Genomic prediction for Nordic Red Cattle using one-step and selection index blending

Su, Guohai; Madsen, Per; Nielsen, U.; Mäntysaari, Esa; Aamand, G.P.; Christensen, Ole; Lund, Mogens Sandø

doi:10.3168/jds.2011-4804

Cited by 107 publications

(139 citation statements)

References 35 publications

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“…On the other hand, the multi-step method has the flexibility to use prediction methods such as Bayesian methods and could easily use EBV from multi-trait acrosscountry evaluation in the reference population including genotyped foreign bulls. Some studies have compared the single-step and multi-step methods (Su et al, 2012;Lourenco et al, 2014). However, few studies have compared the results obtained with and without genotyped cows analyzed by both single-step and multi-step methods.…”

Section: Prediction Methodsmentioning

confidence: 99%

Effect of genotyped cows in the reference population on the genomic evaluation of Holstein cattle

Uemoto

Osawa

Saburi

2017

Animal

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This study evaluated the dependence of reliability and prediction bias on the prediction method, the contribution of including animals (bulls or cows), and the genetic relatedness, when including genotyped cows in the progeny-tested bull reference population. We performed genomic evaluation using a Japanese Holstein population, and assessed the accuracy of genomic enhanced breeding value (GEBV) for three production traits and 13 linear conformation traits. A total of 4564 animals for production traits and 4172 animals for conformation traits were genotyped using Illumina BovineSNP50 array. Single-and multi-step methods were compared for predicting GEBV in genotyped bull-only and genotyped bull-cow reference populations. No large differences in realized reliability and regression coefficient were found between the two reference populations; however, a slight difference was found between the two methods for production traits. The accuracy of GEBV determined by single-step method increased slightly when genotyped cows were included in the bull reference population, but decreased slightly by multi-step method. A validation study was used to evaluate the accuracy of GEBV when 800 additional genotyped bulls (POPbull) or cows (POPcow) were included in the base reference population composed of 2000 genotyped bulls. The realized reliabilities of POPbull were higher than those of POPcow for all traits. For the gain of realized reliability over the base reference population, the average ratios of POPbull gain to POPcow gain for production traits and conformation traits were 2.6 and 7.2, respectively, and the ratios depended on heritabilities of the traits. For regression coefficient, no large differences were found between the results for POPbull and POPcow. Another validation study was performed to investigate the effect of genetic relatedness between cows and bulls in the reference and test populations. The effect of genetic relationship among bulls in the reference population was also assessed. The results showed that it is important to account for relatedness among bulls in the reference population. Our studies indicate that the prediction method, the contribution ratio of including animals, and genetic relatedness could affect the prediction accuracy in genomic evaluation of Holstein cattle, when including genotyped cows in the reference population.Keywords: inclusion of cows, dairy cattle, small population, reliability, prediction bias ImplicationsThe size of the reference population strongly influences genomic evaluation. If the size of the progeny-tested bull reference population is small, inclusion of domestic cows must be considered. In order to maximize the prediction accuracy by including genotyped cows in the reference population, we must investigate how the reliability and prediction bias are affected by the inclusion of cows under several conditions. The results of this study indicate that the prediction method, the contribution of included animals, and genetic relatedness should be taken account in designin...

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Section: Prediction Methodsmentioning

confidence: 99%

Effect of genotyped cows in the reference population on the genomic evaluation of Holstein cattle

Uemoto

Osawa

Saburi

2017

Animal

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“…In this study, ω = 0.20 was chosen according to the previous studies in Improving genomic predictions in Danish Jersey Nordic cattle populations 2012c;Gao et al, 2012). In this setting, the GBLUP model is equivalent to a GBLUP including a genomic effect and a residual polygenic effect accounting for 80% and 20% of total additive genetic variance, respectively.…”

