|
Identifying quantitative trait loci by model
selection allowing for epistasis
Dr. Ani Manichaikul
Department of Biostatistics
Johns Hopkins University
Estimating the number and
locations of quantitative trait loci (QTL) and their interactions is a
crucial step toward identifying genes responsible for variation in
experimental crosses. We treat the problem as one of model selection
and present a penalized likelihood approach. We extend the work of
Broman and Speed (2002)
to allow for pairwise interactions between QTL while still aiming to
control false positives, an even greater concern with
epistatic interactions in the model space. We formulate a conservative
version of the penalized likelihood which provides strict control over
the rate of extraneous QTL, as well as a more liberal approach aimed at
detecting epistatic QTL with weak main effects. We demonstrate our
model selection criteria as exploratory tools in analysis of backcross
and intercross data. Through simulation studies, we show that
reasonable power to detect true positives is also achieved.
|