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

Claude Lebreton | Zendy; Peter M. Visscher | Zendy; Chris Haley | Zendy; Andrei Semikhodskii | Zendy; S. A. Quarrie | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

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

Author(s) -

Claude Lebreton,

Peter M. Visscher,

Chris Haley,

Andrei Semikhodskii,

S. A. Quarrie

Publication year - 1998

Publication title -

genetics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.792

H-Index - 246

eISSN - 1943-2631

pISSN - 0016-6731

DOI - 10.1093/genetics/150.2.931

Subject(s) - quantitative trait locus , biology , pleiotropy , genetics , nonparametric statistics , population , trait , family based qtl mapping , linkage (software) , doubled haploidy , locus (genetics) , range (aeronautics) , selection (genetic algorithm) , statistics , gene mapping , mathematics , chromosome , computer science , gene , artificial intelligence , phenotype , demography , materials science , sociology , programming language , composite material

A novel method using the nonparametric bootstrap is proposed for testing whether a quantitative trait locus (QTL) at one chromosomal position could explain effects on two separate traits. If the single-QTL hypothesis is accepted, pleiotropy could explain the effect on two traits. If it is rejected, then the effects on two traits are due to linked QTLs. The method can be used in conjunction with several QTL mapping methods as long as they provide a straightforward estimate of the number of QTLs detectable from the data set. A selection step was introduced in the bootstrap procedure to reduce the conservativeness of the test of close linkage vs. pleiotropy, so that the erroneous rejection of the null hypothesis of pleiotropy only happens at a frequency equal to the nominal type I error risk specified by the user. The approach was assessed using computer simulations and proved to be relatively unbiased and robust over the range of genetic situations tested. An example of its application on a real data set from a saline stress experiment performed on a recombinant population of wheat (Triticum aestivum L.) doubled haploid lines is also provided.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research