Detection of QTLs for heading time and photoperiod response in wheat using a doubled-haploid population
Bibliographic record
Abstract
The genetic basis of heading time in wheat (Triticum aestivum L.) was investigated through the study of flowering under normal autumn sown field conditions as well as photoperiod responses under a controlled environment. Quantitative trait loci (QTLs) for these traits were mapped in a doubled-haploid (DH) population derived from a cross between the wheat cultivars 'Courtot' and 'Chinese Spring'. A molecular marker linkage map of this cross that was previously constructed based on 187 DH lines and 380 markers was used for QTL mapping. The genome was well covered (85%) except for chromosomes 1D and 4D, and a set of anchor loci regularly spaced over the genome (one marker each 15.5 cM) was chosen for marker regression analysis. The presence of a QTL was declared at a significance threshold of alpha = 0.005. The population was grown under field conditions in Clermont-Ferrand, France during two years (1994-1995), in Norwich, U.K. over one year (1998), and also under controlled environments in Norwich. For each trait, between 2 and 4 QTLs were identified with individual effects ranging between 6.3% and 44.4% of the total phenotypic variation. Two QTLs were detected that simultaneously affected heading time and photoperiod response. For heading time, these two QTLs were detected in more than one year. One QTL located on chromosome arm 2BS near the locus Xfbb121-2B, co-segregated with the gene Ppd-B1 known to be involved in photoperiod response. This chromosome region explained a large part of the variation (23.4-44.4% depending on the years or the traits). Another region located on chromosome arm 7BS between the loci Xfbb324-7B and Xfbb53-7B also had a strong effect (7.3-15.3%). This region may correspond to a QTL for earliness per se.
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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 | 0.000 |
| 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".