Implementing Genomic Information in Breeding · MSc Thesis in Agrobiology 45 ECTS Faculty of...

Implementing Genomic Information in Breeding

Schemes of Danish Warmblood Horses

MSc Thesis in Agrobiology

45 ECTS

Faculty of Science and Technology, Department of Molecu lar Biology and Genetics - Centre for Quantitative Genetics and Genomics, Aarhus Un iversity

By

Sophie Axelle Grønnegård Favrelle

Student ID: 201205357 / AU451846

Main supervisor: Senior Researcher Anders Christian Sørensen, Department of Molecular Biology and Genetics, Aarhus University

Co-supervisor: Breeding Advisor Karina Christiansen, the D anish Warmblood Association

June 2017

Preface

This master thesis is conducted based on data provided by the Danish Warmblood Association and the horse section at SEGES. Thanks to my supervisor, Anders Christian Sørensen, for very helpful guidance and commitment for the project. Thanks to Karina Christiansen for useful comments and clarifications of the breeding scheme in the Danish Warmblood Association, and thanks to Maiken Holm for providing me with data. Thanks to Aarhus University and the Danish Warmblood Associa-tion, for the opportunity to make this very exciting project. Last, but not least, thanks to all my fellow students for support, help and knowledge sharing during the conduction of this thesis.

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Abstract Horse breeding is characterized by long generation intervals as consequence of late availability of records on performance traits. Selecting young horses for breeding is therefore associated with some uncertainty. Genomic selection has in other species proved to be advantageous for reduction in generation intervals and increases in accuracies of young animals, thereby improving the genet-ic gain. Therefore, it is interesting to see what potential genomic selection could have in horses. The objective of this thesis was to evaluate the potential of genomic selection for improving the current breeding schemes of Danish Warmblood dressage and show jumping horses. In a literature study, basic animal breeding theory and theory behind different selection ap-proaches is presented, followed by an assessment of challenges in current horse breeding schemes and prospect for genomic selection. This is followed by a thorough descriptive analysis of current breeding scheme. Number of foals born each year variated a lot, but in average the past 10 years, 1622 dressage foals and 619 show jumping foals were born each year. Among the stallions approx-imately 2 %, and for the mares approximately 50 %, ended up being selected for breeding. Selec-tion decisions was founded mainly on phenotypic selection decisions. The breeding scheme was characterized by long utilization periods and large differences in extent of use for stallions, and late unset of reproductive career for mares, resulting in generation intervals of around 10 years in average. These results were used to establish a population structure forming the basis for stochas-tic simulations of different scenarios. In total 16 scenarios were simulated for each of the two Danish Warmblood populations, the first one representing current selection practice. The simulation results indicated potential increases of genetic gain of up to 90 %, just by selecting based on estimated breeding values (EBVs) with best linear unbiased predictions (BLUP) instead of phenotypic selection. Additional gains of 30 % were achieved when using genomic selection on 3-year old stallions, assuming accuracies of 0.6 on the SNP-genotypes. High increases in genetic gain were also found by selecting mares based on BLUP-EBVs instead of randomly, and even higher increases when using GS combined with reproductive techniques as embryo transfer. Rates of in-breeding were found to increase more at low accuracies of SNP-genotypes, compared to high. Osteochondrosis was incorporated as one of the indicator traits for the breeding goal traits, and assuming only weak, but favourable genetic correlations, selection towards the breeding goal traits, showed to improve the susceptibility to osteochondrosis. The maximum reduction in generation interval was found to be 6 years, but this was not a result of implementing genomic selection only. The success of GS depends however not only of its potential. Willingness and acceptance towards changes in the breeding scheme among breeders are crucial. Breeding values are encouraged to be published early enough to use them for selection decisions, and different suggestion for increasing the accuracy of selection are proposed. Implementation of GS necessitates additional strategies ensuring continuous genetic variation due to increased rate of inbreeding shown in this study.

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Table of Contents 1 Introduction .................................................................................................................................. 6

2 Abbreviations and notations ........................................................................................................ 8

3 Selection practices in animal breeding schemes and potential of using genomic information .. 9

3.1 Phenotypic selection in animal breeding .............................................................................. 9

3.1.1 Basic breeding theory .................................................................................................... 9

3.1.2 Horse breeding schemes and goals ............................................................................. 10

3.1.3 Estimated breeding values with the Best Linear Unbiased Prediction ........................ 11

3.2 Selection based on genotypes ............................................................................................. 12

3.2.1 Marker assisted selection ............................................................................................ 12

3.2.2 Genomic selection ........................................................................................................ 13

3.3 Incorporating genomic selection into breeding schemes of horses ................................... 15

3.3.1 Optimizing genetic gain in horses ................................................................................ 16

3.3.2 Enhancing the control of inbreeding ........................................................................... 19

3.3.3 Additional advantages of genomic selection in the future .......................................... 19

3.3.4 Challenges of implementing genomic selection in horse breeding schemes .............. 20

4 Current selection practice in the Danish Warmblood Association ............................................ 22

4.1 Breeding goal ....................................................................................................................... 22

4.2 Selection practice ................................................................................................................ 22

4.2.1 Selection of stallions .................................................................................................... 23

4.2.2 Selection of mares ........................................................................................................ 25

4.3 Descriptive analysis ............................................................................................................. 29

4.3.1 Material and methods ................................................................................................. 29

4.3.2 Results .......................................................................................................................... 29

4.4 Simulating current selection practice ................................................................................. 35

5 Paper manuscript ........................................................................................................................ 41

Abstract .......................................................................................................................................... 41

Introduction .................................................................................................................................... 41

Material and methods .................................................................................................................... 42

Genetic evaluation ...................................................................................................................... 45

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Genetic parameters .................................................................................................................... 46

Population structure ................................................................................................................... 46

Traits evaluated .......................................................................................................................... 48

The simulation program ............................................................................................................. 50

Statistical evaluation ................................................................................................................... 50

Results ............................................................................................................................................ 51

The dressage population ............................................................................................................ 51

The show jumping population .................................................................................................... 52

Discussion ....................................................................................................................................... 58

The value of BLUP-EBVs .............................................................................................................. 58

Genotypes as an extra source of information ............................................................................ 60

Taking advantage of the maternal pathway ............................................................................... 63

Reducing the generation interval ............................................................................................... 64

Changes in rates of inbreeding ................................................................................................... 65

Osteochondrosis as indicator trait ............................................................................................. 67

Conclusion ...................................................................................................................................... 68

References ...................................................................................................................................... 68

6 General discussion ...................................................................................................................... 72

6.1 Realizing greater genetic gain prior the implementation of genomic selection ................ 72

6.2 Prospects for genomic selection in Danish Warmblood breeding schemes ....................... 75

7 General conclusion ..................................................................................................................... 79

8 References .................................................................................................................................. 80

9 Appendices ................................................................................................................................. 87

9.1 Appendix I. The breeding goal for Danish Warmblood horses ........................................... 87

9.2 Appendix II. Linear profile scheme for Danish Warmblood dressage horses ..................... 88

9.3 Appendix III. Linear profile scheme for Danish Warmblood show jumping horses............ 89

1 Introduction

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1 Introduction The use of genomic information in animal breeding schemes to select parents for next generation, have within the last decade evolved significantly (Stock and Reents, 2013). The approach was pro-posed by Meuwissen et al. (2001) and the concept is called genomic selection (GS). Genomic selec-tion has proven to be highly beneficial in a number of different species due to the potential of in-creasing the accuracy of estimated breeding values (EBVs) early in life, and decrease the generation interval, thereby improving the genetic gain (Schefers and Weigel, 2012; Meuwissen et al., 2016). In horse breeding schemes, GS has not been implemented yet, even though it is expected to be advantageous for this species as well as it has been in others (Stock et al., 2016). The Danish Warm-blood Association (DWB) is committed to alter this as GS has the potential to improve the genetic gain significantly (Mark et al., 2014). This is due to genomic information being available early in life, contrary to the conventional EBVs, which are not accurate enough, before the horse has reached the age of around ten years (Haberland et al., 2012a). Currently, DWB is placed 5th and 19th on the studbook ranking lists of The World Breeding Federation for Sport Horses (WBSFH) in dressage and show jumping, respectively (WBFSH, 2016). The Danish Warmblood Association see GS as an opportunity to improve the genetic gain, and strengthen their position on an international market, where certain people are willing to spend an amount in the double-digit million range (DKK) to get the best genetics. Besides, if not implementing GS, risk of losing market share and move down the ranking lists exists, when other warmblood associations implements GS in the future. Therefore, DWB is open-minded towards the implementation of GS, and “The GenHorse Project”, where 500 of the most informative Danish Warmblood horses were genotyped to investigate the potential of GS in the Danish Warmblood breeding schemes, was therefore established (Mark et al., 2014). The potential of implementing GS in riding horse breeding schemes have just in recent years started to be investigated. Until now, only few publications on the subject exists, and no riding horse breeding associations have yet implemented GS routinely in their breeding schemes. The objective of this thesis, was therefore to investigate further the poten-tial of implementing GS in Danish Warmblood horse breeding schemes on genetic gains, generation intervals and rates of inbreeding. Different scenarios of implementing GS is assessed by stochastic simulations and compared to current breeding scheme in DWB. The hypothesis is that genomic in-formation can be used to improve the genetic gain in the Danish Warmblood population, and make better conditions for controlling the rate of inbreeding. Furthermore, GS is expected to enable more effective use of the maternal pathway. The results are likely to be useful for DWB in determining how to implement GS, and to prepare for changes in the breeding scheme, which are possibly pre-requisites for the success of GS in the future. Danish Warmblood dressage and show jumping horses are in the thesis treated as two separate populations since their breeding goals are not the same. The thesis is delimited to deal with genetic characteristics only, and does therefore not consider economic aspects of implementing GS. The thesis is divided into four main parts. First, a literature study, where basic animal breeding the-ory and the theory behind selection based on phenotypic and genomic information are presented.

1 Introduction

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This is followed by a review on the prospects of implementing genomic selection in horse breeding schemes and existing literature on the subject (section 3). Second, a thorough description on the current selection practice in DWB is made, supported by descriptive statistics on real data received from the horse section at SEGES (The Danish Knowledge Centre for Agriculture) (section 4). Third part, consists of a paper manuscript, where the simulation study and the different elements forming the basis for the simulations are presented, followed by a presentation and discussion of the simu-lation results (section 5). The fourth and last part, comprises a general discussion of the three pre-ceding parts, with focus on the potential to implement genomic selection in the breeding schemes of Danish Warmblood horses (section 6). The final part is completed with an overall conclusion (sec-tion 7).

2 Abbreviations and notations

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2 Abbreviations and notations BLUP Best linear unbiased prediction DWB The Danish Warmblood Association EBV Estimated breeding value GEBV Genomic estimated breeding value GI Generation interval GS Genomic selection IBD Identical by descent IBS Identical by state LD Linkage disequilibrium LSD Least significant difference MAS Marker assisted selection MME Mixed model equation OC Susceptibility to osteochondrosis QTL Quantitative trait loci PD Performance in high-level dressage competition PS Performance in high-level show jumping competition Sire+ Stallion, sire to 0.5 % or more of the total number of offspring born in a year. Sire- Stallion, sire to less than 0.5 % of the total number of offspring born in a year. SNP Single nucleotide polymorphisms TBV True breeding value YC Young horse conformation YD Young horse dressage ability YS Young horse show jumping ability 𝐹 Inbreeding coefficient ∆𝐹 Rate of inbreeding ∆𝐺 Genetic gain ℎ2 Heritability 𝐼 Total merit index 𝑖 Selection intensity 𝐿 Generation interval 𝑟𝐴𝐼 Accuracy between true and estimated breeding value 𝑟𝑔 Genetic correlation 𝜎𝑎 Additive genetic standard deviation 𝑉𝑎𝑟 Genetic variance 𝑤 Weight of a given trait in the index

3 Selection practices in animal breeding schemes and potential of using genomic information

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3 Selection practices in animal breeding schemes and potential of using ge-nomic information

3.1 Phenotypic selection in animal breeding

3.1.1 Basic breeding theory The aim of animal breeding schemes is to achieve genetic gain in a pre-defined direction, specified by a breeding goal (Groen et al., 1997). This can be accomplished through selection of individuals in the population, who are superior to the average of their parents, and approximates the goal the most. If done properly, selection will result in genetic gain. When animal breeders wish to obtain genetic gain in certain traits, they often select based on visual, or at least measurable, expressions of the traits, known as phenotypes. But what is being exposed for selection are the underlying genes that codes for the phenotypes; the genotypes. The genotypes are already established at conception, and thus cannot be changed. Environmental factors are another component, besides genotypes that determines how the phenotype is expressed, and contrary to genotypes, environmental factors can be changed. Thus, environmental factors can be responsible for a significant part of the phenotypic expression, and should therefore be accounted for in the evaluation of phenotypes (Tolley, 1984). Selection intensity, genetic standard deviation, accuracy of selection and generation interval, are all parameters that affects the genetic gain (Burns et al., 2004) (see equation 3.1). The selection inten-sity reflects the difference between the mean of selected parents for next generation, and the pop-ulation mean in units of the standard normal distribution. The accuracy reflects the correlation be-tween the EBV and the true breeding value (TBV) (Weller, 2016). The genetic standard deviation reflects the range of possible values for a given trait (Schefers and Weigel, 2012), and the generation interval reflects the mean age of the parents, when they contribute to the next generation of indi-viduals in the population (Tolley, 1984).

∆𝐺𝑝𝑒𝑟 𝑦𝑒𝑎𝑟 = 𝑖∙𝑟𝐴𝐼∙𝜎𝑎𝐿 (3.1)

where ∆𝐺𝑝𝑒𝑟 𝑦𝑒𝑎𝑟 is the genetic gain per year, 𝑖 is the selection intensity, 𝑟𝐴𝐼 is the accuracy between TBV and EBV, 𝜎𝑎 is the additive genetic standard deviation, 𝐿 is the generation interval

(Falconer and Mackay, 1996).

Another component affecting the genetic gain is the heritability. The heritability reflects how much of the phenotypic variation that can be explained by the genotypic variation, and thus how much of the variation is inherited from the parents. If the heritability is high, the potential for genetic gain is high, and contrary if it is low, it will be hard to achieve genetic gain through selection (Tolley, 1984). Some of the parameters included in the equation for genetic gain are easier to change than others, and changing them may alter the genetic gain, but can simultaneously affect other parameters in


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undesirable ways, e.g. increasing the inbreeding level (Tolley, 1984). Thus, animal breeding is about achieving genetic gain in ways, where undesirable factors are minimized.

3.1.2 Horse breeding schemes and goals A prerequisite for selection practices to be well-functioning is to have a breeding goal which is easy to understand and accepted among practitioners and users (Dekkers and Gibson, 1998), e.g. breed-ers and riders. A breeding goal should optimally, specifically define the traits and their relative value, wherein genetic gain is desired (Árnason and Van Vleck, 2000). Breeding associations should there-fore agree on a specific and well-defined breeding goal, where important traits and possibly also economically important traits are included. In horse breeding it is not easy to define traits very spe-cific though, due to many traits being recorded subjectively as they are not easy to measure directly (Koenen et al., 2004). When the traits are not recorded in the same way, both within and between breeding associations, it complicates matters when breeding towards the goal, and when comparing breeding goals with other breeding associations (Koenen and Aldridge, 2002). Koenen et al. (2004) found that definitions of breeding goals often are incomplete and not reflecting the true selection practice in European Warmblood associations. They explained it to be caused by traits being hard to measure and record consistently. When the breeding goal is not easy to understand, breeders will have difficulties in selecting the most ideal animals for breeding (Koenen and Aldridge, 2002). In most animal breeding schemes the breeding goal includes economically weighted traits to max-imize the genetic gain (Weller, 2016). Commonly, economic weights are calculated so they reflect the cost and returns in a production system without any considerations of genetic parameters. This is not the case in most horse breeding schemes though, where derivation of economic weights only have received minor scientific attention (Árnason and Van Vleck, 2000). Reasons for this could be that horse breeding often is carried out on hobby level. Therefore, the requirement to earn signifi-cant profit becomes quite low (SLU, 2001). Thus, scientific effort in this area is not prioritized. Fur-thermore, as horse owners are not directly being paid according to e.g. the height a horse can jump, or how elastic the trot is, as dairy farmers are paid directly for milk yield, it is difficult to determine the value of one unit of expression in many riding horse traits (Árnason and Van Vleck, 2000). Eco-nomic weightings might therefore not be the perfect way to base the selection on (Koenen et al., 2004). Relative weightings based on the importance of the traits in relation to the breeding goal, could be a way to overcome this fact that not all traits can be assigned economic weights. This ap-proach is commonly known as selection based on desired gains, and was presented first time by Yamada et al. (1975). The desired gains approach necessitates pre-chosen relative values of genetic change in all traits included in the breeding goal (Árnason and Van Vleck, 2000), and that may be at least as challenging as defining economic weights. Koenen et al. (2004) found by means of question-naires that many traits were assigned high relative weightings by European warmblood associations, even though the traits were not necessarily included in the verbally described breeding goal. This implies that relative weightings of traits in the breeding goals of riding horses may not always be in accordance with the breeding goal presented for the breeders, and thus is difficult to handle. Nev-ertheless, correct relative weightings of the traits can have an important effect on the genetic gain


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and should therefore be prioritized. In the breeding scheme the direction of the breeding goal is however still the most important (Vandespitte and Hazel, 1977). Árnason and Van Vleck (2000) pro-poses that if derivation of weights is not possible, breeders should be provided with EBVs on all possible traits that may be included in the breeding goal, so they have the opportunity to define their own breeding goal and thereby evaluate the horses according to that. An approximation to the aggregate genotype, which should reflect the breeding goal, relating EBVs and weights of important traits, can be formulated as in equation 3.2.

𝐼 = 𝑤1 ∙ 𝐸𝐵𝑉1 + 𝑤2 ∙ 𝐸𝐵𝑉2 + ⋯+ 𝑤𝑖 ∙ 𝐸𝐵𝑉𝑖 (3.2)

where 𝐼 is the total merit index reflecting the breeding goal, 𝑤𝑖 is the weight of the 𝑖’th trait, 𝐸𝐵𝑉𝑖 is the estimated breeding value for the 𝑖’th trait

(Dekkers and Gibson, 1998).

It is important to note that the traits included in the calculation of EBVs does not necessarily have to be the same as the ones included in the aggregate genotype (Dekkers and Gibson, 1998). This is due to some traits being impossible to measure early in life, and therefore other highly correlated traits, measured early in life, are included in the calculation of EBVs instead.

3.1.3 Estimated breeding values with the Best Linear Unbiased Prediction The EBVs of domesticated animals are traditionally based on phenotype and pedigree information (Meuwissen et al., 2001), and derived from the Best Linear Unbiased Prediction (BLUP) (Burns et al., 2004). The BLUP methodology is generally a further development of the previously developed se-lection index theory, but is more complex as it includes mixed linear model equations (MMEs) (Boichard et al., 2016). The difference is that BLUP can estimate fixed effects on a trait, such as the rider’s effect on a horse’s capacity to jump, and breeding values simultaneously (therefore “Unbi-ased”). Contrary, in the selection index, fixed effects have to be assumed before breeding values can be estimated, even though the fixed effects are rarely known beforehand (Mrode, 2013). When using BLUP to calculate EBVs (BLUP-EBVs), phenotypes of the animal itself and the phenotypes of its relatives are incorporated into an index, which are used in the selection schemes (Boichard et al., 2016). Each animal is given an index by which they are ranked according to the ones having the greatest response to selection for a given selection intensity (Weller, 2016). Selection can then be based on those who have the highest values. BLUP-EBVs are calculated as solution for MMEs, and the model for a single trait can be given as equation 3.3. Breeding goals often consist of multiple traits though. The advantage of multiple trait models over single trait models is that phenotypic and genotypic correlations between the traits are accounted for. This can be very useful information when traits, as previously mentioned, only can be recorded late in life. As a results of accounting for the correlations, reliabilities increase (Mrode, 2013), and gain from selection can be expected (Gengler and Coenraets, 1997).

𝑦𝑖𝑗 = 𝑋𝑏𝑖 + 𝑍𝑎𝑗 + 𝑒𝑖𝑗 (3.3)


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where 𝑦𝑖𝑗 is the phenotypic observation on the 𝑗’th individual belonging to the 𝑖’th level of fixed effects affecting the trait,

𝑏𝑖 is the deviation from the population caused by the fixed effects of the 𝑖’th level, 𝑎𝑗 is the random genetic effects of the 𝑗’th individual,

𝑒𝑖𝑗 is the random residual effects including environmental effects of the 𝑗’th individual, 𝑋 is the incidence matrix that relates phenotypic observations to the fixed effects, 𝑍 is the incidence matrix that relates phenotypic observations to the random effects

(Árnason and Van Vleck, 2000; Mrode, 2013).

The BLUP-EBVs makes it possible to select for easily recordable traits, with moderate to high herit-abilities, in animals without any phenotypes registered (Boichard et al., 2016). Especially for the dairy cattle industry this was beneficial since most of the economically important traits only can be expressed and recorded in females. The low fertility rates of females, contrary the almost unlimited fertility rates of males, assign them to a lower priority than the males in the breeding scheme even though the traits of interest cannot be recorded in males. Thus, the genetic evaluation of the males is highly based on their female relatives (Weller, 2016), and the development of BLUP was therefore very advantageous in such selection schemes. Focusing mostly on the males in the breeding scheme is generally also the case in horse breeding, although for this species it is nearly equally possible to obtain male records of traits of interest as it is to obtain female records. The challenge though arise when selection for traits not as easy to record and with low heritabilities is desired (Boichard et al., 2016). To come with an example, high-level competition traits in horses cannot be recorded before the age of at least seven years and often older, and the traits often have low heritabilities (Ricard et al., 2000). This indicates a need for large numbers of relatives with phenotypic records in high-level competition for the EBVs to become reliable (Ducro et al., 2007a). This causes the genetic gain to become very slow (Goddard and Hayes, 2009), and consequently, traits with low heritability have been neglected or given a low priority in breeding schemes previously (Weller, 2016). To improve on these kinds of traits, identification of genes affecting desired traits in horses would be very ben-eficial as horses that carries them then would be easier to identify and select (Goddard and Hayes, 2009). In many horse breeding associations BLUP-EBVs are developed and calculated once a year, but it does not seem to be a selection tool that breeders use in greater extents (Thorén Hellsten et al., 2006; Dubois and Ricard, 2007).

