Livestock Science 180 (2015) 78–83
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Livestock Science
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1871-14

n Corr
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Allele substitution effects of IGF1, GH and PIT1 markers on estimated
breeding values for weight and reproduction traits in Canchim beef cattle

Daniela do Amaral Grossi a, Natalia Vinhal Grupioni b, Marcos Eli Buzanskas b,
Claudia Cristina Paro de Paz c,d, Luciana Correia de Almeida Regitano e,
Maurício Mello de Alencar e, Flávio Schramm Schenkel a, Danísio Prado Munari b,n

a Centre for Genetic Improvement of Livestock, Department of Animal and Poultry Science, University of Guelph, 50 Stone Road East, Guelph, Canada ON N1G2W1
b Departamento de Ciências Exatas, UNESP – Univ Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Via de Acesso Prof. Paulo Donato
Castellane, Jaboticabal, SP 14884-900, Brazil
c Instituto de Zootecnia, Centro APTA de Bovinos de Corte, Rodovia Carlos Tonanni, km 94, Sertãozinho, SP 14174-000, Brazil
d Departamento de Genética, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP 14049-900, Brazil
e Empresa Brasileira de Pesquisa Agropecuária, EMBRAPA Pecuária Sudeste, Rodovia Washington Luiz, km 234, São Carlos, SP 13560-970, Brazil
a r t i c l e i n f o

Article history:
Received 16 January 2015
Received in revised form
22 July 2015
Accepted 24 July 2015

Keywords:
Animal breeding
Association analysis
Beef cattle
Quantitative trait loci
Regression coefficient
x.doi.org/10.1016/j.livsci.2015.07.018
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esponding author. Fax: þ55 1632024275.
ail address: danisio@fcav.unesp.br (D.P. Muna
a b s t r a c t

The aim of this study was to evaluate the allele substitution effects of IGF1, GH and PIT1 markers on the
estimated breeding values (EBVs) for birth (BW) and weaning weights (WW), weight at 12 (W12) and 18
(W18) months of age, weight (WFC) and age (AFC) at first calving, and scrotal circumference measured at
12 (SC12) and 18 (SC18) months of age in Canchim beef cattle. Maternal effects were considered for birth
(BWmaternal) and weaning (WWmaternal) weight. Regression analyses were carried out considering the
EBVs, obtained from multi-trait analyses, and the deviations for each allele studied (four alleles for the
IGF1 markers and two for GH and PIT1 markers, respectively). According to the results obtained for IGF1,
the substitution effect of the “225” allele was significant (Pr0.05) and presented higher EBVs for BW,
BWmaternal, WW, W12, W18, and SC18; and lower EBVs for WWmaternal. The allele substitution effect
observed for GH gene was significant (Pr0.05) on EBVs for AFC and WFC. The “Valine” allele was re-
sponsible for lower EBVs for AFC and higher EBVs for WFC. For the PIT1 gene, the substitution effect of
the “Hinf�” allele was significant (Pr0.05) on the EBVs for WW, WWmaternal, AFC, and WFC, respec-
tively. Important allele substitution effects were found for weight and reproduction traits in Canchim
cattle. In general, markers on the IGF1 gene are related to higher EBVs for weight, while markers on the
GH and PIT1 showed greater influence on the EBVs for age and weight at first calving. Adding molecular
markers information on the selection process could result in increased genetic gains in Canchim cattle.
Future studies using high-density genotyping platforms may contribute to our understanding of the
effects of these genes on traits of economic importance in the Canchim breed.

& 2015 Elsevier B.V. All rights reserved.
1. Introduction

Molecular markers have been used to identify loci or chromo-
some regions that might be responsible for variations in quanti-
tative traits. Because of the difficulty to identify quantitative trait
loci (QTL), markers presenting linkage disequilibrium with a QTL
may be used to aid on inferences about QTL effects. These markers
can be identified through certain candidate genes or regions.

The insulin-like growth factor type 1 (IGF1), growth hormone
(GH) and pituitary transcription factor (PIT1) are candidate genes
for growth and reproduction traits, because they participate on the
ri).
hormonal system with a fundamental role in regulating animal’s
development. The IGF1 and GH form the somatotropic axis and are
involved in such hormonal system (Renaville et al., 2002), while
PIT1 is a regulatory factor for synthesis of GH, prolactin and
thyrotropin (Oprządek et al., 2003).

In Canchim breed, Pereira et al. (2005), Andrade et al. (2008),
and Carrijo et al. (2008) reported that IGF1, GH, and PIT1 markers
had significant effects on weight traits. Moody et al. (1996) ob-
served significant effects on body weight traits when studying GH,
IGF1, and PIT1 markers in Hereford cattle. According to Ishida et al.
(2010), single nucleotide polymorphisms in the GH gene were
responsible for lighter calves at various ages in Japanese Black
breed. Rogberg-Muñoz et al. (2011) observed association of
weaning weight with IGF1 markers in commercial and experi-
mental Hereford herds. Association of PIT1 markers with

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Table 1
Number of animals (N), means with respective standard deviations (SD), minimum
(Min) and maximum values (Max) for birth weight (BW), weaning weight (WW),
weight at 12 (W12) and 18 (W18) months of age, age (AFC) and weight (WFC) at
first calving and scrotal circumference at 12 (SC12) and 18 (SC18) months of age in
Canchim cattle.

