Dairy Cattle Breeding and Genetics
Summary of Meeting
October 7, 2009
Room 141, Dept Animal & Poultry Science, University of Guelph
Participants:
Ainsley Archer, CGIL
Ryan Barrett, JAC
Jarmila Bohmanova, CGIL
Ted Burnside, GENO
Nicolas Caron, Semex
Shannon Cartwright, OVC
Jacques Chesnais, Semex
Jalal Fatehi, CGIL
Melkaye Gebreselassie, CGIL
Paige Glover CGIL
Lynsay Henderson, OVC
Gladys Huapaya, CDN
Mohsen Jafarikia, CCSI-CGIL
Janusz Jamrozik, CGIL
Gerrit Kistemaker, CDN
Ken Leslie, OVC
Sarah Loker, CGIL
Filippo Miglior, AAFC-CDN
Steve Miller, CGIL
Bethany Muir, HAC
Timothee Neuenschwander, CGIL
Melissa Nixon, HAC
Mehdi Sargolzaei, Semex-CGIL
Flavio Schenkel, CGIL
Asheber Sewalem, AAFC-CDN
Jay Shannon, HAC
Katarzyna Stachowicz, CGIL
Pete Sullivan, CDN
Brian Van Doormaal, CDN
Laurie Wagter, OVC
1. Genomic Methodologies, Applications and Strategies
1.1 Options for combining direct genomic and progeny-test results – P.G.
Sullivan
Genomics is a technology with excellent potential to improve the selection
of dairy sires in Canada, especially for the selection of young bulls prior to
progeny-testing. With genomics, reliabilities of genetic evaluations for young
genotyped animals are much higher than was previously possible with parent
averages. However, there appears to be an over-evaluation of top young sires,
which creates major problems when comparing top-ranking bulls, because top
lists can include many more young sires than they should. The purpose of this
report was to investigate the problem of over-scaled genomic evaluations for
young bulls.
The study used de-regressed proofs as observations and estimated and
summed SNP effects to calculate breeding values (DGV). Blending of EBV and
DGV gave GEBV. The study found that the GEBV for young sires appeared to be
over-scaled relative to proven sires, and that the reliabilities of young sire GEBV
were biased upwards relative to validation studies. This difference may be a
result of reliabilities being approximated using traditional programs, whereas
using genomic methods reliabilities are calculated directly by inverting the matrix.
Also, the current genomic approach incorrectly assumes that the SNP can
explain 100% of the genetic variance. Meanwile, the 50K panel is still a very
sparse representation of the complete bovine genome.
It may be useful to address the problem of over-scaling when DGV are
calculated, instead of at the blending step (when calculating GEBV). Further
investigation is required into use of a discount factor for reliability. More validation
tests need to be performed, and on more traits.
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1.2 Genomic validation with selected data – P. G. Sullivan
Genomic evaluations of young bulls may be on a different scale than that
of proven bulls, causing regressions of progeny proofs (Y) on genomic
predictions (X) of the same bulls to be lower than 1.0. The main concern is that
genomic evaluations for top young bulls may be over-estimated, leading to
greater use of these bulls over proven bulls, which leads to disappointments.
A regression of 1.0 is only possible if there is no pre-selection on Y. Only
random selection of which proven bulls to evaluate via genomics is acceptable.
However, regressions below 1.0 have occurred thus far because genotyping and
genomic evaluation has been avoided for some of the worst bulls. The purpose
of this study was to examine the effects of, and possible solutions to, preselection.
Selection on Y reduces the regression substantially below 1.0, but
subsequent selection on X can bring the regressions back up towards 1.0 gain.
Therefore, validation tests that are based on regression should either include
selection on X if Y is subject to pre-selection, or should account for pre-selection
on Y by basing the test on the expected regression values similar to the
estimates from this study. Genomic validation studies should consider use of
parameters other than simple correlations and regressions, ones that are least
affected by selection.
