XXIV
World´s Poultry Congress 5 - 9
August - 2012 • Salvador - Bahia - Brazil
Genomics in poultry breeding
– Into consolidation phases
Aviagen Limited, Newbridge, EH28 8SZ,
Midlothian, Scotland, UK
Introduction
The main objective of a global poultry breeder
is to deliver products that perform competitively
for a broad number of traits including live
performance, health and welfare in a wide range
of production environments. Selective breeding
based on combining phenotypes and pedigrees
and estimation of breeding values using Best Linear
Avendaño et al., (2010) reviewed the current
state of genomics technologies in poultry which
has shown a significant expansion in SNP panel
development, high throughput genotyping and
sequencing platforms since the release of the first
draft of the chicken genome sequence (International
Chicken Genome Sequencing Consortium, 2004).
In addition, there have been significant strides in
both the development and availability of analytical
methods for incorporating genomics information in
routine breeding programs.
Accuracy of selection is critical for translating
genetic progress from elite populations to realised
improvements in commercial operations. Empirical
results addressing the question of whether
genomics information would significantly contribute
to an increase in accuracy of selection are now
starting to become available. This paper is aimed
to be a continuation of the review by Avendaño
et al., (2010) following the progress of genomics
technology in poultry (broilers, layers and turkeys)
in terms of development and availability of SNP
(Single Nucleotide Polymorphism) panels, statistical
tools for incorporating genomics in routine genetic
evaluations, the role of imputation and realised
improvements in accuracy of selection in key traits
over conventional genetic evaluation methods. The
consolidation of these areas is critical for enabling
the incorporation of genomics information and
tools for routine poultry breeding.
Area: Genetics and Breeding • August 07
Santiago Avendaño, K.A. Watson and A. Kranis
G
enomics in poultry breeding has seen
notable
progress
with
significant
advancements in the development and
availability of high density SNP (Single
Nucleotide Polymorphism) panels in broilers and
layers chickens, and in turkeys. In addition, an
array of statistical tools for incorporating genomics
in routine genetic evaluations is also becoming
increasingly available to poultry breeders. Initial
empirical validations of genomics based genetic
evaluation are encouraging and indicate this
technology can provide improvements in accuracy
over traditional methods. Moreover, strategies
that combine high density and low density
genotypes could substantially reduce the cost of
implementation with minimal loss in accuracy.
While the potential benefits of the implementation
of genomics are significant, it is clear that the
implementation must be cost-effective, particularly
in the context of an extremely competitive market. A
consolidation phase has now been initiated in which
poultry breeding companies will strive for capitalise
on the significant developmental investments. The
next few years will be critical to assess whether
genomics technologies will provide adequate return
for the investment and contribute with significant
benefits to poultry breeding globally.
Unbiased Predictions (BLUP) has been widely used
in poultry breeding and there is evidence of the
benefits translated to the industry (Laughlin, 2009).
The use of genomics information for selection
in commercial poultry breeding offers further
opportunities through extra accuracy of selection,
reduction of generation intervals and exploitation
of new sources of genetic variation (e.g., Dekkers,
2004). Woolliams (2011) highlights the potential
central role of genomics in the so called ‘precision
breeding’ in the wider context of complex breeding
goals aiming for increases in accuracy, maintaining
the well-being of the species and managing genetic
variance within and between breeds.
1
Santiago Avendaño, K.A. Watson and A. Kranis
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August - 2012 • Salvador - Bahia - Brazil
Development
and
availability of SNP panels
SNP panel was developed to provide a dense SNP
panel with maximum information across lines.
The Affymetrix avian HD SNP panel is expected to
become commercially available in summer 2012.
Poultry breeders have led the development of high
density (HD) SNP panels with a focus on describing
the molecular basis of quantitative variation in
commercial populations. The publication of the
draft sequence of the chicken genome (International
Chicken Genome Sequencing Consortium, 2004)
provided the key to allow the poultry breeding
industry to actively pursue the implementation
of genomic selection (GS). However, in contrast
with other livestock populations, until recently, no
commercial avian SNP panel as been available.
