Dissertations / Theses on the topic 'Complex Traits Genetics'
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Bell, Jordana Tzenova. "Epistasis in complex human traits." Thesis, University of Oxford, 2006. http://ora.ox.ac.uk/objects/uuid:547db446-c84c-4a6c-8b5c-ce960f7765c5.
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Nelson, Vicki R. "Transgenerational Genetic Effects In Mouse Models Of Complex Traits." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1278706008.
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Goddard, Katrina Blouke. "Study design issues in the analysis of complex genetic traits /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/9565.
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Joshi, Peter K. "Exploring the inheritance of complex traits in humans." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/21118.
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I explore the genetic and environmental basis of inheritance using modern techniques, in particular high-density genotyping arrays, and older techniques, in particular family history, to explore some longstanding questions about the way we inherit complex traits. Using pedigree data and the parent-offspring regression technique, I estimate narrow sense heritability (h2) of human lifespan in 20th Century Scotland as 0.16, lower than commonly cited studies in other populations. I also observe similar concordance between spouses as between parents and offspring - suggesting my estimate of heritability may include significant within-family environment effects and thus should be considered an upper bound. Using genome-wide array data to identify runs of homozygosity, from 150 cohorts across the world and up to 350,000 subjects per trait, I show that cognitive function and body size are associated with the total length of genome-wide runs of homozygosity. Contrary to earlier reports in substantially smaller samples, no evidence was seen of an influence of homozygosity on blood pressure and low-density lipoprotein (LDL) cholesterol, or ten other cardio-metabolic traits. An association between genome-wide homozygosity and complex traits arises due to directional dominance. Since directional dominance is predicted for traits under directional evolutionary selection, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases have not. The analysis of less common single nucleotide polymorphism (SNP) variants in genome-wide association studies promises to elucidate complex trait genetics but is hampered by low power to reliably detect association, whilst avoiding false positives. I show that addition of 100 population-specific exome sequences to 1,000 genomes global reference data allows more accurate imputation, particularly of less common SNPs (minor allele frequency 1–10%). The imputation improvement corresponds to an increase in effective sample size of 28–38%, for SNPs with a minor allele frequency in the range 1–3%. Inheritance of complex traits remains a field wide open for discovery, both in determining the balance between nature and nurture and discovery of the specific mechanisms by which DNA causes variation in these traits, with the prospect of such discoveries illuminating biological pathways involved and, as knowledge deepens, facilitating prediction.
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Allchin, Lorraine Doreen May. "Statistical methods for mapping complex traits." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:65f392ba-1b64-4b00-8871-7cee98809ce1.
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The first section of this thesis addresses the problem of simultaneously identifying multiple loci that are associated with a trait, using a Bayesian Markov Chain Monte Carlo method. It is applicable to both case/control and quantitative data. I present simulations comparing the methods to standard frequentist methods in human case/control and mouse QTL datasets, and show that in the case/control simulations the standard frequentist method out performs my model for all but the highest effect simulations and that for the mouse QTL simulations my method performs as well as the frequentist method in some cases and worse in others. I also present analysis of real data and simulations applying my method to a simulated epistasis data set. The next section was inspired by the challenges involved in applying a Markov Chain Monte Carlo method to genetic data. It is an investigation into the performance and benefits of the Matlab parallel computing toolbox, specifically its implementation of the Cuda programing language to Matlab's higher level language. Cuda is a language which allows computational calculations to be carried out on the computer's graphics processing unit (GPU) rather than its central processing unit (CPU). The appeal of this tool box is its ease of use as few code adaptions are needed. The final project of this thesis was to develop an HMM for reconstructing the founders of sparsely sequenced inbred populations. The motivation here, that whilst sequencing costs are rapidly decreasing, it is still prohibitively expensive to fully sequence a large number of individuals. It was proposed that, for populations descended from a known number of founders, it would be possible to sequence these individuals with a very low coverage, use a hidden Markov model (HMM) to represent the chromosomes as mosaics of the founders, then use these states to impute the missing data. For this I developed a Viterbi algorithm with a transition probability matrix based on recombination rate which changes for each observed state.
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Luo, Yuqun. "Incorporation of genetic marker information in estimating model parameters for complex traits with data from large complex pedigrees /." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486549482668451.
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Bigdeli, T. Bernard. "Quantitative Genetic Methods to Dissect Heterogeneity in Complex Traits." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2651.
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Etiological models of complex disease are elusive[46, 33, 9], as are consistently replicable findings for major genetic susceptibility loci[54, 14, 15, 24]. Commonly-cited explanations invoke low-frequency genomic variation[41], allelic heterogeneity at susceptibility loci[33, 30], variable etiological trajectories[18, 17], and epistatic effects between multiple loci; these represent among the most methodologically-challenging issues in molecular genetic studies of complex traits. The response has been con- sistently reactionary—hypotheses regarding the relative contributions of known func- tional elements, or emphasizing a greater role of rare variation[46, 33] have undergone periodic revision, driving increasingly collaborative efforts to ascertain greater numbers of participants and which assay a rapidly-expanding catalogue of human genetic variation. Major deep-sequencing initiatives, such as the 1,000 Genomes Project, are currently identifying human polymorphic sites at frequencies previously unassailable and, not ten years after publication of the first major genome-wide association find- ings, re-sequencing has already begun to displace GWAS as the standard for genetic analysis of complex traits. With studies of complex disease primed for an unprecedented survey of human genetic variation, it is essential that human geneticists address several prominent, problematic aspects of this research. Realizations regarding the boundaries of human traits previously considered to be effectively disparate in presentation[44, 39, 35, 27, 25, 12, 4, 13], as well as profound insight into the extent of human genetic diversity[23, 22] are not without consequence. Whereas the resolution of fine-mapping studies have undergone persistent refinement, recent polygenic findings suggest a less discriminant basis of genetic liability, raising the question of what a given, unitary association finding actually represents. Furthermore, realistic expectations regarding the pattern of findings for a particular genetic factor between or even within populations remain unclear. Of interest herein are methodologies which exploit the finite extent of genomic variability within human populations to distinguish single-point and cumulative group differences in liability to complex traits, the range of allele frequencies for which common association tests are appropriate, and the relevant dimensionality of common genetic variation within ethnically-concordant but differentially ascertained populations. Using high-density SNP genotype data, we consider both hypothesis-driven and agnostic (genome-wide) approaches to association analysis, and address specific issues pertaining to empirical significance and the statistical properties of commonly- applied tests. Lastly, we demonstrate a novel perspective of genome-wide genetic “background” through exhaustive evaluation of fundamental, stochastic genetic processes in a sample of matched affected and unaffected siblings selected from high- density schizophrenia families.
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Ashbrook, David. "A systems-genetics analyses of complex phenotypes." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/a-systemsgenetics-analyses-of-complex-phenotypes(a3e7ad8e-b23b-40fd-821e-26a6c1a63d38).html.
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Complex phenotypes are traits which are influenced by many factors, and not just a single gene, as for classical Mendelian traits. The brain, and its resultant behaviour, gives us a large subset of complex phenotypes to examine. Variation in these traits is affected by a range of different influences, both genetic and environmental, including social interactions and the effects of parents. Systems-genetics provides us with a framework in which to examine these complex traits, seeking to connect genetic variants to the phenotypes they cause, through intermediate phenotypes, such as gene expression and protein levels. This approach has been developed to exploit and analyse massive data sets generated for example in genomics and transcriptomics. In the first half of this thesis, I combine genetic linkage data from the BXD recombinant inbred mouse panel with genome-wide association data from humans to identify novel candidate genes, and use online gene annotations and functional descriptions to support these candidates. Firstly, I discovered MGST3 as a novel regulator of hippocampus size, which may be linked to neurodegenerative disorders. Secondly, I identified that CMYA5, MCTP1, TNR and RXRG are associated with mouse anxiety-like phenotypes and human bipolar disorder, and provide evidence that MCTP1, TNR and RXRG may be acting via inter-cellular signalling in the striatum. The second half of this thesis uses different cross-fostering designs between genetically variable BXD lines and the genetically uniform C57BL/6J strain to identify indirect genetic effects and the loci underlying them. With this, I have found novel loci expressed in mothers that alter offspring behaviour, novel loci expressed in offspring affecting the level of maternal care, and novel loci expressed in offspring, which alter the behaviour of their nestmates, as well as the level of maternal care they receive. Further I provide evidence of co-adaptation between maternal and offspring genotypes, and a positive indirect genetic effect of offspring on their nestmates, supportive of a role for kin selection. Finally, I demonstrate that the BXD lines can be used to investigate genes with parent-of-origin dependent expression, which have an indirect genetic effect on maternal care. In conclusion, this thesis identifies a number of novel loci, and in some cases genes, associated with complex traits. Not only are these techniques applicable to other phenotypes and other questions, but the candidates I identify can now be examined further in vitro or in vivo.
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Valenzuela, Robert Keams. "Predictive Modeling for Complex Traits: Normal Human Pigmentation Variation." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/145309.
