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Background <p>Genomic prediction is widely used in pig breeding, but phenotypic prediction of complex traits such as disease resilience remains limited because genotypes alone do not capture infection-induced regulatory responses, environmental and management effects, or their interactions. Blood molecular profiles measured in young healthy pigs reflect both genetic and non-genetic influences and may improve prediction of performance under disease challenge. We evaluated whether integrating multiple blood-based omics layers with genomic data improves prediction of production and disease resilience phenotypes in pigs exposed to a polymicrobial disease challenge.</p> Results <p>Data were from 836 healthy pigs from 15 batches with transcriptomic, proteomic, and metabolomic profiles measured in blood collected at ~27 days of age, before transfer into a natural polymicrobial disease challenge at ~40 days of age. Pigs were also genotyped using a commercial 650&#xa0;K marker array. We analyzed 21 traits related to growth, health scores, antibiotic treatments, mortality, feed efficiency, and carcass traits using best linear unbiased prediction (BLUP) animal models with random animal effects based on relationship matrices constructed from genomic (G), transcriptomic (T), proteomic (P), and metabolomic (M) data. Across traits, G-BLUP explained the largest proportion of phenotypic variance for most traits. However, T-, P-, or M-BLUP explained similar or greater variance than G-BLUP for several growth and health traits recorded before challenge. Adding T and/or M to G-BLUP generally increased variance explained and improved prediction accuracy for pre-challenge growth rate and health scores, and for mortality and carcass weight after challenge. Models combining G, T, and M often yielded the highest accuracies, whereas adding P did not consistently improve accuracy. For later grow-finish traits, gains from multi-omics were smaller and less consistent.</p> Conclusions <p>Blood multi-omics profiles from healthy young pigs can improve prediction of performance and disease resilience beyond genomic data alone. Gains were greatest for traits recorded before challenge and for some resilience traits expressed soon after pathogen exposure, suggesting that pre-challenge molecular profiles capture latent resilience potential. These findings support the use of pre-challenge blood multi-omics as biomarkers for precision management and as a basis for breeding and management strategies targeting disease resilience in pigs.</p>

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Prediction of disease resilience of pigs using multi-omics data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月2日

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  • Yulu Chen,
  • Steven Lonergan,
  • Kyu-Sang Lim,
  • Jian Cheng,
  • Elda Dervishi,
  • Michael K. Dyck,
  • Edward Steadham,
  • PigGen Canada,
  • Frederic Fortin,
  • John C. S. Harding,
  • Graham S. Plastow,
  • Jack C. M. Dekkers

摘要

Background

Genomic prediction is widely used in pig breeding, but phenotypic prediction of complex traits such as disease resilience remains limited because genotypes alone do not capture infection-induced regulatory responses, environmental and management effects, or their interactions. Blood molecular profiles measured in young healthy pigs reflect both genetic and non-genetic influences and may improve prediction of performance under disease challenge. We evaluated whether integrating multiple blood-based omics layers with genomic data improves prediction of production and disease resilience phenotypes in pigs exposed to a polymicrobial disease challenge.

Results

Data were from 836 healthy pigs from 15 batches with transcriptomic, proteomic, and metabolomic profiles measured in blood collected at ~27 days of age, before transfer into a natural polymicrobial disease challenge at ~40 days of age. Pigs were also genotyped using a commercial 650 K marker array. We analyzed 21 traits related to growth, health scores, antibiotic treatments, mortality, feed efficiency, and carcass traits using best linear unbiased prediction (BLUP) animal models with random animal effects based on relationship matrices constructed from genomic (G), transcriptomic (T), proteomic (P), and metabolomic (M) data. Across traits, G-BLUP explained the largest proportion of phenotypic variance for most traits. However, T-, P-, or M-BLUP explained similar or greater variance than G-BLUP for several growth and health traits recorded before challenge. Adding T and/or M to G-BLUP generally increased variance explained and improved prediction accuracy for pre-challenge growth rate and health scores, and for mortality and carcass weight after challenge. Models combining G, T, and M often yielded the highest accuracies, whereas adding P did not consistently improve accuracy. For later grow-finish traits, gains from multi-omics were smaller and less consistent.

Conclusions

Blood multi-omics profiles from healthy young pigs can improve prediction of performance and disease resilience beyond genomic data alone. Gains were greatest for traits recorded before challenge and for some resilience traits expressed soon after pathogen exposure, suggesting that pre-challenge molecular profiles capture latent resilience potential. These findings support the use of pre-challenge blood multi-omics as biomarkers for precision management and as a basis for breeding and management strategies targeting disease resilience in pigs.