Effects of Unknown Dam on Genetic
Evaluations of Different Breeds
Pete Sullivan
Introduction
Recently, large differences were observed between genetic evaluations of Milking Shorthorn
cows with unknown dams relative to cows with known dams. Similar differences may also exist
for other breeds. Differences such as these are presumably due to genetic group effects in the
genetic evaluation models. Current definitions of genetic groups are described in Sullivan and
Miglior (2006) for conformation traits and in Miglior et al (2005) for production traits.
Questions have been raised about the validity of genetic group solutions, particularly for
unknown dams, but follow-up research has not yet been conducted. Therefore, the purpose of
the present paper was to investigate the effects of missing dams in more detail for all breeds, and
to consider potential improvements for the genetic evaluation models.
Data and Methods
Data used were from the April 2008 official evaluation for each breed. Cows with a sire of the
same breed were considered in each analysis.
Differences in LPI and LPI components were estimated for cows with unknown dams relative to
cows with known dams using a relatively simple model that targets the effects of unknown dam
genetic groups on the LPI. The fixed-effects model for each LPI trait (y) was:
y = Xb + Wc + e
Class effects are in vector b and covariates (linear regressions) are in vector c. The only class
effect was year of birth of the cow. Covariates were the corresponding LPI observation of the
sire and the amount of the dam that was unknown (i.e. 0 or 1). Separate covariates for the
unknown dam effect were fit for each 5-year interval of cow birth years.
Following review of the results for LPI traits, further investigation was conducted by applying
the above model to conformation EBV of classified cows, and adding an additional covariate
effect for the final score of the cow. The assumption was that the unknown dam effects should
be relatively close to zero from such a model, although this would only be true if unknown dam
effects were not confounded with effects in the genetic evaluation model that were excluded
from the simplified model of the present study. For example, if cows with unknown dam were
on average a different age than cows with known dams, or were from inferior or superior
environments (herds), then estimates of unknown dam effect from the present study would
include those other confounded effects. Additional investigations were also conducted, as
described in the sections below.
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 1 of 16
Results and Discussion
The effects of unknown dam were very dependent on the breed of evaluation, being generally
positive for the minor breeds and small or negative for the major breeds (Table 1). For Milking
Shorthorn, the advantage of unknown dam was more than 1000 LPI points with similar portions
coming from the production and durability components of the LPI. In contrast, the magnitudes
of unknown dam effects were less than 500 LPI points for all other breeds.
Table 1. Effect of the dam being unknown (relative to known) on LPI evaluations of cows born
between 2001 and 2005. The estimation model for each trait also included the class
effect of cow birth year and a linear regression on sire’s LPI trait value.
Breed
Trait
ms
cn
gu
bs
je
ay
ho
LPI Durability
476
59
-98
164
-213
-57
-101
LPI Production
615
399
537
196
250
-291
-351
LPI Health & Fertility
-34
-31
-103
34
29
95
52
LPI
1077
449
325
391
67
-253
-394
It was of interest to know if the estimated effects for unknown dam were reasonable or if they
were a symptom of potential problems in the evaluation systems. To check this, further studies
were conducted for final score. The evaluation system should generate effects that are consistent
with the observations being analyzed. Genetic effects are linear functions of the observations,
and vice versa. Therefore, by fitting the observation as a covariate effect on the evaluation, the
expectation was that the unknown dam effect should essentially be eliminated. This result was
not observed (Table 2). While the effect of unknown dam was generally reduced in magnitude
by adding the cow’s classification score into the model, substantial effects of unknown dam
remained, particularly when fitting a small value for the genetic-groups variance ratio (ratio of
residual variance over genetic-groups variance; VR). This analysis could be improved by further
adding contemporary group and age-stage-time effects to the model, but these variables were not
readily available for this analysis.
Table 2. Effect of the dam being unknown (relative to known) on conformation EBV of cows
born between 2001 and 2005.
