Animal (2013), 7:5, pp 705–713 & The Animal Consortium 2012
`doi:10.1017/S1751731112002248
`
`animal
`
`Integrating genomic selection into dairy cattle breeding
`programmes: a review
`
`A. Bouquet- and J. Juga
`
`DepartmentofAgriculturalSciences,UniversityofHelsinki,POBox27,FI-00014,Helsinki,Finland
`
`(Received 20 December 2011; Accepted 14 September 2012; First published online 3 December 2012)
`
`Extensivegeneticprogresshasbeenachievedindairycattlepopulationsonmanytraitsofeconomicimportancebecauseof
`efficientbreedingprogrammes.Successoftheseprogrammeshasreliedonprogenytestingofthebestyoungmalestoaccurately
`assesstheirgeneticmeritandhencetheirpotentialforbreeding.Overthelastfewyears,theintegrationofdensegenomic
`informationintostatisticaltoolsusedtomakeselectiondecisions,commonlyreferredtoasgenomicselection,hasenabledgains
`inpredictingaccuracyofbreedingvaluesforyounganimalswithoutownperformance.Thepossibilitytoselectanimalsatanearly
`stageallowsdefiningnewbreedingstrategiesaimedatboostinggeneticprogresswhilereducingcosts.Thefirstobjectiveofthis
`articlewastoreviewmethodsusedtomodelandoptimizebreedingschemesintegratinggenomicselectionandtodiscusstheir
`relativeadvantagesandlimitations.Thesecondobjectivewastosummarizethemainresultsandperspectivesontheuseof
`genomicselectioninpracticalbreedingschemes,onthebasisoftheexampleofdairycattlepopulations.Twomaindesigns
`ofbreedingprogrammesintegratinggenomicselectionwerestudiedindairycattle.Genomicselectioncanbeusedeitherfor
`pre-selectingmalestobeprogenytestedorforselectingmalestobeusedasactivesiresinthepopulation.Thefirstoption
`producesmoderategeneticgainswithoutchangingthestructureofbreedingprogrammes.Thesecondoptionleadstolarge
`geneticgains,uptodoublethoseofconventionalschemesbecauseofamajorreductioninthemeangenerationinterval,butit
`requiresgreaterchangesinbreedingprogrammestructure.Theliteraturesuggeststhatgenomicselectionbecomesmoreattractive
`whenitiscoupledwithembryotransfertechnologiestofurtherincreaseselectionintensityonthedam-to-sirepathway.Theuse
`ofgenomicinformationalsooffersnewopportunitiestoimprovepreservationofgeneticvariation.However,recentsimulation
`studieshaveshownthatputtingconstraintsongenomicinbreedingratesfordefiningoptimalcontributionsofbreedinganimals
`couldsignificantlyreduceachievablegeneticgain.Finally,thearticlesummarizesthepotentialofgenomicselectiontoincludenew
`traitsinthebreedinggoaltomeetsocietaldemandsregardinganimalhealthandenvironmentalefficiencyinanimalproduction.
`
`Keywords: genomic selection, breeding programme, dairy cattle, genetic gain, inbreeding rates
`
`Implications
`
`Introduction
`
`The practical use of genomic information to select animals,
`or genomic selection, is currently revolutionizing the orga-
`nization of dairy cattle breeding schemes. The success of
`this new technology is because of increased genetic progress
`on both the bull and cow genetic pathways by reducing
`costs compared with conventional selection schemes,
`but also by the potential of using this rich source of infor-
`mation to manage genetic resources. Practical results of
`the implementation of genomic selection in dairy cattle
`breeding schemes are of great importance for other live-
`stock species in which genomic selection is now under
`consideration.
`
`- E-mail: alban.bouquet@gmail.com
`
`Use of molecular information to make selection decisions
`in breeding programmes was envisaged decades ago
`(Smith, 1967; Soller, 1978). Although conceptually simple,
`the implementation of genetic markers into breeding pro-
`grammes has been rather limited for technological reasons. The
`recent availability in dense panels of single nucleotide poly-
`morphism (SNP) markers has offered new opportunities to do
`so. The basic concept underlying such an approach is to use
`dense genetic markers as proxies to detect genome regions
`involved in a trait – the quantitative trait loci (QTL) – and exploit
`them for selection purposes. SNP genotyping technology has
`enabled profiling many animals for thousands of marker loci in
`a single analysis, thus with a low cost per marker (Williams,
`2005). Capitalizing on this rich source of information permitted
`estimation of breeding values for young candidates with higher
`
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`Bouquet and Juga
`
`accuracy than before. The principle of genomic evaluation
`models is to take advantage of both genotypic and phenotypic
`data available in a training (also called ‘reference’) population
`to build prediction equations of the genetic merit of individuals
`(Meuwissen et al., 2001). These equations can then be
`applied to selection candidates having genotypes but no
`phenotypes. Diverse approaches have been proposed to
`estimate genomically enhanced breeding values (GEBV), as
`reviewed by Hayes etal. (2009).
