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The Nature, Scope And Impact Of Some Whole-
Genome Analyses In Beef Cattle
D.J. Garrick*†
Introduction
Artificial selection has proven to be effective at altering the performance of animal
production systems. Nevertheless, selection based upon assessment of the genetic
superiority of candidates is suboptimal as a result of errors in the prediction of genetic merit.
Conventional breeding programs sometimes address this issue by extending phenotypic
measurements on selection candidates to include correlated indicator traits (e.g. ultrasound),
or by delaying selection decisions beyond puberty until phenotypic performance can be
observed on progeny or other relatives. Extending the generation interval in order to
increase the accuracy of selection has the unfortunate consequence of reducing annual rates
of gain compared to schemes that can accurately select parents of the next generation by
puberty. Furthermore, such delays often increase costs of the breeding scheme. Marker-
Assisted and Whole-Genome Selection (WGS) are aimed at reducing prediction errors at
pubertal assessment of merit by exploiting information on the transmission of chromosome
fragments from parents to selection candidates, in conjunction with knowledge of the relative
impact of particular chromosome fragments on performance. Whole-Genome Analyses
(WGA) refers to those studies that are undertaken to determine the relative impact (i.e.
substitution effects) of various chromosome fragments identified using high-density SNP
genotypes. The Illumina BovineSNP50® is currently the preferred approach for genotyping
Bos taurus cattle but is likely to be superseded in 2010 by Illumina and Affymetrix arrays
currently in development with reported marker densities increased by at least an order of
magnitude. In order for WGS to influence breeding programs and the rate or cost of genetic
gains, WGA must be undertaken, and genomic prediction tools made available for breeders
and other industry stakeholders to cost-effectively adopt in their breeding programs. This
paper reviews the nature or kind of studies currently underway, the scope or extent of some
of those studies, and comments on the likely impact of WGS in terms of predictive value.
Whole-Genome Analyses
Critical issues to consider in the adoption of WGA are the motivation, the choice of
population, the choice of individuals to genotype, the availability of existing trait
information, and the opportunity to collect novel phenotypes. Following implementation of
findings from such analyses into selection tools, the principal concerns in relation to resultant
* Department of Animal Science, Iowa State University, Ames, IA 50011, USA
† Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand
tools are their reliability and cost. Such information would allow the development of rational
breeding programs that appropriately exploit these new technologies.
Motivation for Whole-Genome Analyses. There are three principal reasons for WGA.
These are: to develop improved tools for prediction; to find genomic regions and genes that
cause variation; and to develop proprietary know-how that can be marketed as a business
proposition to one or more sectors of the industry.
Public-domain research is being undertaken by animal breeders to develop methodologies
and computing strategies and characterize predictive ability. Such work is typically
government or industry funded and logically follows previous research developing prediction
methodologies and characterizing associated information such as heritabilities, genetic and
phenotypic correlations. Allied research includes the implementation details required to
incorporate the findings in national cattle evaluation (NCE) systems for routine application.
Prior to the availability of high-density SNP platforms, genomic research focused on
discovering and exploiting one or a few quantitative trait loci (QTL). In the last decade, the
focus has shifted to concurrently exploiting knowledge of the whole genome. That process
involves a discovery or training phase that determines the informative SNP and quantifies
their predictive ability in some population, rather like the estimated breeding value (EBV) of
a chromosome fragment. Collectively, this process leads to a linear function or predictive
key that can be used to predict genomic merit by summing up the values of the chromosome
fragments carried by an animal outside the training population.
Some geneticists and physiologists with an interest in genetics and causation are undertaking
public-domain research, principally to locate QTL, to fine map to discover, understand, and
perhaps patent, the underlying quantitative trait nucleotides. These researchers are taking
advantage of the reduction in labor and genotyping costs from using high-density SNP arrays
that provide comprehensive genomic coverage, rather than targeting only candidate genes
(which assumes local or cis- gene action accounts for variation), or sparsely genotyping up to
a few hundred microsatellite markers chosen to provide information content and genome
coverage. These QTL studies typically rely on third parties to implement the findings by
purchasing the rights to market SNP tests to industry, and validating the findings.