Section: Datamentioning

confidence: 99%

“…Until now, only about 1200 to 1400 Danish progeny-tested bulls (depending on trait) are available to be used as reference bulls. Due to the small reference population, accuracy of genomic prediction in the Danish Jersey is much lower than in the Danish Holstein and Red Cattle populations 2012c;Gao et al, 2012;Thomasen et al, 2012). Therefore, it is important to find efficient approaches to improve accuracy of genomic prediction in this population.…”

Section: Introductionmentioning

confidence: 99%

Sharing reference data and including cows in the reference population improve genomic predictions in Danish Jersey

Nielsen

et al. 2016

Animal

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Small reference populations limit the accuracy of genomic prediction in numerically small breeds, such like Danish Jersey. The objective of this study was to investigate two approaches to improve genomic prediction by increasing size of reference population in Danish Jersey. The first approach was to include North American Jersey bulls in Danish Jersey reference population. The second was to genotype cows and use them as reference animals. The validation of genomic prediction was carried out on bulls and cows, respectively. In validation on bulls, about 300 Danish bulls (depending on traits) born in 2005 and later were used as validation data, and the reference populations were: (1) about 1050 Danish bulls, (2) about 1050 Danish bulls and about 1150 US bulls. In validation on cows, about 3000 Danish cows from 87 young half-sib families were used as validation data, and the reference populations were: (1) about 1250 Danish bulls, (2) about 1250 Danish bulls and about 1150 US bulls, (3) about 1250 Danish bulls and about 4800 cows, (4) about 1250 Danish bulls, 1150 US bulls and 4800 Danish cows. Genomic best linear unbiased prediction model was used to predict breeding values. De-regressed proofs were used as response variables. In the validation on bulls for eight traits, the joint DK-US bull reference population led to higher reliability of genomic prediction than the DK bull reference population for six traits, but not for fertility and longevity. Averaged over the eight traits, the gain was 3 percentage points. In the validation on cows for six traits (fertility and longevity were not available), the gain from inclusion of US bull in reference population was 6.6 percentage points in average over the six traits, and the gain from inclusion of cows was 8.2 percentage points. However, the gains from cows and US bulls were not accumulative. The total gain of including both US bulls and Danish cows was 10.5 percentage points. The results indicate that sharing reference data and including cows in reference population are efficient approaches to increase reliability of genomic prediction. Therefore, genomic selection is promising for numerically small population.

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“…Several results have been reported around the world. Several authors reported that the reliabilities of genomic estimated breeding values (GEBV) were substantially greater than breeding values from estimated breeding values (EBV) based on pedigree information [29]. The accuracy of selection was different between countries [12].…”

Section: Applications In Genomicsmentioning

confidence: 99%

“…The accuracy of selection was different between countries [12]. The accuracy was dependent on the size of reference population, the heritability of the trait studied, the statistical models and approaches used for prediction of genetic values for quantitative traits, and the method achieved to estimate the accuracy [12,27,29]. Ref.…”

Section: Applications In Genomicsmentioning

confidence: 99%

Bayesian Modeling in Genetics and Genomicsvvv

Zaabza¹,

Gara²,

Rekik³

2017

Bayesian Inference

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This chapter provides a critical review of statistical methods applied in animal and plant breeding programs, especially Bayesian methods. Classical and Bayesian procedures are presented in pedigree-based and marker-based models. The flexibility of the Bayesian approaches and their high accuracy of prediction of the breeding values are illustrated. We show a tendency of the superiority of Bayesian methods over best linear unbiased prediction (BLUP) in accuracy of selection, but some difficulties on elicitation of some complex prior distributions are investigated. Genetic models including marker and pedigree information are more accurate than statistical models based on markers or pedigree alone.

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Genomic prediction for Nordic Red Cattle using one-step and selection index blending

Cited by 107 publications

References 35 publications

Effect of genotyped cows in the reference population on the genomic evaluation of Holstein cattle

Effect of genotyped cows in the reference population on the genomic evaluation of Holstein cattle

Sharing reference data and including cows in the reference population improve genomic predictions in Danish Jersey

Bayesian Modeling in