A Comparison of Statistical Approaches for Detecting Genes Influencing Poisson-Distributed Traits
by Mark J. Giganti, Nathan A. Johnson, and Steven T. Garren.
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.
Backcross data, Permutation tests,
Poisson distribution, Quantitative trait loci
Mark J. Giganti, firstname.lastname@example.org
Nathan A. Johnson, email@example.com
Steven T. Garren, firstname.lastname@example.org
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