`The use of genomic information to make selection decisions,
`or genomic selection, was shown to greatly increase the tech-
`nical and economic efficiency of dairy cattle breeding pro-
`grammes (Schaeffer, 2006; Ko¨ nig etal., 2009). Indeed, genetic
`progress was achieved in conventional progeny testing (PT)
`schemes via the wide use of the very best progeny-tested bulls,
`which was enabled by means of artificial insemination (AI).
`Because selection in dairy cattle is undertaken on traits
`expressed by females, the PT step was necessary to generate a
`daughter group whose performance was used to predict the
`genetic merit of bulls with high accuracy. However, PT implies
`long generation intervals and huge costs related to bull main-
`tenance and progeny-group constitution. Furthermore, only a
`limited number of young sires can be progeny tested each year
`owing to structural constraints. The choice of young males was
`therefore critical, whereas the accuracy of mid-parental Best
`Linear Unbiased Prediction (BLUP) breeding values was low
`at a young age. Because genomic selection alleviates some of
`these costs and technical constraints, the dairy cattle breeding
`industry has rapidly integrated genomic information into
`selection programmes. Many efforts have been devoted (so far)
`to improving the statistical models used for genomic evalua-
`tions (Hayes etal., 2009). Research has also been undertaken
`to answer questions arising about the practical use of genomic
`selection in breeding programmes. Such questions are also
`likely to emerge in other populations in which application of
`genomic selection is being considered.
`To date, two main approaches have been used to predict
`the response to genomic selection. Hence, the first objective of
`this review was to describe these methods, which rely on either
`infinitesimal or finite locus models (FLMs), and to discuss lim-
`itations in their application. The second aim was to summarize
`the main conclusions reached on the subject by considering the
`example of dairy cattle populations. Critical factors influencing
`the efficiency of genomic selection were reviewed, as were future
`challenges faced in designing breeding schemes. The present
`review mainly focused on factors affecting the technical efficiency
`of programmes that was assessed through three criteria, namely
`(i) the annual genetic gain (DG), (ii) the variability of this response
`and (iii) the impact of selection procedures on maintenance of
`genetic diversity accessed via inbreeding rates (DF).
`
`Modelling the efficiency of genomic
`breeding programmes
`
`Modellinggenomicselectionwiththeselectionindextheory
`The selection index theory was proposed to model the
`overall gain in accuracy expected from using genomic
`
`706
`
`information at some selection stages (Lande and Thompson,
`1990; Dekkers, 2007). To do so, SNP information of geno-
`typed individuals is summarized into the direct genomic
`value (DGV). In practice, DGV is modelled as an infinitesimal
`indicator trait, which is highly heritable and genetically cor-
`related with the selected trait. This indicator trait is modelled
`with a heritability of 1, meaning that genotyping errors are
`ignored and genotyped animals will receive no information
`from DGV of relatives. In this approach, the genetic corre-
`lation considered between the DGV and the selected trait
`reflects the desired level of accuracy for genomic predictions
`(Dekkers, 2007). Assuming the infinitesimal model and
`multivariate normal distribution of genotypic and phenotypic
`values, the selection index theory offers a convenient fra-
`mework to optimally combine both information sources.
`Because no molecular data are simulated in this approach,
`the inbreeding rate generated by a scheme must be assessed
`with analytical formulas in deterministic models or with
`pedigrees in stochastic simulations. The main appeal of this
`approximate approach lies in its computational efficiency
`and straightforward implementation into existing software
`using either deterministic prediction models or stochastic
`simulations.
`It was extended to multi-trait selection by
`Dekkers (2007), and was also referred to as pseudo-genomic
`selection by Buch etal. (2012a).
