MétaCan
Menu
Back to cohort
Record W2998591982 · doi:10.3390/ani10010053

Whole-Genome Signatures of Selection in Sport Horses Revealed Selection Footprints Related to Musculoskeletal System Development Processes

2019· article· en· W2998591982 on OpenAlexaff
Siavash Salek Ardestani, Mehdi Aminafshar, Mohammad Bagher Zandi, Mohammad Hossein Banabazi, Mehdi Sargolzaei, Younes Miar

Bibliographic record

VenueAnimals · 2019
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsDalhousie UniversityUniversity of Guelph
Fundersnot available
KeywordsSelection (genetic algorithm)GenomeGenomic selectionComputational biologyBiologyEvolutionary biologyComputer scienceGeneticsArtificial intelligenceGeneSingle-nucleotide polymorphismGenotype

Abstract

fetched live from OpenAlex

Selective breeding has led to gradual changes at the genome level of horses. Deciphering selective pressure patterns is progressive to understand how breeding strategies have shaped the sport horse genome; although, little is known about the genomic regions under selective pressures in sport horse breeds. The major goal of this study was to shed light on genomic regions and biological pathways under selective pressures in sport horses. In this study, whole-genome sequences of 16 modern sport and 35 non-sport horses were used to investigate the genomic selective signals of sport performance, by employing fixation index, nucleotide diversity, and Tajima's D approaches. A total number of 49 shared genes were identified using these approaches. The functional enrichment analysis for candidate genes revealed novel significant biological processes related to musculoskeletal system development, such as limb development and morphogenesis, having been targeted by selection in sport breeds.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.030
GPT teacher head0.341
Teacher spread0.311 · 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

Citations24
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAnimalsSame topicVeterinary Equine Medical ResearchFrench-language works237,207