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Shen, Xia. "Novel Statistical Methods in Quantitative Genetics : Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation." Doctoral thesis, Uppsala universitet, Beräknings- och systembiologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-170091.
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This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models. In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL mapping in terms of precision. Double hierarchical generalized linear models, and a non-iterative simplified version, were implemented and applied to fit data of an entire genome. These whole genome models were shown to have good performance in both QTL mapping and genomic prediction. A re-analysis of a publicly available GWAS data set identified significant loci in Arabidopsis that control phenotypic variance instead of mean, which validated the idea of variance-controlling genes. The works in the thesis are accompanied by R packages available online, including a general statistical tool for fitting random effects models (hglm), an efficient generalized ridge regression for high-dimensional data (bigRR), a double-layer mixed model for genomic data analysis (iQTL), a stochastic IBD matrix calculator (MCIBD), a computational interface for QTL mapping (qtl.outbred), and a GWAS analysis tool for mapping variance-controlling loci (vGWAS).
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Silva, Heyder Diniz. "Aspectos biométricos da detecção de QTL'S ("Quantitative Trait Loci") em espécies cultivadas." Universidade de São Paulo, 2001. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-18102002-162652/.
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O mapeamento de QTL's difere dos demais tipos de pesquisas conduzida em genética. Por se tratar basicamente de um procedimento de testes múltiplos, surge, neste contexto, um problema que se refere ao nível de significância conjunto da análise, e consequentemente, seu poder. Deste modo, avaliou-se, via simulação computacional de dados, o poder de detecção de QTL's da análise de marcas simples, realizada por meio de regressão linear múltipla, utilizando o procedimento stepwise" para seleção das marcas e procedimentos baseados em testes individuais, utilizando os critérios FDR e de Bonferroni para determinação nível de significância conjunto. Os resultados mostraram que o procedimento baseado em regressão múltipla, utilizando o procedimento stepwise" foi mais poderoso em identificar as marcas associadas a QTL's e, mesmo nos casos em que este procedimento apresentou poder ligeiramente inferior aos demais, verificou-se que o mesmo tem como grande vantagem selecionar apenas as marcas mais fortemente ligadas aos QTL's. Dentre os critérios FDR e de Bonferroni, o primeiro mostrou-se, em geral, mais poderoso, devendo ser adotado nos procedimentos de mapeamento por intervalo. Outro problema encontrado na análise de QTL's refere-se µa abordagem da interação QTL's x ambientes. Neste contexto, apresentou-se uma partição da variância da interação genótipos x ambientes em efeitos explicados pelos marcadores e desvios, a partir da qual obtiveram-se os estimadores da proporção da variância genética (pm), e da variância da interação genótipos x ambientes (pms), explicadas pelos marcadores moleculares. Estes estimadores independem de desvios das frequências alélicas dos marcadores em relação µ as esperadas (1:2:1 em uma geração F2, 1:1 em um retrocruzamento, etc.), porém, apresentam uma alta probabilidade de obtenção de estimativas fora do intervalo paramétrico, principalmente para valores elevados destas proporções. Contudo, estas probabilidades podem ser reduzidas com o aumento do número de repetições e/ou de ambientes nos quais as progênies são avaliadas. A partir de um conjunto de dados de produtividade de grãos, referentes µ a avaliação de 68 progênies de milho, genotipadas para 77 marcadores moleculares codominantes e avaliadas em quatro ambientes, verificou-se que as metodologias apresentadas permitiram estimar as proporções pm e pms, bem como classificar as marcas associadas a QTL's, conforme seu nível de interação. O procedimento permitiu ainda a identificação de regiões cromossômicas envolvidas no controle genético do caractere sob estudo conforme sua maior ou menor estabilidade ao longo dos ambientes.In general terms, QTL mapping di®ers from other research ac-tivities in genetics. Being basically a multiple test procedure, problems arise which are related to the joint level of signi¯cance of the analysis, and consequently, to its power. Using computational simulation of data, the power of simple marker analysis, carried out through multiple linear regression, using stepwise procedures to select the markers was obtained. Procedures based on single tests, using both the FDR and the Bonferroni criteria to determinate the joint level of signi¯cance were also used. Results showed that the procedure based on multiple regression, using the stepwise technique, was the most powerful in identifying markers associated to QTL's. However, in cases where its power was smaller, its advantage was the ability to detect only markers strongly associates with QTL's. In comparision with the Bonferroni method, the FDR criterion was in general more powerful, and should be adopted in the interval mapping procedures. Additional problems found in the QTL analysis refer to the QTL x environment interaction. We consider this aspect by par-titioning the genotype x environment interaction variance in components explained by the molecular markers and deviations. This alowed estimating the proportion of the genetic variance (pm), and genotype x environment variance (pms), explained by the markers. These estimators are not a®ected by deviations of allelic frequencies of the markers in relation to the expected values (1:2:1 in a F2 generation, 1:1 in a backcross , etc). However, there is a high probability of obtaining estimates out of the parametric range, specially for high values of this proportion. Nevertheless, these probabilities can be reduced by increasing the number of replications and/or environments where the progenies are evaluated. Based on a set of grain yield data, obtained from the evaluation of 68 maize progenies genotyped for 77 codominant molecular markers, and evaluated as top crosses in four environments, the presented methodologies allowed estimating proportions pm and pms as well the classification of markers associated to QTL's, with respect to its level of genotype x environment interaction. The procedure also allowed the identification of chromosomic regions, involved in the genetical control of the considered trait, according to its stability, in relation to the observed environmental variation.
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Ai, Ni, and 艾妮. "A novel framework for expression quantitative trait loci mapping." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B4715214X.
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Bao, Haikun. "Bayesian hierarchical regression model to detect quantitative trait loci /." Electronic version (PDF), 2006. http://dl.uncw.edu/etd/2006/baoh/haikunbao.pdf.
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Boddhireddy, Prashanth. "Development of highly recombinant inbred populations for quantitative-trait locus mapping." Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1671.
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Pearson, Caroline. "Analysis of a hierarchial Bayesian method for quantitative trait loci /." Electronic version (PDF), 2007. http://dl.uncw.edu/etd/2007-2/pearsonc/carolinepearson.pdf.
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Baldoni, Pedro Luiz 1989. "Modelos lineares generalizados mistos multivariados para caracterização genética de doenças." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/307180.