Trait N Mean SD Min Max

BW (kg) 11,401 35.03 5.68 18.00 52.00
WW (kg) 9035 204.90 40.69 82.00 327.00
W12 (kg) 8889 229.00 47.10 90.00 370.00
W18 (kg) 7863 302.30 60.23 125.00 480.00
AFC (days) 2148 1206.00 178.10 697.00 1736.00
WFC (kg) 1963 439.50 54.94 300.00 600.00
SC12 (cm) 1844 22.66 3.19 13.30 32.00
SC18 (cm) 1519 28.96 3.19 19.00 38.00

D.d.A. Grossi et al. / Livestock Science 180 (2015) 78–83 79
intramuscular fat deposition was found by Thomas et al. (2007) in
Brangus breed. These authors also found association of GH makers
with weight gain, scrotal circumference, and fat thickness,
respectively.

Most of the previously cited studies were conducted con-
sidering a genotypic model, in which the contribution of each
genotype to the phenotypic variation of the trait was estimated.
However, using a statistical model based on allele substitution
effects would be the most fitting from the point of view of genetic
improvement, because the additive effect of each allele could be
estimated independently of the animal's genotype. The estimated
breeding values (EBVs) and variations, such as deregressed EBVs
(Garrick et al., 2009), are indicated to study genomic associations,
mainly when traits are observed in only one sex (i.e. scrotal cir-
cumference) as well as when phenotypes are obtained at older
ages (i.e. stayability). Normally, EBVs are more accurate than raw
phenotypes, however the EBVs depend on the correct removal of
environmental effects, pedigree structure and heritability estimate
to present a desirable accuracy.

Thus, the aim of our study was to evaluate allele substitution
effects for growth and reproduction traits of the Canchim breed on
the genetic markers for IGF1, GH, and PIT1 genes.
2. Material and methods

2.1. Animals and data

Data on birth (BW) and weaning weights (WW), weights at 12
(W12) and 18 (W18) months of age, weight (WFC) and age at the
first calving (AFC), and scrotal circumference at 12 (SC12) and 18
(SC18) months were used in our study. Animals were from a herd
from the Embrapa Southeast Livestock, a Brazilian research facility
which is located in the municipality of São Carlos, state of São
Paulo (22°01′ latitude south, 47°53′ longitude west and 856 m in
altitude). The animals were reared exclusively in a pasture regime,
while receiving mineral supplementation throughout the year.
Details on herd management are described by Mello et al. (2002).

In the Canchim breed, different crossing schemes are used to
create genetic variability (Andrade et al., 2008). In our study, eight
different genetic groups (GG) were classified according to the ge-
netic composition of the animals, sires and dams. Heterotic effects
are present in crossbred populations and are an important source
of variation. In our study, the GG was included in the models, for
genetic parameter estimates, in order to account for part of these
effects.

The statistical analyses were conducted using performance
data on Canchim cattle and on different genetic groups. Given that
Canchim animals are 5/8 Charolais and 3/8 Zebu, the genetic
groups were as follows:

� GG1: offspring from the mating of Charolais with F1 (½ Canchim
½ Zebu).

� GG2: Canchim, offspring from the mating of GG1 with GC1.
� GG3: Canchim, offspring from the mating of GG1 with Canchim.
� GG4: Canchim, offspring from the mating of F1 (½ Zebu ½

Charolais) with F1 ( ½ Zebu ½ Charolais).
� GG5: Canchim, offspring from the mating of F1 (½ Zebu ½

Charolais) with Canchim.
� GG6: Canchim, offspring of Canchim and grand-offspring of

Canchim.
� GG7: Canchim, offspring from the mating of GG2 with GG2; and
� GG8: Canchim, offspring from the mating of GG2 with GG6.
2.2. Genetic parameter estimates

Contemporary groups (CGs) were defined as animals from the
same genetic group (GG1–GG8) that were born in the same year
(from 1949 to 2008) and season (spring, summer, fall or winter).
CGs comprising only one animal and standardized observations
(that were higher than þ3 or lower than �3 standard deviations
in relation to the phenotypic mean) were excluded. The structure
of the data used in the multi-trait analyses is presented in Table 1.

The effects considered in the analysis for each trait are pre-
sented as follows:

� BW: CG as a random effect, sex as a fixed effect, and age of the
dam at calving as a covariate (linear and quadratic effect).

� WW: CG as a random effect, sex as a fixed effect, and age of the
animal at weaning (linear effect) and age of the dam at calving
(linear and quadratic effect) as covariates.

� W12 and W18: CG as a random effect, sex as a fixed effect, and
age of the animal as a covariate (linear effect).

� AFC: CG as a random effect and age of the dam at calving as a
covariate (linear and quadratic effect).

� WFC: CG as a random effect.
� SC12 and SC18: CG as a random effect and age (linear effect) and

weight (linear effect) of the animal as covariates.

The EBVs were estimated in two different analyses. The first
considered the traits measured in both sexes (BW, WW, W12, and
W18) and the second considered the traits measured in only one
of the sexes (AFC, WFC, SC12, and SC18). We conducted all ana-
lyses using the restricted maximum likelihood method, under an
animal model, by means of the ASREML software (Gilmour et al.,
2009). For the multi-trait analysis for AFC, WFC, SC12, and SC18,
the covariances between traits measured on opposite sex were set
to zero.

For BW and WW, the random maternal genetic effect was taken
into consideration, along with the random effects of animal, CG
and residual. For W12, W18, AFC, WFC, SC12, and SC18 the model
included the random effects of animal, CG and residual. The ran-
dom permanent environment effect was not included in the ana-
lyses because the structure of the data, given that around 50% or
more of the dams had only one offspring.

