Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-04-05T14:57:05Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação - Rebecca Cristina Linhares de Carvalho, 2015.pdf: 1343136 bytes, checksum: 87551351118cac05985078a5ee318616 (MD5) / Made available in DSpace on 2016-04-05T14:57:05Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertação - Rebecca Cristina Linhares de Carvalho, 2015.pdf: 1343136 bytes, checksum: 87551351118cac05985078a5ee318616 (MD5) Previous issue date: 2015-02-25 / FACEPE / Parkinsonismo é uma síndrome neurológica em que os neurônios que normalmente produzem o hormônio chamado dopamina se deterioram, causando a perda de controle progressivo do movimento (ex. bradicinesia, a rigidez muscular, o temor de repouso e os reflexos posturais prejudicados). Um mal com sintomas semelhantes ao parkinsonismo é a síndrome ScansWithout Evidence of Dopaminergic Deficits (SWEDDs), na qual os pacientes não apresentam evidências de déficit de dopamina. As causas de parkinsonismo primário como a doença de Parkinson (DP), bem como SWEDDs, não são completamente conhecidos. Os estudos de associações no genoma completo (Genome-wide association studies - GWAS) têm proporcionado ganhos tangíveis para a compreensão da arquitetura genética de doenças complexas, trazendo contribuições consistentes e importantes para DP. GWASs foram realizados no passado com os dados de DP com resultados que influenciaram fortemente desenvolvimentos posteriores. Nesse trabalho, nós desenvolvemos um estudo sobre fatores genéticos que possam contribuir para o entendimento da ocorrência da DP e SWEDDs. Para isso, nós usamos o conjunto de ferramentas de análise de associação envolvendo estudo de caso-controle do método PLINK para executar GWASs, a fim de identificar SNPs que estão associados à DP e SWEDDs. Para fixar o nível de significância dos nossos resultados, nós optamos por usar somente dados reais fornecidos por Parkinson’s Progression Markers Initiative (PPMI). O PPMI é um consórcio internacional projetado para identificar biomarcadores de progressão da DP, tanto para melhorar a compreensão da etiologia da doença, como para fornecer ferramentas cruciais para aumentar a probabilidade de sucesso na elaboração de novos ensaios terapêuticos para DP. Na análise de associação, feita com dados genótipos de três grupos de indivíduos (indivíduos saudáveis, indivíduos com DP e indivíduos com SWEDDs), recuperamos SNPs que mostram forte ligação com PD e SWEDDs, alguns deles já associados na literatura científica com a DP ou a outras doenças degenerativas. Mas, também encontramos cerca de 60 SNPs que não estão relatados na literatura, que mostram evidências de serem fortemente relacionadas com a propensão para DP ou SWEDDs. Estes resultados apresentam alvos promissores para futuros estudos genômicos e podem contribuir para o entendimento da ocorrência da DP e SWEDDs. Curiosamente, embora SWEDDs seja uma doença clinicamente ligada a DP por uma série de sintomas comuns, os SNPs recuperados com as melhores classificações a partir do conjunto de dados DP não faziam parte do conjunto de dados SWEDDs, e vice-versa, o que sugere que esses dois conjuntos de marcadores poderiam ser mais cuidadosamente explorados nos estudos genômicos como SNPs comuns de interesse para as duas doenças. / Parkinsonism is a neurological disorder neurons that normally produce the hormone called dopamine deteriorate, causing progressive loss of movement control (e.g. bradykinesia, muscle rigidity, tremor at rest, and impaired postural reflexes). A syndrome with similar symptoms is Scans Without Evidence of Dopaminergic Deficits (SWEDDs), in which the patients not present evidence of dopaminergic deficits. The causes of primary parkinsonism, or Parkinson’s disease (PD), as well as SWEDDs, are not completely known. Genome-wide association studies (GWASs) have provided substantial contribution to the understanding of the architecture of complex diseases, bringing consistent and important contributions to PD. GWASs have been performed in the past with PD data with results that strongly influenced later developments. In this work, a study of genetic factors that contributes to the set of tools of association analysis for a better understanding of occurency of PD and SWEDDs. To that end, the set of tools of association analysis is involved in a study case for the PLINK method to execute GWASs with the objective of identifying SNPs which are associated to DP and SweDDs. To determine the level of significance of our results, only real data provided by Parkinson’s Progression Markers Initiative (PPMI) was used. PPMI is an international consortium created to indetify biomarkers of DP progression, to better comprehend the etiology of this disease and to provide key tools to increase the probability of success in the development of new therapeutic trials for PD. The association analysis, done with genotype data from three groups of individuals (healthy, affected by PD and affected by SWEDDs), SNPS that have shown strong connection to PD and SWEDDs were recovered, some of them are already linked in the scientific literature to PD or other degenerative diseases. But, we also have found about 60 SNPs that are not reported in the literature, which show evidence to be strongly related to the propensity to PD or SWEDDs. These results are promising targets for future genomic studies and may contribute to the understanding of the occurrence of PD and SWEDDs. Interestingly, although SWEDDs is a disorder clinically linked to PD by a series of common symptoms, the top ranked SNPs recovered from the PD dataset were not part of the SWEDDs dataset and conversely, that suggests that those two sets of markers could be more carefully explored in the genomic studies as common SNPs of interest for the two diseases.

