Implementing Genomic Information in Breeding Schemes of Danish Warmblood Horses

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2 Implementing Genomic Information in Breeding Schemes of Danish Warmblood Horses MSc Thesis in Agrobiology 45 ECTS Faculty of Science and Technology, Department of Molecular Biology and Genetics - Centre for Quantitative Genetics and Genomics, Aarhus University By Sophie Axelle Grønnegård Favrelle Student ID: / AU Main supervisor: Senior Researcher Anders Christian Sørensen, Department of Molecular Biology and Genetics, Aarhus University Co-supervisor: Breeding Advisor Karina Christiansen, the Danish 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 Association, 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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4 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 genetic 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 approaches 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 approximately 2 %, and for the mares approximately 50 %, ended up being selected for breeding. Selection 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 stochastic 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 inbreeding 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. 3

5 Table of Contents 1 Introduction Abbreviations and notations Selection practices in animal breeding schemes and potential of using genomic information Phenotypic selection in animal breeding Basic breeding theory Horse breeding schemes and goals Estimated breeding values with the Best Linear Unbiased Prediction Selection based on genotypes Marker assisted selection Genomic selection Incorporating genomic selection into breeding schemes of horses Optimizing genetic gain in horses Enhancing the control of inbreeding Additional advantages of genomic selection in the future Challenges of implementing genomic selection in horse breeding schemes Current selection practice in the Danish Warmblood Association Breeding goal Selection practice Selection of stallions Selection of mares Descriptive analysis Material and methods Results Simulating current selection practice Paper manuscript Abstract Introduction Material and methods Genetic evaluation

6 Genetic parameters Population structure Traits evaluated The simulation program Statistical evaluation Results The dressage population The show jumping population Discussion The value of BLUP-EBVs Genotypes as an extra source of information Taking advantage of the maternal pathway Reducing the generation interval Changes in rates of inbreeding Osteochondrosis as indicator trait Conclusion References General discussion Realizing greater genetic gain prior the implementation of genomic selection Prospects for genomic selection in Danish Warmblood breeding schemes General conclusion References Appendices Appendix I. The breeding goal for Danish Warmblood horses Appendix II. Linear profile scheme for Danish Warmblood dressage horses Appendix III. Linear profile scheme for Danish Warmblood show jumping horses

7 1 Introduction 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 proposed by Meuwissen et al. (2001) and the concept is called genomic selection (GS). Genomic selection has proven to be highly beneficial in a number of different species due to the potential of increasing 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 Warmblood 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 5 th and 19 th 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 potential 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 information 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 prerequisites 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 theory and the theory behind selection based on phenotypic and genomic information are presented. 6

8 1 Introduction 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 simulation results (section 5). The fourth and last part, comprises a general discussion of the three preceding 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 (section 7). 7

9 2 Abbreviations and notations 2 Abbreviations and notations BLUP Best linear unbiased prediction DWB EBV GEBV GI GS IBD IBS LD LSD MAS MME OC QTL PD PS Sire + Sire - SNP TBV YC YD YS The Danish Warmblood Association Estimated breeding value Genomic estimated breeding value Generation interval Genomic selection Identical by descent Identical by state Linkage disequilibrium Least significant difference Marker assisted selection Mixed model equation Susceptibility to osteochondrosis Quantitative trait loci Performance in high-level dressage competition Performance in high-level show jumping competition Stallion, sire to 0.5 % or more of the total number of offspring born in a year. Stallion, sire to less than 0.5 % of the total number of offspring born in a year. Single nucleotide polymorphisms True breeding value Young horse conformation Young horse dressage ability Young horse show jumping ability F F G h 2 I i L r AI r g σ a Var w Inbreeding coefficient Rate of inbreeding Genetic gain Heritability Total merit index Selection intensity Generation interval Accuracy between true and estimated breeding value Genetic correlation Additive genetic standard deviation Genetic variance Weight of a given trait in the index 8