2.3. DNA extraction and genotyping

The genotype information of the animals are part of the data-
base provided by Embrapa Southeast Livestock (Pereira et al.,
2005; Andrade et al., 2008; Carrijo et al., 2008). A total of 1397,
755, and 517 animals were genotyped using molecular markers on
IGF1 (chromosome 5, GenBank ID X64400), GH (chromosome 19,
GenBank ID M57764), and PIT1 (chromosome 1, GenBank ID

http://ncbi-n:X64400
http://ncbi-n:M57764


Table 2
Allelic frequencies observed for the genetic markers on IGF1, GH and PIT1 genes in
Canchim cattle.

Marker Allele Observed frequency

IGF1 225 0.14
227 0.24
229 0.55
231 0.07

GH L 0.87
V 0.13

PIT1 “Hinfþ” 0.79
“Hinf�” 0.21

Table 4
Number of animals used for regression analyses on the IGF1, GH and PIT1 markers,
with the estimated breeding values for birth weight (BW), weaning weight (WW),
weight at 12 (W12) and 18 (W18) months of age, age (AFC) and weight (WFC) at
first calving, scrotal circumference at 12 (SC12) and 18 (SC18) months of age,
respectively.

Trait Number of animals in the regression analyses

IGF1 GH PIT1

BW 1327 746 515
WW 1325 747 517
W12 1326 747 516
W18 1329 747 516
AFC 1325 740 516
WFC 1327 742 517
SC12 1327 747 513
SC18 1325 744 515

D.d.A. Grossi et al. / Livestock Science 180 (2015) 78–8380
Y15995.1) genes, respectively. The IGF1 marker used in this study
was a microsatellite with four alleles (225, 227, 229, and 231) at
the promoter region of this gene (Kirkpatrick, 1992). The GH
marker had two alleles and was defined by the substitution of the
amino acid leucine (L) to valine (V). This polymorphism is due to a
nucleotide substitution (C/G) at exon 5 and was identified by AluI
restriction digestion of polymerase chain reaction products (PCR–
RFLP) (Lucy et al., 1993). The molecular marker for the PIT1 gene
was located within exon 6, and the alleles (Hinfþ and Hinf�)
were defined by a silent mutation (Dierkes et al., 1998).

The LOKI software, version 2.4.5 (Heath, 2003), was used to
exclude occurrences of possible genotyping errors, resulting in
1332; 748 and 517 animals with known genotype for IGF1, GH and
PIT1, respectively. The allelic frequencies observed in the data files
and the number of animals in each genotype for the genetic
markers are presented in Tables 2 and 3.

2.4. Allele substitution analyses

Analyses on the allele substitution effects of each genetic
marker were performed using the GLM procedure of the SAS
software (SAS 9.1, SAS Institute, Cary, NC, USA). The mean effects
for the allele substitution on the EBVs were estimated by means of
regression analysis, as deviations of the less frequent allele in re-
lation to the more frequent allele (four alleles for the IGF1 marker
and two for GH and PIT1), as described by Stear et al. (1989). If one
of the genotypes was not observed, the regression analysis was
performed using only two points. The dominance effect was as-
sumed to be zero as the EBVs were considered as response vari-
ables. By definition, the EBVs are related to the additive effects and
do not include the dominance deviation, which is defined as the
difference between breeding values and genotypic values when
Table 3
Number of animals (N) in each genotype class for markers on IGF1, GH, and PIT1
genes in Canchim cattle.

IGF1 GH PIT1

Genotype N Genotype N Genotype N

225/225 22 LL 568 Hinfþ/Hinfþ 331
225/227 82 LV 161 Hinfþ/Hinf� 156
225/229 211 VV 19 Hinf�/Hinf� 30
225/231 28
227/227 72
227/229 353
227/231 52
229/229 405
229/231 97
231/231 10
Total 1332 Total 748 Total 517
only a single locus is under consideration (Falconer and Mackay,
1996).

To test the hypothesis that the regression coefficient for each
equation was equal to zero, t-statistics was used. Observations for
which the standardized residual was greater than three or less
than minus three were excluded. The number of animals with
EBVs that were used in each regression analysis is presented in
Table 4. The regression model used was as follows:

y x ejk
j

S

j j jk
1

∑μ β= + +
=

where yk is the EBV for each trait k( ); S is the number of alleles
minus one; μ is the mean of the breeding values; x is the number
of copies for a given allele j( ); β is the linear regression coefficient
(allele substitution effect), and e is the residual effect. For the
marker IGF1, the analyses were performed taking into considera-
tion the substitution of the most frequent allele (“229”), in relation
to the others. For GH and PIT1, the most frequent alleles were
“Leucine” and “Hinfþ”, respectively.
3. Results and discussion

The heritability estimates and the genetic and environmental
correlations obtained in the two multi-trait analyses (BW, WW,
W12, and W18; and AFC, WFC, SC12, and SC18) are presented in
Tables 5 and 6, respectively. Similar results were found for BW and
W12 (Mello et al., 2002), W12 (Mello et al., 2006), W18 (Toral
et al., 2009), AFC and scrotal circumference adjusted at 420 days of
age (Buzanskas et al., 2010), WFC (Gaviolli et al., 2012), and SC12
(Gianlorenço et al., 2003) in the Canchim breed. All the traits
evaluated, with the exception of AFC, presented moderate to high
heritability (from 0.23 to 0.46), thus indicating that these traits
could respond to selection.

