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Journal articles on the topic 'QTL analysi'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Batista, Luiz Fernando Dias, Madeline E. Rivera, Aaron B. Norris, et al. "44 Effect of Quebracho (Schinopsis balansae) extract inclusion in a high roughage diet upon in vitro gas production." Journal of Animal Science 98, Supplement_2 (2020): 53–54. http://dx.doi.org/10.1093/jas/skz397.122.

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Abstract The utilization of natural plant secondary compounds as feed additives in animal nutrition has been extensively studied because of their ability to modify digestive and metabolic functions. Condensed tannin (CT) supplementation can potentially alter ruminal fermentation, and mitigate methane (CH4) emissions. The objective of this study was to determine the effect of quebracho CT extract (QT; Schinopsis balansae) within a roughage-based diet on overall fermentability and CH4 production utilizing the in vitro gas production technique (IVGP). Twenty rumen cannulated steers (227 ± 19 kg)

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2

Barjes Alrawi, Ezzideen, Erica D. Warlick, Qing Cao, et al. "High Peripheral Blood Stem Cell (PBSC) CD34+ Cell Dose Increases the Risk of Chronic Gvhd after Human Leukocyte Antigen (HLA) Matched Sibling Transplantation." Blood 128, no. 22 (2016): 5877. http://dx.doi.org/10.1182/blood.v128.22.5877.5877.

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Abstract CD34+ cell dose is a critical determinant of outcomes after allogeneic PBSC transplantation, with a CD34 dose ≥2.0 x 10e6/kg shown to positively impact hematopoietic engraftment and survival. However, it is unknown whether additional benefits are observed with even higher CD34 cell doses. Therefore, we further explored the effect of intermediate, high and very high CD34 cell doses on the incidence of engraftment, acute and chronic graft-versus-host disease (GVHD) and transplant related mortality (TRM) and on probability of survival and GVHD-Relapse-free survival (GRFS). Three hundred

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3

Takahashi, Hidekazu. "QTL analysis using the Windows QTL Cartographer." Breeding Research 10, no. 1 (2008): 11–14. http://dx.doi.org/10.1270/jsbbr.10.11.

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4

Kang, Yiwei, Miao Zhang, Yue Zhang, et al. "Genetic Mapping of Grain Shape Associated QTL Utilizing Recombinant Inbred Sister Lines in High Yielding Rice (Oryza sativa L.)." Agronomy 11, no. 4 (2021): 705. http://dx.doi.org/10.3390/agronomy11040705.

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Grain shape is a key factor for yield and quality in rice. To investigate the genetic basis of grain shape in the high-yielding hybrid rice variety Nei2You No.6, a set of recombinant inbred sister lines (RISLs) were used to map quantitative trait loci (QTLs) determining grain length (GL), grain width (GW), and length-width ratio (LWR) in four environments. A total of 91 medium/minor-effect QTL were detected using a high-density genetic map consisting of 3203 Bin markers composed of single nucleotide polymorphisms, among which 64 QTL formed 15 clusters. Twelve of 15 clusters co-localized with Q

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5

Mangin, B., P. Thoquet, and N. Grimsley. "Pleiotropic QTL Analysis." Biometrics 54, no. 1 (1998): 88. http://dx.doi.org/10.2307/2533998.

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6

Ukai, Yasuo. "Theory of QTL analysis." Breeding Research 1, no. 1 (1999): 25–31. http://dx.doi.org/10.1270/jsbbr.1.25.

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7

Xu, Peng, Jin Gao, Zhibin Cao, et al. "Fine mapping and candidate gene analysis of qFL-chr1, a fiber length QTL in cotton." Theoretical and Applied Genetics 130, no. 6 (2017): 1309–19. http://dx.doi.org/10.1007/s00122-017-2890-8.

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8

Jia, Xiaoyun, Hongxia Zhao, Jijie Zhu, Shijie Wang, Miao Li, and Guoyin Wang. "Quantitative Trait Loci Mapping and Candidate Gene Analysis for Fiber Quality Traits in Upland Cotton." Agronomy 14, no. 8 (2024): 1719. http://dx.doi.org/10.3390/agronomy14081719.

