Academic literature on the topic 'Quantitative Genetics Model'
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Journal articles on the topic "Quantitative Genetics Model"
Zeng, Z. B., and C. C. Cockerham. "Mutation models and quantitative genetic variation." Genetics 133, no. 3 (March 1, 1993): 729–36. http://dx.doi.org/10.1093/genetics/133.3.729.
Full textTachida, H., and C. C. Cockerham. "A building block model for quantitative genetics." Genetics 121, no. 4 (April 1, 1989): 839–44. http://dx.doi.org/10.1093/genetics/121.4.839.
Full textZhivotovsky, L. A., and M. W. Feldman. "On models of quantitative genetic variability: a stabilizing selection-balance model." Genetics 130, no. 4 (April 1, 1992): 947–55. http://dx.doi.org/10.1093/genetics/130.4.947.
Full textKao, Chen-Hung, and Zhao-Bang Zeng. "Modeling Epistasis of Quantitative Trait Loci Using Cockerham's Model." Genetics 160, no. 3 (March 1, 2002): 1243–61. http://dx.doi.org/10.1093/genetics/160.3.1243.
Full textHill, William G. "Sewall Wright and quantitative genetics." Genome 31, no. 1 (January 1, 1989): 190–95. http://dx.doi.org/10.1139/g89-033.
Full textLuo, L., Y.-M. Zhang, and S. Xu. "A quantitative genetics model for viability selection." Heredity 94, no. 3 (November 10, 2004): 347–55. http://dx.doi.org/10.1038/sj.hdy.6800615.
Full textNagylaki, T. "Geographical variation in a quantitative character." Genetics 136, no. 1 (January 1, 1994): 361–81. http://dx.doi.org/10.1093/genetics/136.1.361.
Full textAndersson, Leif. "Fisher’s quantitative genetic model and the molecular genetics of multifactorial traits." Journal of Animal Breeding and Genetics 135, no. 6 (October 2018): 391–92. http://dx.doi.org/10.1111/jbg.12362.
Full textCaballero, A., and P. D. Keightley. "A pleiotropic nonadditive model of variation in quantitative traits." Genetics 138, no. 3 (November 1, 1994): 883–900. http://dx.doi.org/10.1093/genetics/138.3.883.
Full textEshel, Ilan, and Carlo Matessi. "Canalization, Genetic Assimilation and Preadaptation: A Quantitative Genetic Model." Genetics 149, no. 4 (August 1, 1998): 2119–33. http://dx.doi.org/10.1093/genetics/149.4.2119.
Full textDissertations / Theses on the topic "Quantitative Genetics Model"
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.
Full textBao, Haikun. "Bayesian hierarchical regression model to detect quantitative trait loci /." Electronic version (PDF), 2006. http://dl.uncw.edu/etd/2006/baoh/haikunbao.pdf.
Full textBARBIERI, MIRKO. "THE GENETICS OF LEAF RUST RESISTANCE IN THE MODEL GRASS BRACHYPODIUM DISTACHYON." Doctoral thesis, Università Cattolica del Sacro Cuore, 2009. http://hdl.handle.net/10280/640.
Full textBrachypodium distachyon has been proposed as a model species for the tribe of the Triticeae, which includes wheat and barley. The objective of our study was to identify the genomic regions associated with quantitative resistance to leaf rust in Brachypodium. Leaf rust diseases cause significant reductions annually in yield of cereal crops worldwide. An F2 mapping population of 110 individuals was generated between two Brachypodium inbred lines and a AFLP-based linkage map was developed. The linkage map consists of 192 AFLP loci in ten linkage groups, and spans a total genetic length of 1,231 Kosambi cM. To locate quantitative resistance loci on the map, the 110 F2 plants were evaluated for their reaction to the leaf rust at both seedling and adult plant stages. To improve QTL identification, F2-derived F3 families were studied for resistance to leaf rust in two independent experiments. Two major genomic regions involved in resistance to leaf rust were detected.
Baker, Peter John. "Applied Bayesian modelling in genetics." Thesis, Queensland University of Technology, 2001.
Find full textLeaf, Robert Thomas. "The Evolutionary Effects of Fishing: Implications for Stock Management and Rebuilding." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28636.
Full textPh. D.
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.
Full textDissertação (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
Coffman, Valerie Chest. "Determining Molecular Mechanisms of Cell Division in Fission Yeast by Testing Major Assumptions of the Search, Capture, Pull, and Release Model of Contractile-Ring Assembly." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366202358.
