Abstract
Background
A singlestep blending approach allows genomic prediction using information of genotyped and nongenotyped animals simultaneously. However, the combined relationship matrix in a singlestep method may need to be adjusted because markerbased and pedigreebased relationship matrices may not be on the same scale. The same may apply when a GBLUP model includes both genomic breeding values and residual polygenic effects. The objective of this study was to compare singlestep blending methods and GBLUP methods with and without adjustment of the genomic relationship matrix for genomic prediction of 16 traits in the Nordic Holstein population.
Methods
The data consisted of deregressed proofs (DRP) for 5 214 genotyped and 9 374 nongenotyped bulls. The bulls were divided into a training and a validation population by birth date, October 1, 2001. Five approaches for genomic prediction were used: 1) a simple GBLUP method, 2) a GBLUP method with a polygenic effect, 3) an adjusted GBLUP method with a polygenic effect, 4) a singlestep blending method, and 5) an adjusted singlestep blending method. In the adjusted GBLUP and singlestep methods, the genomic relationship matrix was adjusted for the difference of scale between the genomic and the pedigree relationship matrices. A set of weights on the pedigree relationship matrix (ranging from 0.05 to 0.40) was used to build the combined relationship matrix in the singlestep blending method and the GBLUP method with a polygenetic effect.
Results
Averaged over the 16 traits, reliabilities of genomic breeding values predicted using the GBLUP method with a polygenic effect (relative weight of 0.20) were 0.3% higher than reliabilities from the simple GBLUP method (without a polygenic effect). The adjusted singlestep blending and original singlestep blending methods (relative weight of 0.20) had average reliabilities that were 2.1% and 1.8% higher than the simple GBLUP method, respectively. In addition, the GBLUP method with a polygenic effect led to less bias of genomic predictions than the simple GBLUP method, and both singlestep blending methods yielded less bias of predictions than all GBLUP methods.
Conclusions
The singlestep blending method is an appealing approach for practical genomic prediction in dairy cattle. Genomic prediction from the singlestep blending method can be improved by adjusting the scale of the genomic relationship matrix.
Background
Selection based on dense markers across the genome [1] has become an important component of dairy cattle breeding programs [27]. The accuracy of genomic prediction relies on the amount of information used to derive the prediction equation. In many genomic selection programs, thousands of bulls which have been progeny tested over the last decades have been genotyped and are used as national reference populations. These have been extended by sharing data across countries to include much more information, such as the North American cooperation [8], the EuroGenomics project [7], and the joint Brown Swiss project [9]. Generally, genomic predictions are based on the data of all genotyped animals. However, in practice, not all individuals can be genotyped. To make use of as much information as possible for genetic evaluation, it is appealing to blend the genomic predicted breeding value and the traditional estimated breeding values (EBV) into genomically enhanced breeding values (GEBV) or to perform genomic prediction using all information available simultaneously.
Many studies have shown that a linear model which assumes that effects of all single nucleotide polymorphisms (SNP) are normally distributed with equal variance performs as well as variable selection models for most traits in dairy cattle [2,4]. Because such BLUP models are simple and have low computational requirements, they have become popular approaches for practical genomic prediction. Deregressed proofs (DRP) [10,11] are generally used as the response variable for genomic prediction since they can be easily derived from the EBV that are usually available.
Several blending strategies, including multistep and singlestep approaches, have been proposed to estimate GEBV [4,5,1218]. The core of a singlestep procedure is the integration of the markerbased relationship matrix into the pedigreebased relationship matrix such that information of genotyped and nongenotyped animals is used simultaneously [1315]. Previous study by Su et al. [18] reported that a singlestep procedure resulted in more accurate GEBV than a multistep procedure.
Some studies [1315,18] have reported that the combined relationship matrix in a singlestep method may need to be adjusted because the marker and pedigreebased relationship matrices may not be on the same scale, and different methods to adjust for this have been proposed [1922]. These adjustments may also benefit genomic prediction using other models that integrate marker and pedigreebased relationship matrices, such as a GBLUP model with a polygenic effect.
The purpose of this study was to compare singlestep blending and GBLUP methods with and without adjustment of the genomic relationship matrix for genomic prediction of 16 traits in the Nordic Holstein population. Deregressed proofs were used as response variables in both GBLUP and the singlestep blending methods.
Methods
Data
