Use of genotype x environment interaction model to accommodate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle

C Yao, G de los Campos, MJ VandeHaar, DM Spurlock, LE Armentano, MP Coffey, Y de Haas, RF Veerkamp, CR Staples, EE Connor, Z Wang, MD Hanigan, RJ Tempelman, KA Weigel

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Feed efficiency in dairy cattle has gained much attention recently. Due to the cost-prohibitive measurement of individual feed intakes, combining data from multiple countries is often necessary to ensure an adequate reference population. It may then be essential to model genetic heterogeneity when making inferences about feed efficiency or selecting efficient cattle using genomic information. In this study, we constructed a marker × environment interaction model that decomposed marker effects into main effects and interaction components that were specific to each environment. We compared environment-specific variance component estimates and prediction accuracies from the interaction model analyses, an across-environment analyses ignoring population stratification, and a within-environment analyses using an international feed efficiency data set. Phenotypes included residual feed intake, dry matter intake, net energy in milk, and metabolic body weight from 3,656 cows measured in 3 broadly defined environments: North America (NAM), the Netherlands (NLD), and Scotland (SAC). Genotypic data included 57,574 single nucleotide polymorphisms per animal. The interaction model gave the highest prediction accuracy for metabolic body weight, which had the largest estimated heritabilities ranging from 0.37 to 0.55. The within-environment model performed the best when predicting residual feed intake, which had the lowest estimated heritabilities ranging from 0.13 to 0.41. For traits (dry matter intake and net energy in milk) with intermediate estimated heritabilities (0.21 to 0.50 and 0.17 to 0.53, respectively), performance of the 3 models was comparable. Genomic correlations between environments also were computed using variance component estimates from the interaction model. Averaged across all traits, genomic correlations were highest between NAM and NLD, and lowest between NAM and SAC. In conclusion, the interaction model provided a novel way to evaluate traits measured in multiple environments in which genetic heterogeneity may exist. This model allowed estimation of environment-specific parameters and provided genomic predictions that approached or exceeded the accuracy of competing within- or across-environment models.
Original languageEnglish
Pages (from-to)2007 - 2016
Number of pages10
JournalJournal of Dairy Science
Volume100
Early online date18 Jan 2017
DOIs
Publication statusFirst published - 18 Jan 2017

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dry matter intake
dairy cattle
feed intake
milk
body weight
genotype
energy
genomics
heritability
feed conversion
prediction
Netherlands
Scotland
single nucleotide polymorphism
phenotype
cows
cattle

Keywords

  • Feed efficiency
  • Genomic selection
  • Interaction model

Cite this

Yao, C ; de los Campos, G ; VandeHaar, MJ ; Spurlock, DM ; Armentano, LE ; Coffey, MP ; de Haas, Y ; Veerkamp, RF ; Staples, CR ; Connor, EE ; Wang, Z ; Hanigan, MD ; Tempelman, RJ ; Weigel, KA. / Use of genotype x environment interaction model to accommodate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle. In: Journal of Dairy Science. 2017 ; Vol. 100. pp. 2007 - 2016.
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Yao, C, de los Campos, G, VandeHaar, MJ, Spurlock, DM, Armentano, LE, Coffey, MP, de Haas, Y, Veerkamp, RF, Staples, CR, Connor, EE, Wang, Z, Hanigan, MD, Tempelman, RJ & Weigel, KA 2017, 'Use of genotype x environment interaction model to accommodate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle', Journal of Dairy Science, vol. 100, pp. 2007 - 2016. https://doi.org/10.3168/jds.2016-11606

Use of genotype x environment interaction model to accommodate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle. / Yao, C; de los Campos, G; VandeHaar, MJ; Spurlock, DM; Armentano, LE; Coffey, MP; de Haas, Y; Veerkamp, RF; Staples, CR; Connor, EE; Wang, Z; Hanigan, MD; Tempelman, RJ; Weigel, KA.

