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Статті в журналах з теми "Quantitative genetics":
Mackay, Trudy F. C., Michael Lynch, and Bruce Walsh. "Quantitative Genetics." Evolution 53, no. 1 (February 1999): 307. http://dx.doi.org/10.2307/2640946.
Gunter, Chris. "Quantitative genetics." Nature 456, no. 7223 (December 2008): 719. http://dx.doi.org/10.1038/456719a.
Mackay, Trudy F. C. "QUANTITATIVE GENETICS." Evolution 53, no. 1 (February 1999): 307–9. http://dx.doi.org/10.1111/j.1558-5646.1999.tb05359.x.
Hill, William G. "Sewall Wright and quantitative genetics." Genome 31, no. 1 (January 1, 1989): 190–95. http://dx.doi.org/10.1139/g89-033.
van Buijtenen, J. P. "Genomics and quantitative genetics." Canadian Journal of Forest Research 31, no. 4 (April 1, 2001): 617–22. http://dx.doi.org/10.1139/x00-171.
FRANKHAM, RICHARD. "Quantitative genetics in conservation biology." Genetical Research 74, no. 3 (December 1999): 237–44. http://dx.doi.org/10.1017/s001667239900405x.
Plomin, Robert, and Jenae Neiderhiser. "Quantitative Genetics, Molecular Genetics, and Intelligence." Intelligence 15, no. 4 (October 1991): 369–87. http://dx.doi.org/10.1016/0160-2896(91)90001-t.
Hansen, Thomas F., and Christophe Pélabon. "Evolvability: A Quantitative-Genetics Perspective." Annual Review of Ecology, Evolution, and Systematics 52, no. 1 (November 2, 2021): 153–75. http://dx.doi.org/10.1146/annurev-ecolsys-011121-021241.
Macgregor, Stuart, Sara A. Knott, Ian White, and Peter M. Visscher. "Quantitative Trait Locus Analysis of Longitudinal Quantitative Trait Data in Complex Pedigrees." Genetics 171, no. 3 (July 14, 2005): 1365–76. http://dx.doi.org/10.1534/genetics.105.043828.
Slatkin, Montgomery. "Quantitative Genetics of Heterochrony." Evolution 41, no. 4 (July 1987): 799. http://dx.doi.org/10.2307/2408889.
Дисертації з теми "Quantitative genetics":
Olsson, Charlotta. "Quantitative analysis of disease associated mutations and sequence variants." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2001. http://publications.uu.se/theses/91-554-5018-0/.
Souleman, Dima. "Genetic consequences of colonization of a metal-polluted environment, population genetics and quantitative genetics approaches." Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10006/document.
Natural habitats are more and more destructed and fragmented by urban expansion and human activities. The fragmentation of natural and agricultural areas by buildings and new infrastructures affects the size, connectivity and the quality of habitats. The populations of organisms inhabiting these anthropized territories are then more isolated. However, differentiation between populations of the same organism depends on demographic and genetic processes such as genetic drift, gene flow, mutation and natural selection. Only species that have developed special tolerance mechanisms can persist under changed environmental conditions. The introduction of contaminants such as metals in the environment may influence plants and animals evolution by modifying the evolutionary forces and thus generating differences between populations. In this work, attention was focused on the genetic consequences of metallic pollution on two species, the earthworm Lumbricus terrestris and the plant model Arabidopsis halleri. Two different approaches have been used to study the genetic response to metallic contamination: a population genetic approach was performed in L. terrestris and a quantitative genetic approach was carried on in A. halleri. First, it was a question of identifying and validating new microsatellite markers in L. terrestris. These markers were then used to characterize the neutral genetic diversity in worms collected from agricultural and urban sites. Secondly, genetic architecture of Zn tolerance and Zn hyperaccumulation was conducted investigated for the first time using an intraspecific crossing between metallicolous and non-metallicolous individuals of A. halleri. High density of SNP markers was used to proceed to the QTL mapping step
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.
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.
Keightley, Peter D. "Studies of quantitative genetic variation." Thesis, University of Edinburgh, 1988. http://hdl.handle.net/1842/12340.
Gunn, Melissa Rose School of Biological Earth & Environmental Science UNSW. "The use of microsatellites as a surrogate for quantitative trait variation in conservation." Awarded by:University of New South Wales. School of Biological, Earth and Environmental Science, 2003. http://handle.unsw.edu.au/1959.4/22457.
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/.
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.
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.
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.
Wambach, Tina. "Effects of epistatic interaction on detection and parameter analysis of quantitative trait loci." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33039.
Книги з теми "Quantitative genetics":
Xu, Shizhong. Quantitative Genetics. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83940-6.
Falconer, D. S. Introductionto quantitative genetics. 3rd ed. Harlow: Longman Scientific & Technical, 1989.
Roff, Derek A. Evolutionary quantitative genetics. New York: Chapman & Hall, 1997.
Roff, Derek A. Evolutionary Quantitative Genetics. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-4080-9.
B, Chapman A., ed. General and quantitative genetics. Amsterdam: Elsevier Science Pub. Co., 1985.
Hallauer, Arnel R. Quantitative genetics in maize breeding. 3rd ed. New York: Springer, 2010.
