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Open AccessArticle

Genomic Prediction and Indirect Selection for Grain Yield in US Pacific Northwest Winter Wheat Using Spectral Reflectance Indices from High-Throughput Phenotyping

1
Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA
2
USDA-ARS Plant Science Research Unit, Raleigh, NC 27695, USA
*
Author to whom correspondence should be addressed.
Current address: Integrain Pty Ltd., Bibra Lake 6163, Australia.
Int. J. Mol. Sci. 2020, 21(1), 165; https://doi.org/10.3390/ijms21010165
Received: 5 December 2019 / Revised: 21 December 2019 / Accepted: 22 December 2019 / Published: 25 December 2019
(This article belongs to the Collection Genetics and Molecular Breeding in Plants)
Secondary traits from high-throughput phenotyping could be used to select for complex target traits to accelerate plant breeding and increase genetic gains. This study aimed to evaluate the potential of using spectral reflectance indices (SRI) for indirect selection of winter-wheat lines with high yield potential and to assess the effects of including secondary traits on the prediction accuracy for yield. A total of five SRIs were measured in a diversity panel, and F5 and doubled haploid wheat breeding populations planted between 2015 and 2018 in Lind and Pullman, WA. The winter-wheat panels were genotyped with 11,089 genotyping-by-sequencing derived markers. Spectral traits showed moderate to high phenotypic and genetic correlations, indicating their potential for indirect selection of lines with high yield potential. Inclusion of correlated spectral traits in genomic prediction models resulted in significant (p < 0.001) improvement in prediction accuracy for yield. Relatedness between training and test populations and heritability were among the principal factors affecting accuracy. Our results demonstrate the potential of using spectral indices as proxy measurements for selecting lines with increased yield potential and for improving prediction accuracy to increase genetic gains for complex traits in US Pacific Northwest winter wheat. View Full-Text
Keywords: genetic correlation; genetic gains; genomic prediction; grain yield; high-throughput phenotyping; indirect selection; spectral reflectance indices genetic correlation; genetic gains; genomic prediction; grain yield; high-throughput phenotyping; indirect selection; spectral reflectance indices
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Lozada, D.N.; Godoy, J.V.; Ward, B.P.; Carter, A.H. Genomic Prediction and Indirect Selection for Grain Yield in US Pacific Northwest Winter Wheat Using Spectral Reflectance Indices from High-Throughput Phenotyping. Int. J. Mol. Sci. 2020, 21, 165.

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