Early-life stage phenomic prediction of field agronomic traits across breeding cycles in intermediate wheatgrass
- 1Danforth Plant Science Center
Abstract
Perennial grains represent a promising frontier for sustainable agriculture, but breeding progress is constrained by the accessibility of genotyping and the difficulty of evaluating complex traits expressed for multiple years after establishment across heterogeneous environments. Phenomic selection may help address these challenges by using inexpensive, scalable, high-dimensional phenotypes collected early in development, although the robustness of such predictions across breeding cycles remains uncertain. Here, we compared genomic selection and phenomic selection across two breeding cycles of Thinopyrum intermedium (intermediate wheatgrass; IWG; Kernza(R)), comprising approximately 2,280 individuals from maternal half-sib families evaluated across multiple field sites and years. We constructed relationship matrices from genomic markers and early-life stage phenomic data, including seed and leaf color (HSV), CropReporter multispectral reflectance and indices, and cycle-specific hyperspectral reflectance sensors. Genomic models provided the strongest predictions on average across all field traits in both cycles. Among phenomic predictors, leaf HSV was consistently the most informative, whereas CropReporter and hyperspectral data showed lower and more trait-dependent performance and seed HSV provided little predictive value. Genomic, leaf HSV, and CropReporter models transferred across breeding cycles with little apparent loss of predictive ability relative to within-cycle validation, demonstrating that their predictive signals were not restricted to a single breeding cycle. Early-life stage leaf HSV emerged as a practical, accessible tool for germplasm thinning and early-stage prioritization in perennial breeding programs. Despite limited similarity among relationship matrices, multi-relationship-matrix models rarely improved prediction beyond the stronger constituent single-relationship-matrix model. Together, these results show that early-life stage phenomic data provide reproducible information about agronomic performance expressed years later, but that predictor complexity and data integration do not guarantee improved prediction.
Versions
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BibTeX
@misc{harris2026earlylife,
title = {{Early-life stage phenomic prediction of field agronomic traits across breeding cycles in intermediate wheatgrass}},
author = {Z. N. Harris and J. Braley and E. Cassetta and J. Crain and L. DeHaan and D. Van Tassel and A. Miller and M. J. Rubin},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.28.747871},
url = {https://www.biorxiv.org/content/10.64898/2026.08.28.747871v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Early-life stage phenomic prediction of field agronomic traits across breeding cycles in intermediate wheatgrass
AU - Z. N. Harris
AU - J. Braley
AU - E. Cassetta
AU - J. Crain
AU - L. DeHaan
AU - D. Van Tassel
AU - A. Miller
AU - M. J. Rubin
PY - 2026
DA - 2026/08/28
PB - bioRxiv
DO - 10.64898/2026.08.28.747871
UR - https://www.biorxiv.org/content/10.64898/2026.08.28.747871v1
AB - Perennial grains represent a promising frontier for sustainable agriculture, but breeding progress is constrained by the accessibility of genotyping and the difficulty of evaluating complex traits expressed for multiple years after establishment across heterogeneous environments. Phenomic selection may help address these challenges by using inexpensive, scalable, high-dimensional phenotypes collected early in development, although the robustness of such predictions across breeding cycles remains uncertain. Here, we compared genomic selection and phenomic selection across two breeding cycles of Thinopyrum intermedium (intermediate wheatgrass; IWG; Kernza(R)), comprising approximately 2,280 individuals from maternal half-sib families evaluated across multiple field sites and years. We constructed relationship matrices from genomic markers and early-life stage phenomic data, including seed and leaf color (HSV), CropReporter multispectral reflectance and indices, and cycle-specific hyperspectral reflectance sensors. Genomic models provided the strongest predictions on average across all field traits in both cycles. Among phenomic predictors, leaf HSV was consistently the most informative, whereas CropReporter and hyperspectral data showed lower and more trait-dependent performance and seed HSV provided little predictive value. Genomic, leaf HSV, and CropReporter models transferred across breeding cycles with little apparent loss of predictive ability relative to within-cycle validation, demonstrating that their predictive signals were not restricted to a single breeding cycle. Early-life stage leaf HSV emerged as a practical, accessible tool for germplasm thinning and early-stage prioritization in perennial breeding programs. Despite limited similarity among relationship matrices, multi-relationship-matrix models rarely improved prediction beyond the stronger constituent single-relationship-matrix model. Together, these results show that early-life stage phenomic data provide reproducible information about agronomic performance expressed years later, but that predictor complexity and data integration do not guarantee improved prediction.
ER -