`
`SimulationsbasedonFLMs
`Another approach entails simulating individuals and their
`genotypes at a finite number of markers and QTL. This
`approach allows modelling the dynamics of genetic diversity
`at neutral markers and genetic variance at QTL by explicitly
`considering the discrete nature of genotypes and the finite
`size of genomes (Dekkers etal., 2004). In FLM simulations,
`breeding programmes are generally not started from a
`founder population with genotypes sampled at random. It is
`recommended to first simulate an ancestral population that
`has reached equilibrium with respect
`to mutation and
`genetic drift to ensure that a realistic structure of linkage
`disequilibrium (LD) exists between syntenic loci (Muir, 2007).
`Before simulating the breeding scheme, a base population is
`constituted by sampling individuals from the ancestral
`population. The true breeding values (TBV) of individuals are
`obtained as the sum of effects of QTL alleles that they carry
`on their chromosomes plus a polygenic term, if simulated
`QTLs are not assumed to explain all of the genetic variance.
`Following conventional principles of stochastic simulations,
`phenotypic observations are reconstructed from TBV by
`adding a residual term sampled from known statistical dis-
`tributions to achieve the desired heritability (Dekkers etal.,
`2004). GEBVs are predicted by performing a genomic eva-
`luation with simulated phenotypes and genotypes as inputs
`and by using the variance components assumed in the
`simulation. Hence, the accuracy of genomic predictions is not
`a prior in the model, but rather a result of simulations.
`Finally, inbreeding rates can be estimated in FLM simula-
`tions by using either recorded pedigrees or marker informa-
`tion. Although the pedigree-based estimator of inbreeding
`
`Exhibit 1018
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`ignores the effects of linkage and selection around QTL
`regions, its use is expected to give accurate estimations of
`DF for long genomes, that is, .10 M (Villanueva et al.,
`2005). However, the use of pedigree-based inbreeding esti-
`mates does not provide information about reductions in
`genetic diversity around QTL regions under selection
`(Sonesson etal., 2010). An advantage of FLM simulations is
`that
`they provide information at simulated markers to
`monitor the evolution of autozygosity along the genome.
`Indeed, the proportion of simulated marker loci that is
`homozygous by descent with respect to the base population
`gives an expectation of the ‘realized’ inbreeding coefficient.
`
`Limitsofapplication
`Pseudo-genomic selection and FLM simulations rely on dif-
`ferent genetic models. Given a certain genetic architecture,
`the FLM simulation is expected to be the most accurate
`approach because it models the effects of biological pro-
`cesses and selection procedures on both genetic diversity
`and genetic variance. Although most breeding programme
`simulations have focused on additive effects, sophisticated
`architectures can be simulated with QTL exhibiting dom-
`inance and epistatic effects. However, the choice of para-
`meters is expected to influence the level and maintenance
`of genetic variance and diversity (Hu and Li, 2006). Hence, it
`is safe to test the sensitivity of predictions to different
`architectures when using this prediction method.
`Pseudo-genomic selection is an approximate approach
`that
`requires a set of assumptions with various con-
`sequences on predictions. First, it assumes the infinitesimal
`model and multivariate normal distribution of DGV and
`phenotypic values to combine them optimally into a genomic
`selection index. This supposes that the genetic architecture
`of selected traits complies with the hypotheses of the
`infinitesimal model, that is, traits are controlled by a large
`number of genes with individually small additive effects
`(Bulmer, 1980). Research on model species confirmed the
`relevance of using the infinitesimal model to describe the
`genetic mean and variance of a population for most quanti-
`tative traits because QTL alleles with large effects are rare
`and most of genetic variation is generally due to many loci
`with small effects (Mackay etal., 2009). Thus, this model is
`valid over a short to medium time period, ignoring long-term
`changes in genetic variance due to fluctuations of allele
`frequencies. In addition, when dense marker maps are used,
`DGVs are expected to follow a multivariate normal distri-
`bution as a result of the central limit theorem (Lande and
`Thompson, 1990).
