15 Genetic Improvement of
`
`Dairy Cattle
`
`Vincent Ducrocq 1 and George Wiggans2
`
`1 UMR 1313 Genetique Animate et Biologie Integrative (GABI)
`/NRA, Jouy-en-Josas, France; 2United States Department of Agriculture,
`Beltsville, Maryland, USA
`
`Introduction
`Breeding Objectives
`Derivation of a breeding objective
`Recent evolutions in breeding objectives
`Traits to consider
`Genetic Variation
`Breed differences
`Within-breed variation
`Inbreeding, genetic variability and heterosis
`QTLs and individual genes affecting traits of economic importance
`Genetic Evaluation
`Evaluation models
`Genotype by environment interaction
`National vs international evaluations
`Total merit indexes
`Genetic trends
`Across-breed analysis
`Genomic Selection
`Principles and methods
`SNP chips
`Implementation
`Consequences on the international scene
`Perspectives and challenges
`Design of Breeding Programmes
`AlandMOET
`Breeding programmes with genomic selection
`Minimizing inbreeding
`Conclusions
`References
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`Introduction
`
`As for any species or population, genetic
`improvement of dairy cattle involves determining
`
`© CAB International 2015. The Genetics of Cattle,
`2nd Edn (eds D.J. Garrick and A. Ruvinsky)
`
`a desirable direction for improvement, identifying
`traits that provide information to move in that
`direction, quantifying their heritability, deciding
`how to evaluate them and designing a breeding
`
`371 .
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`Exhibit 1019
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`372
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`V. Ducrocq and G. Wiggans )
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`programme to achieve the goals. With regard
`to these issues, dairy cattle are one of the most
`highly studied of all domesticated species.
`This chapter describes how to determine
`which goals should be established to empha(cid:173)
`size profit or efficiency as the ultimate goal of
`the dairy enterprise. The traits typically meas(cid:173)
`ured are listed along with how they are related
`and the genetic parameters utilized in the
`selection process. Evaluation procedures used
`to establish genetic rankings based on observa(cid:173)
`tions on related animals (genetic evaluations)
`or on genomic information (genomic evalua(cid:173)
`tions) are reviewed. Their incorporation into
`breeding programmes is outlined.
`
`Breeding Objectives
`
`Derivation of a breeding objective
`
`The first step in the design of a breeding pro(cid:173)
`gramme is to specify its goal: the breeding
`objective. The usual purpose of a breeding pro(cid:173)
`gramme is assumed to be the increase of prof(cid:173)
`itability by modifying the genetic mean of key
`traits. Often, this increase must be performed
`under an uncertain future economic environ(cid:173)
`ment, under diverse management systems and
`under some constraints (e.g. quota on overall
`production or constant feed supply or pasture
`area at farm level) . Therefore, the definition of
`the breeding objective starts with an inventory
`of representative management systems and of
`likely future scenarios as well as the description
`of specific constraints to satisfy.
`The derivation of the breeding objective
`involves a profit function that shows how a
`change in each relevant trait influences profita(cid:173)
`bility (Goddard, 1998). This profit function is
`often based on a bioeconomic model of the
`farm and obviously depends on the prices the
`farmer receives for milk and other products, and
`the prices paid for inputs (Groen et al., 1997).
`Typical illustrations can be found in Visscher
`et al. (1994) for pasture-based dairy farming in
`Australia, or Steine et al. (2008) for Norwegian
`Red cattle. Other required characteristics are
`the genetic parameters, the phenotypic means
`and the age structure of the herd at demo(cid:173)
`graphic equilibrium. When the performance
`
`level for one trait is modified by one unit under
`the specified constraints, a new equilibrium is
`reached and the economic efficiency of the
`herd changes. The economic weight of this
`trait is the monetary difference between the
`two situations, or mathematically, the value of
`the partial derivative of the profit function with
`respect to the trait. This weight will be used in
`the construction of a total merit index (TMI),
`i.e. a linear combination of estimated breeding
`values (EBVs) that will serve as selection crite(cid:173)
`rion to generate genetic progress on the breed(cid:173)
`ing objective.
