QTL Identification and Candidate Gene Prediction for Spike-Related Traits in Barley
Bibliographic record
Abstract
Barley (Hordeum vulgare L.) is one of the most important cereal crops in the world, and its production is important to humans. Barley spike morphology is highly correlated with yield and is also a complex multigene-controlled quantitative trait. To date, a considerable number of spike-related quantitative trait loci (QTLs) have been reported in barley, but the large physical distances between most of them and the lack of follow-up studies have made it difficult to use them in molecular-assisted breeding in barley. To explore more novel and yield-enhancing spike QTLs, in this study, a high-density genetic linkage map was developed based on a population of 172 F2:12 recombinant inbred lines (RILs) developed from a cross between the barley variety Yongjiabaidamai (YJ) and Hua 30 (H30), and used to map the spike length (SL), rachis node number (SRN), and spike density (SD). A total of 50 additive QTLs (LOD > 3) were mapped in four environments, four of them being stable and major QTLs. The qSL2-5 overlaps with the zeo1 gene, comparing the gene sequences of both parents and combining with previous studies, zeo1 was determined to be the SL regulatory gene in qSL2-5. The qSRN2-1 overlaps with vrs1, but it has not been previously reported that vrs1 affects SRN. Notably, two novel QTLs, one each on chromosomes 2H (qSL2-1) and 5H (qSL5-1), respectively, were first identified in this study. The qSL2-1 has only 0.06 Mb and contains three high-confidence genes. In addition, this study explored the relationship between three spike traits, and found that SL was affected by both SRN and SD, while there was almost no relationship between SRN and SD. We also explored the effect of these QTLs on grain weight per spike (GWPS) to assess their effect on yield and found that qSRN2-1 and qSL5-1 had a greater effect on GWPS, suggesting that they are potential loci to increase yield.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".