3.2 Selection based on genotypes

3.2.1 Marker assisted selection Within the last decade, development in molecular genetics have made it possible to achieve genetic gain in animal breeding through the use of genomic information (Stock and Reents, 2013). Genetic maps based on DNA markers for most economically important animals were available from approx-imately 2005. These markers made it possible to detect quantitative trait loci (QTL) that were af-fecting essential traits. Detection was possible due to linkage between the marker and the QTL (Weller, 2016). This technique was named “marker assisted selection” (MAS), and consists of two


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steps. Firstly, finding and mapping the QTL that affects the traits found interesting for the specific species. Secondly, incorporation of the QTL into the BLUP-EBVs (Fernando and Grossman, 1989). The first step has its limitations because only the QTL having the largest effect on the traits are found. The QTL with smaller effect on the traits are not possible to find with MAS because they all are declared to have nonsignificant effects on the traits. This has resulted in that no more than 10 % of the genetic variation in the breeding goal can be explained by QTLs, and thereby leaving 90 % of the genetic variation in the breeding goal to be controlled by phenotypic selection. This is, ac-cording to Meuwissen et al. (2016), the reason why selection based on MAS has not been reaching widespread implementation in animal breeding. Development of MAS nevertheless proved to be the starting point for another more successful selection tool in animal breeding; genomic selection (GS) (Stock and Reents, 2013).

3.2.2 Genomic selection Instead of using genomic information from loci with only large effect on the traits, all genomic in-formation, even those loci with extremely small effect, are used in GS (Meuwissen et al., 2001). In 2006 new “single nucleotide polymorphism” (SNP) chips were developed (Weller, 2016). These made it possible to gain information on the SNP genotypes from large number of animals both highly reliable, fast and cost effective (Stock and Reents, 2013). Together with the GS theory proposed by Meuwissen et al. (2001), selection based on genomic EBVs (GEBVs) (Hayes et al., 2009a), selection methods in domestic animal breeding were revolutionized. The theory of GS relies on linkage disequilibrium (LD) between the SNP functioning as marker and the QTL (Goddard and Hayes, 2007). LD is the non-random relationship between alleles at different loci which origin from either migration, selection or genetic drift when the population is finite (Wang, 2005). When LD arises it is due to a newly created allele, surrounded by groups of other alleles that together create what is called a haplotype. If the chromosomal region including this haplotype in the following generations is replicated, the haplotype would most likely remain intact, and complete LD between the newly created allele and each of the surrounding polymorphisms would occur. The newly created allele would then be functioning as predictor of other alleles in a nearby polymorphic region. A SNP is the location of variation in the DNA, where the frequency of the most common base pair in the population is less than 99 % (Brookes, 1999). Thus, the term refers to a location in the genome of an individual, where one nucleotide; A, T, C or G, in a base pair deviates from the one commonly occurring in most other individuals of the population (Mrode, 2013). The SNPs are found throughout the genome, with approximately one SNP per 300-500 base pairs (Weller, 2016). In cattle nearly 30 million SNP markers has been identified (Daetwyler et al., 2014), whereas the Horse Genome Project conducted by the Broad Institute of Havard and Massachusetts Institute of Technology (2007), as part of a project by the National Human Genome Research Institute (NHGRI) (cited by Bailey and Brooks (2013)), only reports 2 million SNP markers identified in horses. In horses, completion of the full genome sequence happened in 2007, and when the genome is completely covered, and markers with high density are available, it becomes possible to obtain genomic information providing information on QTL and adjacent loci (Stock and Reents,


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2013). The SNPs in LD with QTL across the population are in this way functioning as markers for variation in the genes that expresses the traits (Corbin et al., 2010). The success of GS therefore depends on the extent of LD, the rate of which it declines with the distance between the loci in the population (Corbin et al., 2010), and most importantly a good coverage of the genome and high density of the markers, so that the SNPs can capture information about LD (Stock and Reents, 2013). Another important aspect of LD is that it can give information about the population structure, e.g. if it has been through a bottleneck, inbreeding level or if migration or assortative mating have oc-curred (Terwilliger et al., 1998). Incorporating genomic information in the selection procedure requires a reference population, which both have been phenotyped and genotyped for the traits decided to estimate the effects of the SNPs on. The model for estimating SNP effects, assuming 50,000 SNPs, in the reference popula-tion is as equation 3.4. Since estimated marker effects decline under selection, SNP effects must be re-estimated frequently (Stock and Reents, 2013), and as a consequence the need for a reference population supplying information on phenotypes and genotypes will remain an important element in GS in the future (Schefers and Weigel, 2012). Furthermore, a prerequisite for the prediction of the SNP effects to be reliable, the animals in the reference population, needs to be closely related to the animals subjected to selection (Stock and Reents, 2013).

𝑦𝑖 = 𝜇 + 𝑋1𝑖 ∙ 𝑏1 + 𝑋2𝑖 ∙ 𝑏2 + ⋯+ 𝑋50,000𝑖 ∙ 𝑏50,000 + 𝑒𝑖 (3.4)

where 𝑦𝑖 is the phenotype of animal 𝑖, 𝜇 is the overall population mean, 𝑋1𝑖 is the genotype of animal 𝑖, for marker 1, 𝑏 refers to the fixed effect of the marker 𝑒𝑖 is the residual

(Meuwissen et al., 2016).

The reference population functions as a genomic map, where genotyped selection candidates with-out records can be held up against to assess whether they differ in the traits. All SNP effects over the whole genome are estimated as a regression of the phenotype on the genotype in the reference population. Afterwards it is possible to predict GEBVs for all individuals with known genotypes, and subsequently base the selection on these GEBVs without knowing the phenotypes (Samorè and Fontanesi, 2016). This makes it possible to select animals at any age and sex as soon as DNA samples can be taken (Stock and Reents, 2013). The accuracy of GEBVs is though highly affected by the size of the reference population (Habier et al., 2010). The model for deriving GEBVs is exemplified in equation 3.5 and an illustration of the GS system is shown in figure 3.1.

𝐺𝐸𝐵𝑉𝑗 = 𝑋1𝑗 ∙ 𝑥1 + 𝑋2𝑗 ∙ 𝑥2 + ⋯+ 𝑋50,000𝑗 ∙ 𝑥50,000 (3.5)

where 𝑥1 is the predicted effect of SNP 1, 𝑋1𝑗 is the marker genotype of animal 𝑗 for SNP 1.

(Meuwissen et al., 2016).


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Figure 3.1. Illustration of how genomic selection is carried out. Large reference population with known phenotypes and genotypes is used to compute an equation to predict genomic estimated breeding values (GEBVs) on selection candi-dates. The prediction equation combines all marker genotypes (X) with their effect (x) on each trait in the breeding goal to predict GEBVs of each selection candidate. GEBVs are then used to select the best parents for next generation (edited from Van Grevenhof (2011)).

To summarize, the idea behind GS is to: x Use dense marker data to illustrate the genome. x Connect phenotypic data with genotypic data to estimate the marker effects on the traits of

interest. x Use GEBVs as guidelines for which effect the markers have on the phenotypes, and to base

the selection of parents for next generation. (Meuwissen et al., 2001).

3.3 Incorporating genomic selection into breeding schemes of horses In general, the potential of implementing GS in the breeding schemes, relates to the cost and efforts needed to achieve phenotypic data on the traits to be improved. If the phenotypes are expensive, difficult to record, or recorded late in life, GS will have a large potential to increase the genetic gain, whereas in the opposite case, GS will not have quite the same potential (Stock and Reents, 2013). Especially improvement of two parameters, affecting the genetic gain, have driven the development


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and implementation of GS in domesticated animal breeding; the generation interval and the accu-racy of (G)EBVs (Samorè and Fontanesi, 2016), but also the potential to control inbreeding is worth noticing. The potential benefits of GS, does nevertheless not come without any challenges. These, among others, includes obtaining a large enough reference population, allocating sufficient re-sources, establishing international cooperation, and convincing breeders and breeding associations to change selection practices.

3.3.1 Optimizing genetic gain in horses With GS, GEBVs can be predicted with high accuracies on animals without any phenotypes recorded. Meuwissen et al. (2001) found in a simulation study that accuracies of GEBVs based on marker ef-fects alone, could be as high as 0.85. This has proven to be advantageous in animal breeding as the cost on progeny testing can be reduced, and genetic gain is faster due to reduced generation inter-vals (Habier et al., 2007). Thereby GS can provide faster genetic gain than is possible with phenotypic and pedigree based selection (Solberg et al., 2008). Numerous authors found that the generation interval for riding horses is between 8 and 12 years (e.g. Árnason and Van Vleck (2000) and Burns et al. (2004)). This is due to horses being rather old, before they obtain breeding values with rea-sonable reliabilities for the breeding goal traits (Mark et al., 2014), long utilization periods (Dubois and Ricard, 2007), and late start of breeding careers of mares used in competition (Dubois et al., 2008). As horses are sexually mature at the age of 2 years under natural conditions (Pilliner and Davies, 2004), great potential of using GS in horses exists since the generation interval can be re-duced significantly. This has been proven in dairy cattle breeding schemes, where GS provide breed-ers the opportunity to identify genetically superior animals, only a few weeks after they have been born and genotyped. In this way, newly born calves receive GEBVs long time before they are sexually mature. Before GS were implemented in dairy cattle breeding schemes, bull calves were selected based on EBVs when born and afterwards progeny tested. Not until the bull was 4.5 years old the first phenotypes on the progeny were available, and decision could be made whether the bull was good enough as bull sire or not. If the bull was approved he would potentially get his first bull calves for future breeding at the age of at least 5 years. Therefore, GS has revolutionized dairy cattle breed-ing as it has resulted in a substantial drop in the male generation interval from approximately 5 to 2 years, and thereby increased the genetic gain in dairy cattle breeding schemes (Schefers and Weigel, 2012). In figure 3.2 is a timeline showing the selection of colts commonly practiced in cur-rent phenotypic selection practices and the age at which EBVs are public for the breeders to use for selection decisions. Furthermore, an example of how the selection of colts could be practiced with GS, and when GEBVs then could be available for the breeders, is shown. From this figure it is clear to see the impact GS could have on especially the generation interval in the same way as it has in dairy cattle breeding schemes. The publication of breeding values could become at least 6 years faster with GS than with current phenotypic selection practices while maintaining good reliabilities. This may encourage breeders to use younger stallions as sires for next generation, thereby improv-ing the genetic gain by reducing the generation interval considerably.


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Figure 3.2. Timeline of traditional phenotypic selection of colts practiced now (above arrow) and an example of how selection of colts could be practiced if genomic information was implemented (below arrow). Dark boxes highlight when the breeding values are public for the breeders to use.

Dairy cattle breeding is not the only domesticated animal having benefitted from GS. Also the pig and poultry industry have gained from it (Mark et al., 2014). Though, it is not on the generation interval GS has shown to improve the genetic gain the most as the generation intervals already are quite short in the conventional breeding schemes in these species. The main advantage of GS in pig breeding lies in the potential to increase the accuracy of EBVs, especially by improving the possibil-ities to predict maternal traits in boars (Samorè and Fontanesi, 2016) and traits only recorded after slaughter (Lillehammer and Sonesson, 2011). In pig breeding three-breed crosses are made to pro-duce slaughter pigs, and selection is therefore made in purebred lines outside the actual slaughter pig productions (Visscher et al., 2000). In traditional breeding schemes, male piglets are pre-selected right after birth and afterwards tested at test stations, where data on production traits is obtained before final selection of the boars for breeding based on EBVs (Bereskin, 1975). With GS, the boars are still tested and EBVs are calculated, but the EBVs are now used to select those candidates to be genomic tested. It is not all boars that are genomic tested due to the tests still being too expensive. The boars selected for genomic test obtains GEBVs with higher accuracy than if only EBVs were calculated, and final selection takes place based on these GEBVs (Vernersen, 2013). For females, on the other hand, information is scarce in conventional breeding schemes (Lillehammer and Sonesson, 2011), which is why GS also was rather revolutionary for this species. Maternal traits can be genomic selected for in boars, not through their female sibs, as they probably not are available before the final selection of the boars, but through the aunts of the boars. Traits only recorded after slaughter can be genomic selected for in boars, when their full sibs have been slaughtered and recorded for the traits of interest. This information is usually available before the final selection of the boars and thus, high accuracies are possible (Meuwissen et al., 2016). In poultry breeding reduction of the generation interval is not relevant either. Due to the natural short generation interval, poultry breeding has already succeeded in achieving high genetic gain in production traits of both layers and broilers in a very short time, through phenotypic and BLUP based selection. Therefore, potential benefits of GS in poultry are mainly achieved through increasing the accuracy of selection. Primarily in layers, GS have considerable benefits due to increase in accuracy of important traits that are not


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being recorded in both sex (Wolc, 2014) or traits being hard to measure like disease and parasite resistance (Stock and Reents, 2013). According to Meuwissen et al. (2016) the benefits of GS in broilers are not as clear because traits of interest can be recorded in both sex, which make the accuracies acceptable for selection without GS. Breeding companies are nevertheless still investi-gating how GS possibly could improve performance of crossbreeds and certain disease challenge tests that cannot be recorded in the herds where selection and breeding takes place (Meuwissen et al., 2001). As in pig and poultry breeding schemes, improvement of accuracy of EBVs could also have positive effects on the genetic gain in horse breeding. However, this depends largely on the relatedness between the reference population and the genotyped individuals, and the reliabilities of the EBVs in the reference population (Goddard, 2009). As mentioned earlier, high-level competition traits in both dressage and show jumping can only be recorded late in life, and traits recorded early in life, are mainly subjectively recorded. Furthermore, only limited number of offspring is born and pheno-typic recorded in comparison with other species. Consequently, EBVs of especially young riding horses have low accuracies. For instance, Dubois et al. (2008) reported an accuracy of 0.19 on EBVs on young stallions and an accuracy of 0.39 on station tested stallions. As high-level competition traits are the main traits in many riding horse breeding associations (Koenen et al., 2004), the pos-sible improvement of accuracy and hence genetic gain that GS could provide should not be over-seen. Haberland et al. (2012a) showed in a simulation study that accuracies of EBVs significantly increased for young horses without own phenotypes or without offspring with phenotypes, when incorporating genomic information. This makes it possible to lower the generation interval while increasing the genetic gain. Ricard et al. (2013) did however not find same high reliabilities of GS in show jumping horses as has been found in other species. Small sample size (VanRaden, 2008), little relatedness due to inclusion of multiple breeds between the reference population and the selection candidates (Habier et al., 2010), and low accuracy of the pseudo phenotypes (Hayes et al., 2009a), are proposed as possible explanations for the low reliabilities. In the study of Ricard et al. (2013), the reliabilities were therefore not sufficiently improved by GS to suggest implementation in current breeding scheme of horses. This conclusion was based on the reliability results only and therefore without assessing whether GS could shorten the generation interval or benefit the genetic gain in other ways. As GS is beneficial for improvement of traits difficult to record, recorded late in life or even after death (Wolc, 2014), and traits with low heritability (Haberland et al., 2012a), GS would be advanta-geous in horse breeding. This however depends on the availability of phenotypic data of good qual-ity (Stock and Reents, 2013). Lately, increased focus on the longevity of riding horses has emerged. This is a trait hard to measure and can only be recorded after death. Records on longevity are not available at the moment, but corporation between veterinarians and SEGES on registration of dis-eases has started as a pilot project. To start with, osteochondrosis (OCD) (Christiansen, 2011), which is found to be related to longevity (Couroucé-Malblanc et al., 2006) is registered. When enough disease records are available, GS is expected to enable selection against them. Also, traits like con-ception rate, foaling ease and other sex limited traits could in the future be beneficial to be able to


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select for in stallions since the selection intensity in mares is near zero. In relation to this, the ma-ternal pathway in horses could be exploited more with GS, as breeders currently are not strict enough in the selection (Dubois et al., 2008).

3.3.2 Enhancing the control of inbreeding It is not only related to the genetic gain that GS can have valuable effect in horse breeding. Also, inbreeding will be easier to control. This is advantageous as inbreeding reduces the genetic variation in the population by increasing the homozygosity. This can in worst case scenario result in inbreed-ing depression in important traits and affect the genetic gain negatively (Falconer and Mackay, 1996). When using pedigree based BLUP to predict EBVs, records from relatives are incorporated through the relationship matrix. The relationship matrix is based on predictions of the proportion of genes between two individuals that is identical by descent (IBD) (Hayes et al., 2009b). This pre-diction of the average genetic relationship, also known as the covariance (Lynch and Walsh, 1998) between e.g. two full sibs is 0.5 because each of the parents in average share 50 % of their alleles IBD with their offspring (Zapata-Valenzuela et al., 2013). The variance of the Mendelian sampling term, which is defined as the amount of genetic variability between full-sibs due to random inher-itance of alleles from both parents (Bonk et al., 2016) is not accounted for in pedigree based rela-tionship matrices (Avendaño et al., 2005). Therefore, some deviation from the predicted relation-ship will occur without knowledge about it. This can result in unwanted increases in inbreeding due to high covariance between EBVs of related individuals, especially when selection is made early in life on the basis of EBVs that mainly are based on family records (Clark et al., 2013). This problem is solved when using GS because genomic relationship matrices are used instead of the pedigree rela-tionship matrices. Using genomic relationship matrices makes it possible to estimate the relation-ship between the horses more accurately due to the tracing of Mendelian sampling term (Hayes et al., 2009b). The reason why this is possible is that DNA markers can assist in identifying alleles be-tween two individuals that are IBD and identical by state (IBS) (VanRaden, 2008). Therefore, ge-nomic covariance in the genomic relationship matrices are based on realized proportion of alleles that individuals involved actually share (Strandén and Garrick, 2009), and not means like in the ped-igree based relationship matrices. The estimation of relationships between individuals are therefore more accurate when using GS (Hayes et al., 2009b). When obtaining more accurate data about the relationships in a population and the variation within families the inbreeding becomes easier to control and genetic variation easier to maintain. Due to the possibility of selecting two superior full-sibs that with the conventional relationship matrix would be predicted to share 50 % of their alleles IBD, but with the genomic relationship matrix could share considerably less, increase in genetic gain without increasing the level of inbreeding is possible. In this way, implementing GS and select on the basis of GEBVs are expected to increase the genetic gain, while keeping the inbreeding on a low level and thereby maintaining the genetic variation (Clark et al., 2013).

3.3.3 Additional advantages of genomic selection in the future In the future GS will be even more favourable since reproductive technology, e.g. multiple ovulation and embryo transfer (MOET) is continuously developing in many species including horses. Normally


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when using MOET, random embryos are used to produce offspring of selected mares and stallions whose combination is predicted to be superior. Over time GS would make it possible to select the best embryos instead of just one random (Meuwissen et al., 2016). Another rationale for imple-menting GS relates to economy. If ensuring only the best embryos are produced from superior par-ents in the population, the costs for producing non-superior foals for breeding would be lowered. The costs of phenotypic testing potential mares and stallions for breeding could also be reduced with GS (Van Grevenhof et al., 2012). This however might require some good persuasive powers to convince breeders that phenotypic testing should not be prioritized in same extent as it does now with traditional horse breeding schemes.

3.3.4 Challenges of implementing genomic selection in horse breeding schemes Earlier the main reason for GS being challenging to implement in most species, was the price of genomic test, which is now decreasing constantly, and the requirement of large number of markers. These issues have now been overcome in many livestock species due to the development of tech-nology (Van Grevenhof et al., 2012). Technology however, has not been the sole issue limiting the implementation of GS in horse breeding. In e.g. dairy cattle breeding, the success of implementing GS is to a great extent owed to cooperation between countries (Stock and Reents, 2013). Imple-menting GS stimulates international collaborations due to large reference population needed to calculate GEBVs with high accuracies based on the prediction models (Schefers and Weigel, 2012). Bringing together a large reference population is only possible in horse breeding if cooperation be-tween horse breeding associations are established, especially if breeding for traits that are difficult to or costly to record (Van Grevenhof et al., 2012). If using only Danish Warmbloods in the reference population it might not be large enough to compute prediction equation of sufficiently high quality. Though, if the generation interval can be reduced moderately, genetic gain with GS, similar to the gain possible to obtain with BLUP is possible even with a rather small reference population (Van Grevenhof et al., 2012). Van Grevenhof et al., (2012) found that to reach same levels of genetic gain as when selecting based on BLUP-EBVs, a reference population with own phenotypes of approxi-mately 6,000 individuals was suitable when the generation interval was reduced with 20 %, whereas when it was reduced with 50 % a reference population with own phenotypes of approximately 2,000 individuals should be enough. If the reference population was based on progeny phenotypes, num-bers of individuals needed to reach same level of genetic gain as BLUP based selection increased. The exchange of genotypes and joint genomic evaluations between countries and breeding associ-ation would therefore be essential in the implementation of GS because the larger reference popu-lation, the higher accuracies of GEBVs, and the greater potential of GS (Mark et al., 2014). A small reference population will result in GEBVs with low accuracies, which consequently will result in non or only insignificant additional genetic gain compared with traditional phenotypic selection (Van Grevenhof et al., 2012). The first challenge therefore seems to be establishment of a large reference population through cooperation with other warmblood associations for GS to be successfully imple-mented in horse breeding. Furthermore, it has been reported that accuracy of GEBVs decreases the larger number of generations between the reference population and the selection candidates. This


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implies that establishing a large reference population is only the first step in implementing GS, sec-ond step is ensuring continuing high quality data of traits of interest from present generations (Buch et al., 2012). Another challenge would most likely be the horse industry not being ready to allocate as many re-sources that successfully implementation of GS requires. Limited or almost non-existing resources allocated to develop and maintain GS evaluation systems in the horse breeding industry, makes implementation of GS challenging. In this way, the horse industry differs from other livestock spe-cies, where more resources have been appointed. Therefore, cost-effective and simple strategies with reduced numbers of genotyped horses for implementation of GS should be prioritized to start with to ensure successful implementation in the horse sector (Mark et al., 2014). Furthermore, horse breeders would have to change their way of thinking because in current horse breeding schemes, phenotypic performances represent a large proportion of the basis for selection decisions, and selection based on EBVs is not even practiced yet, at least not in greater extents (Koenen et al., 2004).

