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

Cynthia J Coffman¹^,2 , RW Doerge³^,4^,5 , Marta L Wayne⁶ and Lauren M McIntyre²^,4^,5

¹Institute for Clinical and Epidemiological Research, Biostatistics Unit, Durham VA Medical Center (152), Durham, NC 27705 USA

²Duke Medical Center, Department of Biostatistics and Bioinformatics, Durham, NC 27710 USA

³Department of Statistics, Purdue University, West Lafayette, IN 47907 USA

⁴Department of Agronomy, Purdue University, West Lafayette, IN 47907 USA

⁵Computational Genomics, Purdue University, West Lafayette, IN 47907 USA

⁶Department of Zoology, University of Florida, Gainesville, FL 32611-8525 USA

author email corresponding author email

BMC Genetics 2003, 4:10doi:10.1186/1471-2156-4-10


Published:	19 June 2003

Abstract

Background

It has been reported in the quantitative trait locus (QTL) literature that when testing for QTL location and effect, the statistical power supporting methodologies based on two markers and their estimated genetic map is higher than for the genetic map independent methodologies known as single marker analyses. Close examination of these reports reveals that the two marker approaches are more powerful than single marker analyses only in certain cases.

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.

Results

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.

Conclusion

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.