10 3 Selection practices in animal breeding schemes and potential of using genomic information 3 Selection practices in animal breeding schemes and potential of using genomic information 3.1 Phenotypic selection in animal breeding 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 intensity reflects the difference between the mean of selected parents for next generation, and the population mean in units of the standard normal distribution. The accuracy reflects the correlation between 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 individuals in the population (Tolley, 1984). where G per year = i r AI σ a L G per year is the genetic gain per year, i is the selection intensity, r AI is the accuracy between TBV and EBV, σ a is the additive genetic standard deviation, L is the generation interval (3.1) (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 9

11 3 Selection practices in animal breeding schemes and potential of using genomic information 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 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. breeders 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 therefore 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 specific 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 maximize 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 significant profit becomes quite low (SLU, 2001). Thus, scientific effort in this area is not prioritized. Furthermore, 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). Economic 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 approach 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 questionnaires 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. Nevertheless, correct relative weightings of the traits can have an important effect on the genetic gain 10

12 3 Selection practices in animal breeding schemes and potential of using genomic information 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) proposes 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 = w 1 EBV 1 + w 2 EBV w i EBV i (3.2) where I is the total merit index reflecting the breeding goal, w i is the weight of the i th trait, EBV i 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 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 selection 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 Unbiased ). 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). y ij = Xb i + Za j + e ij (3.3) 11

13 3 Selection practices in animal breeding schemes and potential of using genomic information where y ij is the phenotypic observation on the j th individual belonging to the i th level of fixed effects affecting the trait, b i is the deviation from the population caused by the fixed effects of the i th level, a j is the random genetic effects of the j th individual, e ij 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 heritabilities, 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 beneficial 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 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 approximately These markers made it possible to detect quantitative trait loci (QTL) that were affecting 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 12

14 3 Selection practices in animal breeding schemes and potential of using genomic information 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, according 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) Genomic selection Instead of using genomic information from loci with only large effect on the traits, all genomic information, 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 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, 13

15 3 Selection practices in animal breeding schemes and potential of using genomic information 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 occurred (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 population 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). y i = μ + X 1i b 1 + X 2i b X 50,000i b 50,000 + e i (3.4) where y i is the phenotype of animal i, μ is the overall population mean, X 1i is the genotype of animal i, for marker 1, b refers to the fixed effect of the marker e i is the residual (Meuwissen et al., 2016). The reference population functions as a genomic map, where genotyped selection candidates without 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. GEBV j = X 1j x 1 + X 2j x X 50,000j x 50,000 (3.5) where x 1 is the predicted effect of SNP 1, X 1j is the marker genotype of animal j for SNP 1. (Meuwissen et al., 2016). 14

16 3 Selection practices in animal breeding schemes and potential of using genomic information 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 candidates. 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: Use dense marker data to illustrate the genome. Connect phenotypic data with genotypic data to estimate the marker effects on the traits of interest. 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 15

17 3 Selection practices in animal breeding schemes and potential of using genomic information and implementation of GS in domesticated animal breeding; the generation interval and the accuracy 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 resources, establishing international cooperation, and convincing breeders and breeding associations to change selection practices 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 effects alone, could be as high as 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 intervals (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 reasonable 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 reduced significantly. This has been proven in dairy cattle breeding schemes, where GS provide breeders 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 breeding 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 current 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 improving the genetic gain by reducing the generation interval considerably. 16

18 3 Selection practices in animal breeding schemes and potential of using genomic information 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 possibilities 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 produce 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 17

19 3 Selection practices in animal breeding schemes and potential of using genomic information 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 investigating 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 phenotypic 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 possible improvement of accuracy and hence genetic gain that GS could provide should not be overseen. 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 advantageous in horse breeding. This however depends on the availability of phenotypic data of good quality (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 diseases 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 conception rate, foaling ease and other sex limited traits could in the future be beneficial to be able to 18

20 3 Selection practices in animal breeding schemes and potential of using genomic information select for in stallions since the selection intensity in mares is near zero. In relation to this, the maternal pathway in horses could be exploited more with GS, as breeders currently are not strict enough in the selection (Dubois et al., 2008) 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 inbreeding 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 prediction 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 inheritance of alleles from both parents (Bonk et al., 2016) is not accounted for in pedigree based relationship matrices (Avendaño et al., 2005). Therefore, some deviation from the predicted relationship 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 relationship matrices. Using genomic relationship matrices makes it possible to estimate the relationship 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 between two individuals that are IBD and identical by state (IBS) (VanRaden, 2008). Therefore, genomic 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 pedigree 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 fullsibs 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) 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 19

