Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets

Y De Haas, MPL Calus, RF Veerkamp, E Wall, MP Coffey, HD Daetwyler, BJ Hayes, JE Pryce

Research output: Contribution to journalArticle

44 Citations (Scopus)

Abstract

With the aim of increasing the accuracy of genomic estimated breeding values for dry matter intake (DMI) in dairy cattle, data from Australia (AU), the United Kingdom (UK), and the Netherlands (NL) were combined using both single-trait and multi-trait models. In total, DMI records were available on 1,801 animals, including 843 AU growing heifers with records on DMI measured over 60 to 70 d at approximately 200 d of age, and 359 UK and 599 NL lactating heifers with records on DMI during the first 100 d in milk. The genotypes used in this study were obtained from the Illumina Bovine 50K chip (Illumina Inc., San Diego, CA). The AU, UK, and NL genomic data were matched using the single nucleotide polymorphism (SNP) name. Quality controls were applied by carefully comparing the genotypes of 40 bulls that were available in each data set. This resulted in 30,949 SNP being used in the analyses. Genomic predictions were estimated with genomic REML, using ASReml software. The accuracy of genomic prediction was evaluated in 11 validation sets; that is, at least 3 validation sets per country were defined. The reference set (in which animals had both DMI phenotypes and genotypes) was either AU or Europe (UK and NL) or a multi-country reference set consisting of all data except the validation set. When DMI for each country was treated as the same trait, use of a multi-country reference set increased the accuracy of genomic prediction for DMI in UK, but not in AU and NL. Extending the model to a bivariate (AU-EU) or trivariate (AU-UK-NL) model increased the accuracy of genomic prediction for DMI in all countries. The highest accuracies were estimated for all countries when data were analyzed with a trivariate model, with increases of up to 5.5% compared with univariate models within countries.
Original languageEnglish
Pages (from-to)6103 - 6112
Number of pages10
JournalJournal of Dairy Science
Volume95
Publication statusFirst published - 2012

Fingerprint

dry matter intake
dairy cattle
Netherlands
genomics
prediction
single nucleotide polymorphism
heifers
genotype
breeding value
quality control
bulls
animals
phenotype
milk
cattle

Bibliographical note

1023378

Keywords

  • Dry matter intake
  • Genomic prediction
  • Multi-trait genomic REML
  • Validation

Cite this

De Haas, Y ; Calus, MPL ; Veerkamp, RF ; Wall, E ; Coffey, MP ; Daetwyler, HD ; Hayes, BJ ; Pryce, JE. / Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets. In: Journal of Dairy Science. 2012 ; Vol. 95. pp. 6103 - 6112.
@article{b0775cae293049518f382181408bb59b,
title = "Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets",
abstract = "With the aim of increasing the accuracy of genomic estimated breeding values for dry matter intake (DMI) in dairy cattle, data from Australia (AU), the United Kingdom (UK), and the Netherlands (NL) were combined using both single-trait and multi-trait models. In total, DMI records were available on 1,801 animals, including 843 AU growing heifers with records on DMI measured over 60 to 70 d at approximately 200 d of age, and 359 UK and 599 NL lactating heifers with records on DMI during the first 100 d in milk. The genotypes used in this study were obtained from the Illumina Bovine 50K chip (Illumina Inc., San Diego, CA). The AU, UK, and NL genomic data were matched using the single nucleotide polymorphism (SNP) name. Quality controls were applied by carefully comparing the genotypes of 40 bulls that were available in each data set. This resulted in 30,949 SNP being used in the analyses. Genomic predictions were estimated with genomic REML, using ASReml software. The accuracy of genomic prediction was evaluated in 11 validation sets; that is, at least 3 validation sets per country were defined. The reference set (in which animals had both DMI phenotypes and genotypes) was either AU or Europe (UK and NL) or a multi-country reference set consisting of all data except the validation set. When DMI for each country was treated as the same trait, use of a multi-country reference set increased the accuracy of genomic prediction for DMI in UK, but not in AU and NL. Extending the model to a bivariate (AU-EU) or trivariate (AU-UK-NL) model increased the accuracy of genomic prediction for DMI in all countries. The highest accuracies were estimated for all countries when data were analyzed with a trivariate model, with increases of up to 5.5{\%} compared with univariate models within countries.",
keywords = "Dry matter intake, Genomic prediction, Multi-trait genomic REML, Validation",
author = "{De Haas}, Y and MPL Calus and RF Veerkamp and E Wall and MP Coffey and HD Daetwyler and BJ Hayes and JE Pryce",
note = "1023378",
year = "2012",
language = "English",
volume = "95",
pages = "6103 -- 6112",
journal = "Journal of Dairy Science",
issn = "0022-0302",
publisher = "American Dairy Science Association",

}

Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets. / De Haas, Y; Calus, MPL; Veerkamp, RF; Wall, E; Coffey, MP; Daetwyler, HD; Hayes, BJ; Pryce, JE.

