Identification of genomic regions associated with fall dormancy and winter survival in alfalfa (Medicago sativa) to improve persistency and forage yield for the northern climate
Bibliographic record
Abstract
Alfalfa (Medicago sativa L) is foundational to the success of beef, dairy and forage industries in Canada. In Canadian prairie alone, about 2.8 million ha of alfalfa monoculture and alfalfa-based mixtures are cultivated. Understanding the interaction between alfalfa fall dormancy (FD) and winter survival (WS) at a molecular level is crucial for the development of high-yielding winter-hardy alfalfa cultivars with high adaptation to the northern environmental conditions. The objectives of the study were to: 1) investigate the relationship between FD and WS of 27 alfalfa populations with varying FD scores (1 to 10) at Clavet, SK and St-Augustin-de-Desmaures, QC; and 2) conduct a Genome Wide Association Study (GWAS) to identify molecular markers and candidate genes associated with FD and WS. Phenotypic data on fall plant height (FH), spring height (SH), WS, and annual forage biomass were collected from 2019 to 2021. High WS rates (>80%) were observed in alfalfa cultivars with FD = 1-5; 55V48 (FD5), SAR(L)3 (FD4), Peace (FD2), and AC Yellowhead (FD1), at both sites. Importantly, 6010DTF2 (FD6) and CUF(L)3 (FD9) showed good to fair survival rate as well. The correlation between WS and FH was non-significant (r =0.04, P=0.06) at St-Augustin and Clavet (r =-0.07, P=0.1). The GWAS analysis identified 129 SNPs associated FH and SH. Among these, there were six candidate genes, namely SAG39 protease, RICESLEEPER 2 protein, Bax Inhibitor-1, ETHYLENE INSENSITIVE 3-like 3 protein, glutamate-rich WD repeat-containing protein 1, CBBY-like protein with known functions in vital plant processes such as stress responses, cell death, growth regulation, and adaptation to the environmental conditions. Once validated, markers and candidate genes identified in the study could be useful for marker-assisted selection to develop alfalfa cultivars with enhanced winter hardiness and reduced FD in northern Canada.
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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 | 0.001 | 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".