MétaCan
Menu
Back to cohort
Record W4411348433 · doi:10.1101/2025.06.13.659410

Environment-dependent selection impacts heritable developmental stability and trait canalization in rice

2025· preprint· en· W4411348433 on OpenAlexafffund
Taryn S. Dunivant, Irina Ćalić, Conor Gilligan, Zoé Joly‐Lopez, Jae Young Choi, Mignon A. Natividad, Carlo L. U. Cabral, Rolando O. Torres, Georgina V. Vergara, Steven J. Franks, Amelia Henry, Michael D. Purugganan, Simon C. Groen

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsUniversité du Québec à Montréal
FundersNational Institute of Food and AgricultureNational Institutes of HealthCanada Research ChairsGordon and Betty Moore Foundation
KeywordsTraitSelection (genetic algorithm)BiologyStability (learning theory)Evolutionary biologyGeneticsBiotechnologyComputer scienceArtificial intelligenceMachine learning

Abstract

fetched live from OpenAlex

Abstract Canalization, or the maintenance of trait values regardless of environmental or genetic variability, is fundamentally important for maintaining developmental stability. While this concept was described decades ago, we still know relatively little about how canalization is influenced by environmental stress, how it is shaped by natural selection, and the genetic underpinnings of canalization. In this study, we examined natural selection on microenvironmental canalization in rice ( Oryza sativa ) in wet and dry field conditions. We measured developmental stability in genetically identical replicates obtained from geographically widespread Indica and Japonica rice accessions, providing precise estimates of canalization in thousands of plants. We found that drought stress decreased canalization, showing that stress can increase instability. We also found evidence that canalization can evolve, given that canalization of several traits was heritable and under selection. We further uncovered specific genes underlying canalization, with genetic mapping and functional genetic experiments showing that the bZIP transcription factor-encoding gene OsTGA5 / rTGA2.3 , which is part of a module that balances stress response and plant growth, regulates canalization of several traits in an environment-dependent manner. At a genome-wide scale, canalization was associated with lower gene expression stochasticity at an earlier life stage, indicating that expression variation can reduce canalization and increase instability. Trait canalization was also positively correlated to temperature at accessions’ source environments, suggesting that selection on canalization can vary among environments. Overall, our study provides novel insights into the molecular genetic basis of environmental differences in developmental stability and how it might be shaped by selection. Significance Mechanisms stabilizing organismal development in response to genetic mutations or environmental stressors (canalization) have been reported for numerous animals and plants, but their underlying genetic basis and whether they may be shaped by selection remain unclear. Here, we report patterns of drought-induced trait decanalization in populations of rice ( Oryza sativa ) grown in field environments. We determined that trait canalization is heritable and can evolve separately from trait means. We identified the gene OsTGA5 , part of a regulatory module shaping trade-offs between growth and stress responses, as impacting trait canalization. Plants with less noisy gene expression and evolving in warmer environments display greater developmental stability, contributing to the notion that canalization in rice may be adaptive.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.621

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 head0.017
GPT teacher head0.197
Teacher spread0.180 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicRice Cultivation and Yield ImprovementFrench-language works237,207