`When future economic scenarios are too
`vague or when the economic impact of some
`traits is too difficult to determine, it may be pre(cid:173)
`ferred to derive a breeding goal that induces
`genetic gain in a direction of general consensus
`(Olesen et al., 1999). One approach involves
`finding weights for the traits in the breeding
`objective that lead to desired or restricted
`genetic gains. This is also a way to incorporate
`farmer or consumer opinion. For example,
`continuous decline in fertility or resistance to
`mastitis may be regarded as no longer admissi(cid:173)
`ble, while solely economic consideration would
`tolerate the deterioration. On the other hand,
`constraints or restrictions must be included with
`care because they can have a strong negative
`impact on overall benefits. A common practice
`is a two-step approach where a bioeconomic
`model is first developed to derive reference
`weights for the traits in the breeding goal.
`Expected genetic gains under a typical value for
`selection intensity are computed and then rela(cid:173)
`tive weights are empirically modified to get a
`more acceptable response, while controlling its
`overall cost compared with the initial situation.
`In practice, the economic weight for a
`given trait depends on the other traits that are
`included in the profit function. For instance, if
`feed intake is not included in the profit func(cid:173)
`tion, the economic weight for cow body weight
`is positive because increasing body weight
`increases income from sale of cull cows.
`However, if feed intake is included in the profit
`function, but not as a measured trait, the eco(cid:173)
`nomic weight of body weight may be negative
`because larger cows have greater feed require(cid:173)
`ments for maintenance.
`National breeding programmes are often
`compared by contrasting the (relative) economic
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`Exhibit 1019
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`Genetic Improvement of Dairy Cattle
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`373
`
`weights of the traits of interest (Miglior et al. ,
`2005). However, such a comparison may be
`misleading (Cunningham and Taubert, 2009;
`Ducrocq, 2010). First, the relative weights may
`be attached to traits expressed in different
`units, such as phenotypic, genetic or even
`average EBV standard deviations. Furthermore,
`traits are not independent: for example, pro(cid:173)
`ductive life and fertility are genetically corre(cid:173)
`lated, and the weight ascribed to productive
`life differs strongly when the cost of culling due
`to sterility is assigned to productive life, or
`instead, to fertility, even though the expected
`responses are similar. When some traits receive
`a negative weight, the meaning of a relative
`weight assuming a sum of 100% is questiona(cid:173)
`ble. Finally, when the average reliabilities of
`the EBVs included in TMI vary a lot, traits with
`high EBV variability may contribute more to
`the overall ranking of animals than their eco(cid:173)
`nomic weight indicates. This is clearly the case
`for production compared to fertility and dis(cid:173)
`ease traits.
`
`Recent evolutions in
`breeding objectives
`
`For decades, breeding goals in dairy cattle
`included few traits worldwide. These were
`mainly production and type traits, with a strong
`emphasis on production. Exceptions included
`the Scandinavian countries, with far-sighted
`focus on udder health, and fertility and dual(cid:173)
`purpose breeds for which growth and beef
`traits were also valued. Hence in most coun(cid:173)
`tries, functional traits , i.e. traits related to the
`ability to remain productive, fertile and in good
`health with minimum human intervention were
`basically ignored in breeding programmes,
`except indirectly through some morphological
`(type) predictors. As a result, the overall robust(cid:173)
`ness of dairy cows has been decreasing along
`with the continuous and successful increase in
`performance for those production traits under
`selection. This was the consequence of the
`nearly universal negative genetic correlation
`between production and
`fitness
`(Jorjani,
`2007). Functional traits are difficult to select
`because of their low heritability but they often
`have large genetic variability and, therefore,
`
`they can also easily deteriorate . Attention
`towards more sustainable breeding schemes has
`increased tremendously over the past 20 years,
`following the path paved by Scandinavian
`countries. So breeding objectives are now
`broader, more complex but also more balanced
`in many countries. Nowadays, the relative
`weight given to production in breeding objec(cid:173)
`tives is generally between 25 and 50%, and
`functional traits receive larger attention, in
`order to improve long term sustainability of
`dairy production.
