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Genetic Analysis of Female Fertility traits and their relationship to

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					 Genetic Analysis of Reproduction traits and their relationship with conformation
                            Traits in Holstein Cows

      Sajad Toghyani, Abdol Ahad Shadparvar, Mohammad Moradi-Shahrbabak,
                        Mohammad Dadpasand Taromsari

      ABSTRACT

        The main objectives of the present study were to examining genetic aspects of
fertility traits and their relationships with type traits in Iranian Holstein cows. Fertility
traits included calving interval (CI), days from calving to first insemination (DFS), days
open (DO), and gestation length (GL). Type traits were stature (ST), body depth (BD),
fore-udder attachment (FU), rear-udder height (RU), udder depth (UD), rump width
(RW), and angularity (AG). Fertility traits were analyzed using data files containing up
to 26147 records in parities 1 to 8 which were collected from 1980 to 2004. Data on
type traits included 3274 records on first lactation cows. An animal model restricted
maximum likelihood method was used to estimate (co)variance components.
Heritability estimates for fertility traits were low and ranged from 0.040.01 (DFS) to
0.070.013 (CI) and 0.070.002 (GL). Repeatabilities for these traits were estimated to
vary from 0.06 (DFS) to 0.12 (GL). Genetic correlations between fertility traits were
close to zero. Estimated heritabilities for type traits, except for RU, were moderate and
ranged from 0.200.047 (FU) to 0.380.04 (RW). Genetic correlations between fertility
and type traits ranged from zero (DO with ST and FU) to 0.79 (DFS with BD). This
study showed that despite the low heritabilities, there was exploitable genetic variance
in fertility traits. It showed that body volume traits including BD and AG as well as RW
and UD had unfavorable genetic correlations with CI, DFS, and GL. However, these
relationships can be used for indirect selection of dairy cows for fertility traits.

      (Key words: dairy cow, fertility traits, type traits, genetic parameters)




                                             1
      INTRODUCTION

       One of the most important factors affecting economics of dairy herds is the
reproductive performance. Defective reproductive performance, exhibited in lengthened
calving intervals, increased involuntary culling, can result in less milk and fewer calves
per cow per year, less voluntary culling and consequently increased replacement cost,
and, finally, reduced return (Bagnato and Oltenacu, 1994). Adequate fertility
performance can result in more income from milk sale (Dekkers, 1991) and reduced
input costs (Groen et al., 1997). In most countries, milk production is the main objective
in dairy cattle genetic improvement programs. There are unfavorable genetic
relationships between reproductive traits and milk production (Royal et al., 2002) and
therefore, the selection for higher milk yield has resulted in a decline in fertility traits
(Pryce et al., 2004). A study on Canadian dairy cows showed that 25% of cow disposal
occurred because of deficient reproductive performance (Westell et al., 1992). Such
results imply that selection for fertility traits is necessary to genetically maintain or
improve reproductive performance level.

       The efficiency of selection for a trait depends on a number of validated available
records and heritability. In many countries, only a few numbers of herds had an accurate
system for collecting insemination data (Kadarmideen and Coffey, 2001). On the other
hand, the heritability of fertility traits is quite low (Wall et al., 2003). In the case of
traits such as fertility traits, indirect selection can play an important role. This can take
place by using information from traits with higher heritability, more readily available
records, and considerable genetic correlations with fertility traits. Linear type traits may
meet these criteria.

       In some studies phenotypic and genetic relationships between type traits and
longevity were investigated (e.g. Rogers et al., 1991; Larroque and Ducroq, 1999).
Perez-Cabal and Alenda (2002) estimated the genetic relationships between
conformation traits and profit in Spanish Holstein cows and reported that they ranged
from 0.12 for leg side view to 0.37 for suspensory ligament while stature, body depth,
and udder depth had close to zero correlations with profit. They suggested that other
traits affecting profit out of production and longevity, such as fertility and mastitis,
should be studied in order to include new traits in selection indexes.

      Fertility traits are recorded on the basis of milk recording schemes or insemination
data records. Milk recording based traits include calving interval and open days. Traits
from insemination records are days to first insemination, interval from first to last
insemination, number of insemination per service period, non return rate at 56 day and
90 day, and conception rate (Gonzalez-Recio et al., 2004).