1.3 Development of genomic GMACE – P.G. Sullivan and P.M. VanRaden
With genomics, there is increased data sharing between countries on
genotyped bulls. For traditional MACE, it is assumed that each country is
independent, with no data sharing occurring. Hence, traditional MACE assumes
a 0 residual correlation between countries. This study worked to update MACE
software to account for these residual correlations. Not a lot of changes were
required. In the discussion of this report, Jacques Chesnais asked why GMACE
would be necessary when, instead, a multi-trait genomic MACE could work. Pete
Sullivan explained that a multi-trait genomic MACE would take a long time to run,
whereas it took very little time to run GMACE. It is feasible for Interbull to run
GMACE, whereas they would not be able to run a multi-trait genomic MACE.
1.4 Polygenic effects scaling the Genomic Matrix, and discounting
reliabilities – G. Kistemaker and P.G. Sullivan
The genomic relationship (G) matrix is calculated based on SNP
information, and is used in the same way as the A relationship matrix is used in
genetic evaluations. Normally, the G-matrix is scaled so that it looks more like the
A matrix. However, this results in an up-scaling of inbreeding. Both DGV and
reliability are largely determined by the G-matrix. Currently, adjustments have to
be made to the calculation of DGV values and estimated reliabilities. Adjustments
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applied to the calculation of DGV result in low estimated reliabilities of the GEBV
values. These reliabilities, in turn, require adjusting using discount factors. This
report investigated different approaches to calculating DGV values, including
looking at different weights for polygenic effects, and either using the scaled Gmatrix, or the un-scaled G-matrix. The resulting DGV values were combined with
the PA using 6 different levels of discount factors ranging from 0.5 to 1.0 to
determine which level should be used to adjust estimated reliabilities.
It was concluded that if we keep using the same scaling method, the
discount factor should change to 0.80. But if the scaling method is removed, the
discount factor would no longer be required.
Brian Van Doormaal commented that currently there is one scaling of the
G-matrix that occurs once across the whole system for all traits. Then there is a
discount factor of 0.5 across all traits. He added that removing the scaling and
discount factor treats all traits the same. This is a large assumption, as these
changes might not have the same results on all 63 traits currently evaluated.
Others suggested looking into a different kind of scaling. To this, Brian warned
that we have to be careful what the end result of changing scaling coefficients in
the G-matrix would be since it is the inverse of this matrix that is important for
use.
1.5 Weight on polygenic effects and discounted theoretical reliabilities F.S. Schenkel and M. Sargolzaei
This report addressed the concern with over-estimation of reliabilities of
genomic proofs. A validation study was carried out using different weights for the
polygenic effects in the genomic evaluation model and different levels of discount
on the theoretical reliabilities. Based on this validation study, reliabilities are
underestimated, not overestimated. Specifically, with the current weight for
polygenic effects (20%) and the discount factor (0.50) used by CDN, the
expected reliability of GEBV are underestimated for conformation, SCS and
protein yield.
1.6 gebv: Genomic breeding value estimator for livestock – M. Sargolzaei,
F.S. Schenkel and P.M. Van Raden
One of the advantages of G-BLUP over R-BLUP is that individual reliability
for GEBV can be obtained. However, G-BLUP is computationally more
demanding than R-BLUP.
This report introduced gebv software, a genomic evaluation tool for livestock that
efficiently implements G-BLUP and R-BLUP. The software estimates genomic
breeding values using dense SNP maps and a single trait linear model. Important
features of this software include the use of fast block matrix multiplication and
inversion and the ability to run parallel jobs, significantly reducing computing
time.
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1.7 Relationship of traditional and genomic parent averages with the first
official proofs of bulls – N. Caron and J.P. Chesnais
All the validations done so far have all been looking back. This report was
interested looking forward (i.e., estimating the GPA now and what the proof is
going to be for those bulls). The objective of this report was to compare the
predictability of the bull proofs which received their first official proof (GEBV) in
August 2009 using either their traditional or genomic parent averages from April
2009. Differences and correlations between April PA and August GEBV as well
as between April GPA and August GEBV were calculated. In general, it was
found that genomic PAs were better predictors than traditional PAs of the
upcoming first official proofs, with larger correlations between GPA and GEBV. It
was suggested that genomic evaluation should still be used in combination with
progeny testing since GPAs alone cannot identify all top LPI sires.