Private companies, notably Igenity and Pfizer Animal Genetics, are investing in WGA in
order to market proprietary tests. The studies may be undertaken in house, or in
collaboration with publicly funded researchers. Studies represent a “for profit” investment in
research and implementation that is unprecedented in beef cattle improvement but more
common in other livestock species (e.g. chickens and pigs) and in field crops (e.g. maize and
soybeans). In those industries value is typically captured to provide a return on research
investment by marketing and controlling improved germplasm rather than selling tests.
Nature of Whole-Genome Analyses. There are various mating plans to develop resource
populations for WGA. Such studies are expensive and time-consuming if designed matings
are required in advance of the experiment. That time frame is too long for most competitive
grants and for private company investment. Accordingly most studies use existing industry
resources or research herds established for other purposes.
Industry populations have the advantage that they already exist and can be immediately
genotyped. Further, in the case of elite or widely used industry animals, the discovery
individuals will be relevant to the commercial population. In the case of artificial
insemination (AI) sires, they have the further advantage that DNA is readily accessible
despite the disparate ownership or physical location of the animals. The principal source of
information for WGA comes in the form of EBV or expected progeny difference (EPD) from
NCE and is well represented for growth traits, moderately well represented for ultrasound
traits, poorly represented for behavior, reproduction and longevity traits with typically no
information on many other traits such as disease resistance or eating quality. Training on
crossbred sires is seldom an option using NCE data and is limited to those few breed
associations that collect crossbred data.
A U.S. repository of DNA from 1,985 Angus bulls born between 1948 and 2007 have been
assembled by the University of Missouri and Merial (Woodward et al. 2010). These bulls
have generally been widely used in the Angus breed, and are represented in American Angus
Association pedigrees. Accordingly, these bulls have EPDs and accuracies for production:
calving ease direct; birthweight; weaning weight; yearling weight; yearling height; scrotal
circumference; maternal traits: calving ease maternal; milk; mature weight; mature height;
carcass: carcass weight; marbling; ribeye area; fatdepth and some new EPDs like docility
and heifer pregnancy. The accuracies of EPD on old bulls are limited for some traits.
The U.S. Meat Animal Research Center (MARC) has worked with some breed associations
to develop a repository of some 2,026 influential or upcoming bulls in 16 of the most
prominent beef breeds in the U.S. with EPDs from NCE and includes: Angus; Beefmaster;
Brahman; Brangus; Braunvieh; Charolais; Chiangus; Gelbvieh; Hereford; Limousin; Maine-
Anjou; Red Angus; Salers; Santa Gertrudis; Shorthorn; and Simmental. Initial plans for this
repository were to use it to provide genomic predictions of these bulls from training analyses
based on a MARC crossbred population (Thallman, 2009).
The U.S. carcass merit project (CMP) was an industry-funded undertaking initiated in 1998
that collected carcass data, tenderness and sensory attributes on over 8,200 progeny. Some
of the offspring of more than 70 sires across 13 breeds were DNA sampled. The sires were
widely-used AI bulls from various breeds and dams were commercial cows (Thallman et al.
2003). The dataset has been valuable for validation of early genomic tests undertaken in the
U.S. by the National Beef Cattle Evaluation Consortium (NBCEC), the details having been
published on-line by Van Eenennaam et al. (accessible from www.nbcec.org). The CMP
dataset has more recently been high-density genotyped by at least two different organizations
for gene discovery and whole genome prediction, limiting its future value for validation.
Two major studies are being undertaken by Pfizer Animal Genetics in the U.S. in
collaboration with University partners. At Colorado State University, two cohorts each of
about 1,500 composite British and Continental steers from one ranch in Nebraska, have been
extensively phenotyped for feedlot health, particularly respiratory disease and response to
treatment. Sickness was assessed visually, by temperature profiles and by lung damage
scores. Data includes temperament and immunological measures, as well as growth and
carcass information (Brigham et al. 2009). At Iowa State University, several cohorts
representing 2,300 predominately Angus cattle have been assessed for carcass and meat
quality attributes, including tenderness and sensory information, in addition to extensive
phenotyping on traits that might influence the human healthfulness of beef. These healthy
beef traits include mineral and fatty acid composition of key muscles (Reecy et al. 2010).