`
`Traits to consider
`
`The yields of milk, fat and protein are the
`major determinants of income to dairy farmers
`and the most important traits in the objective.
`Their relative economic weights depend on the
`pricing formula by which farmers are paid. If
`the milk is used for manufacturing, protein is
`generally most valuable, fat has some value,
`but milk volume has a negative value because it
`must be transported from farm to factory and
`evaporated to make particular products.
`Other traits commonly included in breed(cid:173)
`ing objectives are health (in particular udder
`health), fertility, calving ease, body weight, milk(cid:173)
`ing speed, temperament and length of produc(cid:173)
`tive life.
`Resistance to mastitis is the health trait of
`major concern in dairy cattle. It represents the
`ability to avoid udder infection or to quickly
`recover after infection. In some cases, resistance
`to a particular pathogen is considered but the lat(cid:173)
`ter is usually unknown. Negative economic con(cid:173)
`sequences of a mastitis event are numerous:
`lower milk production, discarded milk because of
`the presence of antibiotics or inadequate compo(cid:173)
`sition, lower milk payment, increased veterinary
`and labour costs, and increased cow replace(cid:173)
`ment. Indeed, other health traits (lameness, met(cid:173)
`abolic or reproductive disorders) share most of
`these negative impacts.
`Cow fertility influences AI and veterinary
`costs, the interval between calvings and hence
`the pattern and yield from current and later
`lactations. In countries relying heavily on pas(cid:173)
`ture (New Zealand, Ireland) or where male
`calves have a higher value and can be channelled
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`374
`
`V. Ducrocq and G. Wiggans )
`
`towards meat production, fertility has always
`been an important trait. In contrast, in countries
`where dairy calves are of low value and where
`farmers can manage cows with long calving
`intervals so that those cows have long persistent
`lactations, the economic weight of cow fertility
`used to be low. However, even in such a situa(cid:173)
`tion, the degradation of fertility in Holsteins is an
`issue. This has led to an increase in the eco(cid:173)
`nomic weight on fertility traits everywhere.
`Calving ease is valuable because dystocia
`has potentially severe consequences on still(cid:173)
`birth, production, fertility and general health,
`leading to veterinary costs, extra labour costs,
`lost calves and cows, reduced milk yield and
`infertility. In dairy cattle, losses due to difficult
`calvings mostly occur at first calving. The eco(cid:173)
`nomic weight depends heavily on the average
`incidence of dystocia. Calving ease is affected
`by the genetic merit of both the calf and its
`dam; therefore, selection needs to consider
`calving ease as a maternal trait (of the cow) and
`a direct trait of the calf.
`Cows are culled when they are no longer
`economically or physically sustainable. Length
`of productive life (LPL) from first calving to
`culling can be seen as an overall measure of
`her ability to stay productive. If LPL is cor(cid:173)
`rected for the major source of voluntary culling
`(production) , the resulting functional longevity
`depicts her ability to elude involuntary culling
`related to fertility , health or workability prob(cid:173)
`lems . In most selected dairy breeds , the
`proportion of involuntary culling has been
`increasing and voluntary culling on production
`traits has been declining, leading to closer con(cid:173)
`vergence between true and functional longevity.
`Milking speed is of economic value because
`slow milkers increase the labour cost of milking.
`Good temperament, while it may be difficult to
`assign a monetary value to it, is valued highly
`by dairy farmers in Australia and New Zealand
`who milk large numbers of cows and want to
`avoid the disruption and danger caused by
`cows with poor temperament.
