Genomic prediction and indirect selection for grain yield in US Pacific Northwest winter wheat using spectral reflectance indices from high-throughput phenotyping

Authors: LOZADA, DENNIS - Washington State University; GODOY, JAYFRED - Washington State University; Ward, Brian; CARTER, ARRON - Washington State University

Submitted to: International Journal of Molecular Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/22/2019
Publication Date: 12/25/2019
Citation: Lozada, D., Godoy, J.V., Ward, B.P., Carter, A.H. 2019. Genomic prediction and indirect selection for grain yield in US Pacific Northwest winter wheat using spectral reflectance indices from high-throughput phenotyping. International Journal of Molecular Sciences. https://doi.org/10.3390/ijms21010165.

Interpretive Summary: Genomic prediction is a technique which utilizes genome-wide DNA marker data to predict an organism’s phenotypic performance in one or more environments. In the context of plant breeding, genomic prediction involves the genotyping and phenotyping of a set of lines used to train a model, which is then used to predict phenotypic performance for a set of lines which are only genotyped. Genomic prediction can save money in plant breeding, as genome-wide marker data is now far cheaper to obtain than phenotypic data. However, some traits, such as grain yield, are difficult to measure and/or highly influenced by environment. In these cases, correlated secondary traits may be useful for increasing the prediction accuracy for a primary targeted trait of interest. In this study, five different spectral reflectance indices were calculated by scanning winter wheat plants with a spectral radiometer in three separate panels of wheat planted in Lind and Pullman, Washington from 2015 to 2018. The tested panels were genotyped with using 11,089 genome-wide markers. The inclusion of reflection indices in prediction models significantly improved prediction accuracy for grain yield. The relatedness between the panel used to train the model and the panel for which predictions were generated was a primary factor affecting prediction accuracy, as well as the heritability (i.e. the degree to which genotype influences phenotype) of the target trait and the secondary reflection index traits. The results indicate that spectral reflectance data can be used to improve model prediction accuracy for complex traits such as grain yield in wheat grown in the U.S. Pacific Northwest.

Technical Abstract: 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.