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Delahaye-Sourdeix, Manon. "Moving beyond Genome-Wide Association Studies." Thesis, Lyon 1, 2014. http://www.theses.fr/2014LYO10238.
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Les études d'association à grande échelle consistent à étudier la corrélation de plusieurs millions de polymorphismes nucléotidiques avec un risque de cancer chez des milliers d'individus, sans avoir besoin de connaissances préalables sur la fonction biologique de ces variants. Ces études ont été utiles pour établir des hypothèses étiologiques et comprendre l'architecture génétique sous-jacente de plusieurs maladies humaines. Cependant, la plupart des facteurs héréditaires de ces maladies restent inexpliqués. Une partie de cette variation pourrait venir de variants rares qui ne sont pas ciblés par les puces de génotypage actuelles ou encore de variants avec un effet plus modéré voire faible pour lesquels une détection par les études d'association actuelles n'est pas envisageable. Dans ce contexte et comme illustré dans cette thèse, les récentes études d'association peuvent maintenant servir de point de départ pour de nouvelles découvertes, en mettant en place des stratégies innovantes pour étudier à la fois les variants rares et les maladies rares. Nous avons plus particulièrement exploré ces techniques dans le cadre du cancer du poumon, des voies aérodigestives et du lymphome de Hodgkins. L'utilisation de la bioinformatique pour combiner les résultats des études avec d'autres sources d'information, l'intégration de différents types de données génomiques ainsi que l'investigation de la relation entre altérations germinales et somatiques représentent les principales opportunités poursuivies dans ce travail de thèseGenome-wide association (GWA) studies consist in testing up to one million (or more) single nucleotide polymorphisms (SNPs) for their association with cancer risk in thousands of individuals, without requiring any prior knowledge on the functional significance of these variants. These studies have been valuable for establishing etiological hypotheses and understanding the underlying genetic architecture of human diseases. However, most of the heritable factors of these traits remain unexplained. Part of this variation may come from rarer variants that are not targeted by current genotyping arrays or variants with moderate to low effects for which detection by current GWA studies is impractical. In this context and as illustrated in this thesis, GWA studies can now serve as starting points towards further discoveries, looking for new strategies to study both rarer variants and rarer diseases. We have specifically explored these approaches in the context of lung cancer, head and neck cancer and Hodgkin's lymphoma. The use of bioinformatics to combine recent GWA study results with other sources of information, the integration of different types of genomic data as well as the investigation of the interrelationship between germline and somatic alterations represent the main opportunities pursued in this thesis work
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Liu, Jin. "Penalized methods in genome-wide association studies." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/1242.
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Penalized regression methods are becoming increasingly popular in genome-wide association studies (GWAS) for identifying genetic markers associated with disease. However, standard penalized methods such as the LASSO do not take into account the possible linkage disequilibrium between adjacent markers. We propose a novel penalized approach for GWAS using a dense set of single nucleotide polymorphisms (SNPs). The proposed method uses the minimax concave penalty (MCP) for marker selection and incorporates linkage disequilibrium (LD) information by penalizing the difference of the genetic effects at adjacent SNPs with high correlation. A coordinate descent algorithm is derived to implement the proposed method. This algorithm is efficient and stable in dealing with a large number of SNPs. A multi-split method is used to calculate the p-values of the selected SNPs for assessing their significance. We refer to the proposed penalty function as the smoothed MCP (SMCP) and the proposed approach as the SMCP method. Performance of the proposed SMCP method and its comparison with a LASSO approach are evaluated through simulation studies, which demonstrate that the proposed method is more accurate in selecting associated SNPs. Its applicability to real data is illustrated using data from a GWAS on rheumatoid arthritis. Based on the idea of SMCP, we propose a new penalized method for group variable selection in GWAS with respect to the correlation between adjacent groups. The proposed method uses the group LASSO for encouraging group sparsity and a quadratic difference for adjacent group smoothing. We call it smoothed group LASSO, or SGL for short. Canonical correlations between two adjacent groups of SNPS are used as the weights in the quadratic difference penalty. Principal components are used to reduced dimensionality locally within groups. We derive a group coordinate descent algorithm for computing the solution path of the SGL. Simulation studies are used to evaluate the finite sample performance of the SGL and group LASSO. We also demonstrate its applicability on rheumatoid arthritis data.
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Yazdani, Akram. "Statistical Approaches in Genome-Wide Association Studies." Doctoral thesis, Università degli studi di Padova, 2014. http://hdl.handle.net/11577/3423743.
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Genome-wide association studies, GWAS, typically contain hundreds of thousands single nucleotide polymorphisms, SNPs, genotyped for few numbers of samples. The aim of these studies is to identify regions harboring SNPs or to predict the outcomes of interest. Since the number of predictors in the GWAS far exceeds the number of samples, it is impossible to analyze the data with classical statistical methods. In the current GWAS, the widely applied methods are based on single marker analysis that does assess association of each SNP with the complex traits independently. Because of the low power of this analysis for detecting true association, simultaneous analysis has recently received more attention. The new statistical methods for simultaneous analysis in high dimensional settings have a limitation of disparity between the number of predictors and the number of samples. Therefore, reducing the dimensionality of the set of SNPs is required.
This thesis reviews single marker analysis and simultaneous analysis with a focus on Bayesian methods. It addresses the weaknesses of these approaches with reference to recent literature and illustrating simulation studies. To bypass these problems, we first attempt to reduce dimension of the set of SNPs with random projection technique. Since this method does not improve the predictive performance of the model, we present a new two-stage approach that is a hybrid method of single and simultaneous analyses. This full Bayesian approach selects the most promising SNPs in the first stage by evaluating the impact of each marker independently. In the second stage, we develop a hierarchical Bayesian model to analyze the impact of selected markers simultaneously. The model that accounts for related samples places the local-global shrinkage prior on marker effects in order to shrink small effects to zero while keeping large effects relatively large. The prior specification on marker effects, which is hierarchical representation of generalized double Pareto, improves the predictive performance. Finally, we represent the result of real SNP-data analysis through single-maker study and the new two-stage approach.Lo Studio di Associazione Genome-Wide, GWAS, tipicamente comprende centinaia di migliaia di polimorfismi a singolo nucleotide, SNPs, genotipizzati per pochi campioni. L'obiettivo di tale studio consiste nell'individuare le regioni cruciali SNPs e prevedere gli esiti di una variabile risposta. Dal momento che il numero di predittori è di gran lunga superiore al numero di campioni, non è possibile condurre l'analisi dei dati con metodi statistici classici. GWAS attuali, i metodi negli maggiormente utilizzati si basano sull'analisi a marcatore unico, che valuta indipendentemente l'associazione di ogni SNP con i tratti complessi. A causa della bassa potenza dell'analisi a marcatore unico nel rilevamento delle associazioni reali, l'analisi simultanea ha recentemente ottenuto più attenzione. I recenti metodi per l'analisi simultanea nel multidimensionale hanno una limitazione sulla disparità tra il numero di predittori e il numero di campioni. Pertanto, è necessario ridurre la dimensionalità dell'insieme di SNPs. Questa tesi fornisce una panoramica dell'analisi a marcatore singolo e dell'analisi simultanea, focalizzandosi su metodi Bayesiani. Vengono discussi i limiti di tali approcci in relazione ai GWAS, con riferimento alla letteratura recente e utilizzando studi di simulazione. Per superare tali problemi, si è cercato di ridurre la dimensione dell'insieme di SNPs con una tecnica a proiezione casuale. Poiché questo approccio non comporta miglioramenti nella accuratezza predittiva del modello, viene quindi proposto un approccio in due fasi, che risulta essere un metodo ibrido di analisi singola e simultanea. Tale approccio, completamente Bayesiano, seleziona gli SNPs più promettenti nella prima fase valutando l'impatto di ogni marcatore indipendentemente. Nella seconda fase, viene sviluppato un modello gerarchico Bayesiano per analizzare contemporaneamente l'impatto degli indicatori selezionati. Il modello che considera i campioni correlati pone una priori locale-globale ristretta sugli effetti dei marcatori. Tale prior riduce a zero gli effetti piccoli, mentre mantiene gli effetti più grandi relativamente grandi. Le priori specificate sugli effetti dei marcatori sono rappresentazioni gerarchiche della distribuzione Pareto doppia; queste a priori migliorano le prestazioni predittive del modello. Infine, nella tesi vengono riportati i risultati dell'analisi su dati reali di SNP basate sullo studio a marcatore singolo e sul nuovo approccio a due stadi.
