Unveiling the heterosis pattern of modern maize breeding in Southwest China through population structure and genetic diversity analysis
Bibliographic record
Abstract
Maize (Zea mays L.) is an important food crop throughout the world and is also one of the earliest crops to use heterosis. In this study, we evaluated the genetic diversity, population structure, and selective sweep of 100 elite inbred maize lines collected from the current breeding program in Sichuan province, Southwest China, using 5,261,175 high-quality single nucleotide polymorphisms (SNPs). We discovered an abundance of genetic diversities and classified them into four groups. By combining kinship relationships, these groups were further divided into Tropic-local A, Improved-tropic, Tropic-local B, and Improved-local. Genomic differentiation was assessed using Fst values (0.21–0.44) as well as genetic diversity (π = 6.07 × 10−4 − 6.61 × 10−4). We generated 900 (90 × 10) hybrids using 90 and 10 inbred maize lines from 100 diverse maize germplasms. All hybrids were evaluated for 10 traits in three replicate tests across two locations. We found that the patterns of G1 × G3, G1 × G4, G2 × G3, and G3 × G4 exhibited significant heterosis in yield-related traits and have been used in commercial breeding. In addition, we also explored the relationship between 10 traits of hybrid offspring and the number of heterozygous SNP. Under most heterosis modes, the best linear unbiased estimation (BLUE) value of the trait was highly consistent with the trend of deleterious SNPs, but there was a deviation in the G1 × G3 mode. Taken together, the results provide insight into the utilization of the current maize germplasm in Sichuan province to improve hybrid breeding.
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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 | 0.000 | 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) | 0.000 | 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".