AUTHOR=Jackson Robert , Buntjer Jaap B. , Bentley Alison R. , Lage Jacob , Byrne Ed , Burt Chris , Jack Peter , Berry Simon , Flatman Edward , Poupard Bruno , Smith Stephen , Hayes Charlotte , Barber Tobias , Love Bethany , Gaynor R. Chris , Gorjanc Gregor , Howell Phil , Mackay Ian J. , Hickey John M. , Ober Eric S. TITLE=Phenomic and genomic prediction of yield on multiple locations in winter wheat JOURNAL=Frontiers in Genetics VOLUME=14 YEAR=2023 URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1164935 DOI=10.3389/fgene.2023.1164935 ISSN=1664-8021 ABSTRACT=

Genomic selection has recently become an established part of breeding strategies in cereals. However, a limitation of linear genomic prediction models for complex traits such as yield is that these are unable to accommodate Genotype by Environment effects, which are commonly observed over trials on multiple locations. In this study, we investigated how this environmental variation can be captured by the collection of a large number of phenomic markers using high-throughput field phenotyping and whether it can increase GS prediction accuracy. For this purpose, 44 winter wheat (Triticum aestivum L.) elite populations, comprising 2,994 lines, were grown on two sites over 2 years, to approximate the size of trials in a practical breeding programme. At various growth stages, remote sensing data from multi- and hyperspectral cameras, as well as traditional ground-based visual crop assessment scores, were collected with approximately 100 different data variables collected per plot. The predictive power for grain yield was tested for the various data types, with or without genome-wide marker data sets. Models using phenomic traits alone had a greater predictive value (R2 = 0.39–0.47) than genomic data (approximately R2 = 0.1). The average improvement in predictive power by combining trait and marker data was 6%–12% over the best phenomic-only model, and performed best when data from one full location was used to predict the yield on an entire second location. The results suggest that genetic gain in breeding programmes can be increased by utilisation of large numbers of phenotypic variables using remote sensing in field trials, although at what stage of the breeding cycle phenomic selection could be most profitably applied remains to be answered.