Identification of additional loci associated with antibody response to Mycobacterium avium ssp. Paratuberculosis in cattle by GSEA–SNP analysis
Date
2017
Authors
Del Corvo, M.
Luini, M.
Stella, A.
Pagnacco, G.
Ajmone-Marsan, P.
Williams, J.
Minozzi, G.
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Journal article
Citation
Mammalian Genome, 2017; 28(11-12):520-527
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Marcello Del Corvo, Mario Luini, Alessandra Stella, Giulio Pagnacco, Paolo Ajmone-Marsan, John L. Williams, Giulietta Minozzi
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Abstract
Mycobacterium avium subsp. paratuberculosis: (MAP) causes a contagious chronic infection results in Johne's disease in a wide range of animal species, including cattle. Several genome-wide association studies (GWAS) have been carried out to identify loci putatively associated with MAP susceptibility by testing each marker separately and identifying SNPs that show a significant association with the phenotype, while SNP with modest effects are usually ignored. The objective of this study was to identify modest-effect genes associated with MAP susceptibility using a pathway-based approach. The Illumina BovineSNP50 BeadChip was used to genotype 966 Holstein cows, 483 positive and 483 negative for antibody response to MAP, data were then analyzed using novel SNP-based Gene Set Enrichment Analysis (GSEA-SNP) and validated with Adaptive Rank Truncated Product methodology. An allele-based test was carried out to estimate the statistical association for each marker with the phenotype, subsequently SNPs were mapped to the closest genes, considering for each gene the single variant with the highest value within a window of 50 kb, then pathway-statistics were tested using the GSEA-SNP method. The GO biological process "embryogenesis and morphogenesis" was most highly associated with antibody response to MAP. Within this pathway, five genes code for proteins which play a role in the immune defense relevant to response to bacterial infection. The immune response genes identified would not have been considered using a standard GWAS, thus demonstrating that the pathway approach can extend the interpretation of genome-wide association analyses and identify additional candidate genes for target traits.
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© Springer Science+Business Media, LLC 2017