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Genomic-enabled prediction accuracies increased by modeling genotype × environment interaction in durum wheat

Author: Sukumaran, S.
Author: Jarquín, D.
Author: Crossa, J.
Author: Reynolds, M.P.
Year: 2018
Abstract: Genomic prediction studies incorporating genotype × environment (G×E) interaction effects are limited in durum wheat. We tested the genomic-enabled prediction accuracy (PA) of Genomic Best Linear Unbiased Predictor (GBLUP) models?six non-G × E and three G × E models?on three basic cross-validation (CV) schemes? in predicting incomplete field trials (CV2), new lines (CV1), and lines in untested environments (CV0)? in a durum wheat panel grown under yield potential, drought stress, and heat stress conditions. For CV0, three scenarios were considered: (i) leave-one environment out (CV0-Env); (ii) leave one site out (CV0-Site); and (iii) leave 1 yr out (CV0-Year). The reaction norm models with G × E effects showed higher PA than the non-G × E models. Among the CV schemes, CV2 and CV0-Env had higher PA (0.58 each) than the CV1 scheme (0.35). When the average of all the models and CV schemes were considered, among the eight traits? grain yield, thousand grain weight, grain number, days to anthesis, days to maturity, plant height, and normalized difference vegetation index at vegetative (NDVIvg) and grain filling (NDVIllg)?, plant height had the highest PA (0.68) and moderate values were observed for grain yield (0.34). The results indicated that genomic selection models incorporating G × E interaction show great promise for forward prediction and application in durum wheat breeding to increase genetic gains.
Format: PDF
Language: English
Publisher: Crop Science Society of America
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Type: Article
Place of Publication: Madison, U.S
Issue: 2
Volume: 11
DOI: 10.3835/plantgenome2017.12.0112
Agrovoc: DROUGHT
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Journal: The Plant Genome

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  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP

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