`
`Genetic Variation
`
`the rate of
`Genetic parameters quantify
`genetic change that it is possible to achieve .
`
`They are required for estimation of genetic
`merit. Of these parameters, the heritability
`describes what portion of the variation (vari(cid:173)
`ance) in a trait is of genetic origin, and correla(cid:173)
`tions among these traits indicate how genetic
`change in one trait can affect the others.
`When multiple traits are evaluated, covari(cid:173)
`ances indicate to what degree the information
`from one trait influences the others. If an ani(cid:173)
`mal has more than one observation for a trait,
`the repeatability describes the expected simi(cid:173)
`larity among those observations. Other genetic
`parameters include the effects of dominance ,
`individual genes, breed , inbreeding and heter(cid:173)
`osis (crossbreeding).
`
`Breed differences
`
`The world's dairy cattle include Bos taurus and
`Bos indicus breeds. The B. taurus cattle are
`dominant in temperate regions and are noted
`for high production. The B. indicus are preva(cid:173)
`lent in hotter climates and subsistence farming .
`The breeds of the B. taurus population mostly
`arose in Europe. Globally, most animals are
`purebred but crossbreeding programmes have
`been proposed as a way of upgrading indige(cid:173)
`nous cattle to a high producing breed, or as a
`way to obtain the benefits of complementarity
`and heterosis.
`Registry organizations maintain pedigree
`records, which enable animals to be traced to
`the origin of the breed, or its importation.
`With globalization, selection goals around the
`world have converged, as have the technolo(cid:173)
`gies to support high yields, particularly in
`temperate regions. In those environments,
`the Holstein breed has become dominant
`because if its high yield per cow. The Jersey
`has emerged as the primary alternative dairy
`breed , because of high component yields and
`smaller size, along with a collection of so(cid:173)
`called Red breeds. Other breeds, in particular
`dual-purpose breeds, have regional importance,
`such as the Simmental/Montbeliarde breed(s)
`in Europe. Table 15.1 illustrates the differ(cid:173)
`ences in yields for the most common dairy
`breeds. A more complete overview can be
`obtained from the International Committee
`for Animal Recording (ICAR, 2013).
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`Exhibit 1019
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`Genetic Improvement of Dairy Cattle
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`375
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`Table 15.1. 305-day lactation averages by breed and country in 2011. (From www.icar.org.)
`
`Breed
`
`Holstein
`
`Friesian
`Sim mental
`Brown Swiss
`Jersey
`
`Country
`
`Cows (106)
`
`Milk (kg)
`
`Fat(%)
`
`Protein(%)
`
`Canada
`France
`USA
`New Zealand
`Germany
`Germany
`USA
`New Zealand
`
`0.72
`1.72
`3.82
`0.96
`0.89
`0.17
`0.23
`0.35
`
`9,975
`7,873
`10,607
`5,600
`6,922
`7,002
`7,626
`3,946
`
`3.79
`3.97
`3.66
`4.22
`4.11
`4.22
`4.75
`5.52
`
`3.19
`3.38
`3.07
`3.50
`3.48
`3.59
`3.63
`4.00
`
`Within-breed variation
`
`Yield traits
`
`Milk, fat and protein yields are usually defined
`in a standard manner representing produc(cid:173)
`tion in kilogrammes during the 305 days
`(10 months) following calving. Fat and pro(cid:173)
`tein concentrations derived from yields are
`also of interest because they often condition
`milk price. In practice, 305 day yields are
`obtained from periodic (most often monthly)
`measurements of daily production or 'test(cid:173)
`day' yields.
`lactation yields are
`For simplification,
`often regarded as repeated measurements of
`the same genetic trait. Genetic correlations
`among successive lactations provide an indica(cid:173)
`tion of the appropriateness of this assumption.
`Indeed, these correlations are high (>0.85
`between first and later lactations, close to 1
`between later lactations, e.g. Druet et al.,
`2005). One reason for the lower correlation
`with first and later lactations compared to
`between later lactations is that cows reach their
`mature production level at different rates.
