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Статті в журналах з теми "Quantitative trait analyses"
Jiang, C., and Z. B. Zeng. "Multiple trait analysis of genetic mapping for quantitative trait loci." Genetics 140, no. 3 (July 1, 1995): 1111–27. http://dx.doi.org/10.1093/genetics/140.3.1111.
Повний текст джерелаNichols, Krista M., Paul A. Wheeler, and Gary H. Thorgaard. "Quantitative Trait Loci Analyses for Meristic Traits in Oncorhynchus mykiss." Environmental Biology of Fishes 69, no. 1-4 (March 2004): 317–31. http://dx.doi.org/10.1023/b:ebfi.0000022905.72702.0e.
Повний текст джерелаGoffinet, Bruno, and Sophie Gerber. "Quantitative Trait Loci: A Meta-analysis." Genetics 155, no. 1 (May 1, 2000): 463–73. http://dx.doi.org/10.1093/genetics/155.1.463.
Повний текст джерелаHardy, John, Danyah Trabzuni, and Mina Ryten. "Whole genome expression as a quantitative trait." Biochemical Society Transactions 37, no. 6 (November 19, 2009): 1276–77. http://dx.doi.org/10.1042/bst0371276.
Повний текст джерелаMüller-Myhsok, B., and T. Grimm. "Linkage analysis and genetic models in dyslexia — considerations pertaining to discrete trait analysis and quantitative trait analyses." European Child & Adolescent Psychiatry 8, S3 (September 1999): S40—S42. http://dx.doi.org/10.1007/pl00010692.
Повний текст джерелаWilcox, Marsha A., Diego F. Wyszynski, Carolien I. Panhuysen, Qianli Ma, Agustin Yip, John Farrell, and Lindsay A. Farrer. "Empirically derived phenotypic subgroups – qualitative and quantitative trait analyses." BMC Genetics 4, Suppl 1 (2003): S15. http://dx.doi.org/10.1186/1471-2156-4-s1-s15.
Повний текст джерелаPlomin, Robert, and Gerald E. McClearn. "Quantitative trait loci (QTL) analyses and alcohol-related behaviors." Behavior Genetics 23, no. 2 (March 1993): 197–211. http://dx.doi.org/10.1007/bf01067425.
Повний текст джерелаBaes, C., and N. Reinsch. "TIGER: A software system for fine-mapping quantitative trait loci." Archives Animal Breeding 51, no. 4 (October 10, 2008): 402–12. http://dx.doi.org/10.5194/aab-51-402-2008.
Повний текст джерелаXiong, Xinwei, Hui Yang, Bin Yang, Congying Chen, and Lusheng Huang. "Identification of quantitative trait transcripts for growth traits in the large scales of liver and muscle samples." Physiological Genomics 47, no. 7 (July 2015): 274–80. http://dx.doi.org/10.1152/physiolgenomics.00005.2015.
Повний текст джерелаHERNÁNDEZ-SÁNCHEZ, J., A. CHATZIPLI, D. BERALDI, J. GRATTEN, J. G. PILKINGTON, and J. M. PEMBERTON. "Mapping quantitative trait loci in a wild population using linkage and linkage disequilibrium analyses." Genetics Research 92, no. 4 (August 2010): 273–81. http://dx.doi.org/10.1017/s0016672310000340.
