Nonparametric Functional Mapping of QTL

• Activity Number: 22
• Type: Contributed
• Date/Time: Sunday, August 8, 2004 : 2:00 PM to 3:50 PM
• Sponsor: IMS
• Author(s): Jie Yang*+ and George Casella
• Companies: University of Florida and University of Florida
• Keywords: functional mapping ; nonparametric statistics ; mixed model ; genetic algorithm

Abstract

Functional mapping is a powerful tool for detecting major genes responsible for different phenotypic curves, and was first developed by Ma, Casella and Wu (2002). The methodology uses a parametric functional form, usually derived from a biological law, to drive a maximum-likelihood-based test for a significant QTL (quantitative trait loci). However, in many situations there is no obvious functional form and, in such cases, this strategy will not be optimal. Here we propose to use nonparametric function estimation, typically implemented with B-splines, to estimate the underlying functional form of phenotypic trajectories, and then construct a nonparametric test to find evidence of existing quantitative trait loci. Using the representation of a nonparametric regression as a mixed model, we can easily derive a likelihood ratio test statistic. Using that statistic, we can then calculate the p value directly. Simulation studies show that our method is both powerful and quick, and we also provide an application to a real dataset.