Assessing Genetic Distinctness and Redundancy of Plant Germplasm Conserved Ex Situ Based on Published Genomic SNP Data
Bibliographic record
Abstract
Assessing genetic distinctness and redundancy is an important part of plant germplasm characterization. Over the last decade, such assessment has become more feasible and informative, thanks to the advances in genomic analysis. An attempt was made here to search for genebank germplasm with published genomic data and to assess their genetic distinctness and redundancy based on average pairwise dissimilarity (APD). The effort acquired 12 published genomic data sets from CIMMYT, IPK, USDA-ARS, IRRI, and ICRISAT genebanks. The characterized collections consisted of 661 to 55,879 accessions with up to 2.4 million genome-wide SNPs. The assessment generated an APD estimate for each sample. As a higher or lower APD is indicative of more genetic distinctness or redundance for an accession, respectively, these APD estimates helped to identify the most genetically distinct and redundant groups of 100 accessions each and a genetic outlier group with APD estimates larger than five standard deviations in each data set. An APD-based grouping of the conserved germplasm in each data set revealed among-group variances ranging from 1.5 to 53.4% across all data sets. Additional analyses showed that these APD estimations were more sensitive to SNP number, minor allele frequency, and missing data. Generally, 5000 to 10,000 genome-wide SNPs were required for an effective APD analysis. These findings together are encouraging and useful for germplasm management, utilization, and conservation, particularly in the genetic categorization of conserved germplasm.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".