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Journal articles on the topic 'Trait quantitative'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Yamamichi, Masato, and Stephen P. Ellner. "Antagonistic coevolution between quantitative and Mendelian traits." Proceedings of the Royal Society B: Biological Sciences 283, no. 1827 (2016): 20152926. http://dx.doi.org/10.1098/rspb.2015.2926.

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Coevolution is relentlessly creating and maintaining biodiversity and therefore has been a central topic in evolutionary biology. Previous theoretical studies have mostly considered coevolution between genetically symmetric traits (i.e. coevolution between two continuous quantitative traits or two discrete Mendelian traits). However, recent empirical evidence indicates that coevolution can occur between genetically asymmetric traits (e.g. between quantitative and Mendelian traits). We examine consequences of antagonistic coevolution mediated by a quantitative predator trait and a Mendelian pre

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2

Flint, Jonathan. "Mapping quantitative traits and strategies to find quantitative trait genes." Methods 53, no. 2 (2011): 163–74. http://dx.doi.org/10.1016/j.ymeth.2010.07.007.

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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 (2006): 298–309. http://dx.doi.org/10.1007/s00335-005-0147-3.

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Korol, Abraham B., Yefim I. Ronin, Alexander M. Itskovich, Junhua Peng, and Eviatar Nevo. "Enhanced Efficiency of Quantitative Trait Loci Mapping Analysis Based on Multivariate Complexes of Quantitative Traits." Genetics 157, no. 4 (2001): 1789–803. http://dx.doi.org/10.1093/genetics/157.4.1789.

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AbstractAn approach to increase the efficiency of mapping quantitative trait loci (QTL) was proposed earlier by the authors on the basis of bivariate analysis of correlated traits. The power of QTL detection using the log-likelihood ratio (LOD scores) grows proportionally to the broad sense heritability. We found that this relationship holds also for correlated traits, so that an increased bivariate heritability implicates a higher LOD score, higher detection power, and better mapping resolution. However, the increased number of parameters to be estimated complicates the application of this ap

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5

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

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Tsilo, T. J., J. B. Ohm, G. A. Hareland, S. Chao, and J. A. Anderson. "Quantitative trait loci influencing end-use quality traits of hard red spring wheat breeding lines." Czech Journal of Genetics and Plant Breeding 47, Special Issue (2011): S190—S195. http://dx.doi.org/10.17221/3279-cjgpb.

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Wheat bread-making quality is influenced by a complex group of traits including dough visco-elastic characteristics. In this study, quantitative trait locus/loci (QTL) mapping and analysis were conducted for endosperm&nbsp;polymeric proteins together with dough mixing strength and bread-making properties in a population of 139 (MN98550 &times; MN99394) recombinant inbred lines that was evaluated at three environments in 2006. Eleven chromosome regions were associated with endosperm polymeric proteins, explaining 4.2&ndash;31.8% of the phenotypic variation. Most of these polymeric p

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Korol, A. B., Y. I. Ronin, and V. M. Kirzhner. "Interval mapping of quantitative trait loci employing correlated trait complexes." Genetics 140, no. 3 (1995): 1137–47. http://dx.doi.org/10.1093/genetics/140.3.1137.

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Abstract An approach to increase the resolution power of interval mapping of quantitative trait (QT) loci is proposed, based on analysis of correlated trait complexes. For a given set of QTs, the broad sense heritability attributed to a QT locus (QTL) (say, A/a) is an increasing function of the number of traits. Thus, for some traits x and y, H(xy)2(A/a) &gt; or = H(x)2(A/a). The last inequality holds even if y does not depend on A/a at all, but x and y are correlated within the groups AA, Aa and aa due to nongenetic factors and segregation of genes from other chromosomes. A simple relatio

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8

Barton, N. H. "Pleiotropic models of quantitative variation." Genetics 124, no. 3 (1990): 773–82. http://dx.doi.org/10.1093/genetics/124.3.773.

