Comprehensive Meta-Analysis of Maize QTLs Associated With Grain Yield, Flowering Date and Plant Height Under Drought Conditions
Bibliographic record
Abstract
Drought remains the primary abiotic constraint to maize (Zea mays L.) productivity globally. Maize drought response involves several regulatory quantitative traits and complex gene networks. Therefore, precise location of drought-related quantitative trait loci (QTL) is imperative for drought tolerance breeding. Despite numerous studies identifying several drought-related maize QTLs, some QTL from particular genetic backgrounds showed smaller effects or could not be identified at all in different backgrounds, affected by marker sets, experimental design, mapping populations and statistical methods. Herein, therefore; using 457 published maize QTLs conferring for 18 traits, we have performed meta-analysis of data from various experiments to obtain meta-QTL (MQTL), integrate these fruitful QTL and to mine candidate genes related to drought. Resultantly, 24 MQTL with confidence interval (CI) < 5 cm were identified to be hot regions. Additionally, 47 drought related gene loci were observed and several candidate genes of the hot MQTL were reorganized by bioinformatics techniques. Thirteen gene (sod4, taf1, rps1, nthr3, oc13, bas, apx1, asn4, pck2, nac1, gst2, ao1 and kch4) loci of hot MQTL regions were homologous to their corresponding gene sequences from the PlantGDB database (http://www.plantgdb.org/search/). Further, we used a comparative genomics approach to identify the homologous regions of MQTL in rice (Oryza sativa Japonica) database (http://www.gramene.org) and observed that drought-related rice gene ATG6 was homologous to maize candidate genes GRMZM2G027857_T01 and GRMZM2G027857_T02. Conclusively, our identified MQTLs with narrowed CI could be useful for marker-assisted selection and the candidate genes harnessed for maize drought tolerance breeding.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".