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Record W2804613253 · doi:10.1105/tpc.18.00124

Parallel Evolution of Common Allelic Variants Confers Flowering Diversity in <i>Capsella rubella</i>

2018· article· en· W2804613253 on OpenAlexaff
Li Yang, Hui-Na Wang, Xing-Hui Hou, Yu-Pan Zou, Ting‐Shen Han, Xiao‐Min Niu, Jie Zhang, Zhong Zhao, Marco Todesco, Sureshkumar Balasubramanian, Ya‐Long Guo

Bibliographic record

VenueThe Plant Cell · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of British Columbia
FundersCAS-SAFEA International Partnership Program for Creative Research TeamsNational Natural Science Foundation of China
KeywordsBiologyAlleleGenetic diversityDiversity (politics)GeneticsEvolutionary biologyBotanyGeneDemography

Abstract

fetched live from OpenAlex

Flowering time is an adaptive life history trait. Capsella rubella, a close relative of Arabidopsis thaliana and a young species, displays extensive variation for flowering time but low standing genetic variation due to an extreme bottleneck event, providing an excellent opportunity to understand how phenotypic diversity can occur with a limited initial gene pool. Here, we demonstrate that common allelic variation and parallel evolution at the FLC locus confer variation in flowering time in C. rubella. We show that two overlapping deletions in the 5′ untranslated region (UTR) of C. rubella FLC, which are associated with local changes in chromatin conformation and histone modifications, reduce its expression levels and promote flowering. We further show that these two pervasive variants originated independently in natural C. rubella populations after speciation and spread to an intermediate frequency, suggesting a role of this parallel cis-regulatory change in adaptive evolution. Our results provide an example of how parallel mutations in the same 5′ UTR region can shape phenotypic evolution in plants.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.192
Teacher spread0.145 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2018
Admission routes1
Has abstractyes

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