Contributions to methods useful for optimising animal breeding plans [Elektronische Ressource] / vorgelegt von Vinzent Börner

Informations

• Publié par: christian-albrechts-universitat_zu_kiel
• Publié le: 01 janvier 2011
• Nombre de lectures: 17
• Langue: Deutsch
• Poids de l'ouvrage: 1 Mo

Extrait

Aus dem Forschungsbereich Genetik und Biometrie,
des Leibniz Institutes für Nutztierbiologie, Dummerstorf
Contributions to Methods Useful for Optimising Animal
Breeding Plans
Dissertation
zur Erlangung des Doktorgrades
der Agrar- und Ernährungswissenschaftlichen Fakultät
der Christian-Albrechts-Universität zu Kiel
vorgelegt von
Vinzent Börner, MSc.
aus Ueckermünde
Dekanin: Prof. Dr. Karin Schwarz
Erster Berichterstatter: Prof. Dr. Norbert Reinsch
Zweiter Berich Prof. Dr. Georg Thaller
Tag der mündlichen Prüfung: 07.02.2011Gedruckt mit Genehmigung des Dekans der Agrar- und Ernährungswissenschaftlichen
Fakultät der Christian-Albrecht-Universität KielContents
General Introduction 1
1 Gametic Gene Flow Method Accounts for Genomic Imprinting 9
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.2.1 Components of the Gene Flow Method . . . . . . . . . . . . . . . . . . . . 13
1.2.2 Gametic Gene Flow Method . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.2.3 Inbreeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.2.4 Application to a Breeding Programme . . . . . . . . . . . . . . . . . . . . 21
1.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
1.3.1 Genetic Net Present Values . . . . . . . . . . . . . . . . . . . . . . . . . . 27
1.3.2 Inbreeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
1.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
1.4.1 Trait Realisation and Genetic Net Present Values . . . . . . . . . . . . . . 33
1.4.2 Inbreeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
1.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
1.6 Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
1.7 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
1.8 Appendix 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
III Contents
1.9 Appendix 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2 Decorrelated Selection Indices versus Optimum Selection Indices in
Optimising Multistage Dairy Cattle Breeding Schemes regarding Ge-
nomic Selection 45
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
2.2.1 Construction of Selection Indices and the Implementation of GEBV . . . . 50
2.2.2 Genetic Gain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
2.2.3 Breeding Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
2.2.3.1 Breeding Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
2.2.4 Parameter Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.2.5 Maximisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.2.5.1 Combination of Stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.2.5.2 Dynamic Pedigree Information Content . . . . . . . . . . . . . . . . . . . 62
2.2.6 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
2.3.1 Comparison of Methods for Calculating the Genetic Gain . . . . . . . . . 63
2.3.2 Genetic Gain of Optimal Indices . . . . . . . . . . . . . . . . . . . . . . . 72
2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
2.4.1 Comparison of the results for the decorrelated and optimal index . . . . . 72
2.4.2 Results using the optimal index . . . . . . . . . . . . . . . . . . . . . . . . 77
2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
2.6 Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
2.7 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78Contents III
3 Optimisation of Genomic Selection Dairy Cattle Breeding Schemes
regarding different SNP-Chips and Imputing 81
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
3.2.1 Construction of Selection Indices and the Implementation of GEBV . . . . 86
3.2.2 Genetic Gain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
3.2.3 Breeding Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
3.2.4 Breeding Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
3.2.5 Maximisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
3.2.6 Dynamic Pedigree Information Content . . . . . . . . . . . . . . . . . . . 98
3.2.7 Breeding Scheme Similarity Indicator . . . . . . . . . . . . . . . . . . . . . 98
3.2.8 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
3.3.1 Optimisation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
3.3.2 Sensitivity of Maximisation Results to Changes in Accuracy and Cost . . 102
3.3.3 Sensitivity of Results to Changes of Breeding Scheme Struc-
tures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
3.6 Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
3.7 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
3.8 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
General Discussion 121
Gametic Gene Flow Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Genomic Selection in Animal Breeding Plans . . . . . . . . . . . . . . . . . . . . . 122
Decorrelated Selection Indices in Multistage Selection Schemes . . . . . . . . . . . 123IV Contents
Applicability of Maximisation Techniques to Multistage Selection Schemes . . . . 124
Function of Genomic Selection in Multistage Dairy Cattle Breeding Programmes . 126
Biological and Technical Parameters of Dairy Cattle Breeding Program Optimisa-
tions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
Summary 129
Zusammenfassung 133
Bibliography 146
Appendix 147General Introduction
The goal of breeding activities in commercial livestock populations is the increase of the
mean of the genetically based performance capacity concerning one or numerous traits
being summarised in the aggregate genotype via weighting factors. Budgeting breeding
activities is carried out in order to predict this increase per generation and per year if
a certain selection strategy is applied. Furthermore, transforming the genetic gain from
natural units into monetary units due to using economic weights as weighting factors
for the traits in the aggregate genotype, and relating it to the summarised costs of all
the activities, the outcome in terms of the breeding profit can be used to evaluate the
economics of a breeding scheme. Given a certain selection intensity, the genetic gain
is a linear function of the accuracy of breeding value estimates, where this accuracy
relies on two different parameters. First, the amount of information gathered about
the selection candidate, where these information can be phenotypic measurements or
genetic information (direct or marker genotypes) originating from the candidate itself
or its relatives, and the correlations among information sources and with the aggregate
genotype. Second, the capability of the applied statistical model to explain the genetic
variation. Furthermore, since the amount of information is a function of economical
resources as well as of time, budgeting breeding activities has to regard this relation
when optimising selection schemes in order to maximise breeding profit.
The discovery of “genomic imprinting” challenged a major paradigm of animal breeding
12 General Introduction
assuming the effect of an allele on the descendants phenotype as independent of parent-
of-origin. This biochemical mechanism modifies the expression of genes in an individual
due to DNA methylation during gametogenesis in an individual’s parents. This leads
to a partial or complete deactivation of the effect of an affected gene on the phenotype
(Reik et al., 1987; Sapienza et al., 1987). The pattern of this DNA methylation depends
on the sex of the ancestor. Analyses in livestock found imprinted quantitative trait loci
(QTL) and genes (IGF2 in pigs, Callipyge in sheep) as well as significant parts of the
genetic variance to be induced by genomic imprinting, where imprinting affected traits
were related to growth and carcass quality (de Koning et al., 2001a,b, 2000; de Vries
et al., 1994; Stella et al., 2003). Thus, accounting for genomic imprinting in genetic
evaluationsforsuchtraitsleadstoincreasedaccuracyofbreedingvaluesand