In: Journal of Dairy Science, Vol. 95, 2012, p. 6103 - 6112.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets

AU - De Haas, Y

AU - Calus, MPL

AU - Veerkamp, RF

AU - Wall, E

AU - Coffey, MP

AU - Daetwyler, HD

AU - Hayes, BJ

AU - Pryce, JE

N1 - 1023378

PY - 2012

Y1 - 2012

N2 - With the aim of increasing the accuracy of genomic estimated breeding values for dry matter intake (DMI) in dairy cattle, data from Australia (AU), the United Kingdom (UK), and the Netherlands (NL) were combined using both single-trait and multi-trait models. In total, DMI records were available on 1,801 animals, including 843 AU growing heifers with records on DMI measured over 60 to 70 d at approximately 200 d of age, and 359 UK and 599 NL lactating heifers with records on DMI during the first 100 d in milk. The genotypes used in this study were obtained from the Illumina Bovine 50K chip (Illumina Inc., San Diego, CA). The AU, UK, and NL genomic data were matched using the single nucleotide polymorphism (SNP) name. Quality controls were applied by carefully comparing the genotypes of 40 bulls that were available in each data set. This resulted in 30,949 SNP being used in the analyses. Genomic predictions were estimated with genomic REML, using ASReml software. The accuracy of genomic prediction was evaluated in 11 validation sets; that is, at least 3 validation sets per country were defined. The reference set (in which animals had both DMI phenotypes and genotypes) was either AU or Europe (UK and NL) or a multi-country reference set consisting of all data except the validation set. When DMI for each country was treated as the same trait, use of a multi-country reference set increased the accuracy of genomic prediction for DMI in UK, but not in AU and NL. Extending the model to a bivariate (AU-EU) or trivariate (AU-UK-NL) model increased the accuracy of genomic prediction for DMI in all countries. The highest accuracies were estimated for all countries when data were analyzed with a trivariate model, with increases of up to 5.5% compared with univariate models within countries.

AB - With the aim of increasing the accuracy of genomic estimated breeding values for dry matter intake (DMI) in dairy cattle, data from Australia (AU), the United Kingdom (UK), and the Netherlands (NL) were combined using both single-trait and multi-trait models. In total, DMI records were available on 1,801 animals, including 843 AU growing heifers with records on DMI measured over 60 to 70 d at approximately 200 d of age, and 359 UK and 599 NL lactating heifers with records on DMI during the first 100 d in milk. The genotypes used in this study were obtained from the Illumina Bovine 50K chip (Illumina Inc., San Diego, CA). The AU, UK, and NL genomic data were matched using the single nucleotide polymorphism (SNP) name. Quality controls were applied by carefully comparing the genotypes of 40 bulls that were available in each data set. This resulted in 30,949 SNP being used in the analyses. Genomic predictions were estimated with genomic REML, using ASReml software. The accuracy of genomic prediction was evaluated in 11 validation sets; that is, at least 3 validation sets per country were defined. The reference set (in which animals had both DMI phenotypes and genotypes) was either AU or Europe (UK and NL) or a multi-country reference set consisting of all data except the validation set. When DMI for each country was treated as the same trait, use of a multi-country reference set increased the accuracy of genomic prediction for DMI in UK, but not in AU and NL. Extending the model to a bivariate (AU-EU) or trivariate (AU-UK-NL) model increased the accuracy of genomic prediction for DMI in all countries. The highest accuracies were estimated for all countries when data were analyzed with a trivariate model, with increases of up to 5.5% compared with univariate models within countries.

KW - Dry matter intake

KW - Genomic prediction

KW - Multi-trait genomic REML

KW - Validation

M3 - Article

VL - 95

SP - 6103

EP - 6112

JO - Journal of Dairy Science

JF - Journal of Dairy Science

SN - 0022-0302

ER -