94
_________________________ Scientific Journal, FCV-LUZ / Vol. XXXIII, Suppl. Esp., 94 - 97, 2023, https://doi.org/10.52973/rcfcv-wbc013
IMPLEMENTING GENOMIC SELECTION IN THE IMB:
CHALLENGES AND OPPORTUNITIES
Implementing genomic selection in the Italian Mediterranean buffalo:
challenges and opportunities
Stephen Biffani 1 , Mayra Gomez 2 *, Roberta Cimmino 2 , Dario Rossi 2 , Gianluigi Zullo 2 , Richard Negrini 3 ,
Alberto Cesarani 4 , Giuseppe Campanile 5 , Gianluca Neglia 5
1
National Research Council (CNR), Institute of Agricultural Biology and Biotechnology (IBBA), Milan, Italy
2 Italian National Association of Buffalo Breeders, Caserta, Italy. 3 Italian National Breeders Association (AIA), Rome, Italy
4 Department of Agriculture, University of Sassari, Sassari, Italy
5 Department of Veterinary Medicine and Animal Production Federico II University, Naples, Italy
* Corresponding author: Mayra Gómez. (m.gomezcarpio@anasb.it).
ABSTRACT
Single-step genomic best linear unbiased predictor (ssGBLUP)
is a method for jointly estimating breeding values (BV) for geno-
typed and non-genotyped animals. Genomic information in the
Italian Mediterranean Buffalo (IMB) is now available. Its inclu-
sion in the genetic evaluation system could increase both the
accuracy and genetic progress of the traits of interest of the
breed. The study aimed to test the feasibility of ssGBLUP and
show the first results of implementing a genomic evaluation for
production and type traits in the IMB. Phenotypic information
on production (270-day milk, mozzarella yield (MY), protein and
fat kg and %, respectively) and morphology: feet and legs (FL)
and mammary system (MS) were used for this study. Produc-
tion records included 743,904 lactations from 276,451 buffalo
cows born from 1984 to 2019. Morphological traits were from
91,966 buffalo cows from 2004 to 2022. Regarding the geno-
types, 2,017 buffalo cows and 133 bulls were used. Data were
analyzed fitting two multi-trait animal models, a 6-trait model
for production data and a 2-trait model for morphology data.
According to the relationship matrix used, two models were fit-
ted: (i) the BLUP with the numerator relationship matrix (A) and
(ii) the ssGBLUP where A and the genomic relationship matrix
(G) are blended into H. BVs were estimated with BLUP and
ssGBLUP models. The cutoff year used to create the partial
data set was 2012. The correlation, accuracy, dispersion, and
bias statistics were calculated (LR method). Both bulls (N=49)
and cows (N=1288) were used for validations. On average,
the correlation between EBVs from partial and whole datasets
estimated with BLUP and ssGBLUP increased from 6 to 49%
and from 14 to 17% for production and type traits, respectively.
Among the traits analyzed, the most affected by the change
were protein/fat content, MY, and AM. The accuracy increase
for these traits was above 20% when using the ssGBLUP. All
LR statistics also improved for non-genotyped females. These
results showed that implementing ssGBLUP in the breeding
program can generate more accurate predictions for essential
traits in dairy IMB than traditional BLUP.
Keywords: genomics, Italian Mediterranean buffalo, selection.
RESUMEN
El mejor predictor lineal insesgado genómico de un solo paso
(ssGBLUP) es un método para estimar conjuntamente los
valores genéticos (BV) para animales genotipados y no ge-
notipados. La información genómica del búfalo mediterráneo
italiano (IMB) ya está disponible. Su inclusión en el sistema
de evaluación genética podría incrementar tanto la precisión
como el progreso genético de los rasgos de interés de la raza.
El estudio tuvo como objetivo probar la viabilidad de ssGBLUP
y mostrar los primeros resultados de la implementación de una
evaluación genómica para rasgos de producción y tipo en el
IMB. Para este estudio se utilizó información fenotípica sobre
producción (leche a 270 días, rendimiento de kg de mozzarella
(MY), % y kg de proteína y grasa, respectivamente) y morfolo-
gía: pies y piernas (FL) y sistema mamario (MS). Los registros
de producción incluyeron 743.904 lactancias de 276.451 vacas
búfalas nacidas de 1984 a 2019. Los rasgos morfológicos fue-
ron de 91.966 vacas búfalas de 2004 a 2022. En cuanto a los
genotipos, se utilizaron 2.017 vacas búfalas y 133 toros. Los
datos se analizaron ajustando dos modelos animales con múl-
tiples rasgos, un modelo de 6 rasgos para datos de producción
y un modelo de 2 rasgos para datos de morfología. De acuerdo
_________________________ Scientific Journal, FCV-LUZ / Vol. XXXIII, Suppl. Esp., 94 - 97, 2023, https://doi.org/10.52973/rcfcv-wbc013
IMPLEMENTING GENOMIC SELECTION IN THE IMB:
CHALLENGES AND OPPORTUNITIES
Implementing genomic selection in the Italian Mediterranean buffalo:
challenges and opportunities
Stephen Biffani 1 , Mayra Gomez 2 *, Roberta Cimmino 2 , Dario Rossi 2 , Gianluigi Zullo 2 , Richard Negrini 3 ,
Alberto Cesarani 4 , Giuseppe Campanile 5 , Gianluca Neglia 5
1
National Research Council (CNR), Institute of Agricultural Biology and Biotechnology (IBBA), Milan, Italy
2 Italian National Association of Buffalo Breeders, Caserta, Italy. 3 Italian National Breeders Association (AIA), Rome, Italy
4 Department of Agriculture, University of Sassari, Sassari, Italy
5 Department of Veterinary Medicine and Animal Production Federico II University, Naples, Italy
* Corresponding author: Mayra Gómez. (m.gomezcarpio@anasb.it).
