Literature DB >> 24731627

International genetic evaluations for feed intake in dairy cattle through the collation of data from multiple sources.

D P Berry1, M P Coffey2, J E Pryce3, Y de Haas4, P Løvendahl5, N Krattenmacher6, J J Crowley7, Z Wang7, D Spurlock8, K Weigel9, K Macdonald10, R F Veerkamp4.   

Abstract

Feed represents a large proportion of the variable costs in dairy production systems. The omission of feed intake measures explicitly from national dairy cow breeding objectives is predominantly due to a lack of information from which to make selection decisions. However, individual cow feed intake data are available in different countries, mostly from research or nucleus herds. None of these data sets are sufficiently large enough on their own to generate accurate genetic evaluations. In the current study, we collate data from 10 populations in 9 countries and estimate genetic parameters for dry matter intake (DMI). A total of 224,174 test-day records from 10,068 parity 1 to 5 records of 6,957 cows were available, as well as records from 1,784 growing heifers. Random regression models were fit to the lactating cow test-day records and predicted feed intake at 70 d postcalving was extracted from these fitted profiles. The random regression model included a fixed polynomial regression for each lactation separately, as well as herd-year-season of calving and experimental treatment as fixed effects; random effects fit in the model included individual animal deviation from the fixed regression for each parity as well as mean herd-specific deviations from the fixed regression. Predicted DMI at 70 d postcalving was used as the phenotype for the subsequent genetic analyses undertaken using an animal repeatability model. Heritability estimates of predicted cow feed intake 70 d postcalving was 0.34 across the entire data set and varied, within population, from 0.08 to 0.52. Repeatability of feed intake across lactations was 0.66. Heritability of feed intake in the growing heifers was 0.20 to 0.34 in the 2 populations with heifer data. The genetic correlation between feed intake in lactating cows and growing heifers was 0.67. A combined pedigree and genomic relationship matrix was used to improve linkages between populations for the estimation of genetic correlations of DMI in lactating cows; genotype information was available on 5,429 of the animals. Populations were categorized as North America, grazing, other low input, and high input European Union. Albeit associated with large standard errors, genetic correlation estimates for DMI between populations varied from 0.14 to 0.84 but were stronger (0.76 to 0.84) between the populations representative of high-input production systems. Genetic correlations with the grazing populations were weak to moderate, varying from 0.14 to 0.57. Genetic evaluations for DMI can be undertaken using data collated from international populations; however, genotype-by-environment interactions with grazing production systems need to be considered.
Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  confinement; feed intake; grazing; heritability; international collaboration

Mesh:

Year:  2014        PMID: 24731627     DOI: 10.3168/jds.2013-7548

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  10 in total

1.  Introduction of the Modern Methods of Assessing the Breeding Value of Cows in the Selection of Dairy Cattle in the Republic of Kazakhstan.

Authors:  S Abugaliev; L Bupebayeva; R Kulbayev; A Baisabyrova
Journal:  Arch Razi Inst       Date:  2021-12-30

2.  Can We Observe Expected Behaviors at Large and Individual Scales for Feed Efficiency-Related Traits Predicted Partly from Milk Mid-Infrared Spectra?

Authors:  Lei Zhang; Nicolas Gengler; Frédéric Dehareng; Frédéric Colinet; Eric Froidmont; Hélène Soyeurt
Journal:  Animals (Basel)       Date:  2020-05-18       Impact factor: 2.752

3.  Improvement of genomic prediction by integrating additional single nucleotide polymorphisms selected from imputed whole genome sequencing data.

Authors:  Aoxing Liu; Mogens Sandø Lund; Didier Boichard; Emre Karaman; Sebastien Fritz; Gert Pedersen Aamand; Ulrik Sander Nielsen; Yachun Wang; Guosheng Su
Journal:  Heredity (Edinb)       Date:  2019-07-05       Impact factor: 3.821

4.  Effects of Incorporating Dry Matter Intake and Residual Feed Intake into a Selection Index for Dairy Cattle Using Deterministic Modeling.

Authors:  Kerry Houlahan; Flavio S Schenkel; Dagnachew Hailemariam; Jan Lassen; Morten Kargo; John B Cole; Erin E Connor; Silvia Wegmann; Oliveira Junior; Filippo Miglior; Allison Fleming; Tatiane C S Chud; Christine F Baes
Journal:  Animals (Basel)       Date:  2021-04-17       Impact factor: 2.752

5.  Genetic Characterization and Population Connectedness of North American and European Dairy Goats.

Authors:  Marc Teissier; Luiz F Brito; Flavio S Schenkel; Guido Bruni; Pancrazio Fresi; Beat Bapst; Christèle Robert-Granie; Hélène Larroque
Journal:  Front Genet       Date:  2022-06-17       Impact factor: 4.772

6.  International single-step SNPBLUP beef cattle evaluations for Limousin weaning weight.

Authors:  Renzo Bonifazi; Mario P L Calus; Jan Ten Napel; Roel F Veerkamp; Alexis Michenet; Simone Savoia; Andrew Cromie; Jérémie Vandenplas
Journal:  Genet Sel Evol       Date:  2022-09-04       Impact factor: 5.100

7.  Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency.

Authors:  Sunduimijid Bolormaa; Iona M MacLeod; Majid Khansefid; Leah C Marett; William J Wales; Filippo Miglior; Christine F Baes; Flavio S Schenkel; Erin E Connor; Coralia I V Manzanilla-Pech; Paul Stothard; Emily Herman; Gert J Nieuwhof; Michael E Goddard; Jennie E Pryce
Journal:  Genet Sel Evol       Date:  2022-09-06       Impact factor: 5.100

8.  Multiple Country and Breed Genomic Prediction of Tick Resistance in Beef Cattle.

Authors:  Fernando Flores Cardoso; Oswald Matika; Appolinaire Djikeng; Ntanganedzeni Mapholi; Heather M Burrow; Marcos Jun Iti Yokoo; Gabriel Soares Campos; Claudia Cristina Gulias-Gomes; Valentina Riggio; Ricardo Pong-Wong; Bailey Engle; Laercio Porto-Neto; Azwihangwisi Maiwashe; Ben J Hayes
Journal:  Front Immunol       Date:  2021-06-23       Impact factor: 7.561

9.  Semi-supervised learning for genomic prediction of novel traits with small reference populations: an application to residual feed intake in dairy cattle.

Authors:  Chen Yao; Xiaojin Zhu; Kent A Weigel
Journal:  Genet Sel Evol       Date:  2016-11-07       Impact factor: 4.297

10.  A comparative study on rumen ecology of water buffalo and cattle calves under similar feeding regime.

Authors:  Qiyan Wang; Xiaomei Gao; Yunyan Yang; Caixia Zou; Yingbai Yang; Bo Lin
Journal:  Vet Med Sci       Date:  2020-07-13
  10 in total

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