Design of a<i>Brassica rapa</i>core collection for association mapping studiesThis article is one of a selection of papers from the conference “Exploiting Genome-wide Association in Oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”.
Bibliographic record
Abstract
A Brassica rapa collection of 239 accessions, based on two core collections representing different morphotypes from different geographical origins, is presented and its use for association mapping is illustrated for flowering time. We analyzed phenotypic variation of leaf and seed pod traits, plant architecture, and flowering time using data collected from three field experiments and evaluated the genetic diversity with a set of SSR markers. The Wageningen University and Research Centre (WUR) and the Vavilov Research Institute of Plant Industry (VIR) core collections had similar representations of most morphotypes, as illustrated by the phenotypic and genetic variation within these groups. The analysis of population structure revealed five subgroups in the collection, whereas previous studies of the WUR core collection indicated four subgroups; the fifth group identified consisted mainly of oil accessions from the VIR core collection, winter oils from Pakistan, and a number of other types. A very small group of summer oils is described, that is not related to other oil accessions. A candidate gene approach was chosen for association mapping of flowering time with a BrFLC1 biallelic CAPS marker and a BrFLC2 multiallelic SSR marker. The two markers were significantly associated with flowering time, but their effects were confined to certain morphotypes and (or) alleles. Based on these results, we discuss the optimal design for an association mapping population and the need to fix the heterogeneous accessions to facilitate phenotyping and genotyping.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".