21 3 Selection practices in animal breeding schemes and potential of using genomic information 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 implementing GS relates to economy. If ensuring only the best embryos are produced from superior parents 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 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 technology (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). Implementing 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 between 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 approximately 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, numbers 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 association would therefore be essential in the implementation of GS because the larger reference population, 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 implemented 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 20

22 3 Selection practices in animal breeding schemes and potential of using genomic information implies that establishing a large reference population is only the first step in implementing GS, second 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 resources 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 species, 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). 21

23 4 Current selection practice in the Danish Warmblood Association 4 Current selection practice in the Danish Warmblood Association 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 controlled 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, including 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 Appendix III) and subjective evaluations. In the linear profile schemes three to six additional sub-traits 22

24 4 Current selection practice in the Danish Warmblood Association are evaluated. For example, underlying front part is length of shoulder, length of mane etc. All underlying 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 evaluations. 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) 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 stallion 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 associations i (Dansk Varmblod, 2016d). Besides, i Belgish Warmbloedpaard (BWP), Stud-book sbs Le Cheval de Sport Belge (sbs), Koninkliijk Warmbloed Paarden Stamboek Nederland (KWPN), Landespferdezuchtverband Berlin- Brandenburg (BRAND), Landesverband Bayerischer Pferdezu chter (BAVAR), Norwegian Warmblood Association (NWB), Pferdezuchtverband Baden-Wu rttenberg (BAD-WU ), Pferdezuchtverband Rheinland-Pfalz-Saar (ZWEIB), Pferdezuchtverband Sachsen-Thu ringen 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 Francais (SF), Studbook Zangersheide (ZANG), Swedish Warmblood Association (SWB), Verband Hannoverscher Pferdezu chter (Hann), Verband der Zu chter des Holsteiner Pferdes (HOLST), Verband der Zu chter des Oldenburger Pferdes (OLDBG), Verband der Pferdezu chter Mecklenburg-Verpommern (MECKL), Verband der Zu chter und Freunde des Ostpreussischen Warmblutpferdes Trakehner Abstammung (TRAK), Westfa lisches Pferdestammbuch (WESTF). 23

25 4 Current selection practice in the Danish Warmblood Association 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 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, temperament 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 breeding- 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 influence on the breeding goal, will be rejected. Note that this test is voluntary, but must be completed 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 24

26 4 Current selection practice in the Danish Warmblood Association 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 dressage stallions are evaluated based on rideability, capacity, walk, trot and canter, and the show jumping 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 approximate 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 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. 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. 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. 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. The preliminary register of Danish Warmblood horses ( Forregister, abbr. FOR ). 25

27 4 Current selection practice in the Danish Warmblood Association 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: 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 assessed 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. 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. 1-day test is the same as saddle grading, except without evaluation of conformation. 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. 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 championships, which takes place at the same time as the stallion grading show in March. Dressage 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. 26

28 4 Current selection practice in the Danish Warmblood Association 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). 27

29 4 Current selection practice in the Danish Warmblood Association 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 , including both dressage and show jumping mares). 28

30 4 Current selection practice in the Danish Warmblood Association 4.3 Descriptive analysis 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 results 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 highest EBVs or competition results of their sire was used. In this way, the sire determined which category 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 stallions 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 illustrated with blue, and show jumping horses are illustrated with green in the rest of the thesis Results In table 4.1 and 4.2, the number of sires and dams who became parents in the years , 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. 29

31 4 Current selection practice in the Danish Warmblood Association 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 Birth year offspring N Sires N Dams N Offspring N Offspring per sire (avg.) Dressage N Offspring per dam (avg.) N Sires + N Offspring per sire + (avg.) Offspring of sires+ (%) Age when first-time sire + (avg.) Avg N Offspring per sire in lifetime (avg.) 98 N Years 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 Birth year offspring N Sires N Dams N Offspring N Offspring per sire (avg.) Show jumping N Offspring per dam (avg.) N Sires + N Offspring per sire + (avg.) Offspring of sires + (%) Age when first-time sire + (avg.) Avg N Offspring 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. 30

