Relevant bibliographies by topics / Quantitative trait loci

Academic literature on the topic 'Quantitative trait loci'

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Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Quantitative trait loci"

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Mayo, O. "Interaction and quantitative trait loci." Australian Journal of Experimental Agriculture 44, no. 11 (2004): 1135. http://dx.doi.org/10.1071/ea03240.

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Parallel searches for quantitative trait loci (QTL) for growth-related traits in different populations frequently detect sets of QTL that hardly overlap. Thus, many QTL potentially exist. Tools for the detection of QTL that interact are available and are currently being tested. Initial results suggest that epistasis is widespread. Modelling of the first recognised interaction, dominance, continues to be developed. Multigenic interaction appears to be a necessary part of any explanation. This paper covers an attempt to link some of these studies and to draw inferences about useful approaches to understanding and using the genes that influence quantitative traits.
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&NA;. "Quantitative trait loci mapping." Psychiatric Genetics 3, no. 4 (1993): 203–6. http://dx.doi.org/10.1097/00041444-199324000-00001.

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Peters, Luanne L., Amy J. Lambert, Weidong Zhang, Gary A. Churchill, Carlo Brugnara, and Orah S. Platt. "Quantitative trait loci for baseline erythroid traits." Mammalian Genome 17, no. 4 (April 2006): 298–309. http://dx.doi.org/10.1007/s00335-005-0147-3.

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Yan, Jian, and Weikuan Gu. "Parameters of Quantitative Trait Loci." Critical Reviews™ in Eukaryotic Gene Expression 17, no. 4 (2007): 335–46. http://dx.doi.org/10.1615/critreveukargeneexpr.v17.i4.60.

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Mackay, Trudy F. C. "Quantitative trait loci in Drosophila." Nature Reviews Genetics 2, no. 1 (January 2001): 11–20. http://dx.doi.org/10.1038/35047544.

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Plomin, Robert, Gerald E. McClearn, and Grazyna Gora-Maslak. "Quantitative trait loci and psychopharmacology." Journal of Psychopharmacology 5, no. 1 (January 1991): 1–9. http://dx.doi.org/10.1177/026988119100500102.

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Rajon, Etienne, and Joshua B. Plotkin. "The evolution of genetic architectures underlying quantitative traits." Proceedings of the Royal Society B: Biological Sciences 280, no. 1769 (October 22, 2013): 20131552. http://dx.doi.org/10.1098/rspb.2013.1552.

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In the classic view introduced by R. A. Fisher, a quantitative trait is encoded by many loci with small, additive effects. Recent advances in quantitative trait loci mapping have begun to elucidate the genetic architectures underlying vast numbers of phenotypes across diverse taxa, producing observations that sometimes contrast with Fisher's blueprint. Despite these considerable empirical efforts to map the genetic determinants of traits, it remains poorly understood how the genetic architecture of a trait should evolve, or how it depends on the selection pressures on the trait. Here, we develop a simple, population-genetic model for the evolution of genetic architectures. Our model predicts that traits under moderate selection should be encoded by many loci with highly variable effects, whereas traits under either weak or strong selection should be encoded by relatively few loci. We compare these theoretical predictions with qualitative trends in the genetics of human traits, and with systematic data on the genetics of gene expression levels in yeast. Our analysis provides an evolutionary explanation for broad empirical patterns in the genetic basis for traits, and it introduces a single framework that unifies the diversity of observed genetic architectures, ranging from Mendelian to Fisherian.
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Berke, T. G., and T. R. Rocheford. "Quantitative Trait Loci for Tassel Traits in Maize." Crop Science 39, no. 5 (September 1999): 1439–43. http://dx.doi.org/10.2135/cropsci1999.3951439x.

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Banerjee, Samprit, Brian S. Yandell, and Nengjun Yi. "Bayesian Quantitative Trait Loci Mapping for Multiple Traits." Genetics 179, no. 4 (August 2008): 2275–89. http://dx.doi.org/10.1534/genetics.108.088427.

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Yi, Nengjun, Shizhong Xu, Varghese George, and David B. Allison. "Mapping Multiple Quantitative Trait Loci for Ordinal Traits." Behavior Genetics 34, no. 1 (January 2004): 3–15. http://dx.doi.org/10.1023/b:bege.0000009473.43185.43.

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Dissertations / Theses on the topic "Quantitative trait loci"

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Nyström, Per-Erik. "Quantitative trait loci in pig production /." Uppsala : Swedish Univ. of Agricultural Sciences (Sveriges lantbruksuniv.), 1999. http://epsilon.slu.se/avh/1999/91-576-5712-2.pdf.