Повний текст джерелаДисертації з теми "Quantitative trait analyses"
Pita, Fabiano Veraldo da Costa. "Construction of the gametic covariance matrix for quantitative trait loci analyses in outbred populations." Universidade Federal de Viçosa, 2003. http://www.locus.ufv.br/handle/123456789/10501.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico
A aplicação de análises de “Quantitative Trait Loci” (QTL) em populações exogâmicas é desafiadora porque pressuposições simplificadoras não podem ser aplicadas (por exemplo, os alelos QTL não podem ser assumidos fixados em diferentes famílias, o número de alelos QTL segregantes não é conhecido a priori, não há desequilíbrio de ligação entre um dado alelo marcador e um dado alelo QTL). Quando o efeito genotípico do QTL é assumido aleatório no modelo de análise, a matriz de covariância gamética deve ser calculada para a realização das análises em populações exogâmicas. A acurácia dessa matriz é importante para a obtenção de estimativas confiáveis da posição ou efeito do QTL em análises de mapeamento, ou de valores genotípicos em avaliação genética assistida por marcadores. O objetivo do primeiro estudo foi avaliar diferente estratégias já implementadas em programas computacionais (SO- LAR, LOKI, ESIP e MATVEC) para calcular a matriz de coeficientes Idênticos por Descendência (IBD), que é necessária para o mapeamento de QTL em populações exogâmicas. SOLAR utiliza um método baseado em regressão linear, LOKI e ESIP são ambos baseados em “reverse peeling” e o amostrador implementado em MAT VEC amostra indicadores de segregação. Um pedigree com estrutura F2 típica foi simulado com uma família F2 pequena (2 indivíduos) ou grande (20 indivíduos) e marcadores flanqueadores localizados a 2 cM, 5 cM ou 10 cM de distância um do outro, com o QTL localizado no meio do intervalo. A habilidade dessas estratégias em lidar com informações de marcadores perdidas foi avaliada assumindo um dos pais da geração F2 com ou sem informação de marcador. SOLAR nao estimou os coeficientes IBD corretamente para a maior parte das situações simuladas, enquanto que LOKI apre- sentou problemas quando o tamanho da família F2 era grande. ESIP e o amostrador em MATVEC apresentaram bom desempenho em todas as situacões simuladas, com estimativas de coeficientes IBD próximas aos coeficientes verdadeiros. Portanto, ESIP e MATVEC são os softwares mais indicados quando analises genéticas são realizadas em pedigrees com estruturas complexas. O objetivo do segundo estudo foi avaliar o efeito da utilização de uma melhor aproximação da inversa da matriz de covariância gamética para a avaliação genética de grandes populações de animais domésticos. Algoritmos eficientes, baseados no rastreamento dos alelos QTL de um indivíduo em relação aos de seus avós (Probabilidade de Descendência de um QTL - PDQ), podem ser usados para construir a inversa da matriz de covariância gamética diretamente. Mas essa inversa é uma aproximação quando há informação incompleta de marcador. Também, o calculo exato de PDQºs torna-se difícil quando a informação de marcador é incompleta. Nesse estudo, a inversa da matriz de covariãncia gamética para uma pop- ulação exogãmica simulada foi calculada usando o algoritmo eficiente, mas as PDQ's foram calculadas usando um algoritmo Monte Carlo Cadeia de Markov (MCMC). Essa inversa foi utilizada para predizer o valor genético dos indivíduos através de BLUP assistido por marcadores (MABLUP). O efeito dos cálculos de PDQ usando o algoritmo MCMC sobre a acurãcia da MABLUP foi avaliado com base na resposta a seleção realizada, calculada para o pedigree simulado. Os resultados mostraram que quando as PDQ’S foram estimadas usando MCMC a perda em resposta devido ao uso da inversa aproximada pode ser reduzida em aproximadamente 20%, enquanto que em estudos anteriores essa redução foi de 50%. Ainda, quando quatro marcadores bi-alélicos foram utilizados a resposta para MABLUP foi maior e a perda em re- sposta devido a marcadores com informação perdida foi menor, quando comparadas a situação onde apenas dois marcadores bi-alélicos foram utilizados.