The genetic correlations between BW, WW, W12, and W18
were all within the range found in the literature for various breeds
(Castro-Pereira et al., 2007; Toral et al., 2006; Vargas et al., 2014).
Boligon et al. (2007) studying Nellore breed, found negative and
favorable genetic correlation between AFC and SC18 (�0.23) dif-
fering from the genetic correlation obtained in our study (0.08).
For WFC and SC18, the same authors reported genetic correlation
of 0.28. The genetic correlations for AFC and WFC with SC12
(0.0070.20 and �0.2270.10, respectively) were lower than those
reported by Castro-Pereira et al. (2007) and Silva et al. (2000). Due
to the lack of heifers/cows that were direct descendants from bulls
with records, the observed genetic correlations of AFC and WFC
with SC12 and SC18 were unreliable because of the standard
errors.

http://ncbi-n:Y15995.1


Table 5
Heritability estimates (diagonal), genetic (above the diagonal) and environmental (below the diagonal) correlations, and their respective standard errors for birth weight
(BW), weaning weight (WW), weight at 12 (W12) and 18 (W18) months of age, respectively.

Trait BW WW W12 W18 BWmaternal WWmaternal

BW 0.3570.03 0.3670.05 0.3670.05 0.3270.06 0.0670.09 0.0970.07
WW 0.3470.02 0.4270.02 0.7870.02 0.7170.03 0.1470.08 �0.2970.05
W12 0.2870.02 0.8270.01 0.2970.02 0.9870.01 0.2670.07 0.3370.05
W18 0.2570.02 0.7070.01 0.7770.01 0.2470.02 0.2770.07 0.3770.05
BWmaternal – – – – 0.1070.01 0.0970.08
WWmaternal – – – – – 0.0970.01

BWmaternal¼maternal genetic effect for BW; WWmaternal¼maternal genetic effect for WW. (–) Environmental correlations were set to zero.

Table 6
Heritability estimates (diagonal), genetic (above the diagonal) and environmental
(below the diagonal) correlations, and their respective standard errors for age (AFC)
and weight (WFC) at first calving and scrotal circumference at 12 (SC12) and 18
(SC18) months of age, respectively.

Trait AFC WFC SC12 SC18

AFC 0.0470.02 0.1370.19 0.0070.20 0.0870.21
WFC 0.4470.04 0.3170.04 –0.2270.10 �0.1370.11
SC12 – – 0.5070.06 0.8570.04
SC18 – – 0.4470.07 0.5470.06

(–) Environmental correlations were set to zero.

D.d.A. Grossi et al. / Livestock Science 180 (2015) 78–83 81
Although the IGF1, GH, and PIT1 genes were found to be in-
volved in the processes that regulate animal development (Opr-
ządek et al., 2003; Renaville et al., 2002), they may act differently
on body tissues and at different stages of life. This could explain
the different results found for each of the genetic markers used in
our study. Association analyses between the IGF1, GH, and PIT1
markers and traits of economic interest in Canchim cattle were
performed prior to the present study by Pereira et al. (2005),
Andrade et al. (2008) and Carrijo et al. (2008). The present study
differed from these authors particularly in the definitions of the
animal's genetic groups and in the use of multi-trait analyses,
which made it possible to use all the animals with genotype in-
formation for the allele substitution analyses.

The results from the allele substitution effect analyses for the
IGF1 marker are presented in Table 7. The variance explained by
the IGF1 marker was higher for WW and AFC, equal to 6% (coef-
ficient of determination). However, R2 were low for all the traits
studied. According to the results obtained for IGF1, the “225” allele
was significantly associated (Pr0.05) to higher EBVs for BW,
BWmaternal, WW, W12, W18, and SC18; and to lower EBVs for
WWmaternal. Thus, the “225” allele could be related to favorable
responses for body weight and body development in Canchim
cattle. The “231” allele was significantly associated (Pr0.05) with
Table 7
Mean allele substitution effect (represented by the regression coefficients and respectiv
relation to 229), on the EBVs for birth weight (BW), weaning weight (WW), weight at 12
scrotal circumference at 12 (SC12) and 18 (SC18) months of age in Canchim cattle.

Traits 225 P 227

BW (kg) 0.7270.12 o0.01 0.2870.10
BWmaternal (kg) 0.1570.05 o0.01 0.1170.04
WW (kg) 3.8170.90 o0.01 �4.2070.73
WWmaternal (kg) �0.6970.34 0.05 1.7070.28
W12 (kg) 2.6670.83 o0.01 �2.0070.67
W18 (kg) 2.1470.91 0.02 �2.5570.73
AFC (days) 2.8470.49 o0.01 3.2170.40
WFC (kg) 0.0970.85 0.92 �3.4370.68
SC12 (cm) 0.0570.07 0.50 �0.0970.06
SC18 (cm) 0.2770.07 o0.01 �0.0170.07

P¼significance levels; R2¼coefficients of determination.
negative or close to zero EBVs for BW, WW, W12, W18, and WFC,
respectively. The “227” allele did not present significant effect
(P40.05) with SC12 and SC18; while all others were significant
(Pr0.05). The EBVs for the “227” allele were intermediate when
compared to “225” and “231”, respectively.

Similarly to the present study, the allele substitution analyses
performed by Pereira et al. (2005) on Canchim cattle identified the
alleles “225” and “231” as responsible for respectively higher and
lower EBVs for BW, when studying IGF1. For W12, the “231” allele
was also associated with lower EBVs. According to these authors,
the most favorable allele for W12 was “229”, while our results
showed that the most favorable allele for W12 was “225”, followed
by “229”. Thus, the allele “229”, which is also the allele most fre-
quently observed, is an allele assumed to be correlated with better
reproduction and weight performance.