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Turri, Maria Grazia. "Mapping of behavioural quantitative trait loci." Thesis, University of Oxford, 2002. http://ora.ox.ac.uk/objects/uuid:89823fa1-c1d3-49e3-acb9-46da18b12245.

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Anxiety is a common disorder which affects about 25% of the population and whose pathophysiology is still poorly understood. Animal models of disease have been widely used to investigate the molecular basis of human disorders, including psychiatric illnesses. This thesis is about the study of the genetic basis of a mouse model of anxiety. I have carried out a QTL mapping study of behavioural measures thought to model anxiety. I report results from 1,636 mice, assessed for a large number of phenotypes in five ethological tests. Mice belonged to two F2 intercrosses originated by four lines generated in a replicate selection experiment. By comparing mapping results between the two crosses, I have demonstrated that selection operated on the same relatively small number of loci in the four selected lines. Analysis of genetic effect of QTL across phenotypes has allowed me to identify loci with specific roles on different dimensions of anxious behaviour, therefore enhancing our understanding of the anxiety phenotype in mice. For some of these QTL I have also accomplished fine mapping experiments: a locus on chromosome 15 is now contained in an interval of only 3 centimorgans. This work is the basis for further molecular dissection of the genetic loci that underlie anxiety and provides a starting point for the discovery of genes involved in a common psychiatric condition.
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Joehanes, Roby. "Multiple-trait multiple-interval mapping of quantitative-trait loci." Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1605.

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Santana, Morant Dámaris. "Bayesian mapping of multiple quantitative trait loci." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012166.

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Carlborg, Örjan. "New methods for mapping quantitative trait loci /." Uppsala : Dept. of Animal Breeding and Genetics, Swedish Univ. of Agricultural Sciences ([Institutionen för husdjurens genetik], Sveriges lantbruksuniv.), 2002. http://projkat.slu.se/SafariDokument/210.htm.

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Martinez, de la Vega Octavio. "Quantitative trait loci estimation in plant populations." Thesis, University of Reading, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358346.

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Logeswaran, Sayanthan. "Mapping quantitative trait loci in microbial populations." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/4881.

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Linkage between markers and genes that affect a phenotype of interest may be determined by examining differences in marker allele frequency in the extreme progeny of a cross between two inbred lines. This strategy is usually employed when pooling is used to reduce genotyping costs. When the cross progeny are asexual the extreme progeny may be selected by multiple generations of asexual reproduction and selection. In this thesis I will analyse this method of measuring phenotype in asexual cross progeny. The aim is to examine the behaviour of marker allele frequency due to selection over many generations, and also to identify statistically significant changes in frequency in the selected population. I will show that stochasticity in marker frequency in the selected population arises due the finite initial population size. For Mendelian traits, the initial population size should be at least in the low to mid hundreds to avoid spurious changes in marker frequency in the selected population. For quantitative traits the length of time selection is applied for, as well as the initial population size, will affect the stochasticity in marker frequency. The longer selection is applied for, the more chance of spurious changes in marker frequency. Also for quantitative traits, I will show that the presence of epistasis can hinder changes in marker frequency at selected loci, and consequently make identification of selected loci more difficult. I also show that it is possible to detect epistasis from the marker frequency by identifying reversals in the direction of marker frequency change. Finally, I develop a maximum likelihood based statistical model that aims to identify significant changes in marker frequency in the selected population. I will show that the power of this statistical model is high for detecting large changes in marker frequency, but very low for detecting small changes in frequency.
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Yang, Jie. "Nonparametric functional mapping of quantitative trait loci." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0014762.

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Greenshields, David. "Isolation of adaptive quantitative trait loci in Antirrhinum." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/14950.

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Understanding the genetic basis of quantitative traits represents a huge goal in modern molecular and evolutionary biology. Here, the natural genetics of the genus Antirrhinum, within which separate species can be successfully interbred, are used to investigate differences in a range of morphological characteristics. The two species used in the study, Antirrhinum majus and Antirrhinum molle, have become adapted to very diverse environments and consequently exhibit large variance in a wide range of traits. A large-scale FI mutant screen, from a cross between a transposon-active A. majus line and A. molle, isolated segregating mutations for flower size, flower colour, trichome density and branching in self-pollinated F2 populations. Amplified Fragment Length Polymorphism analysis of the F2 and the use of molecular maps have shown the mutations generally correspond to known Quantitative Trait Loci, and the roles of genes linked to these regions are discussed. The technique sheds some light on the molecular and evolutionary mechanisms underpinning diversity in Antirrhinum and has implications for the use of transposon-tagging in locating QTL in other plant systems.
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Ritchey, Brian Michael. "Quantitative Trait Loci Mapping Of Macrophage Atherogenic Phenotypes." Cleveland State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=csu1510080975338565.

