Intersection tests for single marker QTL analysis can be more powerful than two marker QTL analysis

Simulation studies are a commonly used tool to determine the behavior of test statistics under known conditions. We conducted a simulation study to assess the general behavior of an intersection test and a two marker test under a variety of conditions. The study was designed to reveal whether two marker tests are always more powerful than intersection tests, or whether there are cases when an intersection test may outperform the two marker approach.We present a reanalysis of a data set from a QTL study of ovariole number in Drosophila melanogaster.Our simulation study results show that there are situations where the single marker intersection test equals or outperforms the two marker test. The intersection test and the two marker test identify overlapping regions in the reanalysis of the Drosophila melanogaster data. The region identified is consistent with a regression based interval mapping analysis.We find that the intersection test is appropriate for analysis of QTL data. This approach has the advantage of simplicity and for certain situations supplies equivalent or more powerful results than a comparable two marker test.Many authors [1-4] have compared the statistical power of different methods for quantitative trait locus (QTL) mapping. These comparisons have shown that the additional information supplied by the genetic distance between identifiable DNA markers unconfounds the QTL effect from the QTL location, thus making two marker models more powerful than single marker models of detection (for review see Doerge et al. [5]).With the goal of detecting and/or locating QTL, there are two common statistical approaches that can be taken. The first approach is based on ANOVA, or simple linear regression, and performs statistical tests based solely on single DNA marker information. No genetic map is required for single marker analysis, and the calculations are based on phenotypic means and variances within each of the genotypic classes. The second, and more involve