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Porretta'S, Luciano. "MODELS AND METHODS IN GENOME WIDE ASSOCIATION STUDIES." Doctoral thesis, Universite Libre de Bruxelles, 2018. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/265314.
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The interdisciplinary field of systems biology has evolved rapidly over the last few years. Different disciplines have contributed to the development of both its experimental and theoretical branches.Although computational biology has been an increasing activity in computer science for more than a two decades, it has been only in the past few years that optimization models have been increasingly developed and analyzed by researchers whose primary background is Operations Research(OR). This dissertation aims at contributing to the field of computational biology by applying mathematical programming to certain problems in molecular biology.Specifically, we address three problems in the domain of Genome Wide Association Studies}:(i) the Pure Parsimony Haplotyping Under uncertatind Data Problem that consists in finding the minimum number of haplotypes necessary to explain a given set of genotypes containing possible reading errors; (ii) the Parsimonious Loss Of Heterozygosity Problem that consists of partitioning suspected polymorphisms from a set of individuals into a minimum number of deletion areas; (iii) and the Multiple Individuals Polymorphic Alu Insertion Recognition Problem that consists of finding the set of locations in the genome where ALU sequences are inserted in some individual(s).All three problems are NP-hard combinatorial optimization problems. Therefore, we analyse their combinatorial structure and we propose an exact approach to solution for each of them. The proposed models are efficient, accurate, compact, polynomial-sized and usable in all those cases for which the parsimony criterion is well suited for estimation.Option Informatique du Doctorat en Sciences
info:eu-repo/semantics/nonPublished
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Barrett, Jeffrey C. "Design and analysis of genome-wide association studies." Thesis, University of Oxford, 2008. http://ora.ox.ac.uk/objects/uuid:45790b5c-e50c-406a-bb3c-a96868b11a28.
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Despite many years of effort, linkage and candidate gene association studies have yielded disappointingly few risk loci for common human diseases such as diabetes, auto-immune disorders and cancers. Large sample sizes, increased understanding of the patterns of correlation in genetic variation, and plunging genotyping costs have enabled genome-wide association studies, which have good power to detect common risk alleles of modest effect. I present an evaluation of SNP choice in study design and show that overall, despite substantial differences in genotyping technologies, marker selection strategies and number of markers assayed, the first generation platforms all offer good levels of genome coverage (∼ 70%). I next describe the largest such project undertaken to date, the Wellcome Trust Case Control Consortium, which consisted of 2000 cases from each of seven common diseases and 3000 shared controls. It identified nearly two dozen new associations. I demonstrate the importance of careful data quality control, including both standard and unorthodox analyses. I next focus on the association results therein for Crohn’s disease. I present a replication experiment in over 1000 additional Crohn’s patients which unambiguously confirmed six previously published loci and four new loci. Next I describe, in a general context, several issues impeding the combination of genome-wide scans, including data annotation, population structure and differences in genotyping platform. Each of these problems is shown to be tractable with available methods, provided that these methods are applied prudently. I present the results of a meta-analysis of three genome-wide scans for Crohn’s disease. The data showed a striking excess of significant associations, and a replication experiment involving over 4000 independent Crohn’s patients verified twenty new risk loci. Finally, I discuss the early success of genome-wide association and its consequences for further understanding the biology of human disease.
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Li, Shengxu. "Genome-wide association studies of body mass index." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608974.
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Keildson, Sarah. "Model selection strategies in genome-wide association studies." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:bd97c2e3-10e3-4007-9b7b-199e99d04f94.
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Unravelling the genetic architecture of common diseases is a continuing challenge in human genetics. While genome-wide association studies (GWAS) have proven to be successful in identifying many new disease susceptibility loci, the extension of these studies beyond single-SNP methods of analysis has been limited. The incorporation of multi-locus methods of analysis may, however, increase the power of GWAS to detect genes of smaller effect size, as well as genes that interact with each other and the environment. This investigation carried out large-scale simulations of four multi-locus model selection techniques; namely forward and backward selection, Bayesian model averaging (BMA) and least angle regression with a lasso modification (lasso), in order to compare the type I error rates and power of each method. At a type I error rate of ~5%, lasso showed the highest power across varied effect sizes, disease frequencies and genetic models. Lasso penalized regression was then used to perform three different types of analysis on GWAS data. Firstly, lasso was applied to the Wellcome Trust Case Control Consortium (WTCCC) data and identified many of the WTCCC SNPs that had a moderate-strong association (p<10-5) type 2 diabetes (T2D), as well as some of the moderate WTCCC associations (p<10-4) that have since been replicated in a large-scale meta-analysis. Secondly, lasso was used to fine-map the 17q21 childhood asthma risk locus and identified putative secondary signals in the 17q21 region, that may further contribute to childhood asthma risk. Finally, lasso identified three potential interaction effects potentially contributing towards coronary artery disease (CAD) risk. While the validity of these findings hinges on their replication in follow-up studies, the results suggest that lasso may provide scientists with exciting new methods of dissecting, and ultimately understanding, the complex genetic framework underlying common human diseases.
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Parisi, Rosa. "Multi-locus statistical analysis of genome-wide association studies." Thesis, University of Leeds, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535123.
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Yeung, Ming-yiu, and 楊明耀. "Genome wide association studies of biliary atresia in Chinese." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43703847.
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Bhattacharya, Kanishka. "Gene x gene interactions in genome wide association studies." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:6cb7ab29-90df-4d70-bc2f-531f874b79d0.
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Genome wide association studies (GWAS) have revolutionized our approach to mapping genetic determinants of complex human diseases. However, even with success from recent studies, we have typically been able to explain only a fraction of the trait heritability. GWAS are typically analysed by testing for the marginal effects of single variants. Consequently, it has been suggested that gene-gene interactions might contribute to the missing heritability of complex diseases. GWAS incorporating interaction effects have not been routinely applied because of statistical and computational challenges relating to the number of tests performed, genome-wide. To overcome this issue, I have developed novel methodology to allow rapid testing of pairwise interactions in GWAS of complex traits, implemented in the IntRapid software. Simulations demonstrated that the power of this approach was equivalent to computationally demanding exhaustive searches of the genome, but required only a fraction of the computing time. Application of IntRapid to GWAS of a range of complex human traits undertaken by the Wellcome Trust Case Control Consortium (WTCCC) identified several interaction effects at nominal significance, which warrant further investigation in independent studies. In an attempt to fine-map the identified interacting loci, I undertook imputation of the WTCCC genotype data up to the 1000 Genomes Project reference panel (Phase 1 integrated release, March 2012) in the neighbourhood of the lead SNPs. I modified the IntRapid software to take account of imputed genotypes, and identified stronger signals of interaction after imputation at the majority of loci, where the lead SNP often had moved by hundreds of kilobases. The X-chromosome is often overlooked in GWAS of complex human traits, primarily because of the difference in the distribution of genotypes in males and females. I have extended IntRapid to allow for interactions with the X chromosome by considering males and females separately, and combining effect estimates across the sexes in a fixed-effects meta-analysis. Application to genotype data from the WTCCC failed to identify any strong signals of association with the X-chromosome, despite known epidemiological differences between the sexes for the traits considered. The novel methods developed as part of this doctoral work enable a user friendly, computationally efficient and powerful way of implementing genome-wide gene-gene interaction studies. Further work would be required to allow for more complex interaction modelling and deal with the associated computational burden, particularly when using next-generation sequencing (NGS) data which includes a much larger set of SNPs. However, IntRapid is demonstrably efficient in exhaustively searching for pairwise interactions in GWAS of complex traits, potentially leading to novel insights into the genetic architecture and biology of human disease.
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Yi, Hui. "Assessment of Penalized Regression for Genome-wide Association Studies." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64845.
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The data from genome-wide association studies (GWAS) in humans are still predominantly analyzed using single marker association methods. As an alternative to Single Marker Analysis (SMA), all or subsets of markers can be tested simultaneously. This approach requires a form of Penalized Regression (PR) as the number of SNPs is much larger than the sample size. Here we review PR methods in the context of GWAS, extend them to perform penalty parameter and SNP selection by False Discovery Rate (FDR) control, and assess their performance (including penalties incorporating linkage disequilibrium) in comparison with SMA. PR methods were compared with SMA on realistically simulated GWAS data consisting of genotype data from single and multiple chromosomes and a continuous phenotype and on real data. Based on our comparisons our analytic FDR criterion may currently be the best approach to SNP selection using PR for GWAS. We found that PR with FDR control provides substantially more power than SMA with genome-wide type-I error control but somewhat less power than SMA with Benjamini-Hochberg FDR control. PR controlled the FDR conservatively while SMA-BH may not achieve FDR control in all situations. Differences among PR methods seem quite small when the focus is on variable selection with FDR control. Incorporating LD into PR by adapting penalties developed for covariates measured on graphs can improve power but also generate morel false positives or wider regions for follow-up. We recommend using the Elastic Net with a mixing weight for the Lasso penalty near 0.5 as the best method.Ph. D.