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Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4202.12 cM in length. Transgressive segregation p

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9

UKAI, Yasuo. "Quantitative Trait and QTL Analysis." Japanese journal of crop science 68, no. 2 (1999): 179–86. http://dx.doi.org/10.1626/jcs.68.179.

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10

T., Hayashi. "Recent methods for QTL analysis." Japanese Journal of Biometrics 17, no. 1/2 (1996): 91–102. http://dx.doi.org/10.5691/jjb.17.91.

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11

Pérez-Pérez, José Manuel, David Esteve-Bruna, and José Luis Micol. "QTL analysis of leaf architecture." Journal of Plant Research 123, no. 1 (2009): 15–23. http://dx.doi.org/10.1007/s10265-009-0267-z.

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12

van den Berg, J. H., E. E. Ewing, R. L. Plaisted, S. McMurry, and M. W. Bonierbale. "QTL analysis of potato tuberization." Theoretical and Applied Genetics 93, no. 3 (1996): 307–16. http://dx.doi.org/10.1007/bf00223170.

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13

van den Berg, J. H., E. E. Ewing, R. L. Plaisted, S. McMurry, and M. W. Bonierbale. "QTL analysis of potato tuberization." TAG Theoretical and Applied Genetics 93, no. 3 (1996): 307–16. http://dx.doi.org/10.1007/s001220050282.

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14

Hina, Aiman, Yongce Cao, Shiyu Song, et al. "High-Resolution Mapping in Two RIL Populations Refines Major “QTL Hotspot” Regions for Seed Size and Shape in Soybean (Glycine max L.)." International Journal of Molecular Sciences 21, no. 3 (2020): 1040. http://dx.doi.org/10.3390/ijms21031040.

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Seed size and shape are important traits determining yield and quality in soybean. However, the genetic mechanism and genes underlying these traits remain largely unexplored. In this regard, this study used two related recombinant inbred line (RIL) populations (ZY and K3N) evaluated in multiple environments to identify main and epistatic-effect quantitative trait loci (QTLs) for six seed size and shape traits in soybean. A total of 88 and 48 QTLs were detected through composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 15 QTLs were common

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15

Wu, Sanling, Jie Qiu, and Qikang Gao. "QTL-BSA: A Bulked Segregant Analysis and Visualization Pipeline for QTL-seq." Interdisciplinary Sciences: Computational Life Sciences 11, no. 4 (2019): 730–37. http://dx.doi.org/10.1007/s12539-019-00344-9.

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16

TOGASHI, Kenji, Naoyuki YAMAMOTO, Osamu SASAKI, JEO Rege, and Hisato TAKEDA. "Marker-QTL-Association Analysis Incorporating Diversification of QTL Variance and its Application." Nihon Chikusan Gakkaiho 67, no. 11 (1996): 923–29. http://dx.doi.org/10.2508/chikusan.67.923.

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17

Jia, Xiaoyun, Jijie Zhu, Hongxia Zhao, et al. "QTL Mapping and Candidate Gene Analysis for Cotton Fiber Quality and Early Maturity Using F2 and F3 Generations." Plants 14, no. 7 (2025): 1063. https://doi.org/10.3390/plants14071063.

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Cotton is the most important natural fiber-producing crop globally. High-quality fiber and early maturity are equally important breeding goals in the cotton industry. However, it remains challenging to synchronously improve these traits through conventional breeding techniques. To identify additional genetic information relating to fiber quality and early maturity, 11 phenotypic traits for the F2 and F3 generations were tested, and quantitative trait loci (QTL) mapping was performed. Candidate genes were analyzed using published RNA-seq datasets and qRT-PCR assays. All 11 tested traits showed

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18

Chung, Ill-Min, Tae-Ho Ham, Gi-Won Cho, et al. "Study of Quantitative Trait Loci (QTLs) Associated with Allelopathic Trait in Rice." Genes 11, no. 5 (2020): 470. http://dx.doi.org/10.3390/genes11050470.

