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Journal articles on the topic 'Quantitative trait loci'

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Published: 4 June 2021

Last updated: 10 March 2023

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1

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

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Parallel searches for quantitative trait loci (QTL) for growth-related traits in different populations frequently detect sets of QTL that hardly overlap. Thus, many QTL potentially exist. Tools for the detection of QTL that interact are available and are currently being tested. Initial results suggest that epistasis is widespread. Modelling of the first recognised interaction, dominance, continues to be developed. Multigenic interaction appears to be a necessary part of any explanation. This paper covers an attempt to link some of these studies and to draw inferences about useful approaches to understanding and using the genes that influence quantitative traits.

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2

&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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3

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

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4

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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5

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

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6

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

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7

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

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In the classic view introduced by R. A. Fisher, a quantitative trait is encoded by many loci with small, additive effects. Recent advances in quantitative trait loci mapping have begun to elucidate the genetic architectures underlying vast numbers of phenotypes across diverse taxa, producing observations that sometimes contrast with Fisher's blueprint. Despite these considerable empirical efforts to map the genetic determinants of traits, it remains poorly understood how the genetic architecture of a trait should evolve, or how it depends on the selection pressures on the trait. Here, we develop a simple, population-genetic model for the evolution of genetic architectures. Our model predicts that traits under moderate selection should be encoded by many loci with highly variable effects, whereas traits under either weak or strong selection should be encoded by relatively few loci. We compare these theoretical predictions with qualitative trends in the genetics of human traits, and with systematic data on the genetics of gene expression levels in yeast. Our analysis provides an evolutionary explanation for broad empirical patterns in the genetic basis for traits, and it introduces a single framework that unifies the diversity of observed genetic architectures, ranging from Mendelian to Fisherian.

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8

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

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9

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

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10

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

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11

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

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12

Buitenhuis, A. J., T. B. Rodenburg, M. Siwek, S. J. B. Cornelissen, M. G. B. Nieuwland, R. P. M. A. Crooijmans, M. A. M. Groenen, P. Koene, H. Bovenhuis, and J. J. van der Poel. "Quantitative trait loci for behavioural traits in chickens." Livestock Production Science 93, no. 1 (April 2005): 95–103. http://dx.doi.org/10.1016/j.livprodsci.2004.11.010.

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13

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 (February 2007): 51–57. http://dx.doi.org/10.1111/j.1439-0523.2006.01305.x.

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14

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 (October 20, 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 polymeric proteins together with dough mixing strength and bread-making properties in a population of 139 (MN98550 × MN99394) recombinant inbred lines that was evaluated at three environments in 2006. Eleven chromosome regions were associated with endosperm polymeric proteins, explaining 4.2–31.8% of the phenotypic variation. Most of these polymeric proteins QTL coincided with several QTL for dough-mixing strength and bread-making properties. Major QTL clusters were associated with the low-molecular weight glutenin gene Glu-A3, the two high-molecular weight glutenin genes Glu-B1 and Glu-D1, and two regions on chromosome 6D. Alleles at these QTL clusters have previously been proven useful for wheat quality except one of the 6D QTL clusters.

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15

Kao, Chen-Hung, Zhao-Bang Zeng, and Robert D. Teasdale. "Multiple Interval Mapping for Quantitative Trait Loci." Genetics 152, no. 3 (July 1, 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 estimated and analyzed. Using the MIM model, a stepwise selection procedure with likelihood ratio test statistic as a criterion is proposed to identify QTL. This MIM method was applied to a mapping data set of radiata pine on three traits: brown cone number, tree diameter, and branch quality scores. Based on the MIM result, seven, six, and five QTL were detected for the three traits, respectively. The detected QTL individually contributed from ∼1 to 27% of the total genetic variation. Significant epistasis between four pairs of QTL in two traits was detected, and the four pairs of QTL contributed ∼10.38 and 14.14% of the total genetic variation. The asymptotic variances of QTL positions and effects were also provided to construct the confidence intervals. The estimated heritabilities were 0.5606, 0.5226, and 0.3630 for the three traits, respectively. With the estimated QTL effects and positions, the best strategy of marker-assisted selection for trait improvement for a specific purpose and requirement can be explored. The MIM FORTRAN program is available on the worldwide web (http://www.stat.sinica.edu.tw/~chkao/).

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16

Lee, Norman H., Brian J. Haas, Noah E. Letwin, Bryan C. Frank, Truong V. Luu, Qiang Sun, Carrie D. House, et al. "Cross-Talk of Expression Quantitative Trait Loci Within 2 Interacting Blood Pressure Quantitative Trait Loci." Hypertension 50, no. 6 (December 2007): 1126–33. http://dx.doi.org/10.1161/hypertensionaha.107.093138.