The application of Quantitative Trait Loci (QTL) analyses in outbred population is challenging because simplified assumptions do not hold for these populations (e.g., the QTL alleles cannot be assumed fixed in different families, the number of QTL alleles segregating is not known a priori, there is not gametic phase disequilibrium between a given genetic marker allele and a QTL allele). When the QTL genotypic effect is assumed random, the gametic covariance matrix must be calculated to per- form QTL analyses in outbred populations. The accuracy of this matrix is important to obtain reliable estimates of QTL position or effect when applying QTL mapping, or QTL genotypic values when applying Marker Assisted Genetic Evaluation. The objective of the first study was to evaluate the different strategies already imple- mented in softwares (SOLAR, LOKI, ESIP and MATVEC) to calculate the matrix of identical by descent (IBD) coefficients, which is required for QTL mapping anal- ysis in outbred populations. SOLAR uses a regression method, LOKI and ESIP are both based on reverse peeling, and the MAT VEC sampler samples segregation in- dicators. A typical F2 pedigree was simulated with a small (2 offspring) or a large (20 offspring) F2 family, and the flanking markers were simulated 2 CM, 5 CM, or 10 CM apart, with the QTL located in the middle. The ability of these strategies to deal with missing genetic marker information was evaluated assuming one of the F2 parents with or without marker information. SOLAR failed to estimate the correct coefficients at almost all situations simulated, while LOKI showed problems when a large family was present in the pedigree. ESIP and MATVEC sampler performed well at all situations, providing IBD coefficients closed to the true ones. Therefore, ESIP and MATVEC are more indicated when genetic analysis are carried out on complex pedigree structures. The objective of the second study was to evaluate the effect of using a better approximation of the inverse of the gametic covariance matrix on the genetic evaluation of large livestock populations. Efficient algorithms, based on trac- ing the QTL alleles of an individual to its grandmother or grandfather (probability of descent a QTL - PDQ’s), can be used to construct the inverse of the gametic covari- ance matrix directly. But this inverse is an approximation when incomplete marker information is available. Also, computing the exact PDQ’s becomes difficult when marker information is incomplete. In this study, the inverse of the gametic covariance matrix for a simulated outbred pedigree was calculated using the efficient algorithm, but the PDQ’s were calculated using a Markov chain Monte Carlo (MCMC) algo- rithm. This inverse was used to calculate the predicted genetic value of individuals through Marker Assisted Best Linear Unbiased Prediction (MABLUP). The effect of PDQ calculations using the MCMC algorithm on MABLUP accuracy was evaluated based on the realized response to selection for the simulated pedigree. The results showed that by estimating the PDQ’s by MCMC the loss in response because of using an approximate inverse could be reduced to about 20%, while in previous studies this reduction was of 50%. Further, response to MABLUP was greater when four bi-allelic markers were used, and the loss in response due to missing markers was smaller in the case with four markers compared to when only two bi-allelic markers were used.
Tese importada do Alexandria
Masri, Amer. "Use of quantitative trait loci (QTL) affecting muscling in sheep for breeding." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/9526.
Повний текст джерелаPoon, Fong-Yee. "Genetic architecture of neurogenesis in the adult mouse forebrain : insights from quantitative trait locus analyses." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50395.
Повний текст джерелаMedicine, Faculty of
Medical Genetics, Department of
Graduate
Silva, Franklin Magnum de Oliveira. "Integrative analyses of photosynthesis, plant growth, metabolite levels and enzyme activities in an introgression line population of Solanum pennellii." Universidade Federal de Viçosa, 2016. http://www.locus.ufv.br/handle/123456789/21421.
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Fundação de Amparo à Pesquisa do Estado de MInas Gerais
Para identificar regiões genômicas envolvidas na regulação de processos fisiológicos fundamentais, como fotossíntese, respiração e aqueles relacionados, uma população de ILs de Solanum pennellii em fundo genético de S. lycopersicum (M82) foi analisada. Foram estudados parâmetros fisiológicos, metabólicos e de crescimento, que vão desde troca gasosa (por exemplo, taxa de assimilação de CO 2 e condutância estomática), fluorescência da clorofila (por exemplo, taxa de transporte de elétrons e de extinção fotoquímica), bem como parâmetros de crescimento (por exemplo, taxa de crescimento relativo, matéria seca da raiz e parte aérea). Em paralelo, nós também analisamos, por meio de uma plataforma robotizada, os principais intermediários metabólicos (por exemplo, açúcares, amido, nitrato, aminoácidos e proteínas), e a atividade de nove enzimas representativas do metabolismo central do C e N. O objetivo do estudo foi: (1) combinar informações sobre as atividades enzimáticas e os níveis de metabólitos de caule, pecíolo e folha com a biomassa e rendimento de frutos; (2) através do estudo desses três órgãos interligados, examinar o quanto há de conectividade entre a atividade das enzimas e os níveis de metabólitos; (3) fornecer informações preditivas sobre as diferenças de particionamento do C e assimilação N inorgânico; (4) investigar a diversidade genética natural e identificar QTLs relacionados ao metabolimo central e a atividade enzimática no caule, pecíolo e folha. As análises dos dados permitiram a identificação de 67 QTL relacionados à parametros fisiológicos e metabólicos. Além disso, uma anotação abrangente e detalhada destas regiões permitiu apontar um total de 87 genes candidatos que possam controlar as características investigadas. Desses, 70 genes apresentou variantes alélicas relacionadas inserções de elementos transponíveis entre os dois genótipos parentais. As análises metabólicas e enzimática revelaram alta frequência de correlações positivas entre as enzymas, frequência moderada de correlações entre metabólitos relacionados, e baixa correlações entre a atividade das enzimas e os níveis de metabólitos. Tomados em conjunto, vapresentamos o maior estudo de parâmetros de fotossíntese e crescimento em plantas de tomate até à data. Os resultados permitiram a identificação de genes candidatos que podem estar envolvidos na regulação da fotossíntese, metabolismo primário e crescimento da planta, e fornece um recurso genético valioso para a compreensão dos mecanismos bioquímicos envolvidos na regulação do metabolismo primário em tomateiro.