Full textSanture, 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.
Full textCerqueira, 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/.
Full textModelos 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.
Barros, Matheus Souza de. "Progresso genético para produção de grãos obtido em doze anos de melhoramento da população elite de arroz de terras altas." Universidade Federal de Goiás, 2015. http://repositorio.bc.ufg.br/tede/handle/tede/7167.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
The genetic improvement resulted from plant breeding acts decisively in maintaining the supply of agricultural foods like rice. The contribution of this genetic improvement to increase productivity is expressed in many crops by genetic progress which represents the genetic changes in the course of the selection cycles, and promotes the advance of genotypic average towards selection. Among the strategies adopted to increase the gain by selection, the early generation selection proves to be promising. This work has aimed to evaluate the genetic potential for selecting upland rice progenies and estimate the genetic progress for grain yield. The data used in this study were obtained from the progeny yield trials conducted in the period of eleven agricultural years 2002/03 to 2012/13 conducted by Embrapa Rice and Beans. The traits analyzed were grain yield (kg ha-1), plant height (cm) and days to flowering (day). In each year of the experiment, a group of progeny was tested in four to six sites. It was used Federer's augmented block design without replication per site in seven years and with at least three checks, in four years of the series were used two replications. The experimental data within each year were submitted to joint analysis. A mixedeffects linear model was applied for estimating the components of variance by the method of restricted maximum likelihood (REML). From this estimate of the components, it was calculated the genetic and phenotypic parameters, in addition the selective accuracy, the experimental precision coefficient, the experimental variation coefficient and relative variation coefficient. The genetic progress was estimated by the method of generalized linear regression of the adjusted means of progeny groups evaluated in each year by the mixed model approach. The estimates of the relative annual mean gain and the total relative gain for the three studied traits were also obtained. The estimates of genetic variance among progenies for grain yield were highly significant (p < 0.001), except for the progeny group evaluated in 2007. The heritability estimation ranging from 0.22 to 0.69 that, associated with the selective accuracy, indicates the expected level of efficiency with early generation selection in each group of progenies. For Plant height and daysto- flowering, the genetic variance estimates were significant (p ≤ 0.01) in all groups. The variance components for these traits led to a rather high heritability estimation that suggests favorable conditions for selection in early generations. The genetic progress for grain yield (80.5 kg ha-1 yr-1) was highly significant. This value represents a relative annual mean gain of 2.88%. Throughout the period, the cumulative gain was estimated in 32.86% which indicates an increase of 918 kg ha-1 for grain yield. The response for plant height was not significant, suggesting that the height of progenies remained stable over the period. For days-to-flowering, it was detected significant increase in cycle length, indicated by the cumulative increase of the vegetative period in about five days (6.73%). From these results we conclude that early generation selection, adopted by the breeding program, were effective in promoting the genetic gain for grain yield in the elite populations.
O melhoramento genético de plantas atua de modo decisivo na manutenção da oferta de alimentos de origem agrícola como o arroz. A contribuição do melhoramento para o aumento da produtividade em várias culturas é expressa pelo progresso genético, que representa as alterações genéticas, no decorrer dos ciclos seletivos, promovendo o deslocamento da média genotípica do caráter no sentido da seleção. Entre várias estratégias adotadas para aumentar os ganhos com a seleção merece destaque a seleção precoce, que envolve a avaliação de progênies endogâmicas nas gerações F3 ou F4. Este trabalho teve como objetivo avaliar o potencial genético para a seleção em progênies de arroz de terras altas e estimar o progresso genético para produção de grãos. Foram utilizados dados dos ensaios de rendimento de progênies conduzidos no período de onze anos agrícolas, 2002/03 a 2012/13 pela Embrapa Arroz e Feijão. Foram analisados os caracteres produção de grãos (kg.ha-1), altura de plantas (cm) e dias para a floração (dia). Em cada ano da série um grupo de progênies foi testado em experimentos instalados entre quatro a seis locais. Foi empregado o delineamento blocos aumentados de Federer em sete anos da série sem repetição por local e com no mínimo três testemunhas, e em