A simple regression-based method to map quantitative trait loci underlying function-valued phenotypes.
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.
Affiliation: Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706.Show MeSH
Mentions: Following an approach developed by Zeng et al. (2000), we derived profile log-likelihood curves, to visualize the evidence and localization of each QTL in the context of a multiple-QTL model: The position of each QTL was varied one at a time, and at each location for a given QTL, we derived a LOD score comparing the multiple-QTL model with the QTL under consideration at a particular position and the locations of all other QTL fixed to the model with the given QTL omitted. This profile is calculated for each time point, individually, and then the SLOD (or MLOD) profiles are obtained by averaging (or maximizing) across time points. The SLOD and MLOD profiles are shown in Figure 3.
Affiliation: Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706.