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Record W3002897626 · doi:10.1111/rda.13639

Genome‐wide identification of runs of homozygosity islands in the Gyr breed (<i>Bos indicus</i>)

2020· article· en· W3002897626 on OpenAlexfundno aff
Alejandra Maria Toro Ospina, Ricardo António da Silva Faria, Aníbal Eugênio Vercesi Filho, Joslaine Noely dos Santos Gonçalves Cyrillo, Maria Eugênia Zerlotti Mercadante, Rogério Abdallah Curi, Josineudson Augusto II de Vasconcelos Silva

Bibliographic record

VenueReproduction in Domestic Animals · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoAgence Universitaire de la FrancophonieFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsRuns of HomozygosityBiologyInbreedingBreedPopulationGeneticsGenomeGeneGenotypeSingle-nucleotide polymorphismDemography

Abstract

fetched live from OpenAlex

Abstract Runs of homozygosity (ROH) are contiguous homozygous regions of the genome. These regions can be used to identify genes associated with traits of economic interest, as well as inbreeding levels. The aim of the present study was to analyse the length and distribution of ROH islands in Gyr cattle and to identify genes within these regions. A population of 173 animals selected for beef production and a population of 291 animals selected for dairy production were used. Differences in the number of short ROH (ROH 1‐2 Mb ) were observed between the two populations, while the number of long ROH (ROH &gt;16 Mb ) was similar. ROH islands with the highest incidences (&gt;0.50) overlapped in several segments of the genome in the two populations. The genes identified were associated with milk production, growth, reproduction, immune response and resistance traits. Our results contribute to the understanding of how selection can shape the distribution of ROH and ROH islands within the same breed when animals are selected for different purposes such as dairy or beef production.

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)0.001
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 score0.837
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.252
Teacher spread0.238 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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