Breed
Additional
Model Effects
VR*
ms
cn
gu
bs
je
ay
ho
1
7.3
2.3
1.5
2.0
-2.7
-1.4
-2.1
2
5.0
1.7
-2.8
Birth Year
Sire EBV
5
2.8
1.1
-2.9
10
2.0
-1.5
-0.6
0.5
-2.9
-2.6
-2.2
1
7.0
0.8
0.9
2.1
-2.2
-1.0
-1.5
Birth Year
2
4.7
1.8
-2.2
Sire EBV
5
2.5
1.2
-2.3
Classification
10
1.8
-2.9
-1.3
0.6
-2.4
-2.2
-1.6
*
Ratio of residual variance over genetic-groups variance
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 2 of 16
As an alternative approach, observations from the current classification system were partitioned
into the contributing factors in the genetic evaluation model. Averages for each factor were then
compared between cows with unknown dam (U) and cows with known dam (K) by computing
the average difference (U-K). Differences (U-K) for each factor estimated from the official
model (VR=1) and changes to those differences after increasing to VR=10 are shown in Table 3.
Differences in dam contribution were primarily due to the combination of differences in
performance and contemporary group (management) solutions, which provides some evidence
that the evaluation model and system is working properly, or at least as expected for final score.
Table 3. Effect of variance ratio (VR) for genetic groups on the difference between
contributions to classification score of cows classified under the new classification
system (i.e. since August 2005) from an unknown (U) relative to a known (K) dam
(expressed on the scale of official conformation proofs).
Breed
Effect on Classification
ms
cn
gu
bs
je
ay
ho
n/a
Difference (U-K) in Performance
5.1
7.5
-3.1
-4.6
-5.7
-3.2
-5.9
Difference (U-K) in Contributions to Performance when VR* is 1
Dam
6.8
1.9
0.6
1.7
-3.1
-1.7
Sire
-0.6
1.3
-0.3
-0.1
-0.7
-0.3
Mendelian Sampling
0.7
0.3
0.3
-0.0
-0.0
0.1
Age-Stage-Time
-1.8
0.0
-1.3
-0.3
-0.0
-0.8
Contemporary Group
-1.6
1.8
-3.3
-5.7
-1.5
-0.7
Residual
1.6
2.2
0.9
-0.0
-0.4
0.2
-2.3
-1.0
0.0
-0.4
-2.2
0.0
Change to Difference (U-K) in Contributions when Increasing VR from 1 to 10
Dam
-5.3
-4.1
-3.0
-1.5
-0.0
-1.3
Sire
0.0
0.0
0.1
0.1
0.0
0.0
Mendelian Sampling
0.4
0.3
0.4
0.1
0.0
0.1
Age-Stage-Time
0.2
0.2
0.3
0.0
-0.0
0.0
Contemporary Group
2.6
2.1
0.3
0.9
0.0
0.7
Residual
2.0
1.5
1.9
0.4
-0.0
0.4
-0.1
0.0
0.0
0.0
0.0
0.0
*
Ratio of residual variance over genetic-groups variance
The consistent decrease in unknown dam effects across all breeds is explained by the fact that the
solution for recent unknown dams was generally among the highest genetic group solutions, and
increasing the variance ratio reduced the higher solutions towards the average of all genetic
group solutions (solutions for recent unknown dams were positive for all breeds before shifting
the solutions to the published proof scale). The relative changes in contributions, due to the
increase of the variance ratio, provides evidence of moderate to strong confounding between
unknown dam and contemporary group effects for most breeds. If effects were completely
confounded, an increase in one effect would be equally offset by a decrease in the other. When
both effects are treated as random, a portion of each effect goes into the residual. In the present
study, the offsetting change in contemporary group effect was about ½ the magnitude of the
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 3 of 16
change in unknown dam effect for most breeds. This means that by increasing the variance ratio
for genetic groups, some of the bias due to factors not included in the model (e.g. heterosis)
would be shifted from the cow’s EBV into the contemporary group solutions. Almost all of the
remaining effects removed from the EBV of cows with unknown dam shifted into the residuals
(Table 3). Given that pedigree is missing, it seems reasonable to expect larger residual effects.