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Orientador: Hildete Prisco PinheiroDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação
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Resumo: Os Modelos Lineares Generalizados Mistos (MLGM) são uma generalização natural dos Modelos Lineares Mistos (MLM) e dos Modelos Lineares Generalizados (MLG). A classe dos MLGM estende a suposição de normalidade dos dados permitindo o uso de várias outras distribuições bem como acomoda a superdispersão frequentemente observada e também a correlação existente entre observações em estudos longitudiais ou com medidas repetidas. Entretanto, a teoria de verossimilhança para MLGM não é imediata uma vez que a função de verossimilhança marginal não possui forma fechada e envolve integrais de alta dimensão. Para solucionar este problema, diversas metodologias foram propostas na literatura, desde técnicas clássicas como quadraturas numéricas, por exemplo, até métodos sofisticados envolvendo algoritmo EM, métodos MCMC e quase-verossimilhança penalizada. Tais metodologias possuem vantagens e desvantagens que devem ser avaliadas em cada tipo de problema. Neste trabalho, o método de quase-verossimilhança penalizada (\cite{breslow1993approximate}) foi utilizado para modelar dados de ocorrência de doença em uma população de vacas leiteiras pois demonstrou ser robusto aos problemas encontrados na teoria de verossimilhança deste conjunto de dados. Além disto, os demais métodos não se mostram calculáveis frente à complexidade dos problemas existentes em genética quantitativa. Adicionalmente, estudos de simulação são apresentados para verificar a robustez de tal metodologia. A estabilidade dos estimadores e a teoria de robustez para este problema não estão completamente desenvolvidos na literatura
Abstract: Generalized Linear Mixed Models (GLMM) are a generalization of Linear Mixed Models (LMM) and of Generalized Linear Models (GLM). The class of models GLMM extends the normality assumption of the data and allows the use of several other probability distributions, for example, accommodating the over dispersion often observed and also the correlation among observations in longitudinal or repeated measures studies. However, the likelihood theory of the GLMM class is not straightforward since its likelihood function has not closed form and involves a high order dimensional integral. In order to solve this problem, several methodologies were proposed in the literature, from classical techniques as numerical quadrature¿s, for example, up to sophisticated methods involving EM algorithm, MCMC methods and penalized quasi-likelihood. These methods have advantages and disadvantages that must be evaluated in each problem. In this work, the penalized quasi-likelihood method (\cite{breslow1993approximate}) was used to model infection data in a population of dairy cattle because demonstrated to be robust in the problems faced in the likelihood theory of this data. Moreover, the other methods do not show to be treatable faced to the complexity existing in quantitative genetics. Additionally, simulation studies are presented in order to verify the robustness of this methodology. The stability of these estimators and the robust theory of this problem are not completely studied in the literature
Mestrado
Estatistica
Mestre em Estatística
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Galal, Ushma. "The statistical theory underlying human genetic linkage analysis based on quantitative data from extended families." Thesis, University of the Western Cape, 2010. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_2684_1361989724.
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Traditionally in human genetic linkage analysis, extended families were only used in the analysis of dichotomous traits, such as Disease/No Disease. For quantitative traits, analyses initially focused on data from family trios (for example, mother, father, and child) or sib-pairs. Recently however, there have been two very important developments in genetics: It became clear that if the disease status of several generations of a family is known and their genetic information is obtained, researchers can pinpoint which pieces of genetic material are linked to the disease or trait. It also became evident that if a trait is quantitative (numerical), as blood pressure or viral loads are, rather than dichotomous, one has much more power for the same sample size. This led to the
development of statistical mixed models which could incorporate all the features of the data, including the degree of relationship between each pair of family members. This is necessary because a parent-child pair definitely shares half their genetic material, whereas a pair of cousins share, on average, only an eighth. The statistical methods involved here have however been developed by geneticists, for their specific studies, so there does not seem to be a unified and general description of the theory underlying the methods. The aim of this dissertation is to explain in a unified and statistically comprehensive manner, the theory involved in the analysis of quantitative trait genetic data from extended families. The focus is on linkage analysis: what it is and what it aims to do.
There is a step-by-step build up to it, starting with an introduction to genetic epidemiology. This includes an explanation of the relevant genetic terminology. There is also an application section where an appropriate human genetic family dataset is analysed, illustrating the methods explained in the theory sections.
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Neto, Eduardo Leonardecz. "Competição intergenotípica na análise de testes de progênie em essências florestais." Universidade de São Paulo, 2002. http://www.teses.usp.br/teses/disponiveis/11/11137/tde-30102002-160556/.
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No presente trabalho buscou-se introduzir o efeito da competição entre plantas nas análises dos testes de progênie/procedências em essências florestais, com o fim de identificar os seus efeitos e as distorções devidas à sua não observância. Para tanto, foram utilizados ensaios com níveis de precisão e mortalidades diferentes, de cinco espécies, a saber: Gallesia gorarema Vell. Moq., Eucaliptus grandis Hill ex Maider, Eucaliptus citridora Hook, Pinus elliottii Engl. var. elliottii e Araucaria angustifolia (Bert.) O. Ktze. Obtiveram-se as esperanças dos quadrados médios das fontes de variação da análise de variância nos delineamentos aqui utilizados. Com base nestas derivações, foi demonstrado explicitamente o viés nas estimativas de parâmetros genéticos quantitativos. Este viés está diretamente relacionado com a magnitude do coeficiente de regressão b e com a grandeza relativa das somas de quadrados de diferentes efeitos contidos na análise de variância da variável competição. Caso ignorado o efeito de competição, quando este influencia a variável resposta Y, os ponderadores b, que compõem o índice de seleção terão estimativas viesadas, gerando erro na seleção dos indivíduos superiores. Na análise de dados observou-se que a inclusão da competição, de maneira geral, reduziu as estimativas das componentes de variância, e por conseqüência, outras estimativas de parâmetros que são função destes, quando comparado com as estimativas feitas por via das análises sem o ajuste para a competição. A análise com a variável competição não mostrou diferenças significativas para o efeito de progênies. Isto demostra que a competição comportou-se de forma aleatória, o que corrobora para que seja colocada na análise como uma covariável; caso contrário esta teria que ser considerada uma componente da performance e introduzida numa análise multicaracterística. Utilizando as análises com e sem ajuste para a competição, para estimar os valores genéticos e o ganho com a seleção, observou-se que os indivíduos selecionados não são concordantes. Isto indica que os equívocos na seleção podem ser comuns, haja vista que o fato de se ajustar os dados faz com que o posto dos indivíduos tidos por superiores seja alterado. É recomendável considerar os efeitos da competição na análise de dados em que os indivíduos estão sujeitos a competir uns com os outros, no seu desenvolvimento.The aim of this work was to introduce competition effects in the model underlying the analysis of forest tree experiments. Results were compared with analyses in which effects were neglected. Progeny trails with different levels of precision and mortality were used, including the following species: Gallesia gorarema Vell. Moq., Eucaliptus grandis Hill ex Maider, Eucaliptus citridora Hook, Pinus elliottii Engl. var. elliottii and Araucaria angustifolia (Bert.) O. Ktze. Mathematical expectation of mean squares values were derived and the bias of estimates was explicitly shown. Competition effects were found significant in all experiments, but were primarily of random nature. Bias was shown to be directly proportional to the magnitude of the regression parameter b and to the relative magnitude of sums of squares of the competition variable. Including the variable in general lead to a reduction of estimates of variance components and to smaller expected progress from selection. The b coefficients of multi-effect selection index are also biased if competition is ignored. Results indicated that different sets of genotypes could be selected if the analyses of data were carried out with or without the competition effects. Including a competition variable in the analysis of trials in which plants are exposed to competing with each other is recommendable.
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Randall, Joshua Charles. "Large-scale genetic analysis of quantitative traits." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:addfb69d-602c-43e3-ab18-6e6d3b269076.
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Recent advances in genotyping technology coupled with an improved understanding of the architecture of linkage disequilibrium across the human genome have resulted in genome-wide association studies (GWAS) becoming a useful and widely applied tool for discovering common genetic variants associated with both quantitative traits and disease risk. After each GWAS was completed, it left behind a set of genotypes and phenotypes, often including anthropometric measures used as covariates. Genetic associations with anthropometric measures are not well characterized, perhaps due to lack of power to detect them in the sample sizes of individual studies. To improve power to detect variants associated with complex phenotypes such as anthropometric traits, data from multiple GWAS can be combined. This thesis describes the methods and results of several such analyses performed as part of the Genome-wide Investigation of ANThropemtric measures (GIANT) consortium, and compares various different methods that can be used to perform combined analyses of GWAS. In particular, the comparisons focus on comparing differences between meta-analysis methods, in which only summary statistics that result from within-study association testing are shared between studies, and mega-analysis methods in which individual-level genotype and phenotype data is analysed together. Finally, a brief discussion of technological means that have the potential to help overcome some of the challenges associated with performing mega-analyses is offered in order to suggest future work that could be undertaken in this area.
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Gale, Joanne. "Statistical Methods for the Analysis of Quantitative Trait Data in Genetic Association Studies." Thesis, University of Oxford, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.504345.
