Identification of salt-tolerant QTLs with strong genetic background effect using two sets of reciprocal introgression lines in rice
Bibliographic record
Abstract
Effect of genetic background on detection of quantitative trait locus (QTL) governing salinity tolerance (ST) was studied using two sets of reciprocal introgression lines (ILs) derived from a cross between a moderately salinity tolerant japonica variety, Xiushui09 from China, and a drought tolerant but salinity susceptible indica breeding line, IR2061-520-6-9 from the Philippines. Salt toxicity symptoms (SST) on leaves, days to seedling survival (DSS), and sodium and potassium uptake by shoots were measured under salinity stress of 140 mmol/L of NaCl. A total of 47 QTLs, including 26 main-effect QTLs (M-QTLs) and 21 epistatic QTLs (E-QTLs), were identified from the two sets of reciprocal ILs. Among the 26 M-QTLs, only four (15.4%) were shared in the reciprocal backgrounds while no shared E-QTLs were detected, indicating that ST QTLs, especially E-QTLs, were very specific to the genetic background. Further, 78.6% of the M-QTLs for SST and DSS identified in the reciprocal ILs were also detected in the recombinant inbred lines (RILs) from the same cross, which clearly brings out the background effect on ST QTL detection and its utilization in ST breeding. The detection of ILs with various levels of pyramiding of nonallelic M-QTL alleles for ST from Xiushui09 into IR2061-520-6-9 allowed us to further improve the ST in rice.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".