Detailed analyses were not conducted for other solutions that would be directly affected by the
variance ratio for genetic groups (for example cows with unknown sires), but these other effects
would presumably be less affected than were the unknown dams of cows. Extreme solutions for
genetic groups were predominantly the solutions for unknown dams of cows (e.g. CAN-CC in
Figures 1 and 2), although there were exceptions (for example the unknown sires of Jersey cows
(CAN-BC) and Jersey bulls (CAN-BB), Figure 3). It is of interest that decreases in unknown
dam of cow solutions (CAN-CC) were somewhat offset by increases in unknown sire of cow
(CAN-BC) solutions (e.g. Figures 1 and 2), suggesting that confounding may also exist due to
pedigrees having either both or neither parent known rather than a single parent unknown.
Genetic evaluations are run separately by breed, and there are no effects in the model for
crossbreeding (i.e. heterosis) or additive breed differences for crossbred cows. This is usually
not an issue because cows included are usually purebred. In the Milking Shorthorn data,
however, cows with unknown dam may be crossbreds out of Holstein cows. The unknown dam
effect would then include the breed difference between Holstein and Milking Shorthorn plus the
effects of heterosis. The breed difference is an additive genetic effect that belongs in the EBV,
but the heterosis effect is non-additive and should not be part of the EBV.
Heterosis effects are opposite to the effects of inbreeding and will vary in degree of importance
among traits (e.g. Miglior et al, 2008). The significance of heterosis effects would be larger
when crossing animals of different breeds than for crossing unrelated animals within a breed (i.e.
larger than inbreeding effects in Miglior et al, 2008). Assuming a favourable effect on LPI traits,
the heterosis effects could explain some of the unknown dam effect for Milking Shorthorn, as
undesirable bias. In the context of a within-breed evaluation, a better model for Milking
Shorthorn would be one that shifts heterosis effects from the EBV of unknown dams into other
non-genetic effects in the model. Eventually, a multiple-breed model would be preferred.
The variance ratio for genetic group effects was arbitrarily set to 1 in the current evaluation
systems. Many countries treat genetic groups as fixed effects to account for selection bias,
which is equivalent to a variance ratio of zero. Models that account for uncertain parentage
without applying genetic groups are equivalent to applying genetic groups with a variation ratio
equal to the number of potential parents within each genetic group (Sullivan, 1995), which is
generally much higher than 1. The use of a non-zero variance ratio has computational
advantages and the use of a small value is an attempt to strike a balance between computations
(including confounding issues) and accounting for selection bias (Sullivan and Schaeffer, 1994).
Considering the results for Milking Shorthorn, it seems reasonable to increase the variance ratio
to a value between 2 and 5, to reduce the effects of genetic groups on the EBVs of classified
cows with unknown dams. Indications of the relative impacts for recent cows of each breed,
when changing the variance ratio within the range of 1 to 10, can be drawn from Figure 4.
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 4 of 16
Changing the variance ratio would have larger impacts for recent Milking Shorthorn,
Canadienne, and Guernsey cows than for cows of the other breeds.
Creating Variation for Unknown Dam Effects
A concern in addition to the large magnitudes of unknown dam effects with the current genetic
evaluation system, is that animals born in a given year receive identical unknown dam
contributions to their EBV (i.e. we essentially assume that the unknown dams were the same
animal for the entire group of progeny). This can be very problematic if unknown dams in a
given year are genetically very different from herd to herd (e.g. different breeds). A more
reasonable assumption is that the unknown dam effects for a given year were sampled from a
common genetic group (i.e. a common maternal grand-parent average is used, essentially treating
the unknown dams like full-sibs). The unknown grand-parent solutions account for the fact that
the unknown dams were a selected group, similar to the way the unknown dam group solution in
the current official model accounts for the effects of selection. The difference is that by shifting
the selection effect from the unknown group of dams to their parents, each unknown dam
contribution can vary to better reflect the performance data of individual progeny (e.g. classified
cows). Average unknown dam effects within a year will also vary from herd to herd.