`Second,
`in pseudo-genomic selection, the accuracy of
`GEBVs is a prior in the model and assumed to remain con-
`stant over time and to be the same for all individuals. In
`practice, GEBV accuracy is expected to increase depending
`on the size of the training population. Fixing GEBV accura-
`cies to currently achieved values consequently depicts a
`rather pessimistic scenario, which is safe when genomic
`schemes are compared with conventional schemes. However,
`accuracy of GEBVs was shown to be sensitive to many
`
`Genomic selection in breeding programmes
`
`intermingled parameters comprising the genetic architecture
`of traits (Daetwyler et al., 2010), the LD existing between
`markers and QTL (Goddard, 2009; Goddard etal., 2011), the
`statistical model of genomic evaluation (Daetwyler et al.,
`2010; Bastiaansen et al., 2012), the frequency of updating
`prediction equations (Muir, 2007) and the composition of the
`training population and its relationship with selection can-
`didates (Lillehammer etal., 2011; Pszczola etal., 2012). As a
`consequence, different designs of genomic schemes can lead
`to different evolutions of GEBV accuracy (Lillehammer etal.,
`2011). Because pseudo-genomic selection ignores the
`influence of all of these parameters on GEBV accuracy, this
`approach may lead to biased comparisons of genomic
`schemes on the basis of predictions of genetic gain.
`Finally, computational requirements to achieve calcula-
`tions also influence the choice of prediction method. Pseudo-
`genomic selection makes the optimization of breeding
`schemes more tractable than in FLM simulations, especially
`when selection is carried out on a breeding objective that
`includes several genetically correlated traits. However,
`Meuwissen (2009) showed that it was possible in FLM
`simulations to scale down by the same factor the effective
`size of the population, the genome size and the number of
`training records without modifying the achieved level of
`GEBV accuracy. This allows comparison of expected genetic
`gain from different genomic selection schemes at lower
`computational expense.
`
`Impact of genomic selection on selection response
`and its variability
`
`Descriptionofinvestigatedgenomicselectionschemes
`Most studies have compared genomic selection schemes
`with the conventional PT scheme, which was the norm in
`dairy cattle populations until recently. Genomic selection
`was integrated into breeding schemes in two different ways,
`leading to genomic pre-selection (PS) and genomic juvenile
`(JS) schemes. The PS scheme entails using GEBVs to pre-
`select young males for PT. All subsequent steps for the
`selection of males remain the same as in PT schemes.
`Compared with PT schemes, PS schemes allow increasing
`the selection accuracy of young male candidates by using
`their genotypic information.
`In JS schemes, the use of
`genomic information is more aggressive. AI sires are selected
`by using GEBVs among young genotyped males able to
`produce semen. Although GEBVs of young sires are less
`accurate than conventional breeding values estimated for
`progeny-tested bulls, the loss in selection accuracy is com-
`pensated by a huge reduction in generation intervals, as
`there is no need for progeny testing. Genetic gain is then
`rapidly cumulated over shortened generations.
`In both
`genomic schemes, females can be genotyped to increase
`both accuracy and intensity for the selection of females.
`Large variations in predicted DG were found across stu-
`dies, as described by Pryce and Daetwyler (2012). Compared
`with conventional schemes, gains in DG ranged from 19%
`for PS schemes to more than 1100% for JS schemes.
`
`707
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`Exhibit 1018
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`

`Bouquet and Juga
`
`However, these figures must be interpreted with caution,
`considering the specific set of parameters used in each study,
`for example, the heritability of the trait, the accuracy of
`GEBV and the number of genotypings allocated to males and
`females. For instance, for a trait with moderate heritability
`(0.3) – typical of a production trait – setting up a PS scheme
`increased DG by 9% (Lillehammer etal., 2011), 13% (Buch
`et al., 2012a) and 16% (Pryce et al., 2010) compared with
`the former PT scheme. JS schemes were shown to generate
`higher selection response, with increases of 29% (Lillehammer
`etal., 2011), 59% (Pryce etal., 2010), 65% (Buch etal., 2012a)
`and 86% (Colleau etal., 2009) compared with PT schemes.
`Increases in DG because of genomic selection were the
`largest when selection was carried out on traits of low her-
`itability, because genomic data added relatively more infor-
`mation to predict breeding values for these traits. Compared
`with a traditional PT scheme, Lillehammer et al. (2011)
`showed that DG was increased by 29%, 40% and 70% in
`JS schemes for heritability values of 0.30, 0.05 and 0.01,
`respectively. However, levels of GEBV accuracy remained
`generally moderate to low for traits of low heritability.
