MétaCan
Menu
Back to cohort
Record W2738073882 · doi:10.1111/age.12574

Identification of positive selection signatures in pigs by comparing linkage disequilibrium variances

2017· article· en· W2738073882 on OpenAlexaff
Xiuling Li, Shulin Yang, Kunzhe Dong, Zhonglin Tang, Kuihua Li, Bin Fan, Z Wang, Bang Liu

Bibliographic record

VenueAnimal Genetics · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversity of Alberta
FundersChina Scholarship Council
KeywordsBiologyLinkage disequilibriumGeneticsQuantitative trait locusGenotypingSelection (genetic algorithm)SNP genotypingAlleleGenome ScanEvolutionary biologyMicrosatelliteGenotypeHaplotypeGene

Abstract

fetched live from OpenAlex

Selection affects the patterns of linkage disequilibrium (LD) around the site of a beneficial allele with an increase in LD among the hitchhiking alleles. Comparing the differences in regional LD between pig populations could help to identify putative genomic regions with potential adaptations for economic traits. In this study, using Illumina Porcine SNP60K BeadChip genotyping data from 207 Chinese indigenous, 117 South American village and 408 Large White pigs, we estimated the variation of genome-wide LD between populations using the varld program. The top 0.1% standardized VarLD scores were used as a criterion for all comparisons, and compared with LD blocks, a total of four selection signatures on Sus scrofa chromosome (SSC) 7, 9, 13 and 14 were identified in all populations. These signatures overlapped with quantitative trait loci for linoleic acid content, age at puberty, number of muscle fibers per unit area, hip structure and body weight traits in pigs. Among them, one of the signatures (56.5-56.6 Mb on SSC7) in Large White pigs harbored the ADAMTSL3 gene, which is known to affect body length. The findings of this study seem to point toward recent selection in different pig populations. Further investigations are encouraged to confirm the selection signatures detected by varld in the present study.

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 score0.608
Threshold uncertainty score0.489

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 head0.011
GPT teacher head0.265
Teacher spread0.253 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

Explore more

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