Genome-Wide Association Mapping of Salt Tolerance in Barley Germplasm
Bibliographic record
Abstract
Salt stress is the main abiotic factor that limits the productivity of barley ( Hordeum vulgare L.) in global saline-alkali regions. This study employed genome-wide association analysis (GWAS) methods to reveal the genetic basis of salt tolerance in different barley germplasm resources. We evaluated a group of core germplasm resources under controlled salinity conditions and conducted high-resolution GWAS analysis using single nucleotide polymorphism (SNP) markers. Our analysis identified several loci and candidate genes significantly associated with salt stress traits, including those involved in ion homeostasis, osmotic regulation, and stress signaling pathways. A case study focusing on North African germplasm resources highlighted key salt-tolerant genes, such as HvHKT1;5 and HvNHX1 , which further emphasizes their significance in breeding projects. Despite the challenges related to population structure and environmental variations, our research results demonstrate the practicality of GWAS in analyzing complex traits and guiding marker-assisted selection (MAS). This study laid the foundation for breeding salt-tolerant barley varieties and emphasized the value of integrating genomic tools into climate-adaptive agricultural breeding strategies.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".