The development of SNP panels for turkeys has
shown significant progress in the last two years.
The development of the turkey genome sequence
(Dalloul et al., 2010) was able to capitalise on
the progress in chickens to allow rapid genome
assembly at a fraction of the cost of that in the
chicken (i.e. $250,000 vs over $10 million). Indeed,
the turkey genome project is the first example in
animals where the cost of sequencing was less
than the cost of analysis and interpretation of the
results (Dalloul et al., 2010). Aslam et al. (2012)
identified 5.49 millions SNPs through sequencing
the complete genome of 32 individual turkeys from
11 different populations (seven commercial lines,
three heritage varieties and one wild population), of
which 4.9 millions were segregating in at least one
population. This research indicated that the turkey
genome is much less diverse in terms of a relatively
low frequency of heterozygous SNP in comparison
to other livestock species like chicken and pig.
This foundational work provides the basis for the
development of commercial SNP panels for future
application in turkeys with rapid and significant
progress anticipated in the next few years.
Aviagen developed its first SNP panel in 2005,
soon after the release of the draft chicken genome
sequence. Since then, Aviagen with sister companies
Hy-Line International and Lohmann LTZ has been
expanding and refining SNP panels to include the
results of extensive whole genome associations on
a range of economically important traits (Ye et al.,
2006; Powell et al., 2008) and the structure of linkage
disequilibrium (LD) within and between broiler and
layer populations (Andreescu et al., 2007; Abasht
et al., 2006). In 2008, the USDA, Cobb-Vantress
and Hendrix Genetics started a collaborative project
on genome-wide selection which included the
development of a moderate density 60K SNP chip.
The 60K Illumina SNP BeadChip was designed to
include SNPs known to be segregating at high to
medium minor allele frequencies (MAF) in the two
major types of commercial chicken (broilers and
layers; Groenen et al., 2011).
The development of the first a commercially
available HD SNP panel started in 2010 in the UK
through a Defra-BBSRC funded LINK project between
the Roslin Institute, Aviagen, Hy-Line International,
Affymetrix Ltd and in cooperation with the German
Synbreed (project funded by BMBF). The panel
development was based on re-sequenced a large
number of chickens (n=243) from 24 different lines
including commercial broiler and layer lines, and
experimental inbred layer lines (Gheyas et al., 2012).
Sequencing identified a staggering 78 million SNPs
segregating in one or more lines; far in excess of
the 7 million SNPs identified by Rubin et al. (2010).
From the 78 million SNPs a pre-screening chip (1.8
million SNPs) was developed to ensure maximum
performance of the final 600K SNP panel by removing
SNPs which did not work with the Affymetrix
genotyping platform and those which did not show
Mendelian segregation. The final Affymetrix 600K
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World´s Poultry Congress 5 - 9
Area: Genetics and Breeding • August 07
In parallel to the improvements in SNP
genotyping platforms, swift advances in sequencing
technologies have resulted in more accessible
sequencing platforms and a rapid reduction
in the cost of sequencing. This provides the
potential for new genotyping strategies including
imputation of full genomic sequence (Li et al.,
2010) or genotyping-by-sequencing using reduced
representation or shotgun sequence information
(Elshire et al., 2011). Although preliminary, these
sequencing advancements highlight the future
potential for using direct sequences for genomic
selection purposes.
Genomics
statistical
approaches for genetic
evaluations
Early attempts of estimating allelic effects on
traits of economic importance focused on candidategene approaches looking for significance of SNPs
within genes of known physiological function (e.g.
Ye et al., 2006). On the other hand, success stories
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World´s Poultry Congress 5 - 9
August - 2012 • Salvador - Bahia - Brazil
from the incorporation of single SNPs for breeding
purposes are very scarce. A notable exception is the
development of a SNP gene test that allowed the
elimination of a recessive gene (flavin-containing
monooxygenases), responsible for a metabolic
disorder (‘fishy-taint’) in brown-shelled eggs which
was implemented some 10 years ago (Preisinger,
2012).
