Exploration, quantification, and mitigation of systematic error in high-throughput approaches to gene-expression profiling: implications for data reproducibility

Technological and methodological advances in the fields of medical and life-sciences have, over the last 25 years, revolutionised the way in which cellular activity is measured at the molecular level. Three such advances have provided a means of accurately and rapidly quantifying mRNA, from the development of quantitative Polymerase Chain Reaction (qPCR), to DNA microarrays, and second-generation RNA-sequencing (RNA-seq). Despite consistent improvements in measurement precision and sample throughput, the data generated continue to be a ffected by high levels of variability due to the use of biologically distinct experimental subjects, practical restrictions necessitating the use of small sample sizes, and technical noise introduced during frequently complex sample preparation and analysis procedures. A series of experiments were performed during this project to pro le sources of technical noise in each of these three techniques, with the aim of using the information to produce more accurate and more reliable results. The mechanisms for the introduction of confounding noise in these experiments are highly unpredictable. The variance structure of a qPCR experiment, for example, depends on the particular tissue-type and gene under assessment while expression data obtained by microarray can be greatly influenced by the day on which each array was processed and scanned. RNA-seq, on the other hand, produces data that appear very consistent in terms of differences between technical replicates, however there exist large differences when results are compared against those reported by microarray, which require careful interpretation. It is demonstrated in this thesis that by quantifying some of the major sources of noise in an experiment and utilising compensation mechanisms, either pre- or post-hoc, researchers are better equipped to perform experiments that are more robust, more accurate, and more consistent

The promise of genome‐wide SNP genotyping: from population genetics to disease gene identification

Advances in single nucleotide polymorphism (SNP) genotyping technologies have revolutionised our ability to scrutinise the human genome. My PhD research focuses on using these new technologies to catalogue genetic variability in a collection of diverse populations from around the globe, and to determine the role of genetic variants in neurological diseases. First, I present and discuss the analysis of genome‐wide SNP data in individuals from ethnically and geographically diverse human populations uncovering the diversity of genotype, haplotype and copy number variation in these populations. Second, I will describe an autozygosity mapping approach in three Brazilian dystoniaparkinsonism families which lead to the identification of a novel disease‐segregating mutation in the gene PRKRA. Third, I will report on a large genome‐wide association study in Parkinson’s disease, uncovering genetic variability at the SNCA and MAPT loci that are strongly associated with risk for developing disease. Forth, I provide compelling evidence that genetic variants at the SNCA locus are also significantly associated with risk for developing multiple system atrophy. This finding represents the first reproducible risk gene for this devastating disorder, and causally links this condition to the more common neurodegenerative disorder Parkinson’s disease. Finally, I present the results of a comprehensive mutational screening study investigating the frequency and spectrum of sequence and copy number mutations in the parkinsonism genes PRKN and PINK in individuals with early-onset Parkinson’s disease, in multiple system atrophy patients and in normal controls. In summary, the data presented in this thesis emphasise the critical role that genetic variability plays in the pathogenesis of neurological disorders

Integrating snp data and imputation methods into the DNA methylation analysis framework

DNA methylation is a widely studied epigenetic modification that can influence the expression and regulation of functional genes, especially those related to aging, cancer and other diseases. The common goal of methylation studies is to find differences in methylation levels between samples collected under different conditions. Differences can be detected at the site level, but regulated methylation targets are most commonly clustered into short regions. Thus, identifying differentially methylated regions (DMRs) between different groups is of prime interest. Despite advanced technology that enables measuring methylation genome-wide, misinterpretations in the readings can arise due to the existence of single nucleotide polymorphisms (SNPs) in the target sequence. One of the main pre-processing steps in DMR detection methods involves filtering out potential SNP-related probes due to this issue. In this work, it is proposed to leverage the current trend of collecting both SNP and methylation data on the same individual, making it possible to integrate SNP data into the DNA methylation analysis framework. This will enable the originally filtered potential SNPs to be restored if a SNP is not actually present. Furthermore, when a SNP is present or other missing data issues arise, imputation methods are proposed for methylation data. First, regularized linear regression (ridge, LASSO and elastic net) imputation models are proposed, along with a variable screening technique to restrict the number of variables in the models. Functional principal component regression imputation is also proposed as an alternative approach. The proposed imputation methods are compared to existing methods and evaluated based on imputation accuracy and DMR detection ability using both real and simulated data. One of the proposed methods (elastic net with variable screening) shows effective imputation accuracy without sacrificing computation efficiency across a variety of settings, while greatly improving the number of true positive DMR detections --Abstract, page iii

