An SNP-based approach to estimating the mating and relatedness components of plant mating portfolios
Bibliographic record
Abstract
An individual's mating portfolio includes the number and genetic diversity of its mates and the distribution of genetic contributions to resulting offspring. For natural plant populations, microsatellites are useful for quantifying mating outcomes, but mating studies typically involve too few microsatellite markers to determine mate relatedness. In contrast, genotyping-by-sequencing (GBS) identifies thousands of single-nucleotide polymorphisms (SNPs) that enable relatedness assessment of parental plants but is unnecessarily informative and costly for parentage analysis of many offspring. We propose using GBS to identify SNPs for a sample of parental plants to both characterize their relatedness and provide selected markers for identifying seed parents. We specifically describe the development of SNP-SCALE markers and determine their efficacy for identifying the fathers of seed families of known maternal plants. Because GBS identifies many more SNPs than needed for mating analysis, specific SNPs can be selected for marker development based on ideal characteristics (equal allele frequencies, gametic phase disequilibrium). The SNP-SCALE approach produces allele-specific markers with unique size and fluorescent tags that facilitate scoring with few errors. These markers can be assayed relatively inexpensively using multiplex PCR and electrophoresis with a DNA sequencer. For a sample of parental Delphinium glaucum plants and their seeds, 30 marker loci provided adequate discrimination to characterize mating outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".