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17

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 (November 1, 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 QTL and single-factor ANOVA was used for estimating QTL-by-environment interactions. A modest number of QTL were detected per trait, with individual QTL explaining up to 9.5% of the phenotypic variation. Two QTL-by-treatment interactions were found for growth initiation, whereas several QTL-by-treatment interactions were detected among growth cessation traits. This is the first report of QTL interactions with specific environmental signals in forest trees and will assist in the identification of candidate genes controlling these important adaptive traits in perennial plants.

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18

Korol, A. B., Y. I. Ronin, and V. M. Kirzhner. "Interval mapping of quantitative trait loci employing correlated trait complexes." Genetics 140, no. 3 (July 1, 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) > 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 relationship connects H2 (both in single trait and two-trait analysis) with the expected LOD value, ELOD = -1/2N log(1-H2). Thus, situations could exist that from the inequality H(xy)2(A/a) > or = H(x)2(A/a) a higher resolution is provided by the two-trait analysis as compared to the single-trait analysis, in spite of the increased number of parameters. Employing LOD-score procedure to simulated backcross data, we showed that the resolution power of the QTL mapping model can be elevated if correlation between QTs is taken into account. The method allows us to test numerous biologically important hypotheses concerning manifold effects of genomic segments on the defined trait complex (means, variances and correlations).

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19

YI, NENGJUN, and SHIZHONG XU. "Mapping quantitative trait loci with epistatic effects." Genetical Research 79, no. 2 (April 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. The parameters of interest are various QTL effects, including additive, dominance and epistatic effects of QTLs, the locations of identified QTLs and even the number of QTLs. When the number of QTLs is treated as an unknown parameter, the dimension of the model becomes a variable. This requires the reversible jump Markov chain Monte Carlo algorithm. The utility of the proposed method is demonstrated through analysis of simulation data.

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20

Gimelfarb, A. "Pleiotropy as a factor maintaining genetic variation in quantitative characters under stabilizing selection." Genetical Research 68, no. 1 (August 1996): 65–73. http://dx.doi.org/10.1017/s0016672300033899.

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SummaryA model of pleiotropy with N diallelic loci contributing additively to N quantitative traits and stabilizing selection acting on each of the traits is considered. Every locus has a major contribution to one trait and a minor contribution to the rest of them, while every trait is controlled by one major locus and N−1 minor loci. It is demonstrated that a stable equilibrium with the allelic frequency equal to 0·5 in all N loci can be maintained in such a model for a wide range of parameters. Such a ‘totally polymorphic’ equilibrium is maintained for practically any strength of selection and any recombination, if the relative contribution by a minor locus to a trait is less than 20 % of the contribution by a major locus. The dynamic behaviour of the model is shown to be quite complex with a possibility under sufficiently strong selection of multiple stable equilibria and positive linkage disequilibria between loci. It is also suggested that pleiotropy among loci controlling traits experiencing direct selection can be responsible for apparent selection on neutral traits also controlled by these loci.

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21

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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22

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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23

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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24

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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25

Zahn, Laura M. "Cell type–specific quantitative trait loci." Science 369, no. 6509 (September 10, 2020): 1335.11–1337. http://dx.doi.org/10.1126/science.369.6509.1335-k.

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26

Haley, C. "Quantitative Trait Loci Analysis in Animals." Heredity 88, no. 6 (May 27, 2002): 486. http://dx.doi.org/10.1038/sj.hdy.6800068.

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27

Stear, M. J. "Quantitative Trait Loci Analysis in Animals." Veterinary Journal 164, no. 3 (November 2002): 302–3. http://dx.doi.org/10.1053/tvjl.2002.0718.

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28

McMullen, M. D., P. F. Byrne, M. E. Snook, B. R. Wiseman, E. A. Lee, N. W. Widstrom, and E. H. Coe. "Quantitative trait loci and metabolic pathways." Proceedings of the National Academy of Sciences 95, no. 5 (March 3, 1998): 1996–2000. http://dx.doi.org/10.1073/pnas.95.5.1996.

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29

Barnholtz-Sloan, Jill. "Quantitative Trait Loci: Methods and Protocols." American Journal of Human Genetics 71, no. 4 (October 2002): 1000. http://dx.doi.org/10.1086/342664.

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30

Liti, Gianni, Jonas Warringer, and Anders Blomberg. "Mapping Quantitative Trait Loci in Yeast." Cold Spring Harbor Protocols 2017, no. 8 (August 2017): pdb.prot089060. http://dx.doi.org/10.1101/pdb.prot089060.

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31

Wu, Rongling. "Quantitative trait loci: Methods and protocols." American Journal of Human Biology 15, no. 2 (February 27, 2003): 235–36. http://dx.doi.org/10.1002/ajhb.10129.

