Interaction and association analysis of malting related traits in barley
Bibliographic record
Abstract
Barley is considered as a foundation of the brewing and malting industry. Varieties with superior malt quality traits are required for efficient brewing and distillation processes. Among these, the Diastatic Power (DP), wort-Viscosity (VIS), β-glucan content (BG), Malt Extract (ME) and Alpha-Amylase (AA) are controlled by several genes linked to numerous quantitative trait loci (QTL), identified for barley malting quality. One of the well-known QTL, QTL2, associated with barley malting trait present on chromosome 4H harbours a key gene, called as HvTLP8 that has been identified for influencing the barley malting quality through its interaction with β-glucan in a redox-dependent manner. In this study, we examined to develop a functional molecular marker for HvTLP8 in the selection of superior malting cultivars. We first examined the expression of HvTLP8 and HvTLP17 containing carbohydrate binding domains in barley malt and feed varieties. The higher expression of HvTLP8 prompted us to further investigate its role as a marker for malting trait. By exploring the 1000 bp downstream 3' UTR region of HvTLP8, we found single nucleotide polymorphism (SNP) in between Steptoe (feed variety) and Morex (malt variety), which was further validated by Cleaved Amplified Polymorphic Sequence (CAPS) marker assay. Analysis of 91 individuals from the Steptoe x Morex doubled haploid (DH) mapping population revealed CAPS polymorphism in HvTLP8. Highly significant (p<0.001) correlations among ME, AA and DP malting traits were observed. The correlation coefficient (r) between these traits ranged from 0.53 to 0.65. However, the polymorphism in HvTLP8 did not correlate effectively with ME, AA, and DP. Altogether, these findings will help us to further design the experiment regarding the HvTLP8 variation and its association with other desirable traits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".