Seed dormancy in barley (Hordeum vulgare L.): comparative genomics, quantitative trait loci analysis and molecular genetics
Bibliographic record
Abstract
[Truncated abstract] Under prolonged wet and damp conditions, barley grain with low dormancy can germinate precociously, a condition known as preharvest sprouting that causes a number of detrimental effects in grain quality. In particular, preharvest sprouting renders the grain unsuitable for malting. The aim of this study was to take a genomics approach to identify and characterise candidate genes that could be linked to the control of seed dormancy in barley. This thesis developed a bioinformatic strategy that exploited the availability of gene sequences with functional evidence in the model species of Arabidopsis and rice. The bioinformatic strategy integrated phenotypic data (QTL data) and comparative genomics for a targeted approach in identifying candidate genes with a high probability of having a conserved function in cereals. This bioinformatic study identified two candidate genes ERA1 and ABI2 with strong evidence for a role in seed dormancy based on their function in Arabidopsis in abscisic acid (ABA) signal transduction and their co-location to seed dormancy QTLs in Arabidopsis, rice and wheat. In order to establish whether the candidate genes mapped to seed dormancy QTLs in barley, QTL analyses were performed on a double haploid population, not previously studied, developed from a cross between Stirling, a major Australian malting cultivar, and Harrington, a major Canadian malting cultivar. This cross was specifically chosen for this study, as elucidation of chromosomal regions associated with seed dormancy in the background of a malting cultivar would make a significant contribution for the malting industry. '...' Identification of a seed dormancy QTL on the long arm of 3H, in a region syntenic to the wheat chromosome locations of ESTS aligning to the ERA1 and ABI2 genes, laid the foundation for physical and genetic mapping of the candidate genes to investigate whether the genes co-located to the QTL on 3H. Physical mapping of the genes in wheat barley addition lines confirmed their positions on the long arm of 3H. Genetic mapping of the ERA1 gene was performed using a CAPS marker developed in this thesis. The genetic mapping of the ERA1 gene did not place the gene within either of the minor QTLs on 3HL, although segregation distortion may have influenced the map position of this gene. Further investigation is required to resolve the positioning of the ERA1 and ABI2 genes in relation to the 3H seed dormancy QTL. The main outcomes of this study have been 1) identification of candidate genes for further study; 2) identification of QTLs on the long arm of 3H that were previously unknown; 3) demonstration of the potential differences in dormancy that can be achieved through the use of specific gene combinations, highlighting the importance of minor genes and the epistatic interactions that occur between them and; 4) the development of a CAPS marker for the ERA1 gene, which can be used to track the gene in barley breeding programs to observe its association with important agronomic traits. This thesis also pioneered the implementation of several new technologies including multiplex-ready PCR (Hayden et al. 2008) for fluorescence–based SSR genotyping and QTLNetwork (Yang et al. 2008) for statistical analysis of QTLs. Seed dormancy is a complex trait and is likely to involve the interplay of a number of genes that have a role in other developmental and regulatory processes.
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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".