Identification of QTLs Associated with Biological Nitrogen Fixation Traits in Soybean Using a Genotyping‐by‐Sequencing Approach
Bibliographic record
Abstract
Although biological N fixation (BNF) is a key process for economic cultivation of soybean [Glycine max (L.) Merr.], the trait is often neglected in breeding programs, mainly due to difficulties in evaluating nodulation and N fixation parameters. We used a genotyping‐by‐sequencing approach to provide a dense genome‐wide marker coverage with 1448 single nucleotide polymorphisms (SNPs) distributed broadly across the chromosomes of a soybean population. The mapping population was composted of 113 F7:8 recombinant inbred lines, obtained by single‐seed descent method, derived from crossing of soybean cultivars ‘Bossier’ (high BNF capacity) and ‘Embrapa 20’ (medium BNF capacity). The traits evaluated were nodule number (NN), nodule dry weight (NDW), average NDW (ANDW; i.e., NDW/NN), and shoot dry weight (SDW). A genetic map was constructed with 1448 SNPs that generated 35 linkage groups totaling 1793 cM, thus covering ∼72% of the genome based on the consensus linkage map. The estimated heritability for NN, NDW, ANDW, and SDW was 0.41, 0.30, 0.30, and 49.11, respectively. Inclusive composite interval mapping identified two significant quantitative trait loci (QTLs) for ANDW at 67 cM on chromosome 13, with a logarithm of odds (LOD) score of 4.66. The additive effect of this locus was −0.31 mg nodule−1, and it explained 18.13% of the phenotypic variation. Another QTL was identified for SDW at 24 cM on chromosome 19 and exhibited an LOD score of 3.93. The additive effect was of 0.57 g plant−1, and it accounted for 14.93% of the phenotypic variation. Additive genetic effects contribute to the final phenotype. In the present study, Embrapa 20 was the major contributor to ANDW, and Bossier was the major contributor to SDW. The latter was previously reported using simple sequence repeat markers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".