Identifying regions of the wheat genome controlling seed development by mapping expression quantitative trait loci†
Bibliographic record
Abstract
Statistical methods established for the genetic analysis of quantitative traits can be applied to gene expression data. Quantitative trait locus (QTL) analysis can associate the expression of genes or groups of genes with particular genomic regions, and thereby identify regions regulating gene expression. A segregating population of 41 doubled haploid (DH) lines from the hard red spring wheat cross RL4452 x 'AC Domain' was used to map expression level polymorphisms. This population had previously been mapped with microsatellites, and includes a full QTL analysis for agronomic and seed quality traits. Expression analysis on mRNA from developing seed grown in two field locations was conducted on 39 of the 41 DH lines using the Affymetrix GeneChip Wheat Genome Array. Analysis of the hybridization intensity identified 1484 Affymetrix probe sets in the first location and 10,280 probe sets in the second location, where the hybridization intensity varied significantly between genotypes of the population. A common set of 1455 probe sets differing in intensity between genotypes in both locations was used for mapping, and 542 QTLs were identified that each mapped to a single chromosome interval, illustrating that major gene expression QTLs could be found in wheat. Genomic regions corresponding to multiple gene expression QTLs were identified. Comparison of expression mapping data with physical mapping of wheat expressed sequence tag (EST) sequences using rice synteny, as well as logarithm of odds (LOD) score analysis, showed that both cis- and trans-acting expression QTLs were present. Chromosomes 1D and 4B may contain significant trans-regulatory regions in this population.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".