      The relationships between fertility traits and some type traits have been the
subject of some studies. It was shown that some type traits, particularly those correlated
with milk yield are correlated to calving interval (Pryce et al., 2000). Melendez et al.
(2003) reported a negative impact of poor legs and feet on calving interval. Wall et al.
(2005) reported an unfavorable genetic correlation (-0.16) between calving interval and
rump angle, suggesting that animals with high pin bones would have a longer calving
interval. However, they observed no significant relationship between days to first
service and non return rate and rump angle. They also observed unfavorable correlation
between mammary system and udder support and calving interval. In all of these studies


                                             2
the relationship between just the first lactation fertility records and type traits was
examined. In the case of traits such as fertility traits with the possibility of measuring
several records during cow life, use of repeated records may improve the accuracy of
estimated genetic parameters. Hence the objectives of this study were to estimate
genetic parameters for some selected type and fertility traits and their relationships
using repeated records on fertility traits.

                               MATERIALS AND METHODS

      Data

       In this study, the data on Iranian Holstein reproduction and type traits collected by
Animal Breeding Center of Iran during 1980 to 2004, were used. Original data file for
reproduction traits consisted of insemination records that were matched to pedigree,
lactation, and calving performance information to calculate the traits of interest. The
fertility traits selected for this study were calving interval (CI), days open (DO), days
from calving to first insemination (DFS), and gestation length (GL). Conception data
was determined using subsequent calving date that agreed with the latest insemination
data. Gestation length was measured as an interval from the last insemination to
subsequent calving. All parities up to 8th were analyzed.

      The studied type traits were stature (ST), body depth (BD), fore-udder attachment
(FU), rear-udder height (RU), udder depth (UD), rump width (RW), and angularity
(AG). In Iranian classification system, the classifiers scores BD, FU, UD, and AG on a
scale from 1 to 9.

       Records from cows without pedigree information were excluded. Days open was
required to be between 45 and 350d, CI between 300 and 600d, DFS between 30 and
300, and GL between 240 and 290d. Structure of edited data sets and descriptive
statistics for fertility and type traits are presented in tables 1 and 2, respectively.

      Methods

      In this study, the models were developed based on data availability, literature
evidence, genetic evaluation models that are used in other countries, and available
computing facilities. Single-trait model for CI, DO, DFS and GL in a simplified scalar
notation, were as fallows:

      Y= RYS + H(RYS) + AMP + A + PE + E

      DO= RYS + H(RYS) + AMPf + A + PE + E

      Where Y denoted CI, DFS, or GL, RYS was fixed effect of region by year of birth
by season of birth, H(RYS) was the fixed effect of herd within RYS, AMP was the
fixed effect of age at previous calving by month of previous calving by parity, AMPf
was the fixed effect of age at previous calving by month of first insemination by parity,
A was a random animal genetic effect, PE was a random permanent environmental
effect, and E was a random error term.

      The model for analyzing type traits was:


                                             3
      Y= RYS + H(RYS) +  DIM + β AFF +γ AEV + δ BLD + TEC + A + E

     Where Y was the record of particular type trait, RYS was a fixed effect of region
by year and season of evaluation, , β, γ, and δ were the linear regression coefficients of
Y on days in milk (DIM), age at first freshening (AFF), age at evaluation (AEV), and
percent of Holstein heredity (BLD), respectively. The fixed effect of technician was
denoted by TEC, the random effect of animal additive genetic effect and residual effects
were shown by A and E, respectively.

       Variance and covariance components were estimated by restricted maximum
likelihood method using DFREML program (Meyer. 1997). Multiple trait analyses were
performed to obtain estimates for genetic, environmental and phenotypic correlation
between traits.

                               RESULTS AND DISCUSSION

      Estimates of variance components and ratios with respect to the total variance for
various random effects for fertility traits are shown in Table 3. Heritability for CI was
estimated to be 0.070.013, which was similar to estimated heritability obtained by
Muir et al. (2004), but higher than estimates reported by other authors (Ojango and
Pollot, 2001; Wall et al., 2003). This trait can be affected by some managemental
factors such as length of the voluntary waiting period and application of
synchronization products. In some studies ( Marti and Funk, 1994; Dematawewa and
Berger, 1998) days nonpregnant, which is similar to CI considering the relatively small
variation in gestation length, has been analyzed.