ABSTRACT
Single-step genomic best linear unbiased predictor (ssGBLUP)
is a method for jointly estimating breeding values (BV) for geno-
typed and non-genotyped animals. Genomic information in the
Italian Mediterranean Buffalo (IMB) is now available. Its inclu-
sion in the genetic evaluation system could increase both the
accuracy and genetic progress of the traits of interest of the
breed. The study aimed to test the feasibility of ssGBLUP and
show the first results of implementing a genomic evaluation for
production and type traits in the IMB. Phenotypic information
on production (270-day milk, mozzarella yield (MY), protein and
fat kg and %, respectively) and morphology: feet and legs (FL)
and mammary system (MS) were used for this study. Produc-
tion records included 743,904 lactations from 276,451 buffalo
cows born from 1984 to 2019. Morphological traits were from
91,966 buffalo cows from 2004 to 2022. Regarding the geno-
types, 2,017 buffalo cows and 133 bulls were used. Data were
analyzed fitting two multi-trait animal models, a 6-trait model
for production data and a 2-trait model for morphology data.
According to the relationship matrix used, two models were fit-
ted: (i) the BLUP with the numerator relationship matrix (A) and
(ii) the ssGBLUP where A and the genomic relationship matrix
(G) are blended into H. BVs were estimated with BLUP and
ssGBLUP models. The cutoff year used to create the partial
data set was 2012. The correlation, accuracy, dispersion, and
bias statistics were calculated (LR method). Both bulls (N=49)
and cows (N=1288) were used for validations. On average,
the correlation between EBVs from partial and whole datasets
estimated with BLUP and ssGBLUP increased from 6 to 49%
and from 14 to 17% for production and type traits, respectively.
Among the traits analyzed, the most affected by the change
were protein/fat content, MY, and AM. The accuracy increase
for these traits was above 20% when using the ssGBLUP. All
LR statistics also improved for non-genotyped females. These
results showed that implementing ssGBLUP in the breeding
program can generate more accurate predictions for essential
traits in dairy IMB than traditional BLUP.
Keywords: genomics, Italian Mediterranean buffalo, selection.
RESUMEN
El mejor predictor lineal insesgado genómico de un solo paso
(ssGBLUP) es un método para estimar conjuntamente los
valores genéticos (BV) para animales genotipados y no ge-
notipados. La información genómica del búfalo mediterráneo
italiano (IMB) ya está disponible. Su inclusión en el sistema
de evaluación genética podría incrementar tanto la precisión
como el progreso genético de los rasgos de interés de la raza.
El estudio tuvo como objetivo probar la viabilidad de ssGBLUP
y mostrar los primeros resultados de la implementación de una
evaluación genómica para rasgos de producción y tipo en el
IMB. Para este estudio se utilizó información fenotípica sobre
producción (leche a 270 días, rendimiento de kg de mozzarella
(MY), % y kg de proteína y grasa, respectivamente) y morfolo-
gía: pies y piernas (FL) y sistema mamario (MS). Los registros
de producción incluyeron 743.904 lactancias de 276.451 vacas
búfalas nacidas de 1984 a 2019. Los rasgos morfológicos fue-
ron de 91.966 vacas búfalas de 2004 a 2022. En cuanto a los
genotipos, se utilizaron 2.017 vacas búfalas y 133 toros. Los
datos se analizaron ajustando dos modelos animales con múl-
tiples rasgos, un modelo de 6 rasgos para datos de producción
y un modelo de 2 rasgos para datos de morfología. De acuerdo
95
________________________________________________________ Revista Científica, FCV-LUZ / Vol. XXXIII, Supl. Esp., 94 - 97, 2023
con la matriz de relaciones utilizada, se ajustaron dos modelos:
(i) el BLUP con la matriz de relaciones del numerador (A) y (ii)
el ssGBLUP donde A y la matriz de relaciones genómicas (G)
se mezclan en H. Los BV se estimaron con Modelos BLUP y
ssGBLUP. El año de corte utilizado para crear el conjunto de
datos parcial fue 2012. Se calcularon las estadísticas de co-
rrelación, precisión, dispersión y sesgo (método LR). Para las
validaciones se utilizaron toros (N=49) y vacas (N=1288). En
promedio, la correlación entre los EBV de conjuntos de datos
parciales y completos estimados con BLUP y ssGBLUP au-
mentó del 6 al 49 % y del 14 al 17 % para los rasgos de produc-
ción y tipo, respectivamente. Entre los rasgos analizados, los
más afectados por el cambio fueron el contenido de proteína/
grasa, MY y AM. El aumento de precisión para estos rasgos
fue superior al 20 % cuando se utilizó ssGBLUP. Todas las es-
tadísticas de LR también mejoraron para las hembras no ge-
notipadas. Estos resultados mostraron que la implementación
de ssGBLUP en el programa de mejoramiento puede generar
predicciones más precisas para rasgos esenciales en el IMB
lechero que el BLUP tradicional.
