Introduction
Selection for increased meat production and quality in the beef
industry have been the primary emphasis of selection
programmes. Recently, the trend of improving these programmes has
gradually changed from traditional phenotypical selection methods to
genotypic selection by utilizing molecular markers and a better
understanding of DNA polymorphisms that have an effect on carcass and
meat quality may lead to important applications through marker-assisted selection (MAS) programmes (Guo et al., 2016). However, genetic
variations regarding both the quantity and quality of beef already
exist among breeds and even among different populations of the same
breed (Burrow et al., 2001). In 2016, 1.2 million tonnes of red meat was
produced from 9.8 million animals slaughtered in Turkey. The slaughter
population was made up of 3.9 million cattle, 4.1 million sheep, 1.8
million goats and 1400 water buffaloes. Of this production, 1.1
million tonnes (approximately 91 % of total) was composed of beef
(Turkish Statistical Institute, 2016). In some countries, the
beef industry is exclusively based on specific beef herds, but
conversely, cattle farms in many countries, such as Turkey, generally
consist of dairy cattle and dual-purpose breeds, and the number of
beef breeds is limited. Among these, the Holstein breed comprises by far
the most common cattle breed in Turkey, with 5.5 millions purebreds
and 856 000 crossbreeds. Considering the approximately 14 million
total cattle count, the Holstein breed has a significant impact on Turkish
animal husbandry (Turkish Ministry of Food, Agriculture and
Livestock Database, 2015). Therefore, evaluating the potential of
Holstein meat can be considered as a strategically important point in
breeding programmes and meat production (Ardicli et al., 2017a).
In recent years, genes associated with meat quality have been
identified and single nucleotide polymorphisms (SNPs) of many
candidate genes have been specifically determined (Li et al.,
2013). In this context, one of the most investigated genes is bovine
micromolar calcium-activated neutral protease 1 (CAPN1),
which is located on chromosome 29, encoding the large subunit of the
enzyme μ-calpain involved in degrading myofibrillar proteins
under postmortem conditions. CAPN1 was suggested as a genetic
factor influencing the postmortem tenderization process (Koohmaraie,
1996; Page et al., 2002; Miquel et al., 2009). The SNP G316A in exon 9
(alleles C/G) of the CAPN1 gene has been
associated with meat quality traits (Gill et al., 2009; Miquel et al.,
2009; Smith et al., 2009; Bonilla et al., 2010; Mazzucco et al., 2010;
Pinto et al., 2010; Kaneda et al., 2011), final weight and average
daily weight gain (Miquel et al., 2009; Tait et al., 2014). The SNP
V530I in exon 14 of the CAPN1 gene has been associated with
beef tenderness (Corva et al., 2007; Allais et al., 2011), meat colour
and drip loss (Ribeca et al., 2013). The bovine calpastatin
(CAST) gene, mapped to chromosome 7, is considered
a candidate gene for beef tenderness (Schenkel et al., 2006). The S20T
polymorphism in the CAST gene has been shown to be associated
with meat quality traits (Juszczuk-Kubiak et al., 2004). The leptin
(LEP) gene, also known as the “obese gene”, located on
bovine chromosome 4, encodes leptin, which is secreted by adipose
tissue. The concentration of leptin can be involved in food
consumption, energy expenditure and adipose tissue development
(Buchanan et al., 2002; Lagonigro et al., 2003) and plays an indicator
role in marbling, intramuscular fat content, back fat depth and meat
quality grade in feedlot cattle (Geary et al., 2003; Li et al.,
2013). The A80V polymorphism in exon 3 has been shown to be
a candidate marker for milk yield and composition traits (Liefers
et al., 2003; Kulig, 2005; Kulig et al., 2010); however, its
association with meat quality traits has not been fully
depicted. Growth hormone (GH), also known as somatotropin, plays an
important role in growth and metabolism (Di Stasio et al., 2005;
Waters et al., 2011) by interacting with a specific receptor (GHR),
which activates an intracellular signalling pathway (Zhou and Jiang,
2006). Variations in the GHR gene, mapped to chromosome 20,
have been associated with performance traits in cattle (Blott et al.,
2003; Ge et al., 2003; Viitala et al., 2006; Garrett et al., 2008;
Waters et al., 2011). The polymorphism in exon 10 of the GHR
gene has been associated with meat quality (Di Stasio et al., 2005;
Reardon et al., 2010), growth performance and body size traits (Waters
et al., 2011).