       The estimated heritability for DFS was 0.040.01, which was slightly higher than
3.01% obtained by Andersen-Ranberg et al. (2005) for Norwegian dairy cattle, and
considerably lower than 6.1 and 5.8% reported by Weigel and Rekaya (2000) for
Minnesota and California Holstein populations, respectively. Age at calving influences
DFS, as Andersen-Ranberg et al. (2005) found that DFS decreased by increasing age at
first calving from 19.3 to 25.3 mo, and then increased up to 34.7 mo. Furthermore, DFS
is highly affected by the length of the voluntary waiting period, which differs among
herds and among management groups within a herd. Nevertheless, selection for this trait
would favor cows that demonstrate visible estrus early in lactation. Andersen-Ranberg
et al. (2005) reported annual genetic change for DFS in first lactation Norwegian dairy
cattle was 0.11d (unfavorable) in period from 1980 to 1998. This observed trend,
although was unintended correlated response to selection for higher milk yield, reveals
the possibility of making desirable genetic change in DFS by means of applying an
appropriate selection index.

       Calving interval is an economically important trait (Groen et al., 1997). However,
CI requires a record of consecutive calving dates and therefore is only available after a
second calving. Relying just on CI would delay selection decisions on young test bulls.
Early measures on components of CI can be useful in overcoming this problem. Days to
first services are available much earlier and have been shown to be heritable and
therefore could be considered as a useful alternative to CI.

    Estimated heritability for DO was 0.060.008, which was higher than estimates in
some studies (Marti and Funk, 1994; Dematawewa and Berger, 1998). Generally,


                                            4
heritability for DO is estimated to be 0.09 (Dematawewa and Berger, 1998); hence,
our estimate is in good agreement with the previous literature, despite the differences in
data, model and estimation procedures. Marti and Funk (1994) estimated heritability for
DO for group of herds differing in production level, and found that it was larger for the
higher production herds, perhaps because of more consistent management practices
among all cows.

       Estimated heritability for GL was 0.070.002. Gestation length was known as a
trait with relatively small genetic variation (Ojango and Pollot, 2001), however, in the
present study it was shown to be one of the most heritable traits. The reason for this
discrepancy may be the use of information from several parities in this study, while in
other studies only the first lactation record was analyzed. Jamrozik et al. (2005) found
that GL has the highest direct heritability either as heifer trait or cow trait, through
analyzing records on several parities for each cow.

       The effect of permanent environment contributed from 2 to 5% to the total
variance for fertility traits. Jamrozik et al. (2005) estimated the ratio of phenotypic
variance attributable to permanent environmental effects to be less than 2%. Higher
variance ratio for permanent environmental effects in our study is, probably, the result
of considering herd effect as a fixed effect in the model, while it was taken as a random
effect in the model used by Jamrozik et al. (2005). Repeatability estimates for fertility
traits were low and ranged from 0.06 to 0.12. Estimated repeatability for CI was slightly
higher than which was estimated by Ojango and Pollot (2001) (0.09 vs 0.06). The
repeatability for DO was estimated to be 0.10, which was lower than those were
reported by Marti and Funk (1994) (from 0.135 to 0.153, depending on production level
of herd groups) and Dematawewa and Berger (1998) (0.115).

       The low repeatability estimates obtained in this study suggest that fertility traits
are strongly influenced by temporary environmental factors. It would thus be possible to
improve fertility performance through improvement in herd management. The low
repeatability means that a performance record in current parity is not a good indicator of
performance record in the next parities. This fact suggests that in making decision for
culling cows, reproduction performance should take less weight in comparison with
production traits, which are considerably more repeatable.

