Exome QTL-seq maps monogenic locus and QTLs in barley
Bibliographic record
Abstract
BACKGROUND: QTL-seq, in combination with bulked segregant analysis and next-generation sequencing (NGS), is used to identify loci in small plant genomes, but is technically challenging to perform in species with large genomes, such as barley. A combination of exome sequencing and QTL-seq (exome QTL-seq) was used to map the mono-factorial Mendelian locus black lemma and pericarp (Blp) and QTLs for resistance to net blotch disease, a common disease of barley caused by the fungus Pyrenophora teres, which segregated in a population of 100 doubled haploid barley lines. METHODS: The provisional exome sequences were prepared by ordering the loci of expressed genes based on the genome information and concatenating genes with intervals of 200-bp spacer "N" for each chromosome. The QTL-seq pipeline was used to analyze short reads from the exome-captured library. RESULTS: In this study, short NGS reads of bulked total DNA samples from segregants with extreme trait values were subjected to exome capture, and the resulting exome sequences were aligned to the reference genome. SNP allele frequencies were compared to identify the locations of genes/QTLs responsible for the trait value differences between lines. For both objective traits examined, exome QTL-seq identified the monogenic Mendelian locus and associated QTLs. These findings were validated using conventional mapping approaches. CONCLUSIONS: Exome QTL-seq broadens the utility of NGS-based gene/QTL mapping in organisms with large genomes.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".