Palabras clave: genómica, búfalo mediterráneo italiano, se-
lección.
INTRODUCTION
The current breeding goal for the Italian Mediterranean
Buffalo (IMB) centers on enhancing production and functional
traits. Genetic selection in buffaloes has faced obstacles due to
the absence of genealogical data, challenges in implementing
data collection, and poor reproductive performance. Conse-
quently, the full potential of the buffalo has yet to be actualized.
Nevertheless, the Italian Mediterranean buffalo stands out as
the sole breed globally, with a well-established genetic program
that has been in operation for over two decades.
The advent of genomics has significantly transformed the
genetic selection of livestock, although its implementation var-
ies across species. This discrepancy arises from factors such
as population structure, the extensive use of artificial insemina-
tion, and the accessibility of precise phenotypic data. Initially,
methodological approaches restricted genomics application to
large populations, but algorithms now cater to highly heteroge-
neous situations. Among these approaches is the Single-step
Genomic BLUP (ssGBLUP) method. This method has replaced
the multiphase process initially utilized for genomic evaluations
in various farm animal species. The ssGBLUP method employs
the inverse of a relationship matrix H-1 [1], merging the tradi-
tional additive relatedness matrix (A) with the genomic related-
ness matrix (G). Despite earlier challenges related to unknown
parental groups (UPG) and computational costs, recent studies
have demonstrated the efficacy of this method in estimating
Estimated Breeding Values (EBV) across diverse livestock spe-
cies, including dairy animals [2, 3, 4], beef cattle [5], goats [6],
sheep [7, 8], and buffaloes [9, 10].
The enhancement of buffalo genetics holds significant
importance in Italian breeding. Italy ranks sixth globally in buf-
falo milk production and sixteenth in livestock numbers. Conse-
quently, this study assesses the efficacy of genomic models in
predicting breeding values for production traits in Italian Medi-
terranean buffalo.
MATERIALS AND METHODS
Ethics Statement
Animal welfare and use committee approval was unnec-
essary for this study as datasets were obtained from pre-exist-
ing databases based on routine animal recording procedures.
Data
The National Association of Italian Buffalo Breeders
(ANASB) provided data for the present study and regarded pro-
ductive, morphological and pedigree information. Production
records included 743,904 lactations from 276,451 buffalo cows
born from 1984 to 2019. Morphological traits were from 91,966
buffalo cows from 2004 to 2022. Regarding the genotypes, a
total of 2,250 animals were used.
Analysis
Data were analysed fitting two multi-trait animal models,
a 6-trait model for production data and a 2-trait model for mor-
phology data. According to the relationship matrix used, two
models were fitted: (i) the BLUP with the numerator relation-
ship matrix (A) and (ii) the ssGBLUP where A and the genom-
ic relationship matrix (G) are blended into H. Breeding values
were estimated with BLUP and ssGBLUP models. The genet-
ic values were estimated twice to evaluate the models: in the
first test, the candidate animals had their phenotypes available
(complete data), while in the second, they had their phenotypes
masked (partial data). The cutoff year used to create the partial
data set was set at 2012. Therefore, the reduced data set cor-
responded to buffaloes with fine calving in 2012. The following
statistics were calculated to evaluate the models: dispersion,
precision, correlation and bias.
RESULTS AND DISCUSSION
The correlation between the two methods was larger than
90% for production and between 87 and 91% for morphology.
Regarding average reliability, the ssGBLUP method showed a
higher value than to the classic method. As seen in TABLE I,
the increase was between 6 and 41%, depending on the trait.
However, differences are observed between traits, and this is
because they have diverse heritabilities, and this causes the
snps to have a different impact on each trait.
________________________________________________________ Revista Científica, FCV-LUZ / Vol. XXXIII, Supl. Esp., 94 - 97, 2023
con la matriz de relaciones utilizada, se ajustaron dos modelos:
(i) el BLUP con la matriz de relaciones del numerador (A) y (ii)
el ssGBLUP donde A y la matriz de relaciones genómicas (G)
se mezclan en H. Los BV se estimaron con Modelos BLUP y
ssGBLUP. El año de corte utilizado para crear el conjunto de
datos parcial fue 2012. Se calcularon las estadísticas de co-
rrelación, precisión, dispersión y sesgo (método LR). Para las
validaciones se utilizaron toros (N=49) y vacas (N=1288). En
promedio, la correlación entre los EBV de conjuntos de datos
parciales y completos estimados con BLUP y ssGBLUP au-
mentó del 6 al 49 % y del 14 al 17 % para los rasgos de produc-
ción y tipo, respectivamente. Entre los rasgos analizados, los
más afectados por el cambio fueron el contenido de proteína/
grasa, MY y AM. El aumento de precisión para estos rasgos
fue superior al 20 % cuando se utilizó ssGBLUP. Todas las es-
tadísticas de LR también mejoraron para las hembras no ge-
notipadas. Estos resultados mostraron que la implementación
de ssGBLUP en el programa de mejoramiento puede generar
predicciones más precisas para rasgos esenciales en el IMB
lechero que el BLUP tradicional.