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Abstract It is widely held that each gene typically affects many characters, and that each character is affected by many genes. Moreover, strong stabilizing selection cannot act on an indefinitely large number of independent traits. This makes it likely that heritable variation in any one trait is maintained as a side effect of polymorphisms which have nothing to do with selection on that trait. This paper examines the idea that variation is maintained as the pleiotropic side effect of either deleterious mutation, or balancing selection. If mutation is responsible, it must produce alleles whic

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9

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 (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 devel

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10

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

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11

Buitenhuis, A. J., T. B. Rodenburg, M. Siwek, et al. "Quantitative trait loci for behavioural traits in chickens." Livestock Production Science 93, no. 1 (2005): 95–103. http://dx.doi.org/10.1016/j.livprodsci.2004.11.010.

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12

Berke, T. G., and T. R. Rocheford. "Quantitative Trait Loci for Tassel Traits in Maize." Crop Science 39, no. 5 (1999): 1439–43. http://dx.doi.org/10.2135/cropsci1999.3951439x.

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13

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

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14

Xu, C., X. He, and S. Xu. "Mapping quantitative trait loci underlying triploid endosperm traits." Heredity 90, no. 3 (2003): 228–35. http://dx.doi.org/10.1038/sj.hdy.6800217.

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15

Panthee, D. R., V. R. Pantalone, A. M. Saxton, D. R. West, and C. E. Sams. "Quantitative trait loci for agronomic traits in soybean." Plant Breeding 126, no. 1 (2007): 51–57. http://dx.doi.org/10.1111/j.1439-0523.2006.01305.x.

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Hanson, Robert L., and William C. Knowler. "Quantitative trait linkage studies of diabetes-related traits." Current Diabetes Reports 3, no. 2 (2003): 176–83. http://dx.doi.org/10.1007/s11892-003-0042-9.

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Nelson, James C., Cristina Andreescu, Flavio Breseghello, et al. "Quantitative trait locus analysis of wheat quality traits." Euphytica 149, no. 1-2 (2006): 145–59. http://dx.doi.org/10.1007/s10681-005-9062-7.

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18

&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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19

Macgregor, Stuart, Sara A. Knott, Ian White, and Peter M. Visscher. "Quantitative Trait Locus Analysis of Longitudinal Quantitative Trait Data in Complex Pedigrees." Genetics 171, no. 3 (2005): 1365–76. http://dx.doi.org/10.1534/genetics.105.043828.

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20

Jiang, C., and Z. B. Zeng. "Multiple trait analysis of genetic mapping for quantitative trait loci." Genetics 140, no. 3 (1995): 1111–27. http://dx.doi.org/10.1093/genetics/140.3.1111.

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Abstract We present in this paper models and statistical methods for performing multiple trait analysis on mapping quantitative trait loci (QTL) based on the composite interval mapping method. By taking into account the correlated structure of multiple traits, this joint analysis has several advantages, compared with separate analyses, for mapping QTL, including the expected improvement on the statistical power of the test for QTL and on the precision of parameter estimation. Also this joint analysis provides formal procedures to test a number of biologically interesting hypotheses concerning

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21

Svischeva, G. R. "Analysis of quantitative trait loci using hybrid pedigrees: Quantitative traits of animals." Russian Journal of Genetics 43, no. 2 (2007): 200–209. http://dx.doi.org/10.1134/s1022795407020160.

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22

Kao, Chen-Hung, Zhao-Bang Zeng, and Robert D. Teasdale. "Multiple Interval Mapping for Quantitative Trait Loci." Genetics 152, no. 3 (1999): 1203–16. http://dx.doi.org/10.1093/genetics/152.3.1203.

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Abstract A new statistical method for mapping quantitative trait loci (QTL), called multiple interval mapping (MIM), is presented. It uses multiple marker intervals simultaneously to fit multiple putative QTL directly in the model for mapping QTL. The MIM model is based on Cockerham's model for interpreting genetic parameters and the method of maximum likelihood for estimating genetic parameters. With the MIM approach, the precision and power of QTL mapping could be improved. Also, epistasis between QTL, genotypic values of individuals, and heritabilities of quantitative traits can be readily

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23

Jermstad, Kathleen D., Daniel L. Bassoni, Keith S. Jech, Gary A. Ritchie, Nicholas C. Wheeler, and David B. Neale. "Mapping of Quantitative Trait Loci Controlling Adaptive Traits in Coastal Douglas Fir. III. Quantitative Trait Loci-by-Environment Interactions." Genetics 165, no. 3 (2003): 1489–506. http://dx.doi.org/10.1093/genetics/165.3.1489.

