Evidence map›Paper›PMID 42907777›Full record

ArticleGenetics, selection, evolution : GSE2026

Expanding the pig metabolomics QTL catalog.

Emil Ibragimov, Malene Kjelin Morsing, Martin Peter Rydal, Zexi Cai, Hans Stenlund, Jens Peter Nielsen, Merete Fredholm, Peter Karlskov-Mortensen

Abstract read
In one paragraph

Article in Genetics, selection, evolution : GSE, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Emil IbragimovDepatment of Veterinary and Animal Sciences, University of Copenhagen, København, Denmark. emib@sund.ku.dk.ORCID http://orcid.org/0009-0001-2491-1306
Malene Kjelin MorsingDepatment of Veterinary and Animal Sciences, University of Copenhagen, København, Denmark.
Martin Peter RydalDepatment of Veterinary and Animal Sciences, University of Copenhagen, København, Denmark.
Zexi CaiCenter for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
Hans StenlundDepartment of Plant Physiology, Umeå University, Umeå, Sweden.
Jens Peter NielsenDepatment of Veterinary and Animal Sciences, University of Copenhagen, København, Denmark.
Merete FredholmDepatment of Veterinary and Animal Sciences, University of Copenhagen, København, Denmark.
Peter Karlskov-MortensenDepatment of Veterinary and Animal Sciences, University of Copenhagen, København, Denmark.

Funding

Innovationsfonden ID: 4105-00011BNovo Nordisk Fonden NNFSA210073688
6 · The paper itself

Abstract

backgroundUnderstanding the genetic regulation of metabolism is essential for improving animal health and productivity and for strengthening the use of pigs as translational models for human metabolic diseases. Metabolomics, when integrated with genomic data, provides a powerful framework for identifying genetic factors that influence intermediate molecular phenotypes closely linked to physiology. While large-scale metabolomics genome‑wide association studies (GWAS) have been extensively performed in humans, comparable studies in pigs remain limited. Among omics disciplines, metabolomics, the systematic study of metabolites central to physiology, represents the omics layer most closely linked to end-point phenotypes in mammals, such as health and production traits (e.g. growth rate). Metabolite levels are influenced by genetics, diet, and microbiome composition, with human studies showing high heritability and identifying numerous metabolite quantitative trait loci (mQTLs). While such findings have advanced our understanding of disease biology in humans, comparable studies in pigs remain limited.

resultsWe performed GWAS of the serum metabolome in 213 three‑way crossbred pigs [(Landrace × Yorkshire) × Duroc], analyzing 248 metabolites and 30,628 metabolite ratios. We identified 41 significant metabolite-variant associations, which were consolidated into 18 mQTLs based on linkage disequilibrium (LD). Several regions were associated with multiple, biochemically related metabolites, while others showed highly specific effects. Cross-species comparison with the human GWAS catalog revealed candidate genes within seven mQTLs. Through an integrative prioritization strategy, putative causal genes-predominantly enzymes and transporters-were identified for 14 of 18 mQTLs.

conclusionsOur results expand the catalog of genetic loci influencing serum metabolite variation in pigs and highlight both conserved and pig specific components of metabolic regulation. These findings provide a foundation for incorporating metabolomics into pig breeding programs and reinforce the value of pigs as a translational model for studying metabolite-associated traits and diseases in humans.

Indexed as

MetabolomeMetabolomicsQuantitative Trait LociSus scrofaAnimalsGenome-Wide Association StudyPhenotypePolymorphism, Single NucleotideSwine

Identifiers

PMID42907777
PMCPMC13685178

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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.