Data consisted of 5 214 genotyped bulls born between 1974 and 2008 and 9 374 nongenotyped bulls born between 1950 and 2008. The bulls were divided into a training and a validation population by birth date, October 1, 2001. Thus, the training data contained 3 045 genotyped and 8 822 nongenotyped bulls born before this date, and the validation data contained 2 169 genotyped bulls born after this date. Nongenotyped bulls born after October 1, 2001 were not used in training or validation. For the GBLUP methods described below, the training data only included the 3 045 genotyped animals. All 16 traits (subindices) in the Nordic Total Merit index were assessed, including yield, conformation, fertility, and health traits. For each trait, the DRP with reliability less than 0.20 were excluded from the training and the validation data. This removed 1.3%, 2.8% and 3.2% of DRP for birth index, fertility and health, respectively, and less than 0.5% for the other traits. The numbers of individuals in the training and validation datasets differed between traits (Table 1).
Table 1. Heritability (h^{2}) of the traits, number of bulls in training (Train) and validation datasets (Valid_{gen}) for GBLUP and singlestep blending
Marker genotypes were obtained using the Illumina Bovine SNP50 BeadChip (Illumina, SanDiego, CA). The final marker data included 48 073 SNPs for 5 214 bulls after removing SNP with minor allele frequency (MAF) less than 0.01 and locus average GenCall score less than 0.60.
Deregressed proofs (DRP) were used as response variables for genomic prediction in all approaches. Based on EBV data of 14 588 progenytested bulls and pedigree data of 42 144 animals, the deregression was carried out by applying the iterative procedure described in [23,24] using the MiX99 package [25] and with the heritabilities shown in Table 1, which were those used in Nordic cattle routine genetic evaluation. A detailed description of the Nordic cattle genetic evaluation and standardized procedures of EBV is given in http://www.nordicebv.info/Routine+evaluation/ webcite.
Statistical models
Three GBLUP and two singlestep blending methods were used. All analyses were performed with the DMU package [26,27], for estimating both the variance components and breeding values.
Simple GBLUP
The basic GBLUP method [28,29] used to predict direct genomic breeding values (DGV) was:
where y is the data vector of DRP of genotyped bulls, μ is the overall mean, 1 is a vector of ones, Z is a design matrix that allocates records to breeding values, g is a vector of DGV to be estimated, and e is a vector of residuals. It was assumed that
GBLUP with a polygenic effect
where u is the vector of residual polygenic effects that are not captured by the SNP.
Here, we used an equivalent approach. Let
It was assumed that
Adjusted GBLUP with a polygenic effect
The model was the same as the above GBLUP method with a polygenic effect but G was adjusted to be on the same scale as A. Then, the combined relationship matrix was
Original singlestep blending
The original singlestep blending method [15,17,18] uses information from genotyped and nongenotyped individuals simultaneously by combining the genomic relationship matrix G with the pedigreebased numerator relationship matrix A, using the following model:
where y is the vector of DRP for both genotyped and nongenotyped bulls, 1 is a vector of ones, Z is a design matrix, and a is the vector of additive genetic effects, which are the sum of the genomic and the
residual polygenic effects. It was assumed that
where A_{11} is the submatrix of the pedigreebased relationship matrix (A) for genotyped animals, A_{22} is the submatrix of A for nongenotyped animals, A_{12} (or A_{21}) is the submatrix of A for relationships between genotyped and nongenotyped animals, and
Adjusted singlestep blending
In the adjusted singlestep blending method, the G matrix was adjusted for the difference between the original genomic relationship matrix and pedigree relationship matrix (A_{11}), as proposed by previous studies [19,20]. The G matrix was adjusted using two parameters α and β [21], i.e.,
which were derived from the following equations:
Matrix G^{*} was then used to replace G to construct the combined relationship matrix in the singlestep blending method.
The weights ω ranging from 0.05 to 0.40 were used to construct G_{ω} and
Validation
The reliabilities of genomic predictions were measured as squared correlations between the predicted breeding values and DRP for bulls in the validation data, divided by the average reliability of the DRP in validation data. A HotellingWilliams ttest was used to test the difference between the validation correlations obtained from these five prediction methods [30,31]. Bias of genomic predictions was measured as the regression of DRP on the genomic predictions [32].
Results
Genomic predictions using the GBLUP method were improved when a polygenic effect was included (Tables 2 and 3). With a relative weight of 0.2 on the residual polygenic variance, the average reliability of genomic predictions for the 16 traits was 0.363, which was 0.3% points higher than the average reliability from the simple GBLUP. Moreover, the GBLUP method with a polygenic effect reduced bias of genomic predictions. Averaged over the 16 traits, the absolute deviation of the regression coefficient (DRP on genomic prediction) from 1 was 0.093 when using the GBLUP methods with a polygenic effect and 0.107 when using the simple GBLUP method. The GBLUP methods with a polygenic effect slightly reduced also bias in mean, as the intercept in the regression analysis was closer to 0, compared with the simple GBLUP. For the two GBLUP methods with a polygenic effect, adjustment of the genomic relationship matrix had no effect on predictive ability and bias.
