Genetic improvement of feed intake and methane emissions of cattle

Coralia I.V. Manzanilla Pech

Research output: Thesisinternal PhD, WUAcademic

Abstract

Feed costs represent half of the total costs of dairy production. One way to increase profitability of dairy production is to reduce feed costs by improving feed efficiency. As DMI is a trait that varies significantly during and across lactations, it is imperative to understand the underlying genetic architecture of DMI across lactation. Moreover, phenotypes of DMI are scarce, due to the difficulty of recording them (expensive and labor-intensive). Some predictor traits have been suggested to predict DMI. Examples of these predictor traits are those related to production (milk yield (MY) or milk content) or to the maintenance of the cow (body weight (BW) or conformation traits). The ability to determine when predictor traits ideally should be measured in order to achieve an accurate prediction of DMI throughout the whole lactation period is thus important. Recently, with the use of information of single nucleotide polymorphism (SNP) markers, together with phenotypic data and pedigree, genomically estimated breeding values (GEBV) of scarcely recorded traits, such as DMI, have become easier to accurately predict. This approach, combined with predictor traits, could contribute to an increased accuracy of predictions of GEBV of DMI. Methane (CH4) is the second important greenhouse gas, and enteric CH4 is the largest source of anthropogenic CH4, representing 17% of global CH4  emissions. Furthermore, methane emissions represent 2-12% of feed energy losses. Selecting for lower CH4 emitting animals and more feed-efficient animals would aid in mitigating global CH4 emissions. To identify the impact on CH4 emissions of selecting for lower DMI animals, it is important to determine the correlations between DMI and CH4 and to identify whether the same genes that control DMI affect CH4. Therefore, the general objectives of this thesis were to (1) explore the genetic architecture of DMI during lactation, (2) study the relationship of DMI to conformation, production and other related traits, (3) investigate the correlations between DMI and methane traits, and determine the SNP in common between DMI and CH4 through a genome-wide association study (GWAS), and (4) investigate the accuracy of predictions of DMI using predictor traits combined with genomic data.

LanguageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Wageningen University
Supervisors/Advisors
  • Veerkamp, Roel, Promotor
  • de Haas, Yvette, Co-promotor
Award date22 Mar 2017
Place of PublicationWageningen
Publisher
Print ISBNs9789463430692
DOIs
Publication statusPublished - 2017

Fingerprint

methane
genetic improvement
feed intake
cattle
lactation
breeding value
single nucleotide polymorphism
prediction
milk production
animals
body conformation
greenhouse gases
pedigree
profitability
milk yield
labor
feed conversion
genomics
phenotype
cows

Keywords

  • cattle
  • feed intake
  • methane production
  • genetic improvement
  • genetic parameters
  • conformation
  • breeding value
  • animal genetics

Cite this

Manzanilla Pech, C. I. V. (2017). Genetic improvement of feed intake and methane emissions of cattle. Wageningen: Wageningen University. https://doi.org/10.18174/403342
Manzanilla Pech, Coralia I.V.. / Genetic improvement of feed intake and methane emissions of cattle. Wageningen : Wageningen University, 2017. 199 p.
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title = "Genetic improvement of feed intake and methane emissions of cattle",
abstract = "Feed costs represent half of the total costs of dairy production. One way to increase profitability of dairy production is to reduce feed costs by improving feed efficiency. As DMI is a trait that varies significantly during and across lactations, it is imperative to understand the underlying genetic architecture of DMI across lactation. Moreover, phenotypes of DMI are scarce, due to the difficulty of recording them (expensive and labor-intensive). Some predictor traits have been suggested to predict DMI. Examples of these predictor traits are those related to production (milk yield (MY) or milk content) or to the maintenance of the cow (body weight (BW) or conformation traits). The ability to determine when predictor traits ideally should be measured in order to achieve an accurate prediction of DMI throughout the whole lactation period is thus important. Recently, with the use of information of single nucleotide polymorphism (SNP) markers, together with phenotypic data and pedigree, genomically estimated breeding values (GEBV) of scarcely recorded traits, such as DMI, have become easier to accurately predict. This approach, combined with predictor traits, could contribute to an increased accuracy of predictions of GEBV of DMI. Methane (CH4) is the second important greenhouse gas, and enteric CH4 is the largest source of anthropogenic CH4, representing 17{\%} of global CH4  emissions. Furthermore, methane emissions represent 2-12{\%} of feed energy losses. Selecting for lower CH4 emitting animals and more feed-efficient animals would aid in mitigating global CH4 emissions. To identify the impact on CH4 emissions of selecting for lower DMI animals, it is important to determine the correlations between DMI and CH4 and to identify whether the same genes that control DMI affect CH4. Therefore, the general objectives of this thesis were to (1) explore the genetic architecture of DMI during lactation, (2) study the relationship of DMI to conformation, production and other related traits, (3) investigate the correlations between DMI and methane traits, and determine the SNP in common between DMI and CH4 through a genome-wide association study (GWAS), and (4) investigate the accuracy of predictions of DMI using predictor traits combined with genomic data.",
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author = "{Manzanilla Pech}, {Coralia I.V.}",
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Manzanilla Pech, CIV 2017, 'Genetic improvement of feed intake and methane emissions of cattle', Doctor of Philosophy, Wageningen University, Wageningen. https://doi.org/10.18174/403342

Genetic improvement of feed intake and methane emissions of cattle. / Manzanilla Pech, Coralia I.V.

