Genome-wide association study of rice grain width variation
Bibliographic record
Abstract
Seed size is variable within many plant species, and understanding the underlying genetic factors can provide insights into mechanisms of local environmental adaptation. Here we make use of the abundant genomic and germplasm resources available for rice (Oryza sativa) to perform a large-scale genome-wide association study (GWAS) of grain width. Grain width varies widely within the crop and is also known to show climate-associated variation across populations of its wild progenitor. Using a filtered dataset of >1.9 million genome-wide SNPs in a sample of 570 cultivated and wild rice accessions, we performed GWAS with two complementary models, GLM and MLM. The models yielded 10 and 33 significant associations, respectively, and jointly yielded seven candidate locus regions, two of which have been previously identified. Analyses of nucleotide diversity and haplotype distributions at these loci revealed signatures of selection and patterns consistent with adaptive introgression of grain width alleles across rice variety groups. The results provide a 50% increase in the total number of rice grain width loci mapped to date and support a polygenic model whereby grain width is shaped by gene-by-environment interactions. These loci can potentially serve as candidates for studies of adaptive seed size variation in wild grass species.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".