32 4 Current selection practice in the Danish Warmblood Association 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 N sires Years as sire + Figure 4.3. Number of years as sire +. All years from 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 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 , but offspring born in It was assumed that mares having no offspring before 2010 did neither have any offspring before 2005, and thus were first-time foaling in This assumption was made because the data set did not contain information on foals born before Table 4.3. Percentages of first-time foaling dams each year from and their average age. Birth year offspring N First-time foaling dams Dressage First-time foaling dams (%) Age when firsttime foaling (avg.) N First-time foaling dams Show jumping First-time foaling dams (%) Age when firsttime foaling (avg.) Avg N = Number, First-time foaling mare = mares in the data set, having no offspring born in , but offspring born in 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 31

33 4 Current selection practice in the Danish Warmblood Association 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 ). 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 ). 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. 32

34 4 Current selection practice in the Danish Warmblood Association 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 Dressage Show jumping Sire Dam Sire + Dam + Sire Dam Sire + Dam + Avg. age when parent Avg. generation interval 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. 25 Avg. number of offspring Sire age Show jumping Dressage Figure 4.6. Average number of offspring per sire in each sire age class (in years) and each discipline. In figure 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 , 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 percentage 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 33

35 4 Current selection practice in the Danish Warmblood Association only few were selected in DR and FOR. Even though nearly 50 % of the mares born in 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 Not candidates 90.3% Candidates 9.7% Rejected 8.3% Selected 1.4% Figure 4.7. Selection of dressage stallions (numbers based on stallions born in ). 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. Not candidates 86.9% Candidates 13.1% Selected 2.5% Rejected 10.6% Figure 4.8. Selection of show jumping stallions (numbers based on stallions born in ). 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. 34

36 4 Current selection practice in the Danish Warmblood Association Not candidates 51.6% Selected in DH 26.5% Candidates 48.4% Selected in DS 21.1% Selected in FOR 0.7% Selected in DR 0.1% Figure 4.9. Selection of dressage mares (numbers based on mares born in ). 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. Not candidates 57.4% Selected in DH 23.0% Selected in DS 18.3% Candidates 42.6% Selected in FOR 1.2% Selected in DR 0.1% Figure Selection of show jumping mares (numbers based on mares born in ). 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 parameters. In the following simplifications and assumptions made in relation to the simulations are explained. 35

37 4 Current selection practice in the Danish Warmblood Association In table 4.5 the approximate population structure in DWB based on the descriptive analysis in previous 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 N Sire+ N Sire* N Dam N Sire + N Sire* N Dam N selected *N Sire 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 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: 36

38 4 Current selection practice in the Danish Warmblood Association 10% offspring by sire = 200 offspring by a sire = 2.5 offspring pr sire 200 offspring by a sire+ 80 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 osteochondrosis (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 ). Regarding OC, PD and PS, not much literature exists, and therefore, heritabilities and genetic correlations used in the simulations 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. 37

39 4 Current selection practice in the Danish Warmblood Association Table 4.6. Heritability estimates of young horse conformation traits Trait Source Age Studbook h 2 h 2 (avg.) Type Head-neck Front part Topline / back part Frontlimbs Hindlimbs Jönsson et al. (2014a) 3-5 DWB 0.47 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 Viklund et al. (2008) 3 SWB 0.21 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 Jönsson et al. (2014a) 3-5 DWB 0.36 Thorén Hellsten et al. (2009) 3-5 DWB 0.40 Seierø et al. (2016) 3-4 DWB (jumping horses) 0.28 Seierø et al. (2016) 3-4 DWB (jumping horses) 0.21 Crolly (2010b) cited in Christiansen et al. (2010) 3-4 DWB 0.38 Jönsson et al. (2014a) 3-5 DWB 0.32 Thorén Hellsten et al. (2009) 3-5 DWB 0.20 Seierø et al. (2016) 3-4 DWB (jumping horses) 0.17 Jönsson et al. (2014a) 3-5 DWB 0.17 Thorén Hellsten et al. (2009) 3-5 DWB 0.20 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