Distribution of DGV Change for Descendants when Shottle is Excluded
from SNP Estimation – LPI – Brian VanDoormaal
This report was a short investigation that found that EBV is important for
the calculation of DGV. If EBV of Shottle is removed from the calculation, there is
a large drop in LPI that depends on PA.
2. Production Traits
2.1 Application of simultaneous and recursive random regression models
for milk yield and SCS of Canadian Holsteins - J. Jamrozik, J. Bohmanova
and L.R. Schaeffer
The relationships between milk and SCS may involve recursive or
simultaneous effects that cannot be accounted for properly by genetic and
environmental correlations only. Structural equation models allow for modeling
causal pathways between phenotypes. Simultaneous effects refer to the
presence of reciprocal direct effects between traits, whereas recursive effects
postulate that one trait affects the other directly but the reciprocal link does not
occur. This report found that there were no apparent benefits expected due to
fitting causal phenotypic relationships between milk yield and SCS in a random
regression TD model for genetic evaluation purposes. Changes to the Canadian
Test-Day Model with respect to modeling causal relationships between milk
production traits and SCS were not recommended. Brian Van Doormaal added a
comment to the authors suggesting they look at the causal effects for traits that
happen sequentially, rather than on the same test-day.
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2.2 Updated variance components for production traits and somatic cell
score in seven Canadian dairy breeds - J. Bohmanova, F. Miglior and J.
Jamrozik
The Canadian Test-Day Model (CTDM) has undergone recent
improvements. These changes include the addition of a fixed days in milk term,
increasing the order of Legendre polynomials for fixed regressions, pre-adjusting
milk, fat and protein yields for the effect of pregnancy, and increasing the number
of intervals from 4 to 10 for residual variance. Therefore, this report focused on
updating variance components for production traits and SCS. Results of this
report showed that updated estimated variance components had less artefacts
than the previous ones. It is recommended to use these updated variance
component estimates. Pete Sullivan suggested that perhaps an average of the
parameters should be taken across the different breeds. This way, parameters
for breeds of a small sample size (such as Milking Shorthorn) would likely be
closer to the truth. Brian Van Doormaal, however, refuted this by pointing out that
the parameters are different between the different breeds (even the ones without
a small sample size), and they likely should be as this is a reflection of
differences in selection between the different breeds.
4.2 Functional Traits - Female Fertility
4.2.1 Breed of sire effect on fertility of first generation crossbred heifers –
P. Glover, J. Fatehi, L. Schaeffer and E.B. Burnside
Crossbred yearling heifers were compared to purebred Holstein
contemporary heifers for eight fertility traits. Results showed that crossbred
heifers had improved performance in some fertility traits compared to purebred
Holstein heifers. More work is needed in this area, as sample sizes in this work
were very small. For future work, other traits of interest include measured type
traits, survival to calfhood, milk production traits and somatic cell count.
4.4 Functional Traits - Health
4.4.1 Estimation of genetic parameters for measures of calf survival in a
population of dairy calves in New York State – L. Henderson, F. Miglior, A.
Sewalem, D. Kelton, A. Robinson and K. Leslie
Genetic selection for improved health and longevity is a major goal of
dairy cattle breeders. However, little attention has been paid to the genetic
components of calf and heifer survival. The objectives of this study were to
estimate genetic parameters for survival to weaning and survival to exit for a
population of calves from New York State, as well as to associate proofs for the
two calf survival traits with traits routinely evaluated by the Canadian Dairy
Network and the United States Department of Agriculture. Results suggested that
genetic variation exists for the survival traits studied. Also, a large number of
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proofs for conformation traits were significantly correlated with the survival
proofs.
4.4.2 Estimation of genetic parameters for measures of calf health in a
population of dairy calves in New York State – L. Henderson, F. Miglior, A.