`Phenotypic information of an indicator trait genetically cor-
`related with the selected trait and recorded on a large scale
`can be integrated in the genomic evaluation model to improve
`the accuracy of predictions for traits of low heritability when the
`genetic correlation between both traits is large, that is, .0.5
`(Calus and Veerkamp, 2011; Buch etal., 2012a).
`GEBV reliabilities larger than 0.5 to 0.6 are already
`achieved in homogeneous dairy cattle populations with large
`reference populations (e.g. Holstein populations) for traits
`with moderate to high heritability (Harris and Johnson, 2010;
`Su et al., 2010). By contrast, reliabilities of at least 0.8 are
`obtained for breeding values estimated for progeny-tested
`bulls. In breeds with large effective sizes or small reference
`populations, reliability of GEBVs is generally lower (,0.5)
`even for production traits.
`
`Moreemphasisplacedonbulldamselection
`In schemes integrating genomic selection, an important part
`of genetic gain is achieved through the selection of bull
`dams (Schaeffer, 2006). Indeed, the reliability of breeding
`values is strongly increased for genotyped females to a level
`comparable with that achieved for genotyped males.
`Furthermore, GEBVs of bull dams are expected to be less
`biased, because genomic information reduces the weight
`attributed to own performance, which may be subject to
`preferential treatment. With a training population of suffi-
`cient size, large gains are expected from a more accurate
`selection of breeding cows, even when only a small propor-
`tion is genotyped (Sørensen and Sørensen, 2009). Therefore,
`it is relevant to define strategies to optimally allocate gen-
`otyping capacities between young males and females in
`order to maximize genetic gain. Sørensen and Sørensen
`(2009) showed that allocating larger proportions of geno-
`typings to females than males resulted in larger selection
`responses. Indeed, the proportion of males kept for reproduc-
`tion is small in dairy cattle populations. If extra genotyping
`
`708
`
`capacity is spent on males, the second best males according to
`parent average will be genotyped, although they have a lower
`chance of being selected. On the contrary, if extra genotyping
`capacity is spent on females, there are greater chances of
`detecting interesting females among the cow population to
`generate high genetic merit bull calves.
`
`Influenceofreferencepopulationconstitutionongenomic
`predictionaccuracies
`The need for large reference populations to increase GEBV
`accuracy has encouraged collaboration between breeding
`organizations and countries to exchange genotypic data
`(Lund etal., 2011; Wiggans etal., 2011). Collaboration has
`quickly appeared to be one of the cheapest ways to achieve
`higher GEBV reliabilities, although competitive interests
`often impeded the process (VanRaden etal., 2009). In such
`collaborations, all genotypes of selected and unselected
`candidates have to be shared to avoid biases in the esti-
`mation of genomic breeding values, at both the national and
`international scales (Patry etal., 2011).
`Constitution of the reference population also influences
`the level of GEBV accuracy (Habier etal., 2007) and its per-
`sistence over
`time (Lillehammer et al., 2011). Hence,
`breeding programmes should be designed to minimize the
`average relationship within the reference population and
`maximize relationships between candidates and the refer-
`ence population (Pszczola etal., 2012). On the basis of FLM
`simulations of scenarios with reference populations of
`equivalent sizes, Lillehammer et al. (2011) showed that PS
`and JS schemes led to different levels of GEBV accuracy.
`Indeed, in PS schemes, all sires of male candidates have
`been progeny tested and are included in the reference
`population. This implies larger genetic ties between candi-
`dates and reference bulls than in JS schemes, in which only
`grandsires of candidates and older ancestors are included.
`Reducing the number of progeny-tested bulls in PS schemes
`led to a decrease in GEBV accuracy because fewer bulls with
`daughter information could be added each year to the
`reference population. However, Lillehammer et al. (2011)
`showed that the resulting loss in DGcould be compensated
`by increasing the progeny group size. Thus, in PS schemes,
`maintaining maximal testing capacity is required to ensure
`accurate EBVs for bulls in the training population, and sub-
`sequently, accurate GEBVs for candidates, especially for
`traits of low heritability.
`Including cows in the reference population has recently
`been suggested to further increase the reliability of genomic
`predictions because the number of AI sires is generally lim-
`ited in dairy cattle populations (McHugh et al., 2011; Buch
`etal., 2012b). Such a strategy is gaining some interest given
`the increasing popularity of genomic selection in the industry
`and the decreasing costs of genotyping (Strauss, 2010).