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Abstract Quantitative trait loci (QTL) were mapped in the woody perennial Douglas fir (Pseudotsuga menziesii var. menziesii [Mirb.] Franco) for complex traits controlling the timing of growth initiation and growth cessation. QTL were estimated under controlled environmental conditions to identify QTL interactions with photoperiod, moisture stress, winter chilling, and spring temperatures. A three-generation mapping population of 460 cloned progeny was used for genetic mapping and phenotypic evaluations. An all-marker interval mapping method was used for scanning the genome for the presence of

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24

Lan, Hong, Jonathan P. Stoehr, Samuel T. Nadler, Kathryn L. Schueler, Brian S. Yandell, and Alan D. Attie. "Dimension Reduction for Mapping mRNA Abundance as Quantitative Traits." Genetics 164, no. 4 (2003): 1607–14. http://dx.doi.org/10.1093/genetics/164.4.1607.

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AbstractThe advent of sophisticated genomic techniques for gene mapping and microarray analysis has provided opportunities to map mRNA abundance to quantitative trait loci (QTL) throughout the genome. Unfortunately, simple mapping of each individual mRNA trait on the scale of a typical microarray experiment is computationally intensive, subject to high sample variance, and therefore underpowered. However, this problem can be addressed by capitalizing on correlation among the large number of mRNA traits. We present a method to reduce the dimensionality for mapping gene expression data as quanti

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25

Song, Xianliang, and Tianzhen Zhang. "Quantitative trait loci controlling plant architectural traits in cotton." Plant Science 177, no. 4 (2009): 317–23. http://dx.doi.org/10.1016/j.plantsci.2009.05.015.

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26

Šimić, Domagoj, Snežana Mladenović Drinić, Zvonimir Zdunić, et al. "Quantitative Trait Loci for Biofortification Traits in Maize Grain." Journal of Heredity 103, no. 1 (2011): 47–54. http://dx.doi.org/10.1093/jhered/esr122.

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27

Gutiérrez-Gil, B., M. F. El-Zarei, L. Alvarez, et al. "Quantitative trait loci underlying milk production traits in sheep." Animal Genetics 40, no. 4 (2009): 423–34. http://dx.doi.org/10.1111/j.1365-2052.2009.01856.x.

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28

Gao, Y., Z. Q. Du, W. H. Wei, et al. "Mapping quantitative trait loci regulating chicken body composition traits." Animal Genetics 40, no. 6 (2009): 952–54. http://dx.doi.org/10.1111/j.1365-2052.2009.01911.x.

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29

Lightfoot, J. Timothy, Michael J. Turner, Daniel Pomp, Steven R. Kleeberger, and Larry J. Leamy. "Quantitative trait loci for physical activity traits in mice." Physiological Genomics 32, no. 3 (2008): 401–8. http://dx.doi.org/10.1152/physiolgenomics.00241.2007.

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The genomic locations and identities of the genes that regulate voluntary physical activity are presently unknown. The purpose of this study was to search for quantitative trait loci (QTL) that are linked with daily mouse running wheel distance, duration, and speed of exercise. F2 animals ( n = 310) derived from high active C57L/J and low active C3H/HeJ inbred strains were phenotyped for 21 days. After phenotyping, genotyping with a fully informative single-nucleotide polymorphism panel with an average intermarker interval of 13.7 cM was used. On all three activity indexes, sex and strain were

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30

Şahin-Çevik, Mehtap, and Gloria A. Moore. "Quantitative trait loci analysis of morphological traits in Citrus." Plant Biotechnology Reports 6, no. 1 (2011): 47–57. http://dx.doi.org/10.1007/s11816-011-0194-z.

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31

Yang, Runqing, Jiahan Li, and Shizhong Xu. "Mapping quantitative trait loci for traits defined as ratios." Genetica 132, no. 3 (2007): 323–29. http://dx.doi.org/10.1007/s10709-007-9175-0.

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32

Strauss, S. H., R. Lande, and G. Namkoong. "Limitations of molecular-marker-aided selection in forest tree breeding." Canadian Journal of Forest Research 22, no. 7 (1992): 1050–61. http://dx.doi.org/10.1139/x92-140.