The results from the present study, with regard to the asso-
ciations of the IGF1 marker, are in agreement with those reported
in previous studies, in which the genotype class of this marker was
seen to be correlated with BW and W12 (Pereira et al., 2005) and
with BW (Andrade et al., 2008), respectively. In an experiment
involving divergent selection for serum IGF1 concentration, Zhang
et al. (2013) observed significant decrease (Po0.001) on age at
first calving in Angus cattle heifers. Tizioto et al. (2012) found
significant allele substitution effect for IGF1 and yearling weight
(Pr0.017) in the Nellore breed.

The allele substitution effect for the markers of GH and PIT1
genes are presented in Table 8. The variances explained by the GH
and PIT1 markers were higher for AFC and WFC, respectively.
However, R2 were low for all the traits studied. The effect observed
on GH gene was significant (Pr0.05) for the EBVs for AFC and
WFC. The “valine” allele was responsible for lower EBVs for AFC
and higher EBVs for WFC. The PIT1 gene presented a significant
association (Pr0.05) with the EBVs for WW, WWmaternal, AFC, and
WFC. The “Hinf�” allele was shown to be associated with lower
EBVs for WWmaternal and AFC. This allele was also significantly
e standard deviations) of the marker for the IGF1 gene (alleles 225, 227 and 231 in
(W12) and 18 (W18) months of age, age (AFC) and weight (WFC) at first calving and

P 231 P R2

o0.01 –0.6070.15 o0.01 0.05
o0.01 –0.1070.06 0.07 0.02
o0.01 �4.5771.13 o0.01 0.06
o0.01 �0.5070.43 0.25 0.04
o0.01 �5.0571.05 o0.01 0.04
o0.01 �4.9871.14 o0.01 0.03
o0.01 �1.0770.62 0.08 0.06
o0.01 �4.0371.06 o0.01 0.03

0.09 0.0370.08 0.70 0.00
0.91 �0.0970.09 0.29 0.01



Table 8
Mean allele substitution effect (represented by the regression coefficients and respective standard deviations) of the marker for the GH gene (“Valine” allele in relation to
“Leucine”) and PIT1 gene (“Hinf�” allele in relation to “Hinfþ”), on the EBVs for birth weight (BW), weaning weight (WW), weight at 12 (W12) and 18 (W18) months of age,
age (AFC) and weight (WFC) at first calving, and scrotal circumference at 12 (SC12) and 18 (SC18) months of age in Canchim cattle.

Traits GH PIT1

“Valine” P R2 “Hinf�” P R2

BW (kg) �0.0470.15 0.79 0.000 �0.2670.15 0.08 0.006
BWmaternal (kg) 0.0670.06 0.31 0.001 �0.0670.05 0.25 0.002
WW (kg) �1.9271.26 0.13 0.003 2.2671.16 0.05 0.007
WWmaternal (kg) �0.2670.42 0.53 0.000 �0.9670.40 0.02 0.011
W12 (kg) �1.9771.11 0.08 0.004 0.8671.07 0.42 0.001
W18 (kg) �1.8971.18 0.11 0.003 1.0071.16 0.39 0.001
AFC (days) �1.9170.62 o0.01 0.012 �1.8570.65 o0.01 0.015
WFC (kg) 2.2871.12 0.04 0.006 3.5071.04 o0.01 0.021
SC12 (cm) �0.0670.08 0.46 0.000 0.0570.09 0.54 0.000
SC18 (cm) �0.0770.09 0.45 0.000 �0.0270.09 0.80 0.000

P¼significance levels; R2¼coefficients of determination.

D.d.A. Grossi et al. / Livestock Science 180 (2015) 78–8382
associated (Pr0.05) with higher EBVs for WW and WFC.
Substitution of the “Leucine” allele of the GH gene by “Valine”

led to a decrease of 1.91 days and an increase of 2.28 kg in the
EBVs for AFC and WFC, respectively. Regarding to AFC and WFC,
these traits are of great concern on beef cattle production, because
it is desirable that heifers calves at an early age and not so heavy.
According to Gaviolli et al. (2012), dams with greater weight at
maturity would show slow growth rate and later reproduction.
Pereira et al. (2005) observed significant association (Pr0.05) of
GH markers with W12 and found that animals with the “Leucine/
Valine” genotype had higher EBVs when compared to animals
with the “Leucine/Leucine” genotype. However, significant allele
substitution effect for GH was not detected for W12 in our study.

Arango et al. (2014) reported significant association (Po0.01)
between GH markers and weight at first estrus and weight at first
calving in Holstein heifers. Significant effect (Pr0.05) between GH
and longissimus muscle area was observed by Cardoso et al. (2014)
in Nellore animals from three different lines (selection, traditional,
and control). Furthermore, these authors found significant effect
(Pr0.04) of the interaction of GH and the growth hormone re-
ceptor (GHR) with weight at 550 days of age.

The substitution of the PIT1 allele “Hinfþ” by “Hinf�” caused
an increase of 2.26 kg and a decrease of 0.96 kg in the predicted
EBVs for WW and WWmaternal, respectively. The association be-
tween the “Hinf�” allele and higher EBVs for WW was similar to
the results found by Carrijo et al. (2008), i.e. the animals that were
homozygous for “Hinf�” had better performance for WW than the
animals that were heterozygous and homozygous for “Hinfþ”. We
also observed that animals with the “Hinf�” allele had lower AFC
and higher WFC. These results suggest that this allele substitution
could lead to a better reproductive performance in Canchim
heifers.

According to Cardoso et al., (2014), PIT1 markers (named as
POU1F1) were monomorphic in Nellore animals, which was not
assigned to the allele fixation due to selection. Zhang et al. (2009)
found statistical difference (Po0.05) between AA and AB alleles
for body weight at 4 months of age in crossed Qinchuan–Germany
Yellow animals.

