Skip to main content

Improving the accuracy of genomic evaluation for linear body measurement traits using single-step genomic best linear unbiased prediction in Hanwoo beef cattle

Abstract

Background

Recently, there has been a growing interest in the genetic improvement of body measurement traits in farm animals. They are widely used as predictors of performance, longevity, and production traits, and it is worthwhile to investigate the prediction accuracies of genomic selection for these traits. In genomic prediction, the single-step genomic best linear unbiased prediction (ssGBLUP) method allows the inclusion of information from genotyped and non-genotyped relatives in the analysis. Hence, we aimed to compare the prediction accuracy obtained from a pedigree-based BLUP only on genotyped animals (PBLUP-G), a traditional pedigree-based BLUP (PBLUP), a genomic BLUP (GBLUP), and a single-step genomic BLUP (ssGBLUP) method for the following 10 body measurement traits at yearling age of Hanwoo cattle: body height (BH), body length (BL), chest depth (CD), chest girth (CG), chest width (CW), hip height (HH), hip width (HW), rump length (RL), rump width (RW), and thurl width (TW). The data set comprised 13,067 phenotypic records for body measurement traits and 1523 genotyped animals with 34,460 single-nucleotide polymorphisms. The accuracy for each trait and model was estimated only for genotyped animals using five-fold cross-validations.

Results

The accuracies ranged from 0.02 to 0.19, 0.22 to 0.42, 0.21 to 0.44, and from 0.36 to 0.55 as assessed using the PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods, respectively. The average predictive accuracies across traits were 0.13 for PBLUP-G, 0.34 for PBLUP, 0.33 for GBLUP, and 0.45 for ssGBLUP methods. Our results demonstrated that averaged across all traits, ssGBLUP outperformed PBLUP and GBLUP by 33 and 43%, respectively, in terms of prediction accuracy. Moreover, the least root of mean square error was obtained by ssGBLUP method.

Conclusions

Our findings suggest that considering the ssGBLUP model may be a promising way to ensure acceptable accuracy of predictions for body measurement traits, especially for improving the prediction accuracy of selection candidates in ongoing Hanwoo breeding programs.

Background

Improving meat production in beef cattle is an important breeding goal throughout the world because it has a considerable effect on the profitability of the beef industry. Linear body measurement traits are important economic traits in beef cattle, as they provide useful information for understanding the growth and frame size of the animals. The relationship between the linear type traits and economically important traits, such as reproductive traits [1, 2], longevity [3, 4], lifetime production efficiency [5], and growth traits [6, 7], have been extensively researched in both dairy and beef cattle.

Hanwoo cattle are unique to Korea and popular for meat owing to their rapid growth and high quality beef. Body measurements have become routinely collected traits over the last three decades in this breed, which provide valuable resources to study the complete growing period [8]. Moreover, these traits have been proposed as indirect selection criteria for the genetic improvement of meat production in beef cattle [9, 10], and can be harnessed to accelerate the breeding progress. Improving the accuracy of selection for body measurement traits will benefit the beef cattle industry; consequently, these traits are often included in multi-trait genetic evaluations as predictors of performance in beef cattle [11, 12]. To this end, the application of genomic selection could be a promising tool to improve the accuracy of estimation of breeding values of body measurement traits. It refers to selection based on genomic estimated breeding values (GEBV) using genome-wide marker information [13] instead of the traditional selection which uses pedigree-based BLUP [14]. Several statistical methods were developed to predict GEBV from 2001 onwards, among which the genomic best linear unbiased prediction (GBLUP) models and Bayesian variable selection or variable shrinkage models have been widely used [13, 15, 16]. The main differences between these models are the assumptions of the distribution of single nucleotide polymorphism (SNP) effects. Nonetheless, the GBLUP method has become a popular approach for practical genomic evaluations because most traits in livestock species have polygenic nature [17,18,19,20], and also because of its simpler and lower computational demand than other methods [21]. A decade ago, a method based on the GBLUP framework was proposed by Misztal et al. [22], termed the single-step genomic best linear unbiased prediction (ssGBLUP), which uses simultaneously all pedigree, genotypic and phenotypic information from both genotyped and non-genotyped individuals. In this method, the pedigree-based numerator relationship matrix (A) and relationship matrix based on genomic information (G) are combined into a single matrix (H) [22, 23]. The use of ssGBLUP increases the accuracy of genomic prediction compared to the methods using only genotyped individuals [24]. In this line, previous studies have demonstrated that the accuracy of genomic evaluation in many species could be increased by using ssGBLUP compared with pedigree-based BLUP or genomic BLUP [25,26,27,28,29,30,31,32]. Besides, the literature on prediction of the breeding values of linear body measurement traits using genomic evaluations in field data of dairy cattle [25, 33, 34], beef cattle [35,36,37], goats [38,39,40], sheep [41], and pig [42] have been previously reported.