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In rice there are few genetic studies reported for allelopathy traits, which signify the ability of plants to inhibit or stimulate growth of other plants in the environment, by exuding chemicals. QTL analysis for allelopathic traits were conducted with 98 F8 RILs developed from the cross between the high allelopathic parents of ‘Sathi’ and non-allelopathic parents of ‘Nong-an’. The performance of allelopathic traits were evaluated with inhibition rate on root length, shoot length, total length, root weight, shoot weight, and total weight of lettuce as a receiver plant. With 785 polymorphic DNA

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19

Freyer, G., and N. Vukasinovic. "Comparison of granddaughter design and general pedigree design analysis of QTL in dairy cattle: a simulation study." Czech Journal of Animal Science 50, No. 12 (2011): 545–52. http://dx.doi.org/10.17221/4260-cjas.

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Traditional methods for detection and mapping of quantitative trait loci (QTL) in dairy cattle populations are usually based on daughter design (DD) or granddaughter design (GDD). Although these designs are well established and usually successful in detecting QTL, they consider sire families independently of each other, thereby ignoring relationships among other animals in the population and consequently, reducing the power of QTL detection. In this study we compared a traditional GDD with a general pedigree design (GPD) and assessed the precision and power of both methods for detecting and lo

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20

Byrne, Patrick F. "Quantitative Trait Locus (QTL) Analysis 1." Journal of Natural Resources and Life Sciences Education 34, no. 1 (2005): 124. http://dx.doi.org/10.2134/jnrlse.2005.0124.

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21

Byrne, Patrick F. "Quantitative Trait Locus (QTL) Analysis 2." Journal of Natural Resources and Life Sciences Education 34, no. 1 (2005): 124. http://dx.doi.org/10.2134/jnrlse.2005.0124a.

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22

Bekes, F., W. Ma, and K. Gale. "QTL analysis of wheat quality traits." Acta Agronomica Hungarica 50, no. 3 (2002): 249–62. http://dx.doi.org/10.1556/aagr.50.2002.3.3.

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This paper aims to give an overview on the different aspects of QTL analysis of quality traits of wheat through the brief introduction of molecular genetics, cereal chemistry and the statistical methods developed and applied recently in this area. Some examples are also provided, based on the author's research activity carried out in the National Wheat Molecular Marker Program (NWMMP) established in Australia in 1996.

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23

Nganga, Joseph, Mabel Imbuga, and Fuad A. Iraqi. "Comparative genome analysis of trypanotolerance QTL." Veterinary Immunology and Immunopathology 128, no. 1-3 (2009): 216. http://dx.doi.org/10.1016/j.vetimm.2008.10.017.

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24

Iimura, Kazunari, Kimihisa Tasaki, Yoshiko Nakazawa, and Masayuki Amagai. "QTL analysis of strawberry anthracnose resistance." Breeding Research 15, no. 3 (2013): 90–97. http://dx.doi.org/10.1270/jsbbr.15.90.

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25

van den Berg, J. H., E. E. Ewing, R. L. Plaisted, S. McMurry, and M. W. Bonierbale. "QTL analysis of potato tuber dormancy." Theoretical and Applied Genetics 93, no. 3 (1996): 317–24. http://dx.doi.org/10.1007/bf00223171.

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26

Ewing, E. E., R. L. Plaisted, S. McMurry, M. W. Bonierbale, and J. H. van den Berg. "QTL analysis of potato tuber dormancy." TAG Theoretical and Applied Genetics 93, no. 3 (1996): 317–24. http://dx.doi.org/10.1007/s001220050283.

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27

Shimoi, Hitoshi, and Taku Kato. "QTL analysis of a sake yeast." Journal of Biotechnology 136 (October 2008): S746. http://dx.doi.org/10.1016/j.jbiotec.2008.07.1776.

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28

Khan, Nisar A., Stephen M. Githiri, Eduardo R. Benitez, et al. "QTL analysis of cleistogamy in soybean." Theoretical and Applied Genetics 117, no. 4 (2008): 479–87. http://dx.doi.org/10.1007/s00122-008-0792-5.

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29

Verbyla, Arūnas P., Andrew W. George, Colin R. Cavanagh, and Klara L. Verbyla. "Whole-genome QTL analysis for MAGIC." Theoretical and Applied Genetics 127, no. 8 (2014): 1753–70. http://dx.doi.org/10.1007/s00122-014-2337-4.

