Open AccessA Mixed-Model Approach to Mapping Quantitative Trait Loci in Barley on the Basis of Multiple Environment Data
Author(s) -
HansPeter Piepho
Publication year - 2000
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/156.4.2043
Subject(s) - quantitative trait locus , family based qtl mapping , mixed model , biology , trait , population , stability (learning theory) , random effects model , inclusive composite interval mapping , genetics , statistics , computational biology , computer science , gene mapping , mathematics , machine learning , gene , medicine , demography , meta analysis , sociology , chromosome , programming language
In this article, I propose a mixed-model method to detect QTL with significant mean effect across environments and to characterize the stability of effects across multiple environments. I demonstrate the method using the barley dataset by the North American Barley Genome Mapping Project. The analysis raises the need for mixed modeling in two different ways. First, it is reasonable to regard environments as a random sample from a population of target environments. Thus, environmental main effects and QTL-by-environment interaction effects are regarded as random. Second, I expect a genetic correlation among pairs of environments caused by undetected QTL. I show how random QTL-by-environment effects as well as genetic correlations are straightforwardly handled in a mixed-model framework. The main advantage of this method is the ability to assess the stability of QTL effects. Moreover, the method allows valid statistical inferences regarding average QTL effects.