Beyond the genome: the role of functional markers in contemporary plant breeding
Bibliographic record
Abstract
Functional markers (FMs) are derived from polymorphisms that confer phenotypic trait variation, making them powerful tools in plant breeding. Unlike random markers, for which trait associations are unknown, or at best established via linkage or quantitative trait locus (QTL) analysis, FMs are associated with causative polymorphisms, providing precise and reliable information for trait selection. Since the concept of FMs was first proposed in 2003, the emergence and adoption of technologies that were not available at the time have significantly advanced FM discovery and application by enhancing the ability to precisely identify causal variants underlying complex traits, which is a critical prerequisite for FM development. Novel technologies such as high-throughput sequencing, multi-omics, gene editing, and advanced computational tools have enabled the precise identification and functional validation of DNA polymorphisms associated with trait variation. FMs can be used in genomic selection (GS) and modern plant breeding programs by improving selection efficiency and accuracy. While FMs provide numerous benefits, challenges still remain regarding their stability and transferability, and innovative approaches to overcome these limitations are continually being explored. The role of FMs in plant breeding is expected to grow as functional annotation of genomes improves and technologies like genome editing become more accessible. These developments will enable breeders to effectively integrate FMs into breeding pipelines for accelerating genetic gains and addressing global agricultural challenges.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".