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A simple regression-based method to map quantitative trait loci underlying function-valued phenotypes.

Kwak IY, Moore CR, Spalding EP, Broman KW - Genetics (2014)

Bottom Line: However, multiple phenotypes are commonly measured, and recent technological advances have greatly simplified the automated acquisition of numerous phenotypes, including function-valued phenotypes, such as growth measured over time.While methods exist for QTL mapping with function-valued phenotypes, they are generally computationally intensive and focus on single-QTL models.After identifying multiple QTL by these approaches, we can view the function-valued QTL effects to provide a deeper understanding of the underlying processes.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706.

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Power as a function of the percentage of phenotypic variance explained by a single QTL, with additional noise added to the phenotypes. The left column has no additional noise; the center and right columns have independent normally distributed noise added at each time point, with standard deviations 1 and 2, respectively. The three rows correspond to the covariance structure (autocorrelated, equicorrelated, and unstructured). In each panel, SLOD is in red, MLOD is in blue, EE(Wald) is in brown, EE(Residual) is in green, and parametric is in black. The percentage of variance explained by the QTL on the x-axis refers, in each case, to the variance explained in the case of no added noise.
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fig6: Power as a function of the percentage of phenotypic variance explained by a single QTL, with additional noise added to the phenotypes. The left column has no additional noise; the center and right columns have independent normally distributed noise added at each time point, with standard deviations 1 and 2, respectively. The three rows correspond to the covariance structure (autocorrelated, equicorrelated, and unstructured). In each panel, SLOD is in red, MLOD is in blue, EE(Wald) is in brown, EE(Residual) is in green, and parametric is in black. The percentage of variance explained by the QTL on the x-axis refers, in each case, to the variance explained in the case of no added noise.

Mentions: The estimated power to detect the QTL as a function of heritability due to the QTL, for added noise with SD = 0, 1, 2 and the three different covariance structures, is shown in Figure 6. (Corresponding estimates of QTL mapping precision are displayed in Figure S6.) The power of the SLOD, MLOD, and EE(Residual) methods is greatly affected by the introduction of noise. EE(Wald) and the parametric methods are relatively robust to the introduction of noise. Overall, the EE(Wald) method continued to perform best.


A simple regression-based method to map quantitative trait loci underlying function-valued phenotypes.

Kwak IY, Moore CR, Spalding EP, Broman KW - Genetics (2014)

Power as a function of the percentage of phenotypic variance explained by a single QTL, with additional noise added to the phenotypes. The left column has no additional noise; the center and right columns have independent normally distributed noise added at each time point, with standard deviations 1 and 2, respectively. The three rows correspond to the covariance structure (autocorrelated, equicorrelated, and unstructured). In each panel, SLOD is in red, MLOD is in blue, EE(Wald) is in brown, EE(Residual) is in green, and parametric is in black. The percentage of variance explained by the QTL on the x-axis refers, in each case, to the variance explained in the case of no added noise.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4125409&req=5

fig6: Power as a function of the percentage of phenotypic variance explained by a single QTL, with additional noise added to the phenotypes. The left column has no additional noise; the center and right columns have independent normally distributed noise added at each time point, with standard deviations 1 and 2, respectively. The three rows correspond to the covariance structure (autocorrelated, equicorrelated, and unstructured). In each panel, SLOD is in red, MLOD is in blue, EE(Wald) is in brown, EE(Residual) is in green, and parametric is in black. The percentage of variance explained by the QTL on the x-axis refers, in each case, to the variance explained in the case of no added noise.
Mentions: The estimated power to detect the QTL as a function of heritability due to the QTL, for added noise with SD = 0, 1, 2 and the three different covariance structures, is shown in Figure 6. (Corresponding estimates of QTL mapping precision are displayed in Figure S6.) The power of the SLOD, MLOD, and EE(Residual) methods is greatly affected by the introduction of noise. EE(Wald) and the parametric methods are relatively robust to the introduction of noise. Overall, the EE(Wald) method continued to perform best.

Bottom Line: However, multiple phenotypes are commonly measured, and recent technological advances have greatly simplified the automated acquisition of numerous phenotypes, including function-valued phenotypes, such as growth measured over time.While methods exist for QTL mapping with function-valued phenotypes, they are generally computationally intensive and focus on single-QTL models.After identifying multiple QTL by these approaches, we can view the function-valued QTL effects to provide a deeper understanding of the underlying processes.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706.

Show MeSH