Palabras clave: genómica, búfalo mediterráneo italiano, se-
lección.
INTRODUCTION
The current breeding goal for the Italian Mediterranean
Buffalo (IMB) centers on enhancing production and functional
traits. Genetic selection in buffaloes has faced obstacles due to
the absence of genealogical data, challenges in implementing
data collection, and poor reproductive performance. Conse-
quently, the full potential of the buffalo has yet to be actualized.
Nevertheless, the Italian Mediterranean buffalo stands out as
the sole breed globally, with a well-established genetic program
that has been in operation for over two decades.
The advent of genomics has significantly transformed the
genetic selection of livestock, although its implementation var-
ies across species. This discrepancy arises from factors such
as population structure, the extensive use of artificial insemina-
tion, and the accessibility of precise phenotypic data. Initially,
methodological approaches restricted genomics application to
large populations, but algorithms now cater to highly heteroge-
neous situations. Among these approaches is the Single-step
Genomic BLUP (ssGBLUP) method. This method has replaced
the multiphase process initially utilized for genomic evaluations
in various farm animal species. The ssGBLUP method employs
the inverse of a relationship matrix H-1 [1], merging the tradi-
tional additive relatedness matrix (A) with the genomic related-
ness matrix (G). Despite earlier challenges related to unknown
parental groups (UPG) and computational costs, recent studies
have demonstrated the efficacy of this method in estimating
Estimated Breeding Values (EBV) across diverse livestock spe-
cies, including dairy animals [2, 3, 4], beef cattle [5], goats [6],
sheep [7, 8], and buffaloes [9, 10].
The enhancement of buffalo genetics holds significant
importance in Italian breeding. Italy ranks sixth globally in buf-
falo milk production and sixteenth in livestock numbers. Conse-
quently, this study assesses the efficacy of genomic models in
predicting breeding values for production traits in Italian Medi-
terranean buffalo.
MATERIALS AND METHODS
Ethics Statement
Animal welfare and use committee approval was unnec-
essary for this study as datasets were obtained from pre-exist-
ing databases based on routine animal recording procedures.
Data
The National Association of Italian Buffalo Breeders
(ANASB) provided data for the present study and regarded pro-
ductive, morphological and pedigree information. Production
records included 743,904 lactations from 276,451 buffalo cows
born from 1984 to 2019. Morphological traits were from 91,966
buffalo cows from 2004 to 2022. Regarding the genotypes, a
total of 2,250 animals were used.
Analysis
Data were analysed fitting two multi-trait animal models,
a 6-trait model for production data and a 2-trait model for mor-
phology data. According to the relationship matrix used, two
models were fitted: (i) the BLUP with the numerator relation-
ship matrix (A) and (ii) the ssGBLUP where A and the genom-
ic relationship matrix (G) are blended into H. Breeding values
were estimated with BLUP and ssGBLUP models. The genet-
ic values were estimated twice to evaluate the models: in the
first test, the candidate animals had their phenotypes available
(complete data), while in the second, they had their phenotypes
masked (partial data). The cutoff year used to create the partial
data set was set at 2012. Therefore, the reduced data set cor-
responded to buffaloes with fine calving in 2012. The following
statistics were calculated to evaluate the models: dispersion,
precision, correlation and bias.
RESULTS AND DISCUSSION
The correlation between the two methods was larger than
90% for production and between 87 and 91% for morphology.
Regarding average reliability, the ssGBLUP method showed a
higher value than to the classic method. As seen in TABLE I,
the increase was between 6 and 41%, depending on the trait.
However, differences are observed between traits, and this is
because they have diverse heritabilities, and this causes the
snps to have a different impact on each trait.
9613 th World Buffalo Congress ~ 13 er Congreso Mundial de Búfalos / Lectures / Biotechnology & Omics Technologies ___________________
The validation of the results through the LR method [11]
has the purpose of verifying the impact of the snp on the es-
timation and its predictive capacity when an animal does not
have a phenotype. This method is widely used to validate the
transition from genetics to genomics in different livestock spe-
cies, such as cattle [5, 4], ovine [8], chicken [12] e buffalo [10,
13]. In theory, the dispersion, correlation and precision should
be close to 1, and the results show how the ssGBLUP method
tends to predict better than the classical method. While, the
Bias indicates the error that we are making in the estimate.
Overall, ssGBLUP results were better than BLUP’s (TABLE II).