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Melanin pigmentation is a complex trait governed by many genes. Variation in melanin pigmentation within, and between, populations makes it an important trait for assisting in physical identification of an individual in forensic investigations. Utilizing a training sample (n=789) comprised of various ethnicities and SNPs (75) in 24 genes previously implicated in human or animal pigmentation studies, I determined three-SNP multiple linear regression models that accounted for large proportions of pigmentation variation in skin (45.7%), eye color (76.4%), and hair [eumelanin-to-pheomelanin (43.2%) and total melanin (76.3%)], independent of ethnic origin. Rather than implementing stepwise regression, to ascertain the three-SNP predictive models, I devised an algorithm that is likely more robust than stepwise regression. The algorithm consisted of two steps: the first step reduced the pool of 75 SNPs to a pool of 40 by selection of SNPs that were significant (p<0.05) by one-way ANOVA; the second step enabled selection of SNPs for model incorporation based on their frequency in the best-fitted models of all possible combinations of three-SNP models (i.e., 40 choose 3).Prediction models were validated utilizing an independent cohort (n=242, test sample) that was very similar in ethnic composition to the training sample. Relative shrinkage was moderate for skin reflectance (23.4%), eye color (19.4%), and eumelanin-to-pheomelanin (37.3%) of hair, and largest for total melanin (67%) of hair. Additionally, we refined our model-building algorithm, enabling visual comparison of the frequency and co-linearity due to linkage or co-inheritance of SNPs of the best-fitted models. Application of our algorithm to the test sample yielded the same or similar models as the training sample. Two of the three SNPs composing the models were the same, with some variability in the third SNP of the model.
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Uricchio, Lawrence Hart. "Models and forward simulations of selection, human demography, and complex traits." Thesis, University of California, San Francisco, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3681226.
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Evolutionary forces such as recombination, demography, and selection can shape patterns of genetic diversity within populations and contribute to phenotypic variation. While theoretical models exist for each of these forces independently, mathematically modeling their joint impact on patterns of genetic diversity remains very challenging. Fortunately, it is possible to perform forward-in-time computer simulations of DNA sequences that incorporate all of these forces simultaneously. Here, I show that there are trade-offs between computational efficiency and accuracy for simulations of a widely investigated model of recurrent positive selection. I develop a theoretical model to explain this trade-off, and a simple algorithm that obtains the best possible computational performance for a given error tolerance. I then pivot to develop a framework for simulations of human DNA sequences and genetically complex phenotypes, incorporating recently inferred demographic models of human continental groups and selection on genes and non-coding elements. I use these simulations to investigate the power of rare variant association tests in the context of rampant selection and non-equilibrium demography. I show that the power of rare variant association tests is in some cases quite sensitive to underlying assumptions about the relationship between selection and effect sizes. This work highlights both the challenge and the promise of applying forward simulations in genetic studies that seek to infer the parameters of evolutionary models and detect statistical associations.
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Johansson, Åsa. "Genome Variation in Human Populations : Exploring the Effects of Demographic History and the Potential for Mapping of Complex Traits." Doctoral thesis, Uppsala University, Department of Genetics and Pathology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7293.
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A major challenge in human genetics is to understand the genetic variation underlying common diseases. In this thesis, I focus on forces creating differences between individuals and genomic regions, methods for characterizing genomic variation, and the association between genomic and phenotypic variation. Genetic markers are widely used to locate genes associated with different phenotypes. In my first paper, I describe novel algorithms for automatic genotype determination of microsatellite markers, a procedure which is currently both time-consuming and error prone.
The co-segregation of genetic markers in a population leads to non-random association of alleles at different loci - linkage disequilibrium (LD). LD varies throughout the genome and differs between populations due to factors such as their demographic history. In my second paper, I discuss the increased power, for mapping of human traits, that results from studying a population with appreciable levels of LD such as is found in the Swedish Sami population.
Lately, large-scale analyses of single nucleotide polymorphisms (SNPs) have become available and efforts have been made to identify a set of SNPs, which captures most of the genome variation in a population (tagSNPs). In my third paper, I describe the limitations of this approach when applied to data from an independent population sample of randomly ascertained SNPs. The transferability of tagSNPs between populations is poor, presumably due to variation in allele frequencies and the bias towards common SNPs used in most studies.
The level of genomic variation is influenced by population structure, recombination and mutation rate, as well as natural selection. During the exodus from Africa, humans have adapted to new environmental conditions. In my fourth paper, I describe a new method for identifying genomic regions carrying signatures of recent positive selection and apply this to an available dataset of millions of SNPs.
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Ndungu, Anne. "Rare genetic variants and susceptibility to severe bacterial diseases." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:9c5745f9-50f9-469a-8771-2e49e75db7ac.
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Infectious diseases are a major cause of morbidity and mortality worldwide. Streptococcus pneumoniae and Neisseria meningitidis are major causes of severe bacterial disease which can manifest as invasive disease such as bacteraemia and meningitis. Exposure to these pathogens is relatively widespread, yet only a minority of individuals develop invasive disease. A host genetic component to infectious disease susceptibility has been implied from twin and adoptee studies. A role for rare large effect genetic variants in predisposition to infection has been demonstrated through the study of individuals with primary immunodeficiencies. However, a majority of these studies have been undertaken in individuals with a history of recurrent disease or in multi-case families. The relative role of rare genetic variants of moderate to large effect at the population level has not been widely explored. This thesis presents effort made using next generation sequencing methods to identify rare genetic variants that lead to increased susceptibility to bacterial disease focussing on meningococcal disease, pleural infection(empyema), pneumococcal disease and sepsis phenotypes. Using an exome sequencing approach in 13 cases with invasive meningococcal disease, a novel mutation leading to a complement deficiency and increased risk of meningococcal infection was identified and functionally validated in one individual. This mutation in the CFP gene was demonstrated as leading to impaired properdin secretion. Further analysis implicated loss of function mutations in CD4 and ZAP70 as novel loci for meningococcal disease susceptibility. A case control association analysis for sepsis susceptibility highlighted the possible role for small Rho GTPases in sepsis pathology. By aggregating all rare predicted deleterious mutations in a gene, four genes in this pathway, (ROCK2, ARHGAP18, FYN and CDC42BPG) were implicated as having an excess of rare deleterious variants in sepsis samples compared to population controls. A similar approach identified low frequency genetic variants in the CD109 gene as predisposing to empyema susceptibility in children. Finally, preliminary evidence from adult individuals with invasive pneumococcal disease points to a potential role of the RNASE7 gene in invasive pneumococcal disease susceptibility. This association was primarily due to a predicted deleterious missense mutation present in cases and absent in controls. Taken together, these results have identified a number of potential loci with rare variants associated with susceptibility to severe phenotypes of bacterial diseases.
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Chen, Anlu. "Applying Forward Genetic Approaches to Rare Mendelian Disorders and Complex Traits." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1532522241487661.
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Baud, Amelie. "Fine-mapping complex traits in heterogeneous stock rats." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:c762c1af-c899-478f-93e1-305775d5a6f4.
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The fundamental theme my thesis explores is the relationship between genetic variation and phenotypic variation. It addresses three main questions. What is the genetic architecture of traits in the HS? How can sequence information help identifying the sequence variants and genes responsible for phenotypic variation? Are the genetic factors contributing to phenotypic variation in the rat homologous to those contributing to variation in the same phenotype in the mouse? To address these questions, I analysed data collected by the EURATRANS consortium on 1,407 Heterogeneous Stock (HS) rats descended from eight inbred strains through sixty generations of outbreeding. The HS rats were genotyped at 803,485 SNPs and 160 measures relevant to a number of models of disease (e.g. anxiety, type 2 diabetes, multiple sclerosis) were collected. The eight founders of the Stock were genotyped and sequenced. I identified loci in the genome that contribute to phenotypic variation (Quantitative Trait Loci, QTLs), and integrated sequence information with the mapping results to identify the genetic variants underlying the QTLs. I made some important observations about the nature of genetic architecture in rats, and how this compares to mice and humans. I also showed how sequence information can be used to improve mapping resolution, and in some cases to identify causal variants. However, I report an unexpected observation: at the majority of QTLs, the genetic effect cannot be accounted for by a single variant. This finding suggests that genetic variation cannot be reduced to sequence variation. This complexity will need to be taken into account by studies that aim at unravelling the genetic basis of complex traits.
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Tuke, Marcus Aelred. "Exploring the role of low-frequency and structural genetic variation in human complex traits." Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/23687.
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Quantitative traits and disease risk in humans are affected by both genetic and environmental factors. Using genome-wide association studies (GWAS) over the past decade, researchers have been successful in finding common genetic polymorphisms that explain a proportion of the variation in many common phenotypes. Despite these significant leaps forward in our understanding, the heritable components of many traits remain largely unaccounted for. A number of explanations as to the “missing heritability” of complex traits and disease risk have been postulated. This thesis addresses some of the unexplained potential sources of heritable trait variation and explores two of its potential causes: low frequency and structural genetic variation. Chapter 1 provides a background to GWAS, what we have learned from them, discusses the different mechanisms of heritability and reviews the potential explanations for “missing heritability” in complex traits. The chapter then describes low frequency and structural genetic variation and how they fit into the spectrum of genetic variation. Chapter 2 describes a study that tests the extent to which low frequency association signals can be discovered through low pass whole genome sequencing when using well-powered gene expression and biomarker phenotypes as model traits. The study then compares these association signals to 1000 Genomes based imputation in the same individuals. Chapter 3 uses methods to detect the structural forms of the human amylase locus with whole-genome sequencing data. The study detects and validates multi-allelic copy number within this region and finds a lack of evidence of a previous association between structural variation of the amylase locus and obesity and body mass index. Chapter 4 scans for rare copy-number variation (CNV) using SNP microarray data from over 120 thousand individuals at 69 sites that were previously identified as being associated with developmental delay. The chapter aims to refine their prevalence in the general population and attempts to understand their relationship with developmental delay and complex traits. Chapter 5 aims to detect large deletions and duplications genome-wide using SNP microarray data in a sample of over 120 thousand individuals where we have power to detect rare copy number events. I used novel approaches to test their association with 204 clinically relevant complex traits to determine their role in the heritability of complex traits. Chapter 6 discusses the findings from the previous chapters within this thesis. I then continue by describing some limitations of this work and explore the potential further directions for future work in this area of study.