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Awany, Denis. "Leveraging the microbiome in host genome wide association studies." Doctoral thesis, Faculty of Health Sciences, 2021. http://hdl.handle.net/11427/33632.
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Genome-wide association study (GWAS) has emerged as an effective method for detecting genetic polymorphisms associated with expressed phenotypes. Over the past decade, GWAS of human traits and diseases has revolutionized the field of complex disease genetics, identifying hundreds of genetic variants associated with several different phenotypes, ranging from metabolic diseases to cardiovascular and neuropsychiatric conditions. These associations have provided fundamental insights into the genetic architecture of disease susceptibility and led to initial forays into clinical applications, particularly in creation of genetic risk scores for improved disease risk prediction and identification of new drug targets for novel drug development. Despite this gratifying success, however, for almost all complex traits, the identified genetic loci explain only a small proportion, generally less than half, of the estimated heritability. A number of alternative explanations have been offered for this, including undetected genetic effects, unaccounted-for environmental factors, and gene–gene and gene–environment interaction effects. Although there is no consensus on these explanations, it is universally acknowledged that a substantial proportion of the trait heritability is attributable to existence of a large number of undetected genetic variants distributed across the entire allele frequency spectrum, each of which has very small to modest effect on the phenotype, and non host-DNA factors that contribute to phenotypic variation. In parallel to host GWAS, the advent of next-generation sequencing technologies (NGS) that enable culture-independent profiling of microbial communities has led to the rediscovery of the microbiome - the collective genome of the microorganisms that inhabit the body - and the emergence of microbiome-wide association studies. These studies have linked the gut microbiome to a variety of human conditions, ranging from neurological conditions, such as Parkinson's disease and autism, to metabolic diseases, such as obesity, diabetes, and cardiovascular disease. Given the critical importance of the microbiome in host phenotype, it is clear that in order to more comprehensively understand the basis of host phenotypic status, both the host's genotype and microbiome information have to be examined. This thesis explores the dissection of microbial taxa and host genetic polymorphisms associated with human complex traits and diseases, and the interaction of human host genetic polymorphisms with the microbiome. Then, a Bayesian statistical framework, based on the Dirichlet process random effects model, is proposed for identifying microbial species associated with host phenotype. The proposed method uses a weighted combination of phylogenetic and radial basis function kernels to model microbial taxa effects, and a non-parametrically defined latent variable to model latent heterogeneity among samples. Philosophically, the non-parametric specification amounts to the addition of an infinite amount of prior information about all fine details of the parameters being modelled; thus represents an attractive strategy. The utility of the method is demonstrated through simulation experiments and application to real microbiome datasets for schizophrenia, HIV/AIDS, and atherosclerosis diseases, where it is shown that the method is not only robust but also has high statistical power for association inference, resulting in a framework that can contribute to our understanding of the link between the microbiome and human diseases. Understanding the human genetic predisposing factors in concert with this link will make human GWAS fulfil its translational potential, from patient stratifcation and disease risk prediction to identification of new biology and drug discovery.
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Yeung, Ming-yiu. "Genome wide association studies of biliary atresia in Chinese." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43703847.
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Halle, Kari Krizak. "Statistical Methods for Multiple Testing in Genome-Wide Association Studies." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18503.
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In Genome-Wide Association Studies (GWAS) the aim is to look for associationbetween genetic markers and phenotype (disease). For each genetic marker weperform an hypothesis test. Since the number of markers is high (in the order of hundred thousands), we use multiple hypothesis tests. One popular strategy in multippel testing is to estimate an effective number of independent tests, and then use methods based on independent tests to control the total type I error. The focus of this thesis has been to study different methods for estimating the effective number of independent tests. The methods are applied to a large data set on bipolar disorder and schizophrenia in Norwegian individuals from the TOP study at the University of Oslo and Oslo University Hospital (OUS). A key featureof these methods is the correlation between the genetic markers. The methodsconsidered in this thesis are based on either haplotype or genotype correlation andone focus of this thesis has been to study the difference between haplotype andgenotype correlation.
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Barnett, Ian. "SNP-set Tests for Sequencing and Genome-Wide Association Studies." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11416.
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In this dissertation we propose methodology for testing SNP-sets for genetic associations, both for sequencing and genome-wide association studies. Due to the large scale of this kind of data, there is an emphasis on producing methodology that is not only accurate and powerful, but also computationally efficient.
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Michailidou, Kyriaki. "Statistical analyses of genome-wide association studies in breast cancer." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708642.
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Wason, James Maurice Stephen. "The use of multimarker models in genome-wide association studies." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608810.
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Huang, Wenhui. "Towards constructing disease relationship networks using genome-wide association studies." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/46326.
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Background: Genome-wide association studies (GWAS) prove to be a powerful
approach to identify the genetic basis of various human[1] diseases. Here we take
advantage of existing GWAS data and attempt to build a framework to understand the
complex relationships among diseases. Specifically, we examined 49 diseases from all
available GWAS with a cascade approach by exploiting network analysis to study the
single nucleotide polymorphisms (SNP) effect on the similarity between different
diseases. Proteins within perturbation subnetwork are considered to be connection
points between the disease similarity networks.
Results: shared disease subnetwork proteins are consistent, accurate and sensitive to
measure genetic similarity between diseases. Clustering result shows the evidence of
phenome similarity.
Conclusion: our results prove the usefulness of genetic profiles for evaluating disease
similarity and constructing disease relationship networks.Master of Science
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Mittag, Florian [Verfasser]. "Disease risk prediction in genome-wide association studies / Florian Mittag." München : Verlag Dr. Hut, 2016. http://d-nb.info/111116035X/34.
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Antonyuk, Alexander. "Statistical methodology for QTL mapping and genome-wide association studies." Thesis, University of Oxford, 2009. https://ora.ox.ac.uk/objects/uuid:23393c76-b7ef-44c2-a06f-3b23e3a6d936.
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This work deals with statistical tests of association between genetic markers and disease phenotypes. The main criterion used for comparing the tests is statistical power. First we consider animal models and then data from association studies of humans. For the animal section, we analyse a dataset from a prominent mouse experiment which developed a heterogeneous stock of mice via multiple crosses. This stock is characterised by small distances between recombinants which allows fine mapping of genetic loci, but also by uncertainty in haplotypes. We start by highlighting the disadvantages of the currently used approach to deal with this uncertainty and suggest a method that has greater statistical power and is computationally efficient. The method applies the EM algorithm to the broad class of exponential family distributions of phenotypes. We also develop a Bayesian version of the method, for which we extend the widely used IRLS algorithm to maximisation of the weighted posterior. Then we move on to genome-wide association studies (GWAS), where two situations are considered: known and unknown minor allele frequency. First we develop an innovative Bayesian model with the optimal prior for the known population MAF. We demonstrate that not only it is more powerful than any frequentist test considered (the size of the advantage depends on prevalence of the disease and MAF), but also that the frequentist tests change ranking in terms of power. A remarkable property of the frequentist tests, the advantage of discarding part of the data to gain power, is highlighted. The second chapter on GWAS considers the currently more common situation of the unknown MAF, when the Armitage test is known to be the most powerful frequentist method. We show that the suggested model is more powerful in the broad selection of settings considered, including the three different allele effect models: additive, dominant and recessive. For both known and unknown MAF cases we point out that the parameters are constrained and demonstrate how to gain power by taking this constraint into account.
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Earle, Sarah. "Development and application of genome-wide association studies in bacteria." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:fcadbe24-5f0c-452a-9a04-51d2e5af4caf.
