Exploring Presence/Absence Variations in Barley for Agronomic Trait Improvement
Bibliographic record
Abstract
Presence/absence variations (PAVs), a form of structural genomic variation, play a critical role in shaping phenotypic diversity in plants, including crop species like barley ( Hordeum vulgare ), which serves both as a globally important cereal crop and a model organism for genomic studies. In this review, we examined the molecular mechanisms underlying PAV formation, such as transposable element activity and large-scale deletions, and highlighted recent advances in technologies-including pangenome assemblies and high-throughput sequencing-that have enabled the comprehensive detection of PAVs. We further discussed the distribution patterns of PAVs among landraces, cultivars, and wild relatives of barley, emphasizing their evolutionary significance in domestication and adaptation. Functionally, PAVs were found to influence key agronomic traits such as disease resistance, abiotic stress tolerance, and yield-related characteristics. The integration of PAV knowledge into modern breeding programs was explored, with a focus on marker-assisted selection and pangenome-based strategies, including a case study on improving heat tolerance through PAV-informed breeding. Lastly, we considered the potential of combining PAV analysis with multi-omics data and phenotyping platforms, and the role of machine learning in modeling genotype-phenotype relationships. This study underscores the value of PAVs as a genomic resource for precision breeding and highlights future directions in expanding pangenomic datasets and developing breeder-friendly tools to facilitate their practical application.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".