This approach was investigated for the evaluation of final score, by generating a unique pseudo
dam to represent each unknown dam of classified cows. Each pseudo dam had 1 progeny and
was assumed to be 5 years old at calving. Parents of pseudo dams were unknown and
contributed to the solutions of genetic groups currently defined in the official evaluation system.
The average impact of generating pseudo dams on the effect of unknown dams was generally
intermediate between no impact and the impact of increasing the variance ratio on genetic groups
from 1 to 10 (Table 4). For most breeds, the average impact would be similar to increasing the
variance ratio from 1 to 4 (interpreting from Table 2). However, there is also the additional
individual impact of increased correspondence between EBV and performance data for each
classified cow that is assigned a pseudo dam.
Table 4. Effect of the dam being unknown (relative to known) on conformation EBV of cows
born between 2001 and 2005.
Breed
Additional
Model Effects
VR*
ms
cn
gu
bs
je
ay
ho
1
7.3
2.3
1.5
2.0
-2.7
-1.4
-2.1
Birth Year
1p
3.2
-0.6
0.9
1.3
-2.4
-1.9
-2.1
Sire EBV
10
2.0
-1.5
-0.6
0.5
-2.9
-2.6
-2.2
1
7.0
0.8
0.9
2.1
-2.2
-1.0
-1.5
Birth Year
p
Sire EBV
1
2.9
-1.9
0.2
1.4
-1.8
-1.4
-1.5
Classification
10
1.8
-2.9
-1.3
0.6
-2.4
-2.2
-1.6
*
Ratio of residual variance over genetic-groups variance
A unique pseudo dam was created for each classified cow with an unknown dam.
p
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 5 of 16
The relative changes in record contributions were similar using pseudo dams (Table 5) as from
increasing the genetic-groups variance ratio (Table 3). Changes in the average effects of
unknown dam were shifted almost entirely into the contemporary group and residual effects.
Table 5. Effect of pseudo dams on the difference between contributions to classification score
of cows classified under the new classification system (i.e. since August 2005) from
an unknown (U) relative to a known (K) dam (expressed on the scale of official
conformation proofs).
Breed
Effect on Classification
ms
cn
gu
bs
je
ay
ho
n/a
Difference (U-K) in Performance
5.1
7.5
-3.1
-4.6
-5.7
-3.2
-5.9
Difference (U-K) in Contributions to Performance when VR* is 1
Dam
6.8
1.9
0.6
1.7
-3.1
-1.7
Sire
-0.6
1.3
-0.3
-0.1
-0.7
-0.3
Mendelian Sampling
0.7
0.3
0.3
-0.0
-0.0
0.1
Age-Stage-Time
-1.8
0.0
-1.3
-0.3
-0.0
-0.8
Contemporary Group
-1.6
1.8
-3.3
-5.7
-1.5
-0.7
Residual
1.6
2.2
0.9
-0.0
-0.4
0.2
-2.3
-1.0
0.0
-0.4
-2.2
0.0
Change to Difference (U-K) in Contributions to Performance when Using Pseudo Dams
Dam
-3.7
-2.9
-1.1
-1.2
0.4
-0.2
0.1
Sire
0.3
0.0
0.0
0.0
0.0
0.0
0.0
Mendelian Sampling
0.0
0.0
0.0
0.1
-0.0
-0.0
-0.0
Age-Stage-Time
0.1
0.2
0.1
0.1
-0.0
0.0
0.0
Contemporary Group
1.6
1.6
0.2
0.6
-0.1
0.1
-0.0
Residual
1.7
1.2
0.8
0.4
-0.3
0.1
-0.0
*
Ratio of residual variance over genetic-groups variance
Impacts on sire proofs (not shown) were negligible for all breeds and for every alternative model
considered (proof correlations > .999 and almost all bull proof changes less than 0.3 points).
