Quantitative Trait Loci (QTL) Detection in Multicross Inbred Designs

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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The Use of Multiple Markers in a Bayesian Method for Mapping Quantitative Trait Loci

Genetics ◽

10.1093/genetics/143.4.1831 ◽

1996 ◽

Vol 143 (4) ◽

pp. 1831-1842 ◽

Cited By ~ 2

Author(s):

Pekka Uimari ◽

Georg Thaller ◽

Ina Hoeschele

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Posterior Probability ◽

Bayesian Method ◽

Substitution Effect ◽

Qtl Detection ◽

Indicator Variable ◽

Monte Carlo Algorithms ◽

Trait Loci ◽

Residual Variances

Abstract Information on multiple linked genetic markers was used in a Bayesian method for the statistical mapping of quantitative trait loci (QTL). Bayesian parameter estimation and hypothesis testing were implemented via Markov chain Monte Carlo algorithms. Variables sampled were the augmented data (marker-QTL genotypes, polygenic effects), an indicator variable for linkage or nonlinkage, and the parameters. The parameter vector included allele frequencies at the markers and the QTL, map distances of the markers and the QTL, QTL substitution effect, and polygenic and residual variances. The criterion for QTL detection was the marginal posterior probability of a QTL being located on the chromosome carrying the markers, The method was evaluated empirically by analyzing simulated granddaughter designs consisting of 2000 sons, 20 related sires, and their ancestors.

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Feasibility of the grandprogeny design for quantitative trait loci (QTL) detection in purebred beef cattle.

Journal of Animal Science ◽

10.2527/1997.754941x ◽

1997 ◽

Vol 75 (4) ◽

pp. 941 ◽

Cited By ~ 5

Author(s):

D E Moody ◽

D Pomp ◽

D S Buchanan

Keyword(s):

Quantitative Trait Loci ◽

Beef Cattle ◽

Quantitative Trait ◽

Qtl Detection ◽

Trait Loci

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Crop management impacts the efficiency of quantitative trait loci (QTL) detection and use: case study of fruit load×QTL interactions

Journal of Experimental Botany ◽

10.1093/jxb/ert365 ◽

2013 ◽

Vol 65 (1) ◽

pp. 11-22 ◽

Cited By ~ 13

Author(s):

J. Kromdijk ◽

N. Bertin ◽

E. Heuvelink ◽

J. Molenaar ◽

P. H. B. de Visser ◽

...

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Crop Management ◽

Use Case ◽

Qtl Detection ◽

Trait Loci ◽

Fruit Load

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A first generation microsatellite-based linkage map of the Chinese mitten crab Eriocheir sinensis and its application in quantitative trait loci (QTL) detection

Aquaculture ◽

10.1016/j.aquaculture.2015.09.018 ◽

2016 ◽

Vol 451 ◽

pp. 223-231 ◽

Cited By ~ 12

Author(s):

Gao-Feng Qiu ◽

Liang-Wei Xiong ◽

Zhi-Qiang Liu ◽

Yin-Long Yan ◽

Hong Shen

Keyword(s):

Quantitative Trait Loci ◽

Linkage Map ◽

Quantitative Trait ◽

First Generation ◽

Eriocheir Sinensis ◽

Qtl Detection ◽

Chinese Mitten Crab ◽

Mitten Crab ◽

Trait Loci

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Approximate thresholds of interval mapping tests for QTL detection.

Genetics ◽

10.1093/genetics/138.1.235 ◽

1994 ◽

Vol 138 (1) ◽

pp. 235-240 ◽

Cited By ~ 5

Author(s):

A Rebaï ◽

B Goffinet ◽

B Mangin

Keyword(s):

Quantitative Trait Loci ◽

Qtl Mapping ◽

Quantitative Trait ◽

Interval Mapping ◽

Qtl Detection ◽

Recombinant Inbreds ◽

Codominant Markers ◽

Trait Loci ◽

Simulation Results ◽

General Method

Abstract A general method is proposed for calculating approximate thresholds of interval mapping tests for quantitative trait loci (QTL) detection. Simulation results show that this method, when applied to backcross and F2 populations, gives good approximations and is useful for any situation. Programs which calculate these thresholds for backcross, recombinant inbreds and F2 for any given level and any chromosome with any given distribution of codominant markers were written in Fortran 77 and are available under request. The approach presented here could be used to obtain, after suitable calculations, thresholds for most segregating populations used in QTL mapping experiments.

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Investigating the Probability of Sign Inconsistency in the Regression Coefficients of Markers Flanking Quantitative Trait Loci

Genetics ◽

10.1093/genetics/160.4.1697 ◽

2002 ◽

Vol 160 (4) ◽

pp. 1697-1705 ◽

Cited By ~ 1

Author(s):

J T Gene Hwang ◽

Dan Nettleton

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Regression Coefficient ◽

Regression Coefficients ◽

Marker Genotype ◽

Coefficient Estimates ◽

Qtl Detection ◽

Trait Loci ◽

Approximate Probability ◽

Analytic Expression

AbstractEstimates of the locations and effects of quantitative trait loci (QTL) can be obtained by regressing phenotype on marker genotype. Under certain basic conditions, the signs of regression coefficients flanking QTL must be the same. There is no guarantee, however, that the signs of the regression coefficient estimates will be the same. We use sign inconsistency to describe the situation in which there is disagreement between the signs of the estimated regression coefficients flanking QTL. The presence of sign inconsistency can undermine the effectiveness of QTL mapping strategies that presume intervals whose markers have regression coefficient estimates of differing sign to be devoid of QTL. This article investigates the likelihood of sign inconsistency under various conditions. We derive an analytic expression for the approximate probability of sign inconsistency in the single-QTL case. We also examine sign inconsistency probabilities when multiple QTL are present through simulation. We have discovered that the probability of sign inconsistency can be unacceptably high, even when the conditions for QTL detection are otherwise quite favorable.

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