With the availability of denser SNP panels,
genome wide associations (GWAS) approaches
were developed allowing fast scanning of the whole
genome for significance of a large number of traits
within minutes, fitting single or groups (e.g. triplets)
of SNPs (Hassen et al., 2009) or haplotypes (Powell
et al., 2008; Powell et al., 2011). In addition, casecontrol studies, aiming to detect associations for
traits postulated to be regulated by a smaller number
of loci (e.g. disease resistance or susceptibility), are
a promising and cost-efficient strategy, particularly
when based on pooled samples (Peiris et al., 2011)
Therefore, for the purposes of genetic evaluation,
whole-genome breeding values (GEBVs) are
appealing. The first application in animal breeding
of whole genome evaluation using machine learning
was in broilers and aimed to identify a set of SNPs
across the genome as classifiers for dichotomous or
continuous traits (Long et al., 2007, 2008, 2009).
The development of a novel framework, in which
non-parametrically derived kernels were embedded
in a linear model, was shown in broilers to achieve
higher predictive ability in cross-validation studies
The application of genomic selection (Meuwissen
et al., 2001) provides a parametric methodology
that has been shown to accelerate genetic progress
compared to BLUP-based selection in dairy cattle
(VanRaden et al., 2009). Under this approach, all
available SNP markers are fitted simultaneously.
Different prediction methods have been developed:
i. allowing SNP markers to explain different amounts
of variation with all markers fitted in the model,
namely Bayes-A, ii. only a fraction of them having
an effect, namely Bayes-B model (Meuwissen et
al., 2001), and iii. assuming equal variance across
all loci, namely Bayes-C (Habier et al., 2007). Other
models have focused on the feature selection, i.e.
the number of markers fitted in the model. In an
extension of Bayes-C, the proportion of markers ϖ is
jointly estimated from the data, in a model referred
to as Bayes-Cϖ (Habier et al., 2010). The value of ϖ
may range from 0, representing a scenario in which
all markers available are used in the model, to nearly
1, where none but few are markers are fitted. The
former scenario implies a true polygenic architecture
of the trait, while the latter assumes the existence of
few QTLs with large effects.
An alternative method for feature selection
is LASSO, an abbreviation for the least absolute
shrinkage and selection operator (Tibshirani, 1996).
The implementation under a Bayesian context
provides an alternative framework for estimating
GEBVs with good predictive ability in crossvalidation and accommodating the hypothesis that
most markers have near-zero effects but larger
QTLs may also exist (de los Campos et al., 2009).
The predictive ability of LASSO was similar to BayesB/C methods, based on the genomic evaluations of
broiler traits (unpublished data from Aviagen).
Santiago Avendaño, K.A. Watson and A. Kranis
The success of GWAS depends on the architecture
of the trait of interest. Ideally the trait should be
controlled by a small number of genes with large
effects, there is strong linkage disequilibrium
(LD) between QTLs and SNPs, and the biological
pathways are well understood. On the other hand,
for broiler chickens there is evidence that the extent
of LD is low (Andreescu et al., 2007) and indeed
lower than that of layers (Abasht et al., 2009).
Moreover, most economically important traits are
quantitative and accumulated evidence suggests
that genetic variance is controlled from a very large
number of loci with small additive effects, following
the assumptions of the infinitesimal model (Hill,
2010). Using real data, Wang et al. (2012) showed
that when considering windows of 1Mbp to capture
accumulating effects of neighbouring SNPs around
putative QTLs, the proportion of variance explain
from the most significant window didn’t exceed
2% of the total variance, a finding in accordance
with the postulated polygenic nature of quantitative
traits.
studying broiler liveability and feed conversion
rate (Gonzalez-Recio et al., 2008, 2009). These
approaches can be extended to accommodate nonadditive components for more accurate prediction
of future phenotypes (Gianola and de los Campos,
2008).
