Relationship between seminal traits and results of
artificial insemination in Holstein bulls
Amenabar1*, M.E., Karoui2, S., Ugarte1, E., Díaz2, C., Carabaño2, M.J.
1Centro
de Arkaute-ko Zentroa. Campus Agroalimentario de Arkaute. Neiker, E-01080
Vitoria-Gasteiz, Spain
2Departamento de Mejora Genética Animal, INIA, Carretera de La Coruña km 7.5, 28040
Madrid, Spain
Abstract. Data from results of artificial insemination (AI) collected in field
conditions, parameters of semen production recorded in an AI centre (on 502
Holstein bulls) and semen DNA fragmentation (SDF) determined in laboratory
conditions (182 bulls) were joined to study the influence of semen traits on field
fertility. Stepwise regression analyses and principal component analyses were
carried out to investigate this relationship using phenotypic deviations (PD) and
estimated breeding values (EBV) for bull fertility and semen traits. Post
thawing motility and SDF after 6 hours of incubation were the variables
showing larger influence on bull fertility. Overall, these variables explained
32% of the observed variability in bull fertility measured as PD. For EBVs this
proportion was 22% and prefreezing motility was selected instead of the post
thawing motility. The principal components analyses revealed that the main
component, including bull fertility, motility traits and SDF measures (excluding
traits measuring sperm production), explained a 40% of the overall variability.
In order to achieve a good appraisal of the bull fertility, traits measuring aspects
related with sperm vitality such as motility and traits related with survival and
fertilizing potential, such as SDF, ought to be measured.
Keywords: Fertility, semen traits, DNA fragmentation, dairy cattle.
1 Introduction
Fertility has suffered a notorious decline in dairy cattle populations. Most of the
efforts to monitor genetic change for fertility have been directed to evaluate female
fertility. However, fertility can be considered a trait of the female, male or the
combination of both male and female in each mating [1,2,3]. The estimated ‘male
heritability’ is usually very small and how to obtain an accurate measure of service
sire fertility is still unclear [4, 5]. The use of semen traits as male fertility indicators
might be considered as a way to improve the accuracy of male fertility evaluation.
Some studies have shown the positive impact of semen traits routinely collected in AI
*
Corresponding author: mamenabar@neiker.net
centres. However, some authors consider these traits as non-functional because the
association with the fertilizing ability of the spermatozoa has not been proven [6,7].
Functional semen traits such as DNA fragmentation and membrane integrity are not
routinely evaluated in AI centres due to the technical difficulties. The existence of
new techniques that allow an easy determination of semen DNA fragmentation (SDF)
make this trait an additional candidate to reinforce male fertility evaluation in large
scale programmes. Our study aims at determining the relationship of semen traits
routinely evaluated in an AI centre and one functional trait, SDF, on number of
inseminations (INS) and conception rate (CR) measured in field conditions.
2 Material and Methods
Both fertility and seminal traits were appraised by two different measures,
estimated breeding values (EBV) and phenotypic deviations (PD). Fertility data were
extracted from the November 2008 routine genetic evaluation for female fertility
implemented in the north-western population of Spanish Holstein-Friesian cattle and
included insemination records from 1995 to 2008 [8]. Phenotypic deviations for bull
fertility were provided to the authors and resulted from deviating the number of
inseminations per conception (INS) from estimated environmental effects and for the
genetic value of the female and averaged for each service bull. Alternatively,
estimated breeding values (EBV) for male fertility were obtained from the same data
set for CR using two alternative models, one treating the male and female components
as additive (CR+) and another one treating these components as multiplicative (CRx)
factors [3].
Semen traits were provided by the AI centre ABEREKIN, S.A. and included
records for volume per ejaculate (VOL), concentration (CON), mass (MM) and
individual motility (MI) pre-freezing and post-thawing motility (MP) from 502
Holstein bulls born between 1980 y 2006. Breeding value estimates and phenotypic
deviations from these traits were obtained from individual animal models described in
[9]. SDF was also evaluated for 182 bulls from semen samples obtained from the
same AI centre. SDF was tested by the Sperm-Halomax® kit (Halotech DNA, Spain).
This methodology is based on the sperm chromatin dispersion test [10].
Cryopreserved semen samples were thawed by immersion in a 37 C water bath for
30 s. Straws were then incubated in a 37ºC water bath. Determination of sperm SDF
was conduced after 0, 6, 24 and 48, hours of incubation. However, only SDF at 0 (FB)
and 6 hours (F6) of incubation were included in this study because bacterial
contamination appeared in a large proportion of samples at 24 hours and later
determinations.
Two sets of analyses were performed on EBV and on PD. Firstly, stepwise
regression analyses were carried out including the fertility measure as dependent
variable and the semen traits as independent variables. Linear and quadratic
regression coefficients were used for the semen traits. Secondly, principal
components analyses based on the correlation matrix among traits were carried out.
