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

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 s...

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Autores principales: Coffman, Cynthia J, Doerge, RW, Wayne, Marta L, McIntyre, Lauren M
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2003
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC166174/
https://www.ncbi.nlm.nih.gov/pubmed/12816551
http://dx.doi.org/10.1186/1471-2156-4-10
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author Coffman, Cynthia J
Doerge, RW
Wayne, Marta L
McIntyre, Lauren M
author_facet Coffman, Cynthia J
Doerge, RW
Wayne, Marta L
McIntyre, Lauren M
author_sort Coffman, Cynthia J
collection PubMed
description 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.
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spelling pubmed-1661742003-07-26 Intersection tests for single marker QTL analysis can be more powerful than two marker QTL analysis Coffman, Cynthia J Doerge, RW Wayne, Marta L McIntyre, Lauren M BMC Genet Methodology Article 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. BioMed Central 2003-06-19 /pmc/articles/PMC166174/ /pubmed/12816551 http://dx.doi.org/10.1186/1471-2156-4-10 Text en Copyright © 2003 Coffman et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.
spellingShingle Methodology Article
Coffman, Cynthia J
Doerge, RW
Wayne, Marta L
McIntyre, Lauren M
Intersection tests for single marker QTL analysis can be more powerful than two marker QTL analysis
title Intersection tests for single marker QTL analysis can be more powerful than two marker QTL analysis
title_full Intersection tests for single marker QTL analysis can be more powerful than two marker QTL analysis
title_fullStr Intersection tests for single marker QTL analysis can be more powerful than two marker QTL analysis
title_full_unstemmed Intersection tests for single marker QTL analysis can be more powerful than two marker QTL analysis
title_short Intersection tests for single marker QTL analysis can be more powerful than two marker QTL analysis
title_sort intersection tests for single marker qtl analysis can be more powerful than two marker qtl analysis
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC166174/
https://www.ncbi.nlm.nih.gov/pubmed/12816551
http://dx.doi.org/10.1186/1471-2156-4-10
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