There was a moderate increase (18%) in the minimum predic-
tion accuracy with ssGBLUP. This result further confirms the
benefits of including genomic information in the genetic evalua-
tion for production and morphological traits.
When we talk about genetic selection, the precision of
our estimate is essential to guarantee the genetic progress
of our population. The data’s reliability increase is achieved
with the permanent inclusion of information, the validation of
the pedigree, and the meticulous implementation of quality
control measures [14]. Regarding genotypes, various studies
have confirmed that it is beneficial to genotype females; in fact,
including female genotypes is becoming more attractive lately
[15]. In the IMB this aspect is fundamental, since it has a low
number of bulls authorized for artificial fertilization. Therefore,
the genotyping of females is essential. Current data show that
of the total available genotypes, 10% correspond to males and
90% to females. Increasing precision with the inclusion of fe-
males has also been reported in small and large populations
[16]. For example, it was concluded that a less biased and
more reliable GEBV was obtained in Nordic Jersey cattle by
including genotypes from unselected females [17].
The correlation between the total dataset and the partial
dataset (Table II) indicated that the use of genomic information
leads to greater stability of the evaluation for the selection can-
didates (ssGBLUP, 0.51-0.60), which means that the genomic
model predicts better genetic values when the animal does not
have its phenotype. Similar results were shown by Cesarani et
al. [15] in the Italian Simmental cattle, with the ssGBLUP meth-
od obtaining an increase in the correlation of 14%.
With the ssGBLUP, we create a new matrix of combined
relationships that turns out to be more efficient than what we
had with the BLUP method and also perform an adjustment
of the kinship relationships between genotyped and non-gen-
otyped individuals. This adjustment is crucial role in reducing
bias when calculating breeding values from animals with no
phenotypic data available [18].
CONCLUSION
These results obtained by inserting the genotypes into
the calculation of the genetic values move in the expected
direction. These results indicate that ssGBLUP can be imple-
mented in the routine genetic evaluation of Italian Mediterra-
nean Buffalo. This implementation will be beneficial, especially
for those traits with low heritability. It is advisable to continue
increasing the number of genotyped animals more significant
to obtain greater precision increases.
ACKNOWLEDGMENTS
This research was funded by ITALIAN MINISTRY OF
AGRICULTURE (MIPAAF – DISR 07) - Programma di Sviluppo
Rurale Nazionale 2014/2020. Caratterizzazione delle risorse
genetiche animali di interesse zootecnico e salvaguardia della
biodiversità. Sottomisura: 10.2 - Sostegno per la conservazi-
one. l’uso e lo sviluppo sostenibili delle risorse genetiche in ag-
ricoltura. Project: “Bufala Mediterranea Italiana - tecnologie in-
novative per il miglioramento Genetico - BIG” Prot. N. 0215513
11/05/2021. CUP ANASB: J29J21003720005; CUP UNINA:
J69J21003020005.
REFERENCES
[1] Aguilar, I., Misztal, I., Johnson, D.L., Legarra, A., Tsuruta,
S., and Lawlor, T.J. (2010). Hot topic: A unified approach
to utilize phenotypic, full pedigree, and genomic infor-
mation for genetic evaluation of Holstein final score1.
Journal of Dairy Science 93(2), 743-752. doi: https://doi.
org/10.3168/jds.2009-2730.
TABLE I
INCREASED ACCURACY, SSGBLUP VS BLUP
Trait Method Increased (%)
Milk ssGBLUP vs BLUP 10.39
Fat % ssGBLUP vs BLUP 11.84
Protein % ssGBLUP vs BLUP 10.39
Fat Kg ssGBLUP vs BLUP 10.67
Protein Kg ssGBLUP vs BLUP 9.09
Yield ssGBLUP vs BLUP 40.68
Feet and legs ssGBLUP vs BLUP 6.56
Under teat ssGBLUP vs BLUP 7.46
TABLE II
MINIMUM AND MAXIMUM VALUE OBTAINED BY THE
BLUP AND SSGBLUP METHOD
Statistics LR BLUP ssGBLUP
Dispersion 0.64 -1.15 0.80 - 1.06
Correlation 0.37 - 0.57 0.51 - 0.60
Accuracy 0.38 - 0.51 0.45 - 0.52
Bias -0.57 - 0.06 -0.18 - 0.43
The validation of the results through the LR method [11]
has the purpose of verifying the impact of the snp on the es-
timation and its predictive capacity when an animal does not
have a phenotype. This method is widely used to validate the
transition from genetics to genomics in different livestock spe-
cies, such as cattle [5, 4], ovine [8], chicken [12] e buffalo [10,
13]. In theory, the dispersion, correlation and precision should
be close to 1, and the results show how the ssGBLUP method
tends to predict better than the classical method. While, the
Bias indicates the error that we are making in the estimate.
Overall, ssGBLUP results were better than BLUP’s (TABLE II).
There was a moderate increase (18%) in the minimum predic-
tion accuracy with ssGBLUP. This result further confirms the
benefits of including genomic information in the genetic evalua-
tion for production and morphological traits.