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32

Jiang, C., and Z. B. Zeng. "Multiple trait analysis of genetic mapping for quantitative trait loci." Genetics 140, no. 3 (July 1, 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 the nature of genetic correlations between different traits. Among the testing procedures considered are those for joint mapping, pleiotropy, QTL by environment interaction, and pleiotropy vs. close linkage. The test of pleiotropy (one pleiotropic QTL at a genome position) vs. close linkage (multiple nearby nonpleiotropic QTL) can have important implications for our understanding of the nature of genetic correlations between different traits in certain regions of a genome and also for practical applications in animal and plant breeding because one of the major goals in breeding is to break unfavorable linkage. Results of extensive simulation studies are presented to illustrate various properties of the analyses.

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33

Gutiérrez-Gil, B., M. F. El-Zarei, L. Alvarez, Y. Bayón, L. F. de la Fuente, F. San Primitivo, and J. J. Arranz. "Quantitative trait loci underlying milk production traits in sheep." Animal Genetics 40, no. 4 (August 2009): 423–34. http://dx.doi.org/10.1111/j.1365-2052.2009.01856.x.

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34

Gao, Y., Z. Q. Du, W. H. Wei, X. J. Yu, X. M. Deng, C. G. Feng, J. Fei, J. D. Feng, N. Li, and X. X. Hu. "Mapping quantitative trait loci regulating chicken body composition traits." Animal Genetics 40, no. 6 (December 2009): 952–54. http://dx.doi.org/10.1111/j.1365-2052.2009.01911.x.

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35

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

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36

Şahin-Çevik, Mehtap, and Gloria A. Moore. "Quantitative trait loci analysis of morphological traits in Citrus." Plant Biotechnology Reports 6, no. 1 (August 30, 2011): 47–57. http://dx.doi.org/10.1007/s11816-011-0194-z.

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37

Yang, Runqing, Jiahan Li, and Shizhong Xu. "Mapping quantitative trait loci for traits defined as ratios." Genetica 132, no. 3 (August 2, 2007): 323–29. http://dx.doi.org/10.1007/s10709-007-9175-0.

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38

Šimić, Domagoj, Snežana Mladenović Drinić, Zvonimir Zdunić, Antun Jambrović, Tatjana Ledenčan, Josip Brkić, Andrija Brkić, and Ivan Brkić. "Quantitative Trait Loci for Biofortification Traits in Maize Grain." Journal of Heredity 103, no. 1 (November 9, 2011): 47–54. http://dx.doi.org/10.1093/jhered/esr122.

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39

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 (February 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 significant factors, with the F2 animals similar to the high active C57L/J mice in both daily exercise distance and duration of exercise. In the F2 cohort, female mice ran significantly farther, longer, and faster than male mice. QTL analysis revealed no sex-specific QTL but at the 5% experimentwise significance level did identify one QTL for duration, one QTL for distance, and two QTL for speed. The QTL for duration ( DUR13.1) and distance ( DIST13.1) colocalized with the QTL for speed ( SPD13.1). Each of these QTL accounted for ∼6% of the phenotypic variance, whereas SPD9.1 (chromosome 9, 7 cM) accounted for 11.3% of the phenotypic variation. DUR13.1, DIST13.1, SPD13.1, and SPD9.1 were subsequently replicated by haplotype association mapping. The results of this study suggest a genetic basis of voluntary activity in mice and provide a foundation for future candidate gene studies.

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40

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 (May 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 almost two-thirds of the marked genomic sites. Most of the detected associations between marker loci and quantitative traits were highly significant, and could have been detected with fewer than the 1800–1900 plants evaluated in each population. The cumulative, simple effects of marker-linked regions of the genome explained between 8 and 40% of the phenotypic variation for a subset of 25 traits evaluated. Single marker loci accounted for between 0.3% and 16% of the phenotypic variation of traits. Individual plant heterozygosity, as measured by marker loci, was significantly associated with variation in many traits. The apparent types of gene action at the QTLs varied both among traits and between loci for given traits, although overdominance appeared frequently, especially for yield-related traits. The prevalence of apparent overdominance may reflect the effects of multiple QTLs within individual marker-linked regions, a situation which would tend to result in overestimation of dominance. Digenic epistasis did not appear to be important in determining the expression of the quantitative traits evaluated. Examination of the effects of marked regions on the expression of pairs of traits suggests that genomic regions vary in the direction and magnitudes of their effects on trait correlations, perhaps providing a means of selecting to dissociate some correlated traits. Marker-facilitated investigations appear to provide a powerful means of examining aspects of the genetic control of quantitative traits. Modifications of the methods employed herein will allow examination of the stability of individual gene effects in varying genetic backgrounds and environments.