Since there is sufficient pedigree information available in Hanwoo cattle [31, 32], it is expected that the use of ssGBLUP can be influenced to improve genomic prediction accuracy for body measurement traits. Nonetheless, these traits have not yet been investigated in breeding programs for this breed. Therefore, the aim of this study was to evaluate the accuracy of breeding values for linear body measurement traits using conventional BLUP only on genotyped animals (PBLUP-G), conventional BLUP with all animals, GBLUP, and ssGBLUP methods, which provide valuable insights into the application of genomic selection for the studied traits in Hanwoo beef cattle.

Results

Descriptive statistics and estimates of variance components

The number of animals with records, means, minimum, maximum, standard deviations, and phenotypic coefficient of variation for the 10 body measurement traits are shown in Table 1. The mean values of body measurement traits ranged from 21.38 to 165.95 with standard deviation between 2.71 and 9.48. Variance components and heritability estimates for the studied traits are presented in Table 2. The range of heritability for the 10 traits was between 0.11 and 0.40. Among all investigated traits, the estimated heritability of BH was the highest (0.40) and the lowest was for CW and HW (0.11). The standard error for all heritability estimates was less than 0.03 (Table 2).

Table 1 Summary statistics for linear body measurement traits used to estimate variance components in Hanwoo cattle
Table 2 Pedigree-based BLUP variance component estimations and heritability of linear body measurement traits in Hanwoo cattle

Comparison of accuracy and bias for the four models

The predictive accuracies for the 10 traits obtained using PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods are shown in Fig. 1. The accuracies determined using PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods ranged from 0.02 to 0.19, 0.22 to 0.42, 0.21 to 0.44, and 0.36 to 0.55, respectively. The average predictive accuracies across traits were 0.13 for PBLUP-G, 0.34 for PBLUP, 0.33 for GBLUP, and 0.45 for ssGBLUP methods (Fig. 1). The results showed that the average magnitude of improved accuracy from PBLUP to ssGBLUP was 33%, followed by changing the model from GBLUP to ssGBLUP was 43% for the 10 traits. Using data from only genotyped animals, prediction accuracies of the GBLUP method were considerably higher than those of the PBLUP-G method for all traits, whereas the average accuracies using GBLUP were slightly lower than PBLUP method. The results also indicated that when phenotypic data from only genotyped individuals were used, prediction accuracies of the PBLUP-G method were considerably lower than those of other methods for all traits. Furthermore, the ssGBLUP method provided higher accuracies of prediction than both PBLUP and PBLUP-G methods in all body measurement traits. The highest and lowest improvement of accuracy in ssGBLUP over PBLUP was obtained for HW (61%) and CG (14%) traits, respectively.

Fig. 1
figure1

Accuracy of breeding values obtained using PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods. The means and standard errors for body height (BH), body length (BL), chest depth (CD), chest girth (CG), chest width (CW), hip height (HH), hip width (HW), rump length (RL), rump width (RW), and thigh width (TW) in Hanwoo cattle. The white numbers represent standard error (SE). PBLUP-G, pedigree-based best linear unbiased prediction only on genotyped animals; PBLUP, pedigree-based best linear unbiased prediction with all animals; GBLUP, genomic best linear unbiased prediction; ssGBLUP, single-step genomic best linear unbiased prediction

The scale of predictions is an important factor that determines the use of estimated breeding values for genetic evaluation. The regression coefficient of adjusted phenotypes was calculated as a measure of prediction bias (Table 3). The regression coefficients of prediction determined using PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods ranged from 0.01 to 0.85, 0.69 to 1.05, 0.95 to 1.43, and 0.96 to 1.24, respectively, for all traits. The average regression coefficients across the traits were 0.54, 0.96, 1.13, and 1.12 using PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods, respectively. There was no clear trend demonstrating that a model was better than the others regarding unbiased predictions for most traits. In addition, the average absolute deviation of regression coefficients from 1.0 was 0.46, 0.07, 0.15, and 0.13 for PBLUP-G, PBLUP, GBLUP, and ssGBLUP, respectively, indicating that the PBLUP method was the least biased, whereas the predictions from GBLUP and ssGBLUP methods tended to be slightly deflated for most traits (Table 3). However, RMSEs obtained using the ssGBLUP method were lower than those obtained by the PBLUP-G, PBLUP and GBLUP methods for all traits of interest (Table 4).

