Genomic selection in dairy cattle:
`Integration of DNA testing into breeding
`programs
`
`Jonathan M. Schefers*† and Kent A. Weigel*
`*Department of Dairy Science, University of Wisconsin, Madison 53706; and
`†Alta Genetics USA, Watertown, WI 53094
`
`Downloaded from https://academic.oup.com/af/article/2/1/4/4638584 by guest on 27 December 2021
`
`bulls with high genetic merit. Before progeny testing a young bull, the
`average estimated breeding value (EBV) of his sire and dam, which is
`commonly referred to as parent average, was used to select young bulls
`with the highest genetic merit and had an accuracy (reliability) of only 30
`to 40%. In a progeny-testing scheme, a group of elite cows was identi-
`fi ed as potential dams of young bulls (i.e., bull mothers). Progeny test-
`ing was necessary because most traits of economic importance in dairy
`cattle (e.g., milk production) are sex-limited and can be measured only
`in females. These bull mothers were mated to elite progeny-tested sires
`from the previous generation for the specifi c purpose of producing bull
`calves. Once these young bulls reached sexual maturity, which typically
`occurred at about 12 months of age, they were mated to a large number of
`cows on commercial farms, with the goal of producing approximately 100
`daughters. Approximately 3 years later, the daughters of these young bulls
`would begin lactating, and this information was used to calculate the EBV
`of their sires for milk production and other key traits, which typically
`had reliabilities of 75 to 85%. At this point, these bulls were approxi-
`mately 4.5 years of age, and the AI companies would decide which bulls
`should be culled and which bulls should be marketed to dairy farmers for
`the purpose of siring the next generation of replacement heifers. Overall,
`progeny-testing schemes are time consuming and costly because the AI
`companies have to wait many years to obtain genetic predictions with
`suffi cient accuracy for making selection decisions, and in the meantime,
`hundreds of bulls are housed “in waiting” while phenotypes are measured
`on tens of thousands of their daughters. The objective of this review is
`to describe how genomics will affect genetic progress and breeding pro-
`grams in the future.
`
`Factors Affecting the Rate of Genetic Progress
`Four main factors affect the rate of genetic change in a population un-
`dergoing artifi cial selection. The classic equation for explaining the rate of
`genetic change, as described by Falconer (1989), is shown below:
`ir
`σA ,
`L
`where ΔG is genetic change, i is the selection intensity, r is the accuracy
`of selection (or reliability of the EBV), σA is the additive genetic standard
`deviation of the trait of interest, and L is the generation interval. The rate
`of genetic progress can be described in detail for each of the 4 pathways
`of selection according to the sex of the parent and offspring.
`
`∆G
`
`=
`
`Selection Pathway
`Sires of Males. Sires of males (SM) represent the most elite males
`that are selected to be sires of the next generation of young bulls. This
`
`Implications
`Genomic selection offers many advantages with regard to improving
`the rate of genetic gain in dairy cattle breeding programs. The most
`important factors that contribute to faster genetic gain include
` • A greater accuracy of predicted genetic merit for young animals.
` • A shorter generation interval because of heavier use of young,
`genetically superior males and females.
` • An increased intensity of selection because breeders can use
`genomic testing to screen a larger group of potentially elite ani-
`mals.
`By increasing the accuracy and intensity of selection and shortening
`the generation interval, the rate of genetic progress for economically
`important dairy traits can be approximately doubled.
`
`Key words: dairy cattle, DNA testing, genomic selection
`
`Introduction
`The advent of DNA sequencing and high-throughput genomic technol-
`ogies has resulted in the discovery of a large number of single nucleotide
`polymorphisms (SNP) in cattle and other food animal species. Automated
`methods for SNP genotyping are now commercially available, and the use
`of dense SNP arrays that cover the bovine genome and that explain the
`majority of genetic variation in important traits has been proposed by an
`approach called genomic selection or whole-genome selection (Meuwis-
`sen et al., 2001). In practice, genomic selection refers to selection deci-
`sions based on genomic estimated breeding values (GEBV). These GEBV
`are calculated by estimating SNP effects from prediction equations, which
`are derived from a subset of animals in the population (i.e., a reference
`population) that have SNP genotypes and phenotypes for traits of inter-
`est. The accuracy of GEBV depends on the size of the reference popula-
`tion used to derive prediction equations, the heritability of the trait, and
`the extent of relationships between selection candidates and the reference
`population.