In: Journal of Dairy Science, Vol. 100, 18.01.2017, p. 2007 - 2016.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Use of genotype x environment interaction model to accommodate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle

AU - Yao, C

AU - de los Campos, G

AU - VandeHaar, MJ

AU - Spurlock, DM

AU - Armentano, LE

AU - Coffey, MP

AU - de Haas, Y

AU - Veerkamp, RF

AU - Staples, CR

AU - Connor, EE

AU - Wang, Z

AU - Hanigan, MD

AU - Tempelman, RJ

AU - Weigel, KA

PY - 2017/1/18

Y1 - 2017/1/18

N2 - Feed efficiency in dairy cattle has gained much attention recently. Due to the cost-prohibitive measurement of individual feed intakes, combining data from multiple countries is often necessary to ensure an adequate reference population. It may then be essential to model genetic heterogeneity when making inferences about feed efficiency or selecting efficient cattle using genomic information. In this study, we constructed a marker × environment interaction model that decomposed marker effects into main effects and interaction components that were specific to each environment. We compared environment-specific variance component estimates and prediction accuracies from the interaction model analyses, an across-environment analyses ignoring population stratification, and a within-environment analyses using an international feed efficiency data set. Phenotypes included residual feed intake, dry matter intake, net energy in milk, and metabolic body weight from 3,656 cows measured in 3 broadly defined environments: North America (NAM), the Netherlands (NLD), and Scotland (SAC). Genotypic data included 57,574 single nucleotide polymorphisms per animal. The interaction model gave the highest prediction accuracy for metabolic body weight, which had the largest estimated heritabilities ranging from 0.37 to 0.55. The within-environment model performed the best when predicting residual feed intake, which had the lowest estimated heritabilities ranging from 0.13 to 0.41. For traits (dry matter intake and net energy in milk) with intermediate estimated heritabilities (0.21 to 0.50 and 0.17 to 0.53, respectively), performance of the 3 models was comparable. Genomic correlations between environments also were computed using variance component estimates from the interaction model. Averaged across all traits, genomic correlations were highest between NAM and NLD, and lowest between NAM and SAC. In conclusion, the interaction model provided a novel way to evaluate traits measured in multiple environments in which genetic heterogeneity may exist. This model allowed estimation of environment-specific parameters and provided genomic predictions that approached or exceeded the accuracy of competing within- or across-environment models.

AB - Feed efficiency in dairy cattle has gained much attention recently. Due to the cost-prohibitive measurement of individual feed intakes, combining data from multiple countries is often necessary to ensure an adequate reference population. It may then be essential to model genetic heterogeneity when making inferences about feed efficiency or selecting efficient cattle using genomic information. In this study, we constructed a marker × environment interaction model that decomposed marker effects into main effects and interaction components that were specific to each environment. We compared environment-specific variance component estimates and prediction accuracies from the interaction model analyses, an across-environment analyses ignoring population stratification, and a within-environment analyses using an international feed efficiency data set. Phenotypes included residual feed intake, dry matter intake, net energy in milk, and metabolic body weight from 3,656 cows measured in 3 broadly defined environments: North America (NAM), the Netherlands (NLD), and Scotland (SAC). Genotypic data included 57,574 single nucleotide polymorphisms per animal. The interaction model gave the highest prediction accuracy for metabolic body weight, which had the largest estimated heritabilities ranging from 0.37 to 0.55. The within-environment model performed the best when predicting residual feed intake, which had the lowest estimated heritabilities ranging from 0.13 to 0.41. For traits (dry matter intake and net energy in milk) with intermediate estimated heritabilities (0.21 to 0.50 and 0.17 to 0.53, respectively), performance of the 3 models was comparable. Genomic correlations between environments also were computed using variance component estimates from the interaction model. Averaged across all traits, genomic correlations were highest between NAM and NLD, and lowest between NAM and SAC. In conclusion, the interaction model provided a novel way to evaluate traits measured in multiple environments in which genetic heterogeneity may exist. This model allowed estimation of environment-specific parameters and provided genomic predictions that approached or exceeded the accuracy of competing within- or across-environment models.

KW - Feed efficiency

KW - Genomic selection

KW - Interaction model

U2 - 10.3168/jds.2016-11606

DO - 10.3168/jds.2016-11606

M3 - Article

VL - 100

SP - 2007

EP - 2016

JO - Journal of Dairy Science

JF - Journal of Dairy Science

SN - 0022-0302

ER -