The genetic markers studied in our study and their association with
meat yield/quality have also been investigated by other researchers,
but the results were often inconsistent. In addition, there is very
limited information about the effects of these markers on meat yield
and quality of the Holstein breed, which constitutes a significant
proportion in Turkey's meat industry. Therefore, the aim of this
study was to investigate associations of SNPs at CAPN1,
CAST, LEP and GHR genes with carcass
characteristics, meat yield and quality traits.
Materials and methods
Animals, management and slaughter procedures
Data from 400 Holstein bulls randomly selected from a commercial herd,
with a herd size 20 000 cattle, located in South Marmara region and
slaughtered at 14–21 months of age, were used in the study. All
animals were recorded for the Pedigree Project of the Turkish Ministry
of Food, Agriculture and Livestock, and Cattle Breeders
Association. Ethical approval was received from Uludag University
(approval number: 2012-10/05). The farm was located in Bandırma,
northern Balıkesir province
(40∘18′06.0′′ N and
27∘56′28.5′′ E). The animals were
maintained in a semi-open free-stall barn, with straw as
bedding. Maximum and minimum ambient air temperatures (∘C) in
the sheds during the period of the study were 10.1±1.1 and 2.1±0.4 in winter, 19.1±1.4 and 7.06±1.4 in spring and
30.5±1.9 and 16.8±0.8 in summer and relative humidity
percentages (%) were 66.7±1.4, 58.9±2.4 and 70.5±0.8 in the same seasons, respectively. The fattening period were
initiated after 2 weeks of training. During the fattening period,
growing and finishing rations contained corn, potato and tomato pomace
silage; barley straw; barley butter; pasta; corn; corn gluten meal;
corn bran; sugar-beet pulp; soybean meal; sunflower meal; vitamin and
mineral premix; limestone; and salt. The growing ration contained
13.8 % of crude protein and 10.2 MJkg-1 of
metabolizable energy on a dry matter basis and the finishing ration
contained 10.3 % of crude protein and 11.5 MJkg-1 of
energy on a dry matter basis. All animals were fed ad libitum
with the same diets and had full access to water throughout the
experiment. At the end of the finishing period, the animals, in
a non-fasted state, were transported to the nearest slaughterhouse
(40∘21′23.6′′ N and
27∘56′41.1′′ E). The duration of
transport from farm to slaughterhouse was approximately
1–2 h. Prior to slaughter, final live weight (LW) was
recorded by precision scale (100 g sensitivity). Cattle were
stunned by captive bolt before being slaughtered by means of exsanguination and
dressed using standard commercial practices, after being kept for
24 h in paddocks and deprived of feed but with full access to
water. After slaughter, all of the carcasses were electrically
stimulated for a duration of 30 s (60 V), suspended
through the Achilles tendons and chilled for 24 h at
4 ∘C.
Carcass characteristics
In the present study, non-carcass components were removed and then hot
carcass weight (HCW) was determined. Hot carcass did not include
kidneys and perinephric or pelvic fat. Chilled carcass weight (CCW)
was measured after 24 h at 4 ∘C and the dressing
percentage (DP) was calculated based on HCW. After slaughter, carcass
measurements including carcass length (CL), rump length (RL), rump
width (RW), chest width (CW) and inner chest depth (ICD) were measured
with a caliper, cane and ruler according to following anatomic regions
as described by Sagsoz et al. (2005): CL – the distance from the os
pubis to the tip of the first rib; RL – the distance from the os calcaneus
to the median point of the os pubis; RW – from the rump circumference
starting from the point opposite the meat section to the line
connecting the centre of the os pubis and the os calcaneus; CW – outer
side of the half carcass section from the sixth rib tip to the sixth
vertebra; ICD – measured from the sixth rib tip to the sixth vertebra on
the inner side of the half carcass section. Back fat thickness (BFT)
was measured from the lateral side of the m. longissimus
thoracis et lumborum (LTL) at the 12th rib and the same rib surface
was evaluated to calculate the LTL area by using a planimeter
(Ushikata X-Plan 380d III, Tokyo, Japan).