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Books on the topic "Quantitative trait loci"

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Camp, Nicola J., and Angela Cox. Quantitative Trait Loci. New Jersey: Humana Press, 2002. http://dx.doi.org/10.1385/1592591760.

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Rifkin, Scott A., ed. Quantitative Trait Loci (QTL). Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-785-9.

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Weller, J. I., ed. Quantitative trait loci analysis in animals. Wallingford: CABI, 2009. http://dx.doi.org/10.1079/9781845934675.0000.

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Weller, J. I., ed. Quantitative trait loci analysis in animals. Wallingford: CABI, 2001. http://dx.doi.org/10.1079/9780851994024.0000.

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Weller, Joel Ira. Quantitative trait loci analysis in animals. Oxon, UK: CABI Pub., 2001.

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Weller, Joel Ira. Quantitative trait loci analysis in animals. 2nd ed. Cambridge, MA: CABI North American Office, 2009.

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J, Camp Nicola, and Cox Angela 1961-, eds. Quantitative trait loci: Methods and protocols. Totowa, N.J: Humana Press, 2002.

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Salinas-Garcia, Gilberto Eduardo. Mapping quantitative trait loci controlling agronomic traits in Brassica napus L. Birmingham: University of Birmingham, 1996.

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Burns, Malcolm James. Quantitative trait loci mapping in Arabidopsis: Theory and practice. Birmingham: University of Birmingham, 1997.

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Lantbruksuniversitet, Sveriges, ed. Genome analysis of quantitative trait loci in the pig. Uppsala: Sveriges Lantbruksuniversitet, 1997.

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Book chapters on the topic "Quantitative trait loci"

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Cardon, Lon R. "Quantitative Trait Loci." In Behavior Genetic Approaches in Behavioral Medicine, 237–50. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-9377-2_13.

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Xu, Shizhong. "Mapping Quantitative Trait Loci." In Quantitative Genetics, 307–45. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83940-6_18.

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Weller, Joel I. "Mapping Quantitative Trait Loci." In Bovine Genomics, 169–91. Oxford, UK: Wiley-Blackwell, 2012. http://dx.doi.org/10.1002/9781118301739.ch12.

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Knapp, Steven J. "Mapping quantitative trait loci." In Advances in Cellular and Molecular Biology of Plants, 58–96. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1104-1_4.

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Xiong, Dong-Hai, Jian-Feng Liu, Yan-Fang Guo, Yan Guo, Tie-Lin Yang, Hui Jiang, Yuan Chen, Fang Yang, Robert R. Recker, and Hong-Wen Deng. "Quantitative Trait Loci Mapping." In Osteoporosis, 203–35. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-104-8_16.

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Knapp, Steven J. "Mapping quantitative trait loci." In Advances in Cellular and Molecular Biology of Plants, 59–99. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9815-6_5.

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Brandenberger, Luke. "Quantitative Trait Loci (QTL)." In Encyclopedia of Animal Cognition and Behavior, 1–4. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-47829-6_209-1.

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Brandenberger, Luke. "Quantitative Trait Loci (QTL)." In Encyclopedia of Animal Cognition and Behavior, 5839–42. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-55065-7_209.

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Xu, Shizhong. "Mapping Expression Quantitative Trait Loci." In Principles of Statistical Genomics, 395–411. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-0-387-70807-2_25.

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Falchi, 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.

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Conference papers on the topic "Quantitative trait loci"

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Beyer, Andreas, Silpa Suthram, and Trey Ideker. "Uncovering Regulatory Pathways with Expression Quantitative Trait Loci." In 2007 IEEE International Workshop on Genomic Signal Processing and Statistics. IEEE, 2007. http://dx.doi.org/10.1109/gensips.2007.4365837.

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Boone, 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.

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Fu, Chen-Ping, Fernando Pardo-Manuel de Villena, and Leonard McMillan. "Quantitative trait loci mapping with microarray marker intensities." In BCB '14: ACM-BCB '14. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2649387.2649432.

