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Journal articles on the topic 'Quantitative traits'

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Published: 4 June 2021

Last updated: 6 July 2024

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1

Yamamichi, Masato, and Stephen P. Ellner. "Antagonistic coevolution between quantitative and Mendelian traits." Proceedings of the Royal Society B: Biological Sciences 283, no. 1827 (March 30, 2016): 20152926. http://dx.doi.org/10.1098/rspb.2015.2926.

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Coevolution is relentlessly creating and maintaining biodiversity and therefore has been a central topic in evolutionary biology. Previous theoretical studies have mostly considered coevolution between genetically symmetric traits (i.e. coevolution between two continuous quantitative traits or two discrete Mendelian traits). However, recent empirical evidence indicates that coevolution can occur between genetically asymmetric traits (e.g. between quantitative and Mendelian traits). We examine consequences of antagonistic coevolution mediated by a quantitative predator trait and a Mendelian prey trait, such that predation is more intense with decreased phenotypic distance between their traits (phenotype matching). This antagonistic coevolution produces a complex pattern of bifurcations with bistability (initial state dependence) in a two-dimensional model for trait coevolution. Furthermore, with eco-evolutionary dynamics (so that the trait evolution affects predator–prey population dynamics), we find that coevolution can cause rich dynamics including anti-phase cycles, in-phase cycles, chaotic dynamics and deterministic predator extinction. Predator extinction is more likely to occur when the prey trait exhibits complete dominance rather than semidominance and when the predator trait evolves very rapidly. Our study illustrates how recognizing the genetic architectures of interacting ecological traits can be essential for understanding the population and evolutionary dynamics of coevolving species.

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2

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

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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

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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5

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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6

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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7

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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8

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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9

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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10

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

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11

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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12

Nelson, James C., Cristina Andreescu, Flavio Breseghello, Patrick L. Finney, Daisy G. Gualberto, Christine J. Bergman, Roberto J. Peña, et al. "Quantitative trait locus analysis of wheat quality traits." Euphytica 149, no. 1-2 (June 2, 2006): 145–59. http://dx.doi.org/10.1007/s10681-005-9062-7.

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13

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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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

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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16

FitzJohn, Richard G. "Quantitative Traits and Diversification." Systematic Biology 59, no. 6 (September 30, 2010): 619–33. http://dx.doi.org/10.1093/sysbio/syq053.

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17

Skelly, Daniel A., Narayanan Raghupathy, Raymond F. Robledo, Joel H. Graber, and Elissa J. Chesler. "Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples." Genetics 212, no. 3 (May 21, 2019): 919–29. http://dx.doi.org/10.1534/genetics.118.301865.

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Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript–trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative “reference” traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint subsamples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the data set and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation (R = 0.69) between the trait data sets. We demonstrate that this approach outperforms TWAS in identifying associated transcripts. Together, these results demonstrate the validity, reliability, and power of reference trait analysis for identifying relations between complex traits and their molecular substrates.

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18

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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19

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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20

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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21

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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22

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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23

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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24

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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25

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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26

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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27

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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28

Lan, Hong, Jonathan P. Stoehr, Samuel T. Nadler, Kathryn L. Schueler, Brian S. Yandell, and Alan D. Attie. "Dimension Reduction for Mapping mRNA Abundance as Quantitative Traits." Genetics 164, no. 4 (August 1, 2003): 1607–14. http://dx.doi.org/10.1093/genetics/164.4.1607.