`Actually, the recent release of cheaper SNP platforms is likely
`to extend the use of genomic selection to a wider female
`population. Use of imputation techniques makes it feasible
`to use sparser SNP chips to estimate GEBVs with only
`moderate losses in prediction accuracy (Weigel etal., 2010;
`
`Exhibit 1018
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`

`Dassonneville etal., 2011). However, before practical inclu-
`sion of female information in reference populations, the
`issue of preferential treatment, which frequently occurs with
`elite cows, must be tackled because it can introduce bias in
`the estimation of SNP effects, potentially decreasing the
`accuracy of genomic predictions.
`
`Impactonvariabilityofselectionresponse
`In PT schemes, the risk of using a bull with poor genetic merit
`is minimized because of PT. In PS schemes, this risk is further
`reduced owing to the genomic pre-selection step, which
`allows distinguishing with higher accuracy the best young
`males within families before PT (Buch et al., 2012a). In
`JS schemes, the accuracy of breeding values used to select AI
`sires is lower than that obtained after PT. Considering a fixed
`number of AI sires selected each year, the variability of
`selection response obtained by implementing a JS scheme is
`increased relative to conventional schemes (Buch et al.,
`2012a). Thus, in practice, increasing the number of AI sires in
`JS schemes is recommended to mitigate the risk of using
`bulls with poor genetic merit. Another recommendation is to
`use young sires in ‘teams’ because the reliability of the mean
`GEBV of a group of bulls increases with group size. Schefers
`and Weigel (2012) reported that a team of five young bulls,
`each having a GEBV with a reliability of 0.70, will have an
`average GEBV with a reliability of 0.94.
`
`New opportunities to monitor and preserve
`genetic diversity
`
`Impactofgenomicselectiononinbreedingrates
`Exploiting genomic information enables estimation of the
`Mendelian sampling term of young individuals without any
`phenotypic information. Therefore, genomic selection is
`expected to reduce the weight of family information in selection
`decisions by placing the emphasis on Mendelian sampling
`information of young candidates (Daetwyler et al., 2007).
`The largest reductions in inbreeding rates due to the use of
`genomic selection were observed for traits of low heritability
`(Lillehammer et al., 2011) and when a large part of variance
`was explained by markers (de Roos etal., 2011). By screening a
`large population of candidates, genomic selection also facil-
`itates the identification of the least related animals having high
`genetic merit with a higher accuracy than before. In particular,
`McHugh etal. (2011) showed that genotyping a large number
`of females had a very beneficial impact on DFreduction.
`The use of genomic selection to pre-select males for PT
`(i.e. PS scheme) resulted in a clear reduction of per genera-
`tion inbreeding rates compared with PT schemes, for only
`slight modifications of the generation interval (Pryce et al.,
`2010; de Roos et al., 2011; Lillehammer et al., 2011; Buch
`et al., 2012a). Setting up JS schemes also generally led to
`reductions in per generation DFcompared with PT schemes.
`However, because the generation interval was reduced as
`well, annual DF may not be decreased compared with con-
`ventional schemes (Lillehammer et al., 2011; Buch et al.,
`2012a).
`Increasing the number of young AI sires used in
`
`Genomic selection in breeding programmes
`
`JS schemes is an option to curb inbreeding rates without
`markedly reducing genetic gain (Lillehammer etal., 2011).
`Differentiating the number of matings per young bulls on
`the basis of GEBVs is not a sustainable option; it leads to
`a slight increase in genetic gain at the expense of a drastic
`increase in DF(Sørensen and Sørensen, 2009). Finally, schemes
`with mixed use of proven and young genomic bulls were stu-
`died by Colleau et al. (2009) to mimic the situation in which
`some breeders did not accept the risk of using young AI bulls
`having a GEBV of moderate reliability. The authors showed that
`allocating 50% of inseminations in a JS scheme to the 20 best
`bulls having milking daughters led to a small increase in genetic
`gain and a large increase in inbreeding rate. Thus, this practice
`was harmful for genetic diversity unless strict rules were
`defined for the management of genetic resources.