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30

Besnier, François, Arnaud Le Rouzic, and José M. Álvarez-Castro. "Applying QTL analysis to conservation genetics." Conservation Genetics 11, no. 2 (2010): 399–408. http://dx.doi.org/10.1007/s10592-009-0036-5.

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31

Li, Ning, Jian Sun, Jingguo Wang, et al. "QTL analysis for alkaline tolerance of rice and verification of a major QTL." Plant Breeding 136, no. 6 (2017): 881–91. http://dx.doi.org/10.1111/pbr.12539.

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32

Evans, David M., Gu Zhu, David L. Duffy, Grant W. Montgomery, Ian H. Frazer, and Nicholas G. Martin. "Multivariate QTL linkage analysis suggests a QTL for platelet count on chromosome 19q." European Journal of Human Genetics 12, no. 10 (2004): 835–42. http://dx.doi.org/10.1038/sj.ejhg.5201248.

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33

Burke, John M., Shunxue Tang, Steven J. Knapp, and Loren H. Rieseberg. "Genetic Analysis of Sunflower Domestication." Genetics 161, no. 3 (2002): 1257–67. http://dx.doi.org/10.1093/genetics/161.3.1257.

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Abstract Quantitative trait loci (QTL) controlling phenotypic differences between cultivated sunflower and its wild progenitor were investigated in an F3 mapping population. Composite interval mapping revealed the presence of 78 QTL affecting the 18 quantitative traits of interest, with 2–10 QTL per trait. Each QTL explained 3.0–68.0% of the phenotypic variance, although only 4 (corresponding to 3 of 18 traits) had effects &gt;25%. Overall, 51 of the 78 QTL produced phenotypic effects in the expected direction, and for 13 of 18 traits the majority of QTL had the expected effect. Despite be

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34

Goffinet, Bruno, and Sophie Gerber. "Quantitative Trait Loci: A Meta-analysis." Genetics 155, no. 1 (2000): 463–73. http://dx.doi.org/10.1093/genetics/155.1.463.

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Abstract This article presents a method to combine QTL results from different independent analyses. This method provides a modified Akaike criterion that can be used to decide how many QTL are actually represented by the QTL detected in different experiments. This criterion is computed to choose between models with one, two, three, etc., QTL. Simulations are carried out to investigate the quality of the model obtained with this method in various situations. It appears that the method allows the length of the confidence interval of QTL location to be consistently reduced when there are only ver

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35

Huang, W., Z. Xu, Y. Xiong, and B. Zuo. " QTL analysis for carcass composition and meat quality traits on SSC7q1.1-q1.4 region in Large White × Meishan F₂ pigs." Czech Journal of Animal Science 57, No. 6 (2012): 283–89. http://dx.doi.org/10.17221/5963-cjas.

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Significant QTL for carcass and meat quality traits on Sus scrofa chromosome 7 (SSC7) were detected in various Meishan derived resource populations, especially on q1.1-q1.4 region. In order to confirm and narrow the QTL in this region, seven single-nucleotide polymorphisms (SNPs) and one insertion or deletion located in eight genes (BTNL1, SLC39A7, COL21A1, PPARD, GLP1R, MDFI, GNMT, and PLA2G7) were included for linkage mapping in a Large White &times; Meishan resource population, as well as two flanking microsatellite markers (SW2155 and SW352). Ten chromosome-wise significant QTL and two

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36

KIM, JONG-JOO, HONGHUA ZHAO, HAUKE THOMSEN, MAX F. ROTHSCHILD, and JACK C. M. DEKKERS. "Combined line-cross and half-sib QTL analysis of crosses between outbred lines." Genetical Research 85, no. 3 (2005): 235–48. http://dx.doi.org/10.1017/s0016672305007597.

