Association and genome analyses to propose putative candidate genes for malt quality traits
Bibliographic record
Abstract
BACKGROUND: We studied the genetics of nine malt quality traits using association genetics in a panel of North Dakota, ICARDA, and Ethiopian barley lines. Grain samples harvested from Bekoji in 2011 and 2012 were used. RESULTS: The mapping panel revealed strong population structure explained by inflorescence-type, geographic origin, and breeding history. North Dakota germplasm were superior in malt quality traits and they can be donors to improve malt quality properties. We identified 106 marker-trait associations (MTAs) for the nine traits, representing 81 genomic regions across all barley chromosomes. Chromosomes 3H, 5H, and 7H contained most of the MTAs (58.5%). Nearly 18.5% of these genomic regions contained two to three malt quality traits. Within ±250 kb of 81 genomic regions, we recovered 348 barley genes, with some potential impacting malt quality. These include invertase, β-fructofuranosidase, α-glucosidase, serine carboxypeptidase, and bidirectional sugar transporter SWEET14-like protein. Eighteen of these genes were also previously reported in the Hordeum Toolbox, and 17 of them highly expressed during the germination process. CONCLUSION: The results from this study invite further follow-up functional characterization experiments to relate the genes with individual malt quality traits with higher confidence. It also provides germplasm resources for malt barley improvement. © 2018 Society of Chemical Industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".