4 Current selection practice in the Danish Warmblood Association

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4.1 Breeding goal As mentioned, the breeding goal is essential to make an easy understanding and efficient breeding program. It is towards this goal that every selection activity should lead and be based upon. In DWB the overall breeding goal, consisting of multiple traits, is verbally defined as follows:

“We aim to breed a noble, leggy, and supple riding horse with high rideability and a strong health. It has capacity in either jumping or dressage to compete on

international level”.

Additionally, following breeding goals applies for each discipline:

The dressage horse:

“A horse with large and well-carried movements, showing good, active knees and hocks in all three gaits. The walk is lithe, roomy, and regular. The trot is elastic, regular, and with good carriage. The canter is roomy, regular, and with good carriage and balance. Furthermore, good rideability with

courage and willingness to perform is very much desired”.

And the show jumper:

“A vigorous and lithe jumping with great capacity and good technique. Importance is attached to a supple, roomy and balanced canter along with a natural caution, great courage, overview, and a

good rideability” (Dansk Varmblod, 2016a).

For a more comprehensive description of the breeding goal, see Appendix I.

4.2 Selection practice To reach the breeding goal DWB has decided that every mare and stallion selected for breeding in must be graded. Stallions are either approved for breeding and graded, or rejected for breeding and not graded. The mares can obtain grading in one of four grading categories, which is decided by their pedigree and qualities in relation to the breeding goal. It is possible that mares are rejected for breeding, but this rarely happens. The selection of stallions is completely controlled by DWB themselves, whereas the selection of mares only can be stimulated by DWB, but in the end con-trolled by the breeders. The selection is mainly based on phenotypes at a young age, such as conformation, including type, head-neck, frontpart, topline, frontlimbs and hindlimb. Additionally, either dressage ability, includ-ing walk, trot, canter, rideability and capacity, or show jumping ability, including canter, jumping technique, jumping capacity and rideability is part of the young horse evaluation. These traits are evaluated in mares using linear profile schemes (implemented from 2014, see Appendix II and Ap-pendix III) and subjective evaluations. In the linear profile schemes three to six additional sub-traits


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are evaluated. For example, underlying front part is length of shoulder, length of mane etc. All un-derlying sub-traits are given a score (A, B, C, D, E, F, G, H, or I) in relation to how much the sub-trait deviates from the ideal. These scores are then incorporated in the final grade, ranging from 1-10, for each of the traits, and the total score is then used to base decisions on and later calculate BLUP indices (Dansk Varmblod, 2014). The linear profile schemes are not implemented for stallions yet. Hence they are subjectively evaluated only. Furthermore, x-rays for stallions are part of the evalua-tions. Occasionally it happens that older stallions are given the permission to breed, if they have proved their worth in high-level competitions. This is generally only very few (Karina Christiansen, personal com., 2016) as most stallions are castrated after being rejected. Otherwise the overall breeding goal; “capacity to compete on international level” is only indirectly selected for, through the young horse phenotypes having genetic correlations to the overall breeding goal. BLUP-EBVs are not used when selecting young horses, but is considered when older horses are graded and to rank the horses to help breeders make decisions on which horses to breed. The BLUP-EBV on each horse is calculated once a year and consist of three sub-indices; a young horse sub-index based on grading events by DWB (mainly 3-4-year-olds), a championship sub-index based on qualifications and finals in young horse championships held by the Danish Riding Federation (DRF) (4-6-year-olds), and a competition index based on all competition results registered by DRF (from 5-year-olds). Each sub-index is weighted according to the number of offspring the stallion or the mare has (Christiansen, 2015a), and calculated using single-trait models. The BLUP-EBVs are only public on stallions having at least 15 offspring in the young horse sub-index and 15 offspring in the young horse championship sub-index or in the competition sub-index. Because the mares do not get as many offspring as the stallions, they only need their own performance result in two of the three sub-indices or at least one offspring in two of the three sub-indices to get public BLUP-EBVs (Christiansen, 2015a; Dansk Varmblod, 2017b).

4.2.1 Selection of stallions In the DWB, the selection of stallions starts with a pre-selection of mostly two- and three-year-olds in December. The breeders decide themselves if their stallion should be evaluated in the dressage or show jumping discipline, but as the pedigree of the stallion also is assessed in the evaluation, the chance of a stallion with a pedigree dominated by show jumpers being selected as a dressage stal-lion is very small, and the other way around. Therefore, it is normally only one of the disciplines the stallions are evaluated in (Dansk Varmblod, 2016b). To qualify for a pre-selection the stallion must have approved its pedigree. At least four generations back in the pedigree must be evaluated and graded by DWB or other acknowledged breeding associationsi (Dansk Varmblod, 2016d). Besides,

iBelgish Warmbloedpaard (BWP), Stud-book sBs Le Cheval de Sport Belge (sBs), Koninkliijk Warmbloed Paarden Stamboek Nederland (KWPN), Lande-spferdezuchtverband Berlin- Brandenburg (BRAND), Landesverband Bayerischer Pferdezuchter (BAVAR), Norwegian Warmblood Association (NWB), Pferdezuchtverband Baden-Wurttenberg (BAD-WU), Pferdezuchtverband Rheinland-Pfalz-Saar (ZWEIB), Pferdezuchtverband Sachsen-Thuringen e.V. (SATHU), Grænseegnens Holstener Hesteavlsforbund (GHH), Pferdezuchtverband Sachsen-Anhalt (SA), Rheinisches Pferdestammbuch (RHEIN), Springpferdezuchterverband Oldenburg – International e.v., Stud-book Francais du Cheval Anglo-Arabe (AA), Stud-book Francais du Cheval Selle Fran-cais (SF), Studbook Zangersheide (ZANG), Swedish Warmblood Association (SWB), Verband Hannoverscher Pferdezuchter (Hann), Verband der Zuchter des Holsteiner Pferdes (HOLST), Verband der Zuchter des Oldenburger Pferdes (OLDBG), Verband der Pferdezuchter Mecklenburg-Ver-pommern (MECKL), Verband der Zuchter und Freunde des Ostpreussischen Warmblutpferdes Trakehner Abstammung (TRAK), Westfalisches Pferdes-tammbuch (WESTF).


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the mother and the maternal grandmother of the stallion must be graded in one of the two best mare grading categories: DH or DS (for explanation see “Selection of mares”). It is also a requirement that the height of the stallion is at least 162 cm (Dansk Varmblod, 2016c). For the pre-selection of two- and three-year-old show jumping stallions, the stallions are shown loose in free jumping. Afterwards some are rejected, and the few remaining are shown by hand on hard surface. The ones that pass are then shown in lunge. If the stallions are older than three years, they are shown under rider instead of free-jumping and lunge. For the pre-selection of two- and three-year-old dressage stallions, the stallions are shown loose in an arena. Afterwards, some are rejected, and the few remaining are, as the show jumpers, shown by hand on hard surface. The ones that pass this selection stage are then shown in lunge. If the stallions are older than three years, they are shown under rider instead of loose in the arena and lunge (Dansk Varmblod, 2016b). The stallions that have made it through the pre-selection are evaluated at a stallion grading show in March. At this point, they are three years old, or turn three the same year. To qualify for the stallion grading show, the pedigree must be verified with a DNA test and x-rays must be submitted. If the stallion is carrying known genetic defects or x-rays are showing something abnormal believed to have an impact later in life, the stallions are usually rejected unless they possess extraordinary good traits in relation to the breeding goal. Then they can exceptionally be selected, but the abnormalities will then be public for the breeders. Blue Horse Romanov and Sezuan are examples of stallions that have been graded despite having the partly genetic defect; osteochondrosis (Dansk Varmblod, 2016c). At the stallion grading show the stallions are evaluated with same procedure as at the pre-selection. To be approved, the stallion must obtain an overall total score (including conformation and jumping or dressage ability) of at least 8 on a scale ranging from 0-10. There are no restrictions on number of stallions within a certain line that can be approved. If there is a lot of good stallions within the same line of a previously approved stallion, they are usually also approved, but then they are often sold to foreign countries because they are not used as much (Karina Christiansen, personal com., 2016). After approval at the stallion grading show, the stallion must go through a test to get its one year breeding permission. The owner can voluntarily choose if the stallion should participate in a 10-day observation test. During these 10 days, which takes place right after the stallion grading show, tem-perament in the daily handling, both in stable and under rider is assessed and the stallion is trained at a level in accordance to its age. None of the stallions are tested in jumping. At the end the breed-ing- and training committee conducts a training report, and then decides which stallions should be given licence to breed for one year. Stallions that possess bad temper, or other traits with bad in-fluence on the breeding goal, will be rejected. Note that this test is voluntary, but must be com-pleted for the stallion to be permitted for breeding until it has passed a 35-days performance test (Dansk Varmblod, 2017a). If the stallion, before the stallion grading show the following year, passes a 35-days performance test with at least 800 point, it can be awarded with final grading. If the stallion passes the test with


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less than 800 point, but with at least 700 point, it can be given another one-year permission. It can only achieve final grading if it passes a new 35-days test or in young horse championship obtains 800 points or more. The 35-days performance test is conducted to provide information about the temperament and performance potential. Under the test the stallions are trained by professionals, who prepare them for the final test at the end of the period, and the performance of the stallion are evaluated continuously. A test rider also evaluates the stallions twice. In the final test the dres-sage stallions are evaluated based on rideability, capacity, walk, trot and canter, and the show jump-ing stallions are evaluated based on rideability, canter, technique, and capacity (Dansk Varmblod, 2016e). The 10-day and 35-day test applies only to three-, four- and five-year-old stallions. Older stallions can exceptionally be graded if they have achieved very good competition result in age fitting levels. In figure 4.1, the possibilities to achieve final grading for stallions are illustrated together with ap-proximate numbers of how many stallions that are selected in the different selection stages each year. Even though the stallion has reached its final grading, the breeding committee can, at any time withdraw the breeding permission if they find it necessary, e.g. if the stallion turn out to pass on negative traits to its offspring.

4.2.2 Selection of mares There are several ways mares can be graded in DWB. Usually the mares are three or four years old when they are graded, but older mares can also be graded. At three-years-old they are shown loose and by hand, and from four-years-old they are also shown under rider. There are no rules regarding x-rays of mares, but the breeders are encouraged to get x-rays taken before the mares enters their breeding career. When graded, the mares are assigned to one of following categories, and as mentioned previously, it is only colts of mares in the first two categories, DH and DS, who have the possibility to someday become graded stallions.

x The primary stud book of Danish Warmblood horses (“Dansk Hovedstambog”, abbr.; DH). This is the highest achievable level of mare grading’s. To end up in this category the mare should measure at least 160 cm, and in its pedigree, at least three generations back must be graded in DWB or in another acknowledged breeding association. Furthermore, the mare have received at least 8 in general impression.

x The secondary studbook of Danish Warmblood horses (“Dansk Stambog”, abbr.: “DS”). To end up in this category the mare should measure at least 155 cm, and in its pedigree, at least every second generation back must be graded in DWB or in another acknowledged breeding association. Furthermore, the mare have received at least 6 in general impression.

x The register of Danish Warmblood horses (“Dansk Register”, abbr.: DR). To end up in this category the mare should measure at least 148 cm, and in its pedigree, at least every second generation back must be graded in DWB or in another acknowledged breeding association. Furthermore, the mare have received at least 5 in general impression.

x The preliminary register of Danish Warmblood horses (“Forregister”, abbr. “FOR”).


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This category is for mares with unknown pedigree or mares who have a pedigree with less than three graded generations. Minimum height is 148 cm and minimum score in general impression is 5.

The mares can reach their grading in the following ways: x At an exterior grading, the mare is assessed at hand on hard surface and loose in an arena.

It is evaluated on conformation and its gaits, and if it is a show jumping mare it is also as-sessed in free-jumping. For the mare to become dam to a graded stallion, this grading should be accompanied with a grading where the mare is shown under rider, e.g. the 1-day test.

x At the saddle grading, both evaluation of conformation and a riding test is conducted. In both disciplines the mares are shown under its own rider and under a test-rider who do not know the mare beforehand. This is an attempt to account for the environmental deviation coming from the rider. When the mare has its riding-test approved, it is awarded with an “R” before its grading category, e. g. “RDH”.

x 1-day test is the same as saddle grading, except without evaluation of conformation. x Station test is for breeders that do not want or can take the mare to grading themselves.

Here the mares are prepared and trained one month, where they are assessed regarding temperament, rideability and jumping, if relevant, by the trainer. After one month, they are tested with the same procedure as the saddle grading.

x Ability tests are for four-year-old mares, who have already been graded in DWB. The mares participate in the ability test if the owner wishes to have it qualified for young horse cham-pionships, which takes place at the same time as the stallion grading show in March. Dres-sage mares are shown in all gaits under own rider, and show jumping mares are shown in jumping under own rider. If the mare obtains min. 700 points, it will be awarded with an “R” before its grading category.

(Christiansen, 2015b) The mare selection system is illustrated in figure 4.2 together with approximate numbers in each selection step.


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Figure 4.1. Possible pathways a stallion can reach his final grading in the Danish Warmblood Association. Approximate numbers of stallions selected by the Danish Warmblood Association in each step are shown (numbers based on selections in 2015, including both dressage and show jumping stallions).


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Figure 4.2. Possible pathways a mare can be graded in the Danish Warmblood Association. Approximate numbers of mares selected and graded each year are shown (numbers based on selection averages in the years 2005-2010, including both dressage and show jumping mares).


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4.3 Descriptive analysis

4.3.1 Material and methods To get a clearer understanding of how the selection has been in DWB in recent years, descriptive statistics was made on a dataset received from the horse section at SEGES. The dataset contained all Danish Warmblood foals born from 2005 to 2015, their pedigree information, birth year and re-sults from several types of grading events; saddle grading, exterior grading, pre-selection, stallion grading and performance tests. To categorize the Danish Warmblood foals into the categories; dressage or show jumping, the high-est EBVs or competition results of their sire was used. In this way, the sire determined which cate-gory they ended up in. Offspring having an all-round stallion or a thoroughbred as sire were excluded from the dataset because they did not fit either of the categories. This exclusion accounted for 1.7 % of the offspring in the dataset. Due to stallions not being used equally, a distinction was made between sires that was used more intensively (sire+) and sires that was used less intensively (sire-). When only “sire” is used in the following, it refers to all sires, regardless to which extent they were used. Sire+ includes those stal-lions having 0.5 % or more offspring in a year out of the total number of offspring born, and the dams for these offspring are referred to as “dams+”. Sire- are those stallions having below 0.5 % of the offspring born in a year. Furthermore, for the ease of understanding, dressage horses are illus-trated with blue, and show jumping horses are illustrated with green in the rest of the thesis.

4.3.2 Results In table 4.1 and 4.2, the number of sires and dams who became parents in the years 2005-2015, in each discipline, is shown. Also shown is the number of offspring born, and the average number of offspring per sire and dam. The total number of sires+ and the average number of offspring per sire+ are also shown together with percentage of the total number of offspring having a sire+, and the average age when sire+ for the first time.


30

Table 4.1. Number of; dressage horses used as parents, offspring, offspring per sire and dam, sire+, offspring per sire+, and percentage of all offspring having a sire+ and the age when sire+ for the first time. Based on data from 2005-2015.

Dressage Birth

year off-spring

NSires NDams NOff-

spring

NOffspring

per sire (avg.)

NOffspring

per dam (avg.)

NSires+

NOffspring

per sire+

(avg.)

Offspring of sires+

(%)

Age when first-time

sire+ (avg.) 2005 127 1642 1644 13 1 38 37 85 9 2006 132 1649 1651 13 1 38 37 85 9 2007 128 1865 1866 15 1 44 37 88 7 2008 132 2184 2186 17 1 41 46 87 5 2009 132 2117 2117 16 1 40 46 87 6 2010 127 1787 1791 14 1 42 38 88 6 2011 126 1556 1560 12 1 40 34 87 5 2012 120 1323 1329 11 1 34 34 86 7 2013 117 1210 1219 10 1 36 29 87 5 2014 114 1239 1252 11 1 37 29 87 6 2015 106 1210 1222 12 1 39 28 90 6

Avg. 124 1617 1622 13 1 39 36 87 6 NOffspring per sire

+ in lifetime (avg.) 98

NYears as sire+

(avg) 2.7 N = Number, Sire+ = Stallion being sire to 0.5 % or more offspring of the total number of offspring born in one year.

Table 4.2. Number of; show jumping horses used as parents, offspring, offspring per sire and dam, sire+, offspring per sire+, and percentage of all offspring having a sire+ and the age when sire+ for the first time. Based on data from 2005-2015.

Show jumping

Birth year offspring

NSires NDams NOff-

spring NOffspring per

sire (avg.) NOffspring per

dam (avg.) NSires+ NOffspring per

sire+ (avg.)

Offspring of sires+ (%)

Age when first-time sire+

(avg.) 2005 94 825 828 9 1 40 19 92 9 2006 100 813 814 8 1 32 22 86 9 2007 103 824 826 8 1 44 16 85 8 2008 102 739 742 7 1 41 16 88 8 2009 105 743 744 7 1 43 15 87 9 2010 113 653 656 6 1 45 12 82 8 2011 98 562 566 6 1 47 11 91 7 2012 104 431 441 4 1 43 8 78 8 2013 92 419 426 5 1 37 10 87 6 2014 96 407 415 4 1 41 9 89 7 2015 83 350 354 4 1 47 7 93 13 Avg. 100 615 619 6 1 42 13 87 8

NOffspring per sire+ (avg.) 36

Years as sire+ (avg.) 2.8 N = Number, Sire+ = Stallion that is sire to 0.5 % or more offspring of the total number of offspring born in one year.


31

In figure 4.3, the distribution of how many years the stallions were sire+, is shown. Many stallions were not at any time sire+, and only few were sire+ in more years.

Figure 4.3. Number of years as sire+. All years from 2005-2015 are counted in, and it is not necessarily successive years, when a stallion was sire+ in more than one year. N = number.

In table 4.3, for both disciplines, the number and percentage of first-time foaling dams in the years 2010-2015 are shown together with their average age at first foaling. Here, first-time foaling dams were defined as mares in the data set, having no offspring born in 2005-2009, but offspring born in 2010-2015. It was assumed that mares having no offspring before 2010 did neither have any off-spring before 2005, and thus were first-time foaling in 2010-2015. This assumption was made be-cause the data set did not contain information on foals born before 2005.

Table 4.3. Percentages of first-time foaling dams each year from 2010-2015 and their average age.

Dressage Show jumping

Birth year offspring

NFirst-time

foaling dams

First-time foaling dams

(%)

Age when first-time foaling

(avg.)

NFirst-time

foaling dams

First-time foaling dams

(%)

Age when first-time foaling

(avg.) 2010 536 30 10 214 33 9 2011 402 26 8 148 26 9 2012 317 24 8 128 30 9 2013 330 27 7 117 28 9 2014 309 25 7 116 29 9 2015 309 26 8 114 33 9 Avg. 367 26 8 140 30 9

N = Number, First-time foaling mare = mares in the data set, having no offspring born in 2005-2009, but offspring born in 2010-2015.

In figure 4.4, for both disciplines, the age of dams and sires when they became parents, are shown. Note that because of a pregnancy of 11 months for horses, the age when mating is one year less, than explained in the figure. As the figure indicates, the age of the dams in both disciplines were somewhat similar when having offspring. The youngest dams had their offspring at the age of 3 and the oldest at the age of 34. When it comes to the sires, the age when having offspring differed more between the disciplines. Dressage sires were in general a little younger than the show jumping sires. The average ages are illustrated in the boxplots with dots, but are also specified in table 4.4. From

529

118 78 46 32 18 11 3 7 7 2 30

100

200

300

400

500

600

0 1 2 3 4 5 6 7 8 9 10 11

Nsir

es

Years as sire+


32

the average ages an average generation interval of 9 years for dressage horses and almost 11 years for show jumping horses can be computed. This is also shown in table 4.4. Figure 4.5 also show boxplots of the age distribution of the parents when offspring are born, but only for sires+ and dams+. The same picture was shown as when all sires were included; the dressage sires+ tended to be younger than the show jumping sires+. In table 4.4 the generation intervals when including only sires+ and dams+ are shown. The generation intervals tend to decrease slightly for dressage horses, but increased slightly for show jumping horses when only including the sires+ and dams+.

Figure 4.4. Boxplots of age distribution (in years) of all dams and all sires in each discipline when their offspring are born (numbers based on offspring born in 2005-2015). Horizontal lines indicate from bottom and up; youngest age, the 25 % quartile, the median, the 75 % quartile and the oldest age. The black dots indicate the average ages.

Figure 4.5. Boxplots of age distribution (in years) of sires+ and dams+ in each discipline when their offspring are born (numbers based on offspring born in 2005-2015). Horizontal lines indicate from bottom and up; youngest age, the 25 % quartile, the median, the 75 % quartile and the oldest age. The black dots indicate the average ages.


33

Table 4.4 Average ages of sires, dams, sires+ and dams+ (in years) when becoming parents, and the average generation intervals. Numbers are based on data from the years 2005-2015.

Dressage Show jumping Sire Dam Sire+ Dam+ Sire Dam Sire+ Dam+

Avg. age when parent 8.2 10.4 7.8 10.2 10.1 11.2 9.8 12.0 Avg. generation interval 9.3 9.0 10.7 10.9

Sire+ = Stallion that is sire to 0.5 % or more offspring of the total number of offspring born in one year. Dam+ = Dam to the offspring of sire+.

