Using the RTM-GWAS procedure to detect the drought tolerance QTL-allele system at the seedling stage under sand culture in a half-sib population of soybean [<i>Glycine max</i> (L.) Merr.]
Bibliographic record
Abstract
Soybean crops face drought as one of their major yield barriers. Dissecting the complete genetic architecture of drought tolerance (DT) is an ongoing challenge for soybean breeders. A half-sib population with 404 lines consisting of two recombinant inbred line (RIL) populations with M8206 as the joint parent (M8206 × TongShan and ZhengYang × M8206) was established and tested for their DT under sand culture. The population was sequenced using RAD-seq (restriction-site-associated DNA sequencing) filtered with minor allele frequency (MAF) ≥ 0.01, and 55 936 single nucleotide polymorphisms (SNPs) were obtained and organized into 6137 SNPLDBs (SNP linkage disequilibrium blocks). The innovative RTM-GWAS (restricted two stage multi-locus genome-wide association study) identified 46 novel QTLs with 107 alleles on an average of 38.67% of the phenotypic variance (PV) collectively for relative plant length (RPL) and relative plant dry weight (RPDW). The identified quantitative trait loci (QTLs) with their corresponding alleles for RPL and RPDW were structured into QTL-allele matrices, showing the DT genetic architecture of the three parents and half-sib population. From the matrices, the possible best genotype was predicted to have their weighted average value (WAV) over two indicators 1.663, while the top 10 optimal crosses among RILs with 95 th percentile WAV was 0.872–0.960, transgressive over the parents (0.469–0.675) but much less than 1.663, depicting further pyramiding potential. From the detected QTL-allele system, 63 potential candidate genes collectively for both RPL and RPDW indicators explaining on average of 26.94% PV were annotated and χ 2 -tested as a DT potential candidate gene system involving nine biological processes, endorsing the genetic complexity of DT.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".