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Hall, Lynsey Sylvia. "Identifying endophenotypes for depression in Generation Scotland : a Scottish family health study." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28737.
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Depression is the most common psychiatric disorder and the leading cause of disability worldwide. Despite evidence for a genetic component, the genetic aetiology of this disorder remains elusive. To date, only one association study has identified and replicated risk loci for depression. This thesis focuses on aiding genetic discovery by revisiting the depressed phenotype and developing a quantitative trait, using data from Generation Scotland: The Scottish Family Health Study. These analyses aim to test whether this derived quantitative trait has improved statistical power to identify genetic risk variants for depression, relative to the binary classification of case/control. Measures of genetic covariation were used to evaluate and rank ten measures of mood, personality and cognitive ability as endophenotypes for depression. The highest ranking traits were subjected to principal component analysis, and the first principal component used as a quantitative measure of depression. This composite trait was compared to the binary classification of depression in terms of ability to identify risk loci in a genome-wide association study, and phenotypic variance explained by polygenic profile scores for psychiatric disorders. I also compared the composite trait to the univariate traits in terms of their ability to fulfill the endophenotype criteria as described by Gottesman and Gould, namely: being heritable, genetically and phenotypically correlated with depression, state independent, co-segregating with illness in families, and observed at a higher rate in unaffected relatives than in unrelated controls. Four out of ten traits fulfilled most endophenotype criteria, however, only two traits - neuroticism and the general health questionnaire (a measure of current psychological distress) - consistently ranked highest across all analyses. As such, three composite traits were derived incorporating two, three, or four traits. Association analyses of binary depression, univariate traits and composite traits yielded no genome-wide significant results, with most traits performing equivalently. However, composite traits were more heritable and more highly correlated with depression than their constituent traits, suggesting that analyzing these traits in combination was capturing more of the heritable component of depression. Polygenic scores for psychiatric disorders explained more trait variance for the composite traits than the univariate traits, and depression itself. Overall, whilst the composite traits generally obtained more significant results, they did not identify any further insight into the genetic aetiology of depression. This work therefore provides support for the urgent need to redefine the depressed phenotype based on objective and quantitative measures. This is essential for risk stratification, better diagnoses, novel target identification and improved treatment.
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Herzig, Anthony Francis. "Studying the genetic architecture of complex traits in a population isolate." Thesis, Sorbonne Paris Cité, 2019. http://www.theses.fr/2019USPCC110.
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Mon projet de thèse vise à exploiter le potentiel des isolats de population pour étudier la composante génétique des maladies multifactorielles. En effet, les isolats peuvent faciliter l'identification des facteurs génétiques habituellement trop rares en population générale. Cette thèse est composée de deux études principalement : l'imputation génétique et l'analyse de l'héritabilité. Chacune de ces études ont été abordée sous deux angles : l’un théorique, s’appuyant sur une vaste étude de simulations basée sur les caractéristiques de la population isolée du Cilento, permettant d’évaluer des stratégies d’analyse et de déterminer la plus adéquate ; l’autre appliqué, s’appuyant sur l’analyse de données génétiques réelles issues de la même population.L'imputation génétique est une étape cruciale pour effectuer des analyses d'association dans un isolat et représente une méthode peu couteuse pour obtenir les séquences complètes du génome ou de l’exome des individus de la population. L'efficacité de cette approche dépend de la précision de l’imputation ; nous avons donc étudié plusieurs stratégies pour obtenir une précision d'imputation maximale dans un isolat. Nous avons montré que les logiciels utilisant des algorithmes qui s’appuient sur les caractéristiques particulières des isolats n’étaient pas, de façon inattendue, aussi performants que ceux conçus pour les populations générales. De plus, malgré la disponibilité de panels de référence publics contenant plusieurs milliers de chromosomes, nous avons confirmé qu’un panel de référence spécifique de la population d’étude, même de taille très réduite, était essentiel pour la qualité de l’imputation. Ceci était d’autant plus vrai pour les variantes rares.Pour de nombreux traits, il existe des discordances entre les estimations de l'héritabilité obtenues à partir d’individus apparentés et à partir d’individus non apparentés. En particulier, la plupart des chercheurs considère que les effets dominants (non additifs) ne jouent pas un rôle majeur malgré les résultats contrastés des études sur les isolats. Notre deuxième analyse a révélé des mécanismes possibles pour expliquer la disparité de ces estimations publiées entre populations isolées et populations générales. Cela nous a permis de faire des déductions intéressantes pour nos propres analyses dans le Cilento. En particulier, nous avons identifié la possibilité d'une composante de dominance non nulle pour les niveaux de lipoprotéines de basse densité (LDL). Cela nous a amenés à effectuer des analyses d'association pan-génomique des composantes additives et non-additives pour LDL dans le Cilento et nous avons pu identifier des gènes qui avaient déjà été liés au trait dans d'autres études.Dans le contexte de nos deux études, nous avons observé l'importance de conserver l'incertitude génotypique (dosage pour l’imputation, vraisemblance des génotypes pour les données de séquençage). Dans la perspective de cette thèse, nous avons proposé des moyens d’incorporer cette incertitude à certaines méthodes utilisées dans ce projet.Nos résultats concernant les stratégies d'imputation et l'analyse de l'héritabilité seront très utiles pour la poursuite de l'étude de l'isolat de Cilento. Mais, ils seront également instructifs pour les chercheurs travaillant sur d'autres populations isolées et également applicables plus généralement à l'étude des maladies complexesMy thesis project is concerned with tapping the potential of population isolates for the dissection of complex trait architecture. Specifically, isolates can aid the identification of variants that are usually rare in other populations. This thesis principally contains in depth investigations into genetic imputation and heritability analysis in isolates. We approached both of these studies from two main angles; first from a methodological standpoint where we created extensive simulation datasets in order to investigate how the specificities of an isolate should determine strategies for analyses. Secondly, we demonstrated such concepts through analysis of genetic data in the known isolate of Cilento. Imputation is a crucial step to performing association analyses in an isolate and represents a cost-efficient method for gaining dense genetic data for the population. The effectiveness of imputation is of course dependent on its accuracy. Hence, we investigated the wide range of possible strategies to gain maximal imputation accuracy in an isolate. We showed that software using algorithms which specifically evoke known characteristics of isolates were, unexpectedly, not as successful as those designed for general populations. We also demonstrated a very small study specific imputation reference panel performing very strongly in an isolate; particularly for rare variants. For many complex traits, there exist discordances between estimates of heritabilities from studies in closely related individuals and from studies on unrelated individuals. In particular, we noted that most researchers consider dominant (non-additive) genetic effects as unlikely to play a significant role despite contrasting results from previous studies on isolates. Our second analysis revealed possible mechanisms to explain such disparate published heritability estimates between isolated populations and general populations. This allowed us to make interesting deductions from our own heritability analyses of the Cilento dataset, including an indication of a non-null dominance component involved in the distribution of low-density lipoprotein level measurements (LDL). This led us to perform genome-wide association analyses of additive and non-additive components for LDL in Cilento and we were able to identify genes that had been previously linked to the trait in other studies. In the contexts of both of our studies, we observed the importance of retaining genotype uncertainty (genotype dosage following imputation or genotype likelihoods from sequencing data). As a prospective of this thesis, we have proposed ways to incorporate this uncertainty into certain methods used in this project. Our findings for imputation strategies and heritability analysis will be highly valuable for the continued study of the isolate of Cilento but will also be instructive to researchers working on other isolated populations and also applicable to the study of complex diseases in general
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Rossin, Elizabeth Jeffries. "The Proteomic Landscape of Human Disease: Construction and Evaluation of Networks Associated to Complex Traits." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10514.
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Genetic mapping of complex traits has been successful over the last decade, with over 2,000 regions in the genome associated to disease. Yet, the translation of these findings into a better understanding of disease biology is not straightforward. The true promise of human genetics lies in its ability to explain disease etiology, and the need to translate genetic findings into a better understanding of biological processes is of great relevance to the community. We hypothesized that integrating genetics and protein- protein interaction (PPI) networks would shed light on the relationship among genes associated to complex traits, ultimately to help guide understanding of disease biology. First, we discuss the design, testing and implementation of a novel in silico approach (“DAPPLE”) to rigorously ask whether loci associated to complex traits code for proteins that form significantly connected networks. Using a high-confidence set of publically available physical interactions, we show that loci associated to autoimmune diseases code for proteins that assemble into significantly connected networks and that these networks are predictive of new genetic variants associated to the phenotypes in question. Next, we study variation in the electrocardiographic QT-interval, a heritable phenotype that when prolonged is a risk factor for cardiac arrhythmia and sudden cardiac death. We show that a large proportion of QT-associated loci encode proteins that are members of complexes identified by immunoprecipitations in mouse cardiac tissue of proteins known to be causal of Mendelian long-QT syndrome. For several of the identified proteins, we show they affect cardiac ion channel currents in model organisms. Using replication genotyping in 17,500 individuals, we use the complexes to identify genome-wide significant loci that would have otherwise been missed. Finally, we consider whether PPIs can be used to interpret rare and de novo variation discovered through recent technological advances in exome-sequencing. We report a highly connected network underlying de novo variants discovered in an autism trio exome-sequencing effort, and we design, test and implement a novel statistical framework (“DAPPLE/SEQ”) to analyze rare inherited variants in the context of PPIs in a way that significantly boosts power to detect association.