Global assessment of genomic variation in cattle by genome resequencing and high-throughput genotyping

<p>Abstract</p> <p>Background</p> <p>Integration of genomic variation with phenotypic information is an effective approach for uncovering genotype-phenotype associations. This requires an accurate identification of the different types of variation in individual genomes.</p> <p>Results</p> <p>We report the integration of the whole genome sequence of a single Holstein Friesian bull with data from single nucleotide polymorphism (SNP) and comparative genomic hybridization (CGH) array technologies to determine a comprehensive spectrum of genomic variation. The performance of resequencing SNP detection was assessed by combining SNPs that were identified to be either in identity by descent (IBD) or in copy number variation (CNV) with results from SNP array genotyping. Coding insertions and deletions (indels) were found to be enriched for size in multiples of 3 and were located near the N- and C-termini of proteins. For larger indels, a combination of split-read and read-pair approaches proved to be complementary in finding different signatures. CNVs were identified on the basis of the depth of sequenced reads, and by using SNP and CGH arrays.</p> <p>Conclusions</p> <p>Our results provide high resolution mapping of diverse classes of genomic variation in an individual bovine genome and demonstrate that structural variation surpasses sequence variation as the main component of genomic variability. Better accuracy of SNP detection was achieved with little loss of sensitivity when algorithms that implemented mapping quality were used. IBD regions were found to be instrumental for calculating resequencing SNP accuracy, while SNP detection within CNVs tended to be less reliable. CNV discovery was affected dramatically by platform resolution and coverage biases. The combined data for this study showed that at a moderate level of sequencing coverage, an ensemble of platforms and tools can be applied together to maximize the accurate detection of sequence and structural variants.</p

Evolutionary Trajectories of IDH Glioblastomas Reveal a Common Path of Early Tumorigenesis Instigated Years ahead of Initial Diagnosis

We studied how intratumoral genetic heterogeneity shapes tumor growth and therapy response for isocitrate dehydrogenase (IDH)-wild-type glioblastoma, a rapidly regrowing tumor. We inferred the evolutionary trajectories of matched pairs of primary and relapsed tumors based on deep whole-genome-sequencing data. This analysis suggests both a distant origin of de novo glioblastoma, up to 7 years before diagnosis, and a common path of early tumorigenesis, with one or more of chromosome 7 gain, 9p loss, or 10 loss, at tumor initiation. TERT promoter mutations often occurred later as a prerequisite for rapid growth. In contrast to this common early path, relapsed tumors acquired no stereotypical pattern of mutations and typically regrew from oligoclonal origins, suggesting sparse selective pressure by therapeutic measures

Genotype imputation as a cost-saving genomic strategy for South African Sanga cattle: A review

