TY - JOUR
T1 - Structural equation modeling for unraveling the multivariate genomic architecture of milk proteins in dairy cattle
AU - Pegolo, Sara
AU - Yu, Haipeng
AU - Morota, Gota
AU - Bisutti, Vittoria
AU - Rosa, Guilherme J.M.
AU - Bittante, Giovanni
AU - Cecchinato, Alessio
N1 - Funding Information:
This study was funded by the University of Padova (Ricerca Scientifica fondi DOR – 2020, project DOR2001177/20). The authors thank Trento Province, the Italian Brown Swiss Cattle Breeders Association (ANARB), and the Superbrown Consortium of Bolzano and Trento for financial and technical support. We also thank the section editor and the anonymous reviewers for their suggestions and constructive comments. The authors have not stated any conflicts of interest.
Publisher Copyright:
© 2021 American Dairy Science Association
PY - 2021/5
Y1 - 2021/5
N2 - 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-, β-, αS1-, and αS2-CN, and 2 whey proteins, namely β-lactoglobulin (β-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 β-CN and αS1-CN (−0.706) and between β-CN and κ-CN (−0.735). The application of the MMHC algorithm revealed that κ-CN and β-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 β-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 β-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.
AB - 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-, β-, αS1-, and αS2-CN, and 2 whey proteins, namely β-lactoglobulin (β-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 β-CN and αS1-CN (−0.706) and between β-CN and κ-CN (−0.735). The application of the MMHC algorithm revealed that κ-CN and β-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 β-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 β-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.
KW - Bayesian network
KW - cattle
KW - genome-wide association analysis
KW - milk proteins
KW - pathway
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U2 - 10.3168/jds.2020-18321
DO - 10.3168/jds.2020-18321
M3 - Article
C2 - 33663837
AN - SCOPUS:85101876066
SN - 0022-0302
VL - 104
SP - 5705
EP - 5718
JO - Journal of Dairy Science
JF - Journal of Dairy Science
IS - 5
ER -