Статті в журналах: "Quantitative genetics"

1

Mackay, Trudy F. C., Michael Lynch, and Bruce Walsh. "Quantitative Genetics." Evolution 53, no. 1 (February 1999): 307. http://dx.doi.org/10.2307/2640946.

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2

Gunter, Chris. "Quantitative genetics." Nature 456, no. 7223 (December 2008): 719. http://dx.doi.org/10.1038/456719a.

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3

Mackay, Trudy F. C. "QUANTITATIVE GENETICS." Evolution 53, no. 1 (February 1999): 307–9. http://dx.doi.org/10.1111/j.1558-5646.1999.tb05359.x.

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4

Hill, William G. "Sewall Wright and quantitative genetics." Genome 31, no. 1 (January 1, 1989): 190–95. http://dx.doi.org/10.1139/g89-033.

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Some aspects of Wright's great contribution to quantitative genetics and animal breeding are reviewed in relation to current research and practice. Particular aspects discussed are as follows: the utility of his definition of inbreeding coefficient in terms of the correlation of uniting gametes; the maintenance of genetic variation in the optimum model; the inter-relations between past and present animal-breeding practice and the shifting-balance theory of evolution.Key words: quantitative genetics, inbreeding coefficient, genetic variation, evolution.

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5

van Buijtenen, J. P. "Genomics and quantitative genetics." Canadian Journal of Forest Research 31, no. 4 (April 1, 2001): 617–22. http://dx.doi.org/10.1139/x00-171.

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The interaction between genomics and quantitative genetics has been a two-way street. Genomics contributed genetic markers and genetic maps making it possible to study quantitative trait loci (QTLs), and quantitative genetics contributed new theories and computational techniques to deal with the data generated by QTL studies. QTL studies in forest trees have led to the discovery of a few major genes masquerading as quantitative genes, such as genes for rust resistance in several pine species. QTLs for many traits including height growth, leaf traits, wood specific gravity, flowering, frost resistance, disease resistance, and ease of vegetative propagation were found in one or more species. Spring cold hardiness in Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) holds the record for number of QTLs with 14. Generally the number is under seven. The effects are often large, but this may often be due to small population sizes. At this time the impact on forest tree breeding is small, although the potential is certainly there. An interesting marker aided back-crossing program is underway in American chestnut (Castanea dentata (Marsh.) Borkh.).

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6

FRANKHAM, RICHARD. "Quantitative genetics in conservation biology." Genetical Research 74, no. 3 (December 1999): 237–44. http://dx.doi.org/10.1017/s001667239900405x.

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Most of the major genetic concerns in conservation biology, including inbreeding depression, loss of evolutionary potential, genetic adaptation to captivity and outbreeding depression, involve quantitative genetics. Small population size leads to inbreeding and loss of genetic diversity and so increases extinction risk. Captive populations of endangered species are managed to maximize the retention of genetic diversity by minimizing kinship, with subsidiary efforts to minimize inbreeding. There is growing evidence that genetic adaptation to captivity is a major issue in the genetic management of captive populations of endangered species as it reduces reproductive fitness when captive populations are reintroduced into the wild. This problem is not currently addressed, but it can be alleviated by deliberately fragmenting captive populations, with occasional exchange of immigrants to avoid excessive inbreeding. The extent and importance of outbreeding depression is a matter of controversy. Currently, an extremely cautious approach is taken to mixing populations. However, this cannot continue if fragmented populations are to be adequately managed to minimize extinctions. Most genetic management recommendations for endangered species arise directly, or indirectly, from quantitative genetic considerations.

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7

Plomin, Robert, and Jenae Neiderhiser. "Quantitative Genetics, Molecular Genetics, and Intelligence." Intelligence 15, no. 4 (October 1991): 369–87. http://dx.doi.org/10.1016/0160-2896(91)90001-t.

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8

Hansen, Thomas F., and Christophe Pélabon. "Evolvability: A Quantitative-Genetics Perspective." Annual Review of Ecology, Evolution, and Systematics 52, no. 1 (November 2, 2021): 153–75. http://dx.doi.org/10.1146/annurev-ecolsys-011121-021241.