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Miyake, Takeshi. "STATISTICAL GENETIC EVALUATION OF ANIMALS FOR QUANTITATIVE TRAITS UNDER POLYGENIC MODEL AND MIXED INHERITANCE MODEL." Kyoto University, 1999. http://hdl.handle.net/2433/181392.
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Joehanes, Roby. "Multiple-trait multiple-interval mapping of quantitative-trait loci." Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1605.
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Baker, Peter John. "Applied Bayesian modelling in genetics." Thesis, Queensland University of Technology, 2001.
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Wheeler, Eleanor. "Genetic statistical methods for analysis of immune-response quantitative trait data with application to the Belém family study." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612848.
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George, Andrew Winston. "A Bayesian analysis for the mapping of a quantitative trait locus given half-sib data." Thesis, Queensland University of Technology, 1998.
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Joehanes, Roby. "Generalized and multiple-trait extensions to Quantitative-Trait Locus mapping." Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1919.
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Machado, Fábio de Andrade. "Implicações evolutivas da integração morfológica do crânio em Caniformia (Carnivora; Mammalia)." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/41/41131/tde-24042017-160838/.
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O fenótipo de caracteres complexos é o produto final da inter-relação entre genes, vias ontogenéticas e efeitos ambientais. A variação desses fatores não apenas influencia o fenótipo final, mas também como caracteres covariam e evoluem de forma integrada. A seleção natural pode influenciar a integração entre caracteres, levando a mudança de padrões de correlação ao longo do tempo. Assim, uma visão integrativa e dinâmica de fenótipos complexos é essencial para a compreensão da história evolutiva destas estruturas. Na tese atual investiguei a integração morfológica de caracteres cranianos em Caniformes sob duas perspectivas. Em uma primeira abordagem investiguei o padrão de integração morfológica das espécies a partir da comparação das variâncias e covariâncias dos caracteres. Os resultados evidenciam dois principais pontos. O primeiro é que houve uma considerável estabilidade na covariância entre caracteres ao longo de toda a história evolutiva de Carnivora, sugerindo a manutenção dos padrões de desenvolvimento no grupo como um todo. O segundo ponto é que, apesar desta estabilidade, espécies da família Canidae apresentam modificações em sua integração morfológica que os tornam mais similares entre si e mais dissimilares com os demais Carnivora. Essas diferenças estão relacionadas principalmente com caracteres da região facial, que apresentaram maior flexibilidade evolutiva, maiores correlações entre caracteres, e contêm uma maior proporção da variância em Canidae que nos demais Carnivora. Em uma segunda abordagem investiguei as propriedades estatísticas de dois testes baseados na teoria de genética quantitativa: o teste de regressão de autovalores e o teste de correlação de componentes principais (PCs). Estes testes avaliam a proporcionalidade entre padrões de covariância genética e entre-espécies como forma de testar a hipótese nula de deriva genética. Os resultados mostram que o uso de contrastes filogenéticos independentes (PIC) reduz erros do tipo I inflados, principalmente no caso do teste de correlação. Quando PIC são utilizados, o teste de correlação apresenta taxas de erro tipo I nominais para todos os números de espécies. Entretanto, a flutuação do número efetivo populacional (Ne) infla o erro tipo I deste testes. O teste de regressão, apesar de apresentar erro do tipo I inadequado para número de espécies baixo, é robusto a flutuações de Ne. A redução do número de PCs reduz o erro do tipo I a valores nominais à custa de uma redução no poder do teste. O poder de ambos os testes é similar nos diversos cenários avaliados, com uma leve tendência de maior poder para o teste de correlação em números amostrais mais baixos. Adicionalmente, as famílias de Caniformes foram utilizadas como estudo de caso para ambos testes. Os testes foram realizados com métodos paramétricos e não-paramétricos (simulações) e com e sem PIC. Houve rejeição de deriva para quase todas as famílias, com exceção de Mephitidae e Ursidae. Os testes de regressão baseados em simulações se mostraram consistentes com e sem o uso de PIC, apresentando intervalos de confiança menores que os testes paramétricos. Os resultados da presente tese abrem diversas possibilidades de investigação futura, tanto do ponto de vista empírico (em relação a modificações de Canidae e dos processos evolutivos deste grupo e de Ursidae e Mephitidae), assim como metodológicos (aprofundamento das investigações sobre as propriedades dos métodos para investigações macroevolutivas baseados em genética quantitativa)The phenotype of complex characters is the end-product of the interrelations between genes, ontogenetic pathways and environmental effects. The variation in these factors influences not only the final phenotype, but also how characters covary and evolve in an integrated way. Natural selection can influence the interaction among characters, leading to changes in the patterns of integration. Therefore, a integrative and dynamic view of complex phenotypes is essential to the understanding of the evolutionary history of such structures. In the present thesis I investigated the morphological integration of cranial characters in Caniform species in two perspectives. In the first approach I investigated the pattern of morphological integration of the species through the comparison of character variances and covariances. The results of this investigation highlighted two points. The first is that there is considerable stability in the covariance among characters along the evolutionary history of Carnivora, suggesting the maintenance of ontogenetic pathways in the group. The second is that, despite this stability, Canidae species show changes in their morphological integration that make them more similar among each other and more different from the rest of Carnivora. These changes are related mainly to characters from the facial region, which showed a greater evolutionary flexibility, greater correlation among characters, and concentrate a greater proportion of the variance in Canidae than in the rest of Carnivora. In a second approach I evaluated the statistical properties of tests based on quantitative genetics theory: the test of regression of eigenvalues and the test of correlation of principal components (PCs). These tests investigate the proportionality between patterns of genetic and between-species covariance as a way to test the null hypothesis of genetic drift. The results show that the use of phylogenetic independent contrasts (PIC) reduces the inflated type I error, especially in the case of the correlation test. When PIC are employed, the correlation test shows nominal type I error rates for all species sample sizes. However, the oscillation of the effective population size (Ne) inflates type I error rates of these tests. The regression test, despite showing inadequate type I error rates at small species sample sizes, is robust to the oscillation of Ne. The reduction of the number of PCs reduces type I error rates to nominal values at the expense of statistical power. The power of both tests is similar under different scenarios evaluated, with a slight tendency of the correlation test to perform better at small number of species. Additionally, the Caniform families were used as case studies for both tests. Tests were performed using parametric and non-parametric (simulations) techniques, with and without PIC. The drift hypothesis was rejected for almost all families, with the exception of Mephitidae and Ursidae. The regression tests based on simulations were consistent with and without the use of PIC, showing narrower confidence intervals than the ones for parametric tests. The results of the present thesis open a wide range of future investigation opportunities, both from the empirical (relative to the differences in Canidae patterns of morphological integration or the evolutionary processes underlying Ursidae and Mephitidae diversification) and methodological (further investigations of the properties of the quantitative genetics-based tests for macroevolution) points of view
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Pook, Torsten [Verfasser], Henner [Akademischer Betreuer] Simianer, Henner [Gutachter] Simianer, Timothy Mathes [Gutachter] Beissinger, and Hans-Peter [Gutachter] Piepho. "Methods and software to enhance statistical analysis in large scale problems in breeding and quantitative genetics / Torsten Pook ; Gutachter: Henner Simianer, Timothy Mathes Beissinger, Hans-Peter Piepho ; Betreuer: Henner Simianer." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2019. http://d-nb.info/1199608254/34.
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Villemereuil, Pierre de. "Méthodes pour l’étude de l’adaptation locale et application au contexte de l’adaptation aux conditions d’altitude chez la plante alpine Arabis alpina." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAS003/document.