Malgré le succès des insecticides pour le contrôle du paludisme, la transmission continue dans la plupart des pays d’Afrique sub-saharienne. La recherche pour de nouveaux moyens de contrôle (plus spécialement la modification génétique des populations vecteurs), ou l'utilisation plus efficace des contrôles actuels, vont nécessiter une recherche sur les structures de populations de moustiques et les processus d’immunisation qui importent pour la transmission du Plasmodium chez les moustiques sauvages. Par ailleurs, l’utilisation des techniques d’association génomique ‘GWAS’ est basée sur un réelle compréhension des structures des populations.Ma thèse inclura une description detaillée du système immunitaire du moustique, basée sur la recherche actuelle et des comparaisons génomiques; ainsi que des descriptions des principales voies immunitaires, et des gênes potentiels mal-caracterisés qui peuvent être trouvés dans une étude GWAS. Ainsi qu’une description des connaissances actuelles des structures des populations, dont la speciation du gambiae / coluzzii, et les effets des grandes variations structurelles.Je présenterai le développement d’un nouveau moyen d'identification des variations structurelles ; utilisant les techniques d’ “apprentissage automatique” permettant d'identifier les karyotypes directement à partir des séquences haut-débit, menant à des résultats d’une précision sans précédent.Je présenterai également la première vraie cartographie génomique du ‘tout-génome’ du moustique. Les colonies sont fondées par des moustiques sauvages; les fondateurs sont controlées par strates, incluant également des sous-espèces et variations structurelles majeures. Avec ces colonies une méthode innovant de cartographie est utilisée: dans un premier temps, une identification des grandes régions au sein des groupes phenotypées par la perte de hétérogénéité; puis dans un second temps, le génotypage individuel ‘Sequenom’ sera utilisé pour une cartographie exacte. Cette méthode est utilisée pour l’identification d’une région avec un effet phenotypique sur la prévalence des infections dans la nature.Enfin, je suggèrerai comment ces techniques peuvent être importantes à l’avenir pour l’application du contrôle génomique dans la nature. / Despite successes in the use of insecticides in the control of malaria, malaria transmission continues in much of sub-saharan Africa. The search for novel methods of control (in particular genetic modification of vector populations), or of superior implementation of the currently available methods will require both greater knowledge of the population structure of the mosquito, and of the immune processes that are important in the wild. It is important to note that the mapping of novel immune genes, via genome wide association studies (GWAS) is predicated on a firm understanding of the population structure.My thesis will include a detailed description of the mosquito innate immune system based on current research and comparative genomics; this will illustrate the major pathways that might be employed in the anti-malarial response, and some potential uncharacterised genes that might be implicated in any GWAS study. It will also include a summary of what is known about the mosquito’s population structure, in particular the gambiae / coluzzii speciation event and the implication of chromosomal inversions in the speciation process.I will present the development of a novel approach to the identification of chromosomal inversions; using machine-learning techniques in order to call inversion karyotypes directly from sequence, leading to calls of unprecedented accuracy.I will also present the first truly genome-wide association study to have been performed in the mosquito. Strata-controlled populations of mosquitoes were derived from the wild, including restriction on the basis of subspecies and chromosomal inversion. A two-stage mapping design was then devised in which loss-of-heterozygosity is used to identify broad regions in phenotype pools, before fine-resolution mapping by Sequenom genotyping in individuals. This was used to identify a novel locus with a phenotypic effect on infection prevalence.Finally I will describe how these techniques and findings could be important in the future application of genetic control in the wild.