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The concept of evolvability emerged in the early 1990s and soon became fashionable as a label for different streams of research in evolutionary biology. In evolutionary quantitative genetics, evolvability is defined as the ability of a population to respond to directional selection. This differs from other fields by treating evolvability as a property of populations rather than organisms or lineages and in being focused on quantification and short-term prediction rather than on macroevolution. While the term evolvability is new to quantitative genetics, many of the associated ideas and research questions have been with the field from its inception as biometry. Recent research on evolvability is more than a relabeling of old questions, however. New operational measures of evolvability have opened possibilities for understanding adaptation to rapid environmental change, assessing genetic constraints, and linking micro- and macroevolution.

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9

Macgregor, Stuart, Sara A. Knott, Ian White, and Peter M. Visscher. "Quantitative Trait Locus Analysis of Longitudinal Quantitative Trait Data in Complex Pedigrees." Genetics 171, no. 3 (July 14, 2005): 1365–76. http://dx.doi.org/10.1534/genetics.105.043828.

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10

Slatkin, Montgomery. "Quantitative Genetics of Heterochrony." Evolution 41, no. 4 (July 1987): 799. http://dx.doi.org/10.2307/2408889.

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11

Bulmer, M. G., and W. A. Becker. "Manual of Quantitative Genetics." Biometrics 41, no. 4 (December 1985): 1101. http://dx.doi.org/10.2307/2530989.

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12

Goddard, M. E. "Quantitative genetics: Detecting selection." Heredity 90, no. 4 (April 2003): 277. http://dx.doi.org/10.1038/sj.hdy.6800251.

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13

Nichols, R. A. "Quantitative genetics focus issue." Heredity 94, no. 3 (February 24, 2005): 273–74. http://dx.doi.org/10.1038/sj.hdy.6800646.

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14

Slatkin, Montgomery. "QUANTITATIVE GENETICS OF HETEROCHRONY." Evolution 41, no. 4 (July 1987): 799–811. http://dx.doi.org/10.1111/j.1558-5646.1987.tb05854.x.

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15

Lawrence, M. J., H. S. Pooni, and D. Senadhira. "Quantitative genetics of rice." Field Crops Research 61, no. 2 (April 1999): 189–92. http://dx.doi.org/10.1016/s0378-4290(98)00157-9.

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16

Gall, G. A. E. "Manual of quantitative genetics." Aquaculture 54, no. 3 (June 1986): 243–44. http://dx.doi.org/10.1016/0044-8486(86)90331-5.

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17

Falconer, D. "Quantitative genetics in Edinburgh: 1947-1980." Genetics 133, no. 2 (February 1, 1993): 137–42. http://dx.doi.org/10.1093/genetics/133.2.137.

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18

O’Brien, Eleanor K., and Jason B. Wolf. "Evolutionary Quantitative Genetics of Genomic Imprinting." Genetics 211, no. 1 (November 2, 2018): 75–88. http://dx.doi.org/10.1534/genetics.118.301373.

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19

Barton, N. H. "Pleiotropic models of quantitative variation." Genetics 124, no. 3 (March 1, 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 which are only mildly deleterious (s approximately 10(-3)), but nevertheless have significant effects on the trait. Balancing selection can readily maintain high heritabilities; however, selection must be spread over many weakly selected polymorphisms if large responses to artificial selection are to be possible. In both classes of pleiotropic model, extreme phenotypes are less fit, giving the appearance of stabilizing selection on the trait. However, it is shown that this effect is weak (of the same order as the selection on each gene): the strong stabilizing selection which is often observed is likely to be caused by correlations with a limited number of directly selected traits. Possible experiments for distinguishing the alternatives are discussed.

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20

Fournier-Level, Alexandre, Loïc Le Cunff, Camila Gomez, Agnès Doligez, Agnès Ageorges, Catherine Roux, Yves Bertrand, Jean-Marc Souquet, Véronique Cheynier, and Patrice This. "Quantitative Genetic Bases of Anthocyanin Variation in Grape (Vitis vinifera L. ssp. sativa) Berry: A Quantitative Trait Locus to Quantitative Trait Nucleotide Integrated Study." Genetics 183, no. 3 (August 31, 2009): 1127–39. http://dx.doi.org/10.1534/genetics.109.103929.