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The advances to date with quantitative trait locus identification in agronomic crops, which have mostly been with studies of inter- and intra-specific hybrids, are of little relevance to assessing the potential for marker-aided selection in nonhybrid forest tree populations. Although molecular markers provide great opportunities for dissection of quantitative traits in experimental populations, we expect that their near-term usefulness in most operational tree breeding programs will be limited. In addition to cost, this limitation results from quantitative trait locus–marker associations being

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33

Skelly, Daniel A., Narayanan Raghupathy, Raymond F. Robledo, Joel H. Graber, and Elissa J. Chesler. "Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples." Genetics 212, no. 3 (2019): 919–29. http://dx.doi.org/10.1534/genetics.118.301865.

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Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy

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34

Barendse, W. "The transition from quantitative trait loci to diagnostic test in cattle and other livestock." Australian Journal of Experimental Agriculture 45, no. 8 (2005): 831. http://dx.doi.org/10.1071/ea05067.

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The efficient identification of the genes that influence quantitative traits requires: large sample sizes; the analysis of large numbers of polymorphisms in and around genes or surrogates for these; repeated testing in independent samples; the realisation that the inheritance patterns of quantitative trait loci will show the full range of effects found for genes that affect discrete traits; and choosing the appropriate genetic structure of the sample and the kind of DNA polymorphism for the different stages in the identification of the quantitative trait loci. The choice of trait is critical t

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35

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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36

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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37

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

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38

Almasy, Laura, and Diane M. Warren. "Software for quantitative trait analysis." Human Genomics 2, no. 3 (2005): 191. http://dx.doi.org/10.1186/1479-7364-2-3-191.

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39

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

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40

YI, NENGJUN, and SHIZHONG XU. "Mapping quantitative trait loci with epistatic effects." Genetical Research 79, no. 2 (2002): 185–98. http://dx.doi.org/10.1017/s0016672301005511.

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Epistatic variance can be an important source of variation for complex traits. However, detecting epistatic effects is difficult primarily due to insufficient sample sizes and lack of robust statistical methods. In this paper, we develop a Bayesian method to map multiple quantitative trait loci (QTLs) with epistatic effects. The method can map QTLs in complicated mating designs derived from the cross of two inbred lines. In addition to mapping QTLs for quantitative traits, the proposed method can even map genes underlying binary traits such as disease susceptibility using the threshold model.

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41

Edwards, M. D., C. W. Stuber, and J. F. Wendel. "Molecular-Marker-Facilitated Investigations of Quantitative-Trait Loci in Maize. I. Numbers, Genomic Distribution and Types of Gene Action." Genetics 116, no. 1 (1987): 113–25. http://dx.doi.org/10.1093/genetics/116.1.113.

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ABSTRACT Individual genetic factors which underlie variation in quantitative traits of maize were investigated in each of two F2 populations by examining the mean trait expressions of genotypic classes at each of 17–20 segregating marker loci. It was demonstrated that the trait expression of marker locus classes could be interpreted in terms of genetic behavior at linked quantitative trait loci (QTLs). For each of 82 traits evaluated, QTLs were detected and located to genomic sites. The numbers of detected factors varied according to trait, with the average trait significantly influenced by al

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42

Weller, J. I., M. Soller, and T. Brody. "Linkage analysis of quantitative traits in an interspecific cross of tomato (lycopersicon esculentum x lycopersicon pimpinellifolium) by means of genetic markers." Genetics 118, no. 2 (1988): 329–39. http://dx.doi.org/10.1093/genetics/118.2.329.

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Abstract Linkage relationships between loci affecting quantitative traits (QTL) and marker loci were examined in an interspecific cross between Lycopersicon esculentum and Lycopersicon pimpinellifolium. Parental lines differed for six morphological markers and for four electrophoretic markers. Almost 1700 F-2 plants were scored with respect to the genetic markers and also with respect to 18 quantitative traits. Major genes affecting the quantitative traits were not found, but out of 180 possible marker x trait combinations, 85 showed significant quantitative effects associated with the genetic

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43

Lange, Christoph, and John C. Whittaker. "Mapping Quantitative Trait Loci Using Generalized Estimating Equations." Genetics 159, no. 3 (2001): 1325–37. http://dx.doi.org/10.1093/genetics/159.3.1325.