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Xin, Xiachi. "Architecture of human complex trait variation." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31549.
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A complex trait is a trait or disease that is controlled by both genetic and environmental factors, along with their interactions. Trait architecture encompasses the genetic variants and environmental causes of variation in the trait or disease, their effects on the trait or disease and the mechanism by which these factors interact at molecular and organism levels. It is important to understand trait architecture both from a biological viewpoint and a health perspective. In this thesis, I laid emphasis on exploring the influence of familial environmental factors on complex trait architecture alongside the genetic components. I performed a variety of studies to explore the architecture of anthropometric and cardio-metabolic traits, such as height, body mass index, high density lipoprotein content of blood and blood pressure, using a cohort of 20,000 individuals of recent Scottish descent and their phenotype measurements, Single Nucleotide Polymorphism (SNP) data and genealogical information. I extended a method of variance component analysis that could simultaneously estimate SNP-associated heritability and total heritability whilst considering familial environmental effects shared among siblings, couples and nuclear family members. I found that most missing heritability could be explained by including closely related individuals in the analysis and accounting for these close relationships; and that, on top of genetics, couple and sibling environmental effects are additional significant contributors to the complex trait variation investigated. Subsequently, I accounted for couple and sibling environmental effects in Genome- Wide Association Study (GWAS) and prediction models. Results demonstrated that by adding additional couple and sibling information, both GWAS performance and prediction accuracy were boosted for most traits investigated, especially for traits related to obesity. Since couple environmental effects as modelled in my study might, in fact, reflect the combined effect of assortative mating and shared couple environment, I explored further the dissection of couple effects according to their origin. I extended assortative mating theory by deriving the expected resemblance between an individual and in-laws of his first-degree relatives. Using the expected resemblance derived, I developed a novel pedigree study which could jointly estimate the heritability and the degree of assortative mating. I have shown in this thesis that, for anthropometric and cardio-metabolic traits, environmental factors shared by siblings and couples seem to have important effects on trait variation and that appropriate modelling of such effects may improve the outcome of genetic analyses and our understanding of the causes of trait variation. My thesis also points out that future studies on exploring trait architecture should not be limited to genetics because environment, as well as mate choice, might be a major contributor to trait variation, although trait architecture varies from trait to trait.
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Chan, Ying Leong. "Leveraging genetic association data to investigate the polygenic architecture of human traits and diseases." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11372.
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Many human traits and diseases have a polygenic architecture, where phenotype is partially determined by variation in many genes. These complex traits or diseases can be highly heritable and genome-wide association studies (GWAS) have been relatively successful in the identification of associated variants. However, these variants typically do not account for most of the heritability and thus, the genetic architecture remains uncertain.
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Bernhardsson, Carolina. "Molecular population genetics of inducible defense genes in Populus tremula." Doctoral thesis, Umeå universitet, Institutionen för ekologi, miljö och geovetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-54361.
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Plant-herbivore interactions are among the most common of ecological interactions. It is therefore not surprising that plants have evolved multiple mechanisms to defend themselves, using both constitutive chemical and physical barriers and by induced responses which are only expressed after herbivory has occurred. Herbivores, on the other hand, respond to these plant defenses by evolving counter-adaptations which makes defenses less effective or even useless. Adaptation can occur at different geographical scales, with varying coevolutionary interactions across a spatially heterogenous landscape. By looking at the underlying genes responsible for these defensive traits and herbivore related phenotypic traits, it is possible to investigate the coevolutionary history of these plant- herbivore interactions. Here I use molecular population genetic tools to investigate the evolutionary history of several inducible defense genes in European Aspen (Populus tremula) in Sweden. Two genes, belonging to the Polyphenol oxidase gene-family (PPO1 and PPO2), show skews in their site frequency spectrum together with patterns of diversity and divergence from an outgroup which correspond to signatures of adaptive evolution (Paper II). 71 single nucleotide polymorphisms (SNPs) from seven inducible defense genes (PPO1-PPO3, TI2-TI5) show elevated levels of population differentiation compared to control genes (genes not involved in plant defense), and 10 of these defense SNPs show strong signatures of natural selection (Paper III). These 71 defense SNPs also divides a sample of Swedish P. tremula trees into three distinct geographical groups, corresponding to a Southern, Central and Northern cluster, a patterns that is not present in control SNPs (Paper III). The same geographical pattern, with a distinct Northern cluster, is also observed in several phenotypic traits related to herbivory in our common garden in Sävar (Paper IV). These phenotypic traits show patterns of apparent local maladaptation of the herbivore community to the host population which could indicate the presence of “information coevolution” between plants and herbivores (Paper IV). 15 unique defense SNPs also show significant associations to eight phenotypic traits but the causal effects of these SNP associations may be confounded by the geographic structure found in both the underlying genes and in the phenotypic traits. The co-occurrence of population structure in both defense genes and herbivore community traits may be the result from historical events during the post-glacial recolonization of Sweden.Interaktioner mellan växter och herbivorer är bland de vanligaste ekologiska interaktionerna och det är därför inte förvånande att växter har utvecklat flera olika mekanismer för att försvara sig. Dessa försvarsmekanismer består både av konstitutiva kemiska och fysiska barriärer så väl som inducerade försvar som bara är uttryckta efter att en växt har blivit skadad genom betning. Herbivorerna å sin sida svarar på dessa försvar genom att utveckla motanpassningar som gör växternas försvar mindre effektiva eller till och med verkningslösa. Dessa anpassningar kan ske över olika geografiska skalor beroende på om de samevolutionära interaktionerna varierar i ett rumsligt heterogent landskap. Genom att studera de underliggande gener som kontrollerar dessa försvarsegenskaper tillsammans med herbivorrelaterade fenotypiska egenskaper är det möjligt att undersöka den samevolutionära historien av interaktionerna mellan växter och herbivorer. Här använder jag mig av molekylärpopulationsgenetiska verktyg för att undersöka den evolutionära historien i flera inducerade försvarsgener hos asp (Populus tremula) i Sverige. Två gener, som tillhör genfamiljen Polyphenol-oxidaser (PPO1 och PPO2), uppvisar ett frekvensmönster som man förväntar sig vid positiv selektion. Detta mönster kan också ses i dessa geners diversitet samt i divergens från en utgrupp (Uppsats II). 71 ”single nucleotide polymorphisms” (SNPar) från 7 inducerade försvarsgener (PPO1-PPO3, TI2-TI5) visar förhöjda nivåer av populationsdifferentiering jämfört med kontrollgener (gener som inte är involverade i trädens försvar), och 10 av dessa försvars-SNPar visar även tecken på naturlig selektion (Uppsats III). Dessa 71 försvars-SNPar delar in ett urval av svenska aspar i tre distinkta geografiska grupper som beskriver ett sydligt, centralt och nordligt kluster som inte förekommer hos kontroll-SNPar (Uppsats III). Samma geografiska mönster, med ett distinkt nordligt kluster, återfinns däremot i ett antal fenotypiska egenskaper som är relaterade till herbivori i ett odlingsförsök utanför Sävar (Uppsats IV). Dessa fenotypiska egenskaper visar tecken på lokal felanpassning hos herbivorsamhället till den lokala värdpopulationen, vilket kan indikera förekomsten av ett ”samevolutionärt informationsutbyte” mellan växter och herbivorer (Uppsats IV). 15 unika försvars-SNPar påvisar också signifikanta associationer med 8 olika fenotypiska egenskaper, men om dessa har en verklig effekt eller inte är svårt att säga på grund av den geografiska strukturen som förekommer både hos de underliggande generna och hos de fenotypiska egenskaperna. Att denna populationsstruktur förekommer hos både försvarsgener och egenskaper som är förknippade med herbivorsamhället kan däremot vara ett resultat av historiska händelser som skett under aspens post-glaciala återkolonisation av Sverige.
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Benchek, Penelope H. "How Extensive of a Role do Gene-gene Interactions Play in the GeneticArchitecture of Complex Traits?" Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1485511511496427.
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Stahl, Bethany A. "Regressive Evolution of Pigmentation in the Blind Mexican Cavefish Astyanax mexicanus." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439281730.
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Smallbone, Willow. "The impact of Major Histocompatibility Complex composition on fitness and life history traits of a vertebrate model, the guppy (Poecilia reticulata)." Thesis, Cardiff University, 2017. http://orca.cf.ac.uk/108113/.