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L'adaptation locale est un phénomène micro-évolutif qui peut survenir lorsque des populations d'une même espèce sont exposées à des conditions environnementales différentes.Si cet environnement exerce une pression sous forme de sélection naturelle, qu'il existe un potentiel adaptatif au sein des populations et que le flux de gènes est suffisamment modéré, les populations vont alors tendre vers un optimum adaptatif local.Dans cette thèse, je m'intéresse aux moyens méthodologiques de l'étude de l'adaptation locale d'une part, et à l'étude de ce phénomène le long d'un gradient d'altitude chez la plante alpine Arabis alpina d'autre part.Dans la première partie méthodologique, je montre que les méthodes de scan génomique pour détecter les marqueurs génétiques sous sélection peuvent souffrir de forts taux de faux positifs lorsqu'exposées à des jeux de données complexes, mais réalistes.Je présente ensuite une méthode statistique de détection de marqueurs génétiques sous sélection qui, contrairement aux méthodes existantes, utilisent à la fois la notion de différentiation génétique (ou Fst) et une information environnementale.Cette méthode a été développée de manière à limiter son taux de faux positifs de manière générale.J'offre enfin une perspective concernant les liens entre une expérience ancienne en biologie évolutive (l'expérience de jardin commun) et les nouveaux développements moléculaires et statistiques modernes.Dans la seconde partie empirique, je présente une analyse de la démographie d'A. alpina dans six populations naturelles. Outre qu'elle révèle des caractéristiques biologiques intéressantes sur cette espèce (faible espérance de vie, reproduction et survie très différentielle...), cette analyse montre que la croissance diminue et la survie augmente chez cette espèce avec la baisse de la température moyenne (donc avec l'altitude).Puisque ces analyses ne permettent pas d'exclure des hypothèses de dérive et de plasticité phénotype, je présente une analyse en jardin commun sur A. alpina qui permet de lisser les problèmes de plasticité phénotypique et qui, combinée à des analyses moléculaires, permettent d'exclure l'hypothèse de dérive.Les résultats montrent qu'il existe un syndrome phénotypique adaptatif lié à la température moyenne qui tend à des plantes plus petites, plus compactes, qui croissent et se reproduisent moins, dans les milieux froids.À l'aide des données moléculaires et de méthodes de scan génomique, je présente une liste de 40 locus qui peuvent être impliqués dans ce processus.Pour finir, je discute l'ensemble de ces résultats empiriques dans un contexte plus général d'écologie alpine. Je résume ensuite les principaux obstacles méthodologiques à l'étude de l'adaptation locale et je fourni quelques perspectives méthodologiquesLocal adaptation is a micro-evolutionary phenomenon, which arises when populations of the same species are exposed to contrasted environmental conditions.If this environment exert some natural selection pressure, if an adaptive potential exists among the populations and if the gene flow is sufficiently mild, populations are expected to tend toward a local adaptive optimum.In this thesis, I study the methodological means of the study of local adaptation on the one hand, and I investigate this phenomenon along an elevation gradient in the alpine plant Arabis alpina on the other hand.In the first, methodological part, I show that the genome scan methods to detect selection using genetic markers might suffer strong false positive rates when confronted to complex but realistic datasets.I then introduce a statistical method to detect markers under selection, which, contrary to existing methods, make use of both the concept of genetic differentiation (or Fst) and environmental information.This method has been developed in order to reduce its global false positive rate.Finally, I present some perspectives regarding the relationships between the relatively old ``common garden'' experiment and the new developments in molecular biology and statistics.In the second, empirical part, I introduce an analysis of the demographic characteristics of A. alpina in six natural populations. Besides providing interesting biological information on this species (low life expectancy, strongly contrasted reproduction and survival...), these analyses show that growth increase and survival decrease with the decrease of average temperature (hence with altitude).Since these analyses do not allow us to rule out hypotheses such as drift and phenotypic plasticity, I show the results of a common garden experiment which enable us to smooth phenotypic plasticity and, when combined with molecular data, enable us to rule out the hypothesis of drift.The results show the existence of an adaptive phenotypic syndrome, in which plants are smaller, are more compact, grow slower and reproduce less in cold temperature environments.Using the molecular data, I draw a list of 40 locus which might be involved in this adaptive process.In the end, I discuss these empirical findings as a whole to place them in a more general context of alpine ecology. I sum up the main methodological challenges when studying local adaptation and offer some methodological perspectives
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Chavinskaia, Lidia. "La vache globale : la génétique quantitative dans la globalisation de la sélection bovine." Thesis, Paris Est, 2020. http://www.theses.fr/2020PESC2028.
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Cette thèse interdisciplinaire traite la question de la globalisation au travers des organismes vivants impliqués dans les organisations sociotechniques industrielles. La vache, animal qui évolue aux côtés des humains depuis le Néolithique, est porteuse de valeurs et d’enjeux économiques, politiques, culturels et technologiques constitutifs de notre société. La race Holstein, incarnation de l’animal ‘moderne’ par excellence, tel un mix de nature, culture et technoscience, est devenue symbolique de la globalisation dans le monde animal. Présente dans 130 pays et adoptée par de nombreux imaginaires culturels, elle pose néanmoins question des limites biologiques de la globalisation industrielle et marchande. En suivant les acteurs de la sélection bovine au niveau international, la thèse porte un double regard sur cette question depuis le champ des STS (Science and Technology Studies) d’une part et depuis la discipline de la génétique animale d’autre part. Représentés par les scientifiques généticiens, les gènes bovins apparaissent pleinement comme acteurs des processus globalisants inhérents à notre ‘modernité’ technoscientifique. Leurs circulations (ré)agencent des liens invisibles mais non moins constructifs du monde global. Les interactions entre les gènes et leur environnement produisent des effets qui vont bien au-delà des limites physiques du corps animal et façonnent le paysage mondial de la sélection et des productions animales. Les tensions qui s’accentuent entre les acteurs scientifiques et marchands, entre les théories et les pratiques, entre les pays dits développés et ceux en développement appellent à porter un regard différent sur les activités industrielles impliquant les organismes vivants. Ce regard holistique est porté ici au travers de l’unité entre l’animal et son milieu qui dépasse le système de production stricto sensu. La notion d’interaction génotype-milieu issue de la génétique, amenée à l’existence par ses méthodes statistiques et mise en politique dans la globalisation de la sélection bovine permet de rendre compte du lien intrinsèque et complexe entre la vie biologique et la vie socialeThis interdisciplinary thesis questions the issue of globalization through living organisms involved in industrial sociotechnical organizations. The cow, an animal that has evolved alongside humans since Neolithic times, embodies numerous economic, political, cultural and technological issues. The Holstein breed, the incarnation of 'modern' animal as a mix of nature, culture and technoscience, has become symbolic of globalization in the animal world. Present in 130 countries and adopted by many cultural imaginaries, it nevertheless challenges the biological limits of industrial and commercial globalization. By following the actors of cattle breeding at the international level, the thesis brings an original perspective on this question from both STS (Science and Technology Studies) and the discipline of animal genetics. Represented by geneticists, bovine genes fully appear as actors of the globalizing processes of our techno-scientific 'modernity'. Their circulations (re)arrange invisible but constructive links of the global world. The interactions between genes and their environment produce effects that go far beyond the physical boundaries of the animal body and shape the global landscape of animal breeding and production. The growing tensions between scientific and commercial actors, between theories and practices, and between developed and developing countries call for a different view of industrial activities involving living organisms. A holistic approach is adopted here through the unity between the animal and its environment beyond the production system narrowly defined. The notion of 'genotype by environment interaction' issued from statistical genetics and put into politics within the globalization of bovine breeding helps to understand the intrinsic and complex link between biological and social life
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Thompson, Katherine L. "Using ancestral information to search for quantitative trait loci in genome-wide association studies." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1372410951.
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Mignogna, Kristin. "Genome-Wide Systems Genetics of Alcohol Consumption and Dependence." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5946.