Table 2. Reliabilities of genomic predictions using different methods
Table 3. Intercept (INT) and regression coefficient (REG) of DRP on genomic predictions from different methods
Table 4 reports validation reliabilities of GEBV from the two singlestep blending methods and DGV_{ω} from the GBLUP method with a polygenic effect (the adjusted GBLUP method is shown as an example) for the 16 traits, with a relative weight ω = 0.20. The adjusted singlestep blending led to the highest reliability of genomic predictions, followed by the original singlestep blending, and the GBLUP method resulted in the lowest reliability. Reliabilities ranged from 0.206 to 0.503 (average 0.379) for the original singlestep blending, from 0.206 to 0.503 (average 0.382) for the adjusted singlestep blending, and from 0.183 to 0.481 (average 0.363) for the GBLUP method. In general, singlestep blending was better than the GBLUP method and adjusted singlestep blending was better than the original singlestep blending, especially for production traits. On average, reliabilities of genomic breeding values predicted using the original singlestep blending were 1.6 % higher than reliabilities from the adjusted GBLUP method, but 0.3% lower than reliabilities from the adjusted singlestep blending.
Table 4. Reliabilities of genomic predictions using different methods
The regression coefficients (Table 5) ranged from 0.757 to 1.138 (average absolute deviation from 1 equal to 0.084) for the original singlestep blending, from 0.760 to 1.148 (average absolute deviation 0.080) for the adjusted singlestep blending, and from 0.752 to 1.176 (average absolute deviation 0.093) for the adjusted GBLUP method. Predictions from the singlestep blending methods appeared to have less bias than predictions from GBLUP, and predictions from the adjusted singlestep blending has slightly less bias than predictions from the original singlestep blending method. In addition, the two singlestep blending methods led to smaller absolute deviation of the intercept from 0 than the adjusted GBLUP method, indicating less bias in mean.
Table 5. Intercept (INT) and regression coefficient (REG) of DRP on genomic predictions using different methods
Table 6 presents differences between groups of the top 300 bulls based on predictions from the different methods. For all 16 traits, more than 9% of the top 300 bulls based on the adjusted GBLUP method differed from the top 300 bulls based on the two singlestep blending methods. Differences between the two singlestep blending methods were small, except for production traits, which was in agreement with the small differences in reliabilities of GEBV from the two singlestep blending methods.
Table 6. Differences between groups of the top 300 bulls based on genomic prediction using different methods
In order to test the effect of different weighting factors ω in forming G_{ω} and H, eight values of ω between 0.05 and 0.40 were used for the two singlestep blending methods and the two GBLUP methods with a polygenic effect. On average, reliabilities varied from 0.356 to 0.363 over the eight scenarios for the two GBLUP methods, from 0.372 to 0.379 for the original singlestep blending, and from 0.374 to 0.382 for the adjusted singlestep blending (Figure 1). The highest mean reliability was obtained when using a weight of 0.15 or 0.20 for the four methods. The mean absolute deviation of the regression coefficient from 1 varied from 0.080 to 0.104 for the two GBLUP methods, from 0.074 to 0.098 for original singlestep blending and from 0.072 to 0.091 for adjusted singlestep blending (Figure 2). Mean of absolute deviations tended to decrease with increasing weights.
Figure 1. The impact of different weights on reliability of genomic predictions using different methods. GBLUP with a polygenic effect (GBLUPAG), adjusted GBLUP with a polygenic effect (GBLUPAG^{*}), original singlestep blending (Singleori), and adjusted singlestep blending (Singleadj).
Figure 2. The impact of different weights on the mean absolute deviation from 1 of the regression coefficient of DPR on prediction using different methods. GBLUP with a polygenic effect (GBLUPAG), adjusted GBLUP with a polygenic effect (GBLUPAG^{*}), original singlestep blending (Singleori), and adjusted singlestep blending (Singleadj).
Discussion
This study applied three GBLUP and two singlestep blending methods for genomic prediction in Nordic Holsteins. Predictive abilities of the five methods were compared in terms of reliability and bias. Results indicated that both the original singlestep blending and the adjusted singlestep blending were more accurate than the three GBLUP methods because the two singlestep blending approaches used much more information to predict breeding values. Similar results were reported by Su et al. [18] for the Nordic Red population. In the current study, the size of the training dataset for the singlestep blending methods was almost three times as large as that for the three GBLUP methods (Table 1) since DRP of the nongenotyped animals also provided information through a combined relationship matrix. Including pedigree information may also improve genomic predictions because the SNP may not account for all additive genetic variance. As shown in this study, including a residual polygenic effect in the GBLUP methods led to slightly higher reliability of genomic predictions.
