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Record W4408240628 · doi:10.5376/mpb.2025.16.0002

Linkage Drag and Domestication Syndrome: The Genetic Lessons from Rice Evolution

2025· article· en· W4408240628 on OpenAlexvenueno aff
Hui Zhang, Juan Li, Qian Zhu, Xiaoling Zhang, Chunli Wang, Dongsun Lee, Lijuan Chen

Bibliographic record

VenueMolecular Plant Breeding · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsDomesticationBiologyLinkage (software)GeneticsEvolutionary biologyGene

Abstract

fetched live from OpenAlex

The domestication of rice ( Oryza sativa ) from its wild relatives has been a pivotal event in agricultural history, leading to significant genetic changes known as domestication syndrome. This study synthesizes current knowledge on the genetic mechanisms underlying these changes, with a focus on linkage drag and its implications for rice breeding. The severe bottleneck during domestication resulted in a dramatic reduction in genetic diversity in cultivated rice compared to its wild progenitors, O. rufipogon  and O. nivara . Multiple independent domestication events have been identified, contributing to the genetic differentiation between the indica  and japonica  subspeicies. The identification of quantitative trait loci (QTLs) and candidate genes associated with domestication-related traits has provided insights into the clustered distribution of these genes, which may explain the phenomenon of linkage drag. Furthermore, the study of de-domestication in weedy rice has revealed the complexity of genetic changes during the domestication process. This study highlights the importance of understanding the genetic basis of domestication syndrome and linkage drag to improve rice breeding strategies and harness the genetic potential of wild rice species for crop improvement.

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 score0.983
Threshold uncertainty score0.163

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 head0.015
GPT teacher head0.222
Teacher spread0.207 · 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 routes1
Has abstractyes

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