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The Major Histocompatibility Complex (MHC) is a multi-gene family that includes most vertebrate immune genes. Life history traits have been associated with MHC allelic variation, including offspring survival, reproductive success, kin recognition, inbreeding avoidance, body mass gain, mate choice and parasite resistance. The studies reported in this thesis used laboratory and field investigations to identify differences in MHC genetic variation between truly wild, wild type and domesticated conspecifics and the implications of this for fitness, across the entire life history of a vertebrate, the guppy (Poecilia reticulata). Specifically, the effects of host inbreeding and domestication on parasite susceptibility are assessed in relation to MHC allelic and supertype composition. Laboratory studies showed that inbreeding and domestication lead to increased susceptibility to Gyrodactylus turnbulli, which was also linked to the presence of particular functional groups of MHC. A multi-site field sampling supported this finding; revealing that natural parasite communities reflected host MHC functional groups, as well as the river of origin. Truly wild fish had greater MHC genetic diversity than wild type (wild population maintained in the laboratory for ~ 3 years), which, in turn, were more genetically diverse than ornamental (domesticated) conspecifics. The accidental and deliberate release, into the wild, of domesticated fish is common. The release of infected and uninfected ornamental guppies into a wild type laboratory population increased parasite prevalence and abundance, due to the integration of a more susceptible individual into the social group. Mate preference is often linked to MHC similarity, whereby individuals select mates that are dissimilar or optimally similar at the MHC. The effects of sexual selection, MHC similarity and parasitism on mate choice, were assessed, indicating that a combination of factors are important in a female’s preference. Female guppies spent more time interacting with males with redder colouration and less MHC alleles in common. An experimental F1 generation revealed that offspring with parents sharing more MHC alleles and supertypes were more susceptible to parasitic infection. This research suggests that MHC functionality is at least as important as allelic and supertype diversity, with regards to individual fitness and life history traits.
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Freudenthal, Jan Alexander [Verfasser], and Thomas [Gutachter] Schmitt. "Quantitative genetics from genome assemblies to neural network aided omics-based prediction of complex traits / Jan Alexander Freudenthal ; Gutachter: Thomas Schmitt." Würzburg : Universität Würzburg, 2020. http://d-nb.info/1204831718/34.
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Blomquist, Thomas M. "Development of Bimodal Gene Expression Analysis and Allele-Specific Competitive PCR for Investigation of Complex Genetic Traits, Lung Cancer Risk." University of Toledo Health Science Campus / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=mco1277151230.
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Joshi, Shreyas. "IDENTIFICATION OF NOVEL SLEEP RELATED GENES FROM LARGE SCALE PHENOTYPING EXPERIMENTS IN MICE." UKnowledge, 2017. http://uknowledge.uky.edu/biology_etds/42.
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Humans spend a third of their lives sleeping but very little is known about the physiological and genetic mechanisms controlling sleep. Increased data from sleep phenotyping studies in mouse and other species, genetic crosses, and gene expression databases can all help improve our understanding of the process. Here, we present analysis of our own sleep data from the large-scale phenotyping program at The Jackson Laboratory (JAX), to identify the best gene candidates and phenotype predictors for influencing sleep traits.
The original knockout mouse project (KOMP) was a worldwide collaborative effort to produce embryonic stem (ES) cell lines with one of mouse’s 21,000 protein coding genes knocked out. The objective of KOMP2 is to phenotype as many as of these lines as feasible, with each mouse studied over a ten-week period (www.mousephenotype.org). The phenotyping for sleep behavior is done using our non-invasive Piezo system for mouse activity monitoring. Thus far, sleep behavior has been recorded in more than 6000 mice representing 343 knockout lines and nearly 2000 control mice. Control and KO mice have been compared using multivariate statistical approaches to identify genes that exhibit significant effects on sleep variables from Piezo data. Using these statistical approaches, significant genes affecting sleep have been identified. Genes affecting sleep in a specific sex and that specifically affect sleep during daytime and/or night have also been identified and reported.
The KOMP2 consists of a broad-based phenotyping pipeline that consists of collection of physiological and biochemical parameters through a variety of assays. Mice enter the pipeline at 4 weeks of age and leave at 18 weeks. Currently, the IMPC (International Mouse Phenotyping Consortium) database consists of more than 33 million observations. Our final dataset prepared by extracting biological sample data for whom sleep recordings are available consists of nearly 1.5 million observations from multitude of phenotyping assays. Through big data analytics and sophisticated machine learning approaches, we have been able to identify predictor phenotypes that affect sleep in mice. The phenotypes thus identified can play a key role in developing our understanding of mechanism of sleep regulation.
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Finucane, Hilary Kiyo. "Functional and cross-trait genetic architecture of common diseases and complex traits." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112906.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2017Cataloged from PDF version of thesis.
Includes bibliographical references (pages 201-245).
In this thesis, I introduce new methods for learning about diseases and traits from genetic data. First, I introduce a method for partitioning heritability by functional annotation from genome-wide association summary statistics, and I apply it to 17 diseases and traits and many different functional annotations. Next, I show how to apply this method to use gene expression data to identify diseaserelevant tissues and cell types. I next introduce a method for estimating genetic correlation from genome-wide association summary statistics and apply it to estimate genetic correlations between all pairs of 24 diseases and traits. Finally, I consider a model of disease subtypes and I show how to determine a lower bound on the sample size required to distinguish between two disease subtypes as a function of several parameters.
by Hilary Kiyo Finucane.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Mathematics
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Forsberg, Simon. "Complex Trait Genetics : Beyond Additivity." Doctoral thesis, Uppsala universitet, Institutionen för medicinsk biokemi och mikrobiologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-307837.
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The link between the genotype and the phenotype of an organism is immensely complex. Despite this it can, to a great extent, be captured using models that assume that gene variants combine their effects in an additive manner. This thesis explores aspects of genetics that cannot be fully captured using such additive models. Using experimental data from three different model organisms, I study two phenomena that fall outside of the additive paradigm: genetic interactions and genetic variance heterogeneity. Using the model plant Arabidopsis thaliana, we show how important biological insights can be reached by exploring loci that display genetic variance heterogeneity. In the first study, this approach identified alleles in the gene CMT2 associated with the climate at sampling locations, suggesting a role in climate adaption. These alleles affected the genome wide methylation pattern, and a complete knock down of this gene increased the plants heat tolerance. In the second study, we demonstrate how the observed genetic variance heterogeneity was the result of the partial linkage of many functional alleles near the gene MOT1, all contributing to Molybdenum levels in the leaves. Further, we explore genetic interactions using data from dogs and budding yeast (Saccharomyces cerevisiae). In the dog population, two interacting loci were associated with fructosamine levels, a biomarker used to monitor blood glucose. One of the loci displayed the pattern of a selective sweep in some of the studied breeds, suggesting that the interaction is important for the phenotypic breed-differences. In a cross between two strains of yeast, with the advantage of large population size and nearly equal allele frequencies, we identified large epistatic networks. The networks were largely centered on a number of hub-loci and altogether involved hundreds of genetic interactions. Most network hubs had the ability to either suppress or uncover the phenotypic effects of other loci. Many multi-locus allele combinations resulted in phenotypes that deviated significantly from the expectations, had the loci acted in an additive manner. Critically, this thesis demonstrates that non-additive genetic mechanisms often need to be considered in order to fully understand the genetics of complex traits.
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Hemani, Gibran. "Dissecting genetic interactions in complex traits." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6472.
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Of central importance in the dissection of the components that govern complex traits is understanding the architecture of natural genetic variation. Genetic interaction, or epistasis, constitutes one aspect of this, but epistatic analysis has been largely avoided in genome wide association studies because of statistical and computational difficulties. This thesis explores both issues in the context of two-locus interactions. Initially, through simulation and deterministic calculations it was demonstrated that not only can epistasis maintain deleterious mutations at intermediate frequencies when under selection, but that it may also have a role in the maintenance of additive variance. Based on the epistatic patterns that are evolutionarily persistent, and the frequencies at which they are maintained, it was shown that exhaustive two dimensional search strategies are the most powerful approaches for uncovering both additive variance and the other genetic variance components that are co-precipitated. However, while these simulations demonstrate encouraging statistical benefits, two dimensional searches are often computationally prohibitive, particularly with the marker densities and sample sizes that are typical of genome wide association studies. To address this issue different software implementations were developed to parallelise the two dimensional triangular search grid across various types of high performance computing hardware. Of these, particularly effective was using the massively-multi-core architecture of consumer level graphics cards. While the performance will continue to improve as hardware improves, at the time of testing the speed was 2-3 orders of magnitude faster than CPU based software solutions that are in current use. Not only does this software enable epistatic scans to be performed routinely at minimal cost, but it is now feasible to empirically explore the false discovery rates introduced by the high dimensionality of multiple testing. Through permutation analysis it was shown that the significance threshold for epistatic searches is a function of both marker density and population sample size, and that because of the correlation structure that exists between tests the threshold estimates currently used are overly stringent. Although the relaxed threshold estimates constitute an improvement in the power of two dimensional searches, detection is still most likely limited to relatively large genetic effects. Through direct calculation it was shown that, in contrast to the additive case where the decay of estimated genetic variance was proportional to falling linkage disequilibrium between causal variants and observed markers, for epistasis this decay was exponential. One way to rescue poorly captured causal variants is to parameterise association tests using haplotypes rather than single markers. A novel statistical method that uses a regularised parameter selection procedure on two locus haplotypes was developed, and through extensive simulations it can be shown that it delivers a substantial gain in power over single marker based tests. Ultimately, this thesis seeks to demonstrate that many of the obstacles in epistatic analysis can be ameliorated, and with the current abundance of genomic data gathered by the scientific community direct search may be a viable method to qualify the importance of epistasis.
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Saint, Pierre Aude. "Méthodes d'analyse génétique de traits quantitatifs corrélés : application à l'étude de la densité minérale osseuse." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00633981.
