Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat

Abstract

Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots.

Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat

“…While GNDVI is generally positively correlated with GY, because of it being an indicator of the greenness or biomass of the plants, it was negatively correlated with GY in the DS environment as also observed previously (Rutkoski et al 2016). This is most likely due to the range of DTHD in the populations used (the late lines were greener and had a lower yield).…”

Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early‐Generation Wheat Yield Trials

“…Rutkoski et al (2016) reported similar findings in the context of multitrait models (note that the multitrait model used in the present study was only tested with the inclusion of a GRM). This is a clear benefit in the setting of early-generation yield trials, as it allows for the optimization of sparse resource allocation.…”

“…Several previous studies reported that multitrait GS models could be used to increase predictive ability for lowheritability traits that are highly correlated with auxiliary, higher-heritability traits (Jia and Jannink, 2012;Rutkoski et al, 2016;Schulthess et al, 2016;Wang et al, 2016). In the present study, the predictive ability of the GBLUP MV model was greater than that of corresponding single-trait models for intercorrelated traits when using the CV2 cross-validation scheme (Fig.…”

“…Rutkoski et al (2016) and Pszczola et al (2013) reported similar findings, in which the use of a multitrait model including secondary traits had no influence on prediction accuracy if secondary trait phenotypes were not collected for the VP. The second cross-validation scheme they used did not resemble either of our CV2 methods, as it simulated a scenario in which a trait is not assessed in all lines within one environment but is assessed in all other environments.…”

Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat

“…It can also make use of markers, though currently these are more likely to be for alleles associated with genes of major effect-such as Ppd, Vrn, and Rht in wheat (Eagles et al 2014). Nonetheless, since selecting among early generation progeny for expression of complex traits is not always feasible (due in part to very large numbers), it is expected that genomic selection-potentially in combination with high throughput phenotyping of few integrative traits like canopy temperature and vegetative indices (Rutkoski 2016)-will find valuable application in selection of early generations based on strategic crossing for complex physiological traits. Marker assisted selection (MAS) has not delivered as expected (Langridge and Reynolds 2015) but remains a possibility, especially if QTL 9 environment interactions can be more effectively modeled (Millet et al 2016).…”