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Lu, 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.

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Saferali, A., W. Kim, J. Yun, Z. Xu, R. Chase, M. H. Cho, P. Castaldi, and C. P. Hersh. "Splice Quantitative Trait Loci (sQTLs) in Whole Blood and Lung Tissue." In American Thoracic Society 2022 International Conference, May 13-18, 2022 - San Francisco, CA. American Thoracic Society, 2022. http://dx.doi.org/10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a4660.

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High, MD, HY Cho, F. Polack, T. Wiltshire, and S. Kleeberger. "Quantitative Trait Loci Associated with Respiratory Syncytial Virus Susceptibility in Inbred 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.a5985.

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Chua, Z., Y. Y. Sio, and F. T. Chew. "Genome-wide cis-expression-Quantitative Trait Loci (eQTL) in association with asthma." In ERS International Congress 2022 abstracts. European Respiratory Society, 2022. http://dx.doi.org/10.1183/13993003.congress-2022.2593.

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Ghodke-Puranik, Y., J. Zhongbo, W. Fan, M. Jensen, J. Dorschner, D. Vsetecka, S. Amin, et al. "10 Single cell expression quantitative trait LOCI (EQTL) analsis of established lupus-risk loci in patient monocytes." In LUPUS 2017 & ACA 2017, (12th International Congress on SLE &, 7th Asian Congress on Autoimmunity). Lupus Foundation of America, 2017. http://dx.doi.org/10.1136/lupus-2017-000215.10.

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Pavinato, Vitor A. C. "Characterization of quantitative trait loci associated with soybean aphid adaptation to resistant plants." In 2016 International Congress of Entomology. Entomological Society of America, 2016. http://dx.doi.org/10.1603/ice.2016.112119.

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Lundtoft, Christian, Pascal Pucholt, Johanna K. Sandling, Lars Rönnblom, and Niklas Hagberg. "O23 Identification of protein-quantitative trait loci (pQTLs) in the interferon signalling pathway." In 12th European Lupus Meeting. Lupus Foundation of America, 2020. http://dx.doi.org/10.1136/lupus-2020-eurolupus.34.

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Reports on the topic "Quantitative trait loci"

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Hu, Zhiliang, James M. Reecy, and Max F. Rothschild. A Quantitative Trait Loci Resource and Comparison Tool for Pigs: PigQTLDB. Ames (Iowa): Iowa State University, January 2005. http://dx.doi.org/10.31274/ans_air-180814-1068.

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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.

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As is true for all crops, production of Citrus fruit is limited by traits whose characteristics are the products of many genes (i.e. cold hardiness). In order to modify these traits by marker aided selection or molecular genetic techniques, it is first necessary to map the relevant genes. Mapping of quantitative trait loci (QTLs) in perennial plants has been extremely difficult, requiring large numbers of mature plants. Production of suitable mapping populations has been inhibited by aspects of reproductive biology (e.g. incompatibility, apomixis) and delayed by juvenility. New approaches promise to overcome some of these obstacles. The overall objective of this project was to determine whether QTLs for environmental stress tolerance could be effectively mapped in the perennial crop Citrus, using an extensive linkage map consisting of various types of molecular markers. Specific objectives were to: 1) Produce a highly saturated genetic linkage map of Citrus by continuing to place molecular markers of several types on the map. 2) Exploiting recently developed technology and already characterized parental types, determine whether QTLs governing cold acclimation can be mapped using very young seedling populations. 3) Determine whether the same strategy can be transferred to a different situation by mapping QTLs influencing Na+ and C1- exclusion (likely components of salinity tolerance) in the already characterized cross and in new alternative crosses. 4) Construct a YAC library of the citrus genome for future mapping and cloning.
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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.

