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Record W2120907314 · doi:10.1371/journal.pone.0094851

SNPs for Parentage Testing and Traceability in Globally Diverse Breeds of Sheep

2014· article· en· W2120907314 on OpenAlexfundno aff
Michael P. Heaton, K. A. Leymaster, Ted Kalbfleisch, James Kijas, Shannon Clarke, John C. McEwan, J F Maddox, Veronica Basnayake, Dustin T. Petrik, Barry J. Simpson, Timothy P. L. Smith, Carol G. Chitko-McKown

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
FundersInstitute of GeneticsNational Institute of General Medical SciencesAgricultural Research ServiceNational Institutes of HealthUniversity of PeradeniyaAristotle University of ThessalonikiHáskóli ÍslandsCommonwealth Scientific and Industrial Research OrganisationAdnan Menderes ÜniversitesiUniversity College DublinInstitut National de la Recherche AgronomiqueUniversity of New EnglandNational Chemical LaboratoryAgResearchAberystwyth UniversityMeat and Livestock AustraliaKentucky IDeA Networks of Biomedical Research ExcellenceUniversity of CyprusUniversidad de LeónUniversity of AlbertaUniversidad de ZaragozaUniversiteit UtrechtUniversität HohenheimChinese Academy of Agricultural SciencesInternational Atomic Energy AgencyLandbúnaðarháskóli ÍslandsU.S. Department of AgricultureUtah State UniversityCyprus University of TechnologyUniversity of Louisville
KeywordsBreedSingle-nucleotide polymorphismBiologyGeneticsMinor allele frequencySNP genotypingContext (archaeology)Sanger sequencingSireGenotypeSNPDNA sequencingAnimal scienceGene

Abstract

fetched live from OpenAlex

DNA-based parentage determination accelerates genetic improvement in sheep by increasing pedigree accuracy. Single nucleotide polymorphism (SNP) markers can be used for determining parentage and to provide unique molecular identifiers for tracing sheep products to their source. However, the utility of a particular "parentage SNP" varies by breed depending on its minor allele frequency (MAF) and its sequence context. Our aims were to identify parentage SNPs with exceptional qualities for use in globally diverse breeds and to develop a subset for use in North American sheep. Starting with genotypes from 2,915 sheep and 74 breed groups provided by the International Sheep Genomics Consortium (ISGC), we analyzed 47,693 autosomal SNPs by multiple criteria and selected 163 with desirable properties for parentage testing. On average, each of the 163 SNPs was highly informative (MAF≥0.3) in 48±5 breed groups. Nearby polymorphisms that could otherwise confound genetic testing were identified by whole genome and Sanger sequencing of 166 sheep from 54 breed groups. A genetic test with 109 of the 163 parentage SNPs was developed for matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry. The scoring rates and accuracies for these 109 SNPs were greater than 99% in a panel of North American sheep. In a blinded set of 96 families (sire, dam, and non-identical twin lambs), each parent of every lamb was identified without using the other parent's genotype. In 74 ISGC breed groups, the median estimates for probability of a coincidental match between two animals (PI), and the fraction of potential adults excluded from parentage (PE) were 1.1×10(-39) and 0.999987, respectively, for the 109 SNPs combined. The availability of a well-characterized set of 163 parentage SNPs facilitates the development of high-throughput genetic technologies for implementing accurate and economical parentage testing and traceability in many of the world's sheep 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.232
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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

Citations116
Published2014
Admission routes1
Has abstractyes

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