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Widely effective treatment for alcohol use disorder is not yet available, because the exact biological mechanisms that underlie this disorder are not completely understood. One way to gain a better understanding of these mechanisms is to examine the genetic frameworks that contribute to the risk for developing this disorder. This dissertation examines genetic association data in combination with gene expression networks in the brain to identify functional groups of genes associated with alcohol consumption and dependence.
The first study took advantage of the behavioral complexity of human samples, and experimental capabilities provided by mouse models, by co-analyzing gene expression networks in the mesolimbocortical system of acute alcohol-treated mice and human genetic alcohol dependence association data. This study successfully identified ethanol-responsive gene expression networks with overrepresentation of genes suggestively associated with alcohol dependence in an independent human sample, indicating that gene expression networks in mouse models are informative for identifying mechanistic networks relevant to the risk for developing dependence.
The second study aimed to identify quantitative trait loci for voluntary alcohol drinking behaviors under an intermittent ethanol access paradigm, in the genetically complex Diversity Outbred mice. After determining high heritability for alcohol consumption and dependence amongst the progenitor strains, we identified several specific genetic loci associated with these traits. One locus replicated results from a human association study of alcohol consumption, and provided insight to the potentially contributing genes. Finally, we identified alcohol consumption-correlated gene expression networks in the prefrontal cortex of these mice. We also mapped quantitative trait loci for network expression levels, some of which overlapped with the behavioral loci, indicating that the functions represented by these modules mediate the relationship between the genotypes in that region and drinking behaviors. Overall, our studies revealed neuroplastic and ubiquitin-related genes pathways involved in alcohol consumption in mice and humans, and that likely contribute to the risk for developing dependence.
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Lien, Tonje Gulbrandsen. "Statistical Analysis of Quantitative PCR Data." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13094.
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This thesis seeks to develop a better understanding of the analysis of gene expression to find the amount of transcript in a sample. The mainstream method used is called Polymerase Chain Reaction (PCR) and it exploits the DNA's ability to replicate. The comparative CT method estimate the starting fluorescence level f0 by assuming constant amplification in each PCR cycle, and it uses the fluorescence level which has risen above a certain threshold. We present a generalization of this method, where different threshold values can be used. The main aim of this thesis is to evaluate a new method called the Enzymological method. It estimates f0 by considering a cycle dependent amplification and uses a larger part of the fluorescence curves, than the two CT methods. All methods are tested on dilution series, where the dilution factors are known. In one of the datasets studied, the Clusterin dilution-dataset, we get better estimates from the Enzymological method compared to the two CT methods.
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Kafatos, George. "Statistical analysis of quantitative seroepidemiological data." Thesis, Open University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.539408.
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Bruen, Trevor Cormac Vincent. "Discrete and statistical approaches to genetics." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102964.
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This thesis presents a number of major innovations in related but different areas of research. The contributions range along a continuum from mathematical phylogenetics, to development of statistical methodology for detecting recombination and finally to the application of statistical techniques to understand Feline Immunodeficiency Virus (FIV) an important pathogen. An underlying theme is the application of combinatorial and statistical ideas to problems in evolutionary biology and genetics.Chapter 2 and Chapter 3 give a number of results relevant to mathematical phylogenetics, in particular maximum parsimony. Chapter 2 presents a new formulation of maximum parsimony in terms of character subdivision, providing a direct link with the character compatibility problem, also known as the perfect phylogeny problem. Specialization of this result to two characters gives a simple formula based on the intersection graph for calculating the parsimony score for a, pair of characters. Chapter 3 further explores maximum parsimony. In particular, it is shown that a maximum parsimony tree for a sequence of characters minimizes a subtree-prune and regraft (SPR) distance to the sets of trees on which each character is convex. Similar connections are also drawn between the Robinson-Foulds distance and a new variant of Dollo parsimony.
Chapter 4 presents an application of the work in Chapters 2 and 3 to develop a statistical test for detecting recombination. An extensive coalescent based simulation study shows that this new test is both robust and powerful in a variety of different circumstances compared to a number of current methods. In fact, a simple model of mutation rate correlation is shown to mislead a number of competing tests, causing recombination to be falsely inferred. Analysis of empirical data sets confirm that the new test is one of the best approaches to distinguish recurrent mutation from recombination.
Finally, Chapter 5 uses the test developed in Chapter 4 to localize recombinant breakpoints in 14 genomic strains of FIV taken from a wild population of cougars. Based on the technique, three recombinant strains of FIV are identified. Previous studies have focused on the epidemiology and population structure of the virus and this study shows that recombination has also played an important role in the evolution of FIV.
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Baillie, John Kenneth. "Statistical genetics in infectious disease susceptibility." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/17620.
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Death from infectious disease is common heritable, and in many cases a consequence of the host response, rather than direct effects of the pathogen. Since the host response in sepsis is orchestrated by the transmission of a variety of signals, both intra-cellular and inter-cellular, with which we have at least some capacity to intervene, it follows that it should be possible to prevent death through pharmaceutical modulation of inflammatory cascades. So far, it is not. The best candidate therapy for sepsis, activated protein C, failed to live up to initial promise and was ultimately withdrawn from the market in dismal failure. The premise of the work presented here is that a different approach – to develop an understanding of the host response at a genomic level – may yield more tractable insights, specifically into the problem of host susceptibility to influenza, a heritable cause of death in otherwise healthy people and a significant global threat. Since the sequencing of the human genome, it has become possible to identify genomic loci underlying host susceptibility to disease using genome-wide association studies (GWAS), best exemplified by the Wellcome Trust Case Control Consortium. This new technology creates substantial new challenges. The genetic markers associated with a phenotype are rarely causative, frequently in poorly-understood intergenic regions, and tend to have small effect sizes, such that tens or even hundreds of thousands of subjects must be recruited to have sufficient power to detect them. It is therefore not straightforward to translate these genotype-phenotype associations into useful understanding of the role of genes and gene products in disease pathogenesis. Attempts to overcome these challenges in order to discover genomic loci underlying individual susceptibility to infection form the core of this thesis. Ultimately these efforts converge with the development of a new computational method to detect phenotype-associated loci from genome-wide association studies (GWAS) using co-expression at regulatory regions of the genome.
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Oldmeadow, Christopher. "Latent variable models in statistical genetics." Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/31995/1/Christopher_Oldmeadow_Thesis.pdf.
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Understanding the complexities that are involved in the genetics of multifactorial diseases is still a monumental task. In addition to environmental factors that can influence the risk of disease, there is also a number of other complicating factors. Genetic variants associated with age of disease onset may be different from those variants associated with overall risk of disease, and variants may be located in positions that are not consistent with the traditional protein coding genetic paradigm. Latent Variable Models are well suited for the analysis of genetic data. A latent variable is one that we do not directly observe, but which is believed to exist or is included for computational or analytic convenience in a model. This thesis presents a mixture of methodological developments utilising latent variables, and results from case studies in genetic epidemiology and comparative genomics. Epidemiological studies have identified a number of environmental risk factors for appendicitis, but the disease aetiology of this oft thought useless vestige remains largely a mystery. The effects of smoking on other gastrointestinal disorders are well documented, and in light of this, the thesis investigates the association between smoking and appendicitis through the use of latent variables. By utilising data from a large Australian twin study questionnaire as both cohort and case-control, evidence is found for the association between tobacco smoking and appendicitis. Twin and family studies have also found evidence for the role of heredity in the risk of appendicitis. Results from previous studies are extended here to estimate the heritability of age-at-onset and account for the eect of smoking. This thesis presents a novel approach for performing a genome-wide variance components linkage analysis on transformed residuals from a Cox regression. This method finds evidence for a dierent subset of genes responsible for variation in age at onset than those associated with overall risk of appendicitis. Motivated by increasing evidence of functional activity in regions of the genome once thought of as evolutionary graveyards, this thesis develops a generalisation to the Bayesian multiple changepoint model on aligned DNA sequences for more than two species. This sensitive technique is applied to evaluating the distributions of evolutionary rates, with the finding that they are much more complex than previously apparent. We show strong evidence for at least 9 well-resolved evolutionary rate classes in an alignment of four Drosophila species and at least 7 classes in an alignment of four mammals, including human. A pattern of enrichment and depletion of genic regions in the profiled segments suggests they are functionally significant, and most likely consist of various functional classes. Furthermore, a method of incorporating alignment characteristics representative of function such as GC content and type of mutation into the segmentation model is developed within this thesis. Evidence of fine-structured segmental variation is presented.