       Genetic and environmental correlations between fertility traits are shown in Table
4. Generally, the estimated correlations were close to zero. Wall et al. (2003) obtained a
strong genetic and environmental correlations between CI and DFS (0.670.063 and
0.480.002, respectively), and suggested that improving one trait will have a favorable
correlated response on other traits. On the other hand, Muir et al. (2004) reported that
non return rate as a heifer and cow trait were genetically uncorrelated to CI and their
genetic correlations with CI were 0.00 and -0.09, respectively. Jamrozik et al. (2005)
examined relationships between several fertility traits of heifers and cows and found
that in some cases the correlations between traits had relatively large posterior standard
deviations and therefore concluded that they were not always very helpful for deriving
definite conclusions concerning relationships between traits. These evidences, despite
results from Wall et al. (2003), support our low estimated correlations between fertility
traits. These traits can be considered, practically, as independent traits and should be
included together in a selection index, in order to improve all of them.



                                            5
       Estimated variance components and heritabilities for type traits are shown in
Table 5. Estimated heritability for type traits, except for RU, were moderate and varied
from 0.200.047 (FU) to 0.380.04 (RW). Estimated genetic correlations between type
traits and fertility traits are shown in Table 6. Body volume traits including BD and AG
were unfavorably correlated with fertility traits, which means that bigger and thinner
cows will have longer DFS, GL and hence, longer CI. Haile-Mariam et al. (2004) have
shown BD, ST and AG are genetically correlated with CI. Cows that are larger have
higher feeding demands (Perez-Cabal and Alenda, 2002) and more milk production
(Short and Lawlor, 1992), and in most cases tend to consume their own body reserves
(Collard et al., 2000). Because of a negative energy balance that these kinds of animals
exhibit, they have more problems to demonstrate early visible estrus in lactation,
conceive and maintain the calf (Banos et al., 2004). On the other hand, cows with larger
body are more likely to grow bigger calf. Jamrozik et al. (2005) showed that the size of
bigger calf was associated with longer GL, suggesting that the relationship between calf
size and GL is mediating the unfavorable relationship between body volume traits and
GL.

     Udder depth was unfavorably correlated to CI. Wall et al. (2005) found that CI
was genetically correlated to udder support, and mammary system, suggesting that
animals with stronger udder support and better mammary system would have a longer
CI. Generally, a good mammary system is associated with higher milk yields, and
consequently with longer CI.

       Rump width was positively correlated to CI and GL, hence, wider rump
associated with longer CI. This is contrary to the results from other studies (e.g., Cue et
al., 1990; Van Drop et al., 1998) which have shown cows with higher pin bones and
narrower rumps are more likely to have difficult calving, lower non return rate, more
DFS, and therefore longer CI. On the other hand, GL is positively correlated to calf size
(Jamrozik et al., 2005), and hence, one explanation for our finding can be in the
population we studied, cows with wider rump are more likely to have heavier calves and
the relationship between calf size and GL is mediating the unfavorable relationship
between RW and CI.

       Despite results from Wall et al. (2005), our study supports the opinions of
producers and veterinarians about existence a meaningful relationship between fertility
and type traits. In most studies, as well as Wall et al. (2005), information on first
lactation cows were analyzed, however, in the present study records on several parities
were used. Dairy cattle producers' belief may be based on what they will observe in
later lactations, if fertility in mature cows is correlated to type traits.

                                        CONCLUSIONS

        This study demonstrated the presence of genetic variation for fertility traits in the
Holstein cows in Iran. Calving interval and GL had the highest heritability among
fertility traits. Genetic correlations between fertility traits were close to zero, indicating
that they should be considered as independent traits in constructing a selection index.
Fertility traits, particularly CI and DFS were genetically correlated to body volume trait
including BD and AG. Estimated genetic correlations between fertility and type traits
suggest that records on BD, AG, RW, and UD can be used to indirect selection for CI
and DFS.


                                              6
                               ACKNOWLEDGMENTS

The authors are grateful to the Animal Breeding Center of Iran for providing data for
this study.

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Table 1. Summary of data structure, meanSD and coefficient of variance for fertility traits1.