Lipide erfüllen als Energiespeicher und Element verschiedener Verbindungen für den Organismus lebenswichtige Funktionen. Verändert sich ihre Konzentration im Blut spricht man von Dyslipidämien. Diese stellen, insbesondere als Risikofaktor für kardiovaskuläre Erkrankungen, vor allem in Industrienationen ein bedeutendes gesundheitsökonomisches Problem dar. Verschiedene Studien identifizierten in den letzten Jahren zahlreiche Genloci, die den Lipidstoffwechsel beeinflussen. Darunter befinden sich Gene wie APOC1, CETP oder LPL, die aufgrund ihrer Funktionalität eindeutig dem Fettstoffwechsel zuzuordnen sind. Zusätzlich gerieten bisher unbekannte Loci in den Gegenstand der Forschung. Zusammen erklären diese Loci derzeit lediglich 25 - 30 % der phänotypischen Ausprägung. In der vorliegenden Studie wurde mittels genomweiter Assoziationsstudien (GWAS) nach weiteren, potentiell im Zusammenhang mit den Merkmalen HDL-, LDL-Cholesterol und Triglyceriden stehenden Genloci gesucht. Dazu wurde in einer eigenständigen Population, den Sorben aus der Oberlausitz, eine genomweite Assoziationsstudie (N = 839) durchgeführt. Es zeigten sich 13 signifikant assoziierte Loci mit einem P-Wert < 10 5. Anschließend wurden SNPs mit einem P-Wert < 0,01 in der sorbischen Kohorte für eine Metaanalyse mit den Daten der Probanden der Diabetes Genetics Initiative (N ~ 2600) ausgewählt. So konnten 21 Genloci mit einem kombinierten P-Wert < 10 4 bestätigt werden. Von diesen wurden 5 neu identifizierte, bisher nicht publizierte Varianten in der unabhängigen Metabolisches Syndrom Berlin Potsdam Kohorte (N = 2000) repliziert. Mit einem P-Wert von 1,78x10 7 zeigte die Variante rs8135828 im THOC5 Gen für den Parameter HDL-Cholesterol die stärkste Assoziation in der kombinierten Metaanalyse aller 3 Kohorten. Weitere Analysen sind notwendig, um die Funktionen und den Regulationsmechanismus der identifizierten SNPs auf den Fettstoffwechsel genau zu verstehen.

The aim of this thesis is to explore the role of common genetic variation, identified through genome-wide association (GWA) studies, in human traits and diseases, using height as a model polygenic trait, type 2 diabetes as a model common polygenic disease, and maturity onset diabetes of the young (MODY) as a model monogenic disease. The wave of the initial GWA studies, such as the Wellcome Trust Case-Control Consortium (WTCCC) study of seven common diseases, substantially increased the number of common variants associated with a range of different multifactorial traits and diseases. The initial excitement, however, seems to have been followed by some disappointment that the identified variants explain a relatively small proportion of the genetic variance of the studied trait, and that only few large effect or causal variants have been identified. Inevitably, this has led to criticism of the GWA studies, mainly that the findings are of limited clinical, or indeed scientific, benefit. Using height as a model, Chapter 2 explores the utility of GWA studies in terms of identifying regions that contain relevant genes, and in answering some general questions about the genetic architecture of highly polygenic traits. Chapter 3 takes this further into a large collaborative study and the largest sample size in a GWA study to date, mainly focusing on demonstrating the biological relevance of the identified variants, even when a large number of associated regions throughout the genome is implicated by these associations. Furthermore, it shows examples of different features of the genetic architecture, such as allelic heterogeneity and pleiotropy. Chapter 4 looks at the predictive value and, therefore, clinical utility, of variants found to associate with type 2 diabetes, a common multifactorial disease that is increasing in prevalence despite known environmental risk factors. This is a disease where knowledge of the genetic risk has potentially substantial clinical relevance. Finally, Chapter 5 approaches the monogenic-polygenic disease bridge in the direction opposite to that approached in the past: most studies have investigated genes mutated in monogenic diseases as candidates for harboring common variants predisposing to related polygenic diseases. This chapter looks at the common type 2 diabetes variants as modifiers of disease onset in patients with a monogenic but clinically heterogeneous disease, maturity onset diabetes of the young (MODY).

