Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle.

dry matter intake energy corrected milk feed efficiency metabolic body weight

Journal

Journal of dairy science
ISSN: 1525-3198
Titre abrégé: J Dairy Sci
Pays: United States
ID NLM: 2985126R

Informations de publication

Date de publication:
08 Sep 2023
Historique:
received: 07 12 2022
accepted: 05 08 2023
medline: 11 9 2023
pubmed: 11 9 2023
entrez: 10 9 2023
Statut: aheadofprint

Résumé

Feed efficiency has become an increasingly important research topic in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to measure accurately on large populations, making the inclusion of this trait in breeding programs difficult. Understanding how the genetic parameters of feed efficiency and traits related to feed efficiency vary throughout the lactation period is valuable to gain understanding into the genetic nature of feed efficiency. This study used 121,226 dry matter intake (DMI) records, 120,500 energy corrected milk (ECM) records, and 98,975 metabolic body weight (MBW) records, collected on 7,440 first lactation Holstein cows from 6 countries (Canada, Denmark, Germany, Spain, Switzerland, and United States of America), from January 2003 to February 2022. Genetic parameters were estimated using a multiple-trait random regression model with a fourth order Legendre polynomial for all traits. Weekly phenotypes for DMI were re-parameterized using linear regressions of DMI on ECM and MBW, creating a measure of feed efficiency that was genetically corrected for ECM and MBW, referred to as genomic residual feed intake (gRFI). Heritability (SE) estimates varied from 0.15 (0.03) to 0.29 (0.02) for DMI, 0.24 (0.01) to 0.29 (0.03) for ECM, 0.55 (0.03) to 0.83 (0.05) for MBW, and 0.12 (0.03) to 0.22 (0.06) for gRFI. In general, heritability estimates were lower in the first stage of lactation compared with the later stages of lactation. Additive genetic correlations between weeks of lactation varied, with stronger correlations between weeks of lactation that were close together. The results of this study contribute to a better understanding of the change in genetic parameters across the first lactation, providing insight into potential selection strategies to include feed efficiency in breeding programs.

Identifiants

pubmed: 37690722
pii: S0022-0302(23)00620-3
doi: 10.3168/jds.2022-23124
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2023, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Auteurs

K Houlahan (K)

Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada, N1G 2W1.

F S Schenkel (FS)

Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada, N1G 2W1.

F Miglior (F)

Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada, N1G 2W1; Lactanet, Guelph, Ontario, Canada, N1K 1E5.

J Jamrozik (J)

Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada, N1G 2W1; Lactanet, Guelph, Ontario, Canada, N1K 1E5.

R B Stephansen (RB)

Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark.

O González-Recio (O)

Departamento de Producción Animal, E.T.S.I. Agrónomos, Universidad Politécnica, Ciudad Universitaria s/n, 28040 Madrid, Spain.

N Charfeddine (N)

CONAFE, Valdemoro, 28340, Madrid, Spain.

D Segelke (D)

Vereinigte Informationssysteme Tierhaltung w.V. 27283 Verden / Aller.

A M Butty (AM)

Qualitas AG, 6300 Zug, Switzerland.

P Stratz (P)

Qualitas AG, 6300 Zug, Switzerland.

M J VandeHaar (MJ)

Department of Animal Science, Michigan State University, East Lansing, Michigan, United States of America, 48824.

R J Tempelman (RJ)

Department of Animal Science, Michigan State University, East Lansing, Michigan, United States of America, 48824.

K Weigel (K)

Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States of America, 53706.

H White (H)

Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States of America, 53706.

F Peñagaricano (F)

Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States of America, 53706.

J E Koltes (JE)

Department of Animal Science, Iowa State University, Ames, IA, United States of America, 50011.

J E P Santos (JEP)

Department of Animal Sciences, University of Florida, Gainsville, FL, United States of America, 32611.

R L Baldwin (RL)

Animal Genomics and Improvement Lab, USDA, Beltsville, MD, 207052350.

C F Baes (CF)

Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada, N1G 2W1; Institute of Genetics, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland. Electronic address: cbaes@uoguelph.ca.

Classifications MeSH