A regression coefficient of DRP on genomic predictions less than 1 indicates overestimation of the variance of genomic predictions (inflation), while a coefficient larger than 1 indicates underestimation (deflation). The two singlestep blending methods led to less bias than the three GBLUP methods, and the two GBLUP methods with a polygenic effect resulted in less bias than the simple GBLUP method without a polygenic effect. The problem of inflation of genomic predictions is critical in practice [3335] as it can give an unfair advantage to juvenile over older progeny test bulls [17]. Aguilar et al. [17] showed that this bias was reduced by weighting the G and A matrices, and Liu et al. [36] found that including a polygenic effect in a GBLUP model (random regressions on SNP genotypes) led to less bias in genomic predictions. The present study showed that the weighting factor had an effect on the bias of genomic predictions for all traits in the singlestep blending approaches and the GBLUP methods with a polygenic effect. A weight of 0.40 resulted in the smallest minimum absolute deviation from 1 for the regression of GEBV or DGV_{ω} on DRP, averaged over the 16 traits, but a loss of reliability around 0.8%, compared to a weight of 0.20, which led to highest average reliability and an acceptable average absolute deviation of regression coefficient from 1 (Figure 1,2).
The adjusted singlestep blending method resulted in less bias than the original singlestep blending for all settings of the weight factor. In a simulation study, Vitezica et al. [19] also found that the singlestep method was less biased and more accurate when the genomic relationship matrix was adjusted by a constant. Using chicken data, Chen et al. [20] showed that unbiased evaluations can be obtained by adding a constant to the G matrix that is based on current allele frequencies and suggested that the optimal G has average of diagonal and offdiagonal elements close to those of A_{11}. Forni et al. [22] also showed that rescaling the G matrix is a reasonable solution to avoid inflation in pig data. However, in the present study, the adjusted G matrix did not improve genomic predictions in the GBLUP methods with a polygenic effect. This suggests that, based on the present data, adjustment of G has little effect on genomic prediction when only genotyped animals are used, but may be important in other data where there is a large difference in scale between G and A.
The results from the present study indicate that increasing the weighting factor (0.40) reduces bias and that weighting factors around 0.15 to 0.20 give the highest reliability but the optimal weighting factors differed between traits. Similarly, Liu et al. [36] observed that the optimal residual polygenic variance in a GBLUP model (random regressions on SNP genotypes) with a polygenic effect appears to differ among traits. Therefore, traitspecific weighting factors should be used in the singlestep blending methods and the GBLUP methods with a polygenic effect. In the near future, both bulls and heifers may be preselected based on genomic EBV. This will lead to biased predictions of breeding values in both conventional and genomic evaluation procedures. In such situations, appropriate methods to correct the bias of predictions are required [37].
Christensen et al. [21] compared the adjusted and original singlestep blending methods on pig data. In their study, the improvement of prediction reliabilities by adjustment of G matrix is much larger, compared with the results from the current study. This may be because there was more inbreeding in the pig data, which resulted in average values of the diagonal and offdiagonal elements of A_{11} equal to 1.145 and 0.298, and estimates of β and α equal to 0.895 and 0.298, respectively. In the present study, the averages of the diagonal and offdiagonal elements of A_{11}were 1.060 and 0.085, and estimates of β and α were 0.976 and 0.085, i.e. closer to one and zero, respectively. This means that the original G matrix was less adjusted in this study compared to the study on pig data by Christensen et al. [21].
Conclusions
The singlestep blending methods can increase reliability and reduce bias of genomic predictions. The adjusted singlestep blending method performed slightly better than the original singlestep blending method, both with respect to reliability and bias of genomic predictions. The weighting factor used in these singlestep blending methods had a small effect on reliability of genomic prediction but an important effect on bias.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
HG performed statistical analysis and wrote the manuscript. OFC derived the singlestep methods and improved the manuscript. PM provided the software, helped to the analysis and added valuable comments. USN prepared the data. GS and MSL conceived the study, made substantial contribution for the results interpretation and revised the manuscript. MSL, GS and YZ coordinated the project. All authors read and approved the manuscript.
Acknowledgements
The authors thank Danish Cattle Federation, Faba coop, Swedish Dairy Association and Nordic Cattle Genetic Evaluation for providing data. This work was performed in the project “Genomic Selection – from function to efficient utilization in cattle breeding (grant no. 3405100137)”, funded under GUDP by the Danish Directorate for Food, Fisheries and Agri Business, the Milk Levy Fund, VikingGenetics, Nordic Cattle Genetic Evaluation, and Aarhus University.
References