Some research populations that have been used for WGA in the U.S. include descendants
from the MARC Cycle VII germplasm evaluations, that represent crosses including offspring
of recent AI bulls from Angus, Charolais, Gelbvieh, Hereford, Limousin, Red Angus, and
Simmental breeds. Measured traits include feed intake, carcass and tenderness data on some
offspring, puberty records and incidence of disease. The findings will be published in the
public domain, along with Snelling et al. (2010).
Other research populations used for WGA include animals from the USDA Line 1 Hereford
population kept in Miles City, MO, a herd that has been closed and inbred for over 75 years
and all current animals have additive relationships with each other that exceed 50%. In
addition to gene discovery, this population will be used to investigate genomic regions with a
signature of selection and with an excess of heterozygocity (MacNeil, pers. comm.).
The Canadian University of Alberta and University of Guelph are collaborating with animal
and genomic resources, including feed intake and carcass phenotypes for WGA. This
collaboration also includes MARC and the Australian Cooperative Research Center for Beef
Cattle (Beef CRC). The Beef CRC has data from their first cycle including almost 8,000
straight bred Angus, Belmont Red, Brahman, Hereford, Murray Grey, Santa Gertrudis and
Shorthorn cattle plus another 2,000 crossbred individuals representing a range of finishing
environments. The second cycle of the Beef CRC includes almost 4,500 steers and heifers of
two tropical breeds with a wide range of growth, feed efficiency carcass and beef quality
attributes on the steers, and adaptive and reproductive traits on the heifers. Another 6,000
bull calves have been measured for growth and male reproduction traits. More details are in
a review volume at http://www.publish.csiro.au/nid/72/issue/5223.htm. The first stage of
collaboration was limited to exchange of marker effects (Burrow, pers. comm.). The
collaboration includes New Mexico State University, contributing some 800 Brahman-cross
heifers assessed for growth, ultrasound and reproductive performance (Peters et al. 2010).
In Brazil, funding has been approved for WGA in 9 herds of Herefords and Braford cattle
that will be phenotyped for growth, body composition, reproduction and tick resistance, and
for a separate study of reproduction traits in Nelore cattle (Cardoso, pers. comm.).
Research populations that were designed for QTL studies in the microsatellite era include F2
intercrosses of dairy and beef breeds such as Jersey x Limousin (Esmailizadeh et al. 2008),
Holstein x Charolais (Gutierrez-Gil et al. 2009) and an intercross/backcross design of
Brahman x Angus (Amen et al. 2007). Such populations might usefully contribute to WGA.
Scope of Whole-Genome Analyses. In contrast to studies using microsatellite markers, the
scope or extent of WGA studies have principally been limited by the availability of animals
with measured phenotypes or estimated breeding values from progeny tests rather than by the
costs of genotyping. High-density BovineSNP50® genotypes can currently be contained for
under US$200 per animal, in contrast to the cost of microsatellite genotyping which at $5 per
genotype would cost US$1,000 per animal for 200 loci. Furthermore, it is a straightforward
exercise to genotype thousands of animals for the BovineSNP50® in the course of a few
weeks whereas few labs could manage that number of animals in an entire year using
microsatellite technology. Collectively these facts have allowed the scope of discovery
populations to be increased from focused subsamples of the most informative animals to
populations that include every individual or AI bull for which DNA can be obtained.
Impact of Whole-Genome Analyses. Findings from WGA will have no impact on industry
unless they lead to new or improved tools for breeders. Given that WGA results are made
available to industry, their potential impact will depend upon how much the technology can
increase accuracy (i.e. reduce prediction errors) at the point of selection, the traits for which
they can be applied, and the cost of the technology. The realization of the potential impact
will further be limited by the manner in which the industry adopts these tools.