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The combination of QTL mapping studies of synthetic lines and association mapping studies of natural diversity represents an opportunity to throw light on the genetically based variation of quantitative traits. With the positional information provided through quantitative trait locus (QTL) mapping, which often leads to wide intervals encompassing numerous genes, it is now feasible to directly target candidate genes that are likely to be responsible for the observed variation in completely sequenced genomes and to test their effects through association genetics. This approach was performed in grape, a newly sequenced genome, to decipher the genetic architecture of anthocyanin content. Grapes may be either white or colored, ranging from the lightest pink to the darkest purple tones according to the amount of anthocyanin accumulated in the berry skin, which is a crucial trait for both wine quality and human nutrition. Although the determinism of the white phenotype has been fully identified, the genetic bases of the quantitative variation of anthocyanin content in berry skin remain unclear. A single QTL responsible for up to 62% of the variation in the anthocyanin content was mapped on a Syrah × Grenache F1 pseudo-testcross. Among the 68 unigenes identified in the grape genome within the QTL interval, a cluster of four Myb-type genes was selected on the basis of physiological evidence (VvMybA1, VvMybA2, VvMybA3, and VvMybA4). From a core collection of natural resources (141 individuals), 32 polymorphisms revealed significant association, and extended linkage disequilibrium was observed. Using a multivariate regression method, we demonstrated that five polymorphisms in VvMybA genes except VvMybA4 (one retrotransposon, three single nucleotide polymorphisms and one 2-bp insertion/deletion) accounted for 84% of the observed variation. All these polymorphisms led to either structural changes in the MYB proteins or differences in the VvMybAs promoters. We concluded that the continuous variation in anthocyanin content in grape was explained mainly by a single gene cluster of three VvMybA genes. The use of natural diversity helped to reduce one QTL to a set of five quantitative trait nucleotides and gave a clear picture of how isogenes combined their effects to shape grape color. Such analysis also illustrates how isogenes combine their effect to shape a complex quantitative trait and enables the definition of markers directly targeted for upcoming breeding programs.

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21

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 (April 1, 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 approach when a large number of traits are considered simultaneously. Here we present a multivariate generalization of our previous two-trait QTL analysis. The proposed multivariate analogue of QTL contribution to the broad-sense heritability based on interval-specific calculation of eigenvalues and eigenvectors of the residual covariance matrix allows prediction of the expected QTL detection power and mapping resolution for any subset of the initial multivariate trait complex. Permutation technique allows chromosome-wise testing of significance for the whole trait complex and the significance of the contribution of individual traits owing to: (a) their correlation with other traits, (b) dependence on the chromosome in question, and (c) both a and b. An example of application of the proposed method on a real data set of 11 traits from an experiment performed on an F2/F3 mapping population of tetraploid wheat (Triticum durum × T. dicoccoides) is provided.

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22

Sardi, Maria, and Audrey P. Gasch. "Genetic background effects in quantitative genetics: gene-by-system interactions." Current Genetics 64, no. 6 (April 11, 2018): 1173–76. http://dx.doi.org/10.1007/s00294-018-0835-7.

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23

Doebeli, Michael. "Quantitative Genetics and Population Dynamics." Evolution 50, no. 2 (April 1996): 532. http://dx.doi.org/10.2307/2410829.

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24

Mackay, Trudy F. C., and Derek A. Roff. "Quantitative Genetics and Phenotypic Evolution." Evolution 52, no. 2 (April 1998): 635. http://dx.doi.org/10.2307/2411100.

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25

Wehner, Jeanne M., Richard A. Radcliffe, and Barbara J. Bowers. "Quantitative Genetics and Mouse Behavior." Annual Review of Neuroscience 24, no. 1 (March 2001): 845–67. http://dx.doi.org/10.1146/annurev.neuro.24.1.845.

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26

Gordon, Ian L. "Quantitative Genetics of Autogamous F2." Hereditas 134, no. 3 (April 22, 2004): 255–62. http://dx.doi.org/10.1111/j.1601-5223.2001.00255.x.

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27

de Jong, G. "Quantitative Genetics of reaction norms." Journal of Evolutionary Biology 3, no. 5-6 (September 1990): 447–68. http://dx.doi.org/10.1046/j.1420-9101.1990.3050447.x.

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28

Wray, N. R., and P. M. Visscher. "Quantitative genetics of disease traits." Journal of Animal Breeding and Genetics 132, no. 2 (March 30, 2015): 198–203. http://dx.doi.org/10.1111/jbg.12153.

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29

Baker, R. J. "Quantitative genetics in plant breeding." Genome 31, no. 2 (January 15, 1989): 1092. http://dx.doi.org/10.1139/g89-190.

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30

Gordon, Ian L. "Quantitative genetics of intraspecies hybrids." Heredity 83, no. 6 (1999): 757–64. http://dx.doi.org/10.1046/j.1365-2540.1999.00634.x.