The effects of GH and PIT1 on AFC and WFC were similar, i.e.
the most frequent alleles (“Leucine” and “Hinfþ”, respectively).
Evidence of associations were found for the same population data
regarding IGF1 with birth weight and GH with weight at first
calving and with weight at 12 months of age (Grossi et al., 2014).
However, these authors highlighted that the markers of IGF1, GH,
and PIT1 did not influence the phenotypic variance for growth and
reproduction traits after Bonferroni correction for multiple testing.
4. Conclusion

Important allele substitution effects were found for weight and
reproduction traits in Canchim cattle. In general, markers on the
IGF1 gene are related to higher EBVs for pre- and post-weaning
body weight traits, while markers on the GH and PIT1 showed
greater influence on the EBVs for age and weight at first calving.
Adding molecular markers information on the selection process
could result in increased genetic gains in Canchim cattle. Future
studies using high-density genotyping platforms may contribute
to our understanding of the effects of these genes on traits of
economic importance in this breed.
Conflict of interest statement

The authors report that there are no conflicts of interest re-
levant to this publication.
Acknowledgments

We would like to thank FAPESP (Fundação de Amparo à Pes-
quisa do Estado de São Paulo) for the fellowship received by D.A.
Grossi (06/60043-1), N.V. Grupioni (08/09393-7), and M.E. Bu-
zanskas (13/19335-2). D.A. Grossi was supported by a Post-Doc-
toral fellowship at the University of Guelph. L.C.A. Regitano, M.M.
Alencar, D.P. Munari, and C.C.P. Paz were recipients of a CNPq
(Conselho Nacional de Desenvolvimento Científico e Tecnológico)
fellowship. We also thank the Centre for Genetic Improvement of
Livestock for scientific support and Embrapa Southeast Livestock
for providing data.
References

Andrade, P.C., Grossi, D.A., Paz, C.C.P., Alencar, M.M., Regitano, L.C.A., Munari, D.P.,
2008. Association of an insulin-like growth factor 1 gene microsatellite with
phenotypic variation and estimated breeding values of growth traits in Can-
chim cattle. Anim. Genet. 39, 480–485. http://dx.doi.org/10.1111/
j.1365-2052.2008.01755.x.

Arango, J., Echeverri, J.J., López, A., 2014. Association between a polymorphism in
intron 3 of the bovine growth hormone gene and growth traits in Holstein
heifers in Antioquia. Genet. Mol. Res. 13, 6191–6199.

Boligon, A.A., Rorato, P.R.N., Albuquerque, L.G., 2007. Correlações genéticas entre
medidas de perímetro escrotal e características produtivas e reprodutivas de
fêmeas da raça Nelore. Rev. Bras. Zootec. 36, 565–571.

Buzanskas, M.E., Grossi, D.A., Baldi, F., Barrozo, D., Silva, L.O.C., Torres Júnior, R.A.A.,
Munari, D.P., Alencar, M.M., 2010. Genetic associations between stayability and
reproductive and growth traits in Canchim beef cattle. Livest. Sci. 132, 107–112.

http://dx.doi.org/10.1111/j.1365-2052.2008.01755.x
http://dx.doi.org/10.1111/j.1365-2052.2008.01755.x
http://dx.doi.org/10.1111/j.1365-2052.2008.01755.x
http://dx.doi.org/10.1111/j.1365-2052.2008.01755.x
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref2
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref2
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref2
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref2
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref3
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref3
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref3
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref3


D.d.A. Grossi et al. / Livestock Science 180 (2015) 78–83 83
http://dx.doi.org/10.1016/j.livsci.2010.05.008.
Cardoso, D.F., de Souza, F.R.P., de Camargo, G.M.F., Fonseca, P.D., da, S., Fonseca, L.F.

S., Braz, C.U., Boligon, A.A., Mercadante, M.E.Z., de Albuquerque, L.G., Tonhati, H.,
2014. Polymorphism analysis in genes of the somatotropic axis in Nellore cattle
selected for growth. Gene 545, 215–219. http://dx.doi.org/10.1016/j.
gene.2014.05.033.

Carrijo, S.M., Alencar, M.M., Toral, F.L.B., Regitano, L.C.A., 2008. Association of PIT1
genotypes with growth traits in Canchim cattle. Sci. Agric. 65, 116–121.

Castro-Pereira, V.M., Alencar, M.M., Barbosa, P.F., Barbosa, R.T., 2007. Estimativas de
parâmetros genéticos e de ganhos direto e indireto à seleção para caracter-
ísticas de crescimento de machos e fêmeas da raça Canchim. Rev. Bras. Zootec.
36, 1029–1036.

Dierkes, B., Kriegesmann, B., Baumgartner, B.G., Brenig, B., 1998. Partial genomic
structure of the bovine PIT1 gene and characterization of a HinfI transition
polymorphism in exon 6. Anim. Genet., 405.

Falconer, D.S., Mackay, T.F.C., 1996. Introduction to Quantitative Genetics, 4th ed.
Pearson Prentice Hall, Essex, England.

Garrick, D.J., Taylor, J.F., Fernando, R.L., 2009. Deregressing estimated breeding va-
lues and weighting information for genomic regression analyses. Genet. Sel.
Evol. 41, 55. http://dx.doi.org/10.1186/1297-9686-41-55.

Gaviolli, V.R.N., Buzanskas, M.E., Cruz, V.A.R., Savegnago, R.P., Munari, D.P., Freitas,
A.R., Alencar, M.M., 2012. Genetic associations between weight at maturity and
maturation rate with ages and weights at first and second calving in Canchim
beef cattle. J. Appl. Genet. 53, 331–335. http://dx.doi.org/10.1007/
s13353-012-0100-6.