The registered beef cattle industry, like the dairy industry, can be categorized as having four
pathways of selection that influence the rate of genetic gain: sires to breeds sires; sires to
breed cows; cows to breed sires; and cows to breed cows. The commercial beef cattle sector
has unregistered cattle and purchases breeding sires from the registered sector. The genetic
merit of any national beef industry depends upon its historical genetic merit, the annual rate
of genetic change in the registered (nucleus) herds, and the genetic lag between nucleus and
commercial sectors. Tools developed from WGA can be used for WGS in any one or more
of the four selection pathways to increase genetic gain in the nucleus, or to reduce genetic lag
between nucleus and commercial sectors. The business proposition for procuring genetic
tests is quite different in each of these scenarios. Furthermore, the size of the markets are
disproportionate, with greatest numerical potential for testing bulls offered for sale to use as
sires in commercial herds, where their selection has no impact on the rate of genetic gain.
Communication of Results from Whole-Genome Analyses. The last few decades have
been characterized by communication of the results of genetic evaluations in the usual units
of measurement for each trait, in the form of an EBV, EPD, or an index to reflect aggregate
merit. Early attempts to market genetic tests on alternative scales had some success when
tests involved only a single SNP, but have become problematic as the tests have evolved to
include multiple SNPs across genomic regions. Industry confusion has also developed as
sires have been identified that demonstrate apparent conflict between the pedigree-based and
genomic-based merit. In all these circumstances, an appealing approach is to incorporate the
information from genomic testing into NCE (Kachman, 2008; MacNeil et al. 2010) so that
the information is reflected in terms of marker-assisted or genomic-assisted EBV and
associated accuracy, without introducing new jargon, terminologies and interpretations.
This is achievable, but not straightforward. Some would argue that genotyping results would
be accumulated on all animals in central databases; in the same manner as occurs for
pedigree and performance information. This would ensure that results were available on
individuals that were subsequently culled as well as those that were selected, allowing
evaluation systems to account for selection, a requirement for unbiased predictions. Further,
this approach would allow improved predictive technology to be retrospectively applied to
historically collected genotypes. It would also facilitate future activities to in-silico genotype
historical animals, on the basis of a subset of the population genotyped at higher density or
individually sequenced. Centrally stored genotypes might be practical if evaluations are
collectively funded by industry, by government, or both, and where genomic testing tools are
delivered as an industry good. It is less apparent that this model can work where genetic
testing is offered as a for-profit service, and the linear function of informative SNP or
predictive key is the principal intellectual property or know how on which competing testing
companies have based their business. It faces challenges when entities such as breed
associations undertake NCE as a peripheral activity when their core business is selling
pedigree and animal registrations. The breed associations may be challenged by the know-
how and financial costs of changing their software to accommodate these rapidly changing
technologies. Some direct methods for including pedigree and genomic information in a
single evaluation have been proposed (Aguilar et al. 2010), but these approaches will suffer
from computational challenges as the number of genotyped individuals increase, and
currently lack a convincing statistical basis even when computationally feasible.
An alternative approach is to derive genomic EBV solely from marker information and
incorporate that value as a correlated trait in NCE. This has the advantage that a company
selling genetic tests can maintain their proprietary predictive key (and their customers
genotypes) and NCE need only be modified to include correlated information. This
approach is not altogether straightforward as it requires knowledge of the covariance
components relating the marker score to phenotype and faces challenge with the evolution of
molecular keys over time, with increases in the number of routinely genotyped markers, and
perhaps with competing companies using the same or overlapping genetic markers requiring
covariances between molecular scores to be estimated and routinely updated.
Results and discussion
Three critical issues relating to the performance of genomic prediction are: the proportion of
variation that can be predicted within-breed from knowledge of the 50k SNP genotypes; the
extent to which predictive ability erodes when training knowledge is applied to animals of
different breeds; and the ability of a reduced panel to reliably predict performance.
Within-breed predictions from 50k panels. Confidence in genomic predictions can only be
provided by validation in a group of animals not included in the training population.
Training often involves subdividing the data, say into thirds, and training in two-thirds of the
data followed by validation in the other third. Subsets may be chosen so sires do not have
sons in both the training and validation datasets. Such training can be done three times for
different dataset combinations, so that each bull is represented in one validation set. Garrick
(2009) reported results for Angus cattle that vary according to trait and data subset (Table 1),
but the general conclusion is that correlations between genomic predictions from 50k SNP
and realized performance in independent datasets are 0.5-0.7 accounting for 25-50% genetic
variance, equivalent to about 6-16 offspring in a progeny test with heritability of 25%.