`Genetic parameters for lactation yields
`are remarkably similar across countries, with
`heritabilities from 0. 25 to 0. 35 for yields, with
`lower values in extensive or harsh environ(cid:173)
`ments; repeatabilities of 0.50 to 0.60; and
`much higher heritabilities (at least 0.50) for
`fat and protein concentrations. Genetic cor(cid:173)
`relations are high between lactation yields.
`A review of 22 studies in Holstein in different
`countries over the past 20 years indicates cor(cid:173)
`relations of 0. 62 ± 0 .17 between milk and fat
`yields, 0 .84 ± 0 .14 between milk and protein
`yields, 0. 72 ± 0 .12 between fat and protein
`
`yields and 0.48 ± 0.25 between fat and pro(cid:173)
`tein concentrations in the first lactation, with
`similar values in later lactations. Dominance
`variation - due to interactions among genes at
`a specific locus - is most often ignored but can
`reach 5% of the total variance (Van Tassell
`et al., 2000).
`Test-day yields are measurements spe(cid:173)
`cific to a particular testing day, with such tests
`usually being distributed over the whole lacta(cid:173)
`tion. Longitudinal analyses of such data are
`particularly interesting compared to analysis
`of whole lactation yields because they allow a
`more precise description of how genetic and
`non-genetic factors affect production over the
`lactation. For example, the specific effect of a
`herd on a given test day can be accounted for,
`and the effects of month or age at calving or
`stage of gestation and the additive genetic
`merit of the animal on the level and shape of
`the lactation can be accurately modelled.
`Such test-day models have gained popularity
`since the mid-1990s, as special cases of ran(cid:173)
`dom regression models . Heritability estimates
`of test-day production are typically lower at
`the beginning and end of the lactation but
`can be high in mid lactation. Genetic correla(cid:173)
`tions between production traits at different
`stages of lactation are usually high to very
`high (close to 1), except for the beginning
`and the end of lactation. As a result, the over(cid:173)
`all heritability estimates over the lactation are
`definitely higher (up to 0.50) than when the
`total 305 day lactation yields are directly ana(cid:173)
`lysed. Persistency, which describes how steeply
`the production decreases during the lactation,
`and maturity, which expresses how production
`evolves between first and later lactations can
`be specifically evaluated using test-day models.
`
`Exhibit 1019
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`376
`
`V. Ducrocq and G. Wiggans)
`
`Their heritability is generally low (e.g. 0.09-0.16
`for milk persistency according to Jakobsen
`et al., 2002).
`
`Conformation traits
`
`Visual appraisals of cows for conformation
`(also known as type) traits have been collected
`for many years. Improving type traits has been
`advocated as a way to improve fitness, longev(cid:173)
`ity and workability. This view has been altered
`in the recent past: size (or height) is receiving
`considerable attention worldwide in most breeds,
`while its relationship with fitness is often uncer(cid:173)
`tain, and in some production systems, clearly
`unfavourable (Pryce et al., 2009). It is now
`well established that dairy character - or dairy
`form, dairyness or angularity - is unfavourably
`associated with body condition score, fertility
`and mastitis resistance (Lassen et al., 2003).
`Indeed, the objectives of elite breeders regard(cid:173)
`ing type traits often diverge from those of most
`commercial dairymen. Traits such as angular(cid:173)
`ity, body condition score (Pryce et al., 2001),
`body depth or rump angle are useful, but
`mainly as predictors of poor fertility. In con(cid:173)
`trast, udder traits have unambiguous beneficial
`impact on functional longevity, resistance to
`mastitis and milking speed. Udder depth is cer(cid:173)
`tainly the most important udder trait in that
`respect, together with fore and rear attach(cid:173)
`ment, suspensory ligament and teat length and
`placement. A few feet and leg traits (rear leg
`set, foot angle, locomotion) are routinely col(cid:173)
`lected and evaluated nationally and interna(cid:173)
`tionally, but generally have low heritability and
`a disappointingly low correlation with, for
`example, actual longevity. Some countries are
`now investigating other relevant traits better
`related to lameness and longevity, such as
`information on claw disorders collected by
`hoof trimmers (e.g. Van der Linde et al., 2010).