Table 3 Regression coefficients of the adjusted phenotypes of EBV/GEBV for PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods
Table 4 Root of mean square error (RMSE) of EBV/GEBV for PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods

Discussion

The heritability estimates for the 10 body measurement traits in this study were low to relatively high, ranging from 0.11 to 0.40. In the same breed, Choy et al. [43] reported heritability estimates for BL (0.23), CD (0.28), CG (0.27), CW (0.21), HW (0.20), and RW (0.26) at 12 months of age, which were somewhat higher than those seen in our study. These discrepancies could be due to the difference in the numbers of animals measured (approximately 32% more in this study than their study on Hanwoo cattle) and statistical models used for variance component estimations. The estimated heritability of HH was consistent with the study by Zhang et al. [44], where the estimates of heritability for HH at yearling age was 0.38 in Chinese Holstein. The estimate of the heritability of CW in our study was lower than those reported previously for Holstein in the literature [33, 34]. Moreover, our heritability estimates for CD, CW, HW, and TW are within the ranges of the heritability estimates reported by Doyle et al. [45] in Angus, Charolais, Hereford, Limousin, and Simmental cattle.

In this study, we investigated the accuracy of the breeding values for linear body measurement traits using four models, pedigree-based BLUP only on genotyped animals (PBLUP-G), pedigree-based BLUP with all animals (PBLUP), genomic BLUP (GBLUP), and single-step genomic BLUP (ssGBLUP) in Hanwoo beef cattle. Accuracies of obtained breeding values from these models were compared. To maximize the profitability of the beef cattle industry, selection for economically important traits is desirable. Estimations of breeding values for body measurement traits are most important because they are widely used as predictors of growth [7], meat production and longevity [3] traits in the beef cattle industry. Several previous studies on Hanwoo cattle [31, 32] and on other beef cattle breeds [28, 29] have shown that the ssGBLUP outperformed GBLUP or pedigree-based BLUP methods for the prediction of breeding values in carcass and growth traits.

According to our results, the average predictive accuracies of 10 traits obtained using the ssGBLUP model were approximately 33% higher than those obtained using the PBLUP model, which ranged from 14% for CG to 61% for HW trait. The explanation of the observed gain in the accuracy is that the simultaneous use of pedigree, phenotypic, and genomic information in the single-step method provides additional information for estimating breeding values compared to traditional pedigree-based models, which are based on capturing the variation in Mendelian sampling [27]. Moreover, the average gain in accuracy across traits using the ssGBLUP method was approximately 43% higher relative to GBLUP method that only uses data from genotyped animals for all traits, which may reflect the fact that the use of additional phenotypic information from including non-genotyped animals along with a relatively deep pedigree, was available on Hanwoo cattle. On average, the GBLUP method was slightly less accurate than the PBLUP method, which could be attributable to a small number of genotyped reference animals. Another reason is that the PBLUP method utilizes all phenotypic data and pedigree information from all generations to predict breeding values, whereas the GBLUP model uses information only from genotyped animals in the current generation. Based on our results, the GBLUP method considerably outperformed the PBLUP-G model with the same phenotypic data for all traits. Similarly, Lee et al. [31] indicated that methods using genomic information from only genotyped animals performed better than PBLUP and PBLUP-G for carcass traits in Hanwoo cattle. It is important to note, however, that the accuracy of GBLUP using only genotyped animals available on the current generation was 96% of the prediction accuracy of pedigree-based BLUP over all generations. Therefore, it can be argued that the GBLUP method is highly beneficial when pedigree information is unavailable. For instance, in other species such as fish [46] and wild species [47], in which the information on the relationship is not available or can be difficult to keep track of, the GBLUP method can be a useful strategy for improving the accuracy of prediction.

Consistent with our results, Song et al. [42] achieved a lower accuracy with GBLUP method than PBLUP and ssGBLUP methods for all seven body measurement traits in pigs. They also demonstrated that on average, the accuracies of genomic prediction using the ssGBLUP method were higher by 86 and 1% than those using GBLUP and PBLUP, respectively. In another study, Lourenco et al. [48] also showed that GBLUP method was less accurate than PBLUP method for fat percentage trait in all parities using a small number of genotyped animals in the dairy population. Similarly, Abo-Ismail et al. [34] reported that the average reliabilities of EBVs was higher than the average reliabilities of direct genomic breeding values (DGV) using different SNP sets for body conformation traits in the validation population of Holstein cattle.

Some studies have been undertaken to investigate the prediction accuracy of evaluations using the ssGBLUP method for linear type and body measurement traits on different animals such as dairy cattle [25, 33], dairy goat [38,39,40], dairy sheep [41], and pig [42], which highlights that the ssGBLUP method is as accurate as or more accurate than either the PBLUP or GBLUP method for the traits of interest. For instance, Tsuruta et al. [25] exhibited that ssGBLUP method was more accurate than the PBLUP method for 18 linear-type traits in Holsteins. Their results showed that the average reliabilities of 18 traits from the single-trait ssGBLUP model were 86% higher than those from the single-trait BLUP model, and the gain in the reliability of breeding values determined using the multi-trait ssGBLUP model was, on average, 84% compared with that obtained using the multi-trait BLUP for all traits. Similarly, the superiority of ssGBLUP over PBLUP was reported in the estimation of the breeding values for 20 linear-type traits of Holstein cows by Zavadilová et al. [33], who found that the average correlation between the post-progeny test EBV and parent average (0.30) was lower than the average correlation between the post-progeny test EBV and the predicted GEBV (0.40).

