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Статті в журналах з теми "Statistical and quantitative genetics"
Sen, Śaunak, and Gary A. Churchill. "A Statistical Framework for Quantitative Trait Mapping." Genetics 159, no. 1 (September 1, 2001): 371–87. http://dx.doi.org/10.1093/genetics/159.1.371.
Повний текст джерелаNeher, Richard A., and Boris I. Shraiman. "Statistical genetics and evolution of quantitative traits." Reviews of Modern Physics 83, no. 4 (November 10, 2011): 1283–300. http://dx.doi.org/10.1103/revmodphys.83.1283.
Повний текст джерелаSorensen, Daniel. "Developments in statistical analysis in quantitative genetics." Genetica 136, no. 2 (August 21, 2008): 319–32. http://dx.doi.org/10.1007/s10709-008-9303-5.
Повний текст джерелаZou, Fei, Brian S. Yandell, and Jason P. Fine. "Rank-Based Statistical Methodologies for Quantitative Trait Locus Mapping." Genetics 165, no. 3 (November 1, 2003): 1599–605. http://dx.doi.org/10.1093/genetics/165.3.1599.
Повний текст джерелаYe, Shuyun, Rhonda Bacher, Mark P. Keller, Alan D. Attie, and Christina Kendziorski. "Statistical Methods for Latent Class Quantitative Trait Loci Mapping." Genetics 206, no. 3 (May 26, 2017): 1309–17. http://dx.doi.org/10.1534/genetics.117.203885.
Повний текст джерелаChen, Meng, and Christina Kendziorski. "A Statistical Framework for Expression Quantitative Trait Loci Mapping." Genetics 177, no. 2 (July 29, 2007): 761–71. http://dx.doi.org/10.1534/genetics.107.071407.
Повний текст джерелаBarton, N. H., and H. P. de Vladar. "Statistical Mechanics and the Evolution of Polygenic Quantitative Traits." Genetics 181, no. 3 (December 15, 2008): 997–1011. http://dx.doi.org/10.1534/genetics.108.099309.
Повний текст джерелаZou, Fei, Brian S. Yandell, and Jason P. Fine. "Statistical Issues in the Analysis of Quantitative Traits in Combined Crosses." Genetics 158, no. 3 (July 1, 2001): 1339–46. http://dx.doi.org/10.1093/genetics/158.3.1339.
Повний текст джерелаMitchell-Olds, T., and J. Bergelson. "Statistical genetics of an annual plant, Impatiens capensis. I. Genetic basis of quantitative variation." Genetics 124, no. 2 (February 1, 1990): 407–15. http://dx.doi.org/10.1093/genetics/124.2.407.
Повний текст джерелаHoeschele, I., P. Uimari, F. E. Grignola, Q. Zhang, and K. M. Gage. "Advances in Statistical Methods to Map Quantitative Trait Loci in Outbred Populations." Genetics 147, no. 3 (November 1, 1997): 1445–57. http://dx.doi.org/10.1093/genetics/147.3.1445.
Повний текст джерелаДисертації з теми "Statistical and quantitative genetics"
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.
Повний текст джерелаSilva, 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/.
Повний текст джерелаIn 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.
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.
Повний текст джерелаBao, Haikun. "Bayesian hierarchical regression model to detect quantitative trait loci /." Electronic version (PDF), 2006. http://dl.uncw.edu/etd/2006/baoh/haikunbao.pdf.
Повний текст джерелаBoddhireddy, 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.
Повний текст джерелаPearson, 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.
Повний текст джерелаBaldoni, Pedro Luiz 1989. "Modelos lineares generalizados mistos multivariados para caracterização genética de doenças." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/307180.
Повний текст джерелаDissertação (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
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.
Повний текст джерелаTraditionally 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.
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/.
Повний текст джерелаThe 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.
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.
Повний текст джерелаКниги з теми "Statistical and quantitative genetics"
Prem, Narain. Statistical genetics. New York: Wiley, 1990.
Знайти повний текст джерелаStatistical genetics. New York: Wiley, 1993.
Знайти повний текст джерелаFalconer, D. S. Introduction to quantitative genetics. London: Longman, 1989.