                                           CI               DFS        DO            GL

Number of records                          11674            7949       15895         26147

Number of animals with record              6267             4921       5997          13514

Number of total animals in pedigree        9238             8847       10250         20503

Number of sires                            385              395        407           580

Number of RYS2 subclasses                  1352             1071       NS3           1777

Number of parities                         8                8          8             8

MeanSD                                    39564           9757      12466        2796

Coefficient of variance (%)                16.13            58.18      53.31         2.16
1
  CI= calving interval, DFS= days from calving to first insemination, DO= days open, GL=
gestation length.
2
    RYS=fixed effect of region by year and season of evaluation.
3
    NS=the effect of RYS was statistically non significant (P>0.05).




                                                   9
Table 2. Summary of data structure, meanSD and coefficient of variance for type traits1.

                          ST           BD        AG        FU          RU         UD        RW

Number of records         3274         3274      3245      3274        3274       3274      3274
Number of animals
                          3274         3274      3245      3274        3274       3274      3274
with record
Number of total
                          6210         6210      6162      6210        6210       6210      6210
animals in pedigree
Number of sires           284          284       282       284         284        284       284
Number of RYS2
                          69           69        NS3       69          NS3        69        69
subclasses
MeanSD                   139.13.5    5.21.2   6.61.1   6.81.4     25.95.6   5.61.1   19.31.6
Coefficient of
                      2.55      22.04    19.87    20.25     21.44     20.58    8.28
variance (%)
1
  ST= stature, BD= body depth, AG= angularity, FU= fore udder attachment, RU= rear udder
height, UD= udder depth, RW= rump width.
2
    RYS=fixed effect of region by year and season of evaluation.
3
    NS=the effect of RYS was statistically non significant (P>0.05).




                                                 10
Table 3. Estimates of additive genetic variance (  2 ), permanent environmental
                                                                  A

variance (  PE ), temporal environmental variance (  TE ), phenotypic variance (  2 ),
               2                                                2
                                                                                     P

ratios with respect to phenotypic variance for permanent environmental factors ( c 2 ),
heritability ( h 2  SE ) and repeatability ( r) for fertility traits1.
        2
         A          2
                     PE         TE
                                 2
                                            2
                                             P           c2       h 2  SE           r

CI      256.71      109.28     3543.70      3909.69      0.03    0.070.013          0.09

DFS     116.86      44.19      2760.33      2921.38      0.02    0.040.01           0.06

DO      240.52      176.87     3827.70      4245.09      0.04    0.060.008          0.10

GL      2.59        1.60       30.64        34.83        0.05    0.070.002          0.12
1
 CI= calving interval, DFS= days from calving to first insemination, DO= days open,
GL= gestation length.



Table 4. Estimates of genetic (above diagonal) and environmental (below diagonal)
correlation between fertility traits1.
               CI                 DFS                 DO                     GL

CI                                0.0005              0.111                  0.02

DFS            0.0001                                 0.0004                 0.008

DO             0.0005             0.0002                                     0.008

GL             0.0022             0.0012              0.003
1
 CI= calving interval, DFS= days from calving to first insemination, DO= days open,
GL= gestation length.




                                           11
Table 5. Estimates of variance components and heritability for type traits1.
            Additive genetic                 Residual              Heritability

            variance                         variance              (SE)
ST          2.57                             8.00                  0.240.116

BD          0.37                             0.73                  0.340.033

AG          0.22                             0.77                  0.230.031

FU          0.31                             1.28                  0.200.047

RU          2.55                             25.45                 0.090.025

UD          0.27                             0.83                  0.250.033

RW          0.88                             1.45                  0.380.040
1
 ST= stature, BD= body depth, AG= angularity, FU= fore udder attachment, RU= rear
udder height, UD= udder depth, RW= rump width.




Table 6. Estimates of genetic correlation between fertility traits and type traits1.

           ST          BD          AG         FU           RU          UD          RW

CI         0.06        0.35        0.25       -0.02        -0.01       0.24        0.23

DFS        0.01        0.79        0.13       -0.03        -0.01       0.04        0.09

DO         0.00        0.01        0.01       0.00         0.03        0.01        -0.01

GL         0.49        0.34        0.10       0.34         0.13        0.23        0.42
1
  CI= calving interval, DFS= days from calving to first insemination, DO= days open,
GL= gestation length, ST= stature, BD= body depth, AG= angularity, FU= fore udder
attachment, RU= rear udder height, UD= udder depth, RW= rump width.




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posted:9/8/2011
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