PMID- 33663837
OWN - NLM
STAT- MEDLINE
DCOM- 20210426
LR  - 20210426
IS  - 1525-3198 (Electronic)
IS  - 0022-0302 (Linking)
VI  - 104
IP  - 5
DP  - 2021 May
TI  - Structural equation modeling for unraveling the multivariate genomic architecture 
      of milk proteins in dairy cattle.
PG  - 5705-5718
LID - S0022-0302(21)00183-1 [pii]
LID - 10.3168/jds.2020-18321 [doi]
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 kappa-, beta-, alpha(S1)-, and alpha(S2)-CN, and 2 whey proteins, namely 
      beta-lactoglobulin (beta-LG) and alpha-lactalbumin (alpha-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 beta-CN and 
      alpha(S1)-CN (-0.706) and between beta-CN and kappa-CN (-0.735). The application of the MMHC 
      algorithm revealed that kappa-CN and beta-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 kappa-CN, 89 SNP for beta-CN, 
      30 SNP for alpha(S1)-CN, and 14 SNP for alpha(S2)-CN (mostly shared among CN and located 
      on Bos taurus autosome 6) and 15 SNP for beta-LG (mostly located on Bos taurus 
      autosome 11), whereas no SNP passed the significance threshold for alpha-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.
CI  - Copyright (c) 2021 American Dairy Science Association. Published by Elsevier Inc. 
      All rights reserved.
FAU - Pegolo, Sara
AU  - Pegolo S
AD  - Department of Agronomy, Food Natural Resources, Animals and Environment, 
      University of Padua, 35020 Legnaro (PD), Italy. Electronic address: 
      sara.pegolo@unipd.it.
FAU - Yu, Haipeng
AU  - Yu H
AD  - Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and 
      State University, Blacksburg 24061.
FAU - Morota, Gota
AU  - Morota G
AD  - Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and 
      State University, Blacksburg 24061.
FAU - Bisutti, Vittoria
AU  - Bisutti V
AD  - Department of Agronomy, Food Natural Resources, Animals and Environment, 
      University of Padua, 35020 Legnaro (PD), Italy.
FAU - Rosa, Guilherme J M
AU  - Rosa GJM
AD  - Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706; 
      Department of Biostatistics and Medical Informatics, University of Wisconsin, 
      Madison 53792.
FAU - Bittante, Giovanni
AU  - Bittante G
AD  - Department of Agronomy, Food Natural Resources, Animals and Environment, 
      University of Padua, 35020 Legnaro (PD), Italy.
FAU - Cecchinato, Alessio
AU  - Cecchinato A
AD  - Department of Agronomy, Food Natural Resources, Animals and Environment, 
      University of Padua, 35020 Legnaro (PD), Italy.
LA  - eng
PT  - Journal Article
DEP - 20210302
PL  - United States
TA  - J Dairy Sci
JT  - Journal of dairy science
JID - 2985126R
RN  - 0 (Caseins)
RN  - 0 (Milk Proteins)
SB  - IM
MH  - Animals
MH  - Bayes Theorem
MH  - *Caseins/genetics
MH  - Cattle/genetics
MH  - Female
MH  - Genomics
MH  - Latent Class Analysis
MH  - *Milk Proteins
OTO - NOTNLM
OT  - Bayesian network
OT  - cattle
OT  - genome-wide association analysis
OT  - milk proteins
OT  - pathway
EDAT- 2021/03/06 06:00
MHDA- 2021/04/27 06:00
CRDT- 2021/03/05 05:45
PHST- 2020/02/06 00:00 [received]
PHST- 2020/12/31 00:00 [accepted]
PHST- 2021/03/06 06:00 [pubmed]
PHST- 2021/04/27 06:00 [medline]
PHST- 2021/03/05 05:45 [entrez]
AID - S0022-0302(21)00183-1 [pii]
AID - 10.3168/jds.2020-18321 [doi]
PST - ppublish
SO  - J Dairy Sci. 2021 May;104(5):5705-5718. doi: 10.3168/jds.2020-18321. Epub 2021 
      Mar 2.