40 4 Current selection practice in the Danish Warmblood Association Table 4.7. Heritability estimates of young horse dressage ability traits. Trait Source Age Studbook Rider h 2 Walk Trot Canter Capacity* Rideability* Average*** Ducro et al. (2007a) 3-7 KWPN No 0.19 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 Ducro et al. (2007a) 3-7 KWPN No 0.29 Thorén Hellsten et al. (2009) 3-5 SWB Yes 0.38 Thorén Hellsten et al. (2009) 3-5 DWB Yes 0.32 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 Ducro et al. (2007a) 3-7 KWPN No 0.21** 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 0.17** Schade (1996) cited in Thorén Hellsten et al. (2006) 3-4 Hanoverian Yes 0.33** Thorén Hellsten et al. (2009) 3-5 SWB Yes 0.29 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** Thorén Hellsten et al. (2009) 3-5 SWB Yes 0.29 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 All above 2-7 Warmblood horses With/with out h 2 (avg.) 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

41 4 Current selection practice in the Danish Warmblood Association Table 4.8. Heritability estimates of young horse show jumping ability traits. Trait Source Age Studbook Rider h 2 Canter Jumping technique Jumping capacity Rideability* Seierø et al. (2016) 3-4 DWB Mixed 0.32 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** Ducro et al. (2007a) 3-7 KWPN No 0.27 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 Boelling (2010) cited in Christiansen et al. (2010) 3-7 DWB Yes 0.13 Seierø et al. (2016) 3-4 DWB Mixed 0.40 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 Boelling (2010) cited in Christiansen et al. (2010) 3-7 DWB Yes 0.26 Seierø et al. (2016) 3-4 DWB Mixed 0.25 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 Boelling (2010) cited in Christiansen et al. (2010) 3-7 DWB Yes 0.14 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 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. h 2 avg

42 5 Paper manuscript 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. Consequently, generation intervals become long and early selection decisions uncertain. Genomic selection (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 scenarios 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 inbreeding 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. Reduced 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 domesticated species. In horses however, the concept of genomic selection (GS) has not yet been implemented, 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. 41

43 5 Paper manuscript 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 ability, 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 relationship 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 generation interval. Different scenarios of implementing genomic information is assessed through stochastic simulations, and compared to current breeding scheme in DWB. Material and methods Two Danish Warmblood horse populations were simulated. The first reflecting the dressage population, the second reflecting the show jumping population. In each population nine different scenarios 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 accuracy 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 previous scenario, but with the only difference that one factor was changed. A description of the scenarios 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. Young horse conformation (YC) Young horse dressage ability (YD) or Young horse show jumping ability (YS) Susceptibility to osteochondrosis (OC) Performance in high-level dressage competitions (PD) or Performance in high-level show jumping 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). 42

44 5 Paper manuscript Table 1. Description of scenarios simulated. Scenario Scenario 1: Phenotypic selection on 3-year old stallions - current selection practice Scenario 2: Index selection on 3-year old stallions Scenario 3: Genomic selection on 3-year old stallions Description 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 indicator 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 w YC 1 1 w YD/w YS 1 2 w OC -1-1 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 contributing with information to the BLUP-EBV of each 3-year old stallion. In scenario 3, all 1-year old colts were genotyped, and 3-year-old stallions were selected 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: Genomic selection on 4-24-year old stallions Scenario 5: Flexible age structure of 4-24-year old stallions Scenario 6: Index selection on all mares Scenario 7: Genomic selection on 3-year old mares Scenario 8: Flexible age structure of 4-24-year old mares Scenario 9: Embryo transfer on the best mares In scenario 4, all stallions were selected based on BLUP-GEBVs. 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 selected 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. In scenario 6, all mares were selected based on BLUP-EBVs, calculated from multitrait models. In this way, phenotypes from relatives were now contributing with information to the BLUP-EBV of each mare. 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 selected based on BLUP-GEBVs. 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 selected 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. 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 produce 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,

45 5 Paper manuscript Figure 1. Schematic representation of the scenarios simulated. 44