Gianlorenço, V.K., Alencar, M.M., Toral, F.L.B., Mello, S.P., Freitas, A.R., Barbosa, P.F.,
2003. Herdabilidades e Correlações Genéticas de Características de Machos e
Fêmeas, em um Rebanho Bovino da Raça Canchim. Rev. Bras. Zootec. 32,
1587–1593.

Gilmour, A.R., Gogel, B.J., Cullis, B.R., Thompson, R., 2009. ASReml User Guide.
Grossi, D., do, A., Buzanskas, M.E., Grupioni, N.V., de Paz, C.C.P., Regitano, L.C., de, A.,

de Alencar, M.M., Schenkel, F.S., Munari, D.P., 2014. Effect of IGF1, GH, and PIT1
markers on the genetic parameters of growth and reproduction traits in Can-
chim cattle. Mol. Biol. Rep. 42, 245–251. http://dx.doi.org/10.1007/
s11033-014-3767-4.

Heath, S.C., 2003. Loki 2.4.6 – A Package for Multipoint Linkage Analysis on Large
Pedigrees using Reversible Jump Markov Chain Monte Carlo.

Ishida, T., Umebayashi, A., Tsuruta, S., Akashi, R., Harada, H., 2010. Polymorphisms in
growth hormone gene and their associations with calf weight in Japanese Black
cattle. Anim. Sci. J. 81, 623–629. http://dx.doi.org/10.1111/
j.1740-0929.2010.00783.x.

Kirkpatrick, B.W., 1992. Identification of a conserved microsatellite site in the
porcine and bovine insulin-like growth factor-I gene 5′ flank. Anim. Genet. 23,
543–548.

Lucy, M.C., Hauser, S.D., Eppard, P.J., Krivi, G.G., Clark, J.H., Bauman, D.E., Collier, R.J.,
1993. Variants of somatotropin in cattle: gene frequencies in major dairy breeds
and associated milk production. Domest. Anim. Endocrinol. 10, 325–333.

Mello, S.P., Alencar, M.M., Silva, L.O.C., Barbosa, R.T., Barbosa, P.F., 2002. Estimativas
de (co)variâncias e tendências genéticas para pesos em um rebanho Canchim.
Rev. Bras. Zootec. 31, 1707–1714.

Mello, S.P., Alencar, M.M., Toral, F.L.B., Gianlorenço, V.K., 2006. Estimativas de
parâmetros genéticos para características de crescimento e produtividade em
vacas da raça Canchim, utilizando-se inferência bayesiana. Rev. Bras. Zootec. 35,
92–97.

Moody, D.E., Pomp, D., Newman, S., MacNeil, M.D., 1996. Characterization of DNA
polymorphisms in three populations of Hereford cattle and their associations
with growth and maternal EPD in line 1 Herefords. J. Anim. Sci. 74, 1784–1793.

Oprządek, J., Flisikowski, K., Zwierzchowski, L., Dymnicki, E., 2003. Polymorphisms
at loci of leptin (LEP), Pit1 and STAT5A and their association with growth, feed
conversion and carcass quality in Black-and-White bulls. Anim. Sci. Pap. Rep.
21, 135–145.

Pereira, A.P., Alencar, M.M., Oliveira, H.N., Regitano, L.C.A., 2005. Association of GH
and IGF-1 polymorphisms with growth traits in a synthetic beef cattle breed.
Genet. Mol. Biol. 28, 230–236.

Renaville, R., Hammadi, M., Portetelle, D., 2002. Role of the somatotropic axis in the
mammalian metabolism. Domest. Anim. Endocrinol. 23, 351–360.

Rogberg-Muñoz, A., Melucci, L., Prando, A., Villegas-Castagnasso, E.E., Ripoli, M.V.,
Peral-García, P., Baldo, A., Añon, M.C., Giovambattista, G., 2011. Association of
bovine chromosome 5 markers with birth and weaning weight in Hereford
cattle raised under extensive conditions. Livest. Sci. 135, 124–130. http://dx.doi.
org/10.1016/j.livsci.2010.06.160.

Silva, A.M., Alencar, M.M., Freitas, A.R., Barbosa, R.T., Barbosa, P.F., Oliveira, M.C.S.,
Corrêa, L.A., Novaes, A.P., Tullio, R.R., 2000. Herdabilidades e Correlações Ge-
néticas para Peso e Perímetro Escrotal de Machos e Características Reprodutivas
e de Crescimento de Fêmeas, na Raça Canchim. Rev. Bras. Zootec. 29,
2223–2230.

Stear, M.J., Pokorny, T.S., Muggli, N.E., Stone, R.T., 1989. The relationships of birth
weight, preweaning gain and postweaning gain with the bovine major histo-
compatibility system. J. Anim. Sci. 67, 641–649.

Thomas, M.G., Enns, R.M., Shirley, K.L., Garcia, M.D., Garrett, A.J., Silver, G.A., 2007.
Associations of DNA polymorphisms in growth hormone and its transcriptional
regulators with growth and carcass traits in two populations of Brangus bulls.
Genet. Mol. Res. 6, 222–237.

Tizioto, P.C., Meirelles, S.L., Tulio, R.R., Rosa, A.N., Alencar, M.M., Medeiros, S.R., Si-
queira, F., Feijó, G.L.D., Silva, L.O.C., Torres Junior, R.A.A., Regitano, L.C.A., 2012.
Candidate genes for production traits in Nelore beef cattle. Genet. Mol. Res. 11,
4138–4144. http://dx.doi.org/10.4238/2012.September.19.1.