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Since the first genome-wide association study (GWAS) applied to humans in 2005, incredible advances have been made in understanding the genetic basis underlying common human diseases and complex traits. Dramatic technological developments have enabled rapid, inexpensive whole-genome sequencing in large numbers of bacteria, creating intense interest in the large-scale application of GWAS to bacteria. However, fundamental differences between the genomes of humans and bacteria mean that although the methodological developments in the human setting are an invaluable starting point, novel methods tailored specifically to bacteria are required. This thesis concerns the development and application of GWAS in bacteria. Taking lessons from the past decade of human GWAS, I began by assessing the feasibility of GWAS in bacteria by investigating the bacterial genetic basis underlying antimicrobial resistance. I aimed to empirically test the feasibility of bacterial GWAS in light of particular challenges posed by bacteria such as strong population structure, genome-wide linkage disequilibrium and the presence of large accessory genomes. Specifically, I performed a detailed investigation into fusidic acid resistance in Staphylococcus aureus to assess the impact of controlling for population stratification in highly structured populations. This demonstrated the importance of controlling for population structure in reducing the number of false positives, but also the substantial cost in doing so. Testing for lineage-level associations enabled the inference of important lineage-level differences in phenotypes, typically discarded when controlling for population structure. I then went on to apply the methods developed to two further phenotypes. The first, carriage vs invasive disease in Neisseria meningitidis, identified the known hyperinvasive ST-11 lineage to be associated with invasive disease, and suggested that newly-reported variants in genes involved in capsule production and phase variation play an important role in the virulence potential of meningococci in natural populations. I hypothesised that a combination of two particular variants upstream of the gene encoding the virulence factor fHbp (factor H binding protein), produces a second putative FNR box, a binding site for the global transcriptional regulator FNR, which may affect expression of fHbp. Finally, I investigated wild bird vs chicken colonisation in Campylobacter jejuni and identified lineage-level associations in agreement with previously identified host-associated lineage characteristics. I hypothesised that host-associated variants downstream of the CRISPR-Cas region, in genes involved in lipooligosaccharide biosynthesis and the chemotaxis pathway, represent pathways enabling C. jejuni to survive bacteriophages encountered upon colonising a new host. To conclude, I discussed the findings of this thesis and suggested areas for future development where new technologies and methods will enable bacterial GWAS to be further advanced.
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Stanislas, Virginie. "Statistical approaches to detect epistasis in genome wide association studies." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLE040/document.
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De nombreux travaux de recherche portent sur la détection et l’étude des interactions dans les études d’association pangénomique (GWAS). La plupart des méthodes proposées se concentrent principalement sur les interactions entre polymorphismes simples de l’ADN (SNPs), mais des stratégies de regroupement peuvent également être envisagées.Dans cette thèse, nous développons une approche originale pour la détection des interactions à l’échelle des gènes. De nouvelles variables représentant les interactions entre deux gènes sont définies à l’aide de méthodes de réduction de dimension. Ainsi, toutes les informations apportées par les marqueurs génétiques sont résumées au niveau du gène. Ces nouvelles variables d’interaction sont ensuite introduites dans un modèle de régression. La sélection des effets significatifs est réalisée à l’aide d’une méthode de régression pénalisée basée sur le Group LASSO avec contrôle du taux de fausse découvertes.Nous comparons les différentes méthodes de modélisation des variables d’interaction à travers des études de simulations afin de montrer les bonnes performances de notre approche. Enfin, nous illustrons son utilisation pratique pour identifier des interactions entre gènes en analysant deux jeux de données réellesA large amount of research has been devoted to the detection and investigation of epistatic interactions in Genome-Wide Association Studies (GWAS). Most of the literature focuses on interactions between single-nucleotide polymorphisms (SNPs), but grouping strategies can also be considered.In this thesis, we develop an original approach for the detection of interactions at the gene level. New variables representing the interactions between two genes are defined using dimensionality reduction methods. Thus, all information brought from genetic markers is summarized at the gene level. These new interaction variables are then introduced into a regression model. The selection of significant effects is done using a penalized regression method based on Group LASSO controlling the False Discovery Rate.We compare the different methods of modeling interaction variables through simulations in order to show the good performance of our proposed approach. Finally, we illustrate its practical use for identifying gene-gene interactions by analyzing two real data sets
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Yurkiewich, Alexander John. "An Analysis of Genome-Wide Association Studies to Produce Evidence Useful in Guiding Their Reporting and Synthesis." Thesis, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/20686.
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Introduction The present study evaluated reported methodological characteristics of GWAS, investigating relationships between reported methodological characteristics and outcomes observed.
Methods GWAS were identified from NHGRI’s catalogue of GWAS (2005 to 2009). Multivariate meta-regression models (random effects) were produced to identify the impact of reported study characteristics and the strength of relationships between the variables and outcomes.
Results The summary odds ratios for replication components of GWAS in cancer was 1.34 (95% CI 1.25, 1.43) and neuropsychiatric disorders was 1.43 (95% CI 1.30, 1.57). Heterogeneity was accounted for by nature of the control group, relationship between case/control groups, whether cases/controls were drawn from the same population, if data was a primary collection or a build on pre-existing data, if quality assurance was reported, and if the study reported power/sample size.
Conclusion Evidence supports the existence of variability in reporting, with index components demonstrating less variability than replication components in the GWAS.
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Loizides, Charalambos. "Extensions of the case-control design in genome-wide association studies." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:89e057e5-d30f-4125-b210-14d1f2aa37c1.
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The case-control design is one of the most commonly used designs in genome- wide asociation studies. When we increase the sample size of either the controls or, more importantly, the cases, the power of whatever test we use will certainly increase. However increasing the sample size, means that addi- tional individuals need to be genotyped and this implies extra financial costs. However, nowadays with the emergence of genetic studies, a large number of genetic data are available at low or no extra cost. Even though those data may not be completely relevant to the current study, they can still be used to increase the probability to identify true associations. Furthermore, additional information, non-necessarily genetic, can also be used to improve the power of a method. In this thesis we extend the case-control design in order to take ad- vantage of such types of additional data and/or information. We discuss three designs; the case-cohort-control, the kin-cohort and the super-case– case–control–super-control designs. For each of these, we present methods that are adjusted or modified versions of standard case-control methods but we also propose novel ones developed with those extended designs in mind. Ultimately, we describe how those methods can be used in order to increase the power of association tests, especially compared to similar methods of the case-control design.
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Scott, Nigel A. "An Application of Armitage Trend Test to Genome-wide Association Studies." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/74.
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Genome-wide Association (GWA) studies have become a widely used method for analyzing genetic data. It is useful in detecting associations that may exist between particular alleles and diseases of interest. This thesis investigates the dataset provided from problem 1 of the Genetic Analysis Workshop 16 (GAW 16). The dataset consists of GWA data from the North American Rheumatoid Arthritis Consortium (NARAC). The thesis attempts to determine a set of single nucleotide polymorphisms (SNP) that are associated significantly with rheumatoid arthritis. Moreover, this thesis also attempts to address the question of whether the one-sided alternative hypothesis that the minor allele is positively associated with the disease or the two-sided alternative hypothesis that the genotypes at a locus are associated with the disease is appropriate, or put another way, the question of whether examining both alternative hypotheses yield more information.
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Vukcevic, Damjan. "Bayesian and frequentist methods and analyses of genome-wide association studies." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:8f89593e-a4ab-4df0-b297-74194be7891c.
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Recent technological advances and remarkable successes have led to genome-wide association studies (GWAS) becoming a tool of choice for investigating the genetic basis of common complex human diseases. These studies typically involve samples from thousands of individuals, scanning their DNA at up to a million loci along the genome to discover genetic variants that affect disease risk. Hundreds of such variants are now known for common diseases, nearly all discovered by GWAS over the last three years. As a result, many new studies are planned for the future or are already underway. In this thesis, I present analysis results from actual studies and some developments in theory and methodology. The Wellcome Trust Case Control Consortium (WTCCC) published one of the first large-scale GWAS in 2007. I describe my contribution to this study and present the results from some of my follow-up analyses. I also present results from a GWAS of a bipolar disorder sub-phenotype, and a recent and on-going fine mapping experiment. Building on methods developed as part of the WTCCC, I describe a Bayesian approach to GWAS analysis and compare it to widely used frequentist approaches. I do so both theoretically, by interpreting each approach from the perspective of the other, and empirically, by comparing their performance in the context of replicated GWAS findings. I discuss the implications of these comparisons on the interpretation and analysis of GWAS generally, highlighting the advantages of the Bayesian approach. Finally, I examine the effect of linkage disequilibrium on the detection and estimation of various types of genetic effects, particularly non-additive effects. I derive a theoretical result showing how the power to detect a departure from an additive model at a marker locus decays faster than the power to detect an association.
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Wood, Andrew Robert. "Next generation genome-wide association studies in complex human quantitative traits." Thesis, University of Exeter, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.574245.
