MétaCan
Menu
Back to cohort

Evaluation of 16 loci to examine the cross‐species utility of single nucleotide polymorphism arrays

2009· article· en· W2074898751 on OpenAlexaff
Tiziana Sechi, David W. Coltman, James Kijas

Bibliographic record

VenueAnimal Genetics · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversity of Alberta
FundersAustralian Government
KeywordsBiologySingle-nucleotide polymorphismGeneticsSNPEvolutionary biologyGenotypeGenetic diversityGenetic divergenceSNP arrayGenePopulation

Abstract

fetched live from OpenAlex

Large collections of single nucleotide polymorphisms (SNPs) have recently been identified from a number of livestock genomes. This raises the possibility that SNP arrays might be useful for analysis in related species for which few genetic markers are currently available. To address the likely success of such an approach, the aim of this study was to examine the threshold number and position of flanking mutations which act to prevent genotype calls being produced. Sequence diversity was measured across 16 loci containing SNPs known either to work successfully between species or fail between species. In pairwise comparisons between domestic and wild sheep, sequence divergence surrounding working SNP assays was significantly lower than that surrounding non-functional assays. In addition, the location of flanking mismatches tended to be closer to the target SNP in loci that failed to generate genotype calls across species. The magnitude of sequence divergence observed for both working and non-functional assays was compared with the divergence separating domestic sheep from European Mouflon, African Barbary, goat and cattle. The results suggest that the utility of SNP arrays for analysis of shared polymorphism will be restricted to closely related pairs of species. Analysis across more divergent species will, however, be successful for other objectives, such as the identification of the ancestral state of SNPs.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.045
GPT teacher head0.289
Teacher spread0.244 · 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

Citations8
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueAnimal GeneticsSame topicGenetic and phenotypic traits in livestockFrench-language works237,207