Integrated eQTL mapping approach reveals genomic regions regulating candidate genes of the E8-r3 locus in soybean
Bibliographic record
Abstract
Deciphering the gene regulatory networks of critical quantitative trait loci associated with early maturity provides information for breeders to unlock soybean’s (Glycine max (L.) Merr.) northern potential and expand its cultivation range. The E8-r3 locus is a genomic region regulating the number of days to maturity under constant short-day photoperiodic conditions in two early-maturing soybean populations (QS15524F2:F3 and QS15544RIL) belonging to maturity groups MG00 and MG000. In this study, we developed a combinatorial expression quantitative trait loci mapping approach using three algorithms (ICIM, IM, and GCIM) to identify the regions that regulate three candidate genes of the E8-r3 locus (Glyma.04G167900/GmLHCA4a, Glyma.04G166300/GmPRR1a, and Glyma.04G159300/GmMDE04). Using this approach, a total of 2,218 trans (2,061 genes)/7 cis (7 genes) and 4,073 trans (2,842 genes)/3,083 cis (2,418 genes) interactions were mapped in the QS15524F2:F3 and QS15544RIL populations, respectively. From these interactions, we successfully identified two hotspots (F2_GM15:49,385,092-49,442,237 and F2_GM18:1,434,182-1,935,386) and three minor regions (RIL_GM04:17,227,512-20,251,662, RIL_GM04:31,408,946-31,525,671 and RIL_GM13:37,289,785-38,620,690) regulating the candidate genes of E8-r3 and several of their homologs. Based on co-expression network and single nucleotide variant analyses, we identified ALTERED PHLOEM DEVELOPMENT (Glyma.15G263700) and DOMAIN-CONTAINING PROTEIN 21 (Glyma.18G025600) as the best candidates for the F2_GM15:49,385,092-49,442,237 and F2_GM18:1,434,182-1,935,386 hotspots. These findings demonstrate that a few key regions are involved in the regulation of the E8-r3 candidates GmLHCA4a, GmPRR1a, and GmMDE04.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".