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31

Sherman, Paul W., and David F. Westneat. "Multiple mating and quantitative genetics." Animal Behaviour 36, no. 5 (September 1988): 1545–47. http://dx.doi.org/10.1016/s0003-3472(88)80227-6.

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32

Cheng, Kimberly M., and Paul B. Siegel. "Quantitative genetics of multiple mating." Animal Behaviour 40, no. 2 (August 1990): 406–7. http://dx.doi.org/10.1016/s0003-3472(05)80939-x.

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33

Narain, Prem. "Quantitative genetics: past and present." Molecular Breeding 26, no. 2 (February 18, 2010): 135–43. http://dx.doi.org/10.1007/s11032-010-9406-4.

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34

Doebeli, Michael. "QUANTITATIVE GENETICS AND POPULATION DYNAMICS." Evolution 50, no. 2 (April 1996): 532–46. http://dx.doi.org/10.1111/j.1558-5646.1996.tb03866.x.

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35

Mackay, Trudy F. C. "QUANTITATIVE GENETICS AND PHENOTYPIC EVOLUTION." Evolution 52, no. 2 (April 1998): 635–40. http://dx.doi.org/10.1111/j.1558-5646.1998.tb01664.x.

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36

Kresovich, S. "Quantitative genetics in maize breeding." Field Crops Research 23, no. 1 (February 1990): 78–79. http://dx.doi.org/10.1016/0378-4290(90)90102-h.

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37

Gibson, Greg, and Bruce Weir. "The quantitative genetics of transcription." Trends in Genetics 21, no. 11 (November 2005): 616–23. http://dx.doi.org/10.1016/j.tig.2005.08.010.

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38

Williams, Claire G. "Handbook of quantitative forest genetics." Forest Ecology and Management 59, no. 1-2 (June 1993): 177. http://dx.doi.org/10.1016/0378-1127(93)90079-3.

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39

Zeng, Z. B. "Precision mapping of quantitative trait loci." Genetics 136, no. 4 (April 1, 1994): 1457–68. http://dx.doi.org/10.1093/genetics/136.4.1457.

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Abstract Adequate separation of effects of possible multiple linked quantitative trait loci (QTLs) on mapping QTLs is the key to increasing the precision of QTL mapping. A new method of QTL mapping is proposed and analyzed in this paper by combining interval mapping with multiple regression. The basis of the proposed method is an interval test in which the test statistic on a marker interval is made to be unaffected by QTLs located outside a defined interval. This is achieved by fitting other genetic markers in the statistical model as a control when performing interval mapping. Compared with the current QTL mapping method (i.e., the interval mapping method which uses a pair or two pairs of markers for mapping QTLs), this method has several advantages. (1) By confining the test to one region at a time, it reduces a multiple dimensional search problem (for multiple QTLs) to a one dimensional search problem. (2) By conditioning linked markers in the test, the sensitivity of the test statistic to the position of individual QTLs is increased, and the precision of QTL mapping can be improved. (3) By selectively and simultaneously using other markers in the analysis, the efficiency of QTL mapping can be also improved. The behavior of the test statistic under the null hypothesis and appropriate critical value of the test statistic for an overall test in a genome are discussed and analyzed. A simulation study of QTL mapping is also presented which illustrates the utility, properties, advantages and disadvantages of the method.

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40

Zeng, Z. B., and C. C. Cockerham. "Mutation models and quantitative genetic variation." Genetics 133, no. 3 (March 1, 1993): 729–36. http://dx.doi.org/10.1093/genetics/133.3.729.

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Abstract Analyses of evolution and maintenance of quantitative genetic variation depend on the mutation models assumed. Currently two polygenic mutation models have been used in theoretical analyses. One is the random walk mutation model and the other is the house-of-cards mutation model. Although in the short term the two models give similar results for the evolution of neutral genetic variation within and between populations, the predictions of the changes of the variation are qualitatively different in the long term. In this paper a more general mutation model, called the regression mutation model, is proposed to bridge the gap of the two models. The model regards the regression coefficient, gamma, of the effect of an allele after mutation on the effect of the allele before mutation as a parameter. When gamma = 1 or 0, the model becomes the random walk model or the house-of-cards model, respectively. The additive genetic variances within and between populations are formulated for this mutation model, and some insights are gained by looking at the changes of the genetic variances as gamma changes. The effects of gamma on the statistical test of selection for quantitative characters during macroevolution are also discussed. The results suggest that the random walk mutation model should not be interpreted as a null hypothesis of neutrality for testing against alternative hypotheses of selection during macroevolution because it can potentially allocate too much variation for the change of population means under neutrality.