`In dairy cattle breeding programs, genomic selection allows breeders
`to identify genetically superior animals at a much earlier age. In fact, ani-
`mals that have been DNA tested can receive an accurate GEBV before
`they reach sexual maturity. Before the advent of genomic selection, arti-
`fi cial insemination (AI) companies relied on progeny testing to identify
`
`© 2012 Schefers and Weigel.
`doi:10.2527/af.2011-0032
`
`4
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`Animal Frontiers
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`Exhibit 1014
`Select Sires, et al. v. ABS Global
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`Downloaded from https://academic.oup.com/af/article/2/1/4/4638584 by guest on 27 December 2021
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`Figure 1. Timeline of a traditional artifi cial insemination breeding program based on progeny testing. EBV = estimated
`breeding value.
`
`group is chosen based on EBV or GEBV and is typically composed of
`<5% of the males whose semen is marketed to dairy farmers. These bulls
`are often referred to as “sires of sons.”
`
`Sires of Females. Sires of females (SF) represent a larger group of
`males that have been selected based on EBV or GEBV and whose semen
`is used to breed the general population and produce replacement females
`for commercial farms. These bulls are typically referred to as “active AI
`sires.”
`
`Dams of Males. Dams of males (DM) represent a group of elite
`females that are selected based on EBV or GEBV and that usually rank
`among the top 1% of the population. These cows are mated to elite bulls
`from the SM group for the purpose of producing bull calves, and they are
`more commonly referred to as “bull mothers.”
`
`Dams of Females. Dams of females (DF) represent the large
`population of females that are primarily used to produce milk rather than
`breeding stock. These cows, which are often referred to as “commercial
`cows,” are routinely mated to bulls from the SF group to initiate lactation,
`resulting in the next generation of replacement heifers.
`
`Generation Interval
`Because of the heavy reliance on progeny testing for sex-limited dairy
`traits, generation interval is the most important factor affecting the rate of
`genetic change in dairy cattle breeding programs. Generation interval (L)
`is defi ned as the average age of the parents when the progeny are born.
`Biologically, the shortest possible generation interval is the sum of age at
`sexual maturity and gestation length. This limitation can be circumvented
`by using advanced reproductive technologies, such as in vitro fertilization
`(IVF) of prepubertal heifers, but this practice is not commonly used in
`dairy cattle breeding programs.
`As noted earlier, traditional progeny testing is a time-consuming pro-
`cess, and because breeders want highly reliable EBV when making selec-
`tion decisions, the generation interval for the SM pathway is extremely
`
`long. Figure 1 shows the timeline for a traditional progeny-testing scheme,
`which has a generation interval for the SM pathway of approximately 63
`months.
`Genomic selection allows AI companies to make decisions based on
`GEBV, which are available at a very young age. Therefore, younger bulls
`can be used as sires of sons in the SM pathway, and the age at which they
`can be used is limited only by their sexual maturity. Instead of waiting a
`minimum of 4.5 years to use progeny-tested bulls as sires of sons, AI com-
`panies can use the best DNA-tested young bulls as sires of sons by rough-
`ly 1 year of age. This drastically reduces the generation interval in the SM
`pathway and, as noted by Schaeffer (2006), it could lead to doubling of
`the rate of genetic gain. Yearling bulls that have GEBV information but
`lack phenotypic data on their daughters are often referred to as “genomic
`bulls.” There has been an immense shift among the AI companies toward
`the use of genomic bulls in the past 3 years. Some AI companies use al-
`most all genomic bulls as sires of sons, whereas other companies use a
`combination of genomic bulls and progeny-tested bulls. Genomic bulls
`that are considered as sires of sons by AI companies have higher genetic
`merit, on average, but lower average reliability, so AI companies try to
`minimize risk by considering a larger number of genomic bulls as sires of
`sons. Figure 2 illustrates the timeline for an aggressive AI breeding pro-
`gram based on using genomic bulls as sires of sons. The key feature of this
`timeline is that if AI companies are very aggressive in using 1-year-old
`genomic bulls as sires of sons, the generation interval for the SM pathway
`can be reduced to 21 months. Estimation of the SNP effects for computing
`genomic predictions relies on the genotypes and phenotypes of reference
`animals, and in this case, young selection candidates are 3 generations re-
`moved from the most recent data on progeny-tested ancestors in the refer-
`ence population. In the example shown in Figure 2, bull B is 54 months of
`age when phenotypes of his daughters become available for computation
`of his EBV, and by this time, breeders are already using semen from his
`best grandsons to produce his great-grandsons and great-granddaughters.
