Fig.1 Overview of the experimental design. The rapeseed population used in this study was based on crossings of 5 different founder lines (P1 to P5) with a common elite line (L1). The resulting 251 pollinators were crossed with 2 different male-sterile inbred lines (M1 and M2), resulting in 410 test hybrids (A). Across-generation prediction was performed by using NIRS data obtained from the pollinators, grown at 1 location, to predict phenotypic traits of the hybrids, grown at 5 locations (B). Within-generation predictions were performed by using NIRS data and phenotypic traits both obtained from the hybrids (C to E). Here, phenotypic traits were obtained from all 5 locations, while NIRS data was obtained either from all 5 locations (C and E) or from single locations (D). Cross-validation was performed by randomly dividing the hybrid population into 80% for the training set and 20% for the test set with 200 repetitions (B to D) or by using hybrids, which descend from 4 of the 5 original crosses as the training set and the remaining subfamily as test set (E).
Newswise — Utilizing spectral data from near-infrared spectroscopy (NIRS), researchers demonstrated that PS can accurately predict complex traits like seed yield and plant height, even outperforming GS in certain scenarios. This innovative approach promises to revolutionize breeding programs by enhancing efficiency and reducing costs.
Traditionally, plant breeding relies on extensive phenotyping or genomic selection (GS), which uses genetic markers to predict breeding values. While effective, GS can be cost-prohibitive and inaccessible for certain crops. phenomic selection (PS), proposed as a more affordable alternative, leverages NIRS (near-infrared spectroscopy)-derived spectral data instead of genetic markers to predict phenotypic traits. Prior studies have shown PS's success in crops like wheat, maize, and soybean, but its application to hybrid breeding, where parental traits predict hybrid performance, remained untested.
A study (DOI: 10.34133/plantphenomics.0215) published in Plant Phenomics on 24th July 2024 by Lennard Roscher-Ehrig’s team, Justus Liebig University, accelerates breeding cycles and reduces dependency on labor-intensive phenotyping by enabling early-stage selection based on complex traits like seed yield.
The study evaluated PS in 410 hybrid rapeseed test populations grown across multiple environments. Researchers utilized phenomic prediction (PP) based on NIRS data to evaluate hybrid rapeseed performance, comparing it with genomic prediction (GP) and a combined approach incorporating both NIRS and single-nucleotide polymorphism (SNP) data. For within-generation predictions, NIRS data from harvested seeds were used to predict traits such as seed yield, plant height, and flowering time. Results showed that PP significantly outperformed GP for seed yield and plant height, achieving median accuracies of up to 0.56 for seed yield and 0.6 for plant height, compared to GP's highest accuracies of 0.38 and 0.26, respectively. However, for flowering time, GP achieved higher prediction accuracies (up to 0.68) than PP (up to 0.58). The combined approach consistently provided the highest accuracies across all traits, with the best result of 0.69 for flowering time. Predictions based on NIRS data obtained from single locations demonstrated comparable accuracies to those using aggregated data, underscoring PP's adaptability and practical applicability. Additionally, when selecting top-performing genotypes, PP surpassed GP in selecting the top 40 genotypes for seed yield, achieving an accuracy of 0.43 compared to GP's 0.35. Parental PP, which utilized NIRS profiles of pollinators to predict hybrid traits across generations, performed nearly as well as GP for seed yield and plant height, with slight differences in accuracy. Oil and protein content predictions achieved near-perfect accuracies, given their estimation through NIRS. The study highlights PP's efficiency in hybrid breeding, its ability to outperform GP in specific scenarios, and its robustness across varying conditions, establishing it as a viable and cost-effective alternative for trait prediction in rapeseed breeding programs.
PS emerges as a transformative tool for hybrid rapeseed breeding, offering a competitive, cost-effective alternative to genomic selection. Its ability to predict complex traits with high accuracy using existing spectral data positions it as a pivotal method for advancing crop breeding programs. This study underscores PS's potential to drive innovation in agriculture, paving the way for its application in other crops and broader breeding contexts.
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References
DOI
Original Source URL
https://doi.org/10.34133/plantphenomics.0215
Funding information：
The work was funded by Federal Ministry for Food and Agriculture grant 281B200416.
About Plant Phenomics
Science Partner Journal Plant Phenomics is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.
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