<i>qSUB2</i>: A novel QTL with positive epistasis with <i>SUB1</i> locus enhances submergence tolerance in rice
Bibliographic record
Abstract
Abstract Climate change is expected to significantly increase submergence stress of lowland rice ( Oryza sativa ) production in Africa. There is a need to further improve the ability of SUB1 gene to tolerate submergence stress. Genetic control of submergence tolerance was studied in a moderately tolerant to submergence African rice landrace “TOS6454.” Three mapping populations with a total of 1182 lines were developed by crossing TOS6454 with three popular rice varieties of West Africa that are high‐yielding but submergence susceptible, that is, FARO44, FARO52, and FARO60. The screening for submergence was conducted at the Africa Rice Center (AfricaRice) station in Ibadan, Nigeria during 2016–2018. The three mapping populations were genotyped with single nucleotide polymorphic markers either on the Kompetitive allele specific PCR or the DArTseq platforms. Quantitative trait locus (QTL) analysis revealed five different QTLs on chromosomes 1, 2, 3, 4, and 9. The QTLs on chromosomes 2 ( qSUB2 ) and 9 ( qSUB9 ) had consistent large effects and the tolerance alleles were contributed by TOS6454. The QTL on chromosome 9 ( qSUB9 ) had a large effect on submergence tolerance in all three mapping populations. This QTL had a highest logarithm of odds (LOD) score of 30.46 and explained as much as 27.7% phenotypic variation ( R 2 ), with an additive effect of up to 9.39. The QTL on chromosome 2 ( qSUB2) had a highest LOD score of 10.78 and explained up to 10.8% R 2 for this trait. Furthermore, qSUB2 had positive epistasis with qSUB9 which further increased submergence tolerance. A total of 57 lines carrying both qSUB2 and qSUB9 alleles were superior to all other lines in submergence tolerance. qSUB9 co‐segregated with ART5 a marker for the SUB1 gene; thus, it is suggested that qSUB9 is the same as the well‐known SUB1 gene. Pyramiding qSUB2 with the SUB1 gene locus is expected to further improve submergence tolerance in elite rice varieties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".