`Breeders are also using GEBV information to select females at a
`much earlier age. Before the introduction of genomic selection, breed-
`ers generally waited until cows were at least 2 years of age before using
`reproductive technologies, such as multiple ovulation and embryo transfer
`
`January 2012, Vol. 2, No. 1
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`(MOET) or IVF. However, DNA testing of heifer calves provides GEBV
`with suffi cient accuracy to allow selection decisions to be made before
`heifers reach sexual maturity, and the most aggressive breeders are mak-
`ing heavy use of virgin (yearling) heifers as bull mothers. This strategy
`can reduce the generation interval in the DF pathway to 21 months as well.
`
`Selection Intensity
`Two factors affect selection intensity (i) in a breeding program. First,
`selection intensity is dependent on the size of the population. Greater se-
`lection intensity can be achieved in large populations because more selec-
`tion candidates can be screened in search of genetic “outliers.” Second,
`selection intensity is dependent on the proportion of animals selected from
`the population to serve as parents of the next generation. In the era of
`genomic selection, DNA samples can be taken shortly after birth, and AI
`companies can screen thousands of bull calves to fi nd a few elite indi-
`viduals for their breeding programs. In practice, most North American AI
`companies will DNA test and subsequently discard 10 to 20 genetically
`inferior bull calves for every elite young bull that enters the program.
`Since the start of genomic testing in 2009, approximately 33,000 young
`bulls have been DNA tested in North America. Since January 2011, ap-
`proximately 1,000 bulls per month have been tested in North America
`(G. R. Wiggans, US Department of Agriculture, Agricultural Research
`Service, personal communication). Potential bull dams can be tested as
`well, and this gives AI companies and breeders an opportunity to screen
`thousands of potentially elite cows and heifers on commercial farms for
`the purpose of identifying a few superior individuals that can be propa-
`gated by MOET or IVF.
`
`Accuracy of Selection
`In a progeny-testing program, the accuracy of selection (r) depends
`largely on the number of offspring per sire and, hence, on the number of
`cows in progeny test herds that are available for mating to young, unprov-
`en bulls. With genomic selection, accuracy is primarily a function of the
`size of the reference population that is used to estimate SNP effects, which
`
`in turn are used to compute GEBV of selection candidates. This reference
`population may consist of genotyped females that also have phenotypes,
`genotyped males that have daughters with phenotypes, or a combination
`of the two. At present, the reliabilities of GEBV for production traits are
`often 70% or greater in North American Holsteins (Van Raden et al.,
`2009), which is twice the level of reliability associated with traditional
`parent averages that are computed from pedigrees.
`
`Genetic Variation
`The genetic standard deviation (σA) refl ects the underlying genetic
`variability of a given trait within the population. Inbreeding decreases the
`effective population size, which can reduce the amount of genetic varia-
`tion available for selection and reduce the rate of genetic progress. How-
`ever, in comparison with other factors in the equation for genetic progress,
`relatively little can be done to increase the amount of genetic variation
`within a population.
`
`Application of Genomic Selection in Females
`
`Genomic Testing
`Many progressive breeders are using genomic testing for the majority
`of their cows and heifers to identify those females that received the most
`favorable combination of genes from their parents. Currently, a low-den-
`sity (LD) chip with 6,909 SNP (Illumina, 2011a) and a medium-density
`(50K) chip with 54,609 SNP (Illumina, 2011b) are the products used most
`frequently by breeders, and GEBV for production, health, and conforma-
`tion traits can be computed using genotypes from either chip. Recently,
`the LD chip replaced the 3K chip with 2,900 SNP (Illumina, 2001c) be-
`cause of greater gains in reliability and improved readability among SNP
`genotypes. The main difference between the LD chip and 50K chip is cost,
`and the LD chip is more affordable for breeders who wish to genotype a
`large number of cows, heifers, or calves. To combine information from
`SNP chips of different densities when calculating genetic evaluations, im-
`
`Figure 2. Timeline of an aggressive artifi cial insemination breeding program based on the use of genomic bulls as sires of
`sons. GEBV = genomic estimated breeding value; EBV = estimated breeding value.
`
`6
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`Animal Frontiers
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`Exhibit 1014
`Select Sires, et al. v. ABS Global
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`Downloaded from https://academic.oup.com/af/article/2/1/4/4638584 by guest on 27 December 2021
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`putation is used to extend the LD subset of markers to the full 50K set.
