Evidence map›Paper›PMID 42159565›Full record

ArticleJournal of animal science2026

Genetic analysis of High Immune Response technology and relationships with disease resilience in pigs.

Jian Cheng, Julie Schmied, Bonnie Mallard, John C S Harding, Michael K Dyck, Frederic Fortin, Graham S Plastow, PigGen Canada, Jack C M Dekkers

Abstract read
In one paragraph

Article in Journal of animal science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jian ChengDepartment of Animal Science, Iowa State University, Ames, Iowa 50011, United States.
Julie SchmiedDepartment of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada N1G 2W1.
Bonnie MallardDepartment of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada N1G 2W1.
John C S HardingDepartment of Large Animal Clinical Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada S7N 5A2.
Michael K DyckDepartment of Agriculture, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada T6G 2R3.
Frederic FortinCentre de Développement du Porc du Québec Inc, Québec City, Québec, Canada G1V 4M6.
Graham S PlastowDepartment of Agriculture, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada T6G 2R3.ORCID 0000-0002-3774-3110
PigGen CanadaPigGen Canada Research Consortium, Guelph, Ontario, Canada N1H4G8.
Jack C M DekkersDepartment of Animal Science, Iowa State University, Ames, Iowa 50011, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Disease resilience is a viable target for genetic improvement programs in pigs to improve productivity and animal welfare and reduce the use of antibiotics. However, disease resilience is difficult to select for in nucleus breeding programs. Indicator traits that can be measured on (young) pigs without exposure to disease offer possible solutions. The High Immune Response (HIR) technology has been shown to provide such indicator traits in dairy cattle. HIR involves evaluation of antibody-mediated (AMIR) and cell-mediated (CMIR) immune responses. The objectives of this study were to implement HIR technology for young and clinically healthy pigs, to evaluate genetic parameters of, and genomic regions associated with AMIR and CMIR, and estimate genetic correlations of AMIR and CMIR with disease resilience. For the latter, data from a polymicrobial natural disease challenge that pigs were exposed to after HIR evaluation were used. In total, HIR data were collected on 2,295 Yorkshire × Landrace barrows, as well as disease resilience data on these and another 1,800 barrows from the same populations. All pigs had genotypes for 451,389 single-nucleotide polymorphisms (SNPs) across the genome. Both AMIR and CMIR were moderately heritable (0.25 ± 0.06 and 0.20 ± 0.05), with limited evidence of a genetic correlation between them (0.23 ± 0.17). Higher AMIR tended to be genetically correlated with higher growth and health under disease, but the opposite was apparent for CMIR, possibly as a result of the nature of the disease challenge and the clinical disease data that was collected, which tended to emphasize bacterial rather than viral infections. Genetic correlations of AMIR and CMIR with disease resilience traits were also not consistent between two sets of batches of pigs separated in time but this appeared to be the result of genetic differences in disease resilience, possibly because of different disease dynamics, rather than in HIR traits, as the estimate of the genetic correlation between the two sets of batches was low for growth under challenge (0.44 ± 0.38) but high for AMIR (0.86 ± 0.35) and CMIR (0.91 ± 0.48). The major histocompatibility complex was found to explain over 32% of the genetic variation for AMIR, with SNP AX-116767878 explaining the majority of this variation, and was also associated with disease resilience. Three other genomic regions were identified for AMIR, together explaining 12% of genetic variance, while only two regions were identified for CMIR, each explaining less than 2% of genetic variance. All identified regions included genes associated with the immune response. Results suggest that HIR traits, in particular AMIR, are strong candidates as indicator traits that can be measured on young healthy pigs to select for resilience across multiple pathogens.

Indexed as

Disease ResistanceImmunity, CellularSwine DiseasesAnimalsBreedingFemaleGenetic Predisposition to DiseaseGenotypeMalePolymorphism, Single NucleotideSwinedisease resiliencegeneticshigh immune responseimmune responsemajor histocompatibility complexpigs

Identifiers

PMID42159565
PMCPMC13257861

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.