The South African beef cattle population is heterogeneous and consists of a variety of breeds, production systems and breeding goals. Indigenous cattle breeds are uniquely adapted to their native surroundings, necessitating conservation of these breeds as usable genetic resources to sustain efficient production of beef. Current projections indicate positive growth in human population size, with parallel growth in nutritional demand, in the midst of intensifying environmental conditions. Sanga cattle, therefore, are invaluable assets to the South African beef industry. Modern genomic methodologies allow for an extensive insight into the genome architecture of local breeds. The evolution of these methodologies has also provided opportunities to incorporate deoxyribonucleic acid (DNA) information into breed improvement programs in the form of genomic selection (GS). Certain challenges, such as the high cost of generating adequate numbers of dense genotypic profiles and the introduction of ascertainment bias when non-commercial breeds are genotyped with commercial single nucleotide polymorphism (SNP) panels, have caused a lag in progress on the genomics front in South Africa. Genotype imputation is a statistical method that infers unavailable or missing genotypic data based on shared haplotypes within a population using a population or breed representative reference sample. Genotypes are generated in silico, providing an animal with genotypic information for SNP markers that were not genotyped, based on predictive model-based algorithms. The validation of this method for indigenous breeds will enable the development of cost-effective low-density bead chips, allowing more animals to be genotyped, and imputation to high-density information. The improvement in SNP densities, at lower cost, will allow enhanced power in genome-wide association studies (GWAS) and genomic estimated breeding value (GEBV)-based selection for these breeds. To fully reap the benefits of this methodology, however, will require the setting up of accurate and reliable frameworks that are optimized for its application in Sanga breeds. This review paper aims, first, to identify the challenges that have been impeding genomic applications for Sanga cattle and second, to outline the advantages that a method such as genotype imputation might provide.Keywords: breed improvement, developing countries, indigenous breeds, genomic

Developing RNA diagnostics for studying healthy human ageing

Developing strategies to cope with increase in the ageing population and age-related chronic diseases is one of the societies biggest challenges. The characteristics of the ageing process shows significant inter-individual variation. Building genomic signatures that could account for variation in health outcomes with age may facilitate early prognosis of individual age-correlated diseases (e.g. cancer, coronary artery diseases and dementia) and help in developing better targeted treatments provided years in advance of acquiring disabling symptoms for these diseases. The aim of this thesis was to explore methods for diagnosing molecular features of human ageing. In particular, we utilise multi-platform transcriptomics, independent clinical data and classification methods to evaluate which human tissues demonstrate a reproducible molecular signature for age and which clinical phenotypes correlated with these new RNA biomarkers. [Continues.

