Poster 43
Presenter: Il-youp Kwak
Wednesday, 3:00 – 5:00pm
Il-Youp Kwak and Karl W. Broman University of Wisconsin - Madison
Most statistical methods for QTL mapping focus on a single phenotype. 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 height measured over time. While there exist methods for QTL mapping with function-valued phenotypes, they are generally computationally intensive and focus on single-QTL models. We propose two simple fast methods that maintain high power and precision and are amenable to extensions with multiple-QTL models using the penalized likelihood approach of Broman and Speed (2002). After identifying multiple QTL by these approaches, we can view the function-valued QTL effects to provide a deeper understanding of the underlying processes. Our methods have been implemented as a package for R.