Within-breed predictions from reduced SNP panels. The creation of subsets of 600 SNP
markers obtained from choosing the 20 markers on each bovine chromosome with the
highest model frequency, a measure for marker support, was undertaken to repeat the
analyses shown in Table 1 on 600 marker subsets. These data demonstrate relatively little
loss of predictive ability in selectively reducing the panel from 50k to 600 SNP.
Table 1: Correlations between 50k or 600 SNP predictions and EPD for backfat (FAT),
calving ease direct (CED) and maternal (CEM), carcass marbling (MRB), ribeye area (REA),
scrotal circumference (SC), weaning weight direct (WWD) and yearling weight (YWT).
Trait
Train 2 & 3
Predict 1
(50k)
Train 1 & 3
Predict 2
(50k)
Train 2 & 3
Predict 3
(50k)
Overall1
(50k)
Overall
(600 SNP)
FAT
0.71
0.64
0.73
0.69
0.63
CED
0.65
0.47
0.65
0.59
0.61
CEM
0.58
0.56
0.62
0.53
0.55
MRB
0.72
0.73
0.64
0.70
0.67
REA
0.63
0.63
0.60
0.62
0.56
SC
0.60
0.57
0.50
0.55
0.51
WWD
0.65
0.44
0.66
0.52
0.49
YWT
0.69
0.51
0.72
0.56
0.55
1Correlation estimated by pooling estimated variances and covariances
Reduced panels of 600 markers per trait are still too many to populate a single 384 SNP
panel, particularly to simultaneously target several traits. The resulting estimates of the
genetic correlations for 50, 100, 150 or 200 markers were 0.28. 0.29, 0.39 and 0.43
(Woodward et al. 2010). The markers that might populate a single 384 SNP multitrait panel
were further validated by estimating the correlation between marker score and progeny test
performance on a new sample of 275 Angus bulls that were not used in any of the training
analyses. The results were estimated genetic correlations of 0.59 for marbling, 0.32 for
backfat, 0.58 for ribeye area, 0.44 for carcass weight, 0.39 for heifer pregnancy and 0.35 for
yearling weight. Such a panel could account for 10%-35% genetic variation.
Across-breed predictions from 50k panels. The prospect of training in one breed to
predict performance in another is appealing. It may not work well if the genes exhibit
dominance or epistasis, and allele frequencies vary between populations. Linkage
disequilibrium (LD) reflects the ability of alleles at one locus to predict the alleles at another
locus. High-density panels would ideally have at least one SNP in high LD with every QTL.
However, differences in LD between breeds can lead to a marker being a good surrogate of a
causal gene in one breed and less value in another. Few datasets are yet available for across
breed validation. Simulated data using some of the 50k loci as if they were causal genes has
allowed the prospects for across breed prediction to be quantified (Kizilkaya et al. 2010).
Those analyses show poor results when LD among the markers on the panel is relied on to
predict performance. Further, they show that predictive ability erodes considerably when the
number of simulated causal genes is increased. The best-case predictive ability varied from
correlations around 0.4 for 50 genes down to 0.2-0.3 for 500 genes. These correlations
account for up to 18% genetic variation for 50 genes to <10% variation for 500 genes.
Conclusion
Current studies do not well represent the full range of breeds or environments but do include
more traits than those presently available through national cattle evaluation. Fertility traits
remain poorly represented. Predictions from 50k SNP panels might account for 50% genetic
variation when used in the same breed as the training population and substantially less when
used in other breeds. Reduced panels can account for 25%-35% genetic variation for
targeted traits. The prospects for modifying selection programs to exploit high-density 50k
and/or low-density SNP panels looks encouraging, although less so than simulation results.
Future panels can only improve, as further analysis is undertaken on available resource
populations. The role of marker tests as a selection tool is now maturing to the extent that
they are likely to complement, rather than compete with, national cattle evaluation.
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