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Since 2005, genome-wide association (GWA) studies have dominated the field of complex traits. Genetic and environmental factors play a role in causing disease and influencing the variance of a quantitative trait. GWA is a hypothesis-free approach that follows on from candidate gene and linkage studies and has markedly increased the number of loci associated with complex traits. Despite the relative success of GWA studies in identifying several hundreds of phenotypic associations, the genetic component of most complex traits remains largely unaccounted for. The field has now begun to focus its, efforts on the "missing heritability" to enhance the understanding of genetics and the associated biological pathways that underlie the aetiology of complex phenotypes. This thesis presents a series of studies that attempt to address this issue by exploring other sources of variation and statistical models that have not been extensively addressed in GWA studies to date. Chapter 1 is an introduction to genome-wide association studies. In particular it describes the origins of these studies, what we have learnt from them as well as their limitations. Chapter 2 describes a study that shows how multiple signals within a single locus can explain more of the genetic component of a complex trait, using gene expression as a model trait. 2 Chapter 3 describes a study that tests for deviation from additivity (additivity is an assumption of most GWA studies to date) through dominant, recessive and gene-gene interaction analyses using height, body mass index, and waist-hip ratio (adjusted for BMI) as model phenotypes. Chapter 4 describes a study that examines how more signals may be identified by increasing the density of variants through 1000 Genomes based imputation compared to HapMap based imputation. I use 93 phenotypes, all circulating factors, including proteins, ions and vitamins. Chapter 5 describes a study that tests whether more association signals can be discovered through low-coverage whole-genome sequencing. In particular, I compare association testing based on 1000 Genomes based imputation and sequencing. I use gene expression as a model trait. Chapter 6 discusses the research findings from the previous chapters, presents conclusions, and describes future research plans in the field of complex traits for a fuller understanding of the role of genetics. 3
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Hosking, Fay Julie. "Inference from genome-wide association studies using a novel Markov model." Thesis, University of Bristol, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.508085.
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Iotchkova, Valentina Valentinova. "Bayesian methods for multivariate phenotype analysis in genome-wide association studies." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:66fd61e1-a6e3-4e91-959b-31a3ec88967c.
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Most genome-wide association studies search for genetic variants associated to a single trait of interest, despite the main interest usually being the understanding of a complex genotype-phenotype network. Furthermore, many studies collect data on multiple phenotypes, each measuring a different aspect of the biological system under consideration, therefore it can often make sense to jointly analyze the phenotypes. However this is rarely the case and there is a lack of well developed methods for multiple phenotype analysis. Here we propose novel approaches for genome-wide association analysis, which scan the genome one SNP at a time for association with multivariate traits. The first half of this thesis focuses on an analytic model averaging approach which bi-partitions traits into associated and unassociated, fits all such models and measures evidence of association using a Bayes factor. The discrete nature of the model allows very fine control of prior beliefs about which sets of traits are more likely to be jointly associated. Using simulated data we show that this method can have much greater power than simpler approaches that do not explicitly model residual correlation between traits. On real data of six hematological parameters in 3 population cohorts (KORA, UKNBS and TwinsUK) from the HaemGen consortium, this model allows us to uncover an association at the RCL locus that was not identified in the original analysis but has been validated in a much larger study. In the second half of the thesis we propose and explore the properties of models that use priors encouraging sparse solutions, in the sense that genetic effects of phenotypes are shrunk towards zero when there is little evidence of association. To do this we explore and use spike and slab (SAS) priors. All methods combine both hypothesis testing, via calculation of a Bayes factor, and model selection, which occurs implicitly via the sparsity priors. We have successfully implemented a Variational Bayesian approach to fit this model, which provides a tractable approximation to the posterior distribution, and allows us to approximate the very high-dimensional integral required for the Bayes factor calculation. This approach has a number of desirable properties. It can handle missing phenotype data, which is a real feature of most studies. It allows for both correlation due to relatedness between subjects or population structure and residual phenotype correlation. It can be viewed as a sparse Bayesian multivariate generalization of the mixed model approaches that have become popular recently in the GWAS literature. In addition, the method is computationally fast and can be applied to millions of SNPs for a large number of phenotypes. Furthermore we apply our method to 15 glycans from 3 isolated population cohorts (ORCADES, KORCULA and VIS), where we uncover association at a known locus, not identified in the original study but discovered later in a larger one. We conclude by discussing future directions.
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Eleftherochorinou, Charikleia. "Pathway and gene-based analysis of genome wide association studies (GWAS)." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/9175.
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My PhD thesis comprises the development and application of novel strategies to analyse genome-wide association studies (GWAS) in the context of functional pathways. I propose pathway and gene-centric methodologies as complementary tools to the conventional singlemarker analyses to mine further the GWAS hidden information. I developed the cumulative trend (CT) test statistic that assesses the cumulative genetic variation of single nucleotide polymorphisms (SNPs) of genes that interact in the same biological pathway and tests the association between a disease and the pathway as an entity. I applied this methodology to the genotypic data of the Wellcome Trust Case Control Consortium (WTCCC) study on Crohn’s disease (CD), type I diabetes (T1D), rheumatoid arthritis (RA), bipolar disorder, hypertension, type II diabetes, coronary artery disease; I identified highly significant associations between the autoimmune diseases (CD, T1D, RA) and inflammatory pathways; almost no association was identified between the same pathways and the non-inflammatory conditions. I extended my approach to a pathway-based gene stability selection methodology, which selects associated genes in the context of associated pathways. This methodology can be used to prioritise genes for follow up studies. I applied it on two GWAS of RA with different ethnic background and typed on different platforms and I demonstrated replication at the pathway, gene and in-silico functional levels. I finally extended my approach on family trios designed GWAS. I applied it on two casecontrol and family trio datasets of Kawasaki disease (KD). I explored the association between the TGF-β pathway and KD susceptibility. The involvement of this pathway in KD was further validated at the gene expression and protein levels. My proposed methodologies were tested on real datasets and provided reproducible results, which indicates rigor and robustness. I would therefore suggest their application to single or multiple GWAS as a complement to conventional single-SNP analysis.
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Verardo, Lucas Lima. "Gene networks from genome wide association studies for pigs reproductive traits." Universidade Federal de Viçosa, 2015. http://www.locus.ufv.br/handle/123456789/6773.
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Características reprodutivas em suínos como numero de natimortos (SB), numero total de nascidos (TNB) e numero de tetos (NT) são amplamente incluídos em programas de melhoramento devido suas importâncias na indústria. Ao contrário da maioria dos estudos de associação, que consideram fenótipos contínuos com um enfoque Gaussiano, estas características são conhecidas como variáveis discretas, podendo assim, potencialmente seguir outras distribuições como a Poisson. Além disso, apesar de haver vários estudos de associação genômica ampla (GWAS) sendo realizados, somente alguns vem explorando os significados biológicos dos genes identificados nestes estudos. O presente trabalho, usando análises pós-GWAS, fornece uma valiosa fonte de informações sobre genes identificados a partir de estudos de associação para características reprodutivas. As analises de distribuição em modelos genômicos demonstraram a importância em considerar modelos de contagem para SB. Além do mais, diferentes grupos de SNPs e blocos de QTL relevantes entre e dentro de cada estudo foram identificados, direcionando para a possibilidade de diferentes grupos de genes estarem desempenhando funções biológicas relacionadas a uma única característica. Deste modo, destacamos que a diversidade genômica entre populações/ambientes deve ser observada em programas de melhoramento de modo que populações de referência especificas para cada população/ambiente sejam consideradas em estudos genômicos. Com base nestes resultados, nós demonstramos a importância das análises pós-GWAS aumentando o entendimento biológico de genes relevantes para características complexas.
Reproductive traits in pigs, such as number of stillborn (SB), total number born (TNB) and number of teats (NT), are widely included in breeding programs due their importance to the industry. As opposite to most association studies that consider continuous phenotypes under Gaussian assumptions, these traits are characterized as discrete variable, which could potentially follow other distributions, such as the Poisson. In addition, even though many genome wide association studies (GWAS) have been performed, only a few studies have explored biological meanings of genes identified. The present study provided a rich information resource about genes identified using genome wide association approaches for reproductive traits. The distribution analyses in genomic models, highlighted the importance in consider counting models for SB. Moreover, different sets of relevant SNPs and QTL blocks across and within the studies were identified leading to the possibility of different set of genes playing biological roles related to a single complex trait. Thereby, we highlighted the genomic diversity across population/environments to be observed in breeding programs in such a way that population/environments specific reference populations might be considered in genomic analyses. Based on these results, we demonstrated the importance of post-GWAS analyses increasing the biological understanding of relevant genes for complex traits.
