Wheat harvest near Iztaccíhuatl volcano in Juchitepec, Estado de México. Photo: CIMMYT/P. Lowe
With increasing global demand for wheat and increasing constraints (high temperatures, diseases) to wheat’s productivity, wheat breeders are looking for new methodologies to make breeding more efficient. A new study looks at refinements of genomic prediction models to help achieve this.
The authors write that genomic selection is becoming a standard approach to achieving genetic progress in plants, as it gets around the need to field-test the offspring at every cycle, but that the models commonly used in plant breeding are based on datasets of only a few hundred genotyped individual plants.
This study used pedigree and genomic data from nearly 59,000 wheat lines evaluated in different environments, as well as genomic and pedigree information in a model that incorporated genotype X environment interactions to predict the performance of wheat lines in Mexican and South Asian environments.
They found that models using markers (and pedigree) had higher prediction accuracies than models using only phenotypic data. Models that included genomic x environment had higher prediction accuracies than models that do not include interaction.
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