Image processing and genome-wide association studies in sunflower identify loci associated with seed-coat characteristics
Bibliographic record
Abstract
Abstract Sunflower seeds (technically achenes) are characterized by a wide spectrum of sizes, shapes, and colors. These traits are genetically correlated with the branching plant architecture loci, which were introgressed into restorer lines to facilitate efficient hybrid production. To break this genetic correlation between branching and seed traits, high resolution mapping of the genes that regulate seed traits is necessary. Recent progress in genomics permits acquisition of comprehensive genotyping data for a large diversity panel, yet a major constraint for exploring the genetic basis of important phenotypes across large diversity panels is the ability to screen and characterize them efficiently. Here, we implement a cost-effective image analysis pipeline to phenotype seed characteristics in a large sunflower diversity panel comprised of 287 individuals that represents most of the genetic variation in cultivated sunflower. A genome-wide association analysis was performed for seed-coat size and shape traits and significant signals were identified around genes regulating phytohormone activity. In addition, significant seed-coat color QTLs were identified and candidate genes that effect pigmentation were detected including a phytomelanin regulating gene on chromosome 17. Finally, QTLs associated with the seed-coat striped pattern were identified and phytohormone regulating candidate genes were detected. The implementation of image analysis phenotyping for GWAS allowed efficient screening of a large diversity panel and identification of valuable genetic factors effecting seed characteristics at the finest resolution to date.
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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 | 0.001 | 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".