Inter-individual variation of the human epigenome &amp; applications

Genome-wide association studies (GWAS) have led to the discovery of genetic variants influencing human phenotypes in health and disease. However, almost two decades later, most human traits can still not be accurately predicted from common genetic variants. Moreover, genetic variants discovered via GWAS mostly map to the non-coding genome and have historically resisted interpretation via mechanistic models. Alternatively, the epigenome lies in the cross-roads between genetics and the environment. Thus, there is great excitement towards the mapping of epigenetic inter-individual variation since its study may link environmental factors to human traits that remain unexplained by genetic variants. For instance, the environmental component of the epigenome may serve as a source of biomarkers for accurate, robust and interpretable phenotypic prediction on low-heritability traits that cannot be attained by classical genetic-based models. Additionally, its research may provide mechanisms of action for genetic associations at non-coding regions that mediate their effect via the epigenome. The aim of this thesis was to explore epigenetic inter-individual variation and to mitigate some of the methodological limitations faced towards its future valorisation.Chapter 1 is dedicated to the scope and aims of the thesis. It begins by describing historical milestones and basic concepts in human genetics, statistical genetics, the heritability problem and polygenic risk scores. It then moves towards epigenetics, covering the several dimensions it encompasses. It subsequently focuses on DNA methylation with topics like mitotic stability, epigenetic reprogramming, X-inactivation or imprinting. This is followed by concepts from epigenetic epidemiology such as epigenome-wide association studies (EWAS), epigenetic clocks, Mendelian randomization, methylation risk scores and methylation quantitative trait loci (mQTL). The chapter ends by introducing the aims of the thesis.Chapter 2 focuses on stochastic epigenetic inter-individual variation resulting from processes occurring post-twinning, during embryonic development and early life. Specifically, it describes the discovery and characterisation of hundreds of variably methylated CpGs in the blood of healthy adolescent monozygotic (MZ) twins showing equivalent variation among co-twins and unrelated individuals (evCpGs) that could not be explained only by measurement error on the DNA methylation microarray. DNA methylation levels at evCpGs were shown to be stable short-term but susceptible to aging and epigenetic drift in the long-term. The identified sites were significantly enriched at the clustered protocadherin loci, known for stochastic methylation in neurons in the context of embryonic neurodevelopment. Critically, evCpGs were capable of clustering technical and longitudinal replicates while differentiating young MZ twins. Thus, discovered evCpGs can be considered as a first prototype towards universal epigenetic fingerprint, relevant in the discrimination of MZ twins for forensic purposes, currently impossible with standard DNA profiling. Besides, DNA methylation microarrays are the preferred technology for EWAS and mQTL mapping studies. However, their probe design inherently assumes that the assayed genomic DNA is identical to the reference genome, leading to genetic artifacts whenever this assumption is not fulfilled. Building upon the previous experience analysing microarray data, Chapter 3 covers the development and benchmarking of UMtools, an R-package for the quantification and qualification of genetic artifacts on DNA methylation microarrays based on the unprocessed fluorescence intensity signals. These tools were used to assemble an atlas on genetic artifacts encountered on DNA methylation microarrays, including interactions between artifacts or with X-inactivation, imprinting and tissue-specific regulation. Additionally, to distinguish artifacts from genuine epigenetic variation, a co-methylation-based approach was proposed. Overall, this study revealed that genetic artifacts continue to filter through into the reported literature since current methodologies to address them have overlooked this challenge.Furthermore, EWAS, mQTL and allele-specific methylation (ASM) mapping studies have all been employed to map epigenetic variation but require matching phenotypic/genotypic data and can only map specific components of epigenetic inter-individual variation. Inspired by the previously proposed co-methylation strategy, Chapter 4 describes a novel method to simultaneously map inter-haplotype, inter-cell and inter-individual variation