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Accurate predictions of barley phenotypes using genomewide markers and environmental covariates.

Authors :
Neyhart, Jeffrey L.
Silverstein, Kevin A. T.
Smith, Kevin P.
Source :
Crop Science; Sep/Oct2022, Vol. 62 Issue 5, p1821-1833, 13p
Publication Year :
2022

Abstract

Predicting the performance of new plant genotypes under new environmental conditions could accelerate the development of locally adapted and climate resilient cultivars. Enabling these predictions may rely on extending the genomewide prediction framework to include environmental covariates (EC), but such models have generally been tested under limited, less breeding‐realistic contexts. Using a barley (Hordeum vulgare L.) multi‐environment dataset, our objectives were to compare multi‐environment prediction models and scenarios that target genotypes from different breeding generations, use different levels and timescales of ECs, and are applied to different agronomic and quality traits. When predicting the phenotypes of previously tested genotypes in untested environments, models that included the interaction of genomewide markers and pre‐selected in‐season ECs resulted in more accurate predictions (rMG or rMP) within (rMG = 0.56–0.94) and across (rMP = 0.63–0.87) environments; similar accuracy was achieved within (rMP = 0.46–0.89) and across (rMP = 0.87–0.95) locations when using only ECs from realistically available historical climate data. Shifting the prediction target to a distinct, untested offspring population slightly reduced model performance within environments or locations, but rMP across environments (rMP = 0.60–0.86) or locations (rMP = 0.87–0.94) remained very high. Though we achieved moderately high rMP for most traits in the challenging scenario of predicting the offspring population in holdout environments, the similarity between training and target environments, like that between populations, will be a limiting factor for enabling accurate predictions of new genotypes under new growing conditions. Core Ideas: Genomewide prediction with select environmental covariates led to accurate whole‐phenotype predictions.Historical climate data enabled a more breeding‐realistic framework for predicting phenotypes in new locations.Predictive ability was improved for both tested genotypes and untested offspring across new environments.Genetic and environmental similarity may have limited accuracy of untested offspring in holdout environments. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0011183X
Volume :
62
Issue :
5
Database :
Complementary Index
Journal :
Crop Science
Publication Type :
Academic Journal
Accession number :
159470296
Full Text :
https://doi.org/10.1002/csc2.20782