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AbstractThe advent of sophisticated genomic techniques for gene mapping and microarray analysis has provided opportunities to map mRNA abundance to quantitative trait loci (QTL) throughout the genome. Unfortunately, simple mapping of each individual mRNA trait on the scale of a typical microarray experiment is computationally intensive, subject to high sample variance, and therefore underpowered. However, this problem can be addressed by capitalizing on correlation among the large number of mRNA traits. We present a method to reduce the dimensionality for mapping gene expression data as quantitative traits. We used a blind method, principal components, and a sighted method, hierarchical clustering seeded by disease relevant traits, to define new traits composed of a small collection of promising mRNAs. We validated the principle of our approach by mapping the expression levels of metabolism genes in a population of F2-ob/ob mice derived from the BTBR and C57BL/6J strains. We found that lipogenic and gluconeogenic mRNAs, which are known targets of insulin action, were closely associated with the insulin trait. Multiple interval mapping and Bayesian interval mapping of this new trait revealed significant linkages to chromosome regions that were contained in loci associated with type 2 diabetes in this same mouse sample. As a further statistical refinement, we show that principal component analysis also effectively reduced dimensions for mapping phenotypes composed of mRNA abundances.

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29

Houle, D. "Comparing evolvability and variability of quantitative traits." Genetics 130, no. 1 (January 1, 1992): 195–204. http://dx.doi.org/10.1093/genetics/130.1.195.

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Abstract There are two distinct reasons for making comparisons of genetic variation for quantitative characters. The first is to compare evolvabilities, or ability to respond to selection, and the second is to make inferences about the forces that maintain genetic variability. Measures of variation that are standardized by the trait mean, such as the additive genetic coefficient of variation, are appropriate for both purposes. Variation has usually been compared as narrow sense heritabilities, but this is almost always an inappropriate comparative measure of evolvability and variability. Coefficients of variation were calculated from 842 estimates of trait means, variances and heritabilities in the literature. Traits closely related to fitness have higher additive genetic and nongenetic variability by the coefficient of variation criterion than characters under weak selection. This is the reverse of the accepted conclusion based on comparisons of heritability. The low heritability of fitness components is best explained by their high residual variation. The high additive genetic and residual variability of fitness traits might be explained by the great number of genetic and environmental events they are affected by, or by a lack of stabilizing selection to reduce their phenotypic variance. Over one-third of the quantitative genetics papers reviewed did not report trait means or variances. Researchers should always report these statistics, so that measures of variation appropriate to a variety of situations may be calculated.

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30

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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31

Howe, Glenn T., Sally N. Aitken, David B. Neale, Kathleen D. Jermstad, Nicholas C. Wheeler, and Tony HH Chen. "From genotype to phenotype: unraveling the complexities of cold adaptation in forest trees." Canadian Journal of Botany 81, no. 12 (December 1, 2003): 1247–66. http://dx.doi.org/10.1139/b03-141.

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Adaptation to winter cold in temperate and boreal trees involves complex genetic, physiological, and developmental processes. Genecological studies demonstrate the existence of steep genetic clines for cold adaptation traits in relation to environmental (mostly temperature related) gradients. Population differentiation is generally stronger for cold adaptation traits than for other quantitative traits and allozymes. Therefore, these traits appear to be under strong natural selection. Nonetheless, high levels of genetic variation persist within populations. The genetic control of cold adaptation traits ranges from weak to strong, with phenological traits having the highest heritabilities. Within-population genetic correlations among traits range from negligible to moderate. Generally, bud phenology and cold hardiness in the fall are genetically uncorrelated with bud phenology and cold hardiness in the spring. Analyses of quantitative trait loci indicate that cold adaptation traits are mostly controlled by multiple genes with small effects and that quantitative trait loci × environment interactions are common. Given this inherent complexity, we suggest that future research should focus on identifying and developing markers for cold adaptation candidate genes, then using multilocus, multi allelic analytical techniques to uncover the relationships between genotype and phenotype at both the individual and population levels. Ultimately, these methods may be useful for predicting the performance of genotypes in breeding programs and for better understanding the evolutionary ecology of forest trees.Key words: association genetics, cold hardiness, dormancy, genecology, bud phenology, quantitative trait loci.

