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Use of mixed model method to estimate genotypic by environmental interactions in a corn breeding program
Abstract
Genotypic by environmental interactions occur when plant genotypes respond differentially to different environmental conditions. Large interactions reduce progress from selection and make hybrid recommendation difficult. Therefore, corn breeders are interested in developing stable hybrids with high yield capability. Mixed model method is a statistical procedure that includes both random and fixed effects in the statistical model and analysis, and compared to most fixed models produces reduced standard errors when comparing differences among genotypes. Mixed model methodologies in data analysis provide additional confidence when making selections and identifying stable genotypes. In this study, 39 corn single-cross hybrids, progeny from crosses involving 32 female inbreds and 16 male inbreds, plus one commercial hybrid were evaluated at 22 locations of the U.S. Corn Belt during 1996. Grain yield and grain moisture data were analyzed using Proc Mixed, GLM, and Varcomp of the Statistical Analysis System (SAS). The objectives of the research were to compare several mixed linear models where hybrid and location effects were variously considered fixed or random, to compare statistical procedures for estimation of variance components, to predict stability of performance of hybrids and parental inbreds, and to predict average genetic values of hybrids and inbreds. Results indicated that standard errors of prediction were reduced by 33% when hybrids and parental inbreds were considered random rather than fixed effects. The three procedures computed the same estimates of variance components when balanced data were used. Proc Mixed and Varcomp gave similar results when analyzing unbalanced data. Phenotypic variance was small (1.526) in Iowa locations, intermediate in Nebraska (3.169) and Indiana (3.467) locations, and large in Illinois (4.885) locations. Analysis of data combined over locations resulted in significant estimates of variance components for grain yield and moisture. Variance components for female by environment interactions and for males were not significant. In some instances differences between pairs of hybrids were predicted with increased accuracy when location and genotypic effects were considered random. Best linear unbiased predictors for grain yield were as effective as Eberhart and Russell's stability parameters in identifying stable-high yielding hybrids. Large genetic values of the two top ranked hybrids were mainly due to large average heterotic values associated with their female parents.
Subject Area
Agronomy|Plant propagation|Genetics
Recommended Citation
Coutino-Estrada, Bulmaro, "Use of mixed model method to estimate genotypic by environmental interactions in a corn breeding program" (1998). ETD collection for University of Nebraska-Lincoln. AAI9826080.
https://digitalcommons.unl.edu/dissertations/AAI9826080