`Other traits such as muscling are recorded in
`dual-purpose breeds (Simmental, Montbeliarde,
`Normande).
`Conformation traits are most often scored
`on linear scales, e.g. on a scale from 1 to 9 .
`Heritability is usually relatively low (0.05-0.20)
`for feet and leg traits, moderate (0.20-0.35)
`for udder traits and moderate to high (0.25-
`0.60) for traits related to size (lnterbull, 2013).
`Cows usually get a final score to summarize
`
`overall conformation. The final score is a com(cid:173)
`bination of scores characterizing udder, body
`or feet and legs quality. Because genetic
`parameters vary between type traits as well as
`the weights used to combine them into a final
`score, composite indices combining genetic
`merit of the elementary traits in a formal way
`are preferable to direct evaluation of final scores.
`
`Workability traits
`
`Workability traits include milking speed and
`temperament. They are often recorded at the
`same time as type traits, on linear scales (e.g. 1
`to 5 in a within herd comparison) or with actual
`measure (milk flow). Except in the latter case
`where larger estimates were found, heritability
`estimates are moderate (0.20-0.25) for milk(cid:173)
`ing speed and low (0.10) for temperament.
`
`Calving traits
`
`Birth weight is seldom recorded in dairy herds,
`whereas dystocia is commonly recorded as a
`calving code (e.g. 1 = no assistance, 2 = easy
`pull, 3 = hard pull, 4 = caesarean). Stillbirth is
`recorded as an all or none trait (alive or dead
`within 24 or 48 h after birth). Calving traits
`are under the influence of the genetic and
`non-genetic characteristics of both the calf
`(direct effect, ease of birth) and its dam (mater(cid:173)
`nal effect, ease of calving). Heritabilities are
`usually quite low (<0.10), especially when
`adult cows or maternal effects are considered
`(Interbull, 2013).
`
`Fertility traits
`
`Female fertility has been neglected in breed(cid:173)
`ing programmes for decades. As a result, it
`has been notably compromised by intensive
`selection for production. Initially, female fer(cid:173)
`tility traits were limited to crude measures
`such as calving intervals or days open, which
`can be directly extracted from milk record(cid:173)
`ing data . However, fertility is a composite
`phenotype that can be broken down into
`various basic traits requiring joint analysis of
`insemination and calving data. Records cor(cid:173)
`responding to natural services are usually
`ignored in analysis of fertility data . Most fertil(cid:173)
`ity traits are considered as genetically different
`
`Exhibit 1019
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`Genetic Improvement of Dairy Cattle
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`377
`
`between heifers and adult cows, the latter
`being challenged by concomitant production.
`Jorjani (2007) classified female fertility traits
`into four groups: ability to conceive (non(cid:173)
`return rates, conception rate , number of
`inseminations) for heifers; ability to conceive
`for adult cows; ability to recycle after calving
`(interval from calving to first Al) ; and interval
`measures of ability to conceive (interval from
`first to successful (or last) AI). Calving inter(cid:173)
`vals and days open are pooled measures of
`these abilities . Gestation length is moder(cid:173)
`ately heritable but does not vary much within
`breed and is rarely considered in breeding
`programmes.