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Individual loci affecting economic traits in dairy cattle (ETL) have been detected via linkage to genetic markers by application of the granddaughter design in the US population and the daughter design in the Israeli population. From these analyses it is not possible to determine allelic frequencies in the population at large, or whether the same alleles are segregating in different families. We proposed to answer this question by application of the "modified granddaughter design", in which granddaughters with a common maternal grandsire are both genotyped and analyzed for the economic traits. The objectives of the proposal were: 1) to fine map three segregating ETL previously detected by a daughter design analysis of the Israeli dairy cattle population; 2) to determine the effects of ETL alleles in different families relative to the population mean; 3) for each ETL, to determine the number of alleles and allele frequencies. The ETL on Bostaurusautosome (BT A) 6 chiefly affecting protein concentration was localized to a 4 cM chromosomal segment centered on the microsatellite BM143 by the daughter design. The modified granddaughter design was applied to a single family. The frequency of the allele increasing protein percent was estimated at 0.63+0.06. The hypothesis of equal allelic frequencies was rejected at p<0.05. Segregation of this ETL in the Israeli population was confirmed. The genes IBSP, SPP1, and LAP3 located adjacent to BM143 in the whole genome cattle- human comparative map were used as anchors for the human genome sequence and bovine BAC clones. Fifteen genes within 2 cM upstream of BM143 were located in the orthologous syntenic groups on HSA4q22 and HSA4p15. Only a single gene, SLIT2, was located within 2 cM downstream of BM143 in the orthologous HSA4p15 region. The order of these genes, as derived from physical mapping of BAC end sequences, was identical to the order within the orthologous syntenic groups on HSA4: FAM13A1, HERC3. CEB1, FLJ20637, PP2C-like, ABCG2, PKD2. SPP, MEP, IBSP, LAP3, EG1. KIAA1276, HCAPG, MLR1, BM143, and SLIT2. Four hundred and twenty AI bulls with genetic evaluations were genotyped for 12 SNPs identified in 10 of these genes, and for BM143. Seven SNPs displayed highly significant linkage disequilibrium effects on protein percentage (P<0.000l) with the greatest effect for SPP1. None of SNP genotypes for two sires heterozygous for the ETL, and six sires homozygous for the ETL completely corresponded to the causative mutation. The expression of SPP 1 and ABCG2 in the mammary gland corresponded to the lactation curve, as determined by microarray and QPCR assays, but not in the liver. Anti-sense SPP1 transgenic mice displayed abnormal mammary gland differentiation and milk secretion. Thus SPP 1 is a prime candidate gene for this ETL. We confirmed that DGAT1 is the ETL segregating on BTA 14 that chiefly effects fat concentration, and that the polymorphism is due to a missense mutation in an exon. Four hundred Israeli Holstein bulls were genotyped for this polymorphism, and the change in allelic frequency over the last 20 years was monitored.
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Hulata, 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.

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The main objective of this project was to initiate a breeding program to produce cold-tolerant and salinity-tolerant synthetic breeds of tilapia, from a base population consisting of a four-species hybrid population created under an earlier BARD project. A secondary objective was to estimate genetic parameters for the traits growth rate under fresh- and salt-water and for cold tolerance. A third objective was to place quantitative trait loci that affect these traits of interest (e.g., growth rate in fresh-water, salt-water and cold tolerance) on the growing linkage map of primarily microsatellite loci. We have encountered fertility problems that were apparently the result of the complex genetic structure of this base population. The failure in producing the first generation of the breeding program has forced us to stop the intended breeding program. Thus, upon approval of BARD office, this objective was dropped and during the last year we have focused on the secondary objective of the original project during the third year of the project, but failed to perform the intended analysis to estimate genetic parameters for the traits of interest. We have succeeded, however, to strengthen the earlier identification of a QTL for cold tolerance by analyzing further segregating families. The results support the existence of a QTL for cold tolerance on linkage group 15, corresponding to UNH linkage group 23. The results also indicate a QTL for the same trait on linkage group 12, corresponding to UNH linkage group 4.
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Gall, Graham A. E., Gideon Hulata, Eric M. Hallerman, Bernard May, and Umiel Nakdimon. Creating and Characterizing Genetic Variation in Tilapia through the Creation of an Artificial Center of Origin. United States Department of Agriculture, February 2000. http://dx.doi.org/10.32747/2000.7574344.bard.

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Five stocks of tilapia [oreochromis niloticus (on), red O. niloticus (ROn), O. aureus (Oa), O. mossambicus (Om), and Sarotherodon galilaeus (Sg)] were used to produce two-way (F1), three-way (3WC) and four-way crosses (4WC). Three 4WC groups, containing equal representation of all four species, formed the base population for a new synthetic stock, called an "artificial center of origin" (ACO). Four genomic maps were created using microsatellite and AFLP markers, two from a 3WC family [Om female and (Oa x ROn) male] and two from a 4WC family [(Om x Oas) females and (Sg x On) male]. Sixty-two loci segregating from the female parent of the 3WC mapped to 14 linkage groups while 214 loci from the male parent mapped to 24 linkage groups. Similarly, 131 loci segregating from the female parent of the 4WC mapped to 26 linkage groups and 118 loci from the male parent mapped to 25 linkage groups. Preliminary screening of an F2 and a 4WC family identified a number of loci associated with cold tolerance and body weight. These loci were clustered in a few linkage groups, suggesting they may be indicative of quantitative trait loci.
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Weller, 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.