In figure 4.6 the average number of offspring per sire in each age class and each discipline are shown. For the dressage stallions, there is a clear tendency of less offspring the older the stallions are, but in the show jumping stallions there are kind of two peaks; the first at the age of 6 and the second at the age of 13 years.

Figure 4.6. Average number of offspring per sire in each sire age class (in years) and each discipline.

In figure 4.7-4.10 percentages of dressage stallions, show jumping stallions, dressage mares and show jumping mares being selection candidates and percentages of actual selections each year, are shown. Selection candidates are those horses pre-selected by the breeders. Every horse fulfilling the regulations made by DWB in relation to breed can be selection candidate if the breeders wants them to be. The numbers in the figures are averages of the years 2005-2010, both years inclusive. A sex ratio of 50 % males and 50 % females were assumed as gender was not noted in the data set. Among the stallions, the candidates from the show jumping population accounted for a larger per-centage than the candidates from the dressage population. This was also the case in the percentages of each population that were finally selected. Regarding the mares in both disciplines, 100 % of the selection candidates chosen by the breeders were selected by DWB in one of the four selection categories, which all gives permission to breed. Most mares were selected in DH or DS, whereas

0

5

10

15

20

25

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Avg.

num

ber o

f offs

prin

g

Sire age

Show jumping Dressage


34

only few were selected in DR and FOR. Even though nearly 50 % of the mares born in 2005-2010 were graded in the DWB, it was only 21 % of the dressage mares and 17 % of the show jumping mares that became brood mares before 2016.

Figure 4.7. Selection of dressage stallions (numbers based on stallions born in 2005-2010). Not candidates = Stallions not considered at pre-selection. Candidates = Stallions considered at pre-selection. Rejected = Stallions rejected at any of the following selection steps. Selected = Stallions selected for breeding.

Figure 4.8. Selection of show jumping stallions (numbers based on stallions born in 2005-2010). Not candidates = Stal-lions not considered at pre-selection. Candidates = Stallions considered at pre-selection. Rejected = Stallions rejected at any of the following selection steps. Selected = Stallions selected for breeding.

Not candidates90.3%

Rejected 8.3%

Selected 1.4%Candidates 9.7%

Not candidates86.9%

Rejected 10.6%

Selected 2.5%Candidates 13.1%


35

Figure 4.9. Selection of dressage mares (numbers based on mares born in 2005-2010). Not candidates = Mares not con-sidered for breeding at grading events. Candidates = Mares considered for breeding at grading events. Selected in DH = Mare accepted in The primary stud book of Danish Warmblood horses. Selected in DS = Mare accepted in The secondary studbook of Danish Warmblood horses. Selected in DR = Mare accepted in The register of Danish Warmblood horses. Selected in FOR = Mare accepted in The preliminary register of Danish Warmblood horses.

Figure 4.10. Selection of show jumping mares (numbers based on mares born in 2005-2010). Not candidates = Mares not considered for breeding at grading events. Candidates = Mares considered for breeding at grading events. Selected in DH = Mare accepted in The primary stud book of Danish Warmblood horses. Selected in DS = Mare accepted in The secondary studbook of Danish Warmblood horses. Selected in DR = Mare accepted in The register of Danish Warmblood horses. Selected in FOR = Mare accepted in The preliminary register of Danish Warmblood horses.

4.4 Simulating current selection practice To simulate the current selection practice in DWB certain simplifications and assumptions were made. These relates to the population structure, number of traits evaluated and genetic parame-ters. In the following simplifications and assumptions made in relation to the simulations are ex-plained.

Not candidates51.6% Selected in DH

26.5% Selected in DS21.1%

Selected in DR0.1%

Selected in FOR0.7%

Candidates48.4%

Not candidates57.4%

Selected in DH23.0% Selected in DS

18.3%

Selected in DR0.1%

Selected in FOR1.2%

Candidates 42.6%


36

In table 4.5 the approximate population structure in DWB based on the descriptive analysis in pre-vious section is shown. Since only whole numbers, both in the simulation program and in reality, can be selected for breeding, these numbers were adjusted in the simulations. Furthermore, the numbers of foals born each year were kept constant at 2,000 foals per population per year, even though it varied quite a lot between years in the actual Danish Warmblood populations. The number of mares were adjusted accordingly.

Table 4.5. The population structure forming the basis for the simulations.

Dressage Show jumping

Age when mating NSire+ NSire* NDam NSire

+ NSire* NDam

3 5.0 15.0 125.0 3.3 8.3 100.0 4 5.0 15.0 125.0 3.3 8.3 100.0 5 10.0 15.0 125.0 3.3 8.3 100.0 6 2.5 15.0 125.0 5.0 12.5 100.0 7 2.5 10.0 166.7 5.0 12.5 100.0 8 2.5 10.0 166.7 1.7 3.6 166.7 9 2.5 10.0 166.7 1.7 3.6 166.7

10 0.7 2.0 166.7 1.7 3.6 166.7 11 0.7 2.0 166.7 1.7 3.6 166.7 12 0.7 2.0 166.7 1.7 3.6 166.7 13 0.7 2.0 41.7 1.7 3.6 166.7 14 0.7 2.0 41.7 0.9 3.6 45.5 15 0.7 2.0 41.7 0.9 2.5 45.5 16 0.7 2.0 41.7 0.9 2.5 45.5 17 0.7 2.0 41.7 0.9 2.5 45.5 18 0.7 2.0 41.7 0.9 2.5 45.5 19 0.7 2.0 41.7 0.9 2.5 45.5 20 0.7 2.0 41.7 0.9 2.5 45.5 21 0.7 2.0 41.7 0.9 2.5 45.5 22 0.7 2.0 41.7 0.9 2.5 45.5 23 0.7 2.0 41.7 0.9 2.5 45.5 24 0.7 2.0 41.7 0.9 2.5 45.5

Nselected 40 120 2000 40 100 2000 *NSire represents all sires, including sire+ and sire-.

In both disciplines the proportion of foals by a sire+ was ≈90 %, resulting in each sire+ having 45 offspring per year:

90% 𝑜𝑓𝑓𝑠𝑝𝑟𝑖𝑛𝑔 𝑏𝑦 𝑠𝑖𝑟𝑒+ = 1800 𝑜𝑓𝑓𝑠𝑝𝑟𝑖𝑛𝑔 𝑏𝑦 𝑎 𝑠𝑖𝑟𝑒+ →1800 𝑜𝑓𝑓𝑠𝑝𝑟𝑖𝑛𝑔 𝑏𝑦 𝑎 𝑠𝑖𝑟𝑒+

40 𝑠𝑖𝑟𝑒+

= 45 𝑜𝑓𝑓𝑠𝑝𝑟𝑖𝑛𝑔 𝑝𝑟 𝑠𝑖𝑟𝑒+

The number of foals by a sire- is for the dressage population 2 or 3:


37

10% 𝑜𝑓𝑓𝑠𝑝𝑟𝑖𝑛𝑔 𝑏𝑦 𝑠𝑖𝑟𝑒− = 200 𝑜𝑓𝑓𝑠𝑝𝑟𝑖𝑛𝑔 𝑏𝑦 𝑎 𝑠𝑖𝑟𝑒− →200 𝑜𝑓𝑓𝑠𝑝𝑟𝑖𝑛𝑔 𝑏𝑦 𝑎 𝑠𝑖𝑟𝑒+

80 𝑠𝑖𝑟𝑒−

= 2.5 𝑜𝑓𝑓𝑠𝑝𝑟𝑖𝑛𝑔 𝑝𝑟 𝑠𝑖𝑟𝑒−

The proportion of foals by a sire- is for the show jumping population 3 or 4:

10% 𝑜𝑓𝑓𝑠𝑝𝑟𝑖𝑛𝑔 𝑏𝑦 𝑠𝑖𝑟𝑒− = 200 𝑜𝑓𝑓𝑠𝑝𝑟𝑖𝑛𝑔 𝑏𝑦 𝑎 𝑠𝑖𝑟𝑒− →200 𝑜𝑓𝑓𝑠𝑝𝑟𝑖𝑛𝑔 𝑏𝑦 𝑎 𝑠𝑖𝑟𝑒+

60 𝑠𝑖𝑟𝑒−

= 3.33 𝑜𝑓𝑓𝑠𝑝𝑟𝑖𝑛𝑔 𝑝𝑟 𝑠𝑖𝑟𝑒−

Relative to the numerous number of traits evaluated by DWB, the number of traits in the simulations were reduced to four general traits for each population. These included young horse conformation (YC), young horse dressage ability (YD) or show jumping ability (YS), susceptibility to osteochondro-sis (OC) and the breeding goal traits; performance in international high-level dressage competitions (PD) or show jumping competitions (PS). Genetic parameters (estimates of heritabilities and genetic correlations) found in literature for these traits were either varying a lot or very limited. Therefore, genetic heritabilities used for YC, YD and YS in the simulations were based on averages findings of several authors since more literature exists on these traits (see table 4.6-4.8). Regarding OC, PD and PS, not much literature exists, and therefore, heritabilities and genetic correlations used in the sim-ulations were based on single findings on PD and PS by Viklund et al. (2010), and on OC by Stock and Distl (2006a), Stock and Distl (2006b), Stock and Distl (2008) and Van Grevenhof (2011). Genetic correlations were also decided from single findings in publications of same authors.


38

Table 4.6. Heritability estimates of young horse conformation traits

Trait Source Age Studbook h2 h2 (avg.)

Type

Jönsson et al. (2014a) 3-5 DWB 0.47

0.36

Viklund et al. (2008) 3 SWB 0.38 Thorén Hellsten et al. (2009) 3-5 DWB 0.45 Thorén Hellsten et al. (2009) 3-5 SWB 0.33

Seierø et al. (2016) 3-4 DWB (jumping horses) 0.29 Jönsson et al. (2014b) 4-5 SWB 0.23

Head-neck

Viklund et al. (2008) 3 SWB 0.21

0.24 Jönsson et al. (2014a) 3-5 DWB 0.35 Jönsson et al. (2014b) 4-5 SWB 0.20 Koenen et al. (1995) 4 KWPN 0.21

Front part Jönsson et al. (2014a) 3-5 DWB 0.36

0.35 Thorén Hellsten et al. (2009) 3-5 DWB 0.40 Seierø et al. (2016) 3-4 DWB (jumping horses) 0.28

Topline / back part

Seierø et al. (2016) 3-4 DWB (jumping horses) 0.21

0.30 Crolly (2010b) cited in Christiansen et al. (2010) 3-4 DWB 0.38


Frontlimbs

Thorén Hellsten et al. (2009) 3-5 DWB 0.20 0.18 Seierø et al. (2016) 3-4 DWB (jumping horses) 0.17


Hindlimbs Thorén Hellsten et al. (2009) 3-5 DWB 0.20

0.16 Seierø et al. (2016) 3-4 DWB (jumping horses) 0.16 Jönsson et al. (2014a) 3-5 DWB 0.13

Average* All above 3-5 Warmblood horses 0.27 DWB = Danish Warmblood, SWB = Swedish Warmblood, KWPN = Dutch Warmblood. *Including; type, head-neck, frontpart, topline, frontlimbs and hindlimbs as in the linear profile of DWB.


39

Table 4.7. Heritability estimates of young horse dressage ability traits.

Trait Source Age Studbook Rider h2 h2 (avg.)

Walk

Ducro et al. (2007a) 3-7 KWPN No 0.19

0.28

Thorén Hellsten et al. (2009) 3-5 SWB Yes 0.33 Thorén Hellsten et al. (2009) 3-5 DWB Yes 0.20

Wallin et al. (2003) 4 SWB Yes 0.27 Borowska et al. (2011) 2-4 PWB No 0.41

Schade (1996) cited in Thorén Hellsten et al. (2006) 3-4 Hanoverian Yes 0.25

Trot


0.34


Wallin et al. (2003) 4 SWB Yes 0.23 Borowska et al. (2011) 2-4 PWB No 0.44

Schade (1996) cited in Thorén Hellsten et al. (2006) 3-4 Hanoverian Yes 0.37

Canter

Ducro et al. (2007a) 3-7 KWPN No 0.21**

0.25


Wallin et al. (2003) 4 SWB Yes 0.17** Schade (1996) cited in Thorén

Hellsten et al. (2006) 3-4 Hanoverian Yes 0.33**

Rideabil-ity*

Thorén Hellsten et al. (2009) 3-5 SWB Yes 0.29

0.30

Thorén Hellsten et al. (2009) 3-5 DWB Yes 0.22 Borowska et al. (2011) 3-4 PBW Yes 0.43**

Schade (1996) cited in Thorén Hellsten et al. (2006) 3-4 Hanoverian Yes 0.30**

Teegen et al. (2008) 3 Trakehner Yes 0.28**

Capacity*

Thorén Hellsten et al. (2009) 3-5 SWB Yes 0.29

0.25 Thorén Hellsten et al. (2009) 3-5 DWB Yes 0.22 Boelling (2010) cited in

Christiansen et al. (2010) 3-7 DWB Yes 0.25

Aver-age*** All above 2-7 Warmblood

horses With/with

out 0.28

DWB = Danish Warmblood, SWB = Swedish Warmblood, KWPN = Dutch Warmblood, PBW = Polish Warmblood. *Rideability and capacity are often evaluated both by a judge and a test-rider, but as it is only the evaluation by the judge that is included in the breeding value estimation of Danish Warmbloods (Thorén Hellsten et al., 2009), it is the heritability estimate based on the judge evaluation that is prioritized firstly to define YD. **The estimate is based on an evaluation of both dressage and jumping horses together. *** Including; walk, trot, canter, rideability and capacity.


40

Table 4.8. Heritability estimates of young horse show jumping ability traits.

Trait Source Age Studbook Rider h2 h2 avg.

Canter

Seierø et al. (2016) 3-4 DWB Mixed 0.32

0.21

Boelling (2010) cited in Christiansen et al. (2010) 3-7 DWB No 0.22

Boelling (2010) cited in Christiansen et al. (2010) 3-7 DWB Yes 0.14

Wallin et al. (2003) 4 SWB Yes 0.17** Ducro et al. (2007a) 3-7 KWPN No 0.21**

Jumping technique


0.24

Thorén Hellsten et al. (2009) 3-5 DWB Yes 0.26 Thorén Hellsten et al. (2009) 3-5 SWB Yes 0.25

Boelling (2010) cited in Christiansen et al. (2010) 3-7 DWB No 0.27


Jumping ca-pacity


0.30

Thorén Hellsten et al. (2009) 3-5 SWB Yes 0.21 Boelling (2010) cited in

Christiansen et al. (2010) 3-7 DWB No 0.33


Rideability*


0.28

Thorén Hellsten et al. (2009) 3-5 DWB Yes 0.26 Boelling (2010) cited in

Christiansen et al. (2010) 3-7 DWB No 0.33


Borowska et al. (2011) 3-4 PWB Yes 0.43** Teegen et al. (2008) 3 Trakehner Yes 0.28**

Schade (1996) cited in Thorén Hellsten et al. (2006) 3-4 Hanoverian Yes 0.30**

Average*** All above 3-7 Warmblood horses

With/with out 0.26

DWB = Danish Warmblood, SWB = Swedish Warmblood, KWPN = Dutch Warmblood, PBW = Polish Warmblood. * Rideability is often evaluated both by a judge and a test-rider, but as it is only the evaluation by the judge that is included in the breeding value estimation of Danish Warmbloods (Thorén Hellsten et al., 2009), it is only heritability estimate based on the judge evaluation that is prioritized firstly to define YS. **The estimate is based on an evaluation of both dressage and jumping horses together. ***Including; canter, jumping technique, jumping capacity and rideability.

5 Paper manuscript

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5 Paper manuscript

Simulating the Potential of Genomic Selection in Danish Warmblood Horses

S. A. G. Favrelle Department of Molecular Biology and Genetics, Centre for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark.

Abstract Phenotypic records of breeding goal traits in sport horses are not available until late in life. Conse-quently, generation intervals become long and early selection decisions uncertain. Genomic selec-tion (GS) has proven to be efficient in reducing generation intervals and increasing accuracies of selection in other species while improving the genetic gain. The objective of this study was therefore to demonstrate the potential of GS in Danish Warmblood horse breeding schemes. Different sce-narios incorporating genomic information were investigated and compared with current selection practice in both the dressage and the show jumping population. Results indicated large potential of selecting based on estimated breeding values prior to implementation of GS. Using GS on 3-year old stallions increased the genetic gain with 30 % at high accuracies of the SNP-genotypes. Increases of 50 % in genetic gain were observed by selecting mares based on BLUP-EBVs instead of randomly, and higher increases were found when using GS combined with reproductive techniques as embryo transfer. Incorporating SNP-genotypes at low accuracies resulted in higher increases in rates of in-breeding than at high accuracies. Selection towards the breeding goal traits showed to improve the susceptibility to osteochondrosis by assuming only weak, but favourable genetic correlations. Re-duced generation intervals were obtained, but were not result of implementing GS only. Overall, this study showed large potential of genetic gain prior to implementation of GS, and potential of GS to improve the genetic gain of Danish Warmblood horses further compared to current practice.

Keywords: Genomic selection, horse breeding, breeding values, accuracy of selection, generation interval, inbreeding.

Introduction Using genomic information for genetic evaluations in animal breeding schemes, have within the last decade evolved considerably (Stock and Reents, 2013) and is now routinely used in several domes-ticated species. In horses however, the concept of genomic selection (GS) has not yet been imple-mented, neither in sport nor in racing horses (Stock et al., 2016). This is in spite expectations of great potential of genetic gain due to high generation intervals and low accuracies on young horses in current horse breeding schemes. In recent years though, the interest for GS in horses has started to emerge. The Danish Warmblood Association (DWB) is among the horse breeding association wishing to strengthen their breeding scheme by including genomic information in their evaluations.

5 Paper manuscript

42

In current breeding schemes of Danish Warmblood horses Best Linear Unbiased Prediction (BLUP) is used to estimate three single-trait breeding values of young horse dressage or show jumping abil-ity, young horse championships and competition results of dressage or show jumping. However, these estimated breeding values (BLUP-EBVs) are not published until several offspring records are available. Consequently they are not available at the time of selection. Several indicator traits of young horses are therefore used to base phenotypic selection decisions on instead. These might not be the most efficient selection criterions. Therefore, GS is expected to increase the accuracies at a younger age, such genomic estimated breeding values (GEBVs) can be used to make more accurate selection decisions on, and thereby increase the genetic gain. In return, this will make it possible to lower the generation interval and utilise better the maternal pathway, where the selection intensity is low. Genomic selection is furthermore expected to make it easier to control inbreeding in the population due to better estimates of the Mendelian sampling variance when using genomic rela-tionship matrices (Hayes et al., 2009b). The aim of this study was to demonstrate the potential of GS in the breeding schemes of Danish Warmblood horses on the genetic gain of the breeding goal traits, rates of inbreeding and genera-tion interval. Different scenarios of implementing genomic information is assessed through stochas-tic simulations, and compared to current breeding scheme in DWB.

Material and methods Two Danish Warmblood horse populations were simulated. The first reflecting the dressage popu-lation, the second reflecting the show jumping population. In each population nine different scenar-ios were considered, and in the scenarios where genomic information was included, each scenario was simulated twice, once with an accuracy of 0.2 on the SNP-genotypes and secondly with an ac-curacy of 0.6 on the SNP-genotypes. In total 16 scenarios were simulated for each population. The scenarios were made as succeeding scenarios, meaning that each scenario originated from the pre-vious scenario, but with the only difference that one factor was changed. A description of the sce-narios is presented in table 1, and supported by a schematic overview in figure 1. The traits listed below were included in the simulations. Thus, the traits evaluated were reduced and simplified significantly, compared to reality.

x Young horse conformation (YC) x Young horse dressage ability (YD) or Young horse show jumping ability (YS) x Susceptibility to osteochondrosis (OC) x Performance in high-level dressage competitions (PD) or Performance in high-level show jump-

ing competition (PS)

Since the overall breeding goal in DWB is: “Capacity in either show jumping or dressage to compete on international level”, the breeding goals used in these simulations were PS or PD, according to discipline. These are also the breeding goals of many other sport horse breeds (Koenen et al., 2004).

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Table 1. Description of scenarios simulated. Scenario Description

Scenario 1: Pheno-typic selection on 3-year old stallions - current selection practice

Scenario 1 was made to mimic the current selection practice in DWB. Truncation selection of 3-year old stallions was based on own phenotypes from the three indi-cator traits, and the rest of the stallions were selected by randomly. All mares were selected randomly as well. Relative weights between the traits are shown in the box, and were chosen based on supposed values.

Dressage Show jumping wYC 1 1

wYD/wYS 1 2 wOC -1 -1

Scenario 2: Index selection on 3-year old stallions

In scenario 2, 3-year old stallions were selected based on BLUP-EBVs, calculated from multi-trait models. In this way, phenotypes from relatives were also contrib-uting with information to the BLUP-EBV of each 3-year old stallion.

Scenario 3: Ge-nomic selection on 3-year old stallions

In scenario 3, all 1-year old colts were genotyped, and 3-year-old stallions were se-lected based on BLUP-GEBVs. BLUP-GEBVs were calculated for all male candidates, except from base animals older than 1 year, but only used for selection decisions of 3-year olds stallions. Thus, 4-24-year old stallions were still selected randomly.

Scenario 4: Ge-nomic selection on 4-24-year old stal-lions

In scenario 4, all stallions were selected based on BLUP-GEBVs.

Scenario 5: Flexible age structure of 4-24-year old stal-lions

In scenario 5, the number of selected 3-year old stallions was the same as previous scenarios, but now there were no restrictions on how many stallions should be se-lected in each age class on the 4-24-year olds stallions. Due to restrictions on the number of selected 3-year old stallions, the number selected in the other age classes was limited to this number.

Scenario 6: Index selection on all mares

In scenario 6, all mares were selected based on BLUP-EBVs, calculated from multi-trait models. In this way, phenotypes from relatives were now contributing with in-formation to the BLUP-EBV of each mare.

