title: A simple regression method for mapping quantitative trait loci in line crosses using flanking markers.
abstract: The use of flanking marker methods has proved to be a powerful tool for the mapping of quantitative trait loci (QTL) in the segregating generations derived from crosses between inbred lines. Methods to analyse these data, based on maximum-likelihood, have been developed and provide good estimates of QTL effects in some situations. Maximum-likelihood methods are, however, relatively complex and can be computationally slow. In this paper we develop methods for mapping QTL based on multiple regression which can be applied using any general statistical package. We use the example of mapping in an F(2) population and show that these regression methods produce very similar results to those obtained using maximum likelihood. The relative simplicity of the regression methods means that models with more than a single QTL can be explored and we give examples of two lined loci and of two interacting loci. Other models, for example with more than two QTL, with environmental fixed effects, with between family variance or for threshold traits, could be fitted in a similar way. The ease, speed of application and generality of regression methods for flanking marker analysis, and the good estimates they obtain, suggest that they should provide the method of choice for the analysis of QTL mapping data from inbred line crosses.
authors.affiliation: AFRC Institute of Animal Physiology and Genetics Research, Edinburgh Research Station, Roslin, Midlothian EH25 9PS, Edinburgh, U.K.
authors.forename: C S
authors.initials: CS
authors.lastname: Haley
authors.forename: S A
authors.initials: SA
authors.lastname: Knott
mesh_terms: D000818:Animals; D002874:Chromosome Mapping / Q000379:methods* / Q000706:statistics & numerical data; D003198:Computer Simulation; D003433:Crosses, Genetic; D005819:Genetic Markers; D007178:Inbreeding; D016013:Likelihood Functions; D015233:Models, Statistical; D040641:Quantitative Trait Loci; D012044:Regression Analysis
chemical_list: D005819:Genetic Markers