Selection for AI Stud Traits
M. Todd See North Carolina State University Introduction
The adoption of artificial insemination (AI) has had a significant
impact on the structure of the swine genetics industry. It has been reported that AI now accounts
for more than 50 percent of the total swine matings in the United States. This effectively reduces the number of boars
required in the U.S. swine breeding herd and at the same time increases the
importance of high fertility and genetic merit for each boar. While genetic evaluation procedures (BLUP)
to select the top boars for AI are commonplace, attention still needs to be
addressed on levels of inbreeding in boar populations and the cost of boar
production relative to desired selection intensity. In addition, the genetic
control of semen traits has not been extensively studied. Currently boars
selected as "AI quality" are indexed and selected strictly on
performance and carcass characteristics.
The following is a review of the genetic control of male reproductive
traits with discussion towards the opportunity of selecting boars that would be
optimal for semen production and quality, as well as, carcass and production
traits. AI Stud Traits
A complete semen evaluation includes determining the volume of the ejaculate,
the total number of sperms cells and estimating the viability of sperm
cells. Semen should be evaluated
promptly after it is collected. A boar
will usually ejaculate 150 to 250 ml of semen, but the volume can range from 50
to 500 ml (Almond et al., 1998). Sperm
count or concentration is usually reported in millions of sperm cells per ml of
ejaculate. Number of services per
ejaculate can also be evaluated if the number of sperm cells per service is
standardized. Motility is a measure of
the viability of the semen, or the percentage of sperm showing progressive
forward motion. Motility is a
subjective test that requires training and practice by the AI technician. Motility can be reported as both undiluted
semen motility or extended semen motility.
Extended semen motility may also be evaluated over days of storage. Assessing sperm cell morphology is another
way to assess semen viability.
Morphology assessment is a very time consuming process that requires
training, practice and patience. Morphology
assessments in practice will often be denoted as acceptable or
unacceptable. Because of the subjective
nature of many motility and morphology evaluations a good collective measure of
overall semen quality may be the percentage of acceptable collections produced
by a boar. A recent study (Flowers, 1998) examined the
relationships between microscopic estimates of semen quality and fertility for
boar semen. This data represents weekly
ejaculates from 12 mature boars for a 26-week period. Evaluations for motility, morphology and acrosome integrity were
conducted on each ejaculate. Each
ejaculate was extended and used to inseminate at least 5 sows.
The most important
conclusion from this study is that, motility, at best, can be used to establish
the lower limit of acceptability for semen used for insemination. Flowers concluded that motility is of little
analytical value for ejaculates above the 60% level. This is evident in that farrowing rates and number of pigs born
alive were not different among ejaculates estimated to contain more than 60%
motile spermatozoa. As motility
increased beyond 60% reproductive performance remained constant. In contrast, for ejaculates with less than
60% motility, there was a highly correlated, positive linear relationship
between the percentage of motile spermatozoa and reproductive performance. Unfortunately, the same general relationship between estimates of
motility and fertility was present when morphology and acrosome integrity were
considered individually or collectively with motility. It would appear that none of the commonly
used visual estimates of semen quality has strong correlations with farrowing
rates and number of pigs born alive.
However, Flowers concludes that in practice morphology probably is a
more appropriate characteristic to routinely monitor rather than motility. Genetics of Male ReproductionEstimates of heritability for several male reproductive traits are
summarized in Table 1. Testes and
accessory gland measurements have moderate to high heritabilities while
testosterone level and libido traits are slightly less heritable. Testes measurements have a desirable genetic
correlation with total sperm production or percent spermatogenesis (Wilson et
al., 1977; Toelle et al., 1984; Young et al., 1986). Selection lines developed
for increased weight of testes have been show to result in decreased age at
which boars begin producing sperm cells (Rathje et al., 1995), increased daily
sperm production (Huang and Johnson, 1996; Rathje et al., 1995) and increased
concentration of sperm cells (Huang and Johnson, 1996). Boars from lines selected for increased
weight of testes did not differ from controls for percentage of abnormal sperm
cells (Huang and Johnson, 1996). The service sire has also been reported to
have a rather limited effect on litter size, embryonic survival and litter
weight at 21 days. Table 1.
Heritability estimates from male reproductive traits.1
Heritability estimates for semen traits are summarized in Table 2. Sperm quantity and number of services
produced per ejaculate have moderate to high heritabilities and would therefore
be expected to respond to selection.
Sperm cell motility, morphology and concentration (count) are moderate
in heritability. However, the mean
heritability estimates for sperm cell quality in Table 2 may be biased downward
due to the subjective nature of the semen evaluations conducted in some of the
studies (Oh et al., 2000). Genetic
control of sperm cell morphology is supported by Wekerle (1982) who reported
that when sires had an increased incidence of morphological abnormalities (>
30%) their sons also had an incidence of abnormalities greater than 30%. While
of increasingly less importance, halothane positive boars have been reported to
have significantly lower sperm cells per ejaculate (Hillbrand and Glodek, 1984
and Schlenker et al., 1984), lower sperm volume (Schlenker et al., 1984),
reduced forward motility (Schlenker et al., 1984) and increased percentage of
abnormal sperm cells (Hillbrand and Glodek, 1984 and Schlenker et al.,
1984). Table 2. Heritability estimates for semen traits
Brandt and Grandjot (1998) reported genetic
correlations between semen traits and production traits (Table 3) for two lines
of boars in the German hybrid-breeding scheme.
They reported high negative genetic correlations between volume and
density and between density and motility the first relationship is expected and
the second correlation could be explained in part by the subjective nature of
motility scores. A desirable
relationship between density and daily gain and backfat depth was found,
whereas, motility showed an undesirable relationship to both daily gain and
backfat. These results are supported by Hillbrand and Glodek (1983) who also
reported that sperm cell motility had desirable genetic correlation with ham
conformation score and that number of abnormal sperm cells (sperm cell
morphology) had a desirable genetic correlation with sperm cell concentration
and an undesirable relationship with motility.
Brandt and Grandjot (1998) also reported that genetic correlations
between semen quality traits and litter size were all below .04, which could be
explained in part by standardization of services to a constant number of sperm
cells and sperm cell concentration. Table 3. Genetic correlations (pooled SE) between semen traits and production traits
Oh et al. (2000) evaluated semen collection records for 253 AI boars
using the DxMrr random regression routines developed by Meyer (1998). For this analysis the mean number of
services/ejaculate produced during each month of age was determined. Mean observations were then limited to those
from 6 to 27 months of age on 3-month intervals. Genetic parameters where then estimated for average number of
services/ejaculate by age of boar classification using a model that included
season and year as fixed effects, additive genetic effect of the boar,
permanent environmental effect of the boar and random error. Heritability estimates for number of
services/ejaculate by age of AI boar and genetic correlations between ages for
number of services/ejaculate are presented in Table 4. These results indicate that the heritability
for number of services/ejaculate is quite similar across age of boar
classification and is similar to previously reported literature (Rothschild and
Bidanel, 1998). Genetic correlations
between ages for number of services/ejaculate were close to one for adjacent
observations but decreased as the period between observations increased,
especially after 12 months. Permanent
environmental effects on number of services/ejaculate would appear to be
negligible (Table 5) but highly correlated between ages. Table 4. Heritabilities (diagonal) and additive genetic correlations (off-diagonal) for number of services/ejaculate by age of AI boar
Table 5. Permanent environmental effects (diagonal) and correlations (off-diagonal) for number of services/ejaculate by age of AI boar
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