Meuwissen THE, Hayes BJ, Goddard ME: Prediction of total genetic value using genomewide dense marker maps.
Genetics 2001, 157:18191829. PubMed Abstract  Publisher Full Text  PubMed Central Full Text

Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME: Invited review: Genomic selection in dairy cattle: progress and challenges.
J Dairy Sci 2009, 92:433443. PubMed Abstract  Publisher Full Text

Loberg A, Durr JW: Interbull survey on the use of genomic information.
Interbull Bull; Uppsala 2009, 39:314. PubMed Abstract  Publisher Full Text  PubMed Central Full Text

VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, Schenkel FS: Invited review: reliability of genomic predictions for North American Holstein bulls.
J Dairy Sci 2009, 92:1624. PubMed Abstract  Publisher Full Text

Harris BL, Johnson DL: Genomic predictions for New Zealand dairy bulls and integration with national genetic evaluation.
J Dairy Sci 2010, 93:12431252. PubMed Abstract  Publisher Full Text

Su G, Guldbrandtsen B, Gregersen VR, Lund MS: Preliminary investigation on reliability of genomic estimated breeding values in the Danish Holstein population.
J Dairy Sci 2010, 93:11751183. PubMed Abstract  Publisher Full Text

Lund MS, de Ross SPW, de Vries AG, Druet T, Ducrocq V, Fritz S, Guillaume F, Guldbrandtsen B, Liu Z, Reents R: A common reference population from four European Holstein populations increases reliability of genomic predictions.
Genet Sel Evol 2011, 43:43. PubMed Abstract  BioMed Central Full Text  PubMed Central Full Text

Muir B, Doormaal BV, Kistemaker G: International genomic cooperation – North American perspective.
Interbull Bull; Paris 2010, 41:7176. PubMed Abstract  Publisher Full Text  PubMed Central Full Text

Jorjani H, Zumbach B, Dürr J, Santus E: Joint genomic evaluation of BSW populations.
Interbull Bull; Paris 2010, 41:814. PubMed Abstract  Publisher Full Text  PubMed Central Full Text

Goddard ME: A method of comparing sires evaluated in different countries.
Livest Prod Sci 1985, 13:321331. Publisher Full Text

Schaeffer LR: Model for international evaluation of dairy sires.
Livest Prod Sci 1985, 12:105115. Publisher Full Text

Ducrocq V, Liu Z: Combining genomic and classical information in national BLUP evaluations.
Interbull Bull; Barcelona 2009, 40:172177. PubMed Abstract  Publisher Full Text  PubMed Central Full Text

Legarra A, Aguilar I, Misztal I: A relationship matrix including full pedigree and genomic information.
J Dairy Sci 2009, 92:46564663. PubMed Abstract  Publisher Full Text

Misztal I, Legarra A, Aguilar I: Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information.
J Dairy Sci 2009, 92:46484655. PubMed Abstract  Publisher Full Text

Christensen OF, Lund MS: Genomic prediction when some animals are not genotyped.
Genet Sel Evol 2010, 42:2. PubMed Abstract  BioMed Central Full Text  PubMed Central Full Text

Mäntysaari EA, Strandén I: Use of bivariate EBVDGV model to combine genomic and conventional breeding value evaluations. In Proceedings of the 9th World Congress on Genetics Applied to Livestock Production; August 1–6. , Leipzig; 2010.