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Data from an F2 cross between breeds of livestock are typically analysed by least squares line-cross or half-sib models to detect quantitative trait loci (QTL) that differ between or segregate within breeds. These models can also be combined to increase power to detect QTL, while maintaining the computational efficiency of least squares. Tests between models allow QTL to be characterized into those that are fixed (LC QTL), or segregating at similar (HS QTL) or different (CB QTL) frequencies in parental breeds. To evaluate power of the combined model, data wih various differences in QTL allele

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37

TAKAI, Toshiyuki, Akihiro OHSUMI, Yumiko ARAI, et al. "QTL Analysis of Leaf Photosynthesis in Rice." Japan Agricultural Research Quarterly: JARQ 47, no. 3 (2013): 227–35. http://dx.doi.org/10.6090/jarq.47.227.

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38

Kenis, K., and J. Keulemans. "QTL ANALYSIS OF GROWTH CHARACTERISTICS IN APPLE." Acta Horticulturae, no. 663 (December 2004): 369–74. http://dx.doi.org/10.17660/actahortic.2004.663.63.

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39

K., Sato. "QTL analysis and related network in berley." Japanese Journal of Biometrics 17, no. 1/2 (1996): 79–90. http://dx.doi.org/10.5691/jjb.17.79.

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40

Piepho, Hans-Peter, and Klaus Pillen. "Mixed modelling for QTL × environment interaction analysis." Euphytica 137, no. 1 (2004): 147–53. http://dx.doi.org/10.1023/b:euph.0000040512.84025.16.

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41

Vreugdenhil, D., M. Koornneel, and L. I. Sergeeva. "Use of QTL analysis in physiological research." Russian Journal of Plant Physiology 54, no. 1 (2007): 10–15. http://dx.doi.org/10.1134/s1021443707010025.

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42

Hyne, V., and M. J. Kearsey. "QTL analysis: further uses of ‘marker regression’." Theoretical and Applied Genetics 91, no. 3 (1995): 471–76. http://dx.doi.org/10.1007/bf00222975.

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43

Kearsey, M. J., and V. Hyne. "QTL analysis: a simple ‘marker-regression’ approach." Theoretical and Applied Genetics 89, no. 6 (1994): 698–702. http://dx.doi.org/10.1007/bf00223708.

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44

Bhattacharjee, Samsiddhi, Chia-Ling Kuo, Nandita Mukhopadhyay, Guy N. Brock, Daniel E. Weeks, and Eleanor Feingold. "Robust Score Statistics for QTL Linkage Analysis." American Journal of Human Genetics 82, no. 3 (2008): 567–82. http://dx.doi.org/10.1016/j.ajhg.2007.11.012.

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45

Zeng, D. L., L. B. Guo, Y. B. Xu, K. Yasukumi, L. H. Zhu, and Q. Qian. "QTL analysis of seed storability in rice." Plant Breeding 125, no. 1 (2006): 57–60. http://dx.doi.org/10.1111/j.1439-0523.2006.01169.x.

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46

Ritter, E., N. N. Rodríguez-Medina, B. Velásquez, et al. "QTL (QUANTITATIVE TRAIT LOCI) ANALYSIS IN GUAVA." Acta Horticulturae, no. 849 (January 2010): 193–202. http://dx.doi.org/10.17660/actahortic.2010.849.21.

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47

Clarke, Jonathan H., Richard Mithen, James K. M. Brown, and Caroline Dean. "QTL analysis of flowering time inArabidopsis thaliana." Molecular and General Genetics MGG 248, no. 3 (1995): 278–86. http://dx.doi.org/10.1007/bf02191594.

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48

Teng, Sheng, Dali Zeng, Qian Qian, Yasufumi Kunihifo, Danian Huang, and Lihuang Zhu. "QTL analysis of rice low temperature germinability." Chinese Science Bulletin 46, no. 21 (2001): 1800–1803. http://dx.doi.org/10.1007/bf02900554.

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49

Ben-Chaim, Arnon, Yelena Borovsky, Matthew Falise, et al. "QTL analysis for capsaicinoid content in Capsicum." Theoretical and Applied Genetics 113, no. 8 (2006): 1481–90. http://dx.doi.org/10.1007/s00122-006-0395-y.

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50

Parh, D. K., D. R. Jordan, E. A. B. Aitken, et al. "QTL analysis of ergot resistance in sorghum." Theoretical and Applied Genetics 117, no. 3 (2008): 369–82. http://dx.doi.org/10.1007/s00122-008-0781-8.

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