Quantitative trait loci influencing endosperm texture, dough-mixing strength, and bread-making properties of the hard red spring wheat breeding lines
Bibliographic record
Abstract
Wheat end product quality is determined by a complex group of traits including dough viscoelastic characteristics and bread-making properties. Quantitative trait loci (QTL) mapping and analysis were conducted for endosperm texture, dough-mixing strength, and bread-making properties in a population of 139 (MN99394 × MN98550) recombinant inbred lines that were evaluated at three environments in 2006. Based on the genetic map of 534 loci, six QTL were identified for endosperm texture, with the main QTL on chromosomes 1A (R(2) = 6.6%-17.3%), 5A (R(2) = 6.1%-17.1%), and 5D (R(2) = 15.8%-22%). Thirty-four QTL were identified for eight dough-mixing strength and bread-making properties. Major QTL clusters were associated with the low-molecular weight glutenin gene Glu-A3, the two high-molecular weight glutenin genes Glu-B1 and Glu-D1, and two regions on chromosome 6D. Alleles at these QTL clusters have previously been proven useful for wheat quality, except one of the QTL clusters on chromosome 6D. A QTL cluster on chromosome 6D is one of the novel chromosome regions influencing dough-mixing strength and bread-making properties. The QTL for endosperm texture on chromosomes 1A, 5A, and 5B also influenced flour ash content (12.4%-23.3%), flour protein content (10.5%-12.5%), and flour colour (7.7%-13.5%), respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".