  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 growth 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 a
penalized likelihood approach. 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, funqtl.