without these requirements. Specifically, binomial likelihood function-based bootstrap hypothesis test for co-methylation within reads (Binokulars) is a randomization test that can identify jointly regulated CpGs (JRCs) from pooled whole genome bisulfite sequencing (WGBS) data by solely relying on joint DNA methylation information available in reads spanning multiple CpGs. Binokulars was tested on pooled WGBS data in whole blood, sperm and combined, and benchmarked against EWAS and ASM. Our comparisons revealed that Binokulars can integrate a wide range of epigenetic phenomena under the same umbrella since it simultaneously discovered regions associated with imprinting, cell type- and tissue-specific regulation, mQTL, ageing or even unknown epigenetic processes. Finally, we verified examples of mQTL and polymorphic imprinting by employing another novel tool, JRC_sorter, to classify regions based on epigenotype models and non-pooled WGBS data in cord blood. In the future, we envision how this cost-effective approach can be applied on larger pools to simultaneously highlight regions of interest in the methylome, a highly relevant task in the light of the post-GWAS era.Moving towards future applications of epigenetic inter-individual variation, Chapters 5 and 6 are dedicated to solving some of methodological issues faced in translational epigenomics.Firstly, due to its simplicity and well-known properties, linear regression is the starting point methodology when performing prediction of a continuous outcome given a set of predictors. However, linear regression is incompatible with missing data, a common phenomenon and a huge threat to the integrity of data analysis in empirical sciences, including (epi)genomics. Chapter 5 describes the development of combinatorial linear models (cmb-lm), an imputation-free, CPU/RAM-efficient and privacy-preserving statistical method for linear regression prediction on datasets with missing values. Cmb-lm provide prediction errors that take into account the pattern of missing values in the incomplete data, even at extreme missingness. As a proof-of-concept, we tested cmb-lm in the context of epigenetic ageing clocks, one of the most popular applications of epigenetic inter-individual variation. Overall, cmb-lm offer a simple and flexible methodology with a wide range of applications that can provide a smooth transition towards the valorisation of linear models in the real world, where missing data is almost inevitable. Beyond microarrays, due to its high accuracy, reliability and sample multiplexing capabilities, massively parallel sequencing (MPS) is currently the preferred methodology of choice to translate prediction models for traits of interests into practice. At the same time, tobacco smoking is a frequent habit sustained by more than 1.3 billion people in 2020 and a leading (and preventable) health risk factor in the modern world. Predicting smoking habits from a persistent biomarker, such as DNA methylation, is not only relevant to account for self-reporting bias in public health and personalized medicine studies, but may also allow broadening forensic DNA phenotyping. Previously, a model to predict whether someone is a current, former, or never smoker had been published based on solely 13 CpGs from the hundreds of thousands included in the DNA methylation microarray. However, a matching lab tool with lower marker throughput, and higher accuracy and sensitivity was missing towards translating the model in practice. Chapter 6 describes the development of an MPS assay and data analysis pipeline to quantify DNA methylation on these 13 smoking-associated biomarkers for the prediction of smoking status. Though our systematic evaluation on DNA standards of known methylation levels revealed marker-specific amplification bias, our novel tool was still able to provide highly accurate and reproducible DNA methylation quantification and smoking habit prediction. Overall, our MPS assay allows the technological transfer of DNA methylation microarray findings and models to practical settings, one step closer towards future applications.Finally, Chapter 7 provides a general discussion on the results and topics discussed across Chapters 2-6. It begins by summarizing the main findings across the thesis, including proposals for follow-up studies. It then covers technical limitations pertaining bisulfite conversion and DNA methylation microarrays, but also more general considerations such as restricted data access. This chapter ends by covering the outlook of this PhD thesis, including topics such as bisulfite-free methods, third-generation sequencing, single-cell methylomics, multi-omics and systems biology.<br/