Previously, the performance of ssGBLUP has been reported to be better than either the pedigree-based BLUP or GBLUP for milk production traits, udder type traits, and somatic cell scores in French dairy goats and delivered a 61 to 96% gain in the accuracy of genomic prediction for udder type traits [38, 39]. Furthermore, Oget et al. [41] showed that ssGBLUP performed more accurately than pedigree-based BLUP for type traits consisting of teat angle (15.84%) and udder depth (26.07%) in Lacaune dairy sheep.

Overall, our findings indicate that ssGBLUP generally generated higher prediction accuracy than the other three methods for body measurement traits in Hanwoo cattle, which could be implemented in practical breeding programs.

Conclusions

This study aimed to improve the accuracy of genomic prediction through the incorporation of the information of genotyped and non-genotyped animals into a genetic evaluation for body measurement traits in Hanwoo beef cattle. Four methods were also compared, PBLUP-G, PBLUP, GBLUP, and ssGBLUP in terms of accuracy. Our results demonstrate that the ssGBLUP provides a more accurate prediction than both traditional BLUP (PBLUP-G and PBLUP) and GBLUP for all the studied traits. It is worth noting that the ssGBLUP yielded on average 43% higher accuracy than GBLUP and 33% higher accuracy than PBLUP on body measurement traits. Therefore, the ssGBLUP can be considered as an alternative for effectively improving the prediction accuracy of selection candidates in ongoing Hanwoo breeding schemes.

Methods

Pedigree and phenotypic data

The dataset used in this study was provided by the Hanwoo Improvement Center of the National Agricultural Cooperative Federation and included 8452 bulls and 4615 steers born between 1989 and 2015. The pedigree consisted of 50,220 animals, which were traced back to 11 generations. The phenotypic data of body measurement traits were recorded in centimeters as continuous traits at the age of 12 months. The measured traits included body height (BH), body length (BL), chest depth (CD), chest girth (CG), chest width (CW), hip height (HH), hip width (HW), rump length (RL), rump width (RW), and thurl width (TW) which their details are illustrated in Fig. S1. Descriptive statistics for each trait are shown in Table 1.

Genotypic data

Genotyping data used in genomic evaluations in this study were available for 1679 individuals that had been genotyped using Illumina BovineSNP50K (n = 959) and HD 777 K (n = 720) BeadChip (Illumina Inc., San Diego, CA, USA). From both the 50 K and 777 K SNP chips only the identical locations were used and 45,304 common SNPs were found. Animals with more than 10% of missing genotype data (n = 73) and without a phenotype for the traits of interest (n = 33) as well as animals with Mendelian conflicts (n = 11) or deviation errors between the pedigree and genomic relations (n = 39) were excluded from the final analyses. The Mendelian conflicts were investigated using all SNP to determine wrong relationships for sire-offspring pairs. The exclusion threshold of Mendelian conflicts was assumed two percentages according to the default of PreGSf90 program [49]. To detect deviation errors between the pedigree and genomic data, the relationship matrix based on pedigree (A) and SNP genotypes (G) were compared. A total of 39 individuals showed unreasonable deviations based on their A and G relationship coefficients possibly due to DNA sampling errors and thus were eliminated. Among these, duplicated individuals (n = 8) which might have been genotyped twice with different IDs had their G coefficients close to 1.0 and A coefficients close to 0 (n = 4) or 0.25 (n = 4). For the remaining individuals (n = 31), either the G coefficients were near 0 while the A coefficients were close to 0.25, in this case, they would have been mistakenly recorded as half-sib individuals, or the G coefficients were close to 0.25 and the A coefficients were near 0 as would be half-sibs mistakenly recorded as unrelated. SNPs with unknown positions (302 SNPs) and those located on sex chromosomes (1150 SNPs) were removed from the analyses after quality control. Furthermore, the SNPs with call rates lower than 0.98 (2677 SNPs), minor allele frequencies lower than 0.01 (6684 SNPs), and a maximum difference between observed and expected frequency of 0.15 as a departure of heterozygous from the Hardy-Weinberg equilibrium (31 SNPs) were excluded. The missing genotypes were imputed in the BEAGLE software [50]. Finally, the genotypes for 34,460 SNP markers from 1523 animals (369 bulls and 1154 steers) were used for the analyses.

