TITLE:
Mixed Model, AMMI and Eberhart-Russel Comparison via Simulation on Genotype × Environment Interaction Study in Sugarcane
AUTHORS:
Guilherme Moraes Ferraudo, Dilermando Perecin
KEYWORDS:
Plant Breeding, Data Simulation, Genotype-Environment Interaction (GEI) Detection Methods, R Computing Environment, REML/BLUP
JOURNAL NAME:
Applied Mathematics,
Vol.5 No.14,
July
28,
2014
ABSTRACT: Brazil is the world leader in sugarcane production and the largest sugar
exporter. Developing new varieties is one of the main factors that contribute
to yield increase. In order to select the best genotypes, during the final
selection stage, varieties are tested in different environments (locations and
years), and breeders need to estimate the phenotypic performance for main
traits such as tons of cane yield per hectare (TCH) considering the genotype ×
environment interaction (GEI) effect. Geneticists and biometricians have used
different methods and there is no clear consensus of the best method. In this
study, we present a comparison of three methods, viz. Eberhart-Russel (ER),
additive main effects and multiplicative interaction (AMMI) and mixed model
(REML/BLUP), in a simulation study performed in the R computing environment to
verify the effectiveness of each method in detecting GEI, and assess the
particularities of each method from a statistical standpoint. In total, 63
cases representing different conditions were simulated, generating more than 34
million data points for analysis by each of the three methods. The results show
that each method detects GEI differently in a different way, and each has some
limitations. All three methods detected GEI effectively, but the mixed model
showed higher sensitivity. When applying the GEI analysis, firstly it is
important to verify the assumptions inherent in each method and these limitations
should be taken into account when choosing the method to be used.