Association Mapping Considering Allele Dosage: An Example of Forage Traits in an Interspecific Segmental Allotetraploid <i>Urochloa</i> spp. Panel
Bibliographic record
Abstract
The breeding process in tropical segmental allopolyploid forage Urochloa is challenging due to the complex genetic control of the traits. Knowledge about genes associated with forage traits, expressed in the different cutting seasons, are extremely useful to support breeding programs and development of new cultivars. Thus, the aims of our study were (i) to identify genomic regions related to forage traits through genome‐wide association studies (GWAS), and (ii) to verify the influence of allele dosage on these results. A panel of 272 genotypes of Urochloa spp. [U. brizantha (Hoscht. ex A. Rich.) R. Webster × U. ruziziensis (Hoscht. ex A. Rich.) R. Webster] was evaluated in both the wet and dry seasons. The GWAS analyses were performed with 26,535 single nucleotide polymorphisms (SNPs) obtained by genotyping‐by‐sequencing (GBS) using diploid and tetraploid allele dosage configurations. Furthermore, we evaluated scenarios including additive, dominance, and epistatic effects. Seven candidate genomic regions associated with the main forage traits of Urochloa spp. were identified. The importance of the diploid and tetraploid molecular configuration in GWAS analyses for segmental allopolyploid species was demonstrated to identify the genomic behavior of important regions. Results demonstrated that it is possible to identify the same regions using both ploidy configurations; however, in some cases, the allele substitution effect can be biased mainly for regions with dominance and epistatic effects. Finally, this study contributes to the understanding of genetic control of tropical forage traits and genomics to accelerate the selection and reduce the cost to release new cultivars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".