quatro anos os ensaios foram duplicados. Os dados foram submetidos à análise conjunta dos experimentos dentro de cada ano. Por meio de um modelo linear de efeitos mistos foram estimados os componentes de variância pelo método da máxima verossimilhança restrita ou residual (REML). A partir da estimativa dos componentes foram calculados os parâmetros genéticos e fenotípicos. O progresso genético foi estimado pelo método da regressão linear generalizada das médias ajustadas dos grupos de progênies avaliadas em cada ano, pela abordagem de modelos mistos. Foram obtidas as estimativas do ganho médio relativo anual e do ganho relativo total. As estimativas de variância genética, para produção de grãos, entre progênies foram altamente significativas (p<0,001), exceto pelo grupo de progênie avaliado em 2007. As estimativas de herdabilidade variaram de 0,22 a 0,69, que associadas às estimativas de acurácia seletiva indicam o nível de eficiência esperado com a seleção precoce em cada grupo de progênies. Para os caracteres altura de plantas e dias para floração as estimativas de variância genética foram significativas (p≤0,01) em todos os grupos. Os componentes de variância para esses caracteres conduziram a estimativas de herdabilidade bastante elevadas, sugerindo, por tanto, condições favoráveis para seleção em gerações iniciais. O progresso genético estimado para produção de grãos (80,5 kg.ha-1ano-1) foi altamente significativo, representando um ganho relativo médio anual de 2,88%. Em todo o período o ganho acumulado estimado foi de 32,86%, que equivale ao incremento em produtividade de 918 kg ha-1. A resposta para altura de plantas não foi significativa, sugerindo que a estatura das progênies avaliadas permaneceu estável ao longo do período. Para dias para floração houve aumento significativo na duração do ciclo, expresso pelo acréscimo acumulado de aproximadamente cinco dias (6,73%) na duração do ciclo vegetativo. Conclui-se que a estratégia de seleção precoce, adotada pelo programa de melhoramento, foi eficiente em promover o progresso genético para o caráter produção de grãos nas populações elites.
Books on the topic "Quantitative Genetics Model"
Silson, Roy G. A predictive additive model for quantitative genetics: Principles and results. Tring: Greenfield, 1993.
Find full textSilson, Roy G. Additive gene systems: An explanation for problems in evolution and selection. Herts: Greenfield, 1988.
Find full text1955-, Banzhaf Wolfgang, and Eeckman Frank H, eds. Evolution and biocomputation: Computational models of evolution. Berlin: Springer, 1995.
Find full textE, Tollefson Ann, ed. Adenovirus Methods and Protocols: Volume 1: Adenoviruses, Ad Vectors, Quantitation, and Animal Models. Totowa, NJ: Humana Press, 2007.
Find full textJarnecke, Amber M., and Susan C. South. Behavior and Molecular Genetics of the Five Factor Model. Edited by Thomas A. Widiger. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199352487.013.25.
Full textWalsh, Bruce, and Michael Lynch. The Infinitesimal Model and Its Extensions. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0024.
Full textQuantitative Genetics in the Wild. Oxford University Press, 2014.
Find full textCharmantier, Anne, Dany Garant, and Loeske E. B. Kruuk. Quantitative Genetics in the Wild. Oxford University Press, 2014.
Find full textWalsh, Bruce, and Michael Lynch. Maintenance of Quantitative Genetic Variation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0028.
Full textWalsh, Bruce, and Michael Lynch. Evolution and Selection of Quantitative Traits. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.001.0001.
Full textBook chapters on the topic "Quantitative Genetics Model"
Xu, Shizhong. "Mixed Model Analysis of Genetic Variances." In Quantitative Genetics, 195–213. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83940-6_12.
Full textLyons, P. A., and L. S. Wicker. "Localising Quantitative Trait Loci in the NOD Mouse Model of Type 1 Diabetes." In Genes and Genetics of Autoimmunity, 208–25. Basel: KARGER, 1999. http://dx.doi.org/10.1159/000060488.
Full textHall, Brian K. "A Quantitative Genetics Model for Morphological Change in Development and Evolution." In Evolutionary Developmental Biology, 321–32. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-3961-8_20.
Full textMorota, 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.
Full textBürger, Reinhard. "Dynamical Models in Quantitative Genetics." In Lecture Notes in Economics and Mathematical Systems, 75–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-662-00545-3_7.
Full textDawid, Herbert, Karl Doerner, Richard F. Hartl, Marc Reimann, Georg Dorffner, Thomas Fent, Markus Feurstein, Andreas Mild, Martin Natter, and Alfred Taudes. "Genetic-based machine learning." In Quantitative Models of Learning Organizations, 23–46. Vienna: Springer Vienna, 2002. http://dx.doi.org/10.1007/978-3-7091-6158-6_3.
Full textGabriel, W. "Quantitative Genetic Models for Parthenogenetic Species." In Population Genetics and Evolution, 73–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73069-6_8.
