학술논문

Genotype by environment model predictive ability in Miscanthus.
Document Type
Article
Source
GCB Bioenergy. Jan2024, Vol. 16 Issue 1, p1-10. 10p.
Subject
*ENERGY crops
*MISCANTHUS
*GENOTYPE-environment interaction
*PREDICTION models
*GENOTYPES
*SWITCHGRASS
Language
ISSN
1757-1693
Abstract
Miscanthus is a genus of perennial grasses native to East Asia that shows promise as a biofuel energy source. Breeding efforts for increasing biofuel capability in this genus have focused on two species, namely M. sinensis (Msi) and M. sacchariflorus (Msa). For these efforts to succeed, it is critical that both Msi and Msa, as well as their interspecific crosses, can be grown at a wide range of latitudes. Therefore, the purpose of this study was to investigate how well existing data from Msi and Msa trials grown at locations throughout the northern hemisphere can train state‐of‐the‐art genomic selection (GS) models to predict genomic estimated breeding values (GEBVs) of dry yield for untested Msi and Msa accessions in untested environments. We found that accounting for genotype by environment interaction in the GS model did not notably improve predictive ability. Additionally, we observed that locations at lower latitudes showed higher predictive ability relative to locations at higher latitudes. These results suggest that it is crucial to increase the number of trial locations at higher latitude locations to investigate the source of this correlation. This will make it possible to train GS models using data from environments that are similar to growing conditions at the locations targeted by Msi and Msa breeders. Such an increase of trial locations in target environments could pave the way toward advancing breeding efforts for overwintering ability in Msi and Msa, and ultimately support the potential of Miscanthus as a biofuel crop. We evaluated the extent to which existing data from two Miscanthus species grown at multiple locations can be used for genomic prediction. We assessed the ability of two multi‐environment genomic prediction models to predict breeding values of yield for genotypes and environments that were not included in a training set. We found that considering genotype‐by‐environment interactions did not substantially improve predictive ability, and that predictive abilities tended to be lower at locations at higher latitudes. [ABSTRACT FROM AUTHOR]