Distributions of changes to cow EBV are shown in Table 6. The largest individual changes were
for Milking Shorthorn and Canadienne cows. Brown Swiss had a slightly larger proportion of
cows with changes, but fewer extreme changes compared with the other two breeds. The largest
individual changes were for classified cows with unknown dams, and were consistent with the
changes in unknown dam effects summarized in Tables 2 and 4. Approximate reliabilities, and
EDCs for MACE evaluations, are completely unaffected by the introduction of pseudo dams,
because assumptions for unknown dams in reliability and EDC approximations are unchanged.
Patterns of change in the distributions of EBVs for unknown dams are shown in Figures 5
through 11 for each of the 7 evaluated breeds. The dashed black lines are bounds of the 95%
confidence interval for the estimated effects of known dams. Similarly, the red and blue solid
lines are the respective bounds for genetic group solutions from the current model and pseudo
dam solutions from the proposed model. For breeds other than Holstein, all unknown dams
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 6 of 16
within a given year had the same EBV (genetic group solution) under the current genetic
evaluation system, except for birth years that included cows classified under both the previous
and current classification systems. This is because the 2-trait genetic evaluation model allows
for slightly different genetic group effects on classification records from the previous versus
current classification systems (Sullivan et al, 2006). For Holstein cows there was a range of
unknown dam genetic group solutions in all years, which was due to the inclusion of regional
genetic groups in the current official model for Holsteins only.
Table 6. Changes to cow EBV for overall conformation (% of cows) when replacing genetic
groups with pseudo dams, for unknown dams of classified cows.
Breed
EBV change
ms
cn
gu
bs
je
ay
ho
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
0.1
0.7
1.3
1.2
6.2
83.5
6.7
0.3
0.0
0.2
0.4
0.4
2.6
84.5
11.5
0.3
0.2
0.0
0.1
1.1
98.2
0.6
0.0
0.1
1.1
4.3
79.5
14.6
0.4
0.0
0.4
97.6
2.0
0.0
0.1
0.9
97.6
1.5
0.0
0.0
1.4
95.9
2.8
0.0
Trends and averages of unknown dam genetic group effects from the current official system were
generally within the range of proposed pseudo dam effects for all breeds and years. The only
notable exceptions were recent Milking Shorthorn and Canadienne cows, but very few of those
cows had unknown dams (24 and 11 respectively, classified between 2000 and 2003). The
recent genetic group solutions for Milking Shorthorn were higher than the upper limit (97.5
percentile) for known dams, while the pseudo dam solutions were in the upper range but rarely
exceeded the upper limit of known dams. It seems much more reasonable that some unknown
dams are very superior, while others are much closer to breed average, as was the case for
pseudo dams. The reduction in unknown dam effects, with the addition of pseudo dams to the
evaluation, had a significant impact on the top LPI list for Milking Shorthorn cows, dropping the
number of cows in the top 20 that had an unknown dam from 10 to 6. The typical change for top
Milking Shorthorn cows with unknown dams was a drop of about 200 LPI points. The top list of
Canadienne cows was virtually unaffected by the addition of pseudo dams, because the current
top cows in that breed all had a known Canadienne dam. Most of the top Brown Swiss cows had
a known dam, but the few with an unknown dam had LPI changes in the range of -104 to +72
points (most dropped by about 100 LPI points). Changes in LPI for cows with known dam were
mostly zero, and the non-zero changes were usually around 30 points.
