Genome-enable prediction for health traits using high-density SNP panel in US Holstein cattle.
Bayesian regression models
dairy
genomic prediction
reproduction
Journal
Animal genetics
ISSN: 1365-2052
Titre abrégé: Anim Genet
Pays: England
ID NLM: 8605704
Informations de publication
Date de publication:
Mar 2020
Mar 2020
Historique:
accepted:
22
11
2019
pubmed:
8
1
2020
medline:
29
9
2020
entrez:
8
1
2020
Statut:
ppublish
Résumé
The objective of this study was to compare accuracies of different Bayesian regression models in predicting molecular breeding values for health traits in Holstein cattle. The dataset was composed of 2505 records reporting the occurrence of retained fetal membranes (RFM), metritis (MET), mastitis (MAST), displaced abomasum (DA), lameness (LS), clinical endometritis (CE), respiratory disease (RD), dystocia (DYST) and subclinical ketosis (SCK) in Holstein cows, collected between 2012 and 2014 in 16 dairies located across the US. Cows were genotyped with the Illumina BovineHD (HD, 777K). The quality controls for SNP genotypes were HWE P-value of at least 1 × 10
Identifiants
pubmed: 31909828
doi: 10.1111/age.12892
pmc: PMC7065151
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
192-199Subventions
Organisme : USDA NIFA AFRI Translational Genomics for Improved Fertility of Animals
ID : 2013-68004
Informations de copyright
© 2020 The Authors. Animal Genetics published by John Wiley & Sons Ltd on behalf of Stichting International Foundation for Animal Genetics.
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