To identify genomic regions involved in the regulation of fundamental physiological processes such as photosynthesis, respiration and underlying traits, a population of 71 Solanum pennellii introgression lines (ILs) in the genetic background of S. lycopersicum (M82) was analyzed. We determined IL phenotypes physiological, metabolic and growth related traits, ranging from gas- exchange parameters (e.g. CO 2 assimilation rates and stomatal conductance), chlorophyll fluorescence parameters (e.g. electron transport rate and photochemical quenching) as well as growth related traits (e.g. relative growth rate, shoot and root dry matter accumulation). In parallel, we also analyzed by robotized platform the major metabolic intermediates (e.g. sugars and starch), and the activities of nine representative enzymes from central C and N metabolism. We aimed: (1) combine information about enzyme activities and metabolite levels from stem, petiole and leaf with biomass and fruit yield; (2) by studying these three interconnected organs, examine how much connectivity exists between enzyme activities and metabolite levels; (3) provide predictive information about differences in C partitioning and inorganic N assimilation; (4) investigate the natural genetic diversity and identify QTL controlling variation of enzyme activities and metabolite levels in stem, petiole and leaf. Data analyses allowed identification of 67 physiological and metabolic QTL. Additionally, a comprehensive and detailed annotation of these regions allowed to point out a total of 87 candidate genes that might control the investigated traits. Out of those, 70 genes showed allelic variants related to differentially transposable element insertions pattern between both parental genotypes. Furthermore, the results revealed high frequency of positive correlations between enzyme activities, moderate frequency of correlations between related metabolites, and few correlations between enzyme activities and metabolite levels. Taken together, we present the largest study of photosynthetic and growth parameters in tomato plants to date. Our results allowed the identification of candidate genes that might be involved in the regulation of photosynthesis, primary metabolismo and plant growth, and provide an valuable genetic resource to understanding of the biochemical mechanisms involved in the regulation of primary metabolism in tomato plants.
Shimomura, Koichiro. "Quantitative trait locus analysis of agronomic traits in weedy cucumber lines for breeding." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263362.
Повний текст джерелаJoehanes, Roby. "Multiple-trait multiple-interval mapping of quantitative-trait loci." Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1605.
Повний текст джерелаConde-Martinez, F. Victor. "Quantitative trait loci and bulk segregant analysis to identify drought-related traits in maize (Zea mays L.)." Thesis, University of East Anglia, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432430.
Повний текст джерелаYao, Ping. "Quantitative trait loci mapping and candidate gene analysis for growth and carcass traits on two bovine chromosomes." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4576.
Повний текст джерелаThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on May 7, 2009) Includes bibliographical references.
Marklund, Lena. "Genome analysis of quantitative trait loci in the pig /." Uppsala : Swedish Univ. of Agricultural Sciences (Sveriges lantbruksuniv.), 1997. http://epsilon.slu.se/avh/1997/91-576-5416-6.gif.
Повний текст джерелаAtkinson, Jennifer L. "Quantitative trait locus analysis of growth in Arabidopsis thaliana." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/11892.
Повний текст джерелаКниги з теми "Quantitative trait analyses"
Weller, Joel Ira. Quantitative trait loci analysis in animals. Oxon, UK: CABI Pub., 2001.