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 7 of 16
Under the proposed implementation strategy, pseudo dams would completely replace regional
genetic groups for the Holstein evaluation, potentially reducing but not eliminating average
regional differences in unknown dam effects. At the same time the range of individual
adjustments for unknown Holstein dam effects would increase, both within and among regions
(Figure 11). The introduction of pseudo dams still allows for differences in unknown dam
effects among regions, but also for differences among herds within a region, and even among
cows within each herd.
This research targeted the effects of unknown dams of cows on conformation evaluations, and
could be extended both to other traits and to other groups of unknown parents (i.e. selection
paths) (e.g. unknown sires of cows and unknown parents of local versus foreign bulls). It is
difficult to extrapolate the present results to other traits, because data structures and methods to
account for unknown parent effects are not completely consistent across the different evaluation
systems. For conformation traits, less impact is expected for the other unknown-parent selection
paths than was found for unknown dams of cows, but allowing for variation of unknown parent
effects may have similar beneficial effects for all selection paths.
Recommendations
The recommendation is to generate unique pseudo cows for unknown dams of classified cows in
all breeds; immediately for Milking Shorthorn and Canadienne and after submission to the next
Interbull test run for the other breeds. This change has no effect on bull proofs or EDCs, should
have negligible impact on Interbull evaluations, and can therefore be considered a minor change
for which Interbull trend validation tests are not required. Additional research is suggested to
consider the use of pseudo parents for all selection paths currently used to define genetic groups,
for conformation and for other traits.
Miglior, F., Kistemaker, G. and Sullivan, P. 2005. Changes in genetic group strategy for
Canadian test day model. Dairy Cattle Breeding and Genetics Committee Meeting, March.
Miglior, F., Van Doormaal, B. and Kistemaker, G. 2008. Phenotypic analysis of inbreeding
depression for traits measured in Canadian dairy cattle breeds. Dairy Cattle Breeding and
Genetics Committee Meeting, April 14.
Sullivan, P.G. 1995. Alternatives for genetic evaluation with uncertain parentage. Can. J. Anim.
Sci. 75:31-36.
Sullivan, P.G and Miglior, F. 2006. Re-definition of genetic groups for conformation Traits.
Dairy Cattle Breeding and Genetics Committee Meeting, March.
Sullivan P.G. and Schaeffer L.R. 1994. Fixed versus random genetic groups, in: Proc. 6th
World Congr. Genet. Appl. to Lives. Prod. Guelph, Canada, pp. 483-486.
Sullivan, P., Kistemaker, G. and Van Doormaal, B. 2006. Impacts of Changes to the Genetic
Evaluation System for Conformation Traits. Dairy Cattle Breeding and Genetics
Committee Meeting, March.
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 8 of 16
Conformation Proofs for Genetic Groups
Milking Shorthorn 0804 (VR=1)
20
15
CAN-CC
10
CAN-BC
CAN-CB
5
CAN-BB
0
-5
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Conformation Proofs for Genetic Groups
Milking Shorthorn 0804 (VR=2)
20
15
10
CAN-CC
CAN-BC
CAN-CB
5
CAN-BB
0
-5
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Figure 1. Effect of variance ratio on genetic group solutions for Milking Shorthorn.
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 9 of 16
Conformation Proofs for Genetic Groups
Brown Swiss 0804 (VR=1)
15
10
5
CAN-CC
CAN-BC
0
CAN-CB
CAN-BB
-5
-10
-15
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Conformation Proofs for Genetic Groups
Brown Swiss 0804 (VR=2)
15
10
5
0
-5
CAN-CC
CAN-BC
CAN-CB
CAN-BB
-10
-15
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Figure 2. Effect of variance ratio on genetic group solutions for Brown Swiss.