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Dehman, Alia. "Spatial clustering of linkage disequilibrium blocks for genome-wide association studies." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLE013/document.
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Avec le développement récent des technologies de génotypage à haut débit, l'utilisation des études d'association pangénomiques (GWAS) est devenue très répandue dans la recherche génétique. Au moyen de criblage de grandes parties du génome, ces études visent à caractériser les facteurs génétiques impliqués dans le développement de maladies génétiques complexes. Les GWAS sont également basées sur l'existence de dépendances statistiques, appelées déséquilibre de liaison (DL), habituellement observées entre des loci qui sont proches dans l'ADN. Le DL est défini comme l'association non aléatoire d'allèles à des loci différents sur le même chromosome ou sur des chromosomes différents dans une population. Cette caractéristique biologique est d'une importance fondamentale dans les études d'association car elle permet la localisation précise des mutations causales en utilisant les marqueurs génétiques adjacents. Néanmoins, la structure de blocs complexe induite par le DL ainsi que le grand volume de données génétiques constituent les principaux enjeux soulevés par les études GWAS. Les contributions présentées dans ce manuscrit comportent un double aspect, à la fois méthodologique et algorithmique. Sur le plan méthodologie, nous proposons une approche en trois étapes qui tire profit de la structure de groupes induite par le DL afin d'identifier des variants communs qui pourraient avoir été manquées par l'analyse simple marqueur. Dans une première étape, nous effectuons une classification hiérarchique des SNPs avec une contrainte d'adjacence et en utilisant le DL comme mesure de similarité. Dans une seconde étape, nous appliquons une approche de sélection de modèle à la hiérarchie obtenue afin de définir des blocs de DL. Enfin, nous appliquons le modèle de régression Group Lasso sur les blocs de DL inférés. L'efficacité de l'approche proposée est comparée à celle des approches de régression standards sur des données simulées, semi-simulées et réelles de GWAS. Sur le plan algorithmique, nous nous concentrons sur l'algorithme de classification hiérarchique avec contrainte spatiale dont la complexité quadratique en temps n'est pas adaptée à la grande dimension des données GWAS. Ainsi, nous présentons, dans ce manuscrit, une mise en œuvre efficace d'un tel algorithme dans le contexte général de n'importe quelle mesure de similarité. En introduisant un paramètre $h$ défini par l'utilisateur et en utilisant la structure de tas-min, nous obtenons une complexité sous-quadratique en temps de l'algorithme de classification hiérarchie avec contrainte d'adjacence, ainsi qu'une complexité linéaire en mémoire en le nombre d'éléments à classer. L'intérêt de ce nouvel algorithme est illustré dans des applications GWASWith recent development of high-throughput genotyping technologies, the usage of Genome-Wide Association Studies (GWAS) has become widespread in genetic research. By screening large portions of the genome, these studies aim to characterize genetic factors involved in the development of complex genetic diseases. GWAS are also based on the existence of statistical dependencies, called Linkage Disequilibrium (LD) usually observed between nearby loci on DNA. LD is defined as the non-random association of alleles at different loci on the same chromosome or on different chromosomes in a population. This biological feature is of fundamental importance in association studies as it provides a fine location of unobserved causal mutations using adjacent genetic markers. Nevertheless, the complex block structure induced by LD as well as the large volume of genetic data arekey issues that have arisen with GWA studies. The contributions presented in this manuscript are in twofold, both methodological and algorithmic. On the methodological part, we propose a three-step approach that explicitly takes advantage of the grouping structure induced by LD in order to identify common variants which may have been missed by single marker analyses. In thefirst step, we perform a hierarchical clustering of SNPs with anadjacency constraint using LD as a similarity measure. In the second step, we apply a model selection approach to the obtained hierarchy in order to define LD blocks. Finally, we perform Group Lasso regression on the inferred LD blocks. The efficiency of the proposed approach is investigated compared to state-of-the art regression methods on simulated, semi-simulated and real GWAS data. On the algorithmic part, we focus on the spatially-constrained hierarchical clustering algorithm whose quadratic time complexity is not adapted to the high-dimensionality of GWAS data. We then present, in this manuscript, an efficient implementation of such an algorithm in the general context of anysimilarity measure. By introducing a user-parameter $h$ and using the min-heap structure, we obtain a sub-quadratic time complexity of the adjacency-constrained hierarchical clustering algorithm, as well as a linear space complexity in thenumber of items to be clustered. The interest of this novel algorithm is illustrated in GWAS applications
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Ehret, Georg B., Teresa Ferreira, Daniel I. Chasman, Anne U. Jackson, Ellen M. Schmidt, Toby Johnson, Gudmar Thorleifsson, et al. "The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals." NATURE PUBLISHING GROUP, 2016. http://hdl.handle.net/10150/623255.
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To dissect the genetic architecture of blood pressure and assess effects on target organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry, and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure-associated loci, of which 17 were new; 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney. Our findings expand current knowledge of blood pressure-related pathways and highlight tissues beyond the classical renal system in blood pressure regulation.
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Ng, Esther. "Integration of genetic data and genomic annotation in the analysis of genome wide association studies." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:fd0d2dab-d52c-4d8c-9321-5e88628bf528.
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This thesis explored the utility of genome wide association studies (GWAS) and meta-analysis to identify variants associated with serum pollutants levels and metabolic phenotypes. The secondary aims of these thesis were to annotate these variants with regulatory information from databases and to investigate the role of copy number variation in influencing gene expression and phenotype. In the second and third chapters, we identified single nucleotide polymorphisms (SNPs) associated with pollutant and metabolic phenotypes. An example of a novel association was the relationship between SNPs in the ABCG2 gene and octachlorodibenzo-p-dioxin (OCDD). In the third chapter, we identified associations between metabolic phenotypes and SNPs. An example of an identified association was that between serum apolipoprotein B levels and rs7412, which is consistent with other GWAS. To fine map these loci and assign functional annotation, I created Bayesian credible sets and checked for overlap between SNPs in these credible sets and regulatory marks in the Encyclopaedia of DNA Elements (ENCODE) database. Annotating these credible set SNPs with functional information revealed various histone modifications and transcription factors that overlapped. This study was also successful in identifying copy number variants (CNVs) from the PIVUS cohort. There were moderately strong associations between CNVs and some of the phenotypes studied. These associations did not appear to be mediated by the SNP within the CNVs, as the latter had higher P values of associations. In addition, I identified several clinically relevant CNV-expression quantitative trait loci (CNV-eQTL) associations a separate cohort of healthy individuals. Some of these associations were cell specific and/or context specific. Many of these CNVs contained SNPs which are lead SNPs in GWAS studies on a variety of different phenotypes. In conclusion, this thesis was successful in identifying SNPs and CNVs associated with phenotypes, as well as annotating some of these variants with regulatory information. Further work is needed to clarify the mechanisms of regulation.
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Catarino, C. B. "Genetic studies of the common epilepsies : genome-wide association studies in the partial epilepsies." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1418934/.
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This thesis discusses four studies, looking for genetic determinants of common epilepsies: 1) A genome-wide association study (GWAS) of partial epilepsies (PE), which was the first published GWAS in the field of epilepsy (Chapter 4). 2) A GWAS of mesial temporal lobe epilepsy (MTLE) with hippocampal sclerosis (HS) (Chapter 5). 3) A case series of patients with refractory MTLE, operated and found to have large microdeletions at 16p13.11, 15q11.2 and others (Chapter 6). 4) A clinical, genetic and neuropathologic study of a series of patients with Dravet syndrome (DS), diagnosed as adults, including genotype-phenotype correlation analysis (Chapter 7). The main findings include: 1) The GWAS of PE has not yielded any genome-wide significant association with common genetic variants, possibly because of insufficient power and phenotypical heterogeneity. It is, however, a strong foundation for further studies, illustrating the feasibility of large multicentre GWAS in the epilepsies (Chapter 4). 2) The GWAS of MTLEHS yielded a borderline genome-wide statistically significant association with three common genetic variants close or intronic to the SCN1A gene, especially in MTLEHS with antecedents of childhood febrile seizures (Chapter 5). 3) Large microdeletions at 16p13.11 and others were found in patients with MTLEHS and not only in idiopathic non-lesional epilepsies. Good outcome after resective epilepsy surgery is possible in “typical” MTLEHS even with large microdeletions (Chapter 6). 4) The identification of a cohort of adults with DS, not diagnosed as children, allowed the description of long-term evolution through adulthood and recognition of clinical features shared later in the evolution. Over sixty percent had SCN1A mutations. Missense mutations were more frequent in patients who survived through adulthood, with truncating mutations and large deletions only found in those who died in early childhood. Medication changes after diagnosis led in some cases to better seizure control, cognition and quality of life. Further evidence for DS as encephalopathy came from post mortem histopathology, with no neuronal loss found in cerebral cortex or hippocampus.