The Development, Validation and Implementation of a Broad-Based ADME Genotyping Assay into Research and Clinical Trials

Afin d’adresser la variabilité interindividuelle observée dans la réponse pharmacocinétique à de nombreux médicaments, nous avons créé un panel de génotypage personnalisée en utilisant des méthodes de conception et d’élaboration d’essais uniques. Celles-ci ont pour but premier de capturer les variations génétiques présentent dans les gènes clés impliqués dans les processus d'absorption, de distribution, de métabolisme et d’excrétion (ADME) de nombreux agents thérapeutiques. Bien que ces gènes et voies de signalement sont impliqués dans plusieurs mécanismes pharmacocinétiques qui sont bien connues, il y a eu jusqu’à présent peu d'efforts envers l’évaluation simultanée d’un grand nombre de ces gènes moyennant un seul outil expérimental. La recherche pharmacogénomique peut être réalisée en utilisant deux approches: 1) les marqueurs fonctionnels peuvent être utilisés pour présélectionner ou stratifier les populations de patients en se basant sur des états métaboliques connus; 2) les marqueurs Tag peuvent être utilisés pour découvrir de nouvelles corrélations génotype-phénotype. Présentement, il existe un besoin pour un outil de recherche qui englobe un grand nombre de gènes ADME et variantes et dont le contenu est applicable à ces deux modèles d'étude. Dans le cadre de cette thèse, nous avons développé un panel d’essais de génotypage de 3,000 marqueurs génétiques ADME qui peuvent satisfaire ce besoin. Dans le cadre de ce projet, les gènes et marqueurs associés avec la famille ADME ont été sélectionnés en collaboration avec plusieurs groupes du milieu universitaire et de l'industrie pharmaceutique. Pendant trois phases de développement de cet essai de génotypage, le taux de conversion pour 3,000 marqueurs a été amélioré de 83% à 97,4% grâce à l'incorporation de nouvelles stratégies ayant pour but de surmonter les zones d'interférence génomiques comprenant entre autres les régions homologues et les polymorphismes sous-jacent les régions d’intérêt. La précision du panel de génotypage a été validée par l’évaluation de plus de 200 échantillons pour lesquelles les génotypes sont connus pour lesquels nous avons obtenu une concordance > 98%. De plus, une comparaison croisée entre nos données provenant de cet essai et des données obtenues par différentes plateformes technologiques déjà disponibles sur le marché a révélé une concordance globale de > 99,5%. L'efficacité de notre stratégie de conception ont été démontrées par l'utilisation réussie de cet essai dans le cadre de plusieurs projets de recherche où plus de 1,000 échantillons ont été testés. Nous avons entre autre évalué avec succès 150 échantillons hépatiques qui ont été largement caractérisés pour plusieurs phénotypes. Dans ces échantillons, nous avons pu valider 13 gènes ADME avec cis-eQTL précédemment rapportés et de découvrir et de 13 autres gènes ADME avec cis eQTLs qui n'avaient pas été observés en utilisant des méthodes standard. Enfin, à l'appui de ce travail, un outil logiciel a été développé, Opitimus Primer, pour aider pour aider au développement du test. Le logiciel a également été utilisé pour aider à l'enrichissement de cibles génomiques pour d'expériences séquençage. Le contenu ainsi que la conception, l’optimisation et la validation de notre panel le distingue largement de l’ensemble des essais commerciaux couramment disponibles sur le marché qui comprennent soit des marqueurs fonctionnels pour seulement un petit nombre de gènes, ou alors n’offre pas une couverture adéquate pour les gènes connus d’ADME. Nous pouvons ainsi conclure que l’essai que nous avons développé est et continuera certainement d’être un outil d’une grande utilité pour les futures études et essais cliniques dans le domaine de la pharmacocinétique, qui bénéficieraient de l'évaluation d'une longue liste complète de gènes d’ADME.In order to better assess the inter-individual variability observed in a patient’s pharmacokinetic response to many medications, we have created a custom genotyping panel that uses unique assay designs to analyze variation present in key genes involved in the absorption, distribution, metabolism and excretion (ADME) of many therapeutic agents. These genes and pathways involved in most pharmacokinetic mechanisms are well known. However, as yet, there has been little effort to develop tools that can interrogate a large number of variations in most known drug metabolizing genes simultaneously within a single experimental tool. Pharmacogenomic research has historically been conducted using two approaches: targeted studies that screen a small number of specific functional markers to identify known metabolic status phenotypes, and genome-wide studies that identify novel genetic correlations with drug response phenotypes. Thus, a gap currently exists for a targeted ADME research tool that can evaluate a large number of key ADME genes and variants in a format that can be applicable to both types of study designs. As part of this thesis, we have developed a 3000 SNP broad based ADME genotyping panel that can address this need. Genes and markers for the genotyping panel were selected in collaboration with many groups from both academia and the pharmaceutical industry in an effort to capture all pertinent genes and metabolic pathways that have been implicated in drug metabolism. The final assay design was composed of over 3000 markers in 181 genes. Over three phases of iterative development, the assay conversion rate for the 3000 markers was improved from 83.0% to 97.4% through the incorporation of novel design strategies to overcome areas of genomic interference such as regions of homology and underlying polymorphisms. Accuracy of the assay was validated by screening more than 200 samples of known genotype with a concordance of 99%. Additionally, data from the assay has also been compared to data from different technological platforms and has an overall concordance of 99.5%. The effectiveness of the design strategy was demonstrated in the successful utilization of the assay in the screening of over 1000 samples which identified several novel pharmacogenetic associations between ADME variations and adverse drug reactions in children. Another goal of this thesis was to demonstrate what added benefit/utility the 3000 SNP ADME panel would have when compared to currently available genotyping assays. Using 150 extensively investigated liver samples, the broad based assay was not only able to detect and validate 13 previously reported cis eQTLs in ADME genes but further identified an additional 13 novel ADME cis eQTLs that had never been observed before, doubling the number previously identified using standard methods on the same samples. Finally, in support of this work, a number of bioinformatic tools had to be developed to help expedite this research. These tools have been further refined and are currently being used to assist with enrichment of genomic targets for next generation sequencing experiments. In conclusion, this work has led to a better understanding of ADME genetics and the nuances of assaying ADME genes. The content and designs of the developed assay sets it apart from currently available commercial assays that contain only functional markers in a small number of genes or do not have adequate coverage across ADME genes. The assay has the ability to play a significant role in pharmacogenomic studies to identify known and novel pharmacogenomic biomarkers. These will lead to improved biomarkers that will help better stratify pharmaceutical clinical trial populations or assist physicians to select better, more personalized, efficacious and safer therapies for their patients