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Structural equation modeling for unraveling the multivariate genomic architecture of milk proteins in dairy cattle.
Pegolo, Sara; Yu, Haipeng; Morota, Gota; Bisutti, Vittoria; Rosa, Guilherme J M; Bittante, Giovanni; Cecchinato, Alessio.
Afiliação
  • Pegolo S; Department of Agronomy, Food Natural Resources, Animals and Environment, University of Padua, 35020 Legnaro (PD), Italy. Electronic address: sara.pegolo@unipd.it.
  • Yu H; Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg 24061.
  • Morota G; Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg 24061.
  • Bisutti V; Department of Agronomy, Food Natural Resources, Animals and Environment, University of Padua, 35020 Legnaro (PD), Italy.
  • Rosa GJM; Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison 53792.
  • Bittante G; Department of Agronomy, Food Natural Resources, Animals and Environment, University of Padua, 35020 Legnaro (PD), Italy.
  • Cecchinato A; Department of Agronomy, Food Natural Resources, Animals and Environment, University of Padua, 35020 Legnaro (PD), Italy.
J Dairy Sci ; 104(5): 5705-5718, 2021 May.
Article em En | MEDLINE | ID: mdl-33663837
The aims of this study were to investigate potential functional relationships among milk protein fractions in dairy cattle and to carry out a structural equation model (SEM) GWAS to provide a decomposition of total SNP effects into direct effects and effects mediated by traits that are upstream in a phenotypic network. To achieve these aims, we first fitted a mixed Bayesian multitrait genomic model to infer the genomic correlations among 6 milk nitrogen fractions [4 caseins (CN), namely κ-, ß-, αS1-, and αS2-CN, and 2 whey proteins, namely ß-lactoglobulin (ß-LG) and α-lactalbumin (α-LA)], in a population of 989 Italian Brown Swiss cows. Animals were genotyped with the Illumina BovineSNP50 Bead Chip v.2 (Illumina Inc.). A Bayesian network approach using the max-min hill-climbing (MMHC) algorithm was implemented to model the dependencies or independence among traits. Strong and negative genomic correlations were found between ß-CN and αS1-CN (-0.706) and between ß-CN and κ-CN (-0.735). The application of the MMHC algorithm revealed that κ-CN and ß-CN seemed to directly or indirectly influence all other milk protein fractions. By integrating multitrait model GWAS and SEM-GWAS, we identified a total of 127 significant SNP for κ-CN, 89 SNP for ß-CN, 30 SNP for αS1-CN, and 14 SNP for αS2-CN (mostly shared among CN and located on Bos taurus autosome 6) and 15 SNP for ß-LG (mostly located on Bos taurus autosome 11), whereas no SNP passed the significance threshold for α-LA. For the significant SNP, we assessed and quantified the contribution of direct and indirect paths to total marker effect. Pathway analyses confirmed that common regulatory mechanisms (e.g., energy metabolism and hormonal and neural signals) are involved in the control of milk protein synthesis and metabolism. The information acquired might be leveraged for setting up optimal management and selection strategies aimed at improving milk quality and technological characteristics in dairy cattle.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Caseínas / Proteínas do Leite Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Caseínas / Proteínas do Leite Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Ano de publicação: 2021 Tipo de documento: Article