Statistical methods

Estimation of variance components

The variance components and heritabilities were estimated implementing AIREMLF90 software [49], using the pedigree-based single-trait animal model as follows:

$$ \mathbf{y}=\mathbf{Xb}+\mathbf{Zu}+\mathbf{e} $$
(1)

where y is the vector of the observations for the trait of interest, b is the vector of the fixed effects, including batch-test place-sex [the batch was formed twice every year and it represents the year and season of selection at 6 months of age and test place is the place where animals were reared after selection (164 levels)], birth place [the county where the farms were located (111 levels)], and age at the recorded date as a covariate; u is the vector of additive genetic effects of the individuals; X is the incidence matrix of b; Z is the incidence matrix of u, and e is the vector of the residuals. It was assumed that u ~ N (0, Aσa2) and e ~ N (0, Iσe2), where A was a pedigree-based genetic relationship matrix and σa2 was the additive genetic variance, and σe2 is the residual variance.

Finally, the adjusted phenotypes (yadj) were obtained for each trait and animal as the residual effects (e) of the y = Xb + e model which \( \hat{\mathbf{b}}={\left({\mathbf{X}}^{\prime}\mathbf{X}\right)}^{-\mathbf{1}}\ {\mathbf{X}}^{\prime}\mathbf{y} \).

Estimation of breeding values

Four methods, a traditional BLUP method with phenotypes only on genotyped animals (PBLUP-G), a traditional BLUP method with pedigree-based relationship matrix (PBLUP), a GBLUP method based on genomic relationship matrix, and a single-trait single-step GBLUP (ssGBLUP) method by combining the relationship matrix constructed from genotyped and non-genotyped individuals and pedigree information, were used to predict breeding values.

Pedigree-based best linear unbiased prediction (PBLUP) model

The BLUP model to predict conventional EBV was:

$$ {\mathbf{y}}_{\mathrm{adj}}=\mathbf{1}\mu +\mathbf{Zu}+\mathbf{e} $$
(2)

where yadj is the vector of the observations for the trait adjusted for fixed effects, 1 is the vector of ones; μ is overall mean; other notations are the same as in the model eq. (1). In addition, the EBVs were obtained using only phenotypes and pedigree on genotyped animals (PBLUP-G model).

Genomic best linear unbiased prediction (GBLUP) model

For estimating genomic breeding values, we used the model (2) above with the following assumptions;

u was the vector of additive genetic effects of only genotyped individuals and Z was the incidence matrix of u. It was assumed that u ~ N (0, Gσa2), where G was the genomic relationship matrix constructed using SNP information as described by VanRaden [15]:

\( \mathbf{G}=\frac{\mathbf{M}{\mathbf{M}}^{\prime }}{2{\sum}_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{p}}_{\mathrm{i}}\ \left(1-{\mathrm{p}}_{\mathrm{i}}\right)} \), where m is the total number of markers (34,460), pi is the allelic frequency of ith marker and M is the matrix of centered genotypes.

Single-step genomic best linear unbiased prediction (ssGBLUP) model

In the ssGBLUP method, the statistical model was similar to that used for traditional evaluation; however, the non-genotyped and genotyped animals were simultaneously included in the hybrid relationship matrix of H that was a combination of A (numerator relationship matrix) and G (the genomic relationship matrix) matrices. The inverse of the H matrix was obtained as the following equation [51, 52] and by employing preGSf90 software [53]:

$$ {\mathbf{H}}^{-1}={\mathbf{A}}^{-1}+\left[\begin{array}{cc}\mathbf{0}& \mathbf{0}\\ {}\mathbf{0}& {\left(0.95\mathbf{G}+0.05{\mathbf{A}}_{\mathbf{22}}\right)}^{-1}-{\mathbf{A}}_{22}^{-1}\end{array}\right] $$

Where A22 is the numerator relationship matrix for genotyped animals.

Validation and prediction of accuracy

In this study, the accuracy and unbiasedness of prediction were obtained using five-fold cross-validation sets for all traits. Genotyped individuals were assigned to five mutually exclusive groups for cross-validation. K-means clustering, according to pedigree relationship coefficients, was used to minimize the relatedness between training and validation sets [54]. The five groups included 360, 356, 174, 466, and 167 individuals, respectively. Each group was used as the validation set, while the remaining genotyped individuals were included in the training set. When using the ssGBLUP and PBLUP methods with phenotypes of all animals, non-genotyped individuals were included in the training set. For each validation set, the prediction accuracy was calculated as the correlation between the vector of adjusted phenotypes and the vector of estimated breeding values, divided by the square root of trait heritability. Regressions of adjusted phenotype on the EBV and the root of mean square error (RMSE) were calculated for all prediction methods.