Scenario 7: Ge-nomic selection on 3-year old mares

In this scenario, all fillies were genotyped at the age of 1-year, and selected as 3-year olds, based on BLUP-GEBVs. From this scenario, all horses were genotyped and se-lected based on BLUP-GEBVs.

Scenario 8: Flexible age structure of 4-24-year old mares

In scenario 8, the number of selected 3-year old mares was the same as previous scenarios, but now there were no restrictions on how many mares should be se-lected in each age class on the 4-24-year olds mares. Even though restrictions were made on the number of selected 3-year old mares, the number selected in the other age classes were not limited to this number since mares were kept alive after being rejected.

Scenario 9: Embryo transfer on the best mares

In scenario 9, embryo transfer (ET) was carried out on the best mares; the 20 % best 3-year-old mares, and the 10 % best 4-24-year-old mares. The mares had as previous scenarios 2,000 foals in total, but because not as many mares were needed to pro-duce 2,000 foals when ET was applied, the number of mares was downscaled. Thus, the number of mares used to produce 2,000 foals in scenario 9a and 9b were 1,670, and the number of mares used in scenario 9c and 9d were 1,668.

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Figure 1. Schematic representation of the scenarios simulated.

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Genetic evaluation In cases where the genetic evaluation was based on phenotype alone, the total merit index (𝐼) for the dressage population, reflecting the breeding goal, PD, was:

𝐼 = 𝑤𝑌𝐶 ∙ 𝐸𝐵𝑉𝑌𝐶 + 𝑤𝑌𝐷 ∙ 𝐸𝐵𝑉𝑌𝐷 + 𝑤𝑂𝐶 ∙ 𝐸𝐵𝑉𝑂𝐶,

where 𝑤 is the relative weight assigned to the trait and 𝐸𝐵𝑉 is the estimated breeding value for the trait. The total merit index for the show jumping population was the same, except for the trait YD being replaced by YS. In cases where genetic evaluation was based on BLUP-GEBVs, pseudo SNP-genotypes were calcu-lated to mimic the sum of SNP effects on each trait. The pseudo SNP-genotypes were treated as four additional traits, and were calculated using the equations in table 2.

Table 2. Expressions used for calculating the co-variance matrix of the pseudo SNP-genotypes*.

SNPYC SNPYD SNPOC SNPPD YC 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑌𝐶,𝑌𝐶 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑌𝐶,𝑌𝐷 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑌𝐶,𝑂𝐶 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑌𝐶,𝑃𝐷 YD 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑌𝐷,𝑌𝐶 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑌𝐷,𝑌𝐷 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑌𝐷,𝑂𝐶 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑌𝐷,𝑃𝐷 OC 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑂𝐶,𝑌𝐶 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑂𝐶,𝑌𝐷 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑂𝐶,𝑂𝐶 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑂𝐶,𝑃𝐷 PD 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑃𝐷,𝑌𝐶 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑃𝐷,𝑌𝐷 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑃𝐷,𝑂𝐶 𝑟𝐴𝐼 ∙ 𝑟𝑔𝑃𝐷,𝑃𝐷

SNPYC 𝑟𝑔𝑌𝐶,𝑌𝐶 𝑟𝐴𝐼2 ∙ 𝑟𝑔𝑌𝐶,𝑌𝐷 𝑟𝐴𝐼

2 ∙ 𝑟𝑔𝑌𝐶,𝑂𝐶 𝑟𝐴𝐼2 ∙ 𝑟𝑔𝑌𝐶,𝑃𝐷

SNPYD 𝑟𝐴𝐼2 ∙ 𝑟𝑔𝑌𝐷,𝑌𝐶 𝑟𝑔𝑌𝐷,𝑌𝐷 𝑟𝐴𝐼

2 ∙ 𝑟𝑔𝑌𝐷,𝑂𝐶 𝑟𝐴𝐼2 ∙ 𝑟𝑔𝑌𝐷,𝑃𝐷

SNPYS 𝑟𝐴𝐼2 ∙ 𝑟𝑔𝑂𝐶,𝑌𝐶 𝑟𝐴𝐼

2 ∙ 𝑟𝑔𝑂𝐶,𝑌𝐷 𝑟𝑔𝑂𝐶,𝑂𝐶 𝑟𝐴𝐼2 ∙ 𝑟𝑔𝑂𝐶,𝑃𝐷

SNPPD 𝑟𝐴𝐼2 ∙ 𝑟𝑔𝑃𝐷,𝑌𝐶 𝑟𝐴𝐼

2 ∙ 𝑟𝑔𝑃𝐷,𝑌𝐷 𝑟𝐴𝐼2 ∙ 𝑟𝑔𝑃𝐷,𝑂𝐶 𝑟𝑔𝑃𝐷,𝑃𝐷

YC = Young horse conformation, YD = Young horse dressage ability, OC = Susceptibility to Osteochondrosis, PD = Performance in high-level dressage competition, SNP = The sum of SNP effects on the given trait. rAI = Accuracy between true and estimated breeding value. rg = Genetic correlation between two given traits. *Expressions for the dressage population only are shown. For the show jumping population, the expressions were the same, except for YD being replaced by YS, and PD being replaced by PS.

Environmental correlations, re, were assumed equal to 0. The multi-trait animal model used for the dressage population was:

[

𝑦𝑌𝐶𝑦𝑌𝐷𝑦𝑂𝐶𝑦𝑃𝐷

𝑦𝑆𝑁𝑃𝑌𝐶𝑦𝑆𝑁𝑃𝑌𝐷𝑦𝑆𝑁𝑃𝑂𝐶𝑦𝑆𝑁𝑃𝑃𝐷]

= 𝑋 ∙

[

𝑏𝑌𝐶𝑏𝑌𝐷𝑏𝑂𝐶𝑏𝑃𝐷

𝑏𝑆𝑁𝑃𝑌𝐶

𝑏𝑆𝑁𝑃𝑌𝐷

𝑏𝑆𝑁𝑃𝑂𝐶

𝑏𝑆𝑁𝑃𝑃𝐷]

+ 𝑍

[

𝑎𝑌𝐶𝑎𝑌𝐷𝑎𝑂𝐶𝑎𝑃𝐷

𝑎𝑆𝑁𝑃𝑌𝐶𝑎𝑆𝑁𝑃𝑌𝐷𝑎𝑆𝑁𝑃𝑂𝐶𝑎𝑆𝑁𝑃𝑃𝐷]

+

[

𝑒𝑌𝐶𝑒𝑌𝐷𝑒𝑂𝐶𝑒𝑃𝐷

𝑒𝑆𝑁𝑃𝑌𝐶𝑒𝑆𝑁𝑃𝑌𝐷𝑒𝑆𝑁𝑃𝑂𝐶𝑒𝑆𝑁𝑃𝑃𝐷]

,

where 𝑦 is the phenotypic observation, 𝑏 is the deviation from the population caused by fixed ef-fects, 𝑎 is the random genetic effect of the individual defining the genomic breeding value, and 𝑒 is the random residual effect. SNP-subscripts denotes the pseudo pheno- or genotype. 𝑋 is the inci-dence matrix relating phenotypic observations to the fixed effects and 𝑍 is the incidence matrix

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relating phenotypic observations to the random effects. For 𝑎 and 𝑒 respectively, it was furthermore assumed that:

[

𝑎𝑌𝐶𝑎𝑌𝐷𝑎𝑂𝐶𝑎𝑃𝐷

𝑎𝑆𝑁𝑃𝑌𝐶𝑎𝑆𝑁𝑃𝑌𝐷𝑎𝑆𝑁𝑃𝑂𝐶𝑎𝑆𝑁𝑃𝑃𝐷]

~𝑀𝑉𝑁

(

[ 00000000]

; 𝐺⨂𝐴

)

and

[

𝑒𝑌𝐶𝑒𝑌𝐷𝑒𝑂𝐶𝑒𝑃𝐷

𝑒𝑆𝑁𝑃𝑌𝐶𝑒𝑆𝑁𝑃𝑌𝐷𝑒𝑆𝑁𝑃𝑂𝐶𝑒𝑆𝑁𝑃𝑃𝐷]

~𝑀𝑉𝑁

(

[ 00000000]

; 𝑅⨂𝐼

)

,

where 𝐺 is the additive genetic variance and covariance matrix for random animal effects, 𝐴 is the relationship matrix, 𝑅 is the variance and covariance matrix for residual effects, and 𝐼 is an identity matrix. The model for the show jumping population was the same, except for YD replaced by YS and PD replaced by PS. In cases where genetic evaluation was based on BLUP-EBVs, this model was also applicable, though in a more simplified form, where only the four first traits were included.

Genetic parameters The genetic parameters used in the simulations are given in table 3. The parameters are based on findings of various authors, as described in the table. Residual variances are calculated based on the heritability estimates and the assumption of an additive genetic variance equal to 1.

Table 3. Genetic correlations between traits (rg) under diagonal, heritabilities (h2) in the diagonal and residual variances (σe

2) in the last column.

YC YD YS OC PD PS 𝝈𝒆𝟐

YC 0.271 2.70 YD 0.772 0.281 2.57 YS 0.382 Na 0.261 2.85 OC -0.054 -0.054 -0.054 0.354 1.86 PD 0.653 0.673 Na -0.054 0.153 5.67 PS 0.253 Na 0.753 -0.054 Na 0.283 2.57

YC = Young horse conformation, YD = Young horse dressage ability, YS = Young horse show jumping ability, OC = Susceptibility to Osteochondrosis, defined as negative correlations being favourable in relation to the breeding goal, PD = Performance in high-level dressage competition, PS = Performance in high-level show jumping competition. 1Calculated as average between findings of various authorsi. 2(Viklund et al., 2008). 3(Viklund et al., 2010). 4Approximations mainly based on findings in relation to OC in the hock (Stock and Distl, 2006b; Stock and Distl, 2006a, 2008; Van Grevenhof, 2011).

Population structure To define the population structure to be used in the simulation program, a descriptive analysis was made on real data on Danish Warmblood foals born in the years 2005-2015. From this, average numbers of sires and dams, offspring per sire and per dam, and the age distribution of mares and

iKoenen et al. (1995), Schade (1996), Wallin et al. (2003), Thorén Hellsten et al. (2006), Ducro et al. (2007), Teegen et al. (2008), Viklund et al. (2008), Thorén Hellsten et al. (2009), Borowska et al. (2011), Jönsson et al. (2014a), Jönsson et al. (2014b), Seierø et al. (2016), Boelling (2010) and Crolly (2010) cited by Christiansen et al. (2010), and Schade (1996) cited by Thorén Hellsten et al. (2006).

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stallions when mating were obtained. In both populations, breeding animals were set to be at least 3 years old and maximum 24 years old in the simulations as the number of breeding animals outside this age interval was limited in DWB. All stallions rejected for breeding were culled, whereas rejected mares were kept alive. Since not all stallions in DWB were used with same intensity, a distinction was made, such that sires used intensively (sire+, defined as stallions being sire to at least 0.5 % of the foals born in a given year) in the simulations were selected first, where after less intensively used sires (sire-, defined as stallions being sire to less than 0.5 % of the foals born in a given year) were selected. In the dressage population, each sire+ was set to have 45 offspring per year, and each sire- was set to have either 2 or 3 offspring per year. In the show jumping population each sire+ was set to have 45 offspring per year, and each sire- was set to have 3 or 4 offspring per year, according to the results of the descriptive analysis. In the simulations 2,000 foals were born per year, and the number of dams, number of foals per sire+ and per sire was adjusted according to this number such that the proportions between actual population structure in DWB and the simulations were the same. Due to the number of sire matings not adding completely up with the number of dams, a surplus of 185 dressage sire matings and 74 show jumping sire matings were removed randomly by the simulation program. In table 4, an overview of the age distribution of breeding animals, used in the simulation, is shown. These represent approximately the real age distribution in DWB as found in the descriptive analysis.

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Table 4. The population structure forming the basis for the simulations. Broken lines indicate 25 % quartiles.

Dressage Show Jumping Age when

mating NSires+ NSires* NDams NSires

+ NSires** NDams

3 5 15 125 4 9 100 4 5 15 125 4 9 100 5 10 15 125 4 9 100 6 3 15 125 5 13 100 7 2 10 167 5 13 100 8 2 10 167 2 4 167 9 2 10 167 2 4 167

10 1 2 167 2 4 167 11 1 2 166 1 4 167 12 1 2 166 1 4 166 13 1 2 42 1 4 166 14 1 2 42 1 3 46 15 1 2 42 1 2 46 16 1 2 42 1 2 46 17 1 2 42 1 2 46 18 1 2 42 1 2 46 19 1 2 42 1 2 45 20 1 2 42 1 2 45 21 1 2 41 1 2 45 22 1 2 41 1 2 45 23 1 2 41 1 2 45 24 1 2 41 1 2 45

Nselected 44 120 2000 42 100 2000 *NSire represents all dressage sires, including sires+ and sires-. Sires- in the age classes 3-8 were set to have 3 offspring each, and sires- in the age classes 9-24 were set to have 2 offspring each. All sires+ were set to have 45 offspring each. **NSire represents all show jumping sires, including sires+ and sires-. Sires- in the age classes 3-4 were set to have 4 offspring each, and sires- in the age classes 5-24 were set to have 3 offspring each. All sires+ were set to have 45 offspring each.

Traits evaluated The breeding goal; performance in international high-level dressage competitions (PD) or show jumping competitions (PS), is not recorded until late in life, and therefore it is important to evaluate highly correlated traits at a young age (Thorén Hellsten et al., 2006). In DWB numerous traits rec-orded from the age of 3 years are used to evaluate young horses. In the simulations, these indicator traits were reduced in number and rather simplified compared to reality, as already mentioned. Thus, they include the traits; YC, YD, YS, OC, PD and PS. Conformation traits are registered by DWB, in most cases when the horse is around 3 years old, at different test and grading events. Jönsson et al. (2014a) found overall young horse conformation to be an important trait in DWB to indicate dressage competition results later in life as they were found to have a genetic correlation of 0.64. Viklund et al. (2010) also found similar genetic correlations between total conformation and dressage competition traits (0.60-0.71). The genetic correlations

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found between total conformation and show jumping competition were not as high though (0.22-0.31). The findings by Viklund et al. (2010) were based on evaluation from the Swedish warmblood association (SWB), and thus, the evaluation traits are not exactly the same as in DWB. Evaluation of conformation traits is however very important and highly prioritized in most breeding associations, and together with the generally high genetic correlations, this makes conformation a fundamental trait to include in the simulations. In DWB, dressage ability or show jumping ability is also evaluated from the age of around 3 years. Jönsson et al. (2014a) found that results from young horse gait evaluations in DWB generally were highly correlated with dressage competition results later in life (0.70-0.83). In young horse evalua-tions in SWB, Viklund et al. (2010) found intermediate to high genetic correlations (0.47-0.73) be-tween gaits and dressage competition traits in SWB. In the Royal Dutch Warmblood Riding Horse Studbook (KWPN), Ducro et al. (2007b) found genetic correlations between young horse gait traits and performance in dressage competition ranging from 0.37 (canter) to 0.72 (walk). Also, Viklund et al. (2010) found in SWB, medium genetic correlations between young horse evaluation of canter and show jumping competition traits (0.33-0.39). High genetic correlations between young horse evaluations of jumping technique and show jumping competition traits (0.87-0.88) were also found. In KWPN, Ducro et al. (2007b) found similar high genetic correlations between young horse jumping traits (technique, take-off, power) and performance in show jumping competition (0.81-0.92). Thus, overall young horse dressage and show jumping ability, generally have intermediate to high genetic correlations, and even though the evaluation traits are not exactly the same between breeding as-sociations, these findings demonstrate that young horse evaluations functions as good indicators for future performance in dressage and show jumping competitions, which is why they were in-cluded in the simulations.

Osteochondrosis is multifactorial in origin and occurs in young and growing horses (Van Grevenhof et al., 2009). It causes developmental disorders, by disturbing the ossification process in the joints, through mineralisation of cartilage and transformation into bone (Van De Lest et al., 1999). In DWB, OC is only registered in stallions and at the earliest when the stallion has passed the pre-selection and candidates for the stallion grading show as at least 3-year old. At this time, breeders are obliged to hand in x-rays, where OC and other abnormalities can be detected. In the future, DWB hope to develop an OC index model to provide breeders OC indices on all stallions, when enough data is collected (Christiansen, 2011). Not many studies have been conducted regarding the relation be-tween OC and future high-level performance in dressage or show jumping, but few have been fo-cusing on other disciplines in the equine industry. Couroucé-Malblanc et al. (2006) found that OC together with other diseases in joints, significantly decreased the longevity of French Standardbred trotters, and Riley et al. (1998) found OC to cause lameness in draft horses. One of the few studies investigating the impact of OC findings in young horses on future performance as riding horses were Verwilghen et al. (2013) who found OC to cause lowered performance in show jumping. Previously it was suggested that OC was present in 10-25% of the horse population across different breeds (Jeffcott, 1996), and more recently, Hilla and Distl (2014), found that 28 % of the horses included in

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the specific study (7,396 Hanoverian Warmblood horses) had at least one occurrence of OC in fet-lock, hock or stifle joints. Retirement from competition and/or culling is mainly due to lameness and orthopaedic diseases including OC. Therefore, the future performance, thus also the sales value, is highly related to the radiographic health status of the horse as it indicates a risk in relation to the longevity of the horse (Van Hoogmoed et al., 2003). Especially for export, radiographic health status is of significant importance (Van Grevenhof, 2011). Therefore, OC and related disorders are empha-sized in many horse breeding associations (Stock and Distl, 2008). This is why the trait was included in the simulations. In literature the heritability estimates of OC in warmblood horses are varying greatly (0.02-0.37) (Pieramati et al., 2003; Stock and Distl, 2008; Van Grevenhof et al., 2009; Distl, 2013), most likely due to the complexity of the trait and differences in data collection and study methods. Distl (2013) reports that OC in hock joints appears to be the most promising for GS to improve, due to a higher heritability (0.19–0.37) than in other joints. Therefore, it is OC in the hock that forms the basis for genetic parameters in relations to this trait in the simulations.

The simulation program The scenarios were assessed by stochastic simulations made in the simulation program ADAM, de-veloped by Pedersen et al. (2009). For each scenario, the program was specified to run a polygenic multi-trait model, and to simulate 50 time steps, with 50 replicates. Each time step was defined as 1 year, approximately corresponding to the reproductive cycle of horses, whose pregnancy lasts for 11 months. The base population is generated from the previously defined population structure, and the program was told to cull horses of both gender at the age of 25 years. The program computed genetic trends and variances for each of the four traits, inbreeding level and generation intervals for each scenario.

Statistical evaluation In the evaluation, the first 20 time steps were thrown away to ensure that the selection had reached a steady state and that base animals were no longer a part of the population. Genetic trends were assessed by the regression coefficient of the mean true breeding value of foals born each year, on the interaction between year of birth and replicate:

∆𝐺𝑗𝑘~𝑡𝑖𝑚𝑒𝑠𝑡𝑒𝑝𝑗 ∙ 𝑟𝑒𝑝𝑙𝑖𝑐𝑎𝑡𝑒𝑘,

where 𝑗 =30,…,50 and 𝑘 =1,…,50.

The rate of inbreeding per generation was not linear, thus:

∆𝐹 = 1 − exp(−𝛽)

where 𝛽 is the regression coefficient estimated from a linear regression of −log (1 − 𝐹) on mean generation coefficient of foals born in each year. The generation interval was simply the average of each replicate. Least significant differences (LSD) were calculated for each parameter (∆GYC, ∆GYD, ∆GYS, ∆GOC, ∆GPD, ∆GPS, ∆F and GI) to identify the scenarios whose mean were statistically different:

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𝐿𝑆𝐷0.95 = 𝑡0.975,49 ∙ √2 ∙ 𝜎��2,

where 𝑡0.975,49 is the t-value for a significance level of 0.05 on a two-tailed t-distribution with 49 degrees of freedom. 𝜎��

2 is the standard error of the mean regression coefficient estimated on the given parameter. It was assumed that:

𝜎(��1−��2) = √𝜎��12 + 𝜎��2

2 ≅ √2 ∙ 𝜎��2

The highest estimate of the standard error within each parameter is therefore used to cover all scenarios, such that only one LSD value is obtained for each parameter. The mean of two parame-ters, whose difference is larger than the LSD value were declared significantly different.

Results Below the results from the simulations are described. They are described separately for both popu-lations, even though they in general show the same patterns.