`The conclusions about the impact of genomic selection
`schemes on genetic diversity critically depend on the time
`unit chosen to express inbreeding rates. In practice, the
`inbreeding rate should be kept under 1% per generation in
`conservation and breeding schemes to avoid undesirable
`effects of
`inbreeding on fitness (Food and Agriculture
`Organization (FAO), 1998). This recommendation indicates
`that an effective population size of at least 50 individuals is
`required so that factors such as natural selection, recombi-
`nation and mutation – which intervene at every generation –
`can counterbalance effects of inbreeding depression (FAO,
`1998). When the selected trait and fitness were not geneti-
`cally correlated, Meuwissen and Woolliams (1994) esti-
`mated that a critical effective size of 30 to 250 individuals
`was required in a selection scheme so that natural selection
`could overcome the effects of inbreeding depression on fit-
`ness, which is consistent with FAO recommendations. How-
`ever, when the selected trait and fitness were negatively
`correlated, even weakly (20.2), Meuwissen and Woolliams
`(1994) showed that it was almost impossible to prevent a
`decline in fitness. In such situations, shortening the generation
`interval makes the accumulation of inbreeding depression
`effects bigger. Thus, in our opinion, it is safe to compare
`breeding schemes by using annual inbreeding rates. In addition,
`the inbreeding rate can be considered a measure of the risk of a
`scheme. Therefore, it seems relevant to express it on the same
`time scale as genetic gain, that is, on an annual basis.
`Although the design of breeding schemes is important to
`limit the rise in inbreeding in a population, there is also a
`need to develop efficient recording systems that are able to
`detect emerging genetic defects (Agerholm et al., 2001).
`In particular, when the generation interval is shortened, it is
`essential to take rapid measures to control the spreading of
`the defect by restricting the use of bulls carrying it. The
`example of Holstein BLAD, CVM and Brachyspina genetic
`defects illustrated how fast a lethal mutation can spread in a
`population of small effective size.
`
`Includinggenomicinformationintheoptimalcontribution
`selection(OCS)method
`In the near future, JS schemes will likely be preferred to
`other designs because they generate larger genetic gain at
`
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`Exhibit 1018
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`Bouquet and Juga
`
`moderate inbreeding rates, if practical recommendations for
`the use of young AI sires are followed. However, it may
`become difficult for breeding managers to select and mate
`animals while monitoring their relationships, as many more
`AI bulls are used and for a shorter time period. It is therefore
`important to develop and promote the use of tools to control
`inbreeding rates. The standard approach used to determine
`contributions of breeding animals, maximizing genetic
`gain at a desired rate of inbreeding, is referred to as OCS
`(Meuwissen, 1997). In OCS, the contribution of a parent is
`the result of a trade-off between its genetic merit and its
`relationship to other individuals (Ferna´ ndez et al., 2011).
`Recently, Sonesson et al. (2010) showed with FLM simula-
`tions that OCS was not able to maintain genetic diversity
`across the whole genome in selected populations when
`relationship coefficients were estimated from pedigree
`information. In fact, it led to a strong reduction in diversity
`around QTL regions by favouring alleles with the largest
`effects. To circumvent this flaw, these authors proposed
`using marker-based relationships, because they more accu-
`rately reflect the genome sharing between individuals than
`pedigree-based expectations. Genomic Optimal Contribution
`Selection (GOCS) enabled a better control of genomic
`inbreeding across the genome. However, it also strongly
`reduced genetic gain. The study design may have influenced
`the results; in particular, the number of simulated QTL was
`relatively limited, which could have implied a strong effect of
`selection on QTL regions. Nevertheless, this result questions
`whether large DG can be achieved when an acceptable
`constraint on DFis considered. It also emphasizes the need
`to determine an acceptable rate of inbreeding in practical
`breeding programmes by integrating recent knowledge
`gained from genomic data. In addition, the ability of GOCS
`to maintain expected and observed heterozygosity in a
`conservation programme was shown to be sensitive to the
`estimator used to access genomic relationships (de Cara
`et al., 2011). This stresses the importance of identifying
`accurate marker-based relationship estimators to maximize
`the efficiency of GOCS in practical applications.
`Finally, after having inferred the relationship between
`chromosome segments of individuals, one can envisage
`strategies to maintain genetic diversity at the chromosome
`level, for instance, to prioritize preservation in genome
`regions exhibiting acute reduction in diversity (Engelsma
`etal., 2011) or to maintain breed-specific genetic variation in
`a hybrid population. It would be interesting to assess whe-
`ther benefits of these approaches are large relative to GOCS,
`which uses genome-wide relationship estimates.
`
`Impa