Availability of data and materials

All data analyzed during this study are included in this published article and its supplementary information file.

Abbreviations

HIC:

Hanwoo Improvement Center

PBLUP:

Pedigree best linear unbiased prediction

GBLUP:

Genomic best linear unbiased prediction

ssGBLUP:

Single-step genomic best linear unbiased prediction

QTL:

Quantitative trait loci

SNP:

Single nucleotide polymorphism

BH:

Body height

BL:

Body length

CD:

Chest depth

CG:

Chest girth

CW:

Chest width

HH:

Hip height

HW:

Hip width

RL:

Rump length

RW:

Rump width

TW:

Thurl width

GEBV:

Genomic estimated breeding value

RMSE:

Root of mean square error

References

  1. 1.

    Gutiérrez JP, Álvarez I, Fernández I, Royo L, Dıez J, Goyache F. Genetic relationships between calving date, calving interval, age at first calving and type traits in beef cattle. Livest Prod Sci. 2002;78(3):215–22.

    Article  Google Scholar 

  2. 2.

    Berry DP, Buckley F, Dillon P, Evans RD, Veerkamp RF. Genetic relationships among linear type traits, milk yield, body weight, fertility and somatic cell count in primiparous dairy cows. Irish J Agric Food Res. 2004:161–76..

  3. 3.

    Forabosco F, Groen A, Bozzi R, Van Arendonk J, Filippini F, Boettcher P, Bijma P. Phenotypic relationships between longevity, type traits, and production in Chianina beef cattle. J Anim Sci. 2004;82(6):1572–80.

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Sewalem A, Kistemaker G, Van Doormaal B. Relationship between type traits and longevity in Canadian jerseys and Ayrshires using a Weibull proportional hazards model. J Dairy Sci. 2005;88(4):1552–60.

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Sawa A, Bogucki M, Krężel-Czopek S, Neja W. Relationship between conformation traits and lifetime production efficiency of cows. ISRN Vet Sci. 2013;2013:124690.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Veerkamp R, Brotherstone S. Genetic correlations between linear type traits, food intake, live weight and condition score in Holstein Friesian dairy cattle. Anim Sci. 1997;64(3):385–92.

    Article  Google Scholar 

  7. 7.

    Afolayan R, Pitchford W, Deland M, McKiernan W. Breed variation and genetic parameters for growth and body development in diverse beef cattle genotypes. Animal. 2007;1(1):13–20.

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Kim S, Alam M, Park M. Breeding initiatives for Hanwoo cattle to thrive as a beef industry–a review study. J Anim Breed Genet. 2017;1:102–24.

    Google Scholar 

  9. 9.

    Maiwashe A, Bradfield M, Theron H, Van Wyk J. Genetic parameter estimates for body measurements and growth traits in south African Bonsmara cattle. Livest Prod Sci. 2002;75(3):293–300.

    Article  Google Scholar 

  10. 10.

    Kahi A, Hirooka H. Genetic and economic evaluation of Japanese black (Wagyu) cattle breeding schemes. J Anim Sci. 2005;83(9):2021–32.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Gutiérrez JP, Goyache F. Estimation of genetic parameters of type traits in Asturiana de los Valles beef cattle breed. J Anim Breed Genet. 2002;119(2):93–100.

    Article  Google Scholar 

  12. 12.

    Mc Hugh N, Evans R, Fahey A, Berry D. Animal muscularity and size are genetically correlated with animal live-weight and price. Livest Sci. 2012;144(1–2):11–9.

    Article  Google Scholar 

  13. 13.

    Meuwissen THE, Hayes BJB, Goddard MEM. Prediction of Total genetic value using genome-wide dense marker maps. Genetics. 2001;157:1819–29.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Henderson CR. Best linear unbiased estimation and prediction under a selection model. Biometrics. 1975:423–47.

  15. 15.

    VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91(11):4414–23.

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Habier D, Fernando RL, Kizilkaya K, Garrick DJ. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics. 2011;12(1):186.

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. Invited review: genomic selection in dairy cattle: Progress and challenges. J Dairy Sci. 2009;92(2):433–43.

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Wang H, Misztal I, Aguilar I, Legarra A, Fernando RL, Vitezica Z, Okimoto R, Wing T, Hawken R, Muir WM. Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens. Front Genet. 2014;5:134.

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Zhang X, Lourenco D, Aguilar I, Legarra A, Misztal I. Weighting strategies for single-step genomic BLUP: an iterative approach for accurate calculation of GEBV and GWAS. Front Genet. 2016;7:151.

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Mehrban H, Lee DH, Moradi MH, IlCho C, Naserkheil M, Ibáñez-Escriche N. Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture. Genet Sel Evol. 2017;49(1):1–13.

