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Record W2005359788 · doi:10.1093/molbev/mss153

Differences in Selection Drive Olfactory Receptor Genes in Different Directions in Dogs and Wolf

2012· article· en· W2005359788 on OpenAlexaff
Rui Chen, David M. Irwin, Ya‐Ping Zhang

Bibliographic record

VenueMolecular Biology and Evolution · 2012
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsUniversity of Toronto
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsPseudogeneBiologyNonsynonymous substitutionBreedDomesticationGeneticsSelection (genetic algorithm)GeneOlfactory receptorNegative selectionPopulationEvolutionary biologyHaplotypeGenomeAlleleReceptor

Abstract

fetched live from OpenAlex

The olfactory receptor (OR) gene family is the largest gene family found in mammalian genomes. It is known to evolve through a birth-and-death process. Here, we characterized the sequences of 16 segregating OR pseudogenes in the samples of the wolf and the Chinese village dog (CVD) and compared them with the sequences from dogs of different breeds. Our results show that the segregating OR pseudogenes in breed dogs are under strong purifying selection, while evolving neutrally in the CVD, and show a more complicated pattern in the wolf. In the wolf, we found a trend to remove deleterious polymorphisms and accumulate nondeleterious polymorphisms. On the basis of protein structure of the ORs, we found that the distribution of different types of polymorphisms (synonymous, nonsynonymous, tolerated, and untolerated) varied greatly between the wolf and the breed dogs. In summary, our results suggest that different forms of selection have acted on the segregating OR pseudogenes in the CVD since domestication, breed dogs after breed formation, and ancestral wolf population, which has driven the evolution of these genes in different directions.

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 score0.306
Threshold uncertainty score0.419

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 head0.059
GPT teacher head0.264
Teacher spread0.206 · 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

Citations25
Published2012
Admission routes1
Has abstractyes

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