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Two New Strategies for Detecting and Understanding QTL × Environment Interactions
Author(s) -
Gauch Hugh G.,
Rodrigues Paulo C.,
Munkvold Jesse D.,
Heffner Elliot L.,
Sorrells Mark
Publication year - 2011
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2010.04.0206
Subject(s) - quantitative trait locus , biology , doubled haploidy , ammi , hordeum vulgare , population , context (archaeology) , trait , family based qtl mapping , gene–environment interaction , genetics , computer science , gene mapping , genotype , ecology , poaceae , gene , paleontology , demography , sociology , chromosome , programming language
Two new strategies are proposed to improve the detection and understanding of quantitative trait loci (QTL), especially those exhibiting QTL × environment interactions (QEI), in the context of experiments conducted in multiple environments. First, a parsimonious Additive Main effects and Multiplicative Interaction (AMMI) model is applied to the phenotypic data to gain accuracy and thereby to increase the logarithm of odds (LOD) scores for QTL detections. Second, the environments are ordered by AMMI parameters that summarize genotype × environment interaction information to reveal consistent patterns and systematic trends that often have an evident ecological or biological interpretation. The combination of greater accuracy for the phenotypic data and systematic trends for the environments provides for more consistent and understandable QTL results. These new strategies are illustrated with two examples: preharvest sprouting scores of a biparental wheat ( Triticum aestivum L.) population from 14 environments spread over 5 yr, and yield for a doubled‐haploid barley ( Hordeum vulgare L.) population tested in 16 environments. AMMI parameters can also provide successful predictions of entire QTL scans for new environments. The statistical methods developed here are of great generality, applicable across microbial and plant populations grown in multiple environments, and may be adapted to animal and human genetic studies.