Genome-Wide Association Study Reveals a Genomic Region Associated with Mite-Recruitment Phenotypes in the Domesticated Grapevine (Vitis vinifera)
Bibliographic record
Abstract
Indirect defenses are plant phenotypes that reduce damage by attracting natural enemies of plant pests and pathogens to leaves. Despite their economic and ecological importance, few studies have investigated the genetic underpinnings of indirect defense phenotypes. Here, we present a genome-wide association study of five phenotypes previously determined to increase populations of beneficial (fungivorous and predacious) mites on grape leaves (genus Vitis): leaf bristles, leaf hairs, and the size, density, and depth of leaf domatia. Using a common garden genetic panel of 399 V. vinifera cultivars, we tested for genetic associations of these phenotypes using previously obtained genotyping data from the Vitis9kSNP array. We found one single nucleotide polymorphism (SNP) significantly associated with domatia density. This SNP (Chr5:1160194) is near two genes of interest: Importin Alpha Isoform 1 (VIT_205s0077g01440), involved in downy mildew resistance, and GATA Transcription Factor 8 (VIT_205s0077g01450), involved in leaf shape development. Our findings are among the first to examine the genomic regions associated with ecologically important plant traits that facilitate interactions with beneficial mites, and suggest promising candidate genes for breeding and genetic editing to increase naturally occurring predator-based defenses in grapevines.
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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".