Знайти повний текст джерелаFalconer, D. S. Introduction to quantitative genetics. 3rd ed. Harlow: Longman, 1989.
Знайти повний текст джерелаFalconer, D. S. Introduction to quantitative genetics. 2nd ed. Burnt Mill, Harlow, Essex, England: Longman Scientific & Technical, 1986.
Знайти повний текст джерелаFalconer, D. S. Introduction to quantitative genetics. 3rd ed. Burnt Mill, Harlow, Essex, England: Longman, Scientific & Technical, 1989.
Знайти повний текст джерелаJ, Camp Nicola, and Cox Angela 1961-, eds. Quantitative trait loci: Methods and protocols. Totowa, N.J: Humana Press, 2002.
Знайти повний текст джерела1947-, Gianola Daniel, ed. Likelihood, Bayesian and MCMC methods in quantitative genetics. New York: Springer-Verlag, 2002.
Знайти повний текст джерелаGenetic data analysis: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1990.
Знайти повний текст джерелаWeir, B. S. Genetic data analysis II: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1996.
Знайти повний текст джерелаЧастини книг з теми "Statistical and quantitative genetics"
Walsh, B. "Evolutionary Quantitative Genetics." In Handbook of Statistical Genetics, 533–86. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch17.
Повний текст джерелаXu, Shizhong. "Basic Concepts of Quantitative Genetics." In Principles of Statistical Genomics, 53–60. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-0-387-70807-2_5.
Повний текст джерелаXu, Shizhong. "Review of Elementary Statistics." In Quantitative Genetics, 39–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83940-6_4.
Повний текст джерелаJansen, R. C. "Quantitative Trait Loci in Inbred Lines." In Handbook of Statistical Genetics, 587–622. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch18.
Повний текст джерелаHöschele, I. "Mapping Quantitative Trait Loci in Outbred Pedigrees." In Handbook of Statistical Genetics, 623–77. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch19.
Повний текст джерелаGianola, D. "Inferences from Mixed Models in Quantitative Genetics." In Handbook of Statistical Genetics, 678–717. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch20.
Повний текст джерелаSieberts, S. K., and E. E. Schadt. "Inferring Causal Associations between Genes and Disease via the Mapping of Expression Quantitative Trait Loci." In Handbook of Statistical Genetics, 296–326. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch9.
Повний текст джерелаBasford, Kaye E. "Statistical Interaction with Quantitative Geneticists to Enhance Impact from Plant Breeding Programs." In Statistics in Genetics and in the Environmental Sciences, 1–15. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8326-9_1.
Повний текст джерелаMorota, Gota, Diego Jarquin, Malachy T. Campbell, and Hiroyoshi Iwata. "Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data." In Methods in Molecular Biology, 269–96. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2537-8_21.
Повний текст джерелаWeller, Joel Ira. "Statistical methodologies for mapping and analysis of quantitative trait loci." In Plant Genomes: Methods for Genetic and Physical Mapping, 181–207. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2442-3_9.
Повний текст джерелаТези доповідей конференцій з теми "Statistical and quantitative genetics"
Fan, QiaoChu, Zi jie Lu, and Yu chen Liu. "Statistical research methods for genetics." In 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), edited by Chi-Hua Chen, Xuexia Ye, and Hari Mohan Srivastava. SPIE, 2022. http://dx.doi.org/10.1117/12.2639273.
Повний текст джерелаRobinson, John-Paul, Purushotham Bangalore, Jelai Wang, and Tapan Mehta. "Powering statistical genetics with the grid." In the 15th ACM Mardi Gras conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1341811.1341856.
Повний текст джерелаGalas, David, James Kunert-Graf, and Nikita Sakhanenko. "Developing an information theory of quantitative genetics." In Entropy 2021: The Scientific Tool of the 21st Century. Basel, Switzerland: MDPI, 2021. http://dx.doi.org/10.3390/entropy2021-09821.
Повний текст джерелаSantana, Roberto, Hossein Karshenas, Concha Bielza, and Pedro Larrañaga. "Quantitative genetics in multi-objective optimization algorithms." In the 13th annual conference companion. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001858.2001911.