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 10 of 16
Conformation Proofs for Genetic Groups
Jersey 0804 (VR=1)
1
-1
-3
-5
CAN-CC
CAN-BC
-7
CAN-CB
CAN-BB
-9
-11
-13
-15
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Conformation Proofs for Genetic Groups
Jersey 0804_gg2 (VR=2)
1
-1
-3
-5
-7
-9
CAN-CC
CAN-BC
CAN-CB
CAN-BB
-11
-13
-15
1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Figure 3. Effect of variance ratio on genetic group solutions for Jersey.
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 11 of 16
Effect of Genetic Group Variance on the
Conformation Proof Assumed for Unknown Dams of
Cows born in 2005
20.0
Proof Scale
15.0
ms
10.0
cn
gu
5.0
bs
0.0
je
ay
-5.0
ho
-10.0
0
2
4
6
8
10
Genetic Groups Variance Ratio
Figure 4. Effect of variance ratio on genetic group solutions for unknown dams of recent cows in
all breeds.
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 12 of 16
Range (95% CI) of Milking Shorthorn Conformation EBV (0804)
for Known Dams versus Dam Genetic Group and Pseudo Dams
20
15
Proof Scale
10
5
Known Dam
0
Genetic Group
-5
Pseudo Dam
-10
-15
-20
1990
1992
1994
1996
1998
2000
2002
Birth Year
Figure 5. Range in Conformation EBV for dams of classified Milking Shorthorn cows.
Range (95% CI) of Canadienne Conformation EBV (0804)
for Known Dams versus Dam Genetic Group and Pseudo Dams
20
15
Proof Scale
10
5
Known Dam
0
Genetic Group
Pseudo Dam
-5
-10
-15
-20
1990
1992
1994
1996
1998
2000
2002
2004
Birth Year
Figure 6. Range in Conformation EBV for dams of classified Canadienne cows.
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 13 of 16
Range (95% CI) of Guernsey Conformation EBV (0804)
for Known Dams versus Dam Genetic Group and Pseudo Dams
20
15
Proof Scale
10
5
Known Dam
0
Genetic Group
Pseudo Dam
-5
-10
-15
-20
1990
1992
1994
1996
1998
2000
2002
2004
Birth Year
Figure 7. Range in Conformation EBV for dams of classified Guernsey cows.
Range (95% CI) of Brown Swiss Conformation EBV (0804)
for Known Dams versus Dam Genetic Group and Pseudo Dams
20
15
Proof Scale
10
5
Known Dam
0
Genetic Group
Pseudo Dam
-5
-10
-15
-20
1990
1992
1994
1996
1998
2000
2002
2004
Birth Year
Figure 8. Range in Conformation EBV for dams of classified Brown Swiss cows.
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 14 of 16
Range (95% CI) of Jersey Conformation EBV (0804)
for Known Dams versus Dam Genetic Group and Pseudo Dams
20
15
Proof Scale
10
5
Known Dam
0
Genetic Group
Pseudo Dam
-5
-10
-15
-20
1990
1992
1994
1996
1998
2000
2002
2004
Birth Year
Figure 9. Range in Conformation EBV for dams of classified Jersey cows.
Range (95% CI) of Ayrshire Conformation EBV (0804)
for Known Dams versus Dam Genetic Group and Pseudo Dams
20
15
Proof Scale
10
5
Known Dam
0
Genetic Group
Pseudo Dam
-5
-10
-15
-20
1990
1992
1994
1996
1998
2000
2002
2004
Birth Year
Figure 10. Range in Conformation EBV for dams of classified Ayrshire cows.
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 15 of 16
Range (95% CI) of Holstein Conformation EBV (0804)
for Known Dams versus Dam Genetic Group and Pseudo Dams
20
15
Proof Scale
10
5
Known Dam
0
Genetic Group
Pseudo Dam
-5
-10
-15
-20
1990
1992
1994
1996
1998
2000
2002
2004
Birth Year
Figure 11. Range in Conformation EBV for dams of classified Holstein cows.
Dairy Cattle Breeding and Genetics Committee Meeting, April 14, 2008 (updated April 26).
Page 16 of 16