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The project’s general objectives were to determine specific polymorphisms at the DNA level responsible for observed quantitative trait loci (QTLs) and to estimate their effects, frequencies, and selection potential in the Holstein dairy cattle breed. The specific objectives were to (1) localize the causative polymorphisms to small chromosomal segments based on analysis of 52 U.S. Holstein bulls each with at least 100 sons with high-reliability genetic evaluations using the a posteriori granddaughter design; (2) sequence the complete genomes of at least 40 of those bulls to 20 coverage; (3) determine causative polymorphisms based on concordance between the bulls’ genotypes for specific polymorphisms and their status for a QTL; (4) validate putative quantitative trait variants by genotyping a sample of Israeli Holstein cows; and (5) perform gene expression analysis using statistical methodologies, including determination of signatures of selection, based on somatic cells of cows that are homozygous for contrasting quantitative trait variants; and (6) analyze genes with putative quantitative trait variants using data mining techniques. Current methods for genomic evaluation are based on population-wide linkage disequilibrium between markers and actual alleles that affect traits of interest. Those methods have approximately doubled the rate of genetic gain for most traits in the U.S. Holstein population. With determination of causative polymorphisms, increasing the accuracy of genomic evaluations should be possible by including those genotypes as fixed effects in the analysis models. Determination of causative polymorphisms should also yield useful information on gene function and genetic architecture of complex traits. Concordance between QTL genotype as determined by the a posteriori granddaughter design and marker genotype was determined for 30 trait-by-chromosomal segment effects that are segregating in the U.S. Holstein population; a probability of <10²⁰ was used to accept the null hypothesis that no segregating gene within the chromosomal segment was affecting the trait. Genotypes for 83 grandsires and 17,217 sons were determined by either complete sequence or imputation for 3,148,506 polymorphisms across the entire genome. Variant sites were identified from previous studies (such as the 1000 Bull Genomes Project) and from DNA sequencing of bulls unique to this project, which is one of the largest marker variant surveys conducted for the Holstein breed of cattle. Effects for stature on chromosome 11, daughter pregnancy rate on chromosome 18, and protein percentage on chromosome 20 met 3 criteria: (1) complete or nearly complete concordance, (2) nominal significance of the polymorphism effect after correction for all other polymorphisms, and (3) marker coefficient of determination >40% of total multiple-regression coefficient of determination for the 30 polymorphisms with highest concordance. The missense polymorphism Phe279Tyr in GHR at 31,909,478 base pairs on chromosome 20 was confirmed as the causative mutation for fat and protein concentration. For effect on fat percentage, 12 additional missensepolymorphisms on chromosome 14 were found that had nearly complete concordance with the suggested causative polymorphism (missense mutation Ala232Glu in DGAT1). The markers used in routine U.S. genomic evaluations were increased from 60,000 to 80,000 by adding markers for known QTLs and markers detected in BARD and other research projects. Objectives 1 and 2 were completely accomplished, and objective 3 was partially accomplished. Because no new clear-cut causative polymorphisms were discovered, objectives 4 through 6 were not completed.
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7

