A SNP-Based Linkage Map and QTL Identification for Resistance to Yam Anthracnose Disease (YAD) in Water Yam (Dioscorea alata)
Bibliographic record
Abstract
Abstract Background: Yam anthracnose disease (YAD) caused by Colletotrichum gloeosporioides is the primary cause of yield loss in water yam (Dioscorea alata), the widely cultivated species of yam. Development of resistant cultivars have been a prime target for sustainable management of anthracnose in water yam. Molecular breeding tools are required to expedite the development of improved yam varieties. QTL analysis using high density genetic maps serve as a powerful tool to discover key locations of quantitave traits. This study aimed at tagging quantitative trait loci (QTL) for anthracnose disease resistance in a bi-parental mapping population of D. alata.Results: In this study, two contrasting parents for yam anthracnose disease reaction and their 204 full- sib offspring were used to develop a high-density genetic linkage map with 3,257 SNP markers by the GBS technique. The total length of the consensus map was 1460.94 cM with an average of 163 markers per chromosome. Four QTLs were detected for anthracnose disease resistance in 4 locations on 3 chromosomes. The proportion of phenotypic variance explained by these QTLs ranged from 10 to 13%. Plant defense response genes including GDSL-like Lipase/Acylhydrolase, Protein kinase domain and F-box protein were also detected within the QTL regions. Conclusion: The results from the present study provide valuable insight into the genetic architecture of anthracnose resistance in water yam. The candidate markers and putative genes identified herewith form a relevant resource to apply marker-assisted selection as alternative to a conventional labor-intensive screening for anthracnose resistance in water yam.
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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 | 0.001 |
| 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) | 0.001 | 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".