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A model selection approach for the identification of quantitative trait loci in experimental crosses
Author(s) -
Broman Karl W.,
Speed Terence P.
Publication year - 2002
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00354
Subject(s) - quantitative trait locus , selection (genetic algorithm) , family based qtl mapping , identification (biology) , trait , quantitative genetics , biology , computer science , artificial intelligence , genetics , genetic variation , gene mapping , gene , ecology , chromosome , programming language
Summary. We consider the problem of identifying the genetic loci (called quantitative trait loci (QTLs)) contributing to variation in a quantitative trait, with data on an experimental cross. A large number of different statistical approaches to this problem have been described; most make use of multiple tests of hypotheses, and many consider models allowing only a single QTL. We feel that the problem is best viewed as one of model selection. We discuss the use of model selection ideas to identify QTLs in experimental crosses. We focus on a back‐cross experiment, with strictly additive QTLs, and concentrate on identifying QTLs, considering the estimation of their effects and precise locations of secondary importance. We present the results of a simulation study to compare the performances of the more prominent methods.