Знайти повний текст джерелаWeller, J. I., ed. Quantitative trait loci analysis in animals. Wallingford: CABI, 2009. http://dx.doi.org/10.1079/9781845934675.0000.
Повний текст джерелаWeller, J. I., ed. Quantitative trait loci analysis in animals. Wallingford: CABI, 2001. http://dx.doi.org/10.1079/9780851994024.0000.
Повний текст джерелаWeller, Joel Ira. Quantitative trait loci analysis in animals. 2nd ed. Cambridge, MA: CABI North American Office, 2009.
Знайти повний текст джерела1957-, Walsh Bruce, ed. Genetics and analysis of quantitative traits. Sunderland, Mass: Sinauer, 1998.
Знайти повний текст джерелаS, Pooni Harpal, ed. Th e genetical analysis of quantitative traits. Cheltenham: Stanley Thornes, 1998.
Знайти повний текст джерелаKearsey, Michael J., and Harpal S. Pooni. The Genetical Analysis of Quantitative Traits. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-4441-2.
Повний текст джерелаLantbruksuniversitet, Sveriges, ed. Genome analysis of quantitative trait loci in the pig. Uppsala: Sveriges Lantbruksuniversitet, 1997.
Знайти повний текст джерелаSebastian, Rachel Louise. The genetic mapping and quantitative trait analysis of Brassica Oleracea. Birmingham: University of Birmingham, 2000.
Знайти повний текст джерелаHazardous materials transportation risk analysis: Quantitative approaches for truck and train. New York: Van Nostrand Reinhold, 1994.
Знайти повний текст джерелаЧастини книг з теми "Quantitative trait analyses"
Nichols, Krista M., Paul A. Wheeler, and Gary H. Thorgaard. "Quantitative trait loci analyses for meristic traits in Oncorhynchus mykiss." In Genetics of Subpolar Fish and Invertebrates, 317–31. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-94-007-0983-6_26.
Повний текст джерелаFalchi, Mario. "Analysis of Quantitative Trait Loci." In Bioinformatics, 297–326. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-429-6_16.
Повний текст джерелаDuffy, David L. "Analysis of Quantitative Trait Loci." In Methods in Molecular Biology, 191–203. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6613-4_11.
Повний текст джерелаXu, Shizhong. "Quantitative Trait-Associated Microarray Data Analysis." In Principles of Statistical Genomics, 383–94. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-0-387-70807-2_24.
Повний текст джерелаAmos, Christopher I., Bo Peng, Yaji Xu, and Jianzhong Ma. "Linkage Analysis of Quantitative Traits." In Handbook on Analyzing Human Genetic Data, 119–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-69264-5_4.
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 джерелаSmith, Randy, Keith Sheppard, Keith DiPetrillo, and Gary Churchill. "Quantitative Trait Locus Analysis Using J/qtl." In Methods in Molecular Biology, 175–88. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60761-247-6_10.
Повний текст джерелаGadau, Jürgen, Christof Pietsch, and Leo W. Beukeboom. "Quantitative Trait Locus Analysis in Haplodiploid Hymenoptera." In Methods in Molecular Biology, 313–28. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-785-9_16.
Повний текст джерелаSham, Pak. "Recent developments in quantitative trait loci analysis." In Behavioral genetics in the postgenomic era., 41–54. Washington: American Psychological Association, 2003. http://dx.doi.org/10.1037/10480-003.
Повний текст джерелаSorensen, Daniel, and Daniel Gianola. "Introduction to Segregation and Quantitative Trait Loci Analysis." In Statistics for Biology and Health, 671–99. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/0-387-22764-4_16.
Повний текст джерелаТези доповідей конференцій з теми "Quantitative trait analyses"
Cozzi, E., M. Prysak, and D. Beier. "Airway Hyperresponsiveness Quantitative Trait Linkage Analyses in Inbred and Outbred Mice." In American Thoracic Society 2009 International Conference, May 15-20, 2009 • San Diego, California. American Thoracic Society, 2009. http://dx.doi.org/10.1164/ajrccm-conference.2009.179.1_meetingabstracts.a2749.