The dressage population In table 5, a summary of the simulation results in the dressage population is given, and in figure 2, 3, and 4, for illustration purposes, the results from four of the different scenarios (1a, 2a, 4b and 9b), in relation to genetic trend and variance of PD, and inbreeding level, are shown. The genetic gain of PD showed a maximum increase more than four times greater than the genetic gain of the current selection practice (∆GPD=0.0464 vs. 0.2008). Generally, a higher ∆GPD was ob-served in scenarios with high accuracy (rAI=0.6), compared to scenarios with low accuracy (rAI=0.2). The genetic gain of PD increased approximately 50 % (∆GPD=0.0464 vs. 0.0686), by using BLUP-EBVs in the selection of 3-year old sires for next generation, instead of own phenotypes (Scenario 1a and 2a, see figure 2). At low accuracies of the SNP-genotypes, implementing selection based on BLUP-GEBVs on 3-year old stallions (scenario 3a), did only increase the genetic gain of PD slightly, whereas at higher accuracies of the SNP-genotypes (scenario 3b) the genetic gain increased with 30 %, com-pared to selection based on BLUP-EBVs. When selecting all stallions based on BLUP-GEBVs instead of only the 3-year olds (scenario 4a and 4b), the genetic gain of PD increased significantly, even at low accuracies of the SNP-genotypes. Making the age structure more flexible compared to current practice resulted only in insignificant increases at low accuracies of the SNP-genotypes (scenario 5a) and only small increases at high accuracies of the SNP-genotypes (scenario 5b). Similar increases in genetic gain of PD (50 %) was shown when selecting mares based on BLUP-EBVs instead of own phenotypes, randomly (scenario 6a; ∆GPD=0.0910 vs. 0.1361 and scenario 6b; ∆GPD=0.1094 vs. 0.1626) as shown when doing the same on the stallion side. Selecting all mares based on BLUP-GEBVs did not show any significant difference in the genetic gain of PD at low accuracies of the SNP-genotypes (scenario 7a), but at high accuracies of the SNP-genotypes (scenario 7b), the increase in genetic gain was significantly different from previous scenario (∆GPD=0.1626 vs. 0.1704). When the age structure of the mares was made more flexible, and all mares were selected based on BLUP-

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GEBVs, regardless of their age (scenario 8a and 8b), the genetic gain of the breeding goal traits in-creased slightly, but still significantly different from previous scenario. When the selection intensity on the mares increased by applying embryo transfer on the best mares (scenario 9a and 9b), the genetic gain of PD increased significantly at both low and high accuracies of the SNP-genotypes (∆GPD=0.1422 vs. 0.1627 and ∆GPD=0.1793 vs. 0.2008, respectively). In figure 3 it is shown that the variance of PD decreases a lot the first 15-20 years of the simulations for scenario 9b, contrary to scenario 1a, 2a and 4b, which increased simultaneously. The genetic gain for the indicator traits, YC, YD and OC (table 5) were also going in favourable direc-tions, but not quite as much as PD. YC and YD followed approximately the same patterns and the genetic gain went from approximately 0.06 in scenario 1a to 0.19 in scenario 9b. OC acted a little different as it dropped considerably from -0.0389 to -0.0062 from scenario 1a to scenario 2a. It increased slightly, but insignificantly in each of the scenarios up to scenario 9a and 9b, and did not reach the same level in any of the other scenarios as scenario 1a. The rate of inbreeding per generation increased from scenario 1a to scenario 9a from 0.0019 to 0.0123. From scenario 1a to scenario 9b the rate of inbreeding per generation increased from 0.0019 to 0.0057. Thus, a larger increase was seen with the low accuracy, than with the high accuracy. Between scenario 1a and scenario 2a, the rate of inbreeding increased (insignificantly) from 00.0019 to 0.0034. Significant increases in rates of inbreeding happened from scenario 3a to scenario 4a and from 3b to 4b, then from scenario 5a to 6a and 5b to 6b and from 8a to 9a. The inbreeding level for four of the scenarios is shown in figure 4. The generation interval was through the scenarios (table 5), reduced from 10.81 years to 6.58 and 6.23 years, for scenarios with low and high accuracies of the SNP-genotypes, respectively. The first drop was seen in scenario 5a and 5b, where the age structure of the stallions was made more flexi-ble. It resulted in a drop from previous scenario on 0.85 and 1.45 years, respectively. From scenario 5 to scenario 7 the generation interval dropped slightly, but the next large drop happened when the age structure of the mares became flexible, where a drop of 2.3 and 2.4 years were seen for low and high accuracies of the SNP-genotypes, respectively in scenario 8a and 8b. Also significant drops was seen from scenario 8 to scenario 9. In figure 5, the genetic gain per year, in units of genetic standard deviations, of PD is shown for every scenario involving the dressage population. Symbolized by similar letters, the figure shows the sce-narios not significantly different at a significance level of 0.05. All scenarios without similar letters were declared significantly different using a LSD test.

The show jumping population In table 6, a summary of the simulation results in the show jumping population is given. The genetic gain of PS showed a maximum increase nearly six times greater than the genetic gain of the current selection practice (∆GPS=0.0345 vs. 0.1970). As were the case for the dressage population, a higher ∆GPS was observed in scenarios with high accuracy (rAI=0.6), compared to scenarios with low accu-racy (rAI=0.2).

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The genetic gain of PS increased approximately 90 % (∆GPS=0.0345 vs. 0.0652), by using BLUP-EBVs in the selection of 3-year old sires for next generation, instead of own phenotypes (scenario 1b and 2b). At low accuracies of the SNP-genotypes, implementing selection based on BLUP-GEBVs on 3-year old stallions (scenario 3c), did only increase the genetic gain of PS slightly, whereas at higher accuracies of the SNP-genotypes (scenario 3d) the genetic gain increased with 31 %, compared to selection based on BLUP-EBVs. When selecting all stallions based on BLUP-GEBVs instead of only the 3-year olds (scenario 4c and 4d), the genetic gain of PS increased significantly, even at low accuracies of the SNP-genotypes. Making the age structure more flexible compared to current practice resulted only in insignificant increases at low accuracies of the SNP-genotypes (scenario 5c) and only small increases at high accuracies of the SNP-genotypes (scenario 5d). Similar increases in genetic gain of PS (45-50 %) was shown when selecting mares based on BLUP-EBVs instead of own phenotypes, randomly (scenario 6c; ∆GPS=0.0858 vs. 0.1314 and scenario 6d; ∆GPS=0.1071 vs. 0.1649) as shown when doing the same on the stallion side. Selecting all mares based on BLUP-GEBVs did not show any significant difference in the genetic gain of PS at low accuracies of the SNP-genotypes (scenario 7c), but at high accuracies of the SNP-genotypes (scenario 7d), the increase in genetic gain was sig-nificantly different from previous scenario (∆GPS=0.1549 vs. 0.1668). When the age structure of the mares was made more flexible, and all mares were selected based on BLUP-GEBVs, regardless of their age (scenario 8c and 8d), the genetic gain of the breeding goal traits increased significantly from previous scenario. When the selection intensity on the mares increased by applying embryo transfer on the best mares (scenario 9c and 9d), the genetic gain of PS increased significantly at both low and high accuracies of the SNP-genotypes (∆GPS=0.1387 vs. 0.1545 and ∆GPS=0.1776 vs. 0.1970, respectively). The genetic gain for the indicator traits, YC, YD and OC (table 6) were also going in favourable direc-tions, but not quite as much as PS. For YC the genetic gain went from 0.035 in scenario 1b to 0.073 in scenario 9d. For YS the genetic gain went from 0.0473 in scenario 1b to 0.1896 in scenario 9d. As in the dressage population, OC acted a little different than the other indicator traits since it dropped considerably from -0.0240 to -0.0040 from scenario 1b to scenario 2b. It increased slightly, but in-significantly in each of the scenarios up to scenario 9c and 9d, and did not reach the same level in any of the other scenarios as scenario 1b. The rate of inbreeding per generation increased from scenario 1b to scenario 9c from 0.0019 to 0.0168. From scenario 1a to scenario 9d the rate of inbreeding per generation increased from 0.0019 to 0.0076. Thus, a larger increase was again seen with the low accuracy, than with the high accuracy. Between scenario 1b and scenario 2b, the rate of inbreeding increased from 00.0019 to ~0.0044. Next significant increases in rate of inbreeding happened from scenario 3c to scenario 4c and from 3d to 4d, then from scenario 5c to 6c and 5d to 6d and from 8c to 9c. The generation interval was through the scenarios (table 6), reduced from 11.50 years to 7.14 and 6.77 years, for scenarios with low and high accuracies of the SNP-genotypes, respectively. The first drop was seen in scenario 5c and 5d, where the age structure of the stallions was made more flexi-ble. It resulted in a drop from previous scenario on 0.80 and 1.35 years, respectively. From scenario

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5 to scenario 7 the generation interval dropped slightly, but the next large drop happened when the age structure of the mares became flexible, where a drop of 2.56 and 2.71 years were seen for low and high accuracies of the SNP-genotypes, respectively in scenario 8c and 8d. Also significant drops was seen from scenario 8c to scenario 9c. In figure 6, the genetic gain per year, in units of genetic standard deviations, of PS is shown for every scenario involving the show jumping population. Symbolized by similar letters, the figure shows the scenarios not significantly different at a significance level of 0.05. All scenarios without similar letters were declared significantly different using a LSD test.

Table 5. Summary of simulation results on the dressage population, with average genetic gain per year (∆G/year) for each of the four traits, rate of inbreeding per generation (∆F/gen) and generation interval (GI) for every scenario. LSD at a significance level of 0.05 (LSD0.95) is also shown.

Scenario ∆GPD/year ∆GYC/year ∆GYD/year ∆GOC/year ∆F/gen GI 1 0.0464

0.0686 0.0623 0.0624 -0.0389 0.0019 10.81

2 0.0752 0.0763 -0.0062 0.0034 10.81 rAI = 0.2 rAI = 0.6 rAI = 0.2 rAI = 0.6 rAI = 0.2 rAI = 0.6 rAI = 0.2 rAI = 0.6 rAI = 0.2 rAI = 0.6 rAI = 0.2 rAI = 0.6

3 0.0720 0.0895 0.0767 0.0827 0.0784 0.0844 -0.0078 -0.0073 0.0033 0.0021 10.81 10.81 4 0.0894 0.1051 0.0879 0.0944 0.0915 0.0966 -0.0074 -0.0087 0.0093 0.0060 10.81 10.81 5 0.0910 0.1094 0.0921 0.0997 0.0950 0.1022 -0.0075 -0.0087 0.0084 0.0047 9.96 9.36 6 0.1361 0.1626 0.1438 0.1521 0.1466 0.1549 -0.0121 -0.0137 0.0107 0.0063 9.23 8.94 7 0.1371 0.1704 0.1414 0.1551 0.1454 0.1595 -0.0150 -0.0113 0.0111 0.0054 9.23 8.90 8 0.1422 0.1793 0.1499 0.1656 0.1533 0.1685 -0.0125 -0.0144 0.0107 0.0050 6.91 6.49 9 0.1627 0.2008 0.1698 0.1855 0.1752 0.1893 -0.0131 -0.0145 0.0123 0.0057 6.58 6.23

LSD0.95 0.0031 0.0036 0.0036 0.0047 0.0016 0.036

Table 6. Summary of simulation results on the show jumping population, with average genetic gain per year (∆G/year) for each of the four traits, rate of inbreeding per generation (∆F/gen) and generation interval (GI) for every scenario. LSD at a significance level of 0.05 (LSD0.95) is also shown.

Scenario ∆GPS/year ∆GYC/year ∆GYS/year ∆GOC/year ∆F/gen GI 1 0.0345 0.0352 0.0473 -0.0240 0.0019 11.50 2 0.0652 0.0290 0.0688 -0.0040 0.0044 11.50 rAI = 0.2 rAI = 0.6 rAI = 0.2 rAI = 0.6 rAI = 0.2 rAI = 0.6 rAI = 0.2 rAI = 0.6 rAI = 0.2 rAI = 0.6 rAI = 0.2 rAI = 0.6

3 0.0686 0.0854 0.0311 0.0326 0.0709 0.0823 -0.0048 -0.0073 0.0039 0.0026 11.50 11.50 4 0.0858 0.1019 0.0370 0.0362 0.0854 0.0957 -0.0091 -0.0085 0.0108 0.0068 11.50 11.50 5 0.0884 0.1071 0.0373 0.0377 0.0886 0.1000 -0.0070 -0.0082 0.0100 0.0059 10.70 10.16 6 0.1314 0.1549 0.0566 0.0580 0.1373 0.1490 -0.0099 -0.0104 0.0134 0.0083 10.04 9.77 7 0.1336 0.1668 0.0544 0.0637 0.1380 0.1586 -0.0101 -0.0123 0.0147 0.0072 10.04 9.73 8 0.1387 0.1776 0.0587 0.0635 0.1450 0.1684 -0.0094 -0.0121 0.0139 0.0067 7.48 7.02 9 0.1545 0.1970 0.0693 0.0734 0.1634 0.1896 -0.0115 -0.0145 0.0168 0.0076 7.14 6.77

LSD0.95 0.0037 0.0049 0.0040 0.0055 0.0023 0.031

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Figure 2. Illustration of the genetic gain as a result of four of the scenarios; 1a representing current practice in DWB, 2a adding index selection on the 3-year old stallions to the current breeding scheme, 4b adding genomic selection with an accuracy of 0.6 on 3-year-old stallions and index selection on the rest of the stallions, and 9b adding index and genomic selection with an accuracy of 0.6 on all mares as well as a more flexible age structure on both sex.

Figure 3. Illustration of the genetic variance of PD as a result of four of the scenarios; 1a representing current practice in DWB, 2a adding index selection on the 3-year old stallions to the current breeding scheme, 4b adding genomic selec-tion with an accuracy of 0.6, on 3-year-old stallions and index selection on the rest of the stallions, and 9b adding index and genomic selection with an accuracy of 0.6 on all mares as well as a more flexible age structure on both sex.

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Figure 4. Illustration of the inbreeding level as a result of four of the scenarios; 1a representing current practice in DWB, 2a adding index selection on the 3-year old stallions to the current breeding scheme, 4b adding genomic selection with an accuracy of 0.6, on 3-year-old stallions and index selection on the rest of the stallions, and 9b adding index and genomic selection with an accuracy of 0.6 on all mares as well as a more flexible age structure on both sex.

Figure 5. Genetic gain per year in performance in high-level dressage competition (PD) for each scenario shown in units of genetic standard deviation. Similar letters above the bars indicate non-significant differences between the scenarios at a significant level of 0.05. At this significance level, differences between the means of the scenarios below 0.0031 standard deviation units were declared non-significant, using the least significant difference (LSD) test.

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Figure 6. Genetic gain per year in performance in high-level show jumping competition (PS) for each scenario shown in units of genetic standard deviations. Similar letters above the bars indicate non-significant differences between the sce-narios at a significant level of 0.05. At significance this level, differences between the means of the scenarios below 0.0037 were declared non-significant, using the least significant difference (LSD) test.

Discussion The four- and six-fold increase in genetic gain of the breeding goal traits, demonstrated in the dres-sage and show jumping population, respectively, implies that huge potential exists in optimizing the current breeding scheme of Danish Warmblood horses. This is discussed in the following.

The value of BLUP-EBVs The substantial increase in genetic gain of the breeding goal trait in both populations as a result of selecting 3-year old stallions based on BLUP-EBVs instead of own phenotypes of indicator traits, suggests that sport horse breeding could benefit much more from using BLUP-EBVs in selection de-cisions, than what is currently being done. Especially in the stallion selection, the decisions made on 3-year olds, on whether to approve or reject the stallions for breeding, are crucial since the decision typically cannot be altered due to castration if the stallion was not approved. This selection decision is supported by information from relatives as well as own performance in indicator traits when using BLUP-EBVs, and thus are more accurate compared to selecting solely on phenotypic records ob-tained that particular weekend at the stallions grading show. That BLUP-EBVs are leading to more accurate decisions is also clear from the increase of approximately 50 % for both populations in the genetic gain of the breeding goal trait when selecting mares based on BLUP-EBVs in scenario 6. Sigurdsson et al. (1997) found similar results as in current study on the Icelandic horse population, whose genetic gain on an overall score increased five-fold after selection based on BLUP-EBVs, de-rived from multi-trait animal models, was implemented in 1983 and started being used by the

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breeders the succeeding years. Also Viklund et al. (2011) found an increase in genetic gain of Swe-dish Warmblood horses, after introducing BLUP-EBVs based on multi-trait models in 1986 based on data from riding horse quality test for 4-year olds; from near 0, the genetic gain per year increased for show jumping to 0.056 and to 0.032 for dressage, in genetic standard deviations. Selection based on BLUP-EBVs have proven to be an effective way of identifying the best animals for breeding, com-pared when using phenotypes only as it includes all sources of information available (Tavernier, 1988). In DWB, BLUP-EBVs are currently calculated once a year, but do not seem to be used much, neither by the breeder nor by the breeding committee in DWB. The same seem to be the case in other warmblood breeds (Koenen et al., 2004; Thorén Hellsten et al., 2006; Dubois and Ricard, 2007). This might have to do with records of the breeding goal traits in sports horses not being available until late in life, and therefore not sufficiently accurate breeding value, according to the breeding associ-ations, can be obtained before the horses are quite old (Jönsson et al., 2014a). Consequently, DWB waits publishing BLUP-EBVs of the stallions until sufficient offspring records are available. The point of using BLUP-EBVs might then be a bit blurry since selection decisions, at least in relation to the stallions, already have been made before the BLUP-EBVs become public. When DWB themselves do not use the BLUP-EBVs in selection decisions, requesting breeders to do so might not be easy. Thus BLUP-EBVs have not really been accepted as an important selection tool by Danish Warmblood breeders. To alter this view the accuracies of BLUP-EBVs ought to be improved in young horses as the simulation results, in terms of the large increase in genetic gain of the breeding goal traits sug-gests are possible. Considerations which should be made in order to realize similar genetic gain in the real Danish Warmblood populations are outlined in the following. The BLUP-EBVs, currently being published by DWB are calculated from three single trait models and summed up in one index, but otherwise a total merit index combining all information from young horse tests and later records does not exist (Jönsson et al., 2014a). This means that even though high genetic correlations have been found in both disciplines, e.g. between young horse performance and later performance in high-level competition (Thorén Hellsten et al., 2006; Viklund et al., 2010), genetic correlations be-tween indicator traits and breeding goal traits are not accounted for in the current calculations of BLUP-EBVs in DWB. To improve the accuracy at a younger age, thereby encouraging the use of BLUP-EBVs for selection decisions, development of a multi-trait model making basis for further genetic progress towards the breeding goal as has been found in this study is encouraged. When all young horses have BLUP-EBVs calculated from multi-trait models next challenge is to start using them for selection decisions such that a high selection intensity can be realized, resulting in genetic gain as shown possible in the simulations. Another reason for using multi-trait models over single-trait models is that bias caused by pre-selection of horses, occurring because not all horses get to com-pete at high level and therefore are not evaluated in these traits (Luehrs-Behnke et al., 2002) to some extent are accounted for (Novotná et al., 2014). In relation to the late publication of BLUP-EBVs for stallions, Thorén Hellsten et al. (2006) suggested that breeding associations should allow not only mares, but also geldings and non-approved stal-lions to participate in events like current mare grading events. Doubling the amount of information

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at a young age, when having records of both sex, could potentially increase the accuracy of BLUP-EBVs at an earlier age than currently. By Gelinder et al. (2002) it was proposed that these young horse tests did not necessarily had to be complex to separate the good ones from the bad. In fact, as Thorén Hellsten et al. (2006) states, the easier it is for the owners to get their horse evaluated, the more motivated they might be, and the more will show up.

Genotypes as an extra source of information The small increases observed for the breeding goal traits in scenario 3a and 3c, where genomic information was added as an extra information source to the BLUP-EBVs to base the selection of 3-year old stallions on, implies that the genetic gain achievable with an accuracy of 0.2 on the SNP genotypes only added slightly more information to the BLUP-EBVs than was already accounted for by the parent average and own phenotypic records of the indicator traits. Selecting all stallions based on BLUP-GEBVs at an accuracy of 0.2 on the SNP-genotypes resulted in a more convincing increase in the genetic gain in both populations. At high accuracies of the SNP-genotypes (rAI=0.6), the genetic gain of the breeding goal trait increased significantly, just by selecting 3-year old stallions based on GEBVs, and even more when selection all stallions based on BLUP-GEBVs. These increases are probably related more to the selection intensities than to the accuracies of the BLUP-GEBVs. In scenario 3, the same selection intensity and the same accuracy of BLUP-GEBVs was used on stallions of all ages, due to only 3-year olds being selected based on BLUP-GEBVs. The rest were selected at random, and therefore any extra information becoming available for the stallion later in life, did not have any effect on the accuracy of their BLUP-GEBVs. The selection intensity as well stayed the same for all stallions as for 3-year old stallions. In scenario 4, all stallions were selected based on BLUP GEBVs. Therefore, when selecting fewer stallions than the year before in the age classes 4-24 years, the selection intensity increased because now the best stallions were selected, instead of random stallions. The accuracy of BLUP-GEBVs also increased when more information became available, contributing together with increased selection intensity to increases in genetic gain of the breeding goal traits as was seen from the results in scenario 4. In Haberland et al. (2012a), a simulations study in sport horses was also conducted. They found that the accuracy of BLUP-EBVs for a low heritable trait (h2=0.15, e.g. as PD in this study) was 0.27, when only phenotypic records from the parents was included in the BLUP-EBV and only minor increases could be achieved by including other close relatives. Larger increases could though be achieved, when including SNP-genotypes in addition to the phenotypic records of the parents. Even when the accuracy of the SNP-genotypes was as low as 0.2 the accuracy of the BLUP-GEBV, including pheno-typic records of both parents and SNP-genotypes, could be improved from 0.27 to approximately 0.32. When including phenotypic records of the parents and own performance records, the accuracy of the BLUP-EBVs was 0.45. This was only improved marginally by including SNP-genotypes at an accuracy of 0.2, indicating that much of the information obtained from the SNP-genotypes, probably already were accounted for in the records of own performance. These findings correspond well to the findings in current simulation study because low accuracy on SNP-genotypes of 0.2, did not seem to be enough to improve the genetic gain considerably. Haberland et al. (2012a) however,