Wisniewski, 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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Blue mold of apple caused by Penicilliumexpansumis a major postharvest disease. Selection for postharvest disease resistance in breeding programs has been ignored in favor of fruit quality traits such as size, color, taste, etc. The identification of postharvest disease resistance as a heritable trait would represent a significant accomplishment and has not been attempted in apple. Furthermore, insight into the biology of the pathogenicity of P. expansumin apple could provide new approaches to postharvest decay management. Hypothesis: Postharvest resistance of apple to P. expansumcan be mapped to specific genetic loci and significant quantitative-trait-loci (QTLs) can be identified that account for a major portion of the population variance. Susceptibility of apple fruit to P. expansumis dependent on the ability of the pathogen to produce LysM effectors that actively suppress primary and/or secondary resistance mechanisms in the fruit. Objectives: 1) Identify QTL(s) and molecular markers for blue mold resistance in GMAL4593 mapping population (‘Royal Gala’ X MalussieversiiPI613981), 2) Characterize the transcriptome of the host and pathogen (P. expansum) during the infection process 3) Determine the function of LysM genes in pathogenicity of P. expansum. Methods: A phenotypic evaluation of blue mold resistance in the GMAL4593 mapping population, conducted in several different years, will be used for QTL analysis (using MapQTL 6.0) to identify loci associated with blue mold resistance. Molecular markers will be developed for the resistance loci. Transcriptomic analysis by RNA-seq will be used to conduct a time course study of gene expression in resistant and susceptible apple GMAL4593 genotypes in response to P. expansum, as well as fungal responses to both genotypes. Candidate resistance genes identified in the transcriptomic study and or bioinformatic analysis will be positioned in the ‘Golden Delicious’ genome to identify markers that co-locate with the identified QTL(s). A functional analysis of LysM genes on pathogenicity will be conducted by eliminating or reducing the expression of individual effectors by heterologous recombination and silencing technologies. LysMeffector genes will also be expressed in a yeast expression system to study protein function. Expected Results: Identification of postharvest disease resistance QTLs and tightly-linked genetic markers. Increased knowledge of the role of effectors in blue mold pathogenic
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Fridman, 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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Heterosis, the enigmatic phenomenon in which whole genome heterozygous hybrids demonstrate superior fitness compared to their homozygous parents, is the main cornerstone of modern crop plant breeding. One explanation for this non-additive inheritance of hybrids is interaction of alleles within the same locus. This proposal aims at screening, identifying and investigating heterosis trait loci (HTL) for different yield traits by implementing a novel integrated mapping approach in Sorghum bicolor as a model for other crop plants. Originally, the general goal of this research was to perform a genetic dissection of heterosis in a diallel built from a set of Sorghum bicolor inbred lines. This was conducted by implementing a novel computational algorithm which aims at associating between specific heterozygosity found among hybrids with heterotic variation for different agronomic traits. The initial goals of the research are: (i) Perform genotype by sequencing (GBS) of the founder lines (ii) To evaluate the heterotic variation found in the diallel by performing field trails and measurements in the field (iii) To perform QTL analysis for identifying heterotic trait loci (HTL) (iv) to validate candidate HTL by testing the quantitative mode of inheritance in F2 populations, and (v) To identify candidate HTL in NAM founder lines and fine map these loci by test-cross selected RIL derived from these founders. The genetic mapping was initially achieved with app. 100 SSR markers, and later the founder lines were genotyped by sequencing. In addition to the original proposed research we have added two additional populations that were utilized to further develop the HTL mapping approach; (1) A diallel of budding yeast (Saccharomyces cerevisiae) that was tested for heterosis of doubling time, and (2) a recombinant inbred line population of Sorghum bicolor that allowed testing in the field and in more depth the contribution of heterosis to plant height, as well as to achieve novel simulation for predicting dominant and additive effects in tightly linked loci on pseudooverdominance. There are several conclusions relevant to crop plants in general and to sorghum breeding and biology in particular: (i) heterosis for reproductive (1), vegetative (2) and metabolic phenotypes is predominantly achieved via dominance complementation. (ii) most loci that seems to be inherited as overdominant are in fact achieving superior phenotype of the heterozygous due to linkage in repulsion, namely by pseudooverdominant mechanism. Our computer simulations show that such repulsion linkage could influence QTL detection and estimation of effect in segregating populations. (iii) A new height QTL (qHT7.1) was identified near the genomic region harboring the known auxin transporter Dw3 in sorghum, and its genetic dissection in RIL population demonstrated that it affects both the upper and lower parts of the plant, whereas Dw3 affects only the part below the flag leaf. (iv) HTL mapping for grain nitrogen content in sorghum grains has identified several candidate genes that regulate this trait, including several putative nitrate transporters and a transcription factor belonging to the no-apical meristem (NAC)-like large gene family. This activity was combined with another BARD-funded project in which several de-novo mutants in this gene were identified for functional analysis.
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Weller, 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.

