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Record W4388529424 · doi:10.1093/jas/skad281.411

PSXII-9 Identifying Selection Signatures for Immune Response and Resilience to Aleutian Disease in Mink Using Genotypes Data

2023· article· en· W4388529424 on OpenAlexaffabout
Guoyu Hu, Duy Ngoc, Ghader Manafiazar, Alyson A. Kelvin, Graham Plastow, Mehdi Sargolzaei, Zhiquan Wang, Younes Miar

Bibliographic record

VenueJournal of Animal Science · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsUniversity of GuelphUniversity of AlbertaUniversity of SaskatchewanDalhousie University
Fundersnot available
KeywordsMinkBiologyAmerican minkSNPImmune systemGeneticsSingle-nucleotide polymorphismZoologyGenotypeGeneEcology

Abstract

fetched live from OpenAlex

Abstract Aleutian disease (AD), a severe immune-complex disease, brings tremendous financial losses to the mink industry. Resilience is the ability of an animal to minimize the influences caused by disturbances and maintain its performance under pathogen exposure. Many AD-positive farms have selected AD-resilient mink based on AD tests and/or AD-resilience indicator traits, such as growth, pelt quality, and reproduction. Selection based on these indicator traits may have changed the patterns of genetic variation and potentially represent a genomic signature that can be used to identify genes subjected to selection. Therefore, the main objective of this study was to identify the selection signatures related to immune response (IRE) and resilience to AD. . A total of 1,411 mink in five color types (black, demi, mahogany, pastel, and stardust) from the Canadian Centre for Fur Animal Research (Truro, Nova Scotia, Canada), which is an AD-positive facility, were genotyped using the Axiom Affymetrix Mink 70K single nucleotide polymorphism (SNP) panel. For the IRE trait, 264 mink were grouped into pairwise groups based on the combined results of counter-immunoelectrophoresis (CIEP) and enzyme-linked immunosorbent assay (ELISA) tests. Individuals in black (n = 29), demi (n = 131), mahogany (n = 76), and pastel (36) color types were also grouped based on combined results of CIEP and ELISA tests, respectively. For resilience traits, two methods were used to group CIEP-positive individuals into pairwise groups: 1) based on their general resilience performance (GRP, n = 30), which was measured by considering individual performance for feed conversion ratio, Kleiber ratio, and pelt quality, and 2) based on female reproductive performance (FRP, CIEP-positive dams only, n = 36), which was measured by the number of kits alive 24h after birth. The pairwise fixation index, nucleotide diversity, and cross-population extended haplotype homozygosity were used to detect selection signatures. Only SNPs detected by at least two methods were considered selection signatures candidates, including 619, 569, and 526 SNPs for IRE, GRP, and FRP traits, respectively. The genes annotated from the candidate SNPs were associated with previously reported traits influenced by AD, including immune system process, growth, reproduction, and pigmentation. Notably, two olfactory-related gene ontology (GO) terms were significant (q < 0.05) for all studied traits, suggesting AD might cause loss of smell in infected mink. Additionally, the differences in genes and GO terms detected across different color types for IRE indicated that mink of different color types may differ in immune response to AD. The mitogen-activated protein kinase (MAPK) signalling pathway was significant (q < 0.05) in Kyoto Encyclopedia of Genes and Genomes pathway analyses for FRP, indicating AD infection may disorder the MAPK signalling pathway, in turn influencing female reproductive performance. Our study has enhanced the understanding of genomic architecture underlying resilience of mink to AD and sheds light on the underlying biological mechanisms involved.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.378
Teacher spread0.339 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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