QTL detection, validation, candidate genes and KASP markers for pre-harvest sprouting tolerance in white grained common wheat
Bibliographic record
Abstract
BACKGROUND: Pre-harvest sprouting (PHS) in common wheat refers to the premature germination of grains on the spike under high humid conditions. This phenomenon increase alpha-amylase activity and degrade starch, resulting in reduced falling number (FN) and compromised milling and baking quality. The genetic control of PHS is complex and polygenic, presenting a significant challenge but also offering opportunities for trait improvement through targeted breeding. RESULTS: A doubled haploid (DH) population comprising 386 lines, derived from a cross between two white-grained spring wheat genotypes SC8021-V2 (PHS-tolerant) and AC Karma (moderately susceptible to PHS), were evaluated for sprouting score (SS) and FN traits across two locations over two crop seasons in India. The population was genotyped using the Infinium iSelect 90K SNP array. Using composite interval mapping (CIM) and R/qtl, 29 main-effect QTL (MQTL) for SS and FN explaining 2.10-8.63% (SS) and 2.10-10.93% (FN) of the phenotypic variation were identified on 13 chromosomes. CIM exclusively revealed 9 MQTL (SS, 7; FN, 1; SS + FN, 1), R/qtl exclusively revealed 8 MQTL (SS, 6; FN, 2), and 12 MQTL (SS, 6; FN, 5; SS + FN, 1) were revealed by both; 16 MQTL were not previously reported. Nineteen first-order epistatic interactions involving 18 SNPs were also detected. We validated six stable MQTL for SS by confirming their favourable alleles in five highly PHS-tolerant DH lines. A set of 368 candidate genes encoding 42 distinct domains were identified; twenty-two were associated with ABA signaling. Candidate genes have multifaceted role regulating seed dormancy, hormone signaling, seed coat formation, starch metabolism, and stress adaptation contributing to PHS resilience. Three breeder-friendly KASP markers for three QTL for sprouting score (two on 2B, one on 4B) were developed and validated. CONCLUSIONS: This study provides novel insights and discovered 16 previously unreported QTL, epistatic interactions, and key candidate genes, highlighting the complex regulation of PHS-related traits. The availability of three diagnostic KASP markers offer practical tools for marker-assisted selection, facilitating the genetic improvement of PHS in common wheat.
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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) | 0.000 | 0.000 |
| 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".