Aguilar I, Misztal I, Johnson DL, Legarra A, Tsuruta S, Lawlor TJ: Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.
J Dairy Sci 2010, 93:743752. PubMed Abstract  Publisher Full Text

Su G, Madsen P, Nielsen US, Mäntysaari EA, Aamand GP, Christensen OF, Lund MS: Genomic prediction for Nordic Red Cattle using onestep and selection index blending.
J Dairy Sci 2012, 95:909917. PubMed Abstract  Publisher Full Text

Vitezica ZG, Aguilar I, Misztal I, Legarra A: Bias in genomic predictions for populations under selection.
Genet Res (Camb) 2011, 93:357366. Publisher Full Text

Chen CY, Misztal I, Aguilar I, Legarra A, Muir WM: Effect of different genomic relationship matrices on accuracy and scale.
J Anim Sci 2011, 89:26732679. PubMed Abstract  Publisher Full Text

Christensen OF, Madsen P, Nielsen B, Ostersen T, Su G: Singlestep methods for genomic evaluation in pigs.
Animal 2012.
in press

Forni S, Aguilar I, Misztal I: Different genomic relationship matrices for singlestep analysis using phenotypic, pedigree and genomic information.
Genet Sel Evol 2011, 43:1. PubMed Abstract  BioMed Central Full Text  PubMed Central Full Text

Jairath L, Dekkers JCM, Schaeffer LR, Liu Z, Burnside EB, Kolstad B: Genetic evaluation for herd life in Canada.
J Dairy Sci 1998, 81:550562. PubMed Abstract  Publisher Full Text

Schaeffer LR: Multiple trait international bull comparisons.
Livest Prod Sci 2001, 69:145153. Publisher Full Text

Strandén I, Mäntysaari EA: A recipe for multiple trait deregression.
Interbull Bull; Riga 2010, 42:2124. PubMed Abstract  Publisher Full Text  PubMed Central Full Text

Madsen P, Jensen J: An User's Guide to DMU, Version 6, Release 5.0. University of Aarhus, Faculty Agricultural Sciences (DJF), Dept of Genetics and Biotechnology, Research Centre Foulum, Tjele, Denmark; 2010.
http://dmu.agrsci.dk/dmuv6_guide.5.0.pdf webcite
PubMed Abstract 
Madsen P, Su G, Labouriau R, Christensen OF: DMU–A package for analyzing multivariate mixed models. In Proceedings of the 9th World Congress on Genetics Applied to Livestock Production: 1–6 August. , Leipzig; 2010.

VanRaden PM: Efficient methods to compute genomic predictions.
J Dairy Sci 2008, 91:44144423. PubMed Abstract  Publisher Full Text

Hayes BJ, Visscher PM, Goddard ME: Increased accuracy of artificial selection by using the realized relationship matrix.
Genet Res (Camb) 2009, 91:4760. Publisher Full Text

Dunn OJ, Clark V: Comparison of tests of the equality of dependent correlation coefficients.

Steiger JH: Tests for comparing elements of a correlation matrix.

Olson KM, Vanraden PM, Tooker ME, Cooper TA: Differences among methods to validate genomic evaluations for dairy cattle.
J Dairy Sci 2011, 94:26132620. PubMed Abstract  Publisher Full Text

Patry C, Ducrocq V: Bias due to genomic selection.
Interbull Bull; Uppsala 2009, 39:7782. PubMed Abstract  Publisher Full Text  PubMed Central Full Text

VanRaden PM, Tooker ME, Cole JB: Can you believe those genomic evaluations for young bulls?

Mäntysaari EA, Liu Z, VanRaden PM: Interbull validation test for genomic evaluations.
Interbull Bull; Paris 2010, 41:1722. PubMed Abstract  Publisher Full Text  PubMed Central Full Text

Liu Z, Seefried FR, Reinhardt F, Rensing S, Thaller G, Reents R: Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction.
Genet Sel Evol 2011, 43:19. PubMed Abstract  BioMed Central Full Text  PubMed Central Full Text

Patry C, Ducrocq V: Accounting for genomic preselection in national BLUP evaluations in dairy cattle.
Genet Sel Evol 2011, 43:30. PubMed Abstract  BioMed Central Full Text  PubMed Central Full Text