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Martínez, Barrio Álvaro. "Novel Bioinformatics Applications for Protein Allergology, Genome-Wide Association and Retrovirology Studies." Uppsala : Acta Universitatis Upsaliensis, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-111932.
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Martínez, Barrio Álvaro. "Novel Bioinformatics Applications for Protein Allergology, Genome-Wide Association and Retrovirology Studies." Doctoral thesis, Uppsala universitet, Centrum för bioinformatik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-111932.
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Recently, the pace of growth in the amount of data sources within Life Sciences has increased exponentially until pose a difficult problem to efficiently manage their integration. The data avalanche we are experiencing may be significant for a turning point in science, with a change of orientation from proprietary to publicly available data and a concomitant acceptance of studies based on the latter. To investigate these issues, a Network of Excellence (EMBRACE) was launched with the aim to integrate the major databases and the most popular bioinformatics software tools. The focus of this thesis is therefore to approach the problem of seamlessly integrating varied data sources and/or distributed research tools. In paper I, we have developed a web service to facilitate allergenicity risk assessment, based on allergen descriptors, in order to characterize proteins with the potential for sensitization and cross-reactivity. In paper II, a web service was developed which uses a lightweight protocol to integrate human endogenous retrovirus (ERV) data within a public genome browser. This new data catalogue and many other publicly available sources were integrated and tested in a bioinformatics-rich client application. In paper III, GeneFinder, a distributed tool for genome-wide association studies, was developed and tested. Useful information based on a particular genomic region can be easily retrieved and assessed. Finally, in paper IV, we developed a prototype pipeline to mine the dog genome for endogenous retroviruses and displaying the transcriptional landscape of these retroviral integrations. Moreover, we further characterized a group that until this point was believed to be primate-specific. Our results also revealed that the dog has been very effective in protecting itself from such integrations. This work integrates different applications in the fields of protein allergology, biotechnology, genome association studies and endogenous retroviruses.EMBRACE NoE EU FP6
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Dom, Huseyin Alper. "Investigation Of Schizophrenia Related Genes And Pathways Through Genome Wide Association Studies." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615642/index.pdf.
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Schizophrenia is a complex mental disorder that is commonly characterized as deterioration of intellectual process and emotional responses and affects 1% of any given population. SNPs are single nucleotide changes that take place in DNA sequences and establish the major percentage of genomic variations. In this study, our goal was to identify SNPs as genomic markers that are related with schizophrenia and investigate the genes and pathways that are identified through the analysis of SNPs. Genome wide association studies (GWAS) analyse the whole genome of case and control groups to identify genetic variations and search for related markers, like SNPs. GWASs are the most common method to investigate genetic causes of a complex disease such as
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schizophrenia because regular linkage studies are not sufficient. Out of 909,622 SNPs analysis of the dbGAP Schizophrenia genotyping data identified 25,555 SNPs with a p-value 5x10-5. Next, combined p-value approach to identify associated genes and pathways and AHP based prioritization to select biologically relevant SNPs with high statistical association are used through METU-SNP software. 6,000 SNPs had an AHP score above 0.4, which mapped to 2,500 genes suggested to be associated with schizophrenia and related conditions. In addition to previously described neurological pathways, pathway and network analysis showed enrichment of two pathways.
Melanogenesis and vascular smooth muscle contraction pathways were found to be highly associated with schizophrenia. We have also shown that these pathways can be organized in one biological network, which might have a role in the molecular etiology of schizophrenia. Overall analysis results revealed two novel candidate genes SOS1 and GUCY1B3 that have a possible relation with schizophrenia.
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桂宏胜 and Hongsheng Gui. "Data mining of post genome-wide association studies and next generation sequencing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/193431.
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Sarkar, Abhishek Kulshreshtha. "Characterizing non-coding hits in genome-wide association studies using epigenetic data." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82392.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 42-46).
Understanding the molecular basis of human disease is one of the greatest challenges of our time, and recent explosion in genetic and genomic datasets are finally putting it within reach. In the last ten years, genome-wide association studies have identified thousands of genetic variants associated with disease. However, the majority of these variants fall outside genes making interpreting their role in disease difficult. In parallel, the ENCODE and Roadmap Epigenomics consortia have produced high resolution annotations of the genome which identify large portions with potential regulatory function. We develop methods to interpret genome-wide association studies using these annotations to generate hypotheses about how associated variants contribute to disease mechanism. In particular, we go beyond the usual stringent p-value threshold to investigate variants with small individual effect sizes which current methods do not have power to detect. Evaluating our methods on the Wellcome Trust Case Control Consortium 7 Disease studies, we find associated variants are enriched in a variety of functional categories even after controlling for various biases. We also find an unprecedented number of variants contribute to this enrichment, supporting our hypothesis that the architecture of these diseases involves combinatorial interaction of many variants with small individual effect sizes.
by Abhishek Kulshreshtha Sarkar.
S.M.
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Christ, Ryan. "Ancestral trees as weighted networks : scalable screening for genome wide association studies." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:8f382d56-2d5d-4a4f-9b39-41700897e02e.
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Several haplotype-based methods have been developed to identify loci where multiple mutations of low to moderate frequency and effect size modulate disease susceptibility. Most such approaches either do not scale to hundreds of thousands of genomes or do not explicitly model recombination and the block-like structure of haplotypes. Using a novel checkpointing technique and a C-core, vectorized implementation of an Hidden Markov Model (HMM) based on the Li & Stephens Model, at each single nucleotide polymorphism (SNP) along the genome, we obtain a local genetic distance between all pairs of haplotypes in a phased dataset. To rapidly test this local distance matrix for association with a phenotype, we derive two finite sample central limit type theorems for quadratic forms which do not require any further assumptions on the matrix other than it is free of outliers, for which we have an easily calculable, formal condition. We combine these results with a novel concentration inequality for Gaussian quadratic forms to upper and lower bound p-values for quadratic forms while avoiding a full eigendecomposition of each matrix. Applying our HMM implementation and quadratic form screening method, we recover known loci associated with malaria susceptibility and uncover new potential associations in a pilot dataset of 6,136 haplotypes.
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Warrier, Varun. "The genetics of autism and related traits." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/275748.
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Autism Spectrum Conditions (henceforth, autism) refers to a group of neurodevelopmental conditions characterized by difficulties in social interaction and communication, difficulties in adjusting to unexpected change, alongside unusually narrow interests and repetitive behaviour, and sensory hyper-sensitivity. Twin and family-based studies have consistently identified high heritabilities for autism and autistic traits, with recent studies converging at 60 – 90% heritability. Common genetic variants are thought to additively contribute to as much as 50% of the total risk for autism. In this thesis, I investigate the contribution of common genetics variants (including SNPs, and InDels) to autism and related traits. In Chapter 1, I discuss the recent advances in the field of autism genetics, focussing on the contribution of common genetic variants to the risk for autism. Chapters 2 – 7 report the results of various studies investigating the genetic correlates of autism and related traits. In Chapter 2, I surveyed the evidence for 552 candidate genes associated with autism, and conducted a meta-analysis for 58 common variants in 27 genes, investigated in at least 3 independent cohorts. Meta-analysis did not identify any SNPs that were replicably associated with autism in the Psychiatric Genetics Consortium genome-wide association study (PGC-GWAS) dataset after Bonferroni correction, suggesting that candidate gene association studies are not statistically well-powered. In Chapters 3 – 7, I conducted genome-wide association studies (GWAS) for 6 traits associated with autism: self-reported empathy (N = 46,861, Chapter 3), cognitive empathy (N = 89,553, Chapter 4), theory of mind in adolescents (N = 4,577, Chapter 5), friendship satisfaction (Neffective = 158,116) and family relationship satisfaction (Neffective = 164,112, both Chapter 6), and systemizing (N = 51,564, Chapter 7). GWAS identified significant loci for self-reported empathy, systemizing, friendship and family relationship satisfaction, and cognitive empathy. Genetic correlation analyses replicably identified a significant negative genetic correlation between autism and family relationship satisfaction and friendship satisfaction, and a significant positive genetic correlation between autism and systemizing. In addition, there was a negative genetic correlation between autism and self-reported empathy. Chapter 8 draws all of these studies together, concluding that there may be at least two independent sources of genetic risk for autism: one stemming from social traits and another from non-social traits. I discuss some future directions about how this can be leveraged using polygenic scores from multiple phenotypes to potentially stratify individuals within the autism spectrum, and both the strengths and limitations of the reported studies.
