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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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(A–D) The regression coefficients estimated for the root tip angle data set: the estimated baseline function (A) and the estimated QTL effects (B-D). The red curves are for the two-QTL model (from the penalized-SLOD criterion) and the blue dashed curves are for the three-QTL model (from the penalized-MLOD criterion). Positive values for the QTL effects indicate that the Cvi allele increases the tip angle phenotype.
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fig4__A_D: (A–D) The regression coefficients estimated for the root tip angle data set: the estimated baseline function (A) and the estimated QTL effects (B-D). The red curves are for the two-QTL model (from the penalized-SLOD criterion) and the blue dashed curves are for the three-QTL model (from the penalized-MLOD criterion). Positive values for the QTL effects indicate that the Cvi allele increases the tip angle phenotype.

Mentions: To further characterize the effects of the QTL in the context of the inferred multiple-QTL models, we fitted the selected multiple-QTL models at each time point, individually. For the models derived by the penalized-SLOD and penalized-MLOD criteria, the estimated baseline function and the estimated QTL effects, as a function of time, are shown in Figure 4. The estimated QTL effects in Figure 4, B–D, are for the difference between the Cvi allele and the Ler allele.


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

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

(A–D) The regression coefficients estimated for the root tip angle data set: the estimated baseline function (A) and the estimated QTL effects (B-D). The red curves are for the two-QTL model (from the penalized-SLOD criterion) and the blue dashed curves are for the three-QTL model (from the penalized-MLOD criterion). Positive values for the QTL effects indicate that the Cvi allele increases the tip angle phenotype.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4__A_D: (A–D) The regression coefficients estimated for the root tip angle data set: the estimated baseline function (A) and the estimated QTL effects (B-D). The red curves are for the two-QTL model (from the penalized-SLOD criterion) and the blue dashed curves are for the three-QTL model (from the penalized-MLOD criterion). Positive values for the QTL effects indicate that the Cvi allele increases the tip angle phenotype.
Mentions: To further characterize the effects of the QTL in the context of the inferred multiple-QTL models, we fitted the selected multiple-QTL models at each time point, individually. For the models derived by the penalized-SLOD and penalized-MLOD criteria, the estimated baseline function and the estimated QTL effects, as a function of time, are shown in Figure 4. The estimated QTL effects in Figure 4, B–D, are for the difference between the Cvi allele and the Ler allele.

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
Related in: MedlinePlus