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Karaman E, Cheng H, Firat MZ, Garrick DJ, Fernando RL. An upper bound for accuracy of prediction using GBLUP. PLoS One. 2016;11(8):e0161054.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Misztal I, Legarra A, Aguilar I. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J Dairy Sci. 2009;92(9):4648–55.

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Legarra A, Aguilar I, Misztal I. A relationship matrix including full pedigree and genomic information. J Dairy Sci. 2009;92(9):4656–63.

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Misztal I, Aggrey SE, Muir WM. Experiences with a single-step genome evaluation. Poult Sci. 2013;92(9):2530–4.

    Article  PubMed  Google Scholar 

  25. 25.

    Tsuruta S, Misztal I, Aguilar I, Lawlor T. Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. J Dairy Sci. 2011;94(8):4198–204.

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Chen C, Misztal I, Aguilar I, Tsuruta S, Meuwissen T, Aggrey S, Wing T, Muir W. Genome-wide marker-assisted selection combining all pedigree phenotypic information with genotypic data in one step: an example using broiler chickens. J Anim Sci. 2011;89(1):23–8.

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Christensen OF, Madsen P, Nielsen B, Ostersen T, Su G. Single-step methods for genomic evaluation in pigs. Animal. 2012;6(10):1565.

    Article  CAS  PubMed  Google Scholar 

  28. 28.

    Onogi A, Ogino A, Komatsu T, Shoji N, Simizu K, Kurogi K, Yasumori T, Togashi K, Iwata H. Genomic prediction in Japanese black cattle: application of a single-step approach to beef cattle. J Anim Sci. 2014;92(5):1931–8.

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Lourenco D, Tsuruta S, Fragomeni B, Masuda Y, Aguilar I, Legarra A, Bertrand J, Amen T, Wang L, Moser D. Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus. J Anim Sci. 2015;93(6):2653–62.

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Ismael A, Løvendahl P, Fogh A, Lund MS, Su G. Improving genetic evaluation using a multitrait single-step genomic model for ability to resume cycling after calving, measured by activity tags in Holstein cows. J Dairy Sci. 2017;100(10):8188–96.

    CAS  Article  PubMed  Google Scholar 

  31. 31.

    Lee J, Cheng H, Garrick D, Golden B, Dekkers J, Park K, Lee D, Fernando R. Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle. Genet Sel Evol. 2017;49(1):1–9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Mehrban H, Lee DH, Naserkheil M, Moradi MH, Ibáñez-Escriche N. Comparison of conventional BLUP and single-step genomic BLUP evaluations for yearling weight and carcass traits in Hanwoo beef cattle using single trait and multi-trait models. PLoS One. 2019;14(10):e0223352.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Zavadilová L, Přibyl J, Vostrý L, Bauer J. Single-step genomic evaluation for linear type traits of Holstein cows in Czech Republic. Anim Sci Paper Rep. 2014;32(3).

  34. 34.

    Abo-Ismail MK, Brito LF, Miller SP, Sargolzaei M, Grossi DA, Moore SS, Plastow G, Stothard P, Nayeri S, Schenkel FS. Genome-wide association studies and genomic prediction of breeding values for calving performance and body conformation traits in Holstein cattle. Genet Sel Evol. 2017;49(1):82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Bolormaa S, Pryce J, Kemper K, Savin K, Hayes B, Barendse W, Zhang Y, Reich C, Mason B, Bunch R. Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle. J Anim Sci. 2013;91(7):3088–104.

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Mäntysaari EA, Evans R, Strandén I. Efficient single-step genomic evaluation for a multibreed beef cattle population having many genotyped animals. J Anim Sci. 2017;95(11):4728–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Pimentel E, Edel C, Emmerling R, Götz K-U. Methods for interim prediction of single-step breeding values for young animals. J Dairy Sci. 2019;102(4):3266–73.

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    Carillier C, Larroque H, Palhière I, Clément V, Rupp R, Robert-Granié C. A first step toward genomic selection in the multi-breed French dairy goat population. J Dairy Sci. 2013;96(11):7294–305.

    CAS  Article  PubMed  Google Scholar 

  39. 39.

    Carillier C, Larroque H, Robert-Granié C. Comparison of joint versus purebred genomic evaluation in the French multi-breed dairy goat population. Genet Sel Evol. 2014;46(1):67.

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Teissier M, Larroque H, Robert-Granie C. Accuracy of genomic evaluation with weighted single-step genomic best linear unbiased prediction for milk production traits, udder type traits, and somatic cell scores in French dairy goats. J Dairy Sci. 2019;102(4):3142–54.

    CAS  Article  PubMed  Google Scholar 

  41. 41.

