Articles
Modelling genotype by environment interaction using genomic and environmental data in apple
Article number
1412_23
Pages
145 – 150
Language
English
Abstract
Genotype by environment interaction (G×E) describes the differential response of plant genotypes in contrasting environments.
Understanding G×E and its prediction are important for breeding and the deployment of new apple varieties.
Multi-environment trials and the apple reference population (apple REFPOP) allow to evaluate the effect of G×E for a diverse set of genotypes.
Large-scale genomic, phenotypic, and environmental data can be incorporated in genomic prediction models estimating G×E, which may lead to increased genomic predictive ability for genotypes in different environments.
However, the large data sets from multi-environment trials increase complexity of the statistical models accounting for G×E and lead to long computational times, which may hinder extensive model exploration.
Here, modelling techniques recently implemented in the software package BGGE designed to increase computational efficiency were used to estimate the genomic predictive ability for the genotypes of the apple REFPOP, which were phenotyped for flowering intensity in ten environments (i.e., ten combinations of location and year). Reported proportions of the variance components related to genetic, environmental, and residual sources of the phenotypic variation contribute to understanding the effect of G×E on the studied trait.
The estimated computation times and predictive abilities show that increased efficiency of multi-environment genomic predictions can be reached using the tested recent modelling techniques.
These improved genomic prediction models will prove valuable for future breeding decisions in apple.
Understanding G×E and its prediction are important for breeding and the deployment of new apple varieties.
Multi-environment trials and the apple reference population (apple REFPOP) allow to evaluate the effect of G×E for a diverse set of genotypes.
Large-scale genomic, phenotypic, and environmental data can be incorporated in genomic prediction models estimating G×E, which may lead to increased genomic predictive ability for genotypes in different environments.
However, the large data sets from multi-environment trials increase complexity of the statistical models accounting for G×E and lead to long computational times, which may hinder extensive model exploration.
Here, modelling techniques recently implemented in the software package BGGE designed to increase computational efficiency were used to estimate the genomic predictive ability for the genotypes of the apple REFPOP, which were phenotyped for flowering intensity in ten environments (i.e., ten combinations of location and year). Reported proportions of the variance components related to genetic, environmental, and residual sources of the phenotypic variation contribute to understanding the effect of G×E on the studied trait.
The estimated computation times and predictive abilities show that increased efficiency of multi-environment genomic predictions can be reached using the tested recent modelling techniques.
These improved genomic prediction models will prove valuable for future breeding decisions in apple.
Authors
M. Jung, H. Muranty, W. Guerra, B.E. García-Gómez, M. Rymenants, B. Studer, G.A.L. Broggini, A. Patocchi
Keywords
genomic prediction, genomic selection, Malus × domestica, reference population, flowering intensity
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