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      Plasma protein levels of young healthy pigs as indicators of disease resilience

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          Abstract

          Selection for disease resilience, which refers to the ability of an animal to maintain performance when exposed to disease, can reduce the impact of infectious diseases. However, direct selection for disease resilience is challenging because nucleus herds must maintain a high health status. A possible solution is indirect selection of indicators of disease resilience. To search for such indicators, we conducted phenotypic and genetic quantitative analyses of the abundances of 377 proteins in plasma samples from 912 young and visually healthy pigs and their relationships with performance and subsequent disease resilience after natural exposure to a polymicrobial disease challenge. Abundances of 100 proteins were significantly heritable (false discovery rate (FDR) <0.10). The abundance of some proteins was or tended to be genetically correlated ( r g) with disease resilience, including complement system proteins ( r g = −0.24, FDR = 0.001) and IgG heavy chain proteins ( r g = −0.68, FDR = 0.22). Gene set enrichment analyses (FDR < 0.2) based on phenotypic and genetic associations of protein abundances with subsequent disease resilience revealed many pathways related to the immune system that were unfavorably associated with subsequent disease resilience, especially the innate immune system. It was not possible to determine whether the observed levels of these proteins reflected baseline levels in these young and visually healthy pigs or were the result of a response to environmental disturbances that the pigs were exposed to before sample collection. Nevertheless, results show that, under these conditions, the abundance of proteins in some immune-related pathways can be used as phenotypic and genetic predictors of disease resilience and have the potential for use in pig breeding and management.

          Abstract

          Plasma protein levels of young healthy pigs are promising as biomarkers to select for disease resilience.

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          Most cited references59

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              Fitting Linear Mixed-Effects Models Usinglme4

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                Author and article information

                Contributors
                Journal
                J Anim Sci
                J Anim Sci
                jansci
                Journal of Animal Science
                Oxford University Press (US )
                0021-8812
                1525-3163
                2023
                13 January 2023
                13 January 2023
                : 101
                : skad014
                Affiliations
                Department of Animal Science, Iowa State University , Ames, IA, USA
                Department of Animal Science, Iowa State University , Ames, IA, USA
                Department of Animal Science, Iowa State University , Ames, IA, USA
                Department of Animal Resources Science, Kongju National University , Yesan, Republic of Korea
                Department of Animal Science, Iowa State University , Ames, IA, USA
                Department of Animal Science, Iowa State University , Ames, IA, USA
                Hendrix Genetics, Swine Business Unit , Boxmeer, The Netherlands
                Department of Agriculture, Food and Nutritional Science, University of Alberta , Edmonton, AB, Canada
                PigGen Canada Research Consortium , Guelph, Ontario, Canada
                Centre de Développement du Porc du Québec Inc. , Québec City, Canada
                Department of Large Animal Clinical Science, University of Saskatchewan , Saskatoon, SK, Canada
                Department of Agriculture, Food and Nutritional Science, University of Alberta , Edmonton, AB, Canada
                Department of Animal Science, Iowa State University , Ames, IA, USA
                Author notes
                Corresponding author: jdekkers@ 123456iastate.edu
                Author information
                https://orcid.org/0000-0003-2208-7242
                https://orcid.org/0000-0002-3774-3110
                Article
                skad014
                10.1093/jas/skad014
                9977353
                36638126
                dc132c48-b913-4188-accf-4439458603b7
                © The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 16 June 2022
                : 11 January 2023
                : 01 March 2023
                Page count
                Pages: 19
                Categories
                Animal Genetics and Genomics
                AcademicSubjects/SCI00960

                disease resilience,indicator traits,pigs,plasma proteins

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