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41

Cheverud, James M., Eric J. Routman, F. A. M. Duarte, Bruno van Swinderen, Kilinyaa Cothran, and Christy Perel. "Quantitative Trait Loci for Murine Growth." Genetics 142, no. 4 (April 1, 1996): 1305–19. http://dx.doi.org/10.1093/genetics/142.4.1305.

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Abstract Body size is an archetypal quantitative trait with variation due to the segregation of many gene loci, each of relatively minor effect, and the environment. We examine the effects of quantitative trait loci (QTLs) on age-specific body weights and growth in the F2 intercross of the LG/J and SM/J strains of inbred mice. Weekly weights (1-10 wk) and 75 microsatellite genotypes were obtained for 535 mice. Interval mapping was used to locate and measure the genotypic effects of QTLs on body weight and growth. QTL effects were detected on 16 of the 19 autosomes with several chromosomes carrying more than one QTL. The number of QTLs for age-specific weights varied from seven at 1 week to 17 at 10 wk. The QTLs were each of relatively minor, subequal effect. QTLs affecting early and late growth were generally distinct, mapping to different chromosomal locations indicating separate genetic and physiological systems for early and later murine growth.

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42

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

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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 very few “actual” QTL locations. An application of the method is given using data from the maize database available online at http://www.agron.missouri.edu/.

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43

Nagylaki, T. "Geographical variation in a quantitative character." Genetics 136, no. 1 (January 1, 1994): 361–81. http://dx.doi.org/10.1093/genetics/136.1.361.

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Abstract A model for the evolution of the local averages of a quantitative character under migration, selection, and random genetic drift in a subdivided population is formulated and investigated. Generations are discrete and nonoverlapping; the monoecious, diploid population mates at random in each deme. All three evolutionary forces are weak, but the migration pattern and the local population numbers are otherwise arbitrary. The character is determined by purely additive gene action and a stochastically independent environment; its distribution is Gaussian with a constant variance; and it is under Gaussian stabilizing selection with the same parameters in every deme. Linkage disequilibrium is neglected. Most of the results concern the covariances of the local averages. For a finite number of demes, explicit formulas are derived for (i) the asymptotic rate and pattern of convergence to equilibrium, (ii) the variance of a suitably weighted average of the local averages, and (iii) the equilibrium covariances when selection and random drift are much weaker than migration. Essentially complete analyses of equilibrium and convergence are presented for random outbreeding and site homing, the Levene and island models, the circular habitat and the unbounded linear stepping-stone model in the diffusion approximation, and the exact unbounded stepping-stone model in one and two dimensions.

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44

Imprialou, Martha, André Kahles, Joshua G. Steffen, Edward J. Osborne, Xiangchao Gan, Janne Lempe, Amarjit Bhomra, et al. "Genomic Rearrangements inArabidopsisConsidered as Quantitative Traits." Genetics 205, no. 4 (February 7, 2017): 1425–41. http://dx.doi.org/10.1534/genetics.116.192823.

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45

Chen, Xin, Fuping Zhao, and Shizhong Xu. "Mapping Environment-Specific Quantitative Trait Loci." Genetics 186, no. 3 (August 30, 2010): 1053–66. http://dx.doi.org/10.1534/genetics.110.120311.

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46

Camussi, A., E. Ottaviano, T. Calinski, and Z. Kaczmarek. "GENETIC DISTANCES BASED ON QUANTITATIVE TRAITS." Genetics 111, no. 4 (December 1, 1985): 945–62. http://dx.doi.org/10.1093/genetics/111.4.945.

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ABSTRACT Morphological data showing continuous distributions, polygenically controlled, may be particularly useful in intergroup classification below the species level; an appropriate distance analysis based on these traits is an important tool in evolutionary biology and in plant and animal breeding.—The interpretation of morphological distances in genetic terms is not easy because simple phenotypic data may lead to biased estimates of genetic distances. Convenient estimates can be obtained whenever it is possible to breed populations according to a suitable crossing design and to derive information from genetic parameters.—A general method for determining genetic distances is proposed. The procedure of multivariate analysis of variance is extended to estimate appropriate genetic parameters (genetic effects). Not only are optimal statistical estimates of parameters obtained but also the procedure allows the measurement of genetic distances between populations as linear functions of the estimated parameters, providing an appropriate distance matrix that can be defined in terms of these parameters. The use of the T 2 statistic, defined in terms of the vector of contrasts specifying the distance, permits the testing of the significance of any distance between any pair of populations that may be of interest from a genetic point of view.—A numerical example from maize diallel data is reported in order to illustrate the procedure. In particular, heterosis effects are used as the basis for estimates of genetic divergence between populations.

