A different path to the summit of Fusarium Head Blight resistance in wheat: developing germplasm with a systemic approach
Bibliographic record
Abstract
A different path to the summit of Fusarium Head Blight resistance in wheat: developing germplasm with a systemic approach In pursuing FHB resistance in wheat, 30 years of conventional breeding efforts in Eastern Canada have brought some progress. Substantial investment and the application in recent years of marker-assisted selection have to date, however, failed to produce agronomic lines that resist FHB as well as Sumai 3. We present here an alternative path, described as the systemic approach. Rather than seeking to introgress specific putative resistance genes, it subjects target germplasm to regimes of repeated cycles of multiple, interacting (biotic and abiotic) stresses in which desirable traits - not always adequately expressed in parental lines - are identified and selected. How can such a seemingly counterintuitive process work? The systemic approach views desired resistance as arising from the interactions of complex regulation mechanisms mediating how a host responds when a pathogen attacks. These constituents of resistance should thus not always be understood simply as discrete Mendelian units. In repeated rounds of selection, the systemic approach captures those rare individuals that embody optimal interactions of traits, and advances them as founders of lines that resist FHB more effectively than if selection focused on FHB alone. In Quebec, we have chosen to select wheat populations under combined pressure from barley yellow dwarf virus (BYDV) infection and FHB. Resistance to FHB and tolerance of BYDV are quantitative traits that interact. BYD increases both the direct losses from FHB and the production of mycotoxins. Selection under virus pressure, therefore, helps identify those individuals which express FHB resistance more effectively. Moreover, the correlates of virus tolerance (physiological efficiency, generalized stress tolerance and yield) point to those plants with better root traits, ability to produce biomass and yield stability. Together with numerous secondary criteria, such selection eliminates all but a few ‘winners’ in each round. Seen from a systemic perspective, the difficulty of identifying good progeny among descendants of crosses with Sumai 3 does not surprise. Deleterious linkages, pleiotropy and epistasis will usually combine in far from optimal expressions of the assembled genetic information. The systemic approach, by contrast, identifies in repeated cycles increasingly optimized expressions of genes, allowing all potential sources of resistance to be explored. Thus resistant lines can readily be derived from the crosses of susceptible parents, an objective rarely sought in conventional, focused approaches. Moreover, wheat plants respond to the systemic approach's powerful stresses with enhanced epigenetic variation, raw material from which broader ranges of heritable traits can be selected. Germplasm that expresses a full range of attractive traits while resisting FHB as effectively as Sumai 3 can now be shown to be much more abundant than previously imagined. Perhaps this promise will entice more wheat workers to try a systemic approach.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".