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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. The left column is for n = 100, the center column is for n = 200, and the right column is for n = 400. 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.
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fig5: Power as a function of the percentage of phenotypic variance explained by a single QTL. The left column is for n = 100, the center column is for n = 200, and the right column is for n = 400. 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.

Mentions: The estimated power to detect the QTL as a function of heritability due to the QTL, for n = 100, 200, 400 and for the three different covariance structures, is shown in Figure 5. With the autocorrelated variance structure, all methods other than the parametric approach gave similar power. With the equicorrelated variance structure, EE(Wald) had higher power than the other four methods, and the parametric approach was second best. In the unstructured variance setting, the EE(Wald) and MLOD methods worked better than the other three methods. EE(Residual) did not work well in this setting.


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. The left column is for n = 100, the center column is for n = 200, and the right column is for n = 400. 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.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: Power as a function of the percentage of phenotypic variance explained by a single QTL. The left column is for n = 100, the center column is for n = 200, and the right column is for n = 400. 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.
Mentions: The estimated power to detect the QTL as a function of heritability due to the QTL, for n = 100, 200, 400 and for the three different covariance structures, is shown in Figure 5. With the autocorrelated variance structure, all methods other than the parametric approach gave similar power. With the equicorrelated variance structure, EE(Wald) had higher power than the other four methods, and the parametric approach was second best. In the unstructured variance setting, the EE(Wald) and MLOD methods worked better than the other three methods. EE(Residual) did not work well in this setting.

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