After merging information from fertility, routine semen traits and SDF, analyses
were performed on 177 records for PD analyses and 182 for EBV analyses. Table 1
shows summary statistics for the semen traits.
Table 1. Number of observations (N), average (Avg.), standard deviation (S.D.),
minimum (Min) and maximum (Max) values for semen traits
Trait
N
Avg.
S.D.
Min
Max
VOL
177
4.86
1.32
1.8
8.83
CON
177
1287.67
274.26
632
1947
NESP
177
6066
2023
1244
13,240
MM
177
4.04
0.41
2.62
4.85
MI
177
84.05
5.41
56.40
89.70
MP
177
53.53
6.32
19.90
67.50
FB
182
3.41
2.41
1.00
20.40
F6
182
3.62
2.43
1.00
19.50
VOL: volume per ejaculate (ml), CON: concentration (No sperm x10 6/ml), NESP: number of
sperm per ejaculate, MM: mass (1-5), MI: individual motility (%), MP: post-thawing motility
(%), FB/F6: semen DNA fragmentation after 0/6 h incubation (% fragmented spermatozoa)
3 Results and Conclusions
In the stepwise regression analyses the motility traits and F6 were selected as
the factors that best explain bull fertility. Results obtained with the PD values are
shown in Table 2.
Table 2. Estimated regression coefficients, standard error (S.E.) and results of the
hypothesis test of null coefficients (t statistic and p values) for regression of PD for
fertility on routine semen traits and percentage of DNA fragmentation
Variable
Intercept
F6
MP
DF
1
1
1
Parameter estimate
0.0012
-0.0167
0.0037
S. E.
0.0065
0.0028
0.0011
t-value
0.18
-6.04
3.43
Pr > |t|
0.8598
<.0001
0.0008
When EBV values were used the selected variables were F6, MM and MI, for both
the additive and multiplicative models. Adjusted R2 ranged from 0.32 for the PD
model to 0.22 for both EBV models. Although the regression coefficient for the
motilities and SDF traits were highly significant, these traits explained only partially
the bull capacity to yield a successful insemination. The fact that field fertility
measures are inaccurate might also explain the low values for the R 2 values. [11] also
found that MI and MP were the selected variables among the semen traits routinely
recorded in AI centres, explaining a 10% of the total observed variability in non
return rates. Kasimanickam et al. [12] and Waterhouse et al. [13] find a significant
contribution of SDF to bull fertility in experimental and field conditions, respectively.
The correlation matrix among fertility measures and semen traits provided by the
Principal Components analyses is shown in Table 3.
Table 3. Correlation matrix among fertility measures and seminal traits provided by
the Principal Components analysis. For seminal traits, correlations for phenotype
deviations (PD) and estimated breeding values (EBV) are shown above and below the
diagonal, respectively
PDINS
EBVCRx
VOL
CON
NESP
MM
MI
MP
PDINS
1.00
0.86
0.05
0.15
0.20
0.32
0.37
0.44
-0.47 -0.49
FB
EBVCRx
0.86
1.00
0.04
0.13
0.17
0.27
0.31
0.37
-0.44 -0.47
VOL
0.04
-0.02
1.00
-0.18
0.65
0.19
0.15
0.13
CON
0.16
0.10
-0.10
1.00
0.59
0.32
0.55
0.18
-0.04 -0.06
NESP
0.16
0.09
0.70
0.52
1.00
0.59
0.68
0.35
-0.09 -0.10
MM
0.17
0.03
0.24
0.42
0.47
1.00
0.89
0.75
-0.30 -0.31
MI
0.36
0.26
0.21
0.62
0.61
0.78
1.00
0.72
-0.34 -0.34
MP
0.38
0.28
0.14
0.11
0.22
0.59
0.68
1.00
-0.39 -0.40
FB
-0.45
-0.37
0.05
-0.05
-0.02
-0.22
-0.33
-0.38
1.00
0.96
F6
-0.46
-0.38
0.04
-0.06
-0.04
-0.23
-0.34
-0.39
0.96
1.00
0.03
F6
0.03
Correlation estimates for PDs and EBVs were similar for all the semen traits. MP
and SDF measures showed the largest correlations with fertility, followed by prefreezing motility. Traits related to production of spermatozoa showed the lowest
correlations probably due to the fact that these can be considered as compensable
traits by modifying the dilution ratio. The principal components analyses based on the
correlation matrix in Table 3 indicated that 4 principal components are needed to
explain 90% or total variability in fertility and semen trait. The first component
mainly relied on fertility, motilities and SDF and explained around 40% of the total
variability. The other principal components lacked an easy biological interpretation.
Similar results were observed for PD and EBV measures.
In conclusion, motility traits that are routinely collected in AI centres are
associated with field fertility. However, in order to achieve a better appraisal of the
bull fertility, traits measuring other aspects than vitality, such as survival and
fertilizing potential, ought to be measured. SDF traits, which have been related with
chromatin condensation and stability, may be critical factors for a successful
insemination.
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