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47

Gianola, Daniel, Bjorg Heringstad, and Jorgen Odegaard. "On the Quantitative Genetics of Mixture Characters." Genetics 173, no. 4 (April 19, 2006): 2247–55. http://dx.doi.org/10.1534/genetics.105.054197.

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48

Tachida, H., and C. C. Cockerham. "A building block model for quantitative genetics." Genetics 121, no. 4 (April 1, 1989): 839–44. http://dx.doi.org/10.1093/genetics/121.4.839.

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Abstract We introduce a quantitative genetic model for multiple alleles which permits the parameterization of the degree, D, of dominance of favorable or unfavorable alleles. We assume gene effects to be random from some distribution and independent of the D's. We then fit the usual least-squares population genetic model of additive and dominance effects in an infinite equilibrium population to determine the five genetic components--additive variance sigma 2 a, dominance variance sigma 2 d, variance of homozygous dominance effects d2, covariance of additive and homozygous dominance effects d1, and the square of the inbreeding depression h--required to treat finite populations and large populations that have been through a bottleneck or in which there is inbreeding. The effects of dominance can be summarized as functions of the average, D, and the variance, sigma 2 D. An important distinction arises between symmetrical and nonsymmetrical distributions of gene effects. With symmetrical distributions d1 = -d2/2 which is always negative, and the contribution of dominance to sigma 2 a is equal to d2/2. With nonsymmetrical distributions there is an additional contribution H to sigma 2 a and -H/2 to d1, the sign of H being determined by D and the skew of the distribution. Some numerical evaluations are presented for the normal and exponential distributions of gene effects, illustrating the effects of the number of alleles and of the variation in allelic frequencies. Random additive by additive (a*a) epistatic effects contribute to sigma 2 a and to the a*a variance, sigma 2/aa, the relative contributions depending on the number of alleles and the variation in allelic frequencies.(ABSTRACT TRUNCATED AT 250 WORDS)

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49

VISSCHER, PETER M., BRIAN McEVOY, and JIAN YANG. "From Galton to GWAS: quantitative genetics of human height." Genetics Research 92, no. 5-6 (December 2010): 371–79. http://dx.doi.org/10.1017/s0016672310000571.

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SummaryHeight has been studied in human genetics since the late 1800s. We review what we have learned about the genetic architecture of this trait from the resemblance between relatives and from genetic marker data. All empirical evidence points towards height being highly polygenic, with many loci contributing to variation in the population and most effect sizes appear to be small. Nevertheless, combining new genetic and genomic technologies with phenotypic measures on height on large samples facilitates new answers to old questions, including the basis of assortative mating in humans, estimation of non-additive genetic variation and partitioning between-cohort phenotypic differences into genetic and non-genetic underlying causes.

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50

Bakker, Theo C. M. "THE STUDY OF INTERSEXUAL SELECTION USING QUANTITATIVE GENETICS." Behaviour 136, no. 9 (1999): 1237–66. http://dx.doi.org/10.1163/156853999501748.

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AbstractIn this review, I stress the importance of incorporating Quantitative Genetics (QG) in the study of sexual selection through female mate choice. A short overview of QG principles and methods of estimating genetic variance and covariance is given. The state of knowledge is summarized as to two QG assumptions (genetic variance in female mating preferences and male sexual traits) and one QG prediction (genetic covariance between preferences and preferred traits) of models of sexual selection. A review is given of studies of repeatability of mating preferences because of recent accumulation of data. The general conclusion is that sexual traits and mating preferences show large genetic variation and are genetically correlated. The extensive genetic variation asks for an explanation that goes beyond the ususal explanations of the maintenance of genetic variation in fitness traits. Two models that explain the high genetic variance in sexual traits are treated in detail: modifier selection and condition dependence. There are many unexplored areas of QG research that could stimulate further research in sexual selection like the study of genetic covariance between mating preferences and good genes, of genetic variances and covariances of multiple male traits and multiple females preferences, of genetic variance in condition, and of condition dependence of mating preferences.

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