Phenotypic and Genomic Analyses of a Fast Neutron Mutant Population Resource in Soybean
Why this work is in the frame
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Bibliographic record
Abstract
Mutagenized populations have become indispensable resources for introducing variation and studying gene function in plant genomics research. In this study, fast neutron (FN) radiation was used to induce deletion mutations in the soybean (Glycine max) genome. Approximately 120,000 soybean seeds were exposed to FN radiation doses of up to 32 Gray units to develop over 23,000 independent M2 lines. Here, we demonstrate the utility of this population for phenotypic screening and associated genomic characterization of striking and agronomically important traits. Plant variation was cataloged for seed composition, maturity, morphology, pigmentation, and nodulation traits. Mutants that showed significant increases or decreases in seed protein and oil content across multiple generations and environments were identified. The application of comparative genomic hybridization (CGH) to lesion-induced mutants for deletion mapping was validated on a midoleate x-ray mutant, M23, with a known FAD2-1A (for fatty acid desaturase) gene deletion. Using CGH, a subset of mutants was characterized, revealing deletion regions and candidate genes associated with phenotypes of interest. Exome resequencing and sequencing of PCR products confirmed FN-induced deletions detected by CGH. Beyond characterization of soybean FN mutants, this study demonstrates the utility of CGH, exome sequence capture, and next-generation sequencing approaches for analyses of mutant plant genomes. We present this FN mutant soybean population as a valuable public resource for future genetic screens and functional genomics research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it