`Imputation refers the prediction of genotypes for missing markers based
`on family information or linkage disequilibrium. A disadvantage of the
`LD chip is that imputation errors can occur, and this can lead to a slight
`reduction in the accuracy of GEBV. Dassonneville et al. (2011) reported
`error rates ranging from 2 to 6% when imputing 50K genotypes from LD
`genotypes.
`Genomic testing services are currently offered by breed associations,
`AI stud services, and some privately owned companies. The LD chip costs
`$43 to $55 per animal, and reliabilities of the resulting GEBV for produc-
`tion traits in Holsteins are approximately 60 to 65%. The 50K chip is more
`costly, at $125 to $135 per animal (Holstein Association USA, 2011), but
`reliabilities for production traits in Holsteins are roughly 70%. For elite
`females that are likely to be bull dams or embryo donors, breeders often
`prefer the 50K chip because of its greater reliability.
`
`Advanced Reproductive Technologies
`Once a breeder identifi es genetically superior females using genomic
`testing, and when these animals reach sexual maturity, they usually be-
`come part of a MOET-based program. Factors such as time, expense, and
`number of available recipients affect the extent to which a breeder decides
`to invest in MOET. The most aggressive breeders use a combination of
`MOET and IVF for their top animals. Typically, elite females undergo
`their fi rst embryo transfer at 12 months of age. If these females produce
`a suffi cient number of viable embryos, they can be superovulated 3 times
`before they reach 15 months of age, at which time they are inseminated
`using a conventional AI service. Pregnancy can be diagnosed at 30 days of
`gestation, and after they are diagnosed as pregnant, these heifers can enter
`an IVF program. The IVF collection, typically called an “aspiration,” is
`the process of harvesting unfertilized oocytes directly from the ovaries
`of the donor animal. These oocytes are fertilized in vitro 1 day after col-
`lection. Once fertilized, the embryos are cultured and grown for 7 days
`of incubation before transfer into recipient females. The main advantage
`of IVF is that once an animal has been confi rmed pregnant, oocytes can
`be aspirated every 2 weeks. Donors can be aspirated safely between 30
`and 100 days of gestation, so if a pregnant female is aspirated at 30 days
`of gestation, it is possible to carry out 6 aspirations before she reaches
`100 days of gestation. Therefore, not only is it possible to create a large
`number of pregnancies from 1 donor, but it is also possible to mate this
`donor to 10 different sires before her fi rst calving (3 superovulations pre-
`breeding, 1 conventional AI breeding, and 6 IVF collections during her
`pregnancy). In extreme cases, some breeders have put highly superior
`females into a continuous MOET and IVF program. For breeders who
`want to generate as many pregnancies as possible from a single donor,
`IVF is the most effi cient approach. It is possible that a donor animal might
`never have a natural calf. However, if a breeder wants to continue to make
`genetic progress, a continuous MOET and IVF program for an individual
`donor is impractical, because over time we would expect this donor to be
`displaced by a younger, genetically superior female.
`
`Sire Selection
`The use of young genomic bulls by AI companies as sires of sons (SM
`pathway) or by breeders as sires of replacement females (SF pathway)
`continues to increase in popularity. Among dairy producers, there has been
`a major shift toward the use of genomic bulls in the SF pathway. Between
`2006 and 2010, the total number of units of semen sold from young dairy
`
`sires increased by 13% (Olson et al., 2011), and the increased acceptance
`of genomics among dairy cattle producers has allowed extensive market-
`ing of genomic bulls. Moreover, König et al. (2009) reported that breeding
`programs that use young genomic bulls would have greater profi t than
`those that rely on conventional progeny testing, provided that at least 20%
`of the inseminations were to genomic bulls that lacked daughter records.
`On many farms, it is becoming the norm to breed virgin heifers to
`genomic bulls because, on average, these yearling heifers have greater
`genetic merit than the lactating cows. As noted earlier, using young, ge-
`nome-tested males in the SM and SF pathways and using young, genome-
`tested females in the DM and DF pathways can reduce generation inter-
`vals in these pathways by 50% or more. Because the reliability of GEBV
`from DNA testing is usually less (approximately 70%) than the reliability
`of EBV from progeny testing (about 85%), breeders tend to use a larger
`group or “team” of young bulls to mitigate risk. The advantage of a team-
`based approach to sire selection is that reliability of the average GEBV for
`a team of young bulls is considerably greater than the individual reliability
`of each bull. The formula for calculating the reliability of a team of bulls
`is given below:
`
`Team reliability
`
`
`
`= −1
`
`1
`
`−
`
`ll bulls in the team
`average reliability of the individua
`number of bulls in the team
`
`.
