A Nonparametric Bootstrap Method for Testing Close Linkage vs. Pleiotropy of Coincident Quantitative Trait Loci

A Nonparametric Bootstrap Method for Testing Close Linkage vs. Pleiotropy of Coincident Quantitative Trait Loci

Claude M. Lebreton^a, Peter M. Visscher^b, Christopher S. Haley^c, Andrei Semikhodskii^a, and Steve A. Quarrie^a
^a John Innes Centre, Norwich NR4 7UH, United Kingdom,
^b Institute of Ecology and Resource Management, University of Edinburgh, Edinburgh EH9 3JG, United Kingdom
^c Roslin Institute, Edinburgh EH25 9PS, United Kingdom

Corresponding author: Claude M. Lebreton, BIOCEM–Groupe Limagrain, 24, avenue des Landais, 63170 Aubière, France., claude.lebreton{at}bbsrc.ac.uk (E-mail).

Communicating editor: T. F. C. MACKAY

A novel method using the nonparametric bootstrap is proposedfor testing whether a quantitative trait locus (QTL) at onechromosomal position could explain effects on two separate traits.If the single-QTL hypothesis is accepted, pleiotropy could explainthe effect on two traits. If it is rejected, then the effectson two traits are due to linked QTLs. The method can be usedin conjunction with several QTL mapping methods as long as theyprovide a straightforward estimate of the number of QTLs detectablefrom the data set. A selection step was introduced in the bootstrapprocedure to reduce the conservativeness of the test of closelinkage vs. pleiotropy, so that the erroneous rejection of thenull hypothesis of pleiotropy only happens at a frequency equalto the nominal type I error risk specified by the user. Theapproach was assessed using computer simulations and provedto be relatively unbiased and robust over the range of geneticsituations tested. An example of its application on a real dataset from a saline stress experiment performed on a recombinantpopulation of wheat (Triticum aestivum L.) doubled haploid linesis also provided.

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