Nonparametric Functional Mapping of Quantitative Trait Loci

• Activity Number: 381
• Type: Invited
• Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
• Sponsor: ENAR
• Author(s): Jie Yang* and George Casella+ and Rongling Wu
• Companies: St. Jude Children's Research Hospital and University of Florida and Penn State
• Keywords: B-splines ; Maximum likelihood ; Mixed model

Abstract

Functional mapping is a useful tool for mapping quantitative trait loci (QTL) that control dynamic traits. Itincorporates mathematical aspects of biological processes into the mixture model-based likelihood setting for QTL mapping, thus increasing the power of QTL detection and the precision of parameter estimation. 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 to estimate the underlying functional form of phenotypic trajectories, and then construct a nonparametric test to find evidence of existing QTL. Using the representation of a nonparametric regression as a mixed model, the final test statistic is a likelihood ratio test. We consider two types of genetic maps: dense maps and general maps, and the testing power is investigated through simulation studies.