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La plupart des maladies humaines ont une étiologie complexe avec des facteurs génétiques et environnementaux qui interagissent. Utiliser des phénotypes corrélés peut augmenter la puissance de détection de locus de trait quantitatif. Ce travail propose d'évaluer différentes approches d'analyse bivariée pour des traits corrélés en utilisant l'information apportée par les marqueurs au niveau de la liaison et de l'association. Le gain relatif de ces approches est comparé aux analyses univariées. Ce travail a été appliqué à la variation de la densité osseuse à deux sites squelettiques dans une cohorte d'hommes sélectionnés pour des valeurs phénotypiques extrêmes. Nos résultats montrent l'intérêt d'utiliser des approches bivariées en particulier pour l'analyse d'association. Par ailleurs, dans le cadre du groupe de travail GAW16, nous avons comparé les performances relatives de trois méthodes d'association dans des données familiales.
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Sharma, Pankaj. "Genetic dissection of complex traits : essential hypertension." Thesis, University of Cambridge, 1998. https://www.repository.cam.ac.uk/handle/1810/275234.
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Groves-Kirkby, Nick. "Genetic analysis of variation in complex traits." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:4541c4e4-4538-4348-bb4b-0df6673344d2.
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Variation is a universal property of life, and much of contemporary genetics research is directed towards understanding the causes of variation in traits. Here I present the results of my investigations into the genetic and other causes of trait variation in humans and mice. I address these questions in the context of two distinct research projects, which use pre-existing data to investigate the causes of trait variation, through a range of analytic techniques. I first extract trait data from historic breeding records from the incipient Collaborative Cross (a genetic reference population of recombinant inbred mice) and use them to map genetic factors affecting litter size and other reproductive traits. Mapping reveals significant quantitative trait loci associated with litter size and time between litters, as well as a number of suggestive loci. I characterise the genetic effects at these loci and investigate candidate genes. The most robust finding, a litter size locus on chromosome 5, explains around 3% of observed variation and 24% of the variation attributable to genetics. Using data obtained from the Netherlands Twin Registry - a longitudinal database of Dutch twins - I investigate the prevalence of parent of origin effects on gene expression traits in peripheral blood in humans. I first phase individuals' genotypes by parental origin and use these genotypes to calculate the heritability of over 44,000 gene expression traits partitioned into that attributable to matching and nonmatching parent of origin. I replicate prior genomewide heritability estimates for many traits, but I find little evidence of widespread parent-of-origin effects on human gene expression in blood. On further examination, the small sample size severely limits the power to detect such effects. Nonetheless, I identify approximately 200 genes enriched for immune system processes that show evidence of parent-of-origin-specific effects on heritability.
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North, Teri-Louise. "Genetic epidemiological and population genetic studies of complex ageing traits." Thesis, University of Bristol, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.685359.
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The ageing process in humans is affected by lifestyle and by our genetics, the mechanisms of which we understand to varying degrees. Unpicking the phenotypic and genetic architecture of ageing traits will explain why there is such variability in the ageing phenotype. I begin with a study of physical and cognitive capability in middle to older aged individuals (Chapter 2). I use a SNP shown to associate with nicotine dependence in a Mendelian Randomization meta-analysis to explore the causality of the association of smoking with ageing, demonstrating how genetic information can be used to improve our understanding of the causal association of lifestyle and ageing traits. In Chapter 3, I meta-analyse the association of carrier status for four Mendelian diseases and physical capability, cognitive capability and lung function in ageing individuals to understand whether presumed asymptomatic heterozygotes present with a characteristic phenotype in later life. This study generated a novel finding: PI-MZ carrier status for alpha 1-antitrypsin deficiency is associated with increased height and increased respiratory capacity. In Chapter 4, I conduct the first metabochip-wide association scan of objective physical capability and self-reported disability in middle to older aged individuals, with the aim of identifying candidates for ageing genes. Lastly (Chapter 5), I conduct a simulation of evolution to test a pleiotropic model developed by Eyre-Walker (2010) regarding the genetic architecture of complex traits. I find good concordance between simulation and theory, although discrepancies arise due to the assumptions of the diffusion model. I discuss the potential portability of the pleiotropic model to the context of complex ageing traits. This thesis applies contrasting approaches to exploring phenotypic and genetic influences on complex ageing trais, enhancing our current understanding of how lifestyle and genetics shape our ageing phenome. In a world of increasing geriatric morbidity, such progress is of burgeoning importance.
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Tang, Ling-fung Paul, and 鄧凌鋒. "Dissecting the genetics of complex trait in mouse: an attempt using public resources and in-houseknockout." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B43572170.
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Modin, Helena. "Multiple sclerosis : linkage analysis and DNA variation in a complex trait /." Stockholm, 2004. http://diss.kib.ki.se/2004/91-7349-792-4/.
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Wu, Song. "A robust approach for genetic mapping of complex traits." [Gainesville, Fla.] : University of Florida, 2008. http://purl.fcla.edu/fcla/etd/UFE0022399.
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Macdonald, Stuart J. "Evolutionary and genomic analyses of complex traits in Drosophila." Thesis, University of Oxford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365832.
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Salfati, Elias Levy Itshak. "Genetic determinants of cardiovascular disease : heritability and genetic risk score." Thesis, Paris 5, 2014. http://www.theses.fr/2014PA05S014/document.
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Les maladies complexes telles que les maladies cardio-Vasculaires (MCV) sont influencées par des facteurs génétiques et environnementaux. L’estimation du risque cardio-Vasculaire chez un individu est généralement évaluée par la sommation des facteurs de risque reconnu des MCV (p. ex. l’âge, le sexe, le tabac, la pression artérielle et le cholestérol). Dernièrement, plusieurs bio-Marqueurs ont été examiné pour leur aptitude à améliorer la prédiction des maladies cardio-Vasculaires au-Delà des facteurs de risques traditionnels. L’intérêt de découvrir de nouveaux loci est incité notamment par les découvertes qui émergent des études d'association pangénomique (GWAS) qui permettent de tester l’association de variation génétique au risque de contracter une maladie commune. Les GWAS ont considérablement amélioré notre connaissance de l'architecture génétique des maladies cardio-Vasculaires, à ce jour plus de 50 variations génétiques sont formellement associées à des maladies cardio-Vasculaires, de même plus de 200 marqueurs génétiques seraient associés à des facteurs de risque cardiovasculaire traditionnels (p. ex. le taux sanguin des lipides, la pression artérielle, l’indice de masse corporelle et le diabète de type 2). Le succès remarquable de ces études d’association, qui a permis l’identification de nombreux bio-Marqueurs, a conduit à une réévaluation des données génétiques dans le but de définir des informations cliniquement utiles pour limiter et mieux prédire les risques de maladies, grâce à une application plus efficace des stratégies de prévention. Dans cette thèse, nous examinons tout d'abord une nouvelle approche pour étudier l'architecture génétique de l'hypertension artérielle (HTA; facteur de risque majeur des maladies cardiovasculaires prématurées), puis nous avons constitué plusieurs modèles pour prédire le risque de développer une maladie coronarienne (MC; type le plus commun de MCV), enfin nous avons déterminé une base génétique commune du principal prédicteur de complications cliniques des maladies coronariennes – l'athérosclérose subclinique - afin d'ajouter une valeur pronostique supplémentaire en plus des scores de risque traditionnels à différents âges. Nous avons estimé l'héritabilité de la première mesure de la pression artérielle systolique (PAS) à ~25%/~45% et à ~30%/~37% pour la pression artérielle diastolique (PAD) chez les sujets d’origine Européenne (N = 8901) et d’origine Africaine (N = 2860) faisant respectivement partie de la cohorte Atherosclerosis Risk in Communities (ARIC), en accord avec les études antérieures. Par ailleurs, nous avons développé un moyen de combiner un score de risque génétique (SRC) – somme des effets génétiques parmi un ensemble de marqueurs – avec une évaluation indépendante du risque clinique, en utilisant un système d'équations log-Linéaire. Nous avons employé cet outil à la prédiction de la maladie coronarienne (MC) dans la cohorte ARIC. L'ajout d'un score de risque génétique (SRG) à un score de risque clinique (SRC) améliore à la fois la discrimination et l'étalonnage des maladies coronariennes dans la cohorte ARIC, et révèle par la même comment cette information génétique influence l'évaluation des risques ainsi que l’approche clinique. Enfin, parmi 1561 cas et 5068 contrôles (de la présence ou non de calcifications coronaires), faisant partie de plusieurs ensembles de données cliniques et génétiques disponibles via la base de données NCBI de Génotypes et Phénotypes (dbGAP), nous avons constaté qu’une augmentation d'un écart-Type dans le score de risque génétique de 49 bio-Marqueurs de MC est associée à 28 % d’augmentation de risque de développer une athérosclérose coronarienne subclinique diagnostiquée à un stade avancé (p=1.43x10-16). Cette augmentation du risque est significative dans chaque catégorie d'âge (de 15 ans en 15 ans) (0,01 > p > 9.4x10-7) et a été remarquablement similaire dans toutes les catégories d'âge (test d'hétérogénéité p = 0.98). (...)Complex diseases such as cardiovascular disease (CVD) are influenced by both genetic and environmental factors. Estimation of an individual’s cardiovascular risk usually involves measurement of risk factors correlated with risk of CVD (e.g. age, sex, smoking, blood pressure, and total cholesterol). Lately, several biomarkers have been evaluated for their ability to improve prediction of cardiovascular disease beyond traditional risk factors. The interest in novel loci is propelled notably by emerging discoveries from the advent of genome-Wide association studies (GWAS) of genetic variants associated with risk for common diseases. GWAS has greatly enhanced our knowledge of the genetic architecture of cardiovascular disease, yielding over 50 variants confirmed to be associated with CVD to date, as well as over 200 associated with traditional cardiovascular risk factors (e.g. lipids, blood pressure, body mass index, and type 2 diabetes mellitus). This recent and continuing