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Evans, Joshua. "Statistical image reconstruction for quantitative computed tomography." VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/2557.
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Statistical iterative reconstruction (SIR) algorithms for x-ray computed tomography (CT) have the potential to reconstruct images with less noise and systematic error than the conventional filtered backprojection (FBP) algorithm. More accurate reconstruction algorithms are important for reducing imaging dose and for a wide range of quantitative CT applications. The work presented herein investigates some potential advantages of one such statistically motivated algorithm called Alternating Minimization (AM). A simulation study is used to compare the tradeoff between noise and resolution in images reconstructed with the AM and FBP algorithms. The AM algorithm is employed with an edge-preserving penalty function, which is shown to result in images with contrast-dependent resolution. The AM algorithm always reconstructed images with less image noise than the FBP algorithm. Compared to previous studies in the literature, this is the first work to clearly illustrate that the reported noise advantage when using edge-preserving penalty functions can be highly dependent on the contrast of the object used for quantifying resolution. A polyenergetic version of the AM algorithm, which incorporates knowledge of the scanner’s x-ray spectrum, is then commissioned from data acquired on a commercially available CT scanner. Homogeneous cylinders are used to assess the absolute accuracy of the polyenergetic AM algorithm and to compare systematic errors to conventional FBP reconstruction. Methods to estimate the x-ray spectrum, model the bowtie filter and measure scattered radiation are outlined which support AM reconstruction to within 0.5% of the expected ground truth. The polyenergetic AM algorithm reconstructs the cylinders with less systematic error than FBP, in terms of better image uniformity and less object-size dependence. Finally, the accuracy of a post-processing dual-energy CT (pDECT) method to non-invasively measure a material’s photon cross-section information is investigated. Data is acquired on a commercial scanner for materials of known composition. Since the pDECT method has been shown to be highly sensitive to reconstructed image errors, both FBP and polyenergetic AM reconstruction are employed. Linear attenuation coefficients are estimated with residual errors of around 1% for energies of 30 keV to 1 MeV with errors rising to 3%-6% at lower energies down to 10 keV. In the ideal phantom geometry used here, the main advantage of AM reconstruction is less random cross-section uncertainty due to the improved noise performance.
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Casale, Francesco Paolo. "Multivariate linear mixed models for statistical genetics." Thesis, University of Cambridge, 2016. https://www.repository.cam.ac.uk/handle/1810/267465.
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In the last decade, genome-wide association studies have helped to advance our understanding of the genetic architecture of many important traits, including diseases. However, the statistical analysis of genotype-phenotype associations remains challenging due to multiple factors. First, many traits have polygenic architectures, which means that they are controlled by a large number of variants with small individual effects. Second, as increasingly deep phenotype data are being generated there is a need for multivariate analysis approaches to leverage multiple related phenotypes while retaining computational efficiency. Additionally, genetic analyses are confronted by strong confounding factors that can create spurious associations when not properly accounted for in the statistical model. We here derive more flexible methods that allow integrating genetic effects across variants and multiple quantitative traits. To do so, we build on the classical linear mixed model (LMM), a widely adopted framework for genetic studies. The first contribution of this thesis is mtSet, an efficient mixed-model approach that enables genome-wide association testing between sets of genetic variants and multiple traits while accounting for confounding factors. In both simulations and real-data applications we demonstrate that mtSet effectively combines the advantages of variant-set and multi-trait analyses. Next, we present a new model for gene-context interactions that builds on mtSet. The proposed interaction set test (iSet) yields increased statistical power for detecting polygenic interactions. Additionally, iSet enables the identification of genetic loci that are associated with different configurations of causal variants across contexts. After benchmarking the proposed method using simulated data, we consider two applications to real datasets, where we investigate genetic effects on gene expression across different cellular contexts and sex-specific genetic effects on lipid levels. Finally, we describe LIMIX, a software framework for the flexible implementation of different LMMs. Most of the models considered in this thesis, including mtSet and iSet, are implemented and available in LIMIX. A unique aspect of the software is an inference framework that allows a large class of genetic models to be defined and, in many cases, to be efficiently fitted by exploiting specific algebraic properties. We demonstrate the utility of this software suite in two applied collaboration projects. Taken together, this thesis demonstrates the value of flexible and integrative modelling in genetics and contributes new statistical methods for genetic analysis. These approaches generalise previous models, yet retain the computational efficiency that is needed to tackle large genetic datasets.
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Csilléry, Katalin. "Statistical inference in population genetics using microsatellites." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3865.
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Statistical inference from molecular population genetic data is currently a very active area of research for two main reasons. First, in the past two decades an enormous amount of molecular genetic data have been produced and the amount of data is expected to grow even more in the future. Second, drawing inferences about complex population genetics problems, for example understanding the demographic and genetic factors that shaped modern populations, poses a serious statistical challenge. Amongst the many different kinds of genetic data that have appeared in the past two decades, the highly polymorphic microsatellites have played an important role. Microsatellites revolutionized the population genetics of natural populations, and were the initial tool for linkage mapping in humans and other model organisms. Despite their important role, and extensive use, the evolutionary dynamics of microsatellites are still not fully understood, and their statistical methods are often underdeveloped and do not adequately model microsatellite evolution. In this thesis, I address some aspects of this problem by assessing the performance of existing statistical tools, and developing some new ones. My work encompasses a range of statistical methods from simple hypothesis testing to more recent, complex computational statistical tools. This thesis consists of four main topics. First, I review the statistical methods that have been developed for microsatellites in population genetics applications. I review the different models of the microsatellite mutation process, and ask which models are the most supported by data, and how models were incorporated into statistical methods. I also present estimates of mutation parameters for several species based on published data. Second, I evaluate the performance of estimators of genetic relatedness using real data from five vertebrate populations. I demonstrate that the overall performance of marker-based pairwise relatedness estimators mainly depends on the population relatedness composition and may only be improved by the marker data quality within the limits of the population relatedness composition. Third, I investigate the different null hypotheses that may be used to test for independence between loci. Using simulations I show that testing for statistical independence (i.e. zero linkage disequilibrium, LD) is difficult to interpret in most cases, and instead a null hypothesis should be tested, which accounts for the “background LD” due to finite population size. I investigate the utility of a novel approximate testing procedure to circumvent this problem, and illustrate its use on a real data set from red deer. Fourth, I explore the utility of Approximate Bayesian Computation, inference based on summary statistics, to estimate demographic parameters from admixed populations. Assuming a simple demographic model, I show that the choice of summary statistics greatly influences the quality of the estimation, and that different parameters are better estimated with different summary statistics. Most importantly, I show how the estimation of most admixture parameters can be considerably improved via the use of linkage disequilibrium statistics from microsatellite data.
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Sperrin, Matthew. "Statistical methodology motivated by problems in genetics." Thesis, Lancaster University, 2010. http://eprints.lancs.ac.uk/49088/.
