of the scope of application of Mendelian randomization. -- La génétique est l’étude de la transmission de traits héréditaires au sein d’une population. Un défi majeur de la génétique moderne est cependant d’expliquer le mécanisme exact par lequel les variations génétiques peuvent, ou non, se traduire par des variations phénotypiques. Ce défi est d’autant plus important dans le cas des traits dits «complexes», qui sont affectés à la fois par des facteurs génétiques et par des facteurs environnementaux. C’est le cas par exemple de la taille adulte, du taux de cholestérol ou encore de certaines maladies, comme le diabète. Les études d’association pangénomique, en anglais genome-wide association studies (GWASs), permettent de tester si des variants génétiques sont statistiquement associés à un phénotype donné. Ces études ont confirmé que la plupart des traits complexes sont influencés par un très large nombre de variants génétiques, dont chacun a souvent un faible effet qui n’aurait pas été détecté sans l’accès à de larges jeux de données. Elles ont également permis d’estimer la part de la variation phénotypique expliquée par l’ensemble des variants (héritabilité au sens étroit). Les résultats de ces GWASs sont souvent publiés sous forme de statistiques synthétiques (pour chaque variant génétique) qui peuvent être utilisées pour réaliser des analyses additionnelles, notamment des analyses de randomisation mendélienne. Celles-ci permettent d’étudier les relations de cause à effet entre différents traits complexes et d’estimer l’effet de causalité d’un trait sur un autre. Les variations génétiques étant théoriquement réparties de façon aléatoire dans une population, la randomisation mendélienne est une alternative aux essais cliniques randomisés. En utilisant les résultats d’association de variants génétiques associés spécifiquement avec un facteur de risque et en mesurant leurs effets sur un autre trait, la randomisation mendélienne permet d’établir une relation de cause à effet entre deux traits. Ces études, permettant la compréhension des causes génétiques à l’origine des traits complexes ainsi que des relations de cause à effet pouvant exister entre ceux-ci, ouvrent la voie au développement de la médecine de précision, une approche prenant en compte toutes les informations concernant un individu (génétiques et environnementales) pour proposer à chacun un diagnostic et un traitement personnalisés. Durant mon doctorat, j’ai été impliquée dans différents projets visant à développer des approches techniques basées sur la randomisation mendélienne. Dans un premier temps, j’ai travaillé sur une méthode appelée GWAS bayésien (bGWAS). Cette méthode utilise des informations provenant de potentiel facteurs de risques identifiés a priori de façon à augmenter la puissance statistique de l’identification de variants génétiques associés à un trait d’intérêt. L’idée est de combiner (1) les effets de causalité des risques facteurs sur le trait d’intérêt (estimés en utilisant la randomisation mendélienne) et (2) les résultats d’association des variants génétiques avec ces facteurs de risque, pour estimer leur effet a priori sur le trait d’intérêt. Cette méthode a notamment été utilisée pour étudier les causes génétiques influençant l’espérance de vie, en prenant en compte plusieurs facteurs de risques tel que certaines maladies ou encore l’indice de masse corporel. Dans un second temps, j’ai travaillé sur des projets visant à proposer des extensions aux méthodes classiques de randomisation mendélienne (MRlap et LHC-MR) pour les rendre plus robustes à certaines sources de biais communément observées, avec pour but d’élargir les possibilités d’application de ces méthodes

Human lifespan: recent trends and genetic determinants

Human lifespan is determined by a complex interplay of genetics, environment, lifestyle and chance. In the UK, life expectancy has increased by roughly three years every decade, but despite longer lives, individuals also spend more years living with chronic disease. With populations greying and periods of morbidity becoming more prolonged, the burden of ageing and age-related disease is set to become a major healthcare challenge. Understanding the factors underlying trends in human lifespan could guide policy interventions to mitigate the burden of disease, while an understanding of the genetics of lifespan could provide insight into the ageing process. The latter could in turn reveal potential therapeutic targets to delay age-related disease and inform which individuals to target based on their genetic risk. In this thesis, I explore human lifespan from these two perspectives. First, I examined trends in mortality and morbidity in two million Scots using hospital admission and death records and found recent improvements in lifespan could be largely explained by improvements in the incidence and survival after hospitalisation of cancers and heart disease. However, I also found recent deteriorations in infectious disease, especially for individuals from lower socioeconomic classes, suggesting a need for a renewed public health focus in this area. Next, I performed a genome-wide association study (GWAS) to find genetic determinants of lifespan using DNA from 27 European cohorts and the lifespans of their parents (one million total). I identified 12 genomic regions affecting survival and found genetic variants across the genome, when aggregated into polygenic scores, could distinguish up to five years of survival between score deciles. Combining the lifespan GWAS with two other GWAS of lifespan-related traits, I identified 78 genes—some of which delay ageing in model organisms— which putatively influence both human lifespan and healthy years of life and which are enriched for haem metabolism. These findings present the most promising targets for therapeutic interventions to date, which may help delay the onset of age-related disease and extend the healthy years of life for all