Sewalem, D. Kelton, A. Robinson and K. Leslie
Many studies have investigated the influence of genetic incommon
diseases of the dairy cow. To date, there has been limited research published on
the genetic effects of common diseases in dairy calves. Preweaning calf
diseases are a very costly concern for dairy producers and, as such, inclusion of
these traits in breeding programs could be valuable. The objectives of this study
were to estimate genetic parameters for preweaning diseases for a population of
calves from New York State, as well as to associate sire proofs for these traits
with traits routinely evaluated in Canada and the US. Results suggested that
significant genetic variation exists for pre-weaning calf health traits. Heritabilities
of the calf health traits were low. Associations between the proofs for these traits
and proofs for routinely evaluated traits in Canada and the US were significant
for a number of traits.
There was discussion about how few daughters per sire there were in this
study. However, the study still showed a good indication of the consistently
negative relationships between calf health traits and many linear body size
conformation traits, and the positive relationships between numerous
reproduction traits and calf health.
4.4.3 Phenotypic and genotypic variation of bovine immune responses in
cohort dairy herds across Canada – K.A. Thompson, N.A. Karrow, K. Leslie,
M. Quinton, F. Miglior and B.A. Mallard
Selection for enhanced immune response has been proposed to minimize
impacts of disease in livestock. Identifying high and low immune responders
(both phenotypically and based on estimated breeding values) and
understanding the genetic diversity of immune response may make it feasible to
include this trait in breeding indices to improve health. This study found that it will
be possible to calculate heritability and estimated breeding values for immune
response. It will be possible to classify cows as high, average and low for
immune response both phenotypically and based on estimated breeding values.
Associations with disease and production will be investigated.
4.4.4 Multivariate analyses of producer-recorded health events and BCS in
Canadian Holstein cattle – T.F.-O. Neuenschwander, F. Miglior, J. Jamrozik,
C. Bastin, O. Berke, D.F. Kelton and L.R. Schaeffer
Body condition score can be used as an indicator trait for health
resistance and fertility. The objectives of this study were to examine the
relationships between BCS and disease resistance in Canadian Holstein using a
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multivariate approach and to apply a longitudinal model to health data. Data on
BCS and health were analyzed with multiple-trait and longitudinal models. The
multiple-trait model included BCS and different times of the lactation as different
traits. The second model was a random regression model with Legendre
polynomials. Generally, there was a positive genetic correlation between BCS
and health. As accurate BCS recording is easier to do than accurate health
recording, BCS could be used as an indicator trait in selection for better health.
For some health traits, however, genetic correlation with BCS was low, so
selection for BCS would only improve health marginally. The best way to use
these traits would be to combine them in a selection index or to use BCS as a
correlated trait in the genetic evaluations of health traits.
During the discussion of this report, the author noted that the multiple-trait
approach may be better than the longitudinal approach since, with the
longitudinal approach, heritability became very low for disease traits after early
lactation. Following this statement, Brian VanDoormaal added that perhaps we
should simplify things and only genetically evaluate disease incidence in the first
50 days in milk, the period of time when these diseases are likely to occur. Ted
Burnside commented that there definitely is a high amount of early culling in first
lactation. First lactation cows are under a large amount of stress. This
considered, it is not necessarily good to ignore health during the rest of the
lactation either.
4.5 Functional Traits – Body Condition Score
4.5.1 Relationship between body condition score, locomotion and dairy
strength with functional longevity in Holsteins – A. Sewalem, G. Kistemaker
and F. Miglior
The objective of this report was to evaluate the impact of body condition
score, locomotion and dairy strength on functional longevity of Canadian
Holsteins. All traits had a statistically significant (P<0.001) association with
functional longevity. There appeared to be a curvinilear relationship between
body condition score and longevity (so that neither thin nor fat cows were
desirable). The relationship between locomotion and longevity was linear. In
other words, cows with reduced lameness appeared to be better for longevity.
There was a linear relationship between dairy strength and longevity. Cows
better for dairy strength had longer longevity.
4.5.2 Genetic relationships between body condition score and reproduction
traits for Canadian first-parity cows – C. Bastin, S. Loker, N. Gengler and F.