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suggested that the lower the accuracy of the BLUP-EBVs without genomic information, the higher gain in accuracy could be achieved when including genomic information. From the results of current study, this does not seem to be the case at accuracies as low as 0.2 on the SNP-genotypes, even though we do not know the exact accuracy of the BLUP-EBVs. But Since the BLUP-EBV of most 3-year old stallions are based mostly on phenotypic records of their parents, and their own phenotypic records of indicator traits, the accuracy of BLUP-EBVs is expected to be low. The estimate of Haberland et al. (2012a) of 0.45 on horses with own and parents phenotypic records, seem to be suitable for the Danish Warmblood horses as well. Therefore, it suggests that there is a lower limit in the accuracy of BLUP-EBVs, in which this rule can be applied. This lower limit may be above 0.2 for the SNP-genotypes since the results of Haberland et al. (2012a) did only show minor increases in accuracies of GEBVs when including SNP-genotypes at an accuracy of 0.2, and the results of cur-rent study also only showed minor increases in genetic gain at this accuracy of SNP genotypes. Ricard et al. (2013) however, did not find similar results in a study where the focus was on the po-tential of GS to increase the accuracies of estimated breeding values. They did only find minor dif-ferences (0.01 to 0.04) between the accuracies of EBVs and GEBVs. In the study by Haberland et al. (2012a), the accuracy of the breeding values increased from 0.45 to 0.65 for a low heritable trait (h2=0.15) when including SNP-genotypes at an accuracy of 0.6. The results from current study, im-plies that comparable increases in accuracy of GEBVs occurred because of the larger increase in genetic gain found in both populations (~30 %) when adding SNP-genotypes as an extra information source to the BLUP-EBV. The increase was as expected as similar effects on genetic gain were found in other species (Schaeffer, 2006; Lillehammer and Sonesson, 2011; Duchemin et al., 2012). In both populations, much is to be gained by implementing GS on 3-year old stallions, if a higher accuracy on the SNP-genotypes than 0.2 can be obtained. It has previously been reported that the accuracy of SNP genotypes possible to obtain, will determine the extent to which GS will be applied in dairy cattle breeding schemes (König and Swalve, 2009). This also seems to be applicable for horse breeding schemes. Mark et al. (2014) found that the accuracy of SNP-genotypes was highly influ-enced by the number of genotyped stallions. Thus, when only genotyping 500 stallions, the expected accuracy of SNP genotypes was around 0.30 for a low heritable trait (h2=0.11) and around 0.42 for a higher heritable trait (h2=0.21). When genotyping above 5,000 stallions the accuracy reached around 0.72 and 0.80, respectively, and hereafter the gain in accuracy of the SNP genotypes was only small. In the simulations, approximately 1,000 colts were born each year, and from scenario 3 they were all genotyped. According to the finding of Mark et al. (2014) 1,000 genotyped stallions would be enough to obtain an accuracy of at least 0.42 on the SNP genotypes for a low heritable trait (h2=0.11), and even more for higher heritable traits. However, even though 1,000 foals were born and genotyped each year, a challenge would still be to obtain high-level performance records of 1,000 horses per year in the reference population. Therefore, the success of GS is very dependent on collaboration with other breeding association or riding federations to get enough phenotypic data on especially the breeding goal traits. Besides the information coming from the SNP-genotypes, additional information obtained from phenotypic records of the parents and the horse itself, not accounted for by the SNP-genotypes, could possibly increase the accuracy of BLUP-GEBVs even

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more. In accordance with this, Haberland et al. (2012a) also predicted that an accuracy of SNP-gen-otypes on low heritable traits of 0.5, could be feasible for genomic selection in horses. Therefore, scenario 3b and 3d seem realistic to carry out in the real populations, in relation to both accuracies of SNP-genotypes and genetic gain of the breeding goal traits. Although even more genetic gain could be realized when selecting all stallions based on BLUP-GEBVs instead of just the 3-year olds, changes in selection schemes as scenario 4 suggests, might not be well received by the breeders nor by the breeding association. In practical terms, scenario 4 suggests that e.g. acknowledged stallions after several years of breeding, suddenly are denied their breeding license due to other stallions having better BLUP-GEBVs. This would most likely not be accepted among breeders, and they would seek towards other breeding associations. A situation like that is definitely not desirable for the breeding associations, which is why scenario 4, in practical terms does not seem suitable as it is now, despite the potential of genetic gain. In the future, when breeders start accepting BLUP-GEBVs as a valuable selection tool, scenario 4 might become more realistic to carry out. When it comes to genotyping all mares, the results showed same patterns as genotyping the stal-lions as they imply that an accuracy of 0.2 on the SNP-genotypes is not enough to alter the genetic gain significantly, whereas an accuracy of 0.6 is. This is in agreement with both Schaeffer (2006), who found that genotyping females could lead to more accurate BLUP-GEBVs, resulting in better selection decisions and further genetic gain, and Dubois et al. (2008) who found female selection to contribute with 25 % of the genetic gain. It could be argued, whether to genotype and select stallions and mares at a younger age than 3 year olds, e.g. 1 month old as they do in dairy cattle breeding. This would minimize the risk of castration of genetically superior stallions, and is suggested to lower the generation interval (Haberland et al., 2012a) while increasing the genetic gain (Mark et al., 2014). Higher risks though, would then be related with selection decisions of the stallions due to own phenotypic records not being available at the time (Haberland et al., 2012a). Thereby the phenotypic records of the parents and the SNP-genotypes would be the only information sources explaining the genetic gain (Mark et al., 2014). Since mares are not permanently rejected, the age of selection decisions is less important. Another argument to wait with genotyping and selection until the age of 3, would be that horses are not sexually mature until the age of 2 (Pilliner and Davies, 2004), therefore it would probably be better to wait another year, where phenotypic records could be obtained, and potentially gain a little more on the accuracy of BLUP-GEBVs. Even though selection based on breeding values can result in considerable increase in genetic gain (e.g. 50 % and 90 % for the dressage and show jumping population, respectively), and therefore comprise a valuable selection tool, their usefulness is completely controlled by the quality of the information used to estimate them. The information should be high quality phenotypic data, rec-orded with high precision. This is barely the case in horse breeding schemes where many traits are recorded subjectively (Duensing et al., 2014) or on scales difficult to obtain sufficiently high reliabil-ities on (e.g. earnings, ranks and scores in competitions (Koenen and Aldridge, 2002)). In relation to the subjective scores, it is important that different judges evaluates the traits to ensure high quality

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data (Duensing et al., 2014). The major limiting factor for the success of using breeding values for selection decisions is therefore probably obtaining enough phenotypic data of sufficient quality (Stock and Reents, 2013). Linear profile schemes of young horse traits have for now been imple-mented for Danish Warmblood mares. These schemes make the evaluations more objective and precise than before, and therefore improves the prerequisites for selection based on breeding val-ues (Haberland, 2013), thus this should be extended to the stallions as well, and maybe also geld-ings. Due to reasonable correlations found in other studies (Viklund et al., 2010; Jönsson et al., 2014a) between young horse traits and performance later in life, phenotypic records of high quality indicator traits, continue to be an important part of the genetic evaluation. Habier et al. (2007) found accuracies of GEBVs to decline over generations because of decreasing relationships between the reference population and the genotyped population, and recombination (Sonesson and Meuwissen, 2009). Therefore re-estimation of SNP effects are required regularly, and this is also why phenotypic records continue to be important.

Taking advantage of the maternal pathway As mentioned, when selection of stallions based on breeding values, either EBVs or GEBVs is ac-cepted amongst breeders the next challenge is to start using them consistently for selection deci-sions, such that high selection intensity can be realized. Attention to the selection intensity is of particularly importance on the mare side of selection, where it in many cases in current breeding schemes is near zero since many breeders are not strict enough in their selection (Dubois et al., 2008). High potential of increasing the genetic gain therefore exist if the maternal pathway is utilised more effectively. This is clearly shown in the simulation results, when selection of mares based on BLUP-EBVs was implemented. Breeding associations are though only able to advice but not decide on the mare side of selection, and this complicates matters. In the minds of breeders having only one or few mares to breed, a bad evaluation of the mare might be ignored while breeding her any-way, and hoping that the stallion will make it up for any qualities that the mare is lacking. Further-more, many horse breeders breed for hobby purposes and do not focus much on profit or genetic gain. Breeding associations therefore need to meet the requirements from two different types of members, and this might be challenging when it comes to making selection decisions related to greatest genetic gain (Haberland, 2013). In the simulations maintaining a high selection intensity on the mares was quite simple, and was one of the reasons why high increases in genetic gain on the breeding goal trait were found in scenario 6 (~50% in both populations), where selection of mares in all age classes was based on BLUP-EBVs instead of just random. Even higher genetic gains were found when selecting based on BLUP-GEBVs with accuracy of 0.6 on the SNP-genotypes. In fact, it might not be quite as simple to make breeders select completely according to breeding values and maintain likewise selection intensities on the mares. When selecting donor mares for embryo transfer, accurate selection decision is of even more im-portance (Haberland et al., 2012a) since increased selection intensity is present on these mares. Given that the best mares are selected as donors, great potential of genetic gain exists in utilizing modern reproductive techniques that enable mares to have more than one offspring a year. In the

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simulation results of scenario 9a, 9b, 9c and 9d, where 20 % of the best 3-year old mares, and 10 % of the best mares above 3 years were having 3 offspring per year, increases of ~11-14 % in genetic gain of the breeding goal traits were obtained compared to scenario 8. This implies that the genetic gain, may be more influenced by the increase in selection intensity, than the increase in accuracy of BLUP-GEBVs, caused by accounting for the SNP-genotypes in addition to phenotypic records from the parents and own phenotypic records. Genomic selection and reproductive techniques as em-bryo transfer are two methodologies that supplements each other well (Spelman and Garrick, 1998) because when increasing the selection pressure on a proportion of mares, the resulting genetic gain will be influenced largely. Therefore, accurate selection decisions of the mares are of even greater importance when using mares for e.g. embryo transfer. As discussed previously, GS can help im-proving this accuracy of selection.

Reducing the generation interval Genomic selection has proven to be an advantageous tool in animal breeding, amongst other due to potential of reducing the generation interval (Habier et al., 2007), and thereby fasten the genetic gain compared to phenotypic and pedigree based selection (Solberg et al., 2008). Applying GS to all stallions alone did not result in any drops in generation interval though (scenario 4). This is an im-portant aspect for the breeding association and the breeders to be aware of because other changes in the breeding scheme will have to be made in order to fully utilise the potential of GS. This is illustrated in scenario 5, where the age structure of stallions older than 3 years were made more flexible, relative to the current practice, and as a consequence the generation interval dropped. The simulation program was no longer forced to select a fixed number of stallions in each age class, but could select the best stallions regardless of age, according to the ones having the best BLUP-GEBVs. Mark et al. (2014) found that the genetic gain when selecting stallions as 3-year olds were 1.5-2.5 times higher than if selecting the stallions as 6-11 years old. Younger individuals therefore tend to be genetically superior over older individuals, at least if any genetic progress is present. Hence, the selected stallions were, due to their superiority, younger than in the previous scenario as repre-sented by the generation intervals which dropped 0.8 and 1.45 years in the dressage population, and 0.85 and 1.34 years in the show jumping population, for low and high accuracy, respectively. The reason why it did not drop more, was that the selection of 3-year olds stallions were still fixed to the same number as previous scenarios. Consequently, the selection intensity stayed the same for stallions aged 4-24 years as the selection intensity of 3-year old stallions. Contrary, in previous scenarios the selection intensity increased the older the stallions became. In practical terms, all stal-lions selected as 3-year olds were allowed to breed every year until the age of around 10 years in both populations in scenario 5. The reduction in selection intensity might be the explanation for why the genetic gain of the breeding goal traits did not increase much. Therefore, starting to use younger stallions more is a prerequisite for taking fully advantage of GS to reduce the generation interval, and if using the stallions up till the age of 10, only minor reductions can be obtained. When the age structure of the mares was made more flexible, and all mares were selected based on BLUP-GEBVs, regardless of their age in scenario 8, the generation interval dropped to 6.5 years

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in the dressage population and 7.0 years in the show jumping population, at an accuracy of 0.6 of the SNP-genotypes. Simultaneously the genetic gain of the breeding goal traits increased slightly. These results clarify even more, the effect of accurate selection decisions and that reduction of generation interval does not come automatically just by implementing GS. Other changes simulta-neously in the breeding schemes are necessary, e.g. using only the best mares instead of basically all mares, and using them at a young age. When these changes are carried out, reproductive tech-niques will constitute an advantageous supplement for GS, provided that sufficiently accurate BLUP-EBVs or -GEBVs can be obtained (Haberland et al., 2012a).

Changes in rates of inbreeding In a recent unpublished study, the rate of inbreeding in the dressage population of Danish Warm-blood horses was found to be 0.12 % per generation (Favrelle, 2016). A possible explanation for the overestimation of the rate of inbreeding in this simulation study (0.19%) is that a closed population was simulated, whereas in reality the population is open and allows new bloodlines to enter the population, e.g. foreign horses and thoroughbreds. Closed populations are generally characterized by higher rates of inbreeding, e.g. 1.9 % per generation in the Frisian horse population (Sevinga et al., 2004), 0.9 % per generation in the Holstein horse population (Roos et al., 2015), 1.0 % per gen-eration in the endangered Old Kladruber horse population (Vostrá-Vydrová et al., 2016). Therefore, simulating a closed population is proposed as the explanation for the overestimated rate of inbreed-ing. The overestimation is though not significantly different. In the show jumping population the rate of inbreeding was slightly underestimated (0.19 %) compared to the findings of (Favrelle, 2016) who found it to be 0.22 %, but is neither considered significantly different. In same study, few blood-lines were found to contribute to large proportion of each population, e.g. Cor de Bryère contrib-uting with 9 % to the show jumping population, and Donnerhall contributing with 6 % to the dres-sage population. Intensive use of few stallions represents a potential risk of loss in genetic variation. Having genomic information, where more knowledge about the Mendelian sampling term is ob-tained when using genomic relationships instead of pedigree based relationships, could help avoid-ing these intensive uses of few bloodlines. Simultaneously loss in genetic variation could be pre-vented since co-selection of full-sib stallions then can be avoided, genetic contribution can be dis-persed more effectively (Daetwyler et al., 2007), and wider screening of potential young stallions for breeding is enabled (Schefers and Weigel, 2012). This requires that strategies for minimum coancestry matings are applied, where inbreeding is reduced without compromising genetic gain (Liu et al., 2016). This is however not applied in this study. Due to truncation selection, the stallions with the highest breeding values for the breeding goal traits were selected no matter their genetic relatedness with other selected stallions. BLUP-EBVs at this young age is explained mostly by the parent average, and consequently the BLUP-EBV of full-sibs will be identical, and BLUP-EBVs of half-sibs will be also be somewhat similar, until own phenotypic records or phenotypic records of off-spring become available (Stock and Reents, 2013). Selecting between families rather than within families, represents a risk of stallions with high BLUP-EBVs being closely related. This is reflected in the rate of inbreeding, which approximately doubled in both populations when selecting 3-year-old

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stallions based on BLUP-EBVs. The steady increase found from scenario 3 and onwards in both pop-ulations at low accuracies of SNP-genotypes, indicates not to rely blindly on BLUP-GEBVs either. By selecting based on BLUP-GEBVs with low accuracies of the SNP-genotypes, only little extra infor-mation on which alleles the foal has inherited from its parents is added next to the pedigree infor-mation and own performance. Therefore full- and half-sibs will still not be differentiated signifi-cantly from each other in their BLUP-GEBVs, and consequently selected stallions will tend to be related as well as the mares, causing the rates of inbreeding to increase. If obtaining higher accura-cies of the SNP-genotypes as simulated in the scenarios with an accuracy of 0.6, the rates of in-breeding were still increasing from scenario 3 to scenario to 6, but here after it stagnated. This is due to more accurate estimates of the Mendelian sampling term, which allow more differentiation between individuals within same families, even before they have phenotypic records (Engelsma et al., 2011). Therefore, in relation to controlling the rate of inbreeding, GS have the largest potential if high accuracies of SNP-genotypes can be obtained, but even then, increased rates of inbreeding should be expected as the simulation results are indicating, when increasing the selection intensity, if selecting completely based on truncation selection on BLUP-GEBVs. While considering the BLUP-GEBVs of the individual stallions, the relationship amongst each other is therefore highly recom-mended to be considered as well in the selection process. The phenomena “The Bulmer Effect” occurs typically when selection begins and the selection inten-sity increases, resulting in a reduction of the genetic variance. After some generations, the genetic variance reaches a steady state between the reduction and reconstruction of variance due to re-combination established during many years of constant selection intensity (Árnason and Van Vleck, 2000). The Bulmer effect was not observable in the first ~18 years of selection in scenario 1-4, though. Actually, in those years the opposite was happening. The increased genetic variance is as-sumed to be caused by differences in selection intensities on stallions and mares. In scenario 1-4, the mares were randomly selected, while there was increased selection on the stallions. This in-creases the overall genetic variance because the mares from the base population will be a part of the breeding population longer than the stallions from the base population. When individuals from the population no longer originated from the base population, the Bulmer effect occurred, and the steady state was reached after ~25 years, corresponding well to the maximum age of breeding mares and stallions in the simulations being 24 years. Scenario 9b looked a little different from the before mentioned scenarios with respect to the genetic variance. This is due to high selection inten-sity on stallions as well as the mares, and the generation interval is lower. Therefore, the base pop-ulation disappears earlier from the breeding population, and the Bulmer effect happens much closer to where the selection begins in scenario 9, and due to increased selection pressure, a lower genetic variance in the population was found. The genetic variance reached after the Bulmer effect was not much lower from the genetic variance before selection in scenario 1-4, whereas it dropped to around 0.75 in scenario 9b, where maximum selection pressure occurred. This suggest that the high genetic gain achieved is not for free, and if strictly using truncation selection on the breeding values,

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the selection may result in reduced genetic gain, due to loss in genetic variation, increased rates of inbreeding and possibly inbreeding depression on a longer term (Árnason and Van Vleck, 2000).

Osteochondrosis as indicator trait Osteochondrosis is in current breeding scheme a sex- and selection-limited trait, only recorded for stallions pre-selected for the stallion grading show. Favourable correlations with the breeding goal trait (-0.05) and a medium heritability (0.35) as defined in this simulation study, indicates a possibil-ity of improving the susceptibility to OC, even though the genetic correlation is small. OC acted a little different than the other indicator traits as the genetic gain dropped considerably after the first scenario, and did not reach the same level in any of the other scenarios as scenario 1. In the first scenario, a relative weight of -1 was given to OC, but in the other scenarios, where selection based on BLUP-EBVs or -GEBVs were introduced, OC was only indirectly selected against because of all weight was given to the breeding goal trait. This explains the difference in genetic gain of OC be-tween first scenario and the rest of the scenarios. Between scenario 2 to 9 no significant differences were seen, taking one scenario at a time, but overall from scenario 2 to 9, the genetic gain of OC was significantly different, suggesting that selecting against OC should not be neglected, especially if high accuracies of SNP-genotypes can be obtained. Previously, selecting against OC has also been recommended by Stock and Distl (2007), due to their expectation of performance of young warm-blood horses not genetically predisposed for OC, being superior to performance of young warm-blood horses genetically predisposed for OC. Also, accounting for OC seem to be possible simulta-neously with selection for performance traits. This was a conclusion by Stock and Distl (2005) who found that the difference between accounting for OC versus not accounting for OC in the selection for performance traits (both dressage and show jumping) had only a small effect on the genetic gain of performance traits, but a large effect on the prevalence of radiographic findings (including OC in the hock), which was lowered considerably in offspring from stallions that were selected based on an index on radiographic findings. Being able to select for health- and longevity related traits as OC, would be of great value for the breeding association since OC reduces the sales value of the horse severely (Van Hoogmoed et al., 2003), and also influences the longevity of the horse (Wallin et al., 2001). Stock and Distl (2007) studied the relationship between OC and performance traits, and found favourable correlations. Based on these findings, and the results of current study selection against OC is highly recommended. For the breeding against OC to be successful, reliable measures of OC should be obtained. When only having phenotypic records of OC, false-negatives, e.g. horses previously treated and cured for OC, will not be detected in x-rays later on. Thus, not very reliable records will be obtained. If imple-menting GS this problem would be solved because of hiding an animal predisposed for OC, would be less likely as the genotype could reveal the susceptibility of OC. The need for reliable phenotypic records are still very important though because if not, successful implementing of genomic selection would not be possible (Sitzenstock et al., 2010).

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Conclusion Selection based on breeding values is without any doubt worth implementing. Especially for traits difficult to record, or becoming available late as is the case with the breeding goal traits of Danish Warmblood horses and most other sport horses, the potential of genetic gain is large. Incorporating genomic information makes it possible, depending on the reachable accuracy on the SNP-genotypes to obtain more knowledge on the “invisible” characteristics of the horse, and thereby taking more accurate selection decisions early in life while improving the genetic gain additionally. Summarizing, revising the breeding scheme of Danish Warmblood horses resulted in the following:

x Routinely use of BLUP-EBVs in selection decisions of young stallions has the potential to in-crease the genetic gain with 50 % in the dressage population and as much as 90 % in the show jumping population.

x Given that accuracies of BLUP-GEBVs are reasonable, great potential of genetic gain exists when using genomic information. Selecting all stallions based on BLUP-GEBVs would be pref-erable, but in practical terms selecting only 3-year olds based on BLUP-GEBV would be more suitable for a horse breeding association as it is now. Due to reasonable correlations found in other studies between young horse traits and performance later in life, phenotypic rec-ords of high quality indicator traits, continue to be important.

x More attention to the mare side of selection showed very high potential for the genetic gain of the breeding goal traits. Increases of 50 % in genetic gain were observed just by selecting based on BLUP-EBVs instead of randomly. Even higher increases were found when using ge-nomic selection combined with reproductive techniques as embryo transfer.

x Reduced generation intervals were obtained, but were not a result of implementing genomic selection only. Simultaneous changes in the breeding schemes are necessary, e.g. breeders will have to use the stallions with superior BLUP-GEBVs, even though higher risks might be related with those.

x Due to increased risk of between family selections, the rate of inbreeding increased more at low accuracies of SNP-genotypes than at high. While assessing the BLUP-GEBVs of the indi-vidual stallions, the relationship amongst them should therefore be considered as well in the selection process, especially if only low accuracies of the SNP-genotypes can be obtained.

x Assuming only weak, but favorable genetic correlations, selection towards the breeding goal traits, showed to improve the susceptibility to osteochondrosis.

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6 General discussion In horse breeding selection at a young age tends to be rather uncertain, due to late availability of phenotypic records of the breeding goal traits (Koenen and Aldridge, 2002). Together with long uti-lisation periods of the stallions and late unset of the reproductive career of the mares (8-9 years old in DWB), this prolongs the generation intervals, which in current breeding scheme of Danish Warm-blood horses was found to be 9 years for the dressage horses and 11 years for the show jumping horses. This were in accordance with other studies of sport horses (Burns et al., 2004; Leroy et al., 2013). Generally, the generation interval for the mares was 2 years longer than the stallions. Con-sequently, the genetic progress becomes slow, e.g. ~0.04 genetic standard deviation units per year in Danish Warmblood horses. However, in this study the genetic gain increased with 50 % in the dressage population and 90 % in the show jumping population, without altering the generation in-terval and without using GS. This suggests that the generation interval is not the only factor restrain-ing the genetic gain in current breeding scheme, but also the challenge of correct selection decisions of young horses. Furthermore, it suggests that a lot of genetic gain can be realized prior to the im-plementation of genomic selection. In the following, current challenges in the breeding scheme are defined and future actions for han-dling them and realizing increased genetic gain in Danish Warmblood breeding schemes as demon-strated to be possible, are proposed.