Meat quality analyses
Meat quality characteristics investigated in the current study were
marbling score (MS), carcass pH and meat colour (L∗,
a∗, b∗, C∗ and
h∗). Marbling was subjectively evaluated corresponding
to fat distribution among the muscle fibres in the
m. longissimus thoracis (LT) at the 12th–13th rib interface to
represent 9 ∘ (practically devoid, traces, slight, small,
modest, moderate, slightly abundant, moderately abundant, abundant) of
marbling (Hilton et al., 1998). Carcass pH was measured in the LT
between the 12th and 13th ribs at 24 h postmortem using
a digital pH meter (Testo 205, Lenzkirch, Germany). Meat colour
parameters including L∗ (lightness),
a∗ (redness) and b∗ (yellowness)
values were evaluated using a spectrocolorimeter (Konica Minolta
CM508d, Minolta Co., Ltd, Osaka, Japan) with illuminant D65 as the
light source. The device was set to make three measurements and take
their average after the calibration corresponding to the standard
white plate. Three-times-repeated colour measurements were performed
from each sample of the LTL after 24 h storage at
4 ∘C on cut surface of fat-free area and the average of these
measurements was evaluated as the final value (Ekiz et al., 2009).
Chroma value (C∗) was calculated as
(a∗2+b∗2)1/2 and hue angle
(h∗) as arctan (b∗/a∗).
Genomic DNA isolation
DNA was isolated from 4 mL blood samples obtained from the
vena jugularis of each bull and collected in K3EDTA tubes
(Vacutest Kima, SRL, Italy) by a phenol–chloroform method as described
by Green and Sambrook (2012). The amount and purity of the DNA samples
was measured with a spectrophotometer (NanoDrop 2000c, Thermo
Scientific, Wilmington, DE, USA). DNA samples were stored at
-80 ∘C until the genotyping was performed.
Markers used and genotyping
In the present study, the polymorphisms in four candidate genes were
genotyped, which included the G316A and the V530I in the
CAPN1 gene, the S20T in the CAST gene, A80V
in the LEP gene and the S555G in the GHR
gene. Marker G316A (GenBank accession number: AF252504) is
a cytosine/guanine (C/G) polymorphism in exon 9 of the
CAPN1 gene that produces an amino acid substitution
(glycine/alanine) in position 316. Marker V530I (GenBank accession
number: AF248054) of the same gene is an adenine/guanine
(A/G) polymorphism in exon 14 that also produces an
amino acid substitution (isoleucine/valine) in position 530 (Page
et al., 2002; Casas et al., 2005). The SNP S20T (GenBank accession
number: AF117813) in the CAST gene is a guanine/cytosine
(G/C) polymorphism located in exon 1C/1D that produces
serine/threonine substitution at protein position 20 (Juszczuk-Kubiak
et al., 2004). Marker A80V (GenBank accession number: AF536174.1) in
exon 3 of the LEP gene expresses the existence of
a cytosine/thymine (C/T) substitution that causes
coding of alanine instead of valine in position 80 (Haegeman
et al., 2000; Lagonigro et al., 2003). Marker S555G (GenBank accession
number: AF140284) is the guanine/adenine (G/A)
polymorphism at position 257 in exon 10 and induces serine/glycine
substitution in position 555 of the GHR gene (Di Stasio
et al., 2005).
Genotyping of the SNPs in the CAPN1, CAST,
LEP and GHR genes was performed by PCR-RFLP. Primer
sequences and PCR conditions for amplification are shown in Table 1.
The PCR amplification was performed in a total volume of
50 µL containing 33.5 µL of ddH2O,
5 µL of 10× buffer, 5 µL of MgSO4,
1 µL of dNTPs (2.5 mM), 2.5 U of Taq DNA
polymerase (Biomatik, Cambridge, Canada, A1003-500U,
5UµL-1), 1 µL (0.025 µM) of
each primer, and 3 µL of the DNA sample at a concentration
of 100 ngµL-1. The DNA amplification reactions were
performed in a thermal cycler (Palm Cycler GC1-96, Corbett Research,
Australia). After amplification, 15 µL of the PCR product
with each SNP was digested in 15 U of the corresponding
restriction enzyme (Table 1) by incubating at 37 ∘C for
16 h. Afterwards, the digestion products were electrophoresed
in 3 % agarose gel (Sigma Aldrich, Steinheim, Germany) at
85–90 V for 1 h after incubation and visualized by
a gel imaging system (DNr-Minilumi, DNR Bio-Imaging Systems, Israel).