`
`For example, if a breeder uses a team composed of 5 young bulls with
`reliabilities of 70% for individual GEBV, the average GEBV for this team
`would have 94% reliability.
`
`Application of Genomic Selection in Males
`
`Genomic Testing
`Since the introduction of genomic selection, the percentage of young
`dairy bulls that have been DNA tested is greater than 90% for the Hol-
`stein, Jersey, and Brown Swiss breeds (Olson et al., 2011). All young bulls
`that are considered for purchase by the major AI stud services are selected
`based on the results of genomic testing. Therefore, genetic gain is maxi-
`mized by screening a vast number of bull calves because this increases
`selection intensity. The limiting factors with respect to testing more bull
`calves are time of the breeder and sire analyst, cost of the DNA test, and
`the willingness of the breeder to feed and house a large number of young
`bulls while waiting for their initial GEBV results.
`Because genomic selection gives AI companies the ability to carry out
`accurate selection decisions at a young age by using DNA testing, these
`companies may decide to cut costs by purchasing fewer bulls, knowing
`that only males with the highest genetic merit will be marketable. In fact,
`there have been discussions among scientists and practitioners about elim-
`inating progeny testing entirely. This could reduce sire development costs
`by up to 92% (Schaeffer, 2006) because the biggest costs associated with
`progeny testing are housing and feeding. However, it is unlikely that AI
`companies will eliminate progeny testing in the short term for 2 reasons.
`First, a global market for progeny-tested dairy bulls still exists, largely
`because genetic evaluations for these bulls have higher reliability. Sec-
`ond, ongoing measurement of daughter phenotypes is essential because
`prediction equations for calculating GEBV (i.e., SNP effects) should be
`updated periodically to increase the size of the reference population, ac-
`
`January 2012, Vol. 2, No. 1
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`Exhibit 1014
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`count for changes over time in herd management practices or the genetic
`background of mates, and maintain high levels of linkage disequilibrium
`between the reference population and the selection candidates. Miscon-
`ceptions exist that genomics eliminates the need for milk recording and
`type classifi cation services. However, it is essential to keep the reference
`population current through continuous and consistent data recording so
`that genomic predictions are not based on a reference population that is
`many generations removed from the current milking dairy cattle popula-
`tion.
`
`Selection of Bull Mothers
`Before genomic selection, breeders and AI companies tended to se-
`lect potential bull mothers based on a multiple-trait index of production,
`health, and conformation traits. However, to be considered as bull moth-
`ers, these cows would need complete pedigrees and would have to meet
`minimum standards for phenotypic production and physical conforma-
`tion. Cows that failed to meet these minimum criteria were excluded as
`bull mothers, and this signifi cantly reduced the number of eligible cows.
`The movement to genomic selection has dramatically increased the pool
`of females that can be considered as potential bull mothers. It is no lon-
`ger necessary to discard every female that has less-than-perfect pedigree,
`production, or conformation data. Furthermore, cows on large commercial
`dairy farms that focused primarily on the sale of milk were rarely consid-
`ered as bull mothers because their pedigrees lacked several consecutive
`generations of high-ranking sires, or because herd management condi-
`tions favored strong performance of the entire group (i.e., pen or string)
`rather than extreme performance of selected animals within the group. In
`fact, management conditions in these large commercial herds might more
`closely refl ect the conditions under which future daughters of these bull
`calves will be expected to perform, as compared with herds from which
`bull calves were typically purchased historically.
`Another important consideration is that breeders and sire analysts can
`capitalize on the tremendous variation that occurs in each mating because
`of Mendelian sampling of alleles from the sire and dam. For example, a
`particular cow may not have an EBV or GEBV that is suffi cient to reach
`elite status, but when mated to an elite sire, her calf might inherit a highly
`favorable combination of alleles that will warrant his purchase by an AI
`company or her use as an IVF donor. Calves of this type that are recog-
`nized as superior through DNA testing can contribute more than just high
`genetic merit; they can also enhance genetic diversity in future genera-
`tions by virtue of their unique pedigrees and genetic backgrounds.
