A Comparison of Statistical Approaches for Detecting Genes Influencing Poisson-Distributed Traits
by Mark J. Giganti, Nathan A. Johnson, and Steven T. Garren.
Abstract:
Abstract: In the field of statistical genetics, many methods
for identifying quantitative trait loci (QTLs), which are
genes influencing phenotypes, require the assumption of
normality in the phenotypic distribution. However, many
applications of QTL analysis do not have normally
distributed phenotypes. In this paper, we compare different
parametric and nonparametric approaches for detecting the
presence of QTLs when phenotypes follow a Poisson
distribution. Using parametric tests, a Wilcoxon rank-sum
test, and permutation methods, we analyzed simulated
genotypes and phenotypes of mice. Our simulations suggest
that the parametric tests are as effective as the
nonparametric tests and computationally faster than the
permutation tests. We conclude that parametric approaches
are capable of performing QTL analysis for Poisson
distributions, even for small phenotypic means.
Key Words:
Backcross data, Permutation tests,
Poisson distribution, Quantitative trait loci
Authors:
Mark J. Giganti, giganti00@yahoo.com
Nathan A. Johnson, johnsonna@wlu.edu
Steven T. Garren, garrenst@jmu.edu
Editor:
Chris Amos,camos@request.mdacc.tmc.edu.sg
READING THE ARTICLE: You can read the article in
portable document (.pdf) format (188144 bytes.)
NOTE: The content of this article is the intellectual property of the authors, who retains all rights to future publication.
This page has been accessed 696 times since July 24, 2006.
Return to the 
Home Page.