MétaCan
Menu
Back to cohort
Record W2412578895 · doi:10.1038/ncomms8960

Divergent sorting of a balanced ancestral polymorphism underlies the establishment of gene-flow barriers in Capsella

2015· article· en· W2412578895 on OpenAlexafffund
Adrien Sicard, Christian Kappel, Emily B. Josephs, Young Wha Lee, Cindy Marona, John R. Stinchcombe, Stephen Wright, Michael Lenhard

Bibliographic record

VenueNature Communications · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversity of Toronto
FundersDeutsche ForschungsgemeinschaftGenome Canada
KeywordsBiologyBalancing selectionAlleleGene flowGeneticsLineage (genetic)Fixation (population genetics)Evolutionary biologyCoalescent theoryGenetic diversityGeneGenetic variationPhylogeneticsPopulation

Abstract

fetched live from OpenAlex

In the Bateson-Dobzhansky-Muller model of genetic incompatibilities post-zygotic gene-flow barriers arise by fixation of novel alleles at interacting loci in separated populations. Many such incompatibilities are polymorphic in plants, implying an important role for genetic drift or balancing selection in their origin and evolution. Here we show that NPR1 and RPP5 loci cause a genetic incompatibility between the incipient species Capsella grandiflora and C. rubella, and the more distantly related C. rubella and C. orientalis. The incompatible RPP5 allele results from a mutation in C. rubella, while the incompatible NPR1 allele is frequent in the ancestral C. grandiflora. Compatible and incompatible NPR1 haplotypes are maintained by balancing selection in C. grandiflora, and were divergently sorted into the derived C. rubella and C. orientalis. Thus, by maintaining differentiated alleles at high frequencies, balancing selection on ancestral polymorphisms can facilitate establishing gene-flow barriers between derived populations through lineage sorting of the alternative alleles.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.235

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 science0.0010.000
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 head0.028
GPT teacher head0.282
Teacher spread0.254 · 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

Citations98
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueNature CommunicationsSame topicGenetic diversity and population structureFrench-language works237,207