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ZUCCALA', MIRIAM. "Follow-up and fine-scale mapping of multiple sclerosis loci identified in genome wide association studies." Doctoral thesis, Università del Piemonte Orientale, 2018. http://hdl.handle.net/11579/105434.
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Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system (CNS). During my PhD program, I identified and functionally characterized sequence variations associated to the risk to develop MS in the Italian continental population. To this end we performed two different parallel analyses on already known MS associated loci and genes: a fine mapping analysis in order to find the primarily associated variant or gene and a burden test analysis on rare and low frequency variants. We identified the strongest non-HLA signal in the Italian population maps in the Tumor Necrosis Factor (ligand) superfamily member 14 (TNFSFI-4) gene encoding for LIGHT, å transmembrane glycoprotein expressed on various immune cells and Involved in dendritic cells (DC) maturation. We demonstrated through a fine-mapping approach that an intronic variant is the primarily associated one and we were able to define its functional role in the regulation of gene transcription and protein production. Subsequently, we focused our attention on LIGHT receptor (TNFRSFT) but we were not able to identify the primary associated variant due to high linkage disequilibrium (LD). Despite this, we observed a cis-eQTL effect for different variants in this region on TNFRSFI4 gene expression. So, based on these evidences, we proposed for these variants a possible role in gene regulation. Gene-gene interaction analysis. burden test and weight genetic risk score on INFSF14-INFRSF14 pathway seemed to confirm our hypothesis that also genes which interact with TNFSF/4, can also play a role in MS pathogenesis. Parallel to these analysis, we conducted a research of rare functional variants in MS associated loci in order to assess if the genes in these regions showed an imbalance of rare variant frequencies (burden) between MS patients and healthy controls. EFC4B/3 was the gene that seemed to show the most promising result especially for disruptive variants.
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Moreira, Gabriel Costa Monteiro. "Genome-wide association studies reveal genomic regions and positional candidate genes for fat deposition in chickens." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/11/11139/tde-17072018-191146/.
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Excess of fat deposition is a negative factor for poultry production, which affects feed efficiency and consequently the costs of meat production. The incorporation of genomic tools in poultry breeding programs may help to accelerate the selection for increased production efficiency. In this context, we genotyped approximately 2,000 42 days-old chickens from two different populations (Brazilian F2 Chicken Resource population and TT broiler Reference Population) using a high-density SNP array (600K, Affymetrix) to estimate genomic heritability of fatness-related traits, to identify genomic regions and positional candidate genes (PCGs) associated with these traits. We performed genome-wide association (GWAS) analysis using GenSel software (Bayesian approach) to identify 1 Mb genomic windows associated with abdominal fat, skin and carcass fat content traits. The search for PCGs were made within each genomic windows associated considering their Gene Ontology (GO) terms and also the literature information. We also integrated into this study NGS-SNPs data from both populations and selection signature regions identified in Brazilian F2 Chicken Resource population to refine the list of PCGs. The genomic heritability values for fatness-related traits were from moderate to high (greater than 0.30). We identified quantitative trait loci (QTL) for abdominal fat, skin and carcass fat content traits harboring several PCGs involved in biological processes of fat deposition. We identified several NGS-SNPs annotated in potential functional regions in our PCGs and some of those were predicted as deleterious and high impact mutations. Besides that, some genes overlapped with selection signature regions in Brazilian F2 Chicken Resource population. Important candidate genes for fat deposition were identified, providing new insights to achieve a better understanding of the genetic control of fat deposition in chickens.O excesso de deposição de gordura é um fator negativo para a produção de aves, o que afeta a eficiência alimentar e consequentemente os custos da produção de carne. A incorporação das ferramentas genômicas em programas de melhoramento de aves pode ajudar a acelerar a seleção para aumentar a eficiência da produção. Neste contexto, genotipamos cerca de 2.000 aves de 42 dias de duas populações diferentes (população F2 experimental brasileira e população de corte referência TT) usando um chip de SNPs de alta densidade (600K, Affymetrix) para estimar a herdabilidade genômica de características relacionadas à deposição de gordura, para identificar regiões genômicas e genes candidatos posicionais (PCGs) associados a essas características. Realizamos análises de associação genômica ampla (GWAS) usando o programa GenSel (abordagem Bayesiana) para identificar janelas genômicas de 1 Mb associadas com características de gordura abdominal, pele e conteúdo de gordura na carcaça. A busca por PCGs foi feita dentro de cada janela genômica associada, considerando os Gene Ontology (GO) terms e também a informação da literatura. Integramos neste estudo NGS-SNPs identificados em animais parentais de ambas as populações, e além disso, regiões de assinaturas de seleção identificadas na população F2 experimental brasileira para refinar a lista de PCGs. Os valores de herdabilidade genômica para as características relacionadas à gordura foram de moderado a alto (maior que 0,30). Identificamos QTL para características de gordura abdominal, pele e conteúdo de gordura na carcaça contendo PCGs envolvidos em processos biológicos de deposição de gordura. Identificamos vários NGS-SNPs anotados em regiões potencialmente funcionais em nossos PCGs e alguns desses foram preditos como mutações deletérias e de alto impacto. Além disso, alguns genes se sobrepuseram com regiões de assinatura de seleção na população F2 experimental brasileira. Foram identificados importantes genes candidatos para a deposição de gordura, fornecendo novos insights para alcançar uma melhor compreensão do controle genético da deposição de gordura em frangos.
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Narayanan, Kanchana. "MAVEN: a tool for Visualization and Functional Analysis of Genome-Wide Association Studies." Cleveland, Ohio : Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1269455528.
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Thesis (Master of Sciences)--Case Western Reserve University, 2010Department of EECS - Computer and Information Sciences Title from PDF (viewed on 2010-05-25) Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center
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Perry, John Richard Bradbury. "Using Genome Wide Association Studies to Understand the Aetiolog of Type 2 Diabetes." Thesis, Exeter and Plymouth Peninsula Medical School, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.510700.
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Giannoulatou, Eleni. "Single nucleotide polymorphism and copy number variant genotyping for genome wide association studies." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543550.
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Lippert, Christoph [Verfasser], and Karsten [Akademischer Betreuer] Borgwardt. "Linear mixed models for genome-wide association studies / Christoph Lippert ; Betreuer: Karsten Borgwardt." Tübingen : Universitätsbibliothek Tübingen, 2014. http://d-nb.info/1162897163/34.
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Patel, Virag. "Use of the LASSO in single and multi-cohort genome-wide association studies." Thesis, University of Leicester, 2018. http://hdl.handle.net/2381/42881.
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Over the past decade, there has been an ever growing interest in genome-wide association studies (GWAS). The role of GWAS is to discover associations between genetic variants; commonly Single Nucleotide Polymorphisms (SNPs) and complex diseases. Due to the ever increasing number of SNPs in GWAS, the commonly used association analyses tend to be univariate models rather than multivariate models. These methods are therefore unable to account for the correlation between SNPs; known as Linkage Disequilibrium (LD). Penalised regression methods have been suggested as an alternative method in GWAS, specifically the Least Absolute Shrinkage and Selection Operator (LASSO). This method has the ability to both shrink regression coefficients and perform variable selection. In this thesis, the use of the LASSO in both single and multi-cohort GWAS is examined. In the context of the single cohort, the LASSO is applied to the GRAPHIC study in an attempt to discover novel associations with Low-density Lipoprotein. This thesis will also address some of the problems with the LASSO such the tuning parameter selection method that should be used for SNP selection and the need for pruning to reduce the dimensionality of the data in order to fit LASSO models. The literature suggests that a pruning or pre-screening method is required to fit LASSO models in GWAS due to the high computational burden of fitting such a model, yet there is little work to address how the dataset should be pruned. A SNP pruning package in R called prune is developed and is utilised in a simulation study to determine which pruning method should be used. The role of the LASSO in multi-cohort studies is also considered specifically in integrative analyses. A new penalised regression method, the Integrative LASSO, is proposed and developed which uses a combination of LASSO, ridge regression and fused LASSO penalties and tested against some of the current methods in the literature in a simulation study.
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Perry, J. R. B. "Using genome wide association studies to understand the aetiology of type 2 diabetes." Thesis, Exeter and Plymouth Peninsula Medical School, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.701067.
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