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AbstractA number of statistical methods are now available to map quantitative trait loci (QTL) relative to markers. However, no existing methodology can simultaneously map QTL for multiple nonnormal traits. In this article we rectify this deficiency by developing a QTL-mapping approach based on generalized estimating equations (GEE). Simulation experiments are used to illustrate the application of the GEE-based approach.

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44

Lee, E. A., L. L. Darrah, and E. H. Coe. "Dosage effects on morphological and quantitative traits in maize aneuploids." Genome 39, no. 5 (1996): 898–908. http://dx.doi.org/10.1139/g96-113.

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Dosage effects generated by either loss or gain of a chromosome segment were used to identify chromosome regions associated with morphological and quantitative characters in maize (Zea mays L.). Using B–A translocation stocks introgressed into a B73Ht background, a chromosome arm dosage series in a Mo17Ht × B73Ht F1 hybrid background was created for 18 of the 20 chromosome arms. The dosage series was then evaluated for 12 quantitatively inherited characters to associate specific phenotypic changes in a trait with a specific chromosome arm. Not only did our results show the familiar aneuploid s

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Caballero, A., and P. D. Keightley. "A pleiotropic nonadditive model of variation in quantitative traits." Genetics 138, no. 3 (1994): 883–900. http://dx.doi.org/10.1093/genetics/138.3.883.

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Abstract A model of mutation-selection-drift balance incorporating pleiotropic and dominance effects of new mutations on quantitative traits and fitness is investigated and used to predict the amount and nature of genetic variation maintained in segregating populations. The model is based on recent information on the joint distribution of mutant effects on bristle traits and fitness in Drosophila melanogaster from experiments on the accumulation of spontaneous and P element-induced mutations. These experiments suggest a leptokurtic distribution of effects with an intermediate correlation betwe

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McINTYRE, LAUREN M., CYNTHIA J. COFFMAN, and R. W. DOERGE. "Detection and localization of a single binary trait locus in experimental populations." Genetical Research 78, no. 1 (2001): 79–92. http://dx.doi.org/10.1017/s0016672301005092.

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The advancements made in molecular technology coupled with statistical methodology have led to the successful detection and location of genomic regions (quantitative trait loci; QTL) associated with quantitative traits. Binary traits (e.g. susceptibility/resistance), while not quantitative in nature, are equally important for the purpose of detecting and locating significant associations with genomic regions. Existing interval regression methods used in binary trait analysis are adapted from quantitative trait analysis and the tests for regression coefficients are tests of effect, not detectio

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Schulman, N. F., S. M. Viitala, D. J. de Koning, J. Virta, A. Mäki-Tanila, and J. H. Vilkki. "Quantitative Trait Loci for Health Traits in Finnish Ayrshire Cattle." Journal of Dairy Science 87, no. 2 (2004): 443–49. http://dx.doi.org/10.3168/jds.s0022-0302(04)73183-5.

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48

Holmbeg, M., and L. Andersson-Eklund. "Quantitative Trait Loci Affecting Health Traits in Swedish Dairy Cattle." Journal of Dairy Science 87, no. 8 (2004): 2653–59. http://dx.doi.org/10.3168/jds.s0022-0302(04)73391-3.

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49

Jia, Baoyan, Xinhua Zhao, Yang Qin, et al. "Identification of quantitative trait loci for leaf traits in rice." Genetika 48, no. 2 (2016): 643–52. http://dx.doi.org/10.2298/gensr1602643j.

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A recombinant inbred lines (RILs) population of 90 lines were developed from a subspecies cross between an indica type cultivar, ?Cheongcheong?, and a japonica rice cultivar, ?Nagdong? was evaluated for leaf traits in 2009. A genetic linkage map consisting of 154 simple sequence repeat (SSR) markers was constructed, covering 1973.6 cM of 12 chromosomes with an average map distance of 13.9 cM between markers. By composite interval mapping method a total of 19 QTLs were identified for the leaf traits on 5 chromosomes (Chr.1, Chr.3, Chr.6, Chr.8 and Chr.11). The percentage of phenotypic variance

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Yang, Runqing, and Shizhong Xu. "Bayesian Shrinkage Analysis of Quantitative Trait Loci for Dynamic Traits." Genetics 176, no. 2 (2007): 1169–85. http://dx.doi.org/10.1534/genetics.106.064279.

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