Genome-wide association studies have very successfully found highly significant disease associations with single nucleotide polymorphisms (SNP) in the Major Histocompatibility Complex for adverse drug reactions, autoimmune diseases and infectious diseases. However, the extensive linkage disequilibrium in the region has made it difficult to unravel the HLA alleles underlying these diseases. Here I present two methods to comprehensively predict 4-digit HLA types from the two types of experimental genome data widely available. The Virtual SNP Imputation approach was developed for genome scan data and demonstrated a high precision and recall (96% and 97% respectively) for the prediction of HLA genotypes. A reanalysis of 6 genome-wide association studies using the HLA imputation method identified 18 significant HLA allele associations for 6 autoimmune diseases: 2 in ankylosing spondylitis, 2 in autoimmune thyroid disease, 2 in Crohn's disease, 3 in multiple sclerosis, 2 in psoriasis and 7 in rheumatoid arthritis. The EPIGEN consortium also used the Virtual SNP Imputation approach to detect a novel association of HLA-A*31:01 with adverse reactions to carbamazepine. For the prediction of HLA genotypes from next generation sequencing data, I developed a novel approach using a naïve Bayes algorithm called HLA-Genotyper. The validation results covered whole genome, whole exome and RNA-Seq experimental designs in the European and Yoruba population samples available from the 1000 Genomes Project. The RNA-Seq data gave the best results with an overall precision and recall near 0.99 for Europeans and 0.98 for the Yoruba population. I then successfully used the method on targeted sequencing data to detect significant associations of idiopathic membranous nephropathy with HLA-DRB1*03:01 and HLA-DQA1*05:01 using the 1000 Genomes European subjects as controls. Using the results reported here, researchers may now readily unravel the association of HLA alleles with many diseases from genome scans and next generation sequencing experiments without the expensive and laborious HLA typing of thousands of subjects. Both algorithms enable the analysis of diverse populations to help researchers pinpoint HLA loci with biological roles in infection, inflammation, autoimmunity, aging, mental illness and adverse drug reactions.

Meta-analysis is a statistical technique that combines results from multiple independent studies to make inferences about parameters of interest. Although it is popular for parameter estimation and hypothesis testing, meta-analytic approaches that incorporate heterogeneous studies have not been fully developed. For heterogeneous studies, we do not expect all of the studies to have the same true underlying effect and the use of the fixed-effects model in a meta-analysis in this situation violates the assumption of homogeneity of effect size. Heterogeneity among studies can arise from multiple sources such as differences in populations by ancestry, differences in study designs, and different impacts of environmental exposures on the effect of the variable of interest. In this thesis, we introduce an analytic strategy and statistical models for meta-analysis of potentially heterogeneous studies. First, we propose a two-stage clustering approach to account for heterogeneity in trans-ethnic meta-analysis of genome-wide association studies (GWAS). Specifically, we cluster studies in the two-stage approach using cohort-specific genetic information prior to meta-analysis to account for between-cluster heterogeneity as well as to bolster within-cluster homogeneity. An extensive simulation study shows that this approach improves power and diminishes computational intensity compared to existing methods for trans-ethnic meta-analysis. Next, under a meta-regression framework, we develop a likelihood ratio test (LRT) statistic to accommodate multiple random effects. We allow multiple sources of heterogeneity in terms of study characteristics and model the heterogeneities as random effects. We show that the proposed LRT maintains a similar or higher power than other existing methods in a simulation study especially when heterogeneity exists. We apply this new approach to meta-analyze genome-wide association data. Lastly, we derive a score test in the same context as our proposed new LRT and show the substantial advantage of the score test in computational efficiency compared to the new LRT. The introduced strategy and methodologies can effectively and efficiently aggregate the evidence from potentially heterogeneous studies in statistical genetics and other research areas.

In the last decade, genome-wide association studies have helped to advance our understanding of the genetic architecture of many important traits, including diseases. However, the statistical analysis of genotype-phenotype associations remains challenging due to multiple factors. First, many traits have polygenic architectures, which means that they are controlled by a large number of variants with small individual effects. Second, as increasingly deep phenotype data are being generated there is a need for multivariate analysis approaches to leverage multiple related phenotypes while retaining computational efficiency. Additionally, genetic analyses are confronted by strong confounding factors that can create spurious associations when not properly accounted for in the statistical model. We here derive more flexible methods that allow integrating genetic effects across variants and multiple quantitative traits. To do so, we build on the classical linear mixed model (LMM), a widely adopted framework for genetic studies. The first contribution of this thesis is mtSet, an efficient mixed-model approach that enables genome-wide association testing between sets of genetic variants and multiple traits while accounting for confounding factors. In both simulations and real-data applications we demonstrate that mtSet effectively combines the advantages of variant-set and multi-trait analyses. Next, we present a new model for gene-context interactions that builds on mtSet. The proposed interaction set test (iSet) yields increased statistical power for detecting polygenic interactions. Additionally, iSet enables the identification of genetic loci that are associated with different configurations of causal variants across contexts. After benchmarking the proposed method using simulated data, we consider two applications to real datasets, where we investigate genetic effects on gene expression across different cellular contexts and sex-specific genetic effects on lipid levels. Finally, we describe LIMIX, a software framework for the flexible implementation of different LMMs. Most of the models considered in this thesis, including mtSet and iSet, are implemented and available in LIMIX. A unique aspect of the software is an inference framework that allows a large class of genetic models to be defined and, in many cases, to be efficiently fitted by exploiting specific algebraic properties. We demonstrate the utility of this software suite in two applied collaboration projects. Taken together, this thesis demonstrates the value of flexible and integrative modelling in genetics and contributes new statistical methods for genetic analysis. These approaches generalise previous models, yet retain the computational efficiency that is needed to tackle large genetic datasets.

