Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows

Ding, Xiangdong; Zhang, Z.; Li, X.; Wang, S.; Wu, Xiaoping; Sun, Dongxiao; Yu, Ying; Liu, J.; Wang, Y.; Zhang, Y.; Zhang, S.; Zhang, Q.

doi:10.3168/jds.2012-6194

Cited by 47 publications

(42 citation statements)

References 32 publications

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“…The results showed that in both CVF and CVR scenarios, the model-based theoretical accuracy was higher than correlation-based validation accuracy for all traits, except for ADG and ADFI in the CVF scenario. This phenomenon of high model-based theoretical accuracy was also observed in previous studies[11, 32, 33], perhaps because the theoretical accuracy was overestimated due to imperfect linkage disequilibrium between markers and causal genes [34]. However, it was observed that ADG and ADFI in CVF scenario had higher correlation-based validation accuracy than model-based theoretical accuracy.…”

Section: Discussionsupporting

confidence: 77%

Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens

Liu¹,

Luo²,

Wang³

et al. 2017

PLoS ONE

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Feed represents the major cost of chicken production. Selection for improving feed utilization is a feasible way to reduce feed cost and greenhouse gas emissions. The objectives of this study were to investigate the efficiency of genomic prediction for feed conversion ratio (FCR), residual feed intake (RFI), average daily gain (ADG) and average daily feed intake (ADFI) and to assess the impact of selection for feed efficiency traits FCR and RFI on eviscerating percentage (EP), breast muscle percentage (BMP) and leg muscle percentage (LMP) in meat-type chickens. Genomic prediction was assessed using a 4-fold cross-validation for two validation scenarios. The first scenario was a random family sampling validation (CVF), and the second scenario was a random individual sampling validation (CVR). Variance components were estimated based on the genomic relationship built with single nucleotide polymorphism markers. Genomic estimated breeding values (GEBV) were predicted using a genomic best linear unbiased prediction model. The accuracies of GEBV were evaluated in two ways: the correlation between GEBV and corrected phenotypic value divided by the square root of heritability, i.e., the correlation-based accuracy, and model-based theoretical accuracy. Breeding values were also predicted using a conventional pedigree-based best linear unbiased prediction model in order to compare accuracies of genomic and conventional predictions. The heritability estimates of FCR and RFI were 0.29 and 0.50, respectively. The heritability estimates of ADG, ADFI, EP, BMP and LMP ranged from 0.34 to 0.53. In the CVF scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR were slightly higher than those for RFI. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.360, 0.284, 0.574 and 0.520, respectively, and the model-based theoretical accuracies were 0.420, 0.414, 0.401 and 0.382, respectively. In the CVR scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR was lower than RFI, which was different from the CVF scenario. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.449, 0.593, 0.581 and 0.627, respectively, and the model-based theoretical accuracies were 0.577, 0.629, 0.631 and 0.638, respectively. The accuracies of genomic predictions were 0.371 and 0.322 higher than the conventional pedigree-based predictions for the CVF and CVR scenarios, respectively. The genetic correlations of FCR with EP, BMP and LMP were -0.427, -0.156 and -0.338, respectively. The correlations between RFI and the three carcass traits were -0.320, -0.404 and -0.353, respectively. These results indicate that RFI and FCR have a moderate accuracy of genomic prediction. Improving RFI and FCR could be favourable for EP, BMP and LMP. Compared with FCR, which can be improved by selection for ADG in typical meat-type chicken breeding programs, selection for RFI could lead to extra improvement in feed efficiency.

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Section: Discussionsupporting

confidence: 77%

Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens

Liu¹,

Luo²,

Wang³

et al. 2017

PLoS ONE

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“…In Ding et al . (), the use of 3087 cows genotyped for 48 676 SNP as a reference population resulted in accuracies between 0.70 and 0.80 in a validation population formed by 67 proven sires.…”

Section: Discussionmentioning

confidence: 96%

Genetic parameters for milk fatty acids, milk yield and quality traits of a Holstein cattle population reared under tropical conditions

Petrini

Iung

Rodriguez

et al. 2016

J Animal Breeding Genetics

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Information about genetic parameters is essential for selection decisions and genetic evaluation. These estimates are population specific; however, there are few studies with dairy cattle populations reared under tropical and sub-tropical conditions. Thus, the aim was to obtain estimates of heritability and genetic correlations for milk yield and quality traits using pedigree and genomic information from a Holstein population maintained in a tropical environment. Phenotypic records (n = 36 457) of 4203 cows as well as the genotypes for 57 368 single nucleotide polymorphisms from 755 of these cows were used. Covariance components were estimated using the restricted maximum likelihood method under a mixed animal model, considering a pedigree-based relationship matrix or a combined pedigree-genomic matrix. High heritabilities (around 0.30) were estimated for lactose and protein content in milk whereas moderate values (between 0.19 and 0.26) were obtained for percentages of fat, saturated fatty acids and palmitic acid in milk. Genetic correlations ranging from -0.38 to -0.13 were determined between milk yield and composition traits. The smaller estimates compared to other similar studies can be due to poor environmental conditions, which may reduce genetic variability. These results highlight the importance in using genetic parameters estimated in the population under evaluation for selection decisions.

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“…Currently, the number of genotyped animals in the Chinese Holstein population is around 3000 and most of them are cows, due to a low number of progeny‐tested bulls available (Ding et al . ). Among these animals, only a few bulls were genotyped with the HD chip.…”

Section: Introductionmentioning

confidence: 97%

Increasing imputation and prediction accuracy for Chinese Holsteins using joint Chinese–Nordic reference population

Lund

Ding

et al. 2014

J Animal Breeding Genetics

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This study investigated the effect of including Nordic Holsteins in the reference population on the imputation accuracy and prediction accuracy for Chinese Holsteins. The data used in this study include 85 Chinese Holstein bulls genotyped with both 54K chip and 777K (HD) chip, 2862 Chinese cows genotyped with 54K chip, 510 Nordic Holstein bulls genotyped with HD chip, and 4398 Nordic Holstein bulls genotyped with 54K chip and with deregressed proofs for five milk production traits. Based on these data, the accuracy of imputation from 54K to HD marker data and the accuracy of genomic predictions in Chinese Holstein were assessed. The allele correct rate increased around 2.7 and 1.7% in imputation from the 54K to the HD marker data for Chinese Holstein bulls and cows, respectively, when the Nordic HD-genotyped bulls were included in the reference data for imputation. However, the prediction accuracy was improved slightly when using the marker data imputed based on the combined HD reference data, compared with using the marker data imputed based on the Chinese HD reference data only. On the other hand, when using the combined reference population including 4398 Nordic Holstein bulls, the accuracy of genomic predictions increased 6.5 percentage points together with a reduction of prediction bias. The HD markers did not outperform the 54K markers in genomic prediction based on the present data. The results indicate that for Chinese Holsteins, it is necessary to genotype more individuals with 54K chip to increase reference population rather than increasing marker density.

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Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows

Cited by 47 publications

References 32 publications

Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens

Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens

Genetic parameters for milk fatty acids, milk yield and quality traits of a Holstein cattle population reared under tropical conditions

Increasing imputation and prediction accuracy for Chinese Holsteins using joint Chinese–Nordic reference population

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