6.1 Realizing greater genetic gain prior the implementation of genomic selection In the selection of young stallions, correct decisions are particularly important since rejected stal-lions usually are castrated. This decision cannot be altered, contrary to the selection decision of mares as mares stay fertile. On the stallion side of selection only 10-13 % are pre-selected by the breeders as selection candidates. Basically, this would be plenty if it was the best stallions that were pre-selected. However, this is not certain. Firstly, due to only 1.4-2.5 % of born stallions being se-lected in the end, many breeders might reject the stallions themselves even before they are given the chance at the pre-selection, simply because the chance of the stallion being selected is very small. Secondly, the price the breeders have to pay for pre-selection, grading, testing, licensing etc., being rather expensive, might keep some away, by which indirectly rejecting a possibly superior stallion. Thirdly, as proposed by Viklund (2010) many breeders having only one or few mares, do not have the facilities to have a stallion at home. Consequently, there is a risk that the stallions are castrated even though they might be genetically superior to other stallions. The fact that ~90 % of the stallions are indirectly rejected for good, indicates the importance of accurate selection deci-sions, which might be one challenge restraining the genetic gain in current breeding scheme. Viklund (2010) suggested a way to make more colts get the chance of being selected, the breeding association could function as communicator between “hobby” breeders and professionals, who have better opportunities to handle young colts. Gathering several young colts on one location, and in professional hands, could make good opportunities for routinely evaluation, while insuring no


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stallions with good prospects of being selected later in life got castrated. In the selection step fol-lowed by the pre-selection, the stallions are selected by the breeding association based on pheno-types recorded at one single weekend. Thereby, stallions having a bad weekend due to whatever reason, risks being rejected even though they might be genetically superior in relation to the breed-ing goal. This complicates further the challenge of correct selection decisions. This study showed that a way to overcome the challenge of accurate selection decisions was selecting based on breed-ing values. BLUP-EBVs derived from multi-trait models have the advantage of increasing the accu-racy of selection (Stock and Distl, 2008) because all information available is used, including the cor-relations between the indicator traits and the breeding goal trait. Thereby genetic gain can be achieved (Gengler and Coenraets, 1997). For now, DWB only publish BLUP-EBVs derived from single-trait models for horses with sufficient offspring records. Thus, the correlations are not taken into account and the breeding values are not available early enough to use them for selection decisions. Therefore, the breeding values do not seem to be valued much neither by breeders nor by the breeding association. Publishing BLUP-EBVs derived from multi-trait models early enough to use them for selection decisions are therefore very much encouraged, even though they might only be explained by the parent average and own phenotypes of indicator traits. Breeders accepting and using BLUP-EBVs as a tool for selection decisions is especially important for the success of imple-menting genomic selection in the future. Therefore, DWB needs to act as a good example and start to select, not blindly due to risk of increased rates of inbreeding, but according to breeding values on the young stallions for genomic selection to be successful in the future. Lowering the associated costs is probably difficult. Therefore, a suggestion could be to make other particularly favourable conditions for breeders of colts with high estimated breeding values, such that it becomes more attractive for the breeders to sign their stallion up for pre-selection. To increase the accuracy of breeding values further, Thorén Hellsten et al. (2006) suggested to test young geldings as well as non-approved stallions the same way as the mares are tested in various grading events. This could as well be a suggestion for DWB, but should be made simple and attractive for non-breeders to show up for. The concept should be designed with a fair price and to make it fun and easy for owners to get their riding horse evaluated, while keeping in mind that they provide the association with useful data. Furthermore, defining a clear breeding goal that is accepted by breeders, and in which traits are defined precisely, should be highly prioritized. Otherwise it cannot be expected that breed-ers selects the best horses for breeding (Koenen and Aldridge, 2002). This also includes assigning correct relative weights to the traits in the breeding goal for maximizing genetic gain. Koenen et al. (2004) indicated the importance of precise communication of the breeding goal with relative weights to the breeders, after finding that verbally defined breeding goals often did not mention relative weights between the traits. This was also the case in DWB, where it was not possible to find any indications of which traits were weighted more and which were weighted less in the breeding goal. Thus, somewhat random weights were chosen in the study, when simulating the current prac-tice.


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On the mare side of selection approximately 50 % of the mares are selected by the breeders as selection candidates. Same challenge here as with the stallions is that it is not certain that it is the best mares that are pre-selected. The chance is nevertheless higher since a larger proportion is hav-ing the chance. Contrary to the stallion selection, in average 100 % of the mares pre-selected by the breeders are also selected for breeding in one of the four mare-grading categories. In agreement with this, Viklund et al. (2011) showed that EBVs of broodmares were not better than EBVs of mares not selected for breeding, derived from multi-trait models, including young horse tests and compe-tition in the Swedish Warmblood population. Consequently, the selection intensity is very low on the mare side of selection. The challenge here might be that two kind of breeders are represented at the mare grading’s. The first group being the “hobby” breeders, who might think it could be fun breeding their own riding horse from their mare. The second group being “professional” breeders who want to breed genetically superior and profitable foals. Therefore, the breeding association should meet the requirements from two very diverse groups of members, which might lead to in-appropriate selection decisions. Furthermore, the mares do not risk being infertile if not pre-se-lected or selected. In this way, the breeders or the association are not in a hurry to make selection decisions as they are with the stallions. This is also reflected by the average age of first foaling being 8-9 years old, and that only 21 % dressage mares and 17 % show jumping mares born from 2005-2010 had offspring before 2016. The selection of mares therefore seems to be influenced by the fact that they are used for competitions before being used for breeding. When altering the selection practice on the mare side in this study, the genetic gain increased significantly. Higher selection intensity might be challenging to achieve in reality as the breeding association is dependent on both types of breeders. The best thing to do is probably to strengthen the awareness of this through informing and recommending in continuation with the grading description. Though, this is presum-ably already attempted to some extent. Another idea could be to make an additional mare grading category, where only mares with breeding values above a specified level were allowed to breed. This category would, in contrast to the other categories, be dynamic such that mares would fall in and out of the category as their breeding value change from year to year. Foals after such mares, could then be branded and sold as e. g. “superior” foals to specify that they are after genetically superior mares contributing particularly to the genetic progress in the population. Thereby, it would be clearer that these foals were attractive in relation to the breeding goal. Higher selection intensity could then be obtained on some of the mares, leading to increased genetic gains while there would still be room for “hobby” breeders in the breeding association. A new mare grading category like this, might also lower the age of first foaling and the generation interval since genetically superior mares tend to be younger. For mares not qualifying for this category, the current mare grading cat-egories would still be applied. As shown in this study, using modern reproductive techniques would increase the genetic gain further. Currently, the use of embryo transfer is assumed mostly to be used on the best mares, who either have proved their worth as broodmares, or proved their worth in high-level competitions, because of the high costs associated with the procedure. Therefore, re-lated challenges are expected to be to use young mares instead of older mares for embryo transfer.


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Especially in relation to this, implementation of genomic selection could really be advantageous. In the future it might even be possible to select the best embryos for embryo transfer, instead of just random embryos (Meuwissen et al., 2016), and thereby increasing the selection intensity.

6.2 Prospects for genomic selection in Danish Warmblood breeding schemes As shown by Haberland et al. (2012b), if SNP-genotypes was part of the breeding value as well as phenotypic records of the parents, even with low accuracies, more accurate decision could be made of young horses, also before they had own phenotypes. This was also shown in this study, where the genetic gain increased when including SNP-genotypes in the breeding values. If Danish Warm-blood breeders had these kind of breeding values available shortly after birth of their colts, the decision of selecting them as candidates for pre-selection would probably be encouraged, resulting in increased chances of the best stallions being selected. Breeders could thereby get a better indi-cation of whether to keep the stallion with purpose of getting him selected when own performance in indicator trait is obtained, or weather to castrate him and sell him as riding horse. In the dairy cattle breeding association, letters or e-mails are sent to breeders of cows who carries a pregnancy assumed to be superior. In this way breeders are encouraged to test their calves. This could also be a possibility in DWB to further encourage breeders to select their colts for pre-selection, but would probably be more suitable after the foal have been born and genotyped, and GEBVs have become available. Thereby, the association signalizes that the colt is interesting for breeding purposes, and breeders will be more aware if having an interesting colt in relation to the breeding goal. These foals could e.g. be signed up for pre-selection with some sort of discount if necessary, making it more attractive for the breeders. Thereby more candidates might show up for pre-selection and get the chance of being selected. Despite very good prospects of implementing genomic selection in horse breeding as discussed pre-viously and as also shown in this study, no practical experiences or proposals of genomic selection routines have yet been presented for horses in scientific literature (Stock et al., 2016). This may be a result of multiple factors. First, the horse industry has not yet been ready to allocate as many resources as needed for developing a successful GS practice. However, DWB received a grant in 2013 to develop and implement GS in Danish Warmblood horses. At that time, they were the first horse breeding association, involved in a GS project, and later interest from other has started to emerge (Karina Christiansen, personal com., 2016). This indicates a growing attention for GS in horse breeding, and is defiantly a step in the right direction. Second, implementing GS requires serious changes in the selection practices, which may not be well accepted by all, e.g. professional stables and stud farms who are doing perfectly well as it is now. With phenotypic selection, professionals having a respected and “known name” may influence the selection decisions in a favourable way because they might have better opportunities than amateurs to show their horse in its best condi-tion. Also, phenotypic evaluations are prioritized highly by breeders in current breeding schemes (Koenen et al., 2004), and also by the breeding association in order to get members committed (Haberland, 2013). By implementing GS, a lot of effort should therefore be made in advising breed-


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ers about that changes needed in the breeding schemes are in their best interest to maximize ge-netic gain, and to keep up with other breeding associations. Furthermore, implementation of GS, does not necessarily mean that phenotypic evaluations should not be prioritized any more as Habier et al. (2007) and Van Grevenhof et al. (2012) suggested to lower the costs. On the contrary, in DWB these could be expanded, as mentioned earlier to include all genders, and in this way, contribute with phenotypic young horse records, and later competition records to the prediction models for GEBVs. Third, collaborations between breeding associations are most likely a prerequisite for estab-lishing a reference population with enough phenotypic records of especially the breeding goal traits and other traits with low heritability (Dürr and Philipsson, 2012). This is because the accuracy of the SNP-genotypes is highly affected by the size of the reference population (Habier et al., 2010). Thus, a small reference population will result in GEBVs with low accuracies and consequently no or only small increases in genetic gain (Van Grevenhof et al., 2012). If using only Danish Warmbloods in the reference population, it might therefore be hard to obtain enough phenotypic records, and predic-tion equations might not be of sufficiently good quality to make the selection decisions more accu-rate, than phenotypic selection. As discussed in previous chapter 1,000 genotypes each year seem to be realistic in DWB to obtain an accuracy of at least 0.5 of the SNP-genotypes. Genotyping this many, would necessitate that nearly all Danish Warmblood foals were genotyped. In DWB, it is man-datory that all foals are pedigree verified with a DNA test, and genotyping in connection with this, would therefore not cause much extra work. A suggestion to ensure as many genotypes as possible, could therefore be to make it mandatory as well, on foals of both genders. The age of genotyping should ideally be right after birth for the colts, whereas genotyping fillies could wait until they were sexually mature, depending on what is most convenient. Next challenge is to obtain 1,000 pheno-types each year. For the young horse traits, this is more realistic in the dressage population, whereas in the show jumping population only around half is born each year. For the breeding goal traits 1,000 phenotypic records seem unlikely to obtain within the Danish Warmblood populations each year. Therefore, for the success of GS, collaboration between countries and breeding associations is a prerequisite. Instead of being competitors, warmblood breeding associations across countries will have to start seeing each other as equals who can benefit from each other. Due to the difference between countries in how to obtain data, comparison may become challenging (Koenen and Aldridge, 2002), and therefore standardization of records from grading and testing events, as well as competition records is necessary across countries. Obtaining a large reference population and improving the quality of the prediction models, by exchanging data and standardizing the way phe-notypes are recorded across countries, could improve the accuracies of the prediction equations for GEBVs and make breeding values across countries more comparable than is the case today. In re-turn, higher accuracies might convince breeders that GS is the way to go in future breeding schemes. Though, this is only applicable provided that the relatedness between the horses used for the ref-erence population and the Danish Warmblood populations is large enough (Goddard, 2009). Koenen and Aldridge (2002) predicted the relatedness between sport horses to be lower than in dairy cattle,


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due to smaller progeny groups of sires and lower genetic correlation between traits, as large varia-tion in data recording exist between horse breeding schemes across countries. However, Thorén Hellsten et al. (2008) found the genetic connectedness between European sport horses to be similar or better to that of dairy cattle, and due to the increase in genetic exchange the past 15-20 years, an increase in growing connectedness was found. Koenen et al. (2004) found that 74 % of the mares covered in DWB, was covered by foreign stallions, and that 75 % of the sires of active stallions in DWB originated from Germany. Large proportions of sires of active stallions in other sport horse association (Swedish Warmblood and Dutch Warmblood) were originating from Germany as well. These results indicate that much genetic exchange occurs in DWB and that relatedness between countries is present. Thus, the relatedness between countries seems to be high enough such that a reference population can be expanded with phenotypic data from foreign horses. The simulation result of this study showed increased rates of inbreeding the more intensive the selection became, e.g. from scenario 3 to scenario 9, with genomic information being incorporated in the selection decisions. This suggests that intensifying the selection with GS, by e.g. intensive use of stallions with highest GEBVs, makes it necessary to establish specific strategies to ensure genetic variation on the long term and that inbreeding is under control (Dürr and Philipsson, 2012), even though GS enables genetic gain, while simultaneously reducing the rate of inbreeding (Daetwyler et al., 2007). One strategy to ensure this in DWB, could be to combine GS with optimal contribution selection (OCS), where inbreeding are limited to a certain level, while the genetic gain is maximised (Meuwissen, 1997). The use of OCS is advantageous compared to truncation selection, where rela-tionships among selected animals are not accounted for. Other more simpler strategies to ensure the genetic variation in DWB when implementing GS, could be to make restrictions on how high the inbreeding coefficient of graded stallions and mares could be, or restrictions on how high the in-breeding coefficient of a resulting foal from a mating between two Danish Warmbloods could be. This might exclude genetically superior horses, but in return the risk of inbreeding depression could be minimized. Additionally, restricting number of male mating’s could be a strategy to distribute mating’s more equally than in current breeding schemes, and to ensure that stallions with highest GEBVs are not used too much, but this might not be well received by stud owners nor by mare owners. Therefore, communication to the breeders about how GS should be used is essential. In the background section it was illustrated that the generation interval theoretically could be low-ered with 6 years when using GEBVs for selection decisions (figure 3.2). In the simulations, it was also shown that it was possible to almost halve the generation interval by implementing different selection strategies incorporating genomic information. Implementing these strategies to lower the generation intervals in DWB significantly requires changes in current breeding scheme that may be rather drastic. Dubois and Ricard (2007) proposed that to reduce the age of first foaling breeders should be encourage to begin the reproductive life of their mares, already at the age of 5. At this age, they would have the time to enter some young horse competitions in addition to their perfor-mance tests, which could ensure high accuracies on the breeding values, while reducing the gener-ation interval. Generally, the show jumping stallions tended to be older than the dressage stallions,


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indicating that show jumping breeders are more likely to choose a stallion when it has proven its worth, whereas dressage breeders are more open-minded towards young and unproven stallions. Even though breeders can be advised and encouraged to use younger horses for breeding, it is therefore not likely that this will result in significant drops in generation intervals since “traditions” speaks for proven stallions, rather than unproven stallions, especially in the show jumping popula-tion. More control over the members in the breeding association to carry out changes in relation to age of breeding horses, might be necessary. This means that DWB for instance will have to make rules regarding the age of breeding stallions and/or mares. Taking away the free choice from breed-ers might not be well received, and DWB would risk losing members. Therefore, it might just have to be accepted that the generation interval is very high in horses, and that only small drops in gen-eration interval can be achieved with GS, unless breeders realizes that lower generation intervals is in their best interest. For GS to be successful in DWB, many factors therefore should be considered and acted on. To start up with GS in DWB, scenario 3, where only 3-year old stallions are selected based on GEBVs, seems suitable. Thereby, breeders will have time to slowly adapt to this new selection tool, while the as-sociation starts using it. As discussed scenario 4, would probably be too drastic a change to start with. The breeding association would probably not become popular by declining already graded stallions who fails to keep up with other stallion’s GEBVs. However, after an adaption period, where publication of GEBVs and selection based upon them by DWB is promoted and clarified for the breeders, there might be a larger tendency among the breeders to also start choosing stallions based on GEBVs. Thereby a reflection of scenario 4 might be obtainable without having to force breeders to select specific stallions by rejecting others. This may also solve the issue that breeders select stallions just because they are “in”, where otherwise good stallions not are selected in same extent due to e.g. injuries, and therefore are not in the spotlight the same way even though they might possess some good characteristics in relation to the breeding goal. The same might be the case when implementing GS on the mare side. When the breeding association starts to use GEBVs as selection tool, breeders will hopefully start to consider the breeding values of their mares as well, and in this way, it might be possible to increase the selection intensity on the mare side, without DWB having to tighten up on the grading rules. This is probably important in horse breeding schemes, such that breeders will still have the feeling of deciding themselves. In the future to im-prove the use of GEBVs, it could be beneficial to publish GEBVs for single traits, and not just GEBVs for the breeding goal traits. In this way, an additional tool would be available for the breeders to use, to find suitable stallions for their mares, and it would then be easier to find a stallion that could make it up for qualities that the mare is lacking. As it is now, for the horses having public BLUP-EBVs, it is not possible to interpret weather the horse is genetically good in relation to e.g. trot or canter, but more if it is generally good or bad. Therefore, many might be discarded by the breeders, even though they possess good qualities in relation to what the individual breeders are looking for. This would maybe require some reduction in number of traits and simplifications, though.

7 General conclusion

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7 General conclusion Genetic gain prior to the implementation of GS in the Danish Warmblood population showed to be feasible. It depends however of willingness and acceptance of selection based on breeding values, and open-mindedness towards changes in the breeding scheme. Publication of breeding values, early enough to use them for selection decisions are therefore encouraged as well as DWB is en-couraged to start selecting based on them. To increase the accuracy of selection for young horses more and earlier involvement from DWB in genetic evaluation of foals, and testing of geldings is proposed. More attention should be made on the selection intensity of the mares. With this purpose a new and dynamic mare grading category for those with highest breeding values is suggested to increase selection intensity, reduce the generation interval, and make it easier to identify mares for modern reproductive techniques. Cooperation between sport horse breeding association and riding federations across countries to establish a large reference population of high quality should be prioritized as it is possibly a prereq-uisite for GS to be successful. When GS becomes reality, genotyping foals of both gender, also those not intended for breeding, and publishing GEBVs shortly after birth is recommended to stimulate correct selection decisions from the breeders. It is suggested that DWB use GEBVs to select 3-year old stallions until breeders are familiar with the new selection practices. Following implementation of GS, strategies for ensuring continuous genetic variation in the populations should be made. Drops in generation interval showed to be obtainable, but depends on thorough information and guidance on the new breeding scheme and its potential, and encouraging the use of stallions ranking high in GEBVs. Effort should be made to make breeders accept and learn to use genomic information as a supplementary selection tool. Based on the results, GS can contribute to more accurate selection decisions of young horses in the breeding schemes of Danish Warmblood horses, resulting in high increases of genetic gains and lowered generation intervals. The acceptance of GEBVs by breeders is however essential for the success of GS in the future.

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9 Appendices

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9 Appendices

9.1 Appendix I. The breeding goal for Danish Warmblood horses

We aim to breed a noble, leggy, and supple riding horse with high rideability and a strong health. It has capacity in either jumping or dressage to compete on inter-national level.

Description of the ideal horse:

A large-framed, harmonic riding horse standing at approx. 165-170 cm. who has a suitable amount of noble blood in his lines (being Thoroughbred, purebred Arab, Shagya Arab, or Anglo Arab), in order to achieve a brave and energetic sport horse, eager to perform. He has a nice and cooperative temper and a good learning abil-ity.

The head is expressive with big eyes that enables a wide field of vision. He has an average length of neck which is well-set-on with a nice arch and topline, being clean through the gullet thus assuring a good connection between head and neck. The shoulders are long and sloping and the withers are well defined and pro-nounced thus assuring an optimum position of the saddle. Good freedom of el-bows. A suitable length of back. Oval costal convexity that enables an optimum placing of the rider’s leg. Good costal length enabling suitable space for lungs and inner organs. Muscular and supple loins. Long, muscular, and well-shaped hind-quarters. A well-set-on tail with a correct tail carriage. Muscular forearm and wide and strong thighs and second-thighs. Strong, clean, and well defined limbs with suitable angles at hocks and pasterns. Well defined joints. Short and flat cannon bones. Pasterns of suitable length and well-shaped hooves with a quality crust.

Great importance is attached to a good durability, and that the breeding stock has an obvious expression of gender and is without hereditary defects.

Description of function:

The dressage horse: A horse with large and well-carried movements, showing good, active knees and hocks in all three gaits. The walk is lithe, roomy, and regu-lar. The trot is elastic, regular, and with good carriage. The canter is roomy, regu-lar, and with good carriage and balance. Furthermore, good rideability with cour-age and willingness to perform is very much desired.

The show jumper: A vigorous and lithe jumping with great capacity and good tech-nique. Importance is attached to a supple, roomy and balanced canter along with a natural caution, great courage, overview, and a good rideability.

Dansk Varmblod (2016a)

9 Appendices

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9.2 Appendix II. Linear profile scheme for Danish Warmblood dressage horses

9 Appendices

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9.3 Appendix III. Linear profile scheme for Danish Warmblood show jumping horses

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