Miglior
As fertility traits are difficult to measure, are often not readily available,
and have low heritabilities, body condition score can serve as a predictor for
estimated breeding values for fertility traits, with an accuracy no greater than the
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genetic correlation between body condition score and the trait of interest. The
objective of this research was to estimate the genetic correlations between BCS
and reproduction traits for Canadian first-parity Holstein and Ayrshire cows using
random regression models. Except with maternal calving ease, favourable
genetic correlations were found between body condition score and fertility and
calving traits studied. Correlations were higher in mid lactation for fertility traits
and in early lactation for calving traits.
4.5.3 Genetic relationships between calving traits and body condition
scores before and after calving for Ayrshires – C. Bastin, S. Loker, N.
Gengler and F. Miglior
It is commonly assumed that over-conditioned cows before calving are at
a greater risk for calving difficulty. Therefore, the objective of this study was to
estimate, using random regression models, the genetic correlation between
calving traits and BCS recorded during the period preceding the calving and
during the following lactation. With the exception of the positive genetic
correlation between maternal calving ease and BCS before calving (which
emphasized the phenotypic relationship between fat cows around calving and
dystocia), genetic correlation correlations between BCS and calving traits were
favourable.
4.5.4 Genetic parameters of body condition score and milk production
traits in Canadian Holsteins – S. Loker, C. Bastin, F. Miglior, A. Sewalem, J.
Fatehi, L.R. Schaeffer and J. Jamrozik
Previous reports in this meeting by Bastin et al. and Neuenschwander et
al., as well as other research, have shown that body condition score is
genetically linked with health and fertility traits in dairy cattle. Before using body
condition score as a tool for improving health and fertility, it is first important to
verify the heritability of this trait and its relationship with other traits of economical
importance. To date, an analysis of body condition score and milk production
traits on a test-day basis has not been made. The objective of this research was
therefore to estimate genetic parameters of body condition score with milk
production traits and somatic cell score using random regression animal models.
This study found that body condition score was a moderately heritable trait
across first lactation in Canadian Holstein dairy cattle. Body condition score had
a weak negative genetic correlation with milk yield at the beginning of lactation,
while the genetic correlation between this trait and protein percentage (positive)
and somatic cell score (negative) were at their strongest during this period.
These results suggest that body condition score may be selected for without a
large negative impact on milk production.
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4.5.5 Genetic analysis of body condition score in the first three lactations in
Canadian Holstein – S. Loker, C. Bastin, F. Miglior, A. Sewalem, J. Fatehi,
L.R. Schaeffer and J. Jamrozik
Higher producing cows are willing to increase the extent and duration of
the negative energy balance state to achieve greater milk production. Extent and
duration of the negative energy balance state are related to reduced health and
fertility. If information on energy balance is taken into account, indirect and
unfavourable effect of increased milk production on health and fertility may be
reduced. Body condition score may be used to indicate energy balance. The
objective of this research was to estimate the genetic parameters of body
condition score in the first three lactations in Canadian Holstein dairy cattle using
a multiple-lactation random regression test-day model. Body condition score was
a heritable trait across lactation for parity 1, 2, and 3, with reasonable genetic
variation, especially in mid to late lactation. Records for body condition score
were highly genetically correlated among the first three lactations.
During the discussion of this study, Bethany Muir added a quick comment
that 70% of the cows scored by Holstein Canada are in first lactation, so it may
be useful to know whether or not body condition score is the same trait in
different lactations, especially if scores collected by Holstein Canada (not just
Valacta data) will be incorporated in the future research of this topic.
4.6 Functional Traits – Milking Speed and Temperament
4.6.1 Estimation of genetic parameters for milking temperament in
Canadian Holsteins – A. Sewalem, G. Kistemaker and F. Miglior
National genetic evaluation of milking temperament was implemented in
2001 using a heritability of 8%. The objective of this report was to assess the
phenotypic trend of temperament over time, and to estimate the genetic
parameter of milking temperament in Holstein cows using more recent data.