`
`Health traits
`
`Milk samples collected to determine fat and
`protein content are also analysed for somatic
`cell counts (SCCs), which, after a normalizing
`transformation , become somatic cell scores
`(SCSs), an indicator of udder health (Ali and
`Shook, 1980). High SCSs are associated
`with clinical or sub-clinical mastitis and
`depressed milk yield . Scandinavian countries
`have a long history of systematic disease data
`collection (Aamand , 2006). In particular, the
`actual occurrence of clinical mastitis is rou(cid:173)
`tinely recorded. More countries are following
`this track, especially in Europe (Austria,
`France). Other health traits include feet and
`legs, reproductive or digestive disorders (e.g.
`Egger-Danner et al ., 2012). Those health
`traits are characterized by a low incidence , a
`low heritability but a large genetic variance.
`
`Longevity
`
`A typical measure of longevity is length of pro(cid:173)
`ductive life (LPL), defined as the number of
`days between first calving and culling. For cows
`still alive, only the current LPL (i.e. a lower
`bound of their 'final' LPL) is known: the obser(cid:173)
`vation is said to be censored. Another charac(cid:173)
`teristic of LPL measures is that environmental
`factors influencing risk of being culled (season,
`parity, herd size, etc.) are changing at the same
`time as LPL is measured. Any statistical ana(cid:173)
`lysis of LPL should take these features into
`account. It is also possible to predict LPL of
`cows still in the herd so they can be analysed
`with a standard linear model. An alternative
`simplified trait is survival (0/1) to the next
`lactation, also called stayability. Heritability
`estimates for longevity are around O .10 or less.
`Because of its low heritability and its relatively
`late availability, LPL information is generally
`combined with early predictors such as type
`traits or SCS to improve longevity evaluations.
`
`Correlation between trait groups
`
`Table 15. 2 reports genetic correlation esti(cid:173)
`mates between traits included in the breeding
`goal in France for the Holstein breed. Udder
`health traits (somatic cell count and clinical
`mastitis) are strongly correlated (0. 70) but
`clearly correspond to distinct traits, themselves
`related to udder conformation traits. Fast milk(cid:173)
`ing Holstein cows have higher SCCs. The situ(cid:173)
`ation is reversed in other breeds such as the
`Montbeliarde, where selection for milking ease
`
`Table 15.2. Estimated genetic correlations between some traits included in the Holstein total merit index
`in Francea.
`
`Trait name and abbreviation
`
`Milk yield
`
`sec
`
`CIM
`
`CRate
`
`IC-1AI MEase UddD
`
`Somatic cell count
`Clinical mastitis
`Conception rate
`Interval calving - 1st Al
`Milking ease
`Udder depth
`Functional longevity
`
`sec
`CIM
`CRate
`IC-1AIC
`MEase
`UddD
`Flong
`
`* b
`-0.26
`-0.22
`-0.42
`
`-0.22
`0.17
`
`0.70
`0.25
`
`-0.37
`0.27
`0.48
`
`0.24
`0.23
`-0.18
`0.30
`0.47
`
`0.16
`
`0.15
`0.47
`
`0.28
`0.17
`
`0.41
`
`aTrait scales are transformed: positive values indicate favourable values, e.g. positive SCC means lower SCC.
`bAbsolute value of genetic correlation less than 0 .15.
`c1nterval between calving and first insemination.
`
`Exhibit 1019
`Select Sires, et al. v. ABS Global
`
`

`

`378
`
`V. Ducrocq and G. Wiggans )
`
`has not been as strong. Ability to conceive and
`ability to recycle are two poorly correlated fer(cid:173)
`tility traits. Functional longevity exhibits a rela(cid:173)
`tively high genetic correlation (close to 0.5)
`with a number of functional traits related to
`udder health (somatic cell count, clinical masti(cid:173)
`tis, udder depth) and fertility (conception rate).
`
`Inbreeding, genetic variability
`and heterosis
`
`An animal is inbred if its parents are related.