Повний текст джерелаYang, I. V., J. Cardwell, W. Zhang, R. Borie, A. Walts, J. Powers, M. Rojas, P. J. Wolters, T. E. Fingerlin, and D. A. Schwartz. "Functional Validation of MUC5B and DSP Genetic Variants in Idiopathic Pulmonary Fibrosis (IPF) by Expression Quantitative Trait Locus (EQTL) and Co-Localization Analyses." In American Thoracic Society 2020 International Conference, May 15-20, 2020 - Philadelphia, PA. American Thoracic Society, 2020. http://dx.doi.org/10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a2256.
Повний текст джерелаHuh, Ik-Soo, Sohee Oh, Eunjin Lee, and Taesung Park. "Compairing quantitative trait analysis to qualitative trait analysis for complex traits disease: A genome wide association study for hyperlipidemia." In 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2010. http://dx.doi.org/10.1109/bibmw.2010.5703825.
Повний текст джерелаBoone, Edward L., Karl Ricanek, and Susan J. Simmons. "Quantitative Trait Loci Analysis Using a Bayesian Framework." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371053.
Повний текст джерелаGaidelys, Vaidas, and Emilija Naudžiūnaitė. "EVALUATION OF THE MATHEMATICAL MODELLING METHODS AVAILABLE IN THE MARKET." In 12th International Scientific Conference „Business and Management 2022“. Vilnius Gediminas Technical University, 2022. http://dx.doi.org/10.3846/bm.2022.725.
Повний текст джерелаGrigorov, Tatiana. "Variabilitatea caracterelor biomorfologice la mutantul calcaroides de orz de primăvară în generațiile M3-M7." In VIIth International Scientific Conference “Genetics, Physiology and Plant Breeding”. Institute of Genetics, Physiology and Plant Protection, Republic of Moldova, 2021. http://dx.doi.org/10.53040/gppb7.2021.39.
Повний текст джерелаVilela, Plínio, Mônica Cachoni, Anderson Vieira, and Luciano Christofoletti. "Train Circulation Planning: Quantitative Approaches." In 2017 Joint Rail Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/jrc2017-2223.
Повний текст джерелаLu, Hong, and Lu Lu. "Expression quantitative trait loci and genetic regulatory network analysis of Fbn1." In INTERNATIONAL SYMPOSIUM ON THE FRONTIERS OF BIOTECHNOLOGY AND BIOENGINEERING (FBB 2019). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5110812.
Повний текст джерелаPurrington, Kristen S., Drakoulis Yannoukakos, Jane Carpenter, Heli Nevanlinna, Angela Cox, Gianluca Severi, Christine Ambrosone, et al. "Abstract 3266: Expression quantitative trait locus analysis of triple negative breast cancer." In Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.am2014-3266.
Повний текст джерелаDowling, Caroline. "Perfect Timing: Quantitative trait locus analysis of flowering time in Cannabis sativa." In ASPB PLANT BIOLOGY 2020. USA: ASPB, 2020. http://dx.doi.org/10.46678/pb.20.1053029.
Повний текст джерелаЗвіти організацій з теми "Quantitative trait analyses"
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.
Повний текст джерелаWeller, Joel I., Ignacy Misztal, and Micha Ron. Optimization of methodology for genomic selection of moderate and large dairy cattle populations. United States Department of Agriculture, March 2015. http://dx.doi.org/10.32747/2015.7594404.bard.
Повний текст джерелаParan, 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.
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 джерелаHulata, Gideon, and Graham A. E. Gall. Breed Improvement of Tilapia: Selective Breeding for Cold Tolerance and for Growth Rate in Fresh and Saline Water. United States Department of Agriculture, November 2003. http://dx.doi.org/10.32747/2003.7586478.bard.
Повний текст джерелаSherman, 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.
Повний текст джерелаFeldman, 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.
Повний текст джерелаSeroussi, Eyal, and George Liu. Genome-Wide Association Study of Copy Number Variation and QTL for Economic Traits in Holstein Cattle. United States Department of Agriculture, September 2010. http://dx.doi.org/10.32747/2010.7593397.bard.
Повний текст джерелаFallik, 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.
Повний текст джерелаWisniewski, Michael E., Samir Droby, John L. Norelli, Noa Sela, and Elena Levin. Genetic and transcriptomic analysis of postharvest decay resistance in Malus sieversii and the characterization of pathogenicity effectors in Penicillium expansum. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7600013.bard.
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