success in discovering increasing numbers of robustly associated genetic markers has led to reassessment of whether genetic data can provide clinically useful information by refining risk prediction and moderating disease risk through a more efficient application of prevention strategies. In this thesis, we first address novel approach to survey the genetic architecture of hypertension (i.e. major risk factor for premature CVD), then construct risk prediction models for coronary artery disease (CAD; i.e. most common type of CVD) and finally establish a common genetic basis of the strongest predictor of clinical complications of CAD, subclinical atherosclerosis, to add incremental prognostic value above traditional risk scores across a range of ages. We show that, for first visit measurements, the heritability is ~25%/~45% and ~30%/~37% for systolic (SBP) and diastolic blood pressure (DBP) in European (N=8,901) and African (N=2,860) ancestry individuals from the Atherosclerosis Risk in Communities (ARIC) cohort, respectively, in accord with prior studies. Then we present a means to combine a polygenic risk score - genetic effects among an ensemble of markers - with an independent assessment of clinical risk using a log-Link function. We apply the method to the prediction of coronary heart disease (CHD) in the ARIC cohort. The addition of a genetic risk score (GRS) to a clinical risk score (CRS) improves both discrimination and calibration for CHD in ARIC and subsequently reveal how this genetic information influences risk assessment and thus potentially clinical management. Finally, Among 1561 cases and 5068 controls, from several clinical and genetic datasets available through the NCBI's database of Genotypes and Phenotypes (dbGAP), we found a one SD increase in the genetic risk score of 49 CAD SNPs was associated with a 28% increased risk of having advanced subclinical coronary atherosclerosis (p = 1.43 x 10-16). This increase in risk was significant in every 15-Year age stratum (.01 > p > 9.4 x 10-7) and was remarkably similar across all age strata (p test of heterogeneity = 0.98). We obtained near identical results and levels of significance when we restricted the genetic risk score to 32 SNPs not associated with traditional risk factors. Accordingly, common variation largely recapitulates the known heritability of blood pressure traits. The vast majority of this heritability varies by chromosome, depending on its length, and is largely concentrated in intronic and intergenic regions of the genome but widely distributed across the common allele frequency spectrum. Respectively, our proposed method to combine genetic information at established susceptibility loci with a nongenetic risk prediction tool facilitates the standardized incorporation of a GRS in risk assessment. (...)
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Abecasis, G. R. "Methods for fine mapping complex traits in human pedigrees." Thesis, University of Oxford, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365700.
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Mahjani, Behrang. "Methods from Statistical Computing for Genetic Analysis of Complex Traits." Doctoral thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-284378.
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The goal of this thesis is to explore, improve and implement some advanced modern computational methods in statistics, focusing on applications in genetics. The thesis has three major directions. First, we study likelihoods for genetics analysis of experimental populations. Here, the maximum likelihood can be viewed as a computational global optimization problem. We introduce a faster optimization algorithm called PruneDIRECT, and explain how it can be parallelized for permutation testing using the Map-Reduce framework. We have implemented PruneDIRECT as an open source R package, and also Software as a Service for cloud infrastructures (QTLaaS). The second part of the thesis focusses on using sparse matrix methods for solving linear mixed models with large correlation matrices. For populations with known pedigrees, we show that the inverse of covariance matrix is sparse. We describe how to use this sparsity to develop a new method to maximize the likelihood and calculate the variance components. In the final part of the thesis we study computational challenges of psychiatric genetics, using only pedigree information. The aim is to investigate existence of maternal effects in obsessive compulsive behavior. We add the maternal effects to the linear mixed model, used in the second part of this thesis, and we describe the computational challenges of working with binary traits.eSSENCE
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Besnier, Francois. "Development of Variance Component Methods for Genetic Dissection of Complex Traits." Doctoral thesis, Uppsala universitet, Centrum för bioinformatik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-101399.
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This thesis presents several developments on Variance component (VC) approach for Quantitative Trait Locus (QTL) mapping. The first part consists of methodological improvements: a new fast and efficient method for estimating IBD matrices, have been developed. The new method makes a better use of the computer resources in terms of computational power and storage memory, facilitating further improvements by resolving methodological bottlenecks in algorithms to scan multiple QTL. A new VC model have also been developed in order to consider and evaluate the correlation of the allelic effects within parental lines origin in experimental outbred crosses. The method was tested on simulated and experimental data and revealed a higher or similar power to detect QTL than linear regression based QTL mapping. The second part focused on the prospect to analyze multi-generational pedigrees by VC approach. The IBD estimation algorithm was extended to include haplotype information in addition to genotype and pedigree to improve the accuracy of the IBD estimates, and a new haplotyping algorithm was developed for limiting the risk of haplotyping errors in multigenerational pedigrees. Those newly developed methods where subsequently applied for the analysis of a nine generations AIL pedigree obtained after crossing two chicken lines divergently selected for body weight. Nine QTL described in a F2 population were replicated in the AIL pedigree, and our strategy to use both genotype and phenotype information from all individuals in the entire pedigree clearly made efficient use of the available genotype information provided in AIL.
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Cabrera, Cárdenas Claudia Paola. "Bioinformatics tools for the genetic dissection of complex traits in chickens." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3864.
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This thesis explores the genetic characterization of the mechanisms underlying complex traits in chicken through the use and development of bioinformatics tools. The characterization of quantitative trait loci controlling complex traits has proven to be very challenging. This thesis comprises the study of experimental designs, annotation procedures and functional analyses. These represent some of the main ‘bottlenecks’ involved in the integration of QTLs with the biological interpretation of high-throughput technologies. The thesis begins with an investigation of the bioinformatics tools and procedures available for genome research, briefly reviewing microarray technology and commonly applied experimental designs. A targeted experimental design based on the concept of genetical genomics is then presented and applied in order to study a known functional QTL responsible for chicken body weight. This approach contrasts the gene expression levels of two alternative QTL genotypes, hence narrowing the QTL-phenotype gap, and, giving a direct quantification of the link between the genotypes and the genetic responses. Potential candidate genes responsible for the chicken body weight QTL are identified by using the location of the genes, their expression and biological significance. In order to deal with the multiple sources of information and exploit the data effectively, a systematic approach and a relational database were developed to improve the annotation of the probes of the ARK-Genomics G. gallus 13K v4.0 cDNA array utilized on the experiment. To follow up the investigation of the targeted genetical genomics study, a detailed functional analysis is performed on the dataset. The aim is to identify the downstream effects through the identification of functional variation found in pathways, and secondly to achieve a further characterization of potential candidate genes by using comparative genomics and sequence analyses. Finally the investigation of the body weight QTL syntenic regions and their reported QTLs are presented.
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Langley, Sarah Raye. "Modelling genetic and genomic interactions underlying gene expression and complex traits." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/10925.
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This study focuses on integrating and applying computational techniques for modelling quantitative traits and complex diseases, such as hypertension and diabetes, using the rat model system and translating the findings to humans. Complex disease traits are heritable, highly polygenic, and influenced by environmental factors. Human studies, like Genome Wide Association Studies (GWAS), have identified many genetic determinants underlying these traits but have provided little information about the functional effects of these variants and mechanisms regulating the disease. This study takes a systems-level approach for looking at the genetic regulation of complex traits in the rat by analysing multiple phenotypes, genomewide genetic variation and gene expression data in multiple tissues. I integrated these multi-modality datasets in the BXH/HXB rat Recombinant Inbred (RI) lines, an established model of the human metabolic syndrome, to identify candidate genes, pathways and networks associated with complex disease phenotypes. I evaluated methods for Expression Quantitative Trait Locus (eQTL) analysis and used sparse Bayesian regression approaches to map eQTLs in the RI lines, delineating a new, large eQTL data resource for the rat genetic community. I have also developed and applied signal processing and time series analysis methods to physiological traits to extract more detailed indices of blood pressure, and integrated these with genetic, expression and eQTL data to inform on the regulation of these traits. Then, using publicly available data, I used comparative genomics approaches to elucidate a set of genes and pathways that can play a role in human diseases. This study has provided a valuable resource for future work in the rat, by means of new eQTLs in multiple tissues, and physiological time series phenotypes and approaches. This has enabled an integrative analysis of these data to give new insights into the regulation of complex traits in rats and humans.
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Onkamo, Päivi. "Genetic mapping of complex traits : the case of Type 1 diabetes." Helsinki : University of Helsinki, 2002. http://ethesis.helsinki.fi/julkaisut/mat/rolfn/vk/onkamo/.
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Broadley, Simon Andrew. "The genetic analysis of a complex trait : multiple sclerosis." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620291.
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O'Connor, Christine. "Dissecting the Genetic Architecture of Complex Traits in the Nematode Caenorhabditis remanei." Thesis, University of Oregon, 2018. http://hdl.handle.net/1794/23756.