`The inbreeding coefficient is the probability
`that an animal receives from both parents the
`same ancestral copy of any particular allele or
`chromosome fragment. The intense selection
`of bulls in most breeds, each with (tens of)
`thousands of daughters, and the use of a
`reduced number of sires of sons at the interna(cid:173)
`tional level have led to a continuous increase in
`inbreeding. The use of close family informa(cid:173)
`tion in genetic evaluation tends to further
`increase inbreeding because the consideration
`of all relationships tends to make the evalua(cid:173)
`tions of family members similar, i.e. more likely
`to be selected together.
`Systematic calculation of inbreeding rela(cid:173)
`tive to a base population that is assumed unre(cid:173)
`lated and non-inbred is feasible in very large
`
`populations. Inbreeding can be strongly under(cid:173)
`estimated when pedigree data is incomplete, or
`pedigree depth, i.e. the equivalent number of
`generations of known parents is low, but meth(cid:173)
`ods have been proposed to account for missing
`ancestors (VanRaden, 1992). Other measures
`of genetic variability less sensitive to missing
`data exist. They are related to the probability of
`gene origin, e.g. the effective number of found(cid:173)
`ers or ancestors, or the number of ancestors
`accounting for 50% of the genes (Boichard
`et al., 1997). They show that actual population
`size is not at all representative of genetic varia(cid:173)
`bility. This is demonstrated by some values
`obtained in Holstein in France: 8 bulls contrib(cid:173)
`uted 50% of the genes in females born between
`2004 and 2007 and the effective number of
`ancestors was 21 (Danchin-Burge et al., 2012).
`This situation is observed in all Holstein popula(cid:173)
`tions. The evolution of inbreeding for the
`Holstein population in the USA is shown in
`Fig. 15.1. The base population consisted of
`animals born before 1960. For 20 years until
`1980, inbreeding increased slowly at about
`0.044%/year. For the next 15 years it rose
`rapidly at 0.275%/year. More recently, during
`the period from 2000, the rate of increase has
`decreased to O .11 %/year.
`A consequence of receiving the same
`genes identical by descent from both ancestors
`
`0.06
`
`0.05
`
`0.04
`
`0)
`C
`'6
`Q) 0.03
`Q)
`.0
`E
`
`0.02
`
`0.01
`
`0--.......... -~~~~-~~~~-~~~-~~~~-~~~~-~~~~-~
`~~~*~~~~~~~~~~~~~~~*~~~~~~~
`~~~~~~~~~~~~~~~~~~~~~~~~~~~
`Birth year
`
`Fig. 15.1. Average inbreeding by birth year for US Holstein cows.
`
`Exhibit 1019
`Select Sires, et al. v. ABS Global
`
`

`

`Genetic Improvement of Dairy Cattle
`
`379
`
`is that the likelihood of homozygosity among
`recessive alleles increases. Homozygosity can
`lead to embryo failure, Mendelian diseases and
`decreased productivity related to inbreeding
`depression (Wigg ans et a I. , 1995). Table 15. 3
`presents estimates of inbreeding depression for
`Holstein cows in the USA.
`Heterosis can be viewed as the opposite
`of inbreeding depression, and results from an
`increase in heterozygosity, reducing the likeli(cid:173)
`hood of deleterious homozygous recessive
`genes. Heterosis measures the degree that off(cid:173)
`spring exceed the average of the performance
`of their parents, the magnitude of which
`depends on the genetic distance between the
`parents. Some estimates of heterosis are pre(cid:173)
`sented in Table 15.3. Heterosis is most appar(cid:173)
`ent in breed crosses. If parental breeds are
`quite different in the trait of interest, the bene(cid:173)
`fit of heterosis is unlikely to make the progeny
`competitive with the higher producing parental
`breed. However, heterosis may contribute a
`significant advantage in fitness. In New Zealand
`where most milk is used in manufacturing,
`cows from the Jersey breed (less milk, but high
`in fat) managed at higher stocking rates than
`their larger Holstein counterparts (more milk,
`but less fat) are perceived as financially com(cid:173)
`petitive on a per hectare basis, and the prog(cid:173)
`eny of crosses betwe