Genome‐wide Association for Plant Height and Flowering Time across 15 Tropical Maize Populations under Managed Drought Stress and Well‐Watered Conditions in Sub‐Saharan Africa
Bibliographic record
Abstract
Genotyping breeding materials is now relatively inexpensive but phenotyping costs have remained the same. One method to increase gene mapping power is to use genome‐wide genetic markers to combine existing phenotype data for multiple populations into a unified analysis. We combined data from 15 biparental populations of maize (Zea mays L.) (>2500 individual lines) developed under the Water‐Efficient Maize for Africa project to perform genome‐wide association analysis. Each population was phenotyped in multilocation trials under water‐stressed and well‐watered environments and genotyped via genotyping‐by‐sequencing. We focused on flowering time and plant height and identified clear associations between known genomic regions and the traits of interest. Out of ∼380,000 single‐nucleotide polymorphisms (SNPs), we found 115 and 108 that were robustly associated with flowering time under well‐watered and drought stress conditions, respectively, and 143 and 120 SNPs, respectively, associated with plant height. These SNPs explained 36 to 80% of the genetic variance, with higher accuracy under well‐watered conditions. The same set of SNPs had phenotypic prediction accuracies equivalent to genome‐wide SNPs and were significantly better than an equivalent number of random SNPs, indicating that they captured most of the genetic variation for these phenotypes. These methods could potentially aid breeding efforts for maize in Sub‐Saharan Africa and elsewhere. The methods will also help in mapping drought tolerance and related traits in this germplasm. We expect that analyses combining data across multiple populations will become more common and we call for the development of algorithms and software to enable routine analyses of this nature.
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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 | 0.001 | 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".