Genetic parameters for mid-infrared-spectroscopy-predicted mastitis phenotypes and related traits.

genetic correlation heritability lactoferrin mastitis mid‐infrared spectroscopy somatic cell count

Journal

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
ISSN: 1439-0388
Titre abrégé: J Anim Breed Genet
Pays: Germany
ID NLM: 100955807

Informations de publication

Date de publication:
29 Apr 2024
Historique:
revised: 09 04 2024
received: 01 02 2023
accepted: 14 04 2024
medline: 29 4 2024
pubmed: 29 4 2024
entrez: 29 4 2024
Statut: aheadofprint

Résumé

Genetic improvement of udder health in dairy cows is of high relevance as mastitis is one of the most prevalent diseases. Since it is known that the heritability of mastitis is low and direct data on mastitis cases are often not available in large numbers, auxiliary traits, such as somatic cell count (SCC) are used for the genetic evaluation of udder health. In previous studies, models to predict clinical mastitis based on mid-infrared (MIR) spectral data and a somatic cell count-derived score (SCS) were developed. Those models can provide a probability of mastitis for each cow at every test-day, which is potentially useful as an additional auxiliary trait for the genetic evaluation of udder health. Furthermore, MIR spectral data were used to estimate contents of lactoferrin, a glycoprotein positively associated with immune response. The present study aimed to estimate heritabilities (h

Identifiants

pubmed: 38682760
doi: 10.1111/jbg.12868
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : COMET-Project D4Dairy (872039)

Informations de copyright

© 2024 The Authors. Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd.

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Auteurs

Lisa Rienesl (L)

Institute of Livestock Sciences, University of Natural Resources and Life Sciences, Vienna, Austria.

Birgit Fuerst-Waltl (B)

Institute of Livestock Sciences, University of Natural Resources and Life Sciences, Vienna, Austria.

Gábor Mészáros (G)

Institute of Livestock Sciences, University of Natural Resources and Life Sciences, Vienna, Austria.

Astrid Koeck (A)

ZuchtData EDV-Dienstleistungen GmbH, Vienna, Austria.

Christa Egger-Danner (C)

ZuchtData EDV-Dienstleistungen GmbH, Vienna, Austria.

Nicolas Gengler (N)

Gembloux Agro-Bio Tech, Université de Liège (ULg), Gembloux, Belgium.

Clément Grelet (C)

Walloon Agricultural Research Center (CRA-W), Gembloux, Belgium.

Johann Sölkner (J)

Institute of Livestock Sciences, University of Natural Resources and Life Sciences, Vienna, Austria.

Classifications MeSH