AUTHOR=Azizinia Shiva , Mullan Daniel , Rattey Allan , Godoy Jayfred , Robinson Hannah , Moody David , Forrest Kerrie , Keeble-Gagnere Gabriel , Hayden Matthew J. , Tibbits Josquin FG. , Daetwyler Hans D. 

TITLE=Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1167221

DOI=10.3389/fpls.2023.1167221

ISSN=1664-462X

ABSTRACT=Historically, end-product quality testing has been costly and required large flour samples and therefore, was generally implemented in late phases of variety development imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher-throughput and non-destructive testing technologies, such as Near Infra-Red (NIR), could enable early-stage testing and effective selection on these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on end-product quality trait accuracy of adding NIR phenotypes as a second trait in genomic best linear unbiased prediction (GBLUP) to increase training population size of six quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). 1400-1900 bread wheat lines were measured during 2012-2019 in laboratory assays along with NIR records of approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5–0.83, except for Flour Swelling Volume 0.19). Prediction accuracies of end-product traits ranged between 0.28–0.64 and increased by 30% through inclusion of NIR predicted phenotypes compared to single trait analysis. There was a high correlation between the magnitude of multi-trait prediction accuracy and genetic correlations between end-product and NIR phenotypes (0.69–0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracies at a level that enables selection for end-product quality traits early in the breeding cycle.