Hallauer, Arnel R. Quantitative genetics in maize breeding. 3rd ed. New York: Springer, 2010.
Falconer, D. S. Introduction to quantitative genetics. 3rd ed. London: Longman Scientific & Technical, 1989.
Falconer, D. S. Introduction to quantitative genetics. 4th ed. Harlow: Prentice Hall, 1996.
Falconer, D. S. Introduction to quantitative genetics. 3rd ed. Burnt Mill, Harlow, Essex, England: Longman, Scientific & Technical, 1989.
Частини книг з теми "Quantitative genetics":
Nagylaki, Thomas. "Quantitative Genetics." In Introduction to Theoretical Population Genetics, 279–339. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-76214-7_10.
Priyadarshan, P. M. "Quantitative Genetics." In PLANT BREEDING: Classical to Modern, 269–98. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7095-3_14.
Princée, F. P. G. "Quantitative Genetics." In Topics in Biodiversity and Conservation, 219–43. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50032-4_16.
Chatterjee, Anindo. "Quantitative Genetics." In Genetics Fundamentals Notes, 1029–76. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7041-1_20.
Kulandhasamy, Maheswari, Sunil Singh, and Indrani Mukherjee. "Quantitative Genetics." In Encyclopedia of Animal Cognition and Behavior, 1–3. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-47829-6_168-1.
Kulandhasamy, Maheswari, Sunil Singh, and Indrani Mukherjee. "Quantitative Genetics." In Encyclopedia of Animal Cognition and Behavior, 5837–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-55065-7_168.
Meredith, William R. "Quantitative Genetics." In Agronomy Monographs, 131–50. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 2016. http://dx.doi.org/10.2134/agronmonogr24.c5.
Campbell, B. Todd, and Gerald O. Myers. "Quantitative Genetics." In Agronomy Monographs, 187–203. Madison, WI, USA: American Society of Agronomy, Inc., Crop Science Society of America, Inc., and Soil Science Society of America, Inc., 2015. http://dx.doi.org/10.2134/agronmonogr57.2013.0024.
Laurentin Táriba, Hernán Eduardo. "Quantitative Genetics." In Agricultural Genetics, 167–78. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37192-9_12.
Xu, Shizhong. "Resemblance between Relatives." In Quantitative Genetics, 135–46. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83940-6_9.
Тези доповідей конференцій з теми "Quantitative genetics":
Galas, 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.
Santana, 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.
Milkevych, 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.
"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.
Bijma, 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.
Davoodi, 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.
"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.
Tsuruta, S., D. A. L. Lourenco, and I. Misztal. "432. Efficient genetic progress for quantitative traits through genomic selection." 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_432.
"The association mapping of quantitative resistance loci to net blotch and spot blotch in barley." 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/plantgen2019-149.
Tortereau, F., C. Marie-Etancelin, D. Marcon, and J. L. Weisbecker. "49. Feed intake can be predicted as quantitative or qualitative traits." 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_49.
Звіти організацій з теми "Quantitative genetics":
Paran, 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.
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.
Blum, 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.
Paran, Ilan, and Molly Jahn. Analysis of Quantitative Traits in Pepper Using Molecular Markers. United States Department of Agriculture, January 2000. http://dx.doi.org/10.32747/2000.7570562.bard.
Moore, Gloria A., Gozal Ben-Hayyim, Charles L. Guy, and Doron Holland. Mapping Quantitative Trait Loci in the Woody Perennial Plant Genus Citrus. United States Department of Agriculture, May 1995. http://dx.doi.org/10.32747/1995.7570565.bard.
Sherman, Amir, Rebecca Grumet, Ron Ophir, Nurit Katzir, and Yiqun Weng. Whole genome approach for genetic analysis in cucumber: Fruit size as a test case. United States Department of Agriculture, December 2013. http://dx.doi.org/10.32747/2013.7594399.bard.
Feldman, Moshe, Eitan Millet, Calvin O. Qualset, and Patrick E. McGuire. Mapping and Tagging by DNA Markers of Wild Emmer Alleles that Improve Quantitative Traits in Common Wheat. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7573081.bard.
Orphan, Victoria, Gene Tyson, Christof Meile, Shawn McGlynn, Hang Yu, Grayson Chadwick, Jeffrey Marlow, et al. Systems Level Dissection of Anaerobic Methane Cycling: Quantitative Measurements of Single Cell Ecophysiology, Genetic Mechanisms, and Microbial Interactions. Office of Scientific and Technical Information (OSTI), December 2017. http://dx.doi.org/10.2172/1414771.
Santa Sepúlveda, Juan David, Jhon Berdugo Cely, Mauricio Soto Suárez, Teresa Mosquera, and Carlos Galeano. A genetic linkage map of tetraploid potato (Solanum tuberosum L.) for Phytophthora infestans and Tecia solanivora quantitative resistance. Corporación Colombiana de Investigación Agropecuaria - AGROSAVIA, 2016. http://dx.doi.org/10.21930/agrosavia.poster.2016.28.
Weller, Joel I., Harris A. Lewin, and Micha Ron. Determination of Allele Frequencies for Quantitative Trait Loci in Commercial Animal Populations. United States Department of Agriculture, February 2005. http://dx.doi.org/10.32747/2005.7586473.bard.