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32

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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33

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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34

Jussila, Katja, Kristen Lyall, Sanna Kuusikko-Gauffin, Marja-Leena Mattila, Rachel Pollock-Wurman, Tuula Hurtig, Leena Joskitt, et al. "Familiality of Quantitative Autism Traits." Scandinavian Journal of Child and Adolescent Psychiatry and Psychology 3, no. 2 (2015): 126–35. http://dx.doi.org/10.21307/sjcapp-2015-013.

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35

GINSBURG, E., and G. LIVSHITS. "Segregation analysis of quantitative traits." Annals of Human Biology 26, no. 2 (January 1999): 103–29. http://dx.doi.org/10.1080/030144699282822.

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36

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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37

Mott, Richard, and Jonathan Flint. "Dissecting Quantitative Traits in Mice." Annual Review of Genomics and Human Genetics 14, no. 1 (August 31, 2013): 421–39. http://dx.doi.org/10.1146/annurev-genom-091212-153419.

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38

Plomin, Robert, Claire M. A. Haworth, and Oliver S. P. Davis. "Common disorders are quantitative traits." Nature Reviews Genetics 10, no. 12 (October 27, 2009): 872–78. http://dx.doi.org/10.1038/nrg2670.

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39

Ratner, Mark. "Identifying Quantitative Traits in Plants." Nature Biotechnology 8, no. 5 (May 1990): 401–3. http://dx.doi.org/10.1038/nbt0590-401.

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40

Marlow, A. J., S. John, and J. Worthington. "Multipoint analysis of quantitative traits." Genetic Epidemiology 14, no. 6 (1997): 845–50. http://dx.doi.org/10.1002/(sici)1098-2272(1997)14:6<845::aid-gepi47>3.0.co;2-n.

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41

Løvendahl, P. "Physiological genetics, its relationship with classical quantitative and molecular genetics." Proceedings of the British Society of Animal Science 2003 (2003): 236. http://dx.doi.org/10.1017/s1752756200013910.

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Abstract:

Genetic variation in physiological regulation has been studied in humans to explain disposition to hereditary metabolic diseases, such as diabetes, dwarfism and acromegaly. In farm animals the aim of such studies has more often focused on understanding individual variation with a view to develop physiologically based indicator traits that could help speed up genetic improvement programmes. To be efficient, any indicator trait needs to be measurable both early in life and in animals of both sexes. The advent of molecular or DNA based genetic markers has presented us with an alternative set of indicator traits. These may be seen both as competitive traits and as traits, which can be used in combination with physiological indicators.

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42

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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Abstract:

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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Yang, Runqing, Quan Tian, and Shizhong Xu. "Mapping Quantitative Trait Loci for Longitudinal Traits in Line Crosses." Genetics 173, no. 4 (June 4, 2006): 2339–56. http://dx.doi.org/10.1534/genetics.105.054775.

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45

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

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46

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

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47

Thomasen, J. R., B. Guldbrandtsen, P. Sørensen, B. Thomsen, and M. S. Lund. "Quantitative Trait Loci Affecting Calving Traits in Danish Holstein Cattle." Journal of Dairy Science 91, no. 5 (May 2008): 2098–105. http://dx.doi.org/10.3168/jds.2007-0602.

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48

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 Udder Morphology Traits in Dairy Sheep." Journal of Dairy Science 91, no. 9 (September 2008): 3672–81. http://dx.doi.org/10.3168/jds.2008-1111.

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Yang, Runqing, and Shizhong Xu. "Bayesian Shrinkage Analysis of Quantitative Trait Loci for Dynamic Traits." Genetics 176, no. 2 (April 15, 2007): 1169–85. http://dx.doi.org/10.1534/genetics.106.064279.

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Casas, E., D. D. Lunstra, and R. T. Stone. "Quantitative trait loci for male reproductive traits in beef cattle." Animal Genetics 35, no. 6 (December 2004): 451–53. http://dx.doi.org/10.1111/j.1365-2052.2004.01190.x.

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