Full textGianola, 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.
Full textZonta, L. A., and S. D. Jayakar. "Models of Fluctuating Selection for a Quantitative Trait." In Population Genetics and Evolution, 102–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73069-6_11.
Full textHill, J., H. C. Becker, and P. M. A. Tigerstedt. "Genetic models and their predictive value." In Quantitative and Ecological Aspects of Plant Breeding, 17–66. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5830-5_2.
Full textConference papers on the topic "Quantitative Genetics Model"
Neophytou, Katerina, Christos A. Nicolaou, Constantinos S. Pattichis, and Christos N. Schizas. "Deriving Quantitative Structure-Activity Relationship Models Using Genetic Programming for Drug Discovery." In 6th International Special Topic Conference on Information Technology Applications in Biomedicine, 2007. IEEE, 2007. http://dx.doi.org/10.1109/itab.2007.4407401.
Full textSoltani, Mohsen Reza, Hiwa Khaledi, Mohammad Bagher Ghofrani, and Amir Abbas Rezaei. "Optimum Design and Sensitivity Analysis of Axial Flow Compressor With Combination of Analytical Method, Qualitative and Quantitative Rules and Genetic Algorithm." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-51033.
Full textPeng, Jiang-Tao, Hai-Yan Wang, An Jiang, Qi-Wei Xie, and Huai-Song Wang. "Quantitative calibration model for Infrared spectroscopy using continuous wavelet transform combined with genetic algorithm." In 2012 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2012. http://dx.doi.org/10.1109/icwapr.2012.6294791.
Full textMa, Yong-guang, Jian-qiang Gao, Liang-yu Ma, Qin Yan, and Peng Tong. "Study on Fault Diagnosis Based on the Qualitative / Quantitative Model of SDG and Genetic Algorithm." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258342.
Full textOcchipinti, Annalisa, and Claudio Angione. "A Computational Model of Cancer Metabolism for Personalised Medicine." In Building Bridges in Medical Science 2021. Cambridge Medicine Journal, 2021. http://dx.doi.org/10.7244/cmj.2021.03.001.3.
Full textIsmail, Mohamed A., Attia H. Gomaa, and Ashraf O. Nassef. "Solving the Multi-Objective Facility Layout Problem Using Evolutionary Multi-Objective Optimization Algorithms." In ASME 2006 International Manufacturing Science and Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/msec2006-21067.
Full textOrta, Adil Han, Shain Azadi, Saeid Hedayatrasa, Nicolaas Bernardus Roozen, Wim Van Paepegem, Mathias Kersemans, and Koen Van Den Abeele. "Identification of the Orthotropic Elastic Tensor of Composites Using Full Field Lamb Wave Energy Velocities and Dispersion Curves." In 2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/qnde2021-75067.
Full textZhou, Yu, Chen Xuedong, Zhichao Fan, and Han Yichun. "An Improved Mechanism-Based Creep Constitutive Model Using Stress-Dependent Creep Ductility." In ASME 2016 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/pvp2016-63447.
Full textJin, Mohui, Xianmin Zhang, and Benliang Zhu. "Design of Compliant Mechanisms Using a Pseudo-Rigid-Body Model Based Topology Optimization Method." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34325.
Full textLawson, Michael J., Ye Li, and Danny C. Sale. "Development and Verification of a Computational Fluid Dynamics Model of a Horizontal-Axis Tidal Current Turbine." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49863.
Full textReports on the topic "Quantitative Genetics Model"
Zhang, 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.
Full textSemerikov, Serhiy O., Illia O. Teplytskyi, Yuliia V. Yechkalo, and Arnold E. Kiv. Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2648.
Full textTanksley, Steven D., and Dani Zamir. Development and Testing of a Method for the Systematic Discovery and Utilization of Novel QTLs in the Production of Improved Crop Varieties: Tomato as a Model System. United States Department of Agriculture, June 1995. http://dx.doi.org/10.32747/1995.7570570.bard.
Full textWeller, 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.
Full textFallik, Elazar, Robert Joly, Ilan Paran, and Matthew A. Jenks. Study of the Physiological, Molecular and Genetic Factors Associated with Postharvest Water Loss in Pepper Fruit. United States Department of Agriculture, December 2012. http://dx.doi.org/10.32747/2012.7593392.bard.
Full textFridman, Eyal, Jianming Yu, and Rivka Elbaum. Combining diversity within Sorghum bicolor for genomic and fine mapping of intra-allelic interactions underlying heterosis. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7597925.bard.
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