`
`Areas of Concern
`
`Inbreeding
`A key concern among dairy breeders is the diffi culty in maintaining
`genetic diversity when mating elite females and males because of genetic
`relationships among these individuals. It has been suggested that the rate
`of inbreeding might decrease in the era of genomic selection because a
`greater number of bulls are being used as sires of sons. A good description
`of the potential impact of genomic selection on the rate of inbreeding was
`given by Hayes et al. (2009):
`
`Consider the selection of young bull calves to become part of a
`progeny test team. In the absence of genomic information, and be-
`
`cause the young calves do not have any daughters, their breeding
`value is predicted as the average of the breeding value of their sire
`and dam. Two full sibs therefore receive the same breeding value,
`and if this is high enough, they will both be selected to form part
`of the progeny test team. If genomic information is available, the
`Mendelian sampling term (the result of the sampling of the sire
`and dam alleles during gamete formation) is captured and 2 full
`sibs receive different breeding values, and may not both be select-
`ed to form part of the team, which leads to a decrease in the rate
`of inbreeding. However, if the generation interval of the breeding
`program is halved to take advantage of the accurate GEBV avail-
`able at birth, the resulting increase in inbreeding per year may be
`greater than the decrease from capturing the Mendelian sampling
`term. (pp. 439–440)
`
`Because genomic selection will allow the generation interval to be
`minimized in dairy cattle breeding programs, it is likely that the rate of in-
`breeding per year will increase. Furthermore, although the major AI com-
`panies consider a large number of genomic bulls as sires of sons, most of
`these young bulls have many ancestors in common, including sires and
`maternal or paternal grandsires. In addition, most of the elite young males
`are closely related to the elite young females in the same population be-
`cause both were created from the same donor dams in MOET and IVF
`programs. For this reason, it is diffi cult to minimize inbreeding when mat-
`ing elite animals. In the future, tools for routinely monitoring the genetic
`diversity of existing animals and managing the expected diversity of their
`future progeny by using genome-based mating programs may help to ad-
`dress this challenge.
`
`Bias
`Patry and Ducrocq (2011) investigated the role of genomic prese-
`lection in young bulls. Because young bulls are selected based on high
`GEBV, they typically have superior Mendelian sampling contributions.
`Conversely, young bulls that are culled because of low GEBV typically
`have inferior Mendelian sampling contributions. Because these selection
`and culling decisions occur before the young bulls have an opportunity
`to sire any progeny, the assumption of random Mendelian sampling in
`genetic evaluations is violated. In the future, this could lead to bias in the
`GEBV of young bulls and heifers; therefore methods that account for bias
`attributable to genomic preselection should be investigated and incorpo-
`rated into genetic evaluation programs.
`
`Conclusions
`Genomic selection already plays an important role in dairy cattle breed-
`ing programs, and this will be the case for the foreseeable future. Increases
`in the accuracy of genetic predictions for young animals will dramatically
`decrease the generation interval, and when coupled with opportunities to
`increase selection intensity, the rate of genetic progress in dairy cattle will
`increase signifi cantly. Many breeders have embraced genomic selection
`and routinely use GEBV when purchasing semen or deciding which cows
`and heifers merit investment in reproductive technologies such as MOET
`and IVF. At the same time, AI companies are aggressively using genomic
`testing when determining which young bulls to purchase, marketing se-
`men to dairy producers, and identifying elite females that can make posi-
`tive genetic contributions to the next generation.
`
`8
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`Animal Frontiers
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`Exhibit 1014
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`Downloaded from https://academic.oup.com/af/article/2/1/4/4638584 by guest on 27 December 2021
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`
`About the Authors
`Jonathan M. Schefers is a reginal sire
`analyst with Alta Genetic USA. Sche-
`fers is responsible for bull procurement
`for Minnesota, Iowa, and the western
`United States. He has purchased more
`than 70 bulls during his 3-year tenure
`with Alta. Schefers received his MS
`degree at the University of Wisconsin-
`Madison and is currently pursuing his
`PhD in dairy science. His research focus
`is genomic selection for improving the
`fatty acid and protein profi le of milk.
`
`Kent A. Weigel is professor and chair of
`the Department of Dairy Science at the
`University of Wisconsin–Madison. He
`also serves as Extension dairy genetics
`specialist for the State of Wisconsin and
`is a key technical consultant for the Na-
`tional Association of Animal Breeders
`and its members. His research focuses
`on genetic improvement of the pro-
`ductivity, health, and fertility of dairy
`cattle by using tools such as genomic
`selection, crossbreeding, advanced re-
`productive technologies, and electronic data capture systems. Weigel
`has published more than 100 peer-reviewed j