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Record W4317734097 · doi:10.3390/biology12020169

Effects of Genetic Mutation Sites in ADR Genes on Modern Chickens Produced and Domesticated by Artificial Selection

2023· article· en· W4317734097 on OpenAlexfundno aff
Tomoyoshi Komiyama

Bibliographic record

VenueBiology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsnot available
FundersInstitute of Genetics
KeywordsBiologyDomesticationSelection (genetic algorithm)MutationGeneGeneticsEvolutionary biology

Abstract

fetched live from OpenAlex

Associations between neurotransmitters, adrenergic receptor (ADR) mutations, and behaviors in chickens produced and domesticated by artificial selection remain unclear. This study investigates the association of neurotransmitters and ADR mutations with egg laying and cockfighting—behaviors associated with significantly different breeding backgrounds—in Shaver Brown and Shamo chickens. Accordingly, the whole sequences of nine ADR genes were determined, and nine amino acid-specific mutation sites from five genes (ADRα1A: S365G, ADRα1D: T440N, ADRα2A: D273E, ADRβ1: N443S, S445N, ADRβ3: R342C, Q404L, and P406S) were extracted. Evolutionary analysis showed that these mutations were not ancestrally derived. These results confirm that the mutations at these sites were artificially selected for domestication and are breed specific. NST population analysis confirmed a difference in the degree of genetic differentiation between the two populations in seven genes. The results further confirm differences in the degree of genetic differentiation between the two populations in Shaver Brown (ADRA1B and ADRA1D) and Shamo (ADRA1A and ADRA2B) chickens, indicating that the ADR gene differs between the two breeds. The effects of artificial selection, guided by the human-driven selection of desirable traits, are reflected in adrenaline gene mutations. Furthermore, certain gene mutations may affect domestication, while others may affect other traits in populations or individuals.

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 score0.865
Threshold uncertainty score0.110

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 head0.018
GPT teacher head0.244
Teacher spread0.226 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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