Повний текст джерелаMilkevych, V., E. Karaman, G. Sahana, L. Janss, Z. Cai, and M. S. Lund. "351. Quantitative trait simulation using MeSCoT software." In World Congress on Genetics Applied to Livestock Production. The Netherlands: Wageningen Academic Publishers, 2022. http://dx.doi.org/10.3920/978-90-8686-940-4_351.
Повний текст джерела"Quantitative real-time PCR as a supplementary tool for molecular cytogenetics." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/plantgen2019-044.
Повний текст джерелаBijma, P., A. D. Hulst, and M. C. M. de Jong. "163. A quantitative genetic theory for infectious diseases." In World Congress on Genetics Applied to Livestock Production. The Netherlands: Wageningen Academic Publishers, 2022. http://dx.doi.org/10.3920/978-90-8686-940-4_163.
Повний текст джерелаDavoodi, P., A. Ehsani, R. Vaez Torshizi, and A. A. Masoudi. "596. Chicken quantitative traits follow the omnigenic model." In World Congress on Genetics Applied to Livestock Production. The Netherlands: Wageningen Academic Publishers, 2022. http://dx.doi.org/10.3920/978-90-8686-940-4_596.
Повний текст джерела"Methods of computer vision to extract the quantitative characteristics of the wheat spike." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/plantgen2019-060.
Повний текст джерелаChiang, Chih-Hung. "Statistical analysis of ultrasonic measurements in concrete." In QUANTITATIVE NONDESTRUCTIVE EVALUATION. AIP, 2002. http://dx.doi.org/10.1063/1.1472938.
Повний текст джерелаЗвіти організацій з теми "Statistical and quantitative genetics"
Ott, Jurg. Statistical Genetics Methods for Localizing Multiple Breast Cancer Genes. Fort Belvoir, VA: Defense Technical Information Center, September 1996. http://dx.doi.org/10.21236/ada326461.
Повний текст джерелаOtt, Jurg. Statistical Genetics Methods for Localizing Multiple Breast Cancer Genes. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada337861.
Повний текст джерелаWeller, Joel I., Derek M. Bickhart, Micha Ron, Eyal Seroussi, George Liu, and George R. Wiggans. Determination of actual polymorphisms responsible for economic trait variation in dairy cattle. United States Department of Agriculture, January 2015. http://dx.doi.org/10.32747/2015.7600017.bard.
Повний текст джерелаBlower, D. J. Some General Quantitative Considerations for the Statistical Analysis of Isoperformance Curves. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada531669.
Повний текст джерелаPeter Striupaitis and R.E. Gaensslen. Quantitative/Statistical Approach to Bullet-to-Firearm Identification with Consecutively Manufactured Barrels. Office of Scientific and Technical Information (OSTI), January 2005. http://dx.doi.org/10.2172/892804.
Повний текст джерелаSuen, Guozhen, Yanhua Chua, and Jincan Xian. Solvability Results for Convex, Quasi-n-Dimensional Curves in Quantitative Statistical Systems In D-Dimensional Space. Web of Open Science, February 2020. http://dx.doi.org/10.37686/qrl.v1i1.4.
Повний текст джерелаParan, Ilan, and Molly Jahn. Genetics and comparative molecular mapping of biochemical and morphological fruit characters in Capsicum. United States Department of Agriculture, March 2005. http://dx.doi.org/10.32747/2005.7586545.bard.
Повний текст джерелаKrommes, J. A., and Chang-Bae Kim. A new'' approach to the quantitative statistical dynamics of plasma turbulence: The optimum theory of rigorous bounds on steady-state transport. Office of Scientific and Technical Information (OSTI), June 1990. http://dx.doi.org/10.2172/6765264.
Повний текст джерелаBlum, Abraham, Henry T. Nguyen, and N. Y. Klueva. The Genetics of Heat Shock Proteins in Wheat in Relation to Heat Tolerance and Yield. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568105.bard.
Повний текст джерелаZhang, Hongbin B., David J. Bonfil, and Shahal Abbo. Genomics Tools for Legume Agronomic Gene Mapping and Cloning, and Genome Analysis: Chickpea as a Model. United States Department of Agriculture, March 2003. http://dx.doi.org/10.32747/2003.7586464.bard.
Повний текст джерела