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The main objectives of this research was to detect the specific polymorphisms responsible for observed quantitative trait loci and develop optimal strategies for genomic evaluations and selection for moderate (Israel) and large (US) dairy cattle populations. A joint evaluation using all phenotypic, pedigree, and genomic data is the optimal strategy. The specific objectives were: 1) to apply strategies for determination of the causative polymorphisms based on the “a posteriori granddaughter design” (APGD), 2) to develop methods to derive unbiased estimates of gene effects derived from SNP chips analyses, 3) to derive optimal single-stage methods to estimate breeding values of animals based on marker, phenotypic and pedigree data, 4) to extend these methods to multi-trait genetic evaluations and 5) to evaluate the results of long-term genomic selection, as compared to traditional selection. Nearly all of these objectives were met. The major achievements were: The APGD and the modified granddaughter designs were applied to the US Holstein population, and regions harboring segregating quantitative trait loci (QTL) were identified for all economic traits of interest. The APGD was able to find segregating QTL for all the economic traits analyzed, and confidence intervals for QTL location ranged from ~5 to 35 million base pairs. Genomic estimated breeding values (GEBV) for milk production traits in the Israeli Holstein population were computed by the single-step method and compared to results for the two-step method. The single-step method was extended to derive GEBV for multi-parity evaluation. Long-term analysis of genomic selection demonstrated that inclusion of pedigree data from previous generations may result in less accurate GEBV. Major conclusions are: Predictions using single-step genomic best linear unbiased prediction (GBLUP) were the least biased, and that method appears to be the best tool for genomic evaluation of a small population, as it automatically accounts for parental index and allows for inclusion of female genomic information without additional steps. None of the methods applied to the Israeli Holstein population were able to derive GEBV for young bulls that were significantly better than parent averages. Thus we confirm previous studies that the main limiting factor for the accuracy of GEBV is the number of bulls with genotypes and progeny tests. Although 36 of the grandsires included in the APGD were genotyped for the BovineHDBeadChip, which includes 777,000 SNPs, we were not able to determine the causative polymorphism for any of the detected QTL. The number of valid unique markers on the BovineHDBeadChip is not sufficient for a reasonable probability to find the causative polymorphisms. Complete resequencing of the genome of approximately 50 bulls will be required, but this could not be accomplished within the framework of the current project due to funding constraints. Inclusion of pedigree data from older generations in the derivation of GEBV may result is less accurate evaluations.
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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.

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The general goal was to identify, map, and tag, with DNA markers, segments of chromosomes of a wild species (wild emmer wheat, the progenitor of cultivated wheat) determining the number, chromosomal locations, interactions, and effects of genes that control quantitative traits when transferred to a cultivated plant (bread wheat). Slight modifications were introduced and not all objectives could be completed within the human and financial resources available, as noted with the specific objectives listed below: 1. To identify the genetic contribution of each of the available wild emmer chromosome-arm substitution lines (CASLs) in the bread wheat cultivar Bethlehem for quantitative traits, including grain yield and its components and grain protein concentration and yield, and the effect of major loci affecting the quality of end-use products. [The quality of end-use products was not analyzed.] 2. To determine the extent and nature of genetic interactions (epistatic effects) between and within homoeologous groups 1 and 7 for the chromosome arms carrying "wild" and "cultivated" alleles as expressed in grain and protein yields and other quantitative traits. [Two experiments were successful, grain protein concentration could not be measured; data are partially analyzed.] 3. To derive recombinant substitution lines (RSLs) for the chromosome arms of homoeologous groups 1 and 7 that were found previously to promote grain and protein yields of cultivated wheat. [The selection of groups 1 and 7 tons based on grain yield in pot experiments. After project began, it was decided also to derive RSLs for the available arms of homoeologous group 4 (4AS and 4BL), based on the apparent importance of chromosome group 4, based on early field trials of the CASLs.] 4. To characterize the RSLs for quantitative traits as in objective 1 and map and tag chromosome segments producing significant effects (quantitative trait loci, QTLs by RFLP markers. [Producing a large population of RSLs for each chromosome arm and mapping them proved more difficult than anticipated, low numbers of RSLs were obtained for two of the chromosome arms.] 5. To construct recombination genetic maps of chromosomes of homoeologous groups 1 and 7 and to compare them to existing maps of wheat and other cereals [Genetic maps are not complete for homoeologous groups 4 and 7.] The rationale for this project is that wild species have characteristics that would be valuable if transferred to a crop plant. We demonstrated the sequence of chromosome manipulations and genetic tests needed to confirm this potential value and enhance transfer. This research has shown that a wild tetraploid species harbors genetic variability for quantitative traits that is interactive and not simply additive when introduced into a common genetic background. Chromosomal segments from several chromosome arms improve yield and protein in wheat but their effect is presumably enhanced when combination of genes from several segments are integrated into a single genotype in order to achieve the benefits of genes from the wild species. The interaction between these genes and those in the recipient species must be accounted for. The results of this study provide a scientific basis for some of the disappointing results that have historically obtained when using wild species as donors for crop improvement and provide a strategy for further successes.
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