Markers can also be used to calculate a DNAbased relationship matrix and predict breeding
values under BLUP, namely GBLUP (VanRaden et al.,
2008; Hayes et al., 2008). Depending on the genetic
architecture of the trait, model performance varies.
In cases of a few large QTLs controlling a trait,
Bayes B appears having better predicting ability,
while in situations approaching the infinitesimal
model GBLUP appears to offer more accurate
GEBVs (Daetwyler et al., 2010). However, Clark et
al. (2011) have used simulations to conclude that
Area: Genetics and Breeding • August 07
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Santiago Avendaño, K.A. Watson and A. Kranis
even for a highly polygenic trait the performance of
Bayes-B is not inferior to GBLUP.
In commercial populations, the availability of
genotyped animals covers recent generations, but
there are a lot of historical data that can also be
used for genetic evaluation. It has been shown that
GEBVs can be blended with BLUP breeding values
to increase the accuracy of selection (VanRaden et
al. 2008, Ducroucq and Liu, 2009). However, these
approaches involve two steps, estimating the GEBVs
and BLUP BVs and then combine them in an index,
so more direct methods have been introduced to
combine pedigree and genotype information in
a single step by constructing a hybrid relationship
matrix including individuals with and without
genotypes (Misztal et al., 2009, Aguilar, 2011,
Meuwissen et al., 2011). Studies in dairy cattle
suggest that single-step genomic predictions may
achieve higher gains compared to GEBV blending
(Su et al., 2012). Chen et al., (2011) presented
the implementation of a single-step approach in a
broiler scenario. While the single-step approaches
are appealing from a computational point of
view, they present the risk of leading to biased
GEBV prediction unless the scales of genomic and
pedigree relationships are the same (Miztal, 2011).
Meuwissen et al., (2011) provide a novel framework
for unbiased GEBV prediction in a single-step
context.
The role of genotype
imputation strategies
August - 2012 • Salvador - Bahia - Brazil
methods to predict HD genotypes in selection
candidates after combining their LD genotypes and
parental HD genotypes, can significantly reduce
genotyping costs. Habier et al., 2009 have shown
in simulations that under a low density genomic
selection approach, an evenly spaced LD SNP panel
can achieve comparable accuracy in GS than that of
using HD genotypes for any number of traits over
consecutive generations.
Recent empirical evidence has shown that the
use of imputation methods is effective in chickens.
In broilers, Wang et al. (2011) developed an LD
panel comprising of 384 SNPs optimised for equal
distance and maximum minor allele frequency
between markers. This panel was used to impute the
high-density (HD) genotypes of selection candidates
using training of 3 ancestral generations. The
correlation between true and imputed genotypes
for the testing animals was very high (>0.96). Wolc
et al. (2011) also showed that imputation accuracy
is very high using different methods in layer chickens
and examined the impact of the genotyping
strategy on the HD individuals. When both parents
of LD animals are genotyped on HD panels, the
accuracy of imputation is higher, but only sires are
HD genotyped and dams are LD genotyped a slight
drop is observed (Piyasatian et al., 2010).
The high accuracy of imputation results in high
correlations of GEBVs estimated using imputed
versus true HD genotypes. Piyasatian et al. (2010)
discussed the changes in accuracies of GS using
imputed genotypes across generations and
demonstrated that if one of the parents of selection
individuals is HD genotyped, the loss is minimized.
In Table 1, results from Wang et al. (2011) show
with real data that the accuracy of GS using imputed
genotypes is almost identical to GEBVs using HD
genotypes for a range of GS implementation. These
results suggest that using imputed genotypes for
selection candidates is an effective way to maintain
the accuracy of GEBVs and drastically reduce the
cost of implementation.