Wageningen : Wageningen University, 2017. 199 p.

Research output: Thesisinternal PhD, WUAcademic

TY - THES

T1 - Genetic improvement of feed intake and methane emissions of cattle

AU - Manzanilla Pech, Coralia I.V.

N1 - WU thesis 6604 Includes bibliographic references. - With summary in English

PY - 2017

Y1 - 2017

N2 - Feed costs represent half of the total costs of dairy production. One way to increase profitability of dairy production is to reduce feed costs by improving feed efficiency. As DMI is a trait that varies significantly during and across lactations, it is imperative to understand the underlying genetic architecture of DMI across lactation. Moreover, phenotypes of DMI are scarce, due to the difficulty of recording them (expensive and labor-intensive). Some predictor traits have been suggested to predict DMI. Examples of these predictor traits are those related to production (milk yield (MY) or milk content) or to the maintenance of the cow (body weight (BW) or conformation traits). The ability to determine when predictor traits ideally should be measured in order to achieve an accurate prediction of DMI throughout the whole lactation period is thus important. Recently, with the use of information of single nucleotide polymorphism (SNP) markers, together with phenotypic data and pedigree, genomically estimated breeding values (GEBV) of scarcely recorded traits, such as DMI, have become easier to accurately predict. This approach, combined with predictor traits, could contribute to an increased accuracy of predictions of GEBV of DMI. Methane (CH4) is the second important greenhouse gas, and enteric CH4 is the largest source of anthropogenic CH4, representing 17% of global CH4  emissions. Furthermore, methane emissions represent 2-12% of feed energy losses. Selecting for lower CH4 emitting animals and more feed-efficient animals would aid in mitigating global CH4 emissions. To identify the impact on CH4 emissions of selecting for lower DMI animals, it is important to determine the correlations between DMI and CH4 and to identify whether the same genes that control DMI affect CH4. Therefore, the general objectives of this thesis were to (1) explore the genetic architecture of DMI during lactation, (2) study the relationship of DMI to conformation, production and other related traits, (3) investigate the correlations between DMI and methane traits, and determine the SNP in common between DMI and CH4 through a genome-wide association study (GWAS), and (4) investigate the accuracy of predictions of DMI using predictor traits combined with genomic data.

AB - Feed costs represent half of the total costs of dairy production. One way to increase profitability of dairy production is to reduce feed costs by improving feed efficiency. As DMI is a trait that varies significantly during and across lactations, it is imperative to understand the underlying genetic architecture of DMI across lactation. Moreover, phenotypes of DMI are scarce, due to the difficulty of recording them (expensive and labor-intensive). Some predictor traits have been suggested to predict DMI. Examples of these predictor traits are those related to production (milk yield (MY) or milk content) or to the maintenance of the cow (body weight (BW) or conformation traits). The ability to determine when predictor traits ideally should be measured in order to achieve an accurate prediction of DMI throughout the whole lactation period is thus important. Recently, with the use of information of single nucleotide polymorphism (SNP) markers, together with phenotypic data and pedigree, genomically estimated breeding values (GEBV) of scarcely recorded traits, such as DMI, have become easier to accurately predict. This approach, combined with predictor traits, could contribute to an increased accuracy of predictions of GEBV of DMI. Methane (CH4) is the second important greenhouse gas, and enteric CH4 is the largest source of anthropogenic CH4, representing 17% of global CH4  emissions. Furthermore, methane emissions represent 2-12% of feed energy losses. Selecting for lower CH4 emitting animals and more feed-efficient animals would aid in mitigating global CH4 emissions. To identify the impact on CH4 emissions of selecting for lower DMI animals, it is important to determine the correlations between DMI and CH4 and to identify whether the same genes that control DMI affect CH4. Therefore, the general objectives of this thesis were to (1) explore the genetic architecture of DMI during lactation, (2) study the relationship of DMI to conformation, production and other related traits, (3) investigate the correlations between DMI and methane traits, and determine the SNP in common between DMI and CH4 through a genome-wide association study (GWAS), and (4) investigate the accuracy of predictions of DMI using predictor traits combined with genomic data.

KW - cattle

KW - feed intake

KW - methane production

KW - genetic improvement

KW - genetic parameters

KW - conformation

KW - breeding value

KW - animal genetics

KW - rundvee

KW - voeropname

KW - methaanproductie

KW - genetische verbetering

KW - genetische parameters

KW - bouw (dier)

KW - fokwaarde

KW - diergenetica

U2 - 10.18174/403342

DO - 10.18174/403342

M3 - internal PhD, WU

SN - 9789463430692

PB - Wageningen University

CY - Wageningen

ER -

Manzanilla Pech CIV. Genetic improvement of feed intake and methane emissions of cattle. Wageningen: Wageningen University, 2017. 199 p. https://doi.org/10.18174/403342