Using new data, milking temperament heritability was higher by 62% compared
to what was previously estimated in 2001. The updated genetic parameter had
no large impact on bull proofs, as the correlations among bull proofs was high.
However, reliability of bull proofs increased by more than 6 points.
5.1 Economic Indexes, Breeding Strategies and Inbreeding - LPI
5.1.1 Genetic analysis of return over feed in Canadian Holsteins – J.
Bohmanova, F. Miglior, J. Jamrozik, B.J. Van Doormaal, K.J. Hand and D.
Lazenby
Genetic selection for more profitable animals is the main objective of most
breeding programs. Return over feed (ROF) is a herd profit index developed by
Canadian DHI to evaluate profitability of their cows and make culling decisions in
their herds. This index is based on milk income and feed costs which are the two
most important determinants of cows’ profitability. The objective of this study was
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to estimate variance components and breeding values for ROF through the first
three lactations and to calculate correlations between estimated breeding values
for ROF and other traits currently evaluated in Canada. The study found that
ROF was moderately heritable. Sires’ estimated breeding values for ROF had
favourable correlations with Lifetime Profit Index, estimated breeding values for
production traits and SCS. The ROF is a good indicator of cow profitability.
However, ROF is not recommended to be used for direct selection for profit
because it does not account for differences in heritabilities between components
of profit.
5.2 Economic Indexes, Breeding Strategies and Inbreeding - Inbreeding
5.2.1 Rates of inbreeding and genetic diversity in Canadian Holstein cattle –
K. Stachowicz, M. Sargolzaei, F. Miglior and F.S. Schenkel
The objective of this study was to perform an in-depth analysis of the most
recent pedigree of the Canadian Holstein dairy population in order to assess the
past and current levels of inbreeding and genetic diversity, and to determine the
causes of its loss. Results suggested that the population had a small effective
population size, an accumulation of inbreeding, and genetic diversity loss. The
most prominent cause of genetic erosion of Canadian Holstein population is
genetic drift. Results suggest that cows in Canada are much more genetically
diverse than proven bulls. Therefore, the process of choosing bull dams should
be verified in order to produce bulls less related to the ones currently being used.
Using bulls less related to the Canadian population would be desirable in terms
of conservation of population genetic diversity. The fact that 71% of inbreeding in
this population is due to use of just 10 sires was brought up during discussion.
Jacques Chesnais emphasized the fact that solving this problem would be quite
a challenge, and would require an international effort. Yet, breeders are going to
select top bulls, which happen to be from the same families. Competition would
prevent these breeders from partaking in the effort to increase genetic diversity
via bulls.
Brian Van Doormaal inquired authors about including genomic
relationships in inbreeding analyses. While genomic relationships are a different
measure of inbreeding (i.e., they measure the degree of homozygosity), the
industry needs to figure out how to utilize and include genomic relationships in
calculations of inbreeding.
5.2.2 Assessment of the genetic diversity of Guernsey breed in Canada, the
U.S.A. and South Africa using pedigree data – Melkaye M. G. and Schenkel
F.
The assessment of genetic variability within and between breeds is
essential for developing appropriate genetic conservation strategies. This study
focused on analyzing within breed variability of Guernsey dairy cattle in Canada,
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the U.S.A and South Africa. Guernsey in South Africa had the highest genetic
diversity, while Guernsey in Canada and the USA were highly related. Results of
this study suggest that using sires of higher genetic merit in South Africa, but did
not have a large contribution to the Canadian and U.S.A Guernsey populations
might improve the diversity of these latter populations.
6. DairyGen Reports
6.1 Genetic parameters of feed efficiency in Holsteins – J.F. Hayes
This study addressed the inability of high producing Holstein cows to
ingest sufficient feed to meet their energy requirements. The overall objective of
this project was to estimate population genetic parameters that would help further
understanding of this problem. The significance of the results is that they provide
understanding of the nature of the genetic relationships among feed intake,
production, efficiency of feed utilization, reproduction and health.
The Meeting Adjourned at 4:30 PM.
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