When we talk about genetic selection, the precision of
our estimate is essential to guarantee the genetic progress
of our population. The data’s reliability increase is achieved
with the permanent inclusion of information, the validation of
the pedigree, and the meticulous implementation of quality
control measures [14]. Regarding genotypes, various studies
have confirmed that it is beneficial to genotype females; in fact,
including female genotypes is becoming more attractive lately
[15]. In the IMB this aspect is fundamental, since it has a low
number of bulls authorized for artificial fertilization. Therefore,
the genotyping of females is essential. Current data show that
of the total available genotypes, 10% correspond to males and
90% to females. Increasing precision with the inclusion of fe-
males has also been reported in small and large populations
[16]. For example, it was concluded that a less biased and
more reliable GEBV was obtained in Nordic Jersey cattle by
including genotypes from unselected females [17].
The correlation between the total dataset and the partial
dataset (Table II) indicated that the use of genomic information
leads to greater stability of the evaluation for the selection can-
didates (ssGBLUP, 0.51-0.60), which means that the genomic
model predicts better genetic values when the animal does not
have its phenotype. Similar results were shown by Cesarani et
al. [15] in the Italian Simmental cattle, with the ssGBLUP meth-
od obtaining an increase in the correlation of 14%.
With the ssGBLUP, we create a new matrix of combined
relationships that turns out to be more efficient than what we
had with the BLUP method and also perform an adjustment
of the kinship relationships between genotyped and non-gen-
otyped individuals. This adjustment is crucial role in reducing
bias when calculating breeding values from animals with no
phenotypic data available [18].
CONCLUSION
These results obtained by inserting the genotypes into
the calculation of the genetic values move in the expected
direction. These results indicate that ssGBLUP can be imple-
mented in the routine genetic evaluation of Italian Mediterra-
nean Buffalo. This implementation will be beneficial, especially
for those traits with low heritability. It is advisable to continue
increasing the number of genotyped animals more significant
to obtain greater precision increases.
ACKNOWLEDGMENTS
This research was funded by ITALIAN MINISTRY OF
AGRICULTURE (MIPAAF – DISR 07) - Programma di Sviluppo
Rurale Nazionale 2014/2020. Caratterizzazione delle risorse
genetiche animali di interesse zootecnico e salvaguardia della
biodiversità. Sottomisura: 10.2 - Sostegno per la conservazi-
one. l’uso e lo sviluppo sostenibili delle risorse genetiche in ag-
ricoltura. Project: “Bufala Mediterranea Italiana - tecnologie in-
novative per il miglioramento Genetico - BIG” Prot. N. 0215513
11/05/2021. CUP ANASB: J29J21003720005; CUP UNINA:
J69J21003020005.
REFERENCES
[1] Aguilar, I., Misztal, I., Johnson, D.L., Legarra, A., Tsuruta,
S., and Lawlor, T.J. (2010). Hot topic: A unified approach
to utilize phenotypic, full pedigree, and genomic infor-
mation for genetic evaluation of Holstein final score1.
Journal of Dairy Science 93(2), 743-752. doi: https://doi.
org/10.3168/jds.2009-2730.
TABLE I
INCREASED ACCURACY, SSGBLUP VS BLUP
Trait Method Increased (%)
Milk ssGBLUP vs BLUP 10.39
Fat % ssGBLUP vs BLUP 11.84
Protein % ssGBLUP vs BLUP 10.39
Fat Kg ssGBLUP vs BLUP 10.67
Protein Kg ssGBLUP vs BLUP 9.09
Yield ssGBLUP vs BLUP 40.68
Feet and legs ssGBLUP vs BLUP 6.56
Under teat ssGBLUP vs BLUP 7.46
TABLE II
MINIMUM AND MAXIMUM VALUE OBTAINED BY THE
BLUP AND SSGBLUP METHOD
Statistics LR BLUP ssGBLUP
Dispersion 0.64 -1.15 0.80 - 1.06
Correlation 0.37 - 0.57 0.51 - 0.60
Accuracy 0.38 - 0.51 0.45 - 0.52
Bias -0.57 - 0.06 -0.18 - 0.43
97
________________________________________________________ Scientific Journal, FCV-LUZ / Vol. XXXIII, Suppl. Esp., 94 - 97, 2023
[2] Cesarani, A., Biffani, S., Garcia, A., Lourenco, D.,
Bertolini, G., Neglia, G., et al. (2021a). Genomic in-
vestigation of milk production in Italian buffalo. She-
his Journal of Animal Science 20(1), 539-547. doi:
10.1080/1828051X.2021.1902404.
[3] Legarra, A., and Reverter, A. (2018). Semi-parametric
estimates of population accuracy and bias of predictions
of breeding values and future phenotypes using the LR
method. Genetics Selection Evolution 50(1), 53. doi:
10.1186/s12711-018-0426-6.
[4] Bermann, M., Lourenco, D., Breen, V., Hawken, R., Bri-
to Lopes, F., and Misztal, I. (2021). Modeling genetic
differences of combined broiler chicken populations in
single-step GBLUP. J Anim Sci 99(4). doi: 10.1093/jas/
cabinet056.