An investigation into phenotypic, genetic, and neural correlates of wellbeing

Wellbeing, a key aspect of mental health, is defined as a state of positive subjective experience and optimal psychological functioning. This thesis presents a series of studies devised to comprehensively explore phenotypic, genetic, and neural correlates of wellbeing. The first study (Chapter 2) aimed to compare the heritability and stability of different wellbeing measures in the TWIN-E dataset (N~1600) to discern the most suitable approach for measuring wellbeing for subsequent gene discovery efforts. This twin-based study concluded that multi-item measures of wellbeing such as the COMPAS-W scale, were more heritable and stable than single-item measures. Wellbeing-associated variants were identified via genome-wide association studies (GWAS) and highlighted the need for larger sample size. The subsequent studies were conducted using population-scale data from the UK Biobank comprising ~130,000 participants with phenotypic and genetic data. Thus, in Chapter 3, I constructed a multi-item “wellbeing index” measure using UK Biobank data to investigate its relationship phenotypically and genetically (using GWAS, polygenic scores and LD score regression) with negative mental health indicators (e.g., neuroticism and loneliness), childhood maltreatment and psychiatric illness. I confirmed that SNP-heritability of wellbeing index was higher than both single-item measures and estimates previously reported (SNP-h2 = 8.6%). Moreover, I provide an overview of phenotypic and genetic correlations between wellbeing index and negative mental health indicators. In addition, childhood maltreatment and psychiatric illnesses were associated with reduced wellbeing, with evidence that genetic factors may influence their correlations. In Chapter 4, I investigated the genetic and phenotypic associations between wellbeing index and brain structure, using magnetic resonance image-derived phenotypes from the UK Biobank. This study found associations between wellbeing and volumes of brainstem, cerebellum and subcortical regions, and structural morphology of various cortical regions. Thus, wellbeing is associated with complex structural variations, each with a small effect. Together, this thesis explores the multifaceted nature of wellbeing, elucidating its phenotypic and genetic relationships with related phenotypes, childhood maltreatment, and psychiatric outcomes, and provides novel insights into the associations between wellbeing, its genetic signatures and brain structure

Discovering lesser known molecular players and mechanistic patterns in Alzheimer's disease using an integrative disease modelling approach

Convergence of exponentially advancing technologies is driving medical research with life changing discoveries. On the contrary, repeated failures of high-profile drugs to battle Alzheimer's disease (AD) has made it one of the least successful therapeutic area. This failure pattern has provoked researchers to grapple with their beliefs about Alzheimer's aetiology. Thus, growing realisation that Amyloid-β and tau are not 'the' but rather 'one of the' factors necessitates the reassessment of pre-existing data to add new perspectives. To enable a holistic view of the disease, integrative modelling approaches are emerging as a powerful technique. Combining data at different scales and modes could considerably increase the predictive power of the integrative model by filling biological knowledge gaps. However, the reliability of the derived hypotheses largely depends on the completeness, quality, consistency, and context-specificity of the data. Thus, there is a need for agile methods and approaches that efficiently interrogate and utilise existing public data. This thesis presents the development of novel approaches and methods that address intrinsic issues of data integration and analysis in AD research. It aims to prioritise lesser-known AD candidates using highly curated and precise knowledge derived from integrated data. Here much of the emphasis is put on quality, reliability, and context-specificity. This thesis work showcases the benefit of integrating well-curated and disease-specific heterogeneous data in a semantic web-based framework for mining actionable knowledge. Furthermore, it introduces to the challenges encountered while harvesting information from literature and transcriptomic resources. State-of-the-art text-mining methodology is developed to extract miRNAs and its regulatory role in diseases and genes from the biomedical literature. To enable meta-analysis of biologically related transcriptomic data, a highly-curated metadata database has been developed, which explicates annotations specific to human and animal models. Finally, to corroborate common mechanistic patterns — embedded with novel candidates — across large-scale AD transcriptomic data, a new approach to generate gene regulatory networks has been developed. The work presented here has demonstrated its capability in identifying testable mechanistic hypotheses containing previously unknown or emerging knowledge from public data in two major publicly funded projects for Alzheimer's, Parkinson's and Epilepsy diseases

Estimating abundance of African great apes

All species and subspecies of African great apes are listed by the International Union for the Conservation of Nature as endangered or critically endangered, and populations continue to decline. As human populations and industry expand into great ape habitat, efficient, reliable estimators of great ape abundance are needed to inform conservation status and land-use planning, to assess adverse and beneficial effects of human activities, and to help funding agencies and donors make informed and efficient contributions. Fortunately, technological advances have improved our ability to sample great apes remotely, and new statistical methods for estimating abundance are constantly in development. Following a brief general introduction, this thesis reviews established and emerging approaches to estimating great ape abundance, then describes new methods for estimating animal density from photographic data by distance sampling with camera traps, and for selecting among models of the distance sampling detection function when distance data are overdispersed. Subsequent chapters quantify the effect of violating the assumption of demographic closure when estimating abundance using spatially explicit capture–recapture models for closed populations, and describe the design and implementation of a camera trapping survey of chimpanzees at the landscape scale in Kibale National Park, Uganda. The new methods developed have generated considerable interest, and allow abundances of multiple species, including great apes, to be estimated from data collected during a single photographic survey. Spatially explicit capture–recapture analyses of photographic data from small study areas yielded accurate and precise estimates of chimpanzee abundance, and this combination of methods could be used to enumerate great apes over large areas and in dense forests more reliably and efficiently than previously possible."This work was supported by a St Leonard’s College Scholarship from the University of St Andrews, and the Max Planck Institute for Evolutionary Anthropology." -- Fundin

Clinical utility of sequence-based genotype compared with that derivable from genotyping arrays

Objective: We investigated the common-disease relevant information obtained from sequencing compared with that reported from genotyping arrays. Materials and methods: Using 187 publicly available individual human genomes, we constructed genomic disease ri