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Intersection tests for single marker QTL analysis can be more powerful than two marker QTL analysis
Cynthia J Coffman1, R W Doerge, Marta L Wayne
1Institute for Clinical and Epidemiological Research, Biostatistics Unit, Durham VA Medical Center (152), Durham, NC 27705, USA. cynthia.coffman@duke.edu
BMC Genetics
|June 21, 2003
Summary
The intersection test for quantitative trait locus (QTL) analysis can be as or more powerful than two-marker tests in certain situations. This simpler method is appropriate for QTL data analysis.
Area of Science:
- Genetics
- Statistical genetics
Background:
- Quantitative trait locus (QTL) mapping literature suggests two-marker analyses offer higher statistical power than single-marker analyses.
- However, this is only true in specific scenarios, necessitating further investigation into test statistic behavior.
Purpose of the Study:
- To conduct a simulation study assessing the general behavior of intersection and two-marker tests under various conditions.
- To determine if two-marker tests are always superior or if intersection tests can outperform them.
Main Methods:
- A simulation study was performed to evaluate the statistical power of intersection and two-marker tests.
- Reanalysis of a Drosophila melanogaster dataset on ovariole number was conducted.
Main Results:
- Simulation results indicate that the single-marker intersection test can equal or outperform the two-marker test in certain situations.
- Both tests identified overlapping regions in the Drosophila melanogaster data, consistent with regression-based interval mapping.
Conclusions:
- The intersection test is a suitable approach for quantitative trait locus (QTL) data analysis.
- This method offers simplicity and, in specific cases, provides equivalent or superior statistical power compared to two-marker tests.