Whole Genome Characterization of a Few EMS-Induced Mutants of Upland Rice Variety Nagina 22 Reveals a Staggeringly High Frequency of SNPs Which Show High Phenotypic Plasticity Towards the Wild-Type
Bibliographic record
Abstract
The Indian initiative for creating mutant resource for functional genomics in rice has been instrumental in development of 87,000 EMS induced mutants, of which 7000 are in advanced generations, in the background of Nagina 22, a popular drought and heat tolerant upland cultivar.. Nagina 22 is a pre-green revolution cultivar which is tall and as many as 573 dwarf mutants identified from this resource could be useful as an alternate source of dwarfing. A total of 541 mutants including the macromutants and the trait specific ones, obtained after appropriate screening, are being maintained in the mutant garden. Here, we report the detailed characterization of the 541 mutants based on DUS (distinctness, uniformity and stability) descriptors at two different locations. About 90% of the mutants were found to be similar to the wild type (WT) with high similarity index (>0.6) at both the locations. All 541 mutants were characterized for chlorophyll and epicuticular wax content, while a sub-set of 84 mutants were characterized for their ionome namely, phosphorous, silicon and chloride content. Genotyping of these mutants with 54 genome-wide SSR markers revealed 93% of the mutants to be either completely identical to WT or nearly identical with just one polymorphic locus. Whole genome re-sequencing (WGS) of four mutants which have minimal differences in SSR fingerprint pattern and DUS characters from the WT revealed a staggeringly high number of SNPs on an average (16453 per mutant) in the genic sequences. Of these, nearly 50% of the SNPs led to non-synonymous codons while 30% resulted in synonymous codons. Number of InDels varied from 898-2595 with more than 80% of them being 1-2 bp long. Such a high number of SNPs could pose a serious challenge to identifying gene(s) governing the mutant phenotype by next generation sequencing (NGS) based mapping approaches such as Mutmap. From the WGS data of the WT and the mutants we developed a genic resource of the WT with a novel analysis pipeline. The entire information on this resource along with panicle architecture of 493 mutants is made available in a mutant database EMSgardeN22 (http://14.139.229.201/EMSgardeN22).
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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 | 0.000 | 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".