[5] Herrera, JRV, Flores, EB, Duijvesteijn, N., Moghad-
dar, N., and van der Werf, J.H. (2021). Accuracy of Ge-
nomic Prediction for Milk Production Traits in Philippine
Dairy Buffaloes. Front Genet 12, 682576. doi: 10.3389/
fgene.2021.682576.
[6] Christensen, OF, Madsen, P., Nielsen, B., Ostersen, T.,
and Su, G. (2012). Single-step methods for genomic eva-
luation in pigs. Animal 6(10), 1565-1571.
[7] Cesarani, A., Garcia, A., Hidalgo, J., Degano, L., Vicario,
D., Macciotta, NPP, et al. (2021b). Genomic information
allows for more accurate breeding values for milkability in
dual-purpose Italian Simmental cattle. J Dairy Sci 104(5),
5719-5727. doi: 10.3168/jds.2020-19838.
[8] Cooper, T.A., Wiggans, G.R., and VanRaden, P.M. (Year).
“Including cow information in genomic prediction of Hols-
tein dairy cattle in the US”).
[9] Gao, H., Madsen, P., Nielsen, US, Aamand, GP, Su,
G., Byskov, K., et al. (2015). Including different groups
of genotyped females for genomic prediction in a Nor-
dic Jersey population. Journal of Dairy Science 98(12),
9051-9059. doi: https://doi.org/10.3168/jds.2015-9947.
[10] Amaya Martinez, A., Martinez Sarmiento, R., and Ce-
rón-Muñoz, M. (2020). Genetic evaluations in cattle using
the single-step genomic best linear unbiased predictor.
Agricultural Science and Technology 21(1), 19-31.
[11] Himmelbauer, J., Schwarzenbacher, H., and Fuerst, C.
(2021). Implementation of single-step evaluations for
fitness traits in the German and Austrian Fleckvieh and
Brown Swiss populations. Interbull Bulletin (56), 82-89.
[12] Pimentel, EdCG, Edel, C., Krogmeier, D., Emmerling,
R., and Götz, K.-U. (2021). Effects of use of external in-
formation in Single-Step evaluations for linear type traits
in Brown Swiss. Interbull Bulletin (56), 121-124.
[13] Cesarani , A. , Lourenco , D. , Tsuruta , S. , Legarra , A. , Ni-
colazzi, EL, VanRaden, PM, et al. (2022). Multibreed
genomic evaluation for production traits of dairy cattle
in the United States using single-step genomic best li-
near unbiased predictor. Journal of Dairy Science 105(6),
5141-5152. doi: 10.3168/jds.2021-21505.
[14] Lourenco, D.A., Tsuruta, S., Fragomeni, B.O., Masuda,
Y., Aguilar, I., Legarra, A., et al. (2015). Genetic evalua-
tion using single-step genomic best linear unbiased pre-
dictor in American Angus. J Anim Sci 93(6), 2653-2662.
doi: 10.2527/jas.2014-8836.
[15] Teissier, M., Larroque, H., and Robert-Granié, C. (2018).
Weighted single-step genomic BLUP improves accu-
racy of genomic breeding values for protein content in
French dairy goats: a quantitative trait influenced by a
major gene. Genetics Selection Evolution 50(1), 31. doi:
10.1186/s12711-018-0400-3.
[16] Cesarani, A., Gaspa, G., Correddu, F., Cellesi, M., Di-
mauro, C., and Macciotta, N.P.P. (2019). Genomic selec-
tion of milk fatty acid composition in Sarda dairy sheep:
Effect of different phenotypes and relationship matrices
on heritability and breeding value accuracy. J Dairy Sci
102(4), 3189-3203. doi: 10.3168/jds.2018-15333.
[17] Macedo, F.L., Christensen, O.F., Astruc, J.-M., Aguilar, I.,
Masuda, Y., and Legarra, A. (2020). Bias and accuracy
of dairy sheep evaluations using BLUP and SSGBLUP
with metafounders and unknown parent groups. Gene-
tics Selection Evolution 52(1), 47. doi: 10.1186/s12711-
020-00567-1.
[18] Aspilcueta-Borquis, R.R., Araujo Neto, F.R., Santos, D.J.,
Hurtado-Lugo, N.A., Silva, J.A., and Tonhati, H. (2015).
Multiple-trait genomic evaluation for milk yield and milk
quality traits using genomic and phenotypic data in bu-
ffalo in Brazil. Genet Mol Res 14(4), 18009-18017. doi:
10.4238/2015.December.22.27.
________________________________________________________ Scientific Journal, FCV-LUZ / Vol. XXXIII, Suppl. Esp., 94 - 97, 2023
[2] Cesarani, A., Biffani, S., Garcia, A., Lourenco, D.,
Bertolini, G., Neglia, G., et al. (2021a). Genomic in-
vestigation of milk production in Italian buffalo. She-
his Journal of Animal Science 20(1), 539-547. doi:
10.1080/1828051X.2021.1902404.
