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Artículos de revistas sobre el tema "Quantitative genetics"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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1

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

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2

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

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3

Mackay, Trudy F. C. "QUANTITATIVE GENETICS." Evolution 53, no. 1 (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 (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 (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 res

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6

FRANKHAM, RICHARD. "Quantitative genetics in conservation biology." Genetical Research 74, no. 3 (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

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7

Plomin, Robert, and Jenae Neiderhiser. "Quantitative Genetics, Molecular Genetics, and Intelligence." Intelligence 15, no. 4 (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 (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 researc

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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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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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20

Fournier-Level, Alexandre, Loïc Le Cunff, Camila Gomez, et al. "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 (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 g

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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 (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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22

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

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23

Spandan, Shashwat Dash, and Das Monali. "Quantitative Genetics: A better tool to understand animal population." Science World a monthly e magazine 2, no. 6 (2022): 598–603. https://doi.org/10.5281/zenodo.6617126.

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Quantitative genetics or population genetics is a subfield of genetics that deals with genetic differences within and between populations, and is a part of evolutionary biology. Studies in this branch of biology examine such phenomena as adaptation, speciation, and population structure. The average effect and average excess of a gene substitution are formulated in terms of gene frequencies and inbreeding coefficient. The role of population mean, average gene effect and variances plays an important role in population study.

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24

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

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25

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

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26

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

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27

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

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28

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

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29

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

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30

Walsh, Bruce. ":Evolutionary Quantitative Genetics." Quarterly Review of Biology 99, no. 3 (2024): 185. http://dx.doi.org/10.1086/732068.

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31

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

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32

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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33

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

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34

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

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35

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

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36

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

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37

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

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38

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

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39

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

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40

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

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41

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

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42

Tachida, H., and C. C. Cockerham. "A building block model for quantitative genetics." Genetics 121, no. 4 (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,

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43

Zeng, Z. B. "Precision mapping of quantitative trait loci." Genetics 136, no. 4 (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

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44

Zeng, Z. B., and C. C. Cockerham. "Mutation models and quantitative genetic variation." Genetics 133, no. 3 (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 mutatio

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45

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 (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 carr

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46

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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47

Nagylaki, T. "Geographical variation in a quantitative character." Genetics 136, no. 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

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48

Imprialou, Martha, André Kahles, Joshua G. Steffen, et al. "Genomic Rearrangements inArabidopsisConsidered as Quantitative Traits." Genetics 205, no. 4 (2017): 1425–41. http://dx.doi.org/10.1534/genetics.116.192823.

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49

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

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50

Camussi, A., E. Ottaviano, T. Calinski, and Z. Kaczmarek. "GENETIC DISTANCES BASED ON QUANTITATIVE TRAITS." Genetics 111, no. 4 (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 info

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