Primers sequences (from 5′ to 3′), PCR conditions
and restriction enzymes used for genotyping the polymorphisms in the current
study.
SNP
Allele
PCR
Primers (5′ to 3′)
PCR conditions
Restriction
Reference
name∗
amplicon
enzyme
(bp)
CAPN1
F: 5′GACTGGGGTCTCTGGACTT3′
95 ∘C 5′ (95 ∘C 45 s, 63 ∘C 45 s, 72 ∘C 45 s)
BtgI
Lisa and Di Stasio
G316A
R: 5′GGAACCTCTGGCTCTTGA3′
35 cycles, 72 ∘C 5′
(2009)
CAPN1
A/G
787
F: 5′AGCGCAGGGACCCAGTGA3′
95 ∘C 5′ (95 ∘C 1′, 63 ∘C 1′, 72 ∘C 1′)
AvaII
Soria et al. (2010)
V530I
F: 5′TCCCCTGCCAGTTGTCTGAAG3′
35 cycles, 72 ∘C 5′
CAST
G/C
624
F: 5′TGGGGCCCAATGACGCCATCGATG3′
94 ∘C 5′ (94 ∘C 30 s, 62 ∘C 45 s, 72 ∘C 45 s)
AluI
Juszczuk-Kubiak
S20T
R: 5′GGTGGAGCAGCACTTCTGATCACC3′
32 cycles, 72 ∘C 5′
et al. (2004)
LEP
C/T
458
F: 5′GGGAAGGGCAGAAAGATAG3′
94 ∘C 2′ (94 ∘C 30 s, 57 ∘C 1′, 72 ∘C 30 s)
HphI
Oztabak et al.
A80V
R: 5′CCAAGCTCTCCAAGCTCTC3′
35 cycles, 72 ∘C 15′
(2010)
GHR
G/A
342
F: 5′GCTAACTTCATCGTGGACAAC3′
95 ∘C 5′ (94 ∘C 45 s, 53 ∘C 30 s, 72 ∘C 50 s)
AluI
Di Stasio et al.
S555G
R: 5′CTATGGCATGATTTTGTTCAG3′
35 cycles, 72 ∘C 5′
(2005)
CAPN1 – micromolar
calcium-activated neutral protease 1. CAST – calpastatin.
LEP – leptin. GHR – growth hormone receptor. ∗
SNP names were used according to translation.
Statistical analysis
The Hardy–Weinberg equilibrium (HWE) was tested for all alleles by
using POPGENE software v1.32 (Yeh et al., 2000). The population
genetic indexes including gene heterozygosity (He), effective allele
numbers (Ne) and polymorphism information content (PIC) were estimated
as described by Nei and Roychoudhury (1974) and Botstein
et al. (1980). Association analysis was carried out by the
least-squares method as applied in a general linear model (GLM)
procedure of Minitab (MINITAB®, Pennsylvania, USA,
v17.1.0) according to the following statistical model:
Yijklmnop=μ+Si+Wj+AGk+BGl+CGm+DGn+EGo+eijklmnop,
where Yijklmnop represents the studied traits, μ the overall
mean, Si the fixed effect of season at the slaughter
(i=winter, spring and summer), Wj
the fixed effect of age at slaughter
(j=14–21 months), AGk the
fixed effect of the CAPN1 genotype for the G316A
(k=CC, CG, GG), BGl the
fixed effect of the CAPN1 genotype for the V530I (l=AA, AG, GG), CGm the fixed effect of
the CAST genotype for the S20T (m=CC, CG,
GG), DGn the fixed effect of the
LEP genotype for the A80V (n=CC, CT, TT),
EGo the fixed effect of the GHR
genotype for the S555G (o=AA, AG, GG) and
eijklmnop the random residual effect.