<p>Plant biomass is an abundant source of renewable energy, but the efficiency of its conversion into liquid fuels is low. One reason for this inefficiency is the recalcitrance of biomass to extraction and saccharification of cell wall polysaccharides. This recalcitrance is due to the complex and rigid structure of the plant cell wall. A better understanding of the genes effecting cell wall composition in bioenergy crops could improve feedstock quality and increase conversion efficiency. To identify genetic loci associated with biomass quality traits, we utilized genome-wide association studies (GWAS) in an 840-line <i>Sorghum</i> diversity panel. We identified several QTL from these GWAS including some for lignin composition and saccharification. Linkage disequilibrium (LD) analysis suggested that multiple polymorphisms are driving the association of SNPs within these QTL. Sequencing and further analysis led to the identification of a SNP within the coding region of a gene encoding phenylalanine ammonia-lyase (PAL) that creates a premature stop codon and co-segregates with an increase in the ratio of syringyl (S) to guaiacyl (G) lignin. A comparison of net PAL activity between lines with and without the mutation revealed that this mutation results in decreased PAL activity. </p>

Data from a Croatian isolate population are analysed in a genome-wide association study (GWAS) for a variety of disease-related quantitative traits. A novel genomewide approach to analysing pedigree-based association data called GRAMMAR is utilised. One of the significant findings, for uric acid, is followed up in greater detail, and is replicated in another isolate population, from Orkney. The associated SNPs are located in the SLC2A9 gene, coding for a known glucose transporter, which leads to identification of SLC2A9 as a urate transporter too (Vitart et al., 2008). These SNPs are later implicated in affecting gout, a disease known to be linked with high serum uric acid levels, in an independent study (Dehghan et al., 2008). Subsequently, investigation into different ways in which to use SNP data to identify quantitative trait loci (QTL) for genome-wide association (GWA) studies is performed. Several multi-marker approaches are compared to single SNP analysis using simulated phenotypes and real genotype data, and results show that for rare variants haplotype analysis is the most effective method of detection. Finally, the multi-marker methods are compared with single SNP analysis on the real uric acid data. Interpretation of real data results was complicated due to low sample size, since only founder and unrelated individuals may be used for population-based haplotype analysis, nonetheless, results of the prior analyses of simulated data indicate that multi-marker methods, in particular haplotypes, may greatly facilitate detection of QTL with low minor allele frequency in GWA studies.

Genome-wide association studies (GWAS) have successfully identified thousands of variants robustly associated with hundreds of complex traits, but the biological mechanisms driving these results remain elusive. Functional annotation, describing the roles of known genes and regulatory elements, provides additional information about associated variants. This dissertation explores the potential of these annotations to explain the biology behind observed GWAS results. The first project develops a random-effects approach to genetic fine mapping of trait-associated loci. Functional annotation and estimates of the enrichment of genetic effects in each annotation category are integrated with linkage disequilibrium (LD) within each locus and GWAS summary statistics to prioritize variants with plausible functionality. Applications of this method to simulated and real data show good performance in a wider range of scenarios relative to previous approaches. The second project focuses on the estimation of enrichment by annotation categories. I derive the distribution of GWAS summary statistics as a function of annotations and LD structure and perform maximum likelihood estimation of enrichment coefficients in two simulated scenarios. The resulting estimates are less variable than previous methods, but the asymptotic theory of standard errors is often not applicable due to non-convexity of the likelihood function. In the third project, I investigate the problem of selecting an optimal set of tissue-specific annotations with greatest relevance to a trait of interest. I consider three selection criteria defined in terms of the mutual information between functional annotations and GWAS summary statistics. These algorithms correctly identify enriched categories in simulated data, but in the application to a GWAS of BMI the penalty for redundant features outweighs the modest relationships with the outcome yielding null selected feature sets, due to the weaker overall association and high similarity between tissue-specific regulatory features. All three projects require little in the way of prior hypotheses regarding the mechanism of genetic effects. These data-driven approaches have the potential to illuminate unanticipated biological relationships, but are also limited by the high dimensionality of the data relative to the moderate strength of the signals under investigation. These approaches advance the set of tools available to researchers to draw biological insights from GWAS results.