[3] Legarra, A., and Reverter, A. (2018). Semi-parametric
estimates of population accuracy and bias of predictions
of breeding values and future phenotypes using the LR
method. Genetics Selection Evolution 50(1), 53. doi:
10.1186/s12711-018-0426-6.
[4] Bermann, M., Lourenco, D., Breen, V., Hawken, R., Bri-
to Lopes, F., and Misztal, I. (2021). Modeling genetic
differences of combined broiler chicken populations in
single-step GBLUP. J Anim Sci 99(4). doi: 10.1093/jas/
cabinet056.
[5] Herrera, JRV, Flores, EB, Duijvesteijn, N., Moghad-
dar, N., and van der Werf, J.H. (2021). Accuracy of Ge-
nomic Prediction for Milk Production Traits in Philippine
Dairy Buffaloes. Front Genet 12, 682576. doi: 10.3389/
fgene.2021.682576.
[6] Christensen, OF, Madsen, P., Nielsen, B., Ostersen, T.,
and Su, G. (2012). Single-step methods for genomic eva-
luation in pigs. Animal 6(10), 1565-1571.
[7] Cesarani, A., Garcia, A., Hidalgo, J., Degano, L., Vicario,
D., Macciotta, NPP, et al. (2021b). Genomic information
allows for more accurate breeding values for milkability in
dual-purpose Italian Simmental cattle. J Dairy Sci 104(5),
5719-5727. doi: 10.3168/jds.2020-19838.
[8] Cooper, T.A., Wiggans, G.R., and VanRaden, P.M. (Year).
“Including cow information in genomic prediction of Hols-
tein dairy cattle in the US”).
[9] Gao, H., Madsen, P., Nielsen, US, Aamand, GP, Su,
G., Byskov, K., et al. (2015). Including different groups
of genotyped females for genomic prediction in a Nor-
dic Jersey population. Journal of Dairy Science 98(12),
9051-9059. doi: https://doi.org/10.3168/jds.2015-9947.
[10] Amaya Martinez, A., Martinez Sarmiento, R., and Ce-
rón-Muñoz, M. (2020). Genetic evaluations in cattle using
the single-step genomic best linear unbiased predictor.
Agricultural Science and Technology 21(1), 19-31.
[11] Himmelbauer, J., Schwarzenbacher, H., and Fuerst, C.
(2021). Implementation of single-step evaluations for
fitness traits in the German and Austrian Fleckvieh and
Brown Swiss populations. Interbull Bulletin (56), 82-89.
[12] Pimentel, EdCG, Edel, C., Krogmeier, D., Emmerling,
R., and Götz, K.-U. (2021). Effects of use of external in-
formation in Single-Step evaluations for linear type traits
in Brown Swiss. Interbull Bulletin (56), 121-124.
[13] Cesarani , A. , Lourenco , D. , Tsuruta , S. , Legarra , A. , Ni-
colazzi, EL, VanRaden, PM, et al. (2022). Multibreed
genomic evaluation for production traits of dairy cattle
in the United States using single-step genomic best li-
near unbiased predictor. Journal of Dairy Science 105(6),
5141-5152. doi: 10.3168/jds.2021-21505.
[14] Lourenco, D.A., Tsuruta, S., Fragomeni, B.O., Masuda,
Y., Aguilar, I., Legarra, A., et al. (2015). Genetic evalua-
tion using single-step genomic best linear unbiased pre-
dictor in American Angus. J Anim Sci 93(6), 2653-2662.
doi: 10.2527/jas.2014-8836.
[15] Teissier, M., Larroque, H., and Robert-Granié, C. (2018).
Weighted single-step genomic BLUP improves accu-
racy of genomic breeding values for protein content in
French dairy goats: a quantitative trait influenced by a
major gene. Genetics Selection Evolution 50(1), 31. doi:
10.1186/s12711-018-0400-3.
[16] Cesarani, A., Gaspa, G., Correddu, F., Cellesi, M., Di-
mauro, C., and Macciotta, N.P.P. (2019). Genomic selec-
tion of milk fatty acid composition in Sarda dairy sheep:
Effect of different phenotypes and relationship matrices
on heritability and breeding value accuracy. J Dairy Sci
102(4), 3189-3203. doi: 10.3168/jds.2018-15333.
[17] Macedo, F.L., Christensen, O.F., Astruc, J.-M., Aguilar, I.,
Masuda, Y., and Legarra, A. (2020). Bias and accuracy
of dairy sheep evaluations using BLUP and SSGBLUP
with metafounders and unknown parent groups. Gene-
tics Selection Evolution 52(1), 47. doi: 10.1186/s12711-
020-00567-1.
[18] Aspilcueta-Borquis, R.R., Araujo Neto, F.R., Santos, D.J.,
Hurtado-Lugo, N.A., Silva, J.A., and Tonhati, H. (2015).
Multiple-trait genomic evaluation for milk yield and milk
quality traits using genomic and phenotypic data in bu-
ffalo in Brazil. Genet Mol Res 14(4), 18009-18017. doi:
10.4238/2015.December.22.27.