The models in the present study were selected by evaluating the
adjusted R2 to compare the explanatory power of models with
different numbers of predictors. Markers were initially evaluated
using the significance of genotype effects for each trait. Afterwards,
the interactions between CAPN1, CAST, LEP
and GHR genotypes were added to the model and tested for
significance. When significant associations were identified, the mean
values for each genotype were contrasted using Tukey's test.
Discussion
The primary objective of the current study was to determine whether
DNA markers commonly studied (CAPN1 G316A and V530I,
CAST S20T, LEP A80V and GHR S555G) in
various beef cattle populations could be applied in Holstein bulls,
which comprise by far the most important share of meat
production in Turkey. The present results showed a deviation from HWE
for the LEP A80V and GHR S555G polymorphisms in
Holstein population. Deviations from HWE at particular markers may be
associated with population characteristics. Accordingly, this
disequilibrium can be a result of inbreeding or indirect selection for
these loci from the selection for milk production in the Holstein breed
(Lacorte et al., 2006). Menezes et al. (2006) described a polymorphic
locus as the frequency of the most common allele being lower than
0.95; accordingly all markers used in the present study were
polymorphic. Further, these markers are considered as mildly
informative according to the classification reported by Botstein
et al. (1980). CAPN1, CAST, LEP and
GHR genes were chosen because they have been shown to be
involved in the regulation of appetite, growth rate, carcass traits
and meat quality in many beef cattle breeds, and our results indicated
that CAPN1, CAST and GHR markers may be
associated with carcass traits and meat quality. Among them,
CAPN1 G316A was highly associated with LW and carcass weights
(P<0.001) given that the G is the favourable allele for these
traits. Animals with the GG genotype had +52.7 kg heavier LW
and +34.4 kg heavier HCW compared to the CC genotype in the
present study. In the literature, CAPN1 G316A was evaluated
as an effective marker on beef tenderness in several studies
indicating that the C allele is associated with more tender meat
(Casas et al., 2005; Corva et al., 2007; Gill et al., 2009; Miquel
et al., 2009; Smith et al., 2009; Curi et al., 2010). It has been
shown that, along with loci affecting beef tenderness, other loci
associated with weaning weight and carcass weights were mapped to
bovine chromosome 29 (Casas et al., 2005). Miquel et al. (2009)
reported that final weight and average daily gain differentiated
between the CAPN1 G316A marker genotypes and that choosing
animals with the favourable marker genotype (CC) for tenderness
resulted in a selection of animals with lower average daily gain and
final weight in Angus and Brangus steers. In addition, Pintos and
Corva (2011) found significant associations between the same marker
with birth weight, weaning weight and live weight recorded at 18
months of age in Angus cattle. Ardicli et al. (2017b) reported that
homozygous animals for allele G at the CAPN1 G316A marker
reached the highest final weight and total weight gain in a shorter
fattening period with higher average daily gain in Simmental
bulls. Among the factors considered, it is possible that Warner–Bratzler shear force (WBSF) values show an association with LW and
that selection for this marker may lead to changes in both
traits. Conversely, Corva et al. (2007) and Tait et al. (2014)
reported that final body weight and carcass weight were not affected
by the CAPN1 G316A marker genotypes. The breed of the animals and
the production procedures determine the slaughter weight and carcass
traits (Sañudo et al., 2004) and inconsistent results about the
associations between the same genetic markers with these traits can be
evaluated as a common circumstance. Apart from these associations,
carcass measurements (CL, RL, RW and ICD), BFT and LTL area were
differentiated in the CAPN1 G316A genotypes in the current
study. Animals with the CC genotype had significantly lower values of
the traits mentioned above. In the literature, associations of
CAPN1 G316A marker with carcass and growth traits have been
shown in various cattle populations (Miquel et al., 2009; Pintos and
Corva, 2011). To the best of our knowledge, this is the first study
indicating that a portion of the differences in live and carcass
weight and growth traits associated with this marker may be dependent
on the body size (according to carcass measurement) of the
individual. In the current study, animals with the CC genotype had
11.58 and 11.03 cm2 lower mean value for LTL area, compared
to GG and GC genotypes, respectively. These results indicated that
selecting animals with the GG genotype induced higher values of LW,
carcass weights and measurements and moreover higher BFT and LTL area
as well. This knowledge may be useful for marker-assisted
selection programmes. The trend of beef production in many countries
has gradually changed from meat yield to meat quality. However,
evaluating the ways to improve meat yield may be strategically
important to achieve significant economic benefits in the countries
with meat production deficit. Dairy-type animals, which yield
a higher percent lean and less fat meat when compared with
conventional beef breeds, could be exploited more commonly for beef
production (Ntunde et al., 1977).