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Sequencing the human genome has made vast amounts of potentially useful genetic data accessible. An important challenge in statistics is to develop methodology to extract information from this data. In this thesis, developments are made in two methodological areas that have wide applications in genetics. First, probabilistic methods to deal with the label switching problem in Bayesian mixture models are introduced. Mixture models are used in situations where populations may consist of a number of sub-populations, or as a semi-parametric modelling tool. The label switching problem can prevent meaningful interpretation of the output of Markov Chain Monte Carlo samplers. Specifically, inference on attributes specific to sub-populations can be difficult. Such attributes play an important role in understanding genetic effects. We introduce probabilistic relabelling strategies as a natural way of overcoming the label switching problem, and compare with existing strategies. The comparisons demonstrate that the advantages oered by probabilistic strategies come without loss in parameter estimation ability. Second, we introduce direct eect testing (DET), which is a novel method that distinguishes direct from indirect eects between binary predictors and a binary response. DET consists of two stages: the rst stage nds eects, the second stage infers the uncertainty in determining which predictors cause which eects. The method is useful when it is of interest to recover direct eects between a large number of predictors and the response. This is a common goal in genetics, where we are interested in the eects of variations in the genome on the prevalence of a phenotype. This work includes detailed simulations, comparing the ability of a number of methods at recovering direct eects. DET outperforms existing methods at recovering direct eects in situations where there is high correlation between predictors, and matches their performance when the correlation is moderate or small.
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Lange, Christoph. "Generalized estimating equation methods in statistical genetics." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269921.
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Santure, Anna Wensley, and n/a. "Quantitative genetic models for genomic imprinting." University of Otago. Department of Zoology, 2006. http://adt.otago.ac.nz./public/adt-NZDU20060811.134008.
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A gene is imprinted when its expression is dependent on the sex of the parent from which it was inherited. An increasing number of studies are suggesting that imprinted genes have a major influence on medically, agriculturally and evolutionarily important traits, such as disease severity and livestock production traits. While some genes have a large effect on the traits of an individual, quantitative characters such as height are influenced by many genes and by the environment, including maternal effects. The interaction between these genes and the environment produces variation in the characteristics of individuals. Many quantitative characters are likely to be influenced by a small number of imprinted genes, but at present there is no general theoretical model of the quantitative genetics of imprinting incorporating multiple loci, environmental effects and maternal effects. This research develops models for the quantitative genetics of imprinting incorporating these effects, including deriving expressions for genetic variation and resemblances between relatives. Imprinting introduces both parent-of-origin and generation dependent differences in the derivation of standard quantitative genetic models that are generally equivalent under Mendelian expression. Further, factors such as epistasis, maternal effects and interactions between genotype and environment may mask the effect of imprinting in a quantitative trait. Maternal effects may also mimic a number of signatures in variance and covariance components that are expected in a population with genomic imprinting. This research allows a more comprehensive understanding of the processes influencing an individual�s characteristics.
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Gooley, Theodore Alan. "Quantitative comparisons of statistical methods in image reconstruction." Diss., The University of Arizona, 1990. http://hdl.handle.net/10150/185251.
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Statistical methods for approaching image reconstruction and restoration problems have generated much interest among statisticians in the past decade. In this dissertation, we examine in detail various statistical methods of image reconstruction through the simulation of a multiple-pinhole coded-aperture imaging system for use in emission tomography. We reconstruct each object from a class of 64 total objects, obtaining a reconstruction for each of the 64 originals by several different methods. Among the methods that we use to obtain these reconstructions are maximum likelihood techniques, where we make use of both the popular expectation-maximization (EM) algorithm and a Monte Carlo search routine. We also examine methods that include, in some form, various kinds of prior information. One way of using prior information is through the specification of a prior probability density on the object (or class of objects) to be reconstructed. We investigate the use of Markov random field (MRF) models as a means of specifying the prior densities that will be used to obtain reconstructions. Once given a prior density, these reconstructions are taken to be approximations to the maximum a posteriori (MAP) estimate of the original object. We also investigate reconstructions obtained through other prior densities plus reconstructions obtained by introducing prior information in alternate ways. Once all the reconstructions are obtained, we attempt to answer the important question, "which reconstruction method is 'best'?" We define "best" in this context to be the method that allows a human observer to perform a specified task the most accurately. The task to be performed is to determine whether or not a small protrusion exists on an elliptical object. (This task is motivated by the desire to detect wall-motion abnormalities in the left ventricle of the heart.) We generate 32 objects with protrusions (abnormal objects) and 32 objects without protrusions (normal objects). These objects constitute our class of 64 originals which are reconstructed by the various methods. The reconstruction methods are then analyzed through receiver operating characteristic (ROC) analysis, and a performance index, the area under the curve (AUC), is obtained for each method. Statistical tests are then performed on certain pairs of methods so that the hypothesis that no difference between the AUC's exists can be tested. We found that the reconstruction methods that used the largest amount of (accurate) prior information were generally superior to other methods considered. We also compute calculable figures of merit (FOM) associated with each reconstruction method with the hope that these FOM's will predict the performance of the human observer. Unfortunately, our results indicate that the FOM's that we considered do not correlate well with the performance of the human.
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Remsing, Razvan Alexandru. "Portfolio optimisation with quantitative and qualitative views." Master's thesis, University of Cape Town, 2005. http://hdl.handle.net/11427/4356.
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Includes bibliographical references.Portfolio construction with quantitative and qualitative forecasts is described through the exposition of two asset allocation models. The two models arc the Black-Litterman Asset Allocation moodel and the Qualitative Forecasts : Model developed by Herold Ulf. The models are developed theoretically and made intuitively accessible with real market data examples. Methodology is developed using the two models to transport alpha across benchmarks.
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Keightley, Peter D. "Studies of quantitative genetic variation." Thesis, University of Edinburgh, 1988. http://hdl.handle.net/1842/12340.
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Cerqueira, Pedro Henrique Ramos. "Structural equation models applied to quantitative genetics." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-05112015-145419/.
