Genotyping‐by‐Sequencing on Pooled Samples and its Use in Measuring Segregation Bias during the Course of Androgenesis in Barley
Bibliographic record
Abstract
Estimation of allelic frequencies is often required in breeding but genotyping many individuals at many loci can be expensive. We have developed a genotyping-by-sequencing (GBS) approach for estimating allelic frequencies on pooled samples (Pool-GBS) and used it to examine segregation distortion in doubled haploid (DH) populations of barley ( L.). In the first phase, we genotyped each line individually and exploited these data to explore a strategy to call single nucleotide polymorphisms (SNPs) on pooled reads. We measured both the number of SNPs called and the variance of the estimated allelic frequencies at various depths of coverage on a subset of reads containing 5 to 25 million reads. We show that allelic frequencies could be cost-effectively and accurately estimated at a depth of 50 reads per SNP using 15 million reads. This Pool-GBS approach yielded 1984 SNPs whose allelic frequency estimates were highly reproducible (CV = 10.4%) and correlated ( = 0.9167) with the "true" frequency derived from analysis of individual lines. In a second phase, we used Pool-GBS to investigate segregation bias throughout androgenesis from microspores to a population of regenerated plants. No strong bias was detected among the microspores resulting from the meiotic divisions, whereas significant biases could be shown to arise during embryo formation and plant regeneration. In summary, this methodology provides an approach to estimate allelic frequencies more efficiently and on materials that are unsuitable for individual analysis. In addition, it allowed us to shed light on the process of androgenesis in barley.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".