Cost effectiveness is a sin equanom pre-requisite
for the incorporation of genomics information in
routine poultry breeding programs. In large-scale
commercial poultry pedigree applications of GS,
the cost of genotyping might be prohibitive due to
the large number of selection candidates. Current
costs of HD genotyping are in the range of $150 to
$250 per individual, hence strategies to reduce the
cost of genotyping are clearly appealing to make
implementation economically feasible. The use of
subsets of high density assays to predict GEBVs have
been shown to be effective when only SNPs with
high predictive ability
are included (Long et al.,
Table 1 - Correlations between EBVs from observed versus imputed high-density
2007 Gonzalez-Recio et
SNP genotypes for traits, with different methods for estimulation of SNP effects.
al., 2009; Weigel et al.,
2009).
Trait
No individuals Bayes-A Bayes-B Bayes-C GLUP
Alternatively, the use
of genotype imputation
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World´s Poultry Congress 5 - 9
Body weight
168
0.96
0.96
0.97
0.98
Hen house production
72
0.98
0.98
0.99
0.99
Area: Genetics and Breeding • August 07
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World´s Poultry Congress 5 - 9
Accuracy of
evaluations
August - 2012 • Salvador - Bahia - Brazil
as all selection candidates had individual records
at the time of selection. On the other hand,
for HHP (another reproductive traits) genomics
selection offers potential opportunities as selection
candidates for broiler breeding do not have their
own performance and the prediction of EBVs rely
only on family information (and correlated traits).
genomic
One of the main opportunities for capitalising
on the benefits of genomics selection is through
increases in accuracy of selection when no records
are available on the selection candidates at the
time when selection decisions are made (e.g.
at point of lay). Under this scenario, GS could
impact genetic gain through both increasing the
accuracy of selection, through allowing estimation
of the Mendelian sampling term, and reductions
in generation interval. The increase in accuracy of
selection can be assessed as the correlation between
GEBVs estimated for selection candidates without
considering their own phenotype and their realised
phenotype adjusted for fixed effects after selection
divided by the square root of the trait heritability.
In broiler chickens, the improvement in selection
accuracy constitutes the main vehicle to deliver faster
genetic progress into commercial populations. On
the other hand, in layers where selection decisions
are taken after the accumulation of reproductive
performance on selection candidates, reduction in
generation intervals can have a significant impact.
Chen et al. (2011) combining pedigree and
genotypic information a single step genomic
evaluation (Miztal et al., 2009) obtained accuracies of
up to 50% for a range of commercial traits including
bodyweight at six weeks, breast ultrasound and leg
score. In this case, accuracies were calculated from
the correlation between GEBVs and a ‘total genetic
value’ (as defined by Legarra et al., 2008) and not the
adjusted phenotype of individuals as described above.
Wang et al. (2012 in preparation) combined an
HD panel (36,455 SNPs) on 1,091 individuals with
an LD panel (384 SNPs) on 161 individuals and
compared the accuracy of pedigree based BLUP
(i.e., no record on the individuals) and GS (Bayes-A
and Bayes-B) for Hen House Production (HHP) and
bodyweight (BW). For pedigree BLUP, Bayes-A and
Bayes-B the accuracy was 0.42, 0.67 and 0.77 for
BW, and 0.43, 0.72 and 0.79 for HHP (Figure 1).
This results show that GEBV can yield up to 80%
greater accuracy than pedigree based BLUP. The
results for BWT are for comparison objectives only
Combining several populations is an appealing
strategy to increase the size of the training
population, and it has been suggested that
improvements in accuracy may also be achieved
(Ibanez-Escriche et al., 2009). In broilers, Andrescu
et al. (2010) indicated that the benefit in accuracies
increase with the relatedness of the lines. Simeone
et al. (2012) investigated a single step genomic
evaluation using data on two broiler lines and
showed the feasibility of the approach, exploring
the impact of weighting factors
related to the allelic frequencies
within line in constructing the hybrid
relationship matrix.
Santiago Avendaño, K.A. Watson and A. Kranis
In layers, Wolc et al. (2012) have examined the
persistency of GEBVs over consecutive generations
showing that although GEBVs appear to achieve
higher accuracy of selection from BLUP, they were
also characterised by greater variability. This is
likely attributed to the small size of the validation
population and changes to allelic frequencies across
generations due to selection and drift. In the same
study, GEBVs appear to be more persistent over
time than BLUP BVs, in a scenario where training is
not updated as new generations are added.