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Causal models have been used in different areas of knowledge in order to comprehend the causal associations between variables. Over the past decades, the amount of studies using these models have been growing a lot, especially those related to biological systems where studying and learning causal relationships among traits are essential for predicting the consequences of interventions in such system. Graph analysis (GA) and structural equation modeling (SEM) are tools used to explore such associations. While GA allows searching causal structures that express qualitatively how variables are causally connected, fitting SEM with a known causal structure allows to infer the magnitude of causal effects. Also SEM can be viewed as multiple regression models in which response variables can be explanatory variables for others. In quantitative genetics studies, SEM aimed to study the direct and indirect genetic effects associated to individuals through information related to them, beyond the observed characteristics, such as the kinship relations. In those studies typically the assumptions of linear relationships among traits are made. However, in some scenarios, nonlinear relationships can be observed, which make unsuitable the mentioned assumptions. To overcome this limitation, this paper proposes to use a mixed effects polynomial structural equation model, second or superior degree, to model those nonlinear relationships. Two studies were developed, a simulation and an application to real data. The first study involved simulation of 50 data sets, with a fully recursive causal structure involving three characteristics in which linear and nonlinear causal relations between them were allowed. The second study involved the analysis of traits related to dairy cows of the Holstein breed. Phenotypic relationships between traits were calving difficulty, gestation length and also the proportion of perionatal death. We compare the model of multiple traits and polynomials structural equations models, under different polynomials degrees in order to assess the benefits of the SEM polynomial of second or higher degree. For some situations the inappropriate assumption of linearity results in poor predictions of the direct, indirect and total of the genetic variances and covariance, either overestimating, underestimating, or even assign opposite signs to covariances. Therefore, we conclude that the inclusion of a polynomial degree increases the SEM expressive power.Modelos causais têm sido muitos utilizados em estudos em diferentes áreas de conhecimento, a fim de compreender as associações ou relações causais entre variáveis. Durante as últimas décadas, o uso desses modelos têm crescido muito, especialmente estudos relacionados à sistemas biológicos, uma vez que compreender as relações entre características são essenciais para prever quais são as consequências de intervenções em tais sistemas. Análise do grafo (AG) e os modelos de equações estruturais (MEE) são utilizados como ferramentas para explorar essas relações. Enquanto AG nos permite buscar por estruturas causais, que representam qualitativamente como as variáveis são causalmente conectadas, ajustando o MEE com uma estrutura causal conhecida nos permite inferir a magnitude dos efeitos causais. Os MEE também podem ser vistos como modelos de regressão múltipla em que uma variável resposta pode ser vista como explanatória para uma outra característica. Estudos utilizando MEE em genética quantitativa visam estudar os efeitos genéticos diretos e indiretos associados aos indivíduos por meio de informações realcionadas aos indivíduas, além das característcas observadas, como por exemplo o parentesco entre eles. Neste contexto, é tipicamente adotada a suposição que as características observadas são relacionadas linearmente. No entanto, para alguns cenários, relações não lineares são observadas, o que torna as suposições mencionadas inadequadas. Para superar essa limitação, este trabalho propõe o uso de modelos de equações estruturais de efeitos polinomiais mistos, de segundo grau ou seperior, para modelar relações não lineares. Neste trabalho foram desenvolvidos dois estudos, um de simulação e uma aplicação a dados reais. O primeiro estudo envolveu a simulação de 50 conjuntos de dados, com uma estrutura causal completamente recursiva, envolvendo 3 características, em que foram permitidas relações causais lineares e não lineares entre as mesmas. O segundo estudo envolveu a análise de características relacionadas ao gado leiteiro da raça Holandesa, foram utilizadas relações entre os seguintes fenótipos: dificuldade de parto, duração da gestação e a proporção de morte perionatal. Nós comparamos o modelo misto de múltiplas características com os modelos de equações estruturais polinomiais, com diferentes graus polinomiais, a fim de verificar os benefícios do MEE polinomial de segundo grau ou superior. Para algumas situações a suposição inapropriada de linearidade resulta em previsões pobres das variâncias e covariâncias genéticas diretas, indiretas e totais, seja por superestimar, subestimar, ou mesmo atribuir sinais opostos as covariâncias. Portanto, verificamos que a inclusão de um grau de polinômio aumenta o poder de expressão do MEE.
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Liesch, Rahel. "Statistical Genetics for the Budset in Norway Spruce." Thesis, Uppsala University, Department of Mathematics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121386.
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Wright, David Jonathan. "Investigating statistical homogeneity of a human chromosome." Thesis, Queen Mary, University of London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338927.
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Ngong, Chiano Mathias. "Statistical problems in human genetic linkage analysis." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339750.
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Bergdahl, Joakim, and Lars Almgren. "Quantitative Analysis of Physical and Statistical Properties of Flocks." Thesis, KTH, Teoretisk fysik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-145992.
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Flocking behavior is a common phenomenon in nature in the form of, for instance, flocks of birds or schools of fish. Making the assumption that the members of a flock can be considered a system of interacting particles it is possible to use methods from statistical physics to quantitatively analyze flocks and their properties. This way, a flock can exist in various thermodynamic phases and exhibit phase transitions depending on changes within the flock. In this report the flock analysis is performed with the help of a model originally created by Vicsek et al. The model is governed by certain parameters controlling the interaction between individual flock members. From the results it is possible to see that even small deviations in these parameter values can lead to great phase alterations as well as phase transitions, which strengthens to the assumption that a flock can be considered a system of particles.
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McGivney, Debra F. "Statistical Preconditioning and Quantitative Imaging for Electrical Impedance Tomography." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1365167564.
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Jordan, Joseph Francis Patrick. "Quantitative analysis and statistical mechanics of granular pack structures." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/28618.
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Disordered granular packs present many challenges over regular structures in description, analysis and modelling. Within the granular statistical mechanics, which aims to compute bulk properties from details of the local structure, the quadron description was defined over a decade ago. This quantitative characterisation of disordered systems enumerates the structural degrees of freedom, by dividing the system into tessellating volume elements, quadrons, and assigning each a quantitative structure tensor computed from its shape. Presented in this work is a theoretical derivation of an equipartition principle of the volume, which is the analogue of the well known 3kT/2 in thermal physics. This computation is included in full, for two-dimensional systems, using the quadron description. Following this, quadron tensors and statistics in three dimensional disordered structures are computed and analysed for the first time. A new computer program to achieve this was developed, requiring a new class of solution for cell/pore identification, a custom 3D rendering engine, and several extensions to the quadron description as originally defined. The program was successful in analysing structures from simulations, experimental colloidal suspensions and granular systems of a few thousand grains. By a subsystem-based parallelisation method, much larger data-sets were successfully analysed, including an X-ray tomography experiment with 60,000 non-spherical grains. The quadron statistics of a triaxial shear experiment were computed at multiple stages, and the chirality, a pseudo-vector computed from the structure tensor which measures the deviation from orthogonality of the volume element boundary, develops an anisotropy during the formation of a shear band. While the software performed very well on dense granular packs, its performance in less dense systems or regions, like a shear band, was both slower and less reliable. This was traced to the increased ambiguity in cell structure as the density decreases, and is something that future analysis could improve.
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Li, Jiahan. "Statistical model for mapping quantitative trait loci in autotetraploid." [Gainesville, Fla.] : University of Florida, 2008. http://purl.fcla.edu/fcla/etd/UFE0022877.
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Ye, Chun. "Statistical methods for the analysis of expression quantitative traits." Diss., [La Jolla] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3386752.
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Thesis (Ph. D.)--University of California, San Diego, 2009.Title from first page of PDF file (viewed February 11, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 156-169).
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Dai, Xiaotian. "Novel Statistical Models for Quantitative Shape-Gene Association Selection." DigitalCommons@USU, 2017. https://digitalcommons.usu.edu/etd/6856.
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Other research reported that genetic mechanism plays a major role in the development process of biological shapes. The primary goal of this dissertation is to develop novel statistical models to investigate the quantitative relationships between biological shapes and genetic variants. However, these problems can be extremely challenging to traditional statistical models for a number of reasons: 1) the biological phenotypes cannot be effectively represented by single-valued traits, while traditional regression only handles one dependent variable; 2) in real-life genetic data, the number of candidate genes to be investigated is extremely large, and the signal-to-noise ratio of candidate genes is expected to be very high. In order to address these challenges, we propose three statistical models to handle multivariate, functional, and multilevel functional phenotypes, with applications to biological shape data using different shape descriptors. To the best of our knowledge, there is no statistical model developed for multilevel functional phenotypes. Even though multivariate regressions have been well-explored and these approaches can be applied to genetic studies, we show that the model proposed in this dissertation can outperform other alternatives regarding variable selection and prediction through simulation examples and real data examples. Although motivated ultimately by genetic research, the proposed models can be used as general-purpose machine learning algorithms with far-reaching applications.
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Jung, Min Kyung. "Statistical methods for biological applications." [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3278454.
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Thesis (Ph.D.)--Indiana University, Dept. of Mathematics, 2007.Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6740. Adviser: Elizabeth A. Housworth. Title from dissertation home page (viewed May 20, 2008).
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Mayo, Oliver. "Contributions to quantitative and population genetics : a collection of publications with introduction." Title page, contents and introduction only, 1987. http://web4.library.adelaide.edu.au/theses/09SD/09sdm473.pdf.
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Title from container. Includes bibliographies and indexes. Contributions to quantitative and population genetics -- The biochemical genetics of man -- The theory of plant breeding -- Natural selection and its constraints.
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Choy, Yan-tsun. "Statistical evaluation of mixed DNA stains." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42664287.
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