    Oget C, Teissier M, Astruc J-M, Tosser-Klopp G, Rupp R. Alternative methods improve the accuracy of genomic prediction using information from a causal point mutation in a dairy sheep model. BMC Genomics. 2019;20(1):719.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Song H, Zhang J, Zhang Q, Ding X. Using different single-step strategies to improve the efficiency of genomic prediction on body measurement traits in pig. Front Genet. 2019;9:730.

    Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Choy YH, Lee JG, Mahboob A, Choi TJ, Rho SH. Genetic correlation between live body measurements and beef cutability traits in Hanwoo steers. Asian Australas J Anim Sci. 2017;30(8):1074.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Zhang X, Chu Q, Guo G, Dong G, Li X, Zhang Q, Zhang S, Zhang Z, Wang Y. Genome-wide association studies identified multiple genetic loci for body size at four growth stages in Chinese Holstein cattle. PLoS One. 2017;12(4):e0175971.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Doyle JL, Berry DP, Walsh SW, Veerkamp RF, Evans RD, Carthy TR. Genetic covariance components within and among linear type traits differ among contrasting beef cattle breeds. J Anim Sci. 2018;96(5):1628–39.

    Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Vallejo RL, Leeds TD, Gao G, Parsons JE, Martin KE, Evenhuis JP, Fragomeni BO, Wiens GD, Palti Y. Genomic selection models double the accuracy of predicted breeding values for bacterial cold water disease resistance compared to a traditional pedigree-based model in rainbow trout aquaculture. Genet Sel Evol. 2017;49(1):17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Schwartz MK, Luikart G, Waples RS. Genetic monitoring as a promising tool for conservation and management. Trends Ecol Evol. 2007;22(1):25–33.

    Article  PubMed  Google Scholar 

  48. 48.

    Lourenco D, Misztal I, Tsuruta S, Aguilar I, Ezra E, Ron M, Shirak A, Weller J. Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses. J Dairy Sci. 2014;97(3):1742–52.

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Misztal I, Tsuruta S, Lourenco D, Masuda Y, Aguilar I, Legarra A, Vitezica Z. Manual for BLUPF90 family of programs, vol. 2018; 2018.

    Google Scholar 

  50. 50.

    Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet. 2007;81(5):1084–97.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Aguilar I, Misztal I, Johnson D, Legarra A, Tsuruta S, Lawlor T. Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J Dairy Sci. 2010;93(2):743–52.

    CAS  Article  PubMed  Google Scholar 

  52. 52.

    Aguilar I, Misztal I, Tsuruta S, Wiggans G, Lawlor T. Multiple trait genomic evaluation of conception rate in Holsteins. J Dairy Sci. 2011;94(5):2621–4.

    Article  CAS  PubMed  Google Scholar 

  53. 53.

    Aguilar I, Misztal I, Tsuruta S, Legarra A, Wang H. PREGSF90–POSTGSF90: computational tools for the implementation of single-step genomic selection and genome-wide association with ungenotyped individuals in BLUPF90 programs. In: 10 World Congress on Genetics Applied to Livestock Production (WCGALP): American Society of Animal Science; 2014.

  54. 54.

    Saatchi M, McClure MC, McKay SD, Rolf MM, Kim J, Decker JE, Taxis TM, Chapple RH, Ramey HR, Northcutt SL. Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genet Sel Evol. 2011;43(1):40.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

We are grateful to the staff of the Korean Hanwoo Improvement Center of the National Agricultural Cooperative Federation for supplying data and semen and blood samples of Hanwoo cattle.

Funding

This work was supported by a grant from the IPET Program (No. 20093068), Ministry of Agriculture, Food and Rural Affairs, Republic of Korea and Hankyong National University in 2016. The funding bodies played no role in the design of the study, the collection, analyses, or interpretation of data and in writing of the manuscript.

Author information

Affiliations

Authors

Contributions

DHL and HM conceived and designed the study and contributed to the discussion of results. MN and HM conceived the study and analyzed data. MN drafted the manuscript. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Deuk Hwan Lee.

Ethics declarations

Ethics approval and consent to participate

DNA samples were obtained from semen and/or blood samples collected by veterinarians. Ethics committee approval was not required for semen and blood collection, as they were collected specifically in this study from the Hanwoo Improvement Center (HIC) of the National Agricultural Cooperative Federation, which was involved as a partner in this research project.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Figure S1.

Location of body measurement parts in Hanwoo cattle.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Naserkheil, M., Lee, D.H. & Mehrban, H. Improving the accuracy of genomic evaluation for linear body measurement traits using single-step genomic best linear unbiased prediction in Hanwoo beef cattle. BMC Genet 21, 144 (2020). https://doi.org/10.1186/s12863-020-00928-1

Download citation

Keywords

  • Body measurement traits
  • Single-step
  • Genomic prediction
  • Hanwoo cattle