Toral, F.L.B., Alencar, M.M., Freitas, A.R., 2009. Estruturas de variância residual para
estimação de funções de covariância para o peso de bovinos da raça Canchim.
Rev. Bras. Zootec. 38, 2152–2160.

Toral, F.L.B., Alencar, M.M., Freitas, A.R., 2006. Arranjos para efeitos fixos e estru-
turas de (co) variâncias residuais para análises de medidas repetidas do peso de
bovinos da raça Canchim. Rev. Bras. Zootec. 35, 1951–1958.

Vargas, G., Buzanskas, M.E., Guidolin, D.G.F., Grossi, D.A., Bonifácio, A.S., Lôbo, R.B.,
Fonseca, R., Oliveira, J.A., Munari, D.P., 2014. Genetic parameter estimation for
pre- and post-weaning traits in Brahman cattle in Brazil. Trop. Anim. Health
Prod. 46, 1271–1278. http://dx.doi.org/10.1007/s11250-014-0640-3.

Zhang, C., Liu, B., Chen, H., Lan, X., Lei, C., Zhang, Z., Zhang, R., 2009. Associations of
a HinfI PCR-RFLP of POU1F1 gene with growth traits in Qinchuan cattle. Anim.
Biotechnol. 20, 71–74. http://dx.doi.org/10.1080/10495390802640462.

Zhang, X., Davis, M.E., Moeller, S.J., Ottobre, J.S., 2013. Effects of selection for blood
serum IGF-I concentration on reproductive performance of female Angus beef
cattle. J. Anim. Sci. 91, 4104–4115. http://dx.doi.org/10.2527/jas2012-6217.

http://dx.doi.org/10.1016/j.livsci.2010.05.008
http://dx.doi.org/10.1016/j.livsci.2010.05.008
http://dx.doi.org/10.1016/j.livsci.2010.05.008
http://dx.doi.org/10.1016/j.gene.2014.05.033
http://dx.doi.org/10.1016/j.gene.2014.05.033
http://dx.doi.org/10.1016/j.gene.2014.05.033
http://dx.doi.org/10.1016/j.gene.2014.05.033
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref7
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref7
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref7
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref8
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref8
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref8
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref8
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref8
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref9
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref9
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref9
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref10
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref10
http://dx.doi.org/10.1186/1297-9686-41-55
http://dx.doi.org/10.1186/1297-9686-41-55
http://dx.doi.org/10.1186/1297-9686-41-55
http://dx.doi.org/10.1007/s13353-012-0100-6
http://dx.doi.org/10.1007/s13353-012-0100-6
http://dx.doi.org/10.1007/s13353-012-0100-6
http://dx.doi.org/10.1007/s13353-012-0100-6
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref13
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref13
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref13
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref13
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref13
http://dx.doi.org/10.1007/s11033-014-3767-4
http://dx.doi.org/10.1007/s11033-014-3767-4
http://dx.doi.org/10.1007/s11033-014-3767-4
http://dx.doi.org/10.1007/s11033-014-3767-4
http://dx.doi.org/10.1111/j.1740-0929.2010.00783.x
http://dx.doi.org/10.1111/j.1740-0929.2010.00783.x
http://dx.doi.org/10.1111/j.1740-0929.2010.00783.x
http://dx.doi.org/10.1111/j.1740-0929.2010.00783.x
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref16
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref16
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref16
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref16
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref17
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref17
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref17
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref17
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref18
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref18
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref18
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref18
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref19
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref19
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref19
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref19
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref19
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref20
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref20
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref20
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref20
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref21
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref21
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref21
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref21
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref21
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref22
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref22
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref22
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref22
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref23
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref23
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref23
http://dx.doi.org/10.1016/j.livsci.2010.06.160
http://dx.doi.org/10.1016/j.livsci.2010.06.160
http://dx.doi.org/10.1016/j.livsci.2010.06.160
http://dx.doi.org/10.1016/j.livsci.2010.06.160
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref25
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref25
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref25
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref25
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref25
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref25
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref26
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref26
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref26
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref26
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref27
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref27
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref27
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref27
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref27
http://dx.doi.org/10.4238/2012.September.19.1
http://dx.doi.org/10.4238/2012.September.19.1
http://dx.doi.org/10.4238/2012.September.19.1
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref29
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref29
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref29
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref29
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref30
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref30
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref30
http://refhub.elsevier.com/S1871-1413(15)00356-X/sbref30
http://dx.doi.org/10.1007/s11250-014-0640-3
http://dx.doi.org/10.1007/s11250-014-0640-3
http://dx.doi.org/10.1007/s11250-014-0640-3
http://dx.doi.org/10.1080/10495390802640462
http://dx.doi.org/10.1080/10495390802640462
http://dx.doi.org/10.1080/10495390802640462
http://dx.doi.org/10.2527/jas2012-6217
http://dx.doi.org/10.2527/jas2012-6217
http://dx.doi.org/10.2527/jas2012-6217

	Allele substitution effects of IGF1, GH and PIT1 markers on estimated breeding values for weight and reproduction traits...
	Introduction
	Material and methods
	Animals and data
	Genetic parameter estimates
	DNA extraction and genotyping
	Allele substitution analyses

	Results and discussion
	Conclusion
	Conflict of interest statement
	Acknowledgments
	References