Individual Genomic Distinctness of Rice Germplasm as Measured with an Average Pairwise Dissimilarity of Genome-Wide SNPs and Structural Variants
Bibliographic record
Abstract
The average pairwise dissimilarity (APD) between one plant sample and other assayed samples based on genetic markers was developed in 2006 to assess genetic distinctness and genetic redundancy in a plant germplasm collection. With the availability of abundant genomic variants across a genome, APD can be expanded to measure individual genomic distinctness. This study was conducted to assess the applicability of APD estimates in measuring the individual genomic distinctness of 1789 indica and 854 japonica rice samples based on published genome-wide single-nucleotide polymorphism (SNP) and structural variant (SV) data. It was found that the acquired APD estimates were weakly or not correlated between the SNP and SV data sets in the indica or japonica samples, respectively. For the indica samples, the APD estimates based on the SNP and SV data ranged from 0.1779 to 0.3277 and from 0.2297 to 0.4096, respectively. For the japonica samples, the SNP-based and SV-based APD estimates varied from 0.1774 to 0.3029 and from 0.1534 to 0.3459, respectively. These APD estimates were highly negatively correlated with the estimates of individual inbreeding coefficients and can identify the most genomically distinct rice germplasm that are compatible with those revealed through principal component analysis. Also, a reliable APD estimation was found to require 5000 to 10,000 random genomic SNPs or SVs. These findings together are significant, not only in demonstrating the informativeness of APD estimates in the identification of individuals with variable genomic distinctness, but also in providing guidance for APD applications to measure individual genomic distinctness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".