Figure 1 - Accuracy of pedigree based BLUP (BLUP-ped) and two GBLUP
implementations (Bayes-A and Bayes-B), for 5 weeks Bodyweight (BWT)
and Hen House Production (HHP) in broilers.
Miztal (2011) provides a very
useful and concise FAQs on
genomics aiming to address a
range of key aspects that might
impact the success of genomics
implementation, from datasets
sizes, analytical methods, multitrait evaluations, impact of SNP chip
density and the methodological
challenges ahead.
Area: Genetics and Breeding • August 07
5
XXIV
Final considerations
Santiago Avendaño, K.A. Watson and A. Kranis
Genomics is undoubtedly a fast-growing
and dynamic field in poultry breeding, with the
main breeding organizations making public their
advances through popular articles (e.g.,O’Keefe,
2009) and a significant body of scientific literature.
The conclusions from the review by Avendaño
et al. (2010) remain valid in the context of the
availability of genomics technology for poultry
and the requirements for both capitalizing the
postulated benefits and managing the inherent risk
of fully adopting genomics for routine breeding.
The authors take the liberty of reiterating these in
this paper:
‘…The continuous expansion of SNP panels,
the increasing capabilities of high throughput
genotyping by genotyping providers and the
consolidation of statistical tools for predicting
breeding values using genomics information
suggest that the incorporation of genomics
in routine breeding is potentially within reach
by commercial poultry breeding companies.
On the other hand, with poultry breeding
being such highly competitive market, there is
no margin of error. In this context, exhaustive
validation of SNP or haplotype effects with
independent data is essential to avoid fitting
spurious effects and introducing sources of
bias in routine genetic evaluations. Specific
implementation of genomics for routine
breeding will require a huge deal of flexibility
in terms of accommodating structural changes
in the breeding program structure and to be
able to incorporate one or a combination of
new methods and statistical approaches. Like
any other research and development strategy,
implementation of genomics will have to be
cost effective, and the corresponding tools
repeatable, tractable and capable of dealing
with the high-throughput nature of poultry
breeding…’
Whilst in some scientific quarters the ‘postgenomics era’ seems to be arriving, everything
indicates that the ‘genomics era’ is about to
commence in commercial poultry breeding. The
amalgamation of cutting-edge scientific research
and the development of strategies to deal with the
complexity of decision making in practical poultry
breeding is key to allow a smooth transition of
science into practice.
There are unequivocal signs that genomics
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Area: Genetics and Breeding • August 07
World´s Poultry Congress 5 - 9
August - 2012 • Salvador - Bahia - Brazil
in poultry breeding is in the process of being
consolidated and incorporated in routine
poultry breeding and preliminary results are very
encouraging in terms of benefits in accuracy over
standard selection methods. At the same time,
work still needs to be done to allow cost-effective
implementations. Preisinger, (2012) highlights
the significant cost implication from incorporating
genomics in a layer breeding schemes pragmatically
concluding that although benefits are expected,
cost needs to be reduced to make implementation
feasible.
Breeders strive to deliver benefits in terms of
product performance to its customers and wider
stakeholders (e.g., governments and general
public) around the globe. The next few years will
be critical to assess whether genomics technologies
will provide adequate return for the investment
and contribute with significant benefits to poultry
breeding globally.
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XXIV
WOLC, A., HICKEY, J.M., SARGOLZAEI, M., ARANGO,
J., SETTAR, P., FULTON, J.E., O’SULLIVAN, N.P.,
PREISINGER, P., HABIER, D., FERNANDO, R.,
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FERNANDO, R., GARRICK, D.J., WANG, C. AND
DEKKERS J.C.M (2011) 7th European Symposium on
Poultry Genetics 5-7.10.2011
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Area: Genetics and Breeding • August 07
World´s Poultry Congress 5 - 9
August - 2012 • Salvador - Bahia - Brazil