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41

Lange, Christoph, and John C. Whittaker. "Mapping Quantitative Trait Loci Using Generalized Estimating Equations." Genetics 159, no. 3 (November 1, 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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42

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

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Fernando, R. L., J. Zeng, H. Cheng, D. Habier, A. Wolc, D. J. Garrick, and J. C. M. Dekkers. "036 Discovery of quantitative trait loci using a quantitative trait loci–effects model in a multigenerational pedigree." Journal of Animal Science 94, suppl_2 (April 1, 2016): 16–17. http://dx.doi.org/10.2527/msasas2016-036.

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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 (February 1, 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 markers. The average marker-associated main effect was on the order of 6% of the mean value of the trait. Most of the main effects were apparently due to linkage of QTL to the marker loci rather than to pleiotropy. Fourteen of the traits showed at least one highly significant effect of opposite sign to the overall difference between the parental lines, demonstrating the ability of this design to uncover cryptic genetic variation. Significant variance and skewness effects on the quantitative traits were found to be associated with the genetic markers, suggesting the possible presence of loci affecting the variance and shape of quantitative trait distribution in a population. Most marker-associated quantitative effects showed some degree of dominance, generally in the direction of the L. pimpinellifolium parent. When the significant marker-associated effects were examined in pairs, 12% showed significant interaction effects. The results of this study illustrate the potential usefulness of this type of analysis for the detailed genetic investigation of quantitative trait variation in suitably marked populations.

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Cheverud, James M., and Eric Routman. "Quantitative trait loci: individual gene effects on quantitative characters." Journal of Evolutionary Biology 6, no. 4 (July 1993): 463–80. http://dx.doi.org/10.1046/j.1420-9101.1993.6040463.x.

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Kruglyak, L., and E. S. Lander. "A nonparametric approach for mapping quantitative trait loci." Genetics 139, no. 3 (March 1, 1995): 1421–28. http://dx.doi.org/10.1093/genetics/139.3.1421.

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Abstract Genetic mapping of quantitative trait loci (QTLs) is performed typically by using a parametric approach, based on the assumption that the phenotype follows a normal distribution. Many traits of interest, however, are not normally distributed. In this paper, we present a nonparametric approach to QTL mapping applicable to any phenotypic distribution. The method is based on a statistic ZW, which generalizes the nonparametric Wilcoxon rank-sum test to the situation of whole-genome search by interval mapping. We determine the appropriate significance level for the statistic ZW, by showing that its asymptotic null distribution follows an Ornstein-Uhlenbeck process. These results provide a robust, distribution-free method for mapping QTLs.

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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 to the successful production of diagnostic tests. Since this is the most important single factor affecting whether a test will be commercialised, not only due to the economic importance of the trait, but whether there are easy, alternative methods to improve the trait that are cheaper to implement than a DNA test.

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Yi, Nengjun, and Shizhong Xu. "Bayesian Mapping of Quantitative Trait Loci for Complex Binary Traits." Genetics 155, no. 3 (July 1, 2000): 1391–403. http://dx.doi.org/10.1093/genetics/155.3.1391.

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AbstractA complex binary trait is a character that has a dichotomous expression but with a polygenic genetic background. Mapping quantitative trait loci (QTL) for such traits is difficult because of the discrete nature and the reduced variation in the phenotypic distribution. Bayesian statistics are proved to be a powerful tool for solving complicated genetic problems, such as multiple QTL with nonadditive effects, and have been successfully applied to QTL mapping for continuous traits. In this study, we show that Bayesian statistics are particularly useful for mapping QTL for complex binary traits. We model the binary trait under the classical threshold model of quantitative genetics. The Bayesian mapping statistics are developed on the basis of the idea of data augmentation. This treatment allows an easy way to generate the value of a hypothetical underlying variable (called the liability) and a threshold, which in turn allow the use of existing Bayesian statistics. The reversible jump Markov chain Monte Carlo algorithm is used to simulate the posterior samples of all unknowns, including the number of QTL, the locations and effects of identified QTL, genotypes of each individual at both the QTL and markers, and eventually the liability of each individual. The Bayesian mapping ends with an estimation of the joint posterior distribution of the number of QTL and the locations and effects of the identified QTL. Utilities of the method are demonstrated using a simulated outbred full-sib family. A computer program written in FORTRAN language is freely available on request.

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Kao, Chen-Hung. "Multiple-Interval Mapping for Quantitative Trait Loci Controlling Endosperm Traits." Genetics 167, no. 4 (August 2004): 1987–2002. http://dx.doi.org/10.1534/genetics.103.021642.

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