The CC genotype at the CAPN1 G316A marker was absent or the
frequency was rather low to estimate their association with phenotypic
traits in several studies conducted on various cattle populations
(Curi et al., 2010; Soria et al., 2010; Allais et al., 2011; Li
et al., 2013). However, satisfying results were obtained for the
frequencies of the CC genotype (0.06) and the C allele (0.28) in the
current study. Bovine CAPN1 has been mapped to the telomeric
end of BTA29 (Smith et al., 2000; Page et al., 2002), including
considerable overlap of QTLs regulating not only beef tenderness but
also growth (weaning weight, carcass weight) and feed efficiency
(Casas et al., 2003; Pintos and Corva, 2011). Hence, it is possible to
obtain novel genetic associations among these traits by evaluating
this genomic region.
The amino acid variations may cause a functional change in the
μ-calpain protease. The μ-calpain isoform including V530I and
G316A, or both, may be a functional protein variation in myofibrillar
proteolysis and resulted in differences in meat quality (Page et al.,
2002). The present results indicated that the CAPN1 V530I
marker was significantly associated with meat pH and
L∗ values. Choosing animals with the AA genotype at
CAPN1 V530I marker may have resulted in a selection of animals
with lower L∗ but higher pH values. Improper meat values
of pH>5.8 were observed for the AA genotype. It is worth
noting that ultimate pH is one of the most important indices of meat
quality and high quality-meat has ultimate pH at the range of 5.4–5.6
(Pipek et al., 2003). Moreover, the correlations between meat pH and
all colour parameters proved to be significant. For example, the
increase in meat pH may cause the deterioration of all colour
parameters (Węglarz, 2010). Additionally, environmental
conditions that, for example, cause additional stress on
animals in the pre-slaughter period lead to high postmortem
pH values and should be avoided (Pipek et al., 2003; Węglarz,
2010). Further experiments with larger populations should be conducted
in order to evaluate the consequences of selection for the marker on
optimum meat pH and quality, especially for the colour parameters.
The present results indicated that the CAST S20T polymorphism
was associated with LW and ICD in Holstein bulls (P<0.05). Animals
with the GG genotype displayed a higher mean LW and ICD than those
with the CC and heterozygous genotypes. Studies on the association of
the CAST S20T polymorphism with LW and carcass measurements
in cattle are insufficient and larger populations may be needed to
perform an adequate evaluation. Apart from the associations mentioned
above, the b∗ value indicating the degree of yellow
appearance of meat from GC animals was higher than that estimated in
the meat from those with other genotypes (P<0.05). Consistent with
our results, Juszczuk-Kubiak et al. (2004) reported that the meat from
GC bulls had higher b∗ values and was definitely
darker. One possible connection between the CAST marker and
meat colour may be through the calpain proteolytic system. The
polymorphisms in genes related to calpain/calpastatin activity might
affect meat colour traits, directly or indirectly (Li et al., 2013).
Moreover, assessment of epistasis, genetic linkage and pleiotropy may
be useful to consider different combinations of the polymorphisms
associated with economically important quantitative traits.
The polymorphism A80V of the LEP gene has been reported as an
effective marker on weight and average daily body weight gains (Kulig
and Kmieć, 2009), marbling and carcass traits in feedlot cattle
(Geary et al., 2003; Silva et al., 2014). However, there was no
association between the LEP A80V polymorphism and any of the
phenotypic traits evaluated in the current study. Studies on the
association of the LEP A80V polymorphism with meat and
carcass traits were conducted mostly in beef cattle populations. The
reason for the lack of corresponding result in the present study may
be the breed type.