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A central problem in evolutionary quantitative genetics has been to attempt to dissect the genetic basis of complex traits. A variety of inferential methods have been developed to probe this issue. Here, I use experimental evolution, next generation sequencing and standing genetic variation in the nematode Caenorhabditis remanei to dissect the genetic basis of two model complex traits: oxidative and heat stress response.
Pleiotropy, when one gene affects more than one trait, is an important phenomenon to understand when attempting to understand the genetic architecture of a complex trait. Previous work in the nematode C. elegans found that abiotic stress response is controlled by a handful of genes of major effect, and that mutations in one gene can affect the ability of the organism to respond to multiple types of stressors. I used experimental evolution to probe the extent of pleiotropy between the genes selected for resistance to one of two abiotic stressors: acute heat and oxidative. In contrast to expectations, I find that acute heat stress response and acute oxidative response are polygenic, complex traits. Additionally, I find that the evolved responses do not share a genetic basis. This lack of correlation is reflected at the levels of phenotype, gene expression and genomic response to selection.
In addition to the complex interactions within an organism, the genetic architecture of complex traits and response to selection are affected by population dynamics. Here, I investigate the effect of gene flow on patterns and extent of phenotypic and genetic divergence between populations in distinct environments – a standard lab environment and a chronic heat stress environment. Gene flow of lab-adapted individuals into chronic heat stress adapted populations did not affect phenotypic adaptation, but greatly decreased the number of genomic sites that responded to selection. These results fit predictions that gene flow of non-locally adapted individuals will create an additional barrier for local adaptation, and the strength of selection of locally adapted alleles must not only be greater than the strength of random effects, but also be stronger than the effects of gene flow.
This work includes unpublished co-authored material.2019-01-27
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Manet, Caroline. "Genetic control of susceptibility to Zika virus in the mouse using strains of the Collaborative Cross." Thesis, Paris, Institut agronomique, vétérinaire et forestier de France, 2019. http://www.theses.fr/2019IAVF0029.
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Zika est un Flavivirus émergent transmis à l’Homme par piqûre de moustique. Il a récemment été à l’origine d’épidémies d’envergure mondiale et représente une menace pour la santé publique. L’infection Zika est souvent asymptomatique ou engendre un syndrome grippal bénin. Cependant, des complications sévères ont été associées au virus Zika, telles qu’un syndrome de Guillain-Barré ou des encéphalites chez l’adulte, ainsi que des malformations congénitales comme la microcéphalie. De nombreux facteurs sont susceptibles d’influencer la sensibilité d’un individu au virus Zika, y compris les variants génétiques de l’hôte.Nous avons étudié le rôle des facteurs génétiques de l’hôte dans sa sensibilité à l’infection par le virus Zika. Pour cela, nous avons utilisé des lignées de souris du Collaborative Cross (CC), une population génétique de référence caractérisée par une diversité génétique aussi vaste que celle des populations humaines.Nous avons d’abord montré que le fond génétique de souris déficientes pour le gène du récepteur à l’interféron de type I (Ifnar1) joue un rôle drastique dans leur sensibilité au virus Zika. La diversité génétique des souris CC, préalablement traitées par un anticorps bloquant le récepteur IFNAR, s’exprime par des phénotypes allant d’une résistance complète jusqu’à des formes sévères de la maladie. L’influence des facteurs génétiques de l’hôte s’exerce sur de nombreux paramètres tels que la virémie, la charge virale et les lésions pathologiques dans le cerveau, et enfin le taux de réplication dans les cellules infectées. Les différences de sensibilité entre lignées CC s’avèrent corrélées entre les Flavivirus Zika, Dengue et West-Nile. Nos analyses génétiques ont montré que de multiples gènes à effets faibles sous-tendent ces variations phénotypiques, reflétant la complexité de la sensibilité au virus Zika dans les populations humaines, et permettent d’exclure un rôle majeur du facteur de résistance Oas1b.Nous avons ensuite cherché des gènes agissant comme modificateurs de la sensibilité chez des souris déficientes pour le gène Ifnar1 dans un croisement F2 entre des souris C57BL/6J et 129S2/SvPas portant la mutation. L’analyse génétique a permis l’identification de deux QTLs (Quantitative Trait Locus), l’un contrôlant le pic de virémie et l’autre la survie. Une étude bio-informatique nous a permis d’identifier quelques gènes candidats.Nous avons également étudié comment les facteurs génétiques de l’hôte impactent la réplication virale dans des fibroblastes embryonnaires murins (MEFs) dérivés d’une série de lignées de souris présentant des phénotypes contrastés en réponse à l’infection Zika. Nous avons identifié une augmentation de la réplication virale tardive dans les MEFs de la lignée CC071, résultant d’un retard à l’activation de la réponse interféron (IFN). Des analyses génétique et transcriptomique ont exclus des déficiences causées par des gènes uniques et ont favorisé l’hypothèse d’une combinatoire de gènes exerçant des effets faibles dans la voie d’induction de la réponse IFN.Pour finir, nous avons caractérisé la réponse IFN induite par le virus Zika dans des neurones primaires murins. Cette étude a montré que la capacité des neurones primaires à limiter la réplication virale est moindre que celle des MEFs en raison d’un retard à l’induction de la réponse IFN. Enfin, les facteurs génétiques de l’hôte exercent un rôle critique dans ce contexte puisque les neurones primaires de CC071 présentent un phénotype extrême par comparaison avec des lignées plus résistantes.Notre travail a mis en évidence le rôle des facteurs génétiques de l’hôte dans la pathogénie de l’infection Zika et illustre le potentiel des souris CC dans des études génétiques aussi bien qu’en tant que nouveaux modèles d’infection. Une analyse poussée des lignées aux phénotypes extrêmes permettra d’élucider les mécanismes génétiques de la sensibilité au virus Zika et améliorera notre compréhension de la maladie chez l’HommeZika virus (ZIKV) is a mosquito-transmitted flavivirus responsible for worldwide epidemics and constitutes a major public health threat. The majority of ZIKV infections in humans are either asymptomatic or result in a mild febrile illness. However, some patients develop a more severe, sometimes life-threatening, form of the disease. Recent evidence showed that ZIKV infection can trigger Guillain-Barré syndrome and encephalitis in adults, as well as congenital malformations such as microcephaly. The severity of ZIKV disease in humans depends on many factors, likely including host genetic determinants.We investigated how genome-wide variants could impact the susceptibility to ZIKV infection in mice. To this end, we used mouse strains of the Collaborative Cross (CC), a new genetic reference population encompassing a genetic diversity as broad as that of human populations.First, we described that the susceptibility of Ifnar1 (receptor to type I interferon) knockout mice is largely influenced by their genetic background. We then showed that the genetic diversity of CC mice, which IFNAR was blocked by anti-IFNAR antibody, expressed phenotypes ranging from complete resistance to severe symptoms and death with large variations in the peak and rate of decrease of plasma viral load, in brain viral load, in brain histopathology and in viral replication rate in infected cells. Differences of susceptibility between CC strains were correlated between Zika, Dengue and West Nile viruses. We identified highly susceptible and resistant CC strains as new models to investigate the mechanisms of human ZIKV disease and other flavivirus infections. Genetic analyses revealed that phenotypic variations were driven by multiple genes with small effects, reflecting the complexity of ZIKV disease susceptibility in human population. Notably, our results also ruled out a role of the Oas1b gene in the susceptibility to ZIKV.In a second part, we searched for genes which modify the susceptibility of Ifnar1 knockout mice in an F2 cross between C57BL/6J and 129S2/SvPas mice harboring the mutation. Genetic analysis revealed two Quantitative Trait Locus (QTL) controlling either the peak viremia or the mouse survival. Although these QTLs critical intervals contained hundreds of genes, data mining led us to identify a few candidate causal genes.Then, we investigated how host genetic factors influence viral replication in infected cells using Mouse Embryonic Fibroblasts (MEFs) derived from a series of CC strains with contrasted phenotypes observed in response to ZIKV infection in vivo. MEFs from CC071 strain displayed unique features of increased viral replication rate in late infection. Using transcriptomic analysis, we demonstrated that the phenotype of CC071 infected MEFs resulted from a delayed induction of the type I interferon (IFN) response. Genetic analyses ruled out single gene deficiencies but rather suggested combined effects of multiple factors in the type I IFN induction signaling pathway.Finally, we characterized the ZIKV-induced type I IFN response in MEFs and primary neurons derived from C57BL/6J mouse strain. Primary neurons were less capable than MEFs to control the viral replication due to a delayed IFN response. We later showed that host genetic factors also play a critical role in this context as ZIKV-infected CC071 primary neurons displayed an extreme phenotype compared to neurons from strains that are more resistant.Altogether, our work has unraveled the role of host genes in the pathogeny of ZIKV infection and illustrates the potential of CC mouse strains for genetic studies and as new models of infectious diseases. Extensive analysis of CC strains with extreme phenotypes help us elucidate how genetic variants affect susceptibility as well as immune responses to flaviviral infection and will provide deeper understanding of the pathophysiology of human ZIKV disease
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Luo, Yuqun. "Incorporation of Genetic Marker Information in Estimating Modelparameters for Complex Traits with Data From Large Complex Pedigrees." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1039109696.
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Dahlgren, Andreas. "Analysis of Complex Genetic Traits in Population Cohorts using High-throughput Genotyping Technology." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Universitetsbiblioteket [distributör], 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8291.
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