Growth hormone influences growth and metabolism by interacting with
GHR. The polymorphism S555G in exon 10 of the GHR gene has
previously been shown to be associated with meat quality (Di Stasio
et al., 2005; Reardon et al., 2010), growth and body size (Waters
et al., 2011). Here, this SNP was associated with meat pH and meat
colour parameters (a∗ and
h∗). Animals with GG genotype had higher pH values
compared to the alternative genotypes. Conversely, Reardon
et al. (2010) and Ribeca et al. (2010) found no association between
this marker and meat pH. Environmental factors and pre-slaughter
conditions of the abattoir may offer an explanation for variation in pH
values reported in the different studies. The statistical analysis
revealed that the effect of the GG genotype was significantly greater
than the AA and heterozygote genotypes and that selection of animals
with GG resulted in higher pH. In the study by Reardon et al. (2010),
association of this marker at the GHR gene with
L∗ values of LTL and semimembranosus muscle
was observed but a∗ and b∗ values
were not differentiated between the GHR
genotypes. Conversely, in this study, significant associations were
found between GHR and a∗ and
h∗ values but not L∗ value. Meat
derived from animals with AA genotype seemed to have higher
a∗ values (darker red colour) compared to those with
other genotypes. One possible explanation about this association may
be the effect of meat pH on colour parameters. High postmortem
pH values influence meat colour negatively (Węglarz, 2010). In
this study, AA genotype exhibited low pH and higher red values. Such
genotypic information may have potential for incorporation into
management systems for meat quality.
In the current study, we hypothesized that interactions between
polymorphisms of the selected genes may have significant effects on
meat yield and quality and, thereby, may provide novel perspectives to
evaluate the availability of these polymorphisms. In this case, the
interactions among CAPN1, CAST, LEP and
GHR genotypes were investigated to acquire possible
associations between genotypic combinations and phenotypic
traits. Among these, the LEP A80V × CAST
S20T was associated with DP (P<0.05). Animals with the CCCC
genotype exhibited the highest value of DP. Our results suggested that
the individual genetic effects of these SNPs were not statistically
significant for DP. Interestingly, the combined effects of these
polymorphisms indicated a significant association in the interaction
analysis. Evaluating non-allelic gene interactions and linkage in the
corresponding genomic regions may be required before considering them
in marker-assisted selection. The CAPN1
V530I × GHR S555G and the LEP
A80V × GHR S555G interactions were associated with
variation in pH values (P<0.01). Animals with the AAGG genotype of
the CAPN1 V530I × GHR S555G and animals
with CTGG genotype of the LEP A80V × GHR
S555G had very high pH values (6.56 and 6.09, respectively). High
ultimate pH of meat can result in DFD (dark, firm, dry) meat
occurrence. Moreover, high pH is improper for sorting, confectioning
and vacuum packaging of meat (Pipek et al., 2003; Węglarz,
2010). Selecting animals with these genotypes may have an inadequate
impact to commercial markets. In addition, AAGC animals of the
CAPN1 V530I × CAST S20T exhibited
significantly higher means for RW compared to alternative genotypes
(P<0.01). However, investigation of a larger number of animals
would be desirable, especially because of the genotypes with low
frequency.
Information of SNPs in CAPN1, CAST, LEP and
GHR genes may provide very important clues on how many and
which polymorphisms can explain genetic variations in carcass
characteristics and meat quality to produce constant meat products as
well as to maintain commercial lines. In the current study, the
CAPN1, CAST and GHR genotypes confirmed
significant associations with important traits in adequate numbers of
animals. Thus, G316A and V530I markers in the bovine CAPN1
gene can be used as genetic markers in breeding programmes to improve
meat quantity and quality traits, respectively. Similarly,
GHR S555G and CAST S20T markers may be evaluated in
conventional selection procedures, regarding improvements of meat
colour and carcass traits.
Holstein cattle, which are bred mainly for dairy purposes, carry
a potential for improvement of beef production due to their genetic
variability for beef traits. Therefore, the dual capacity of the Holstein
breed may be used in several countries to cover the shortage in milk
and meat production (Calo et al., 1973). Further genetic studies
should be conducted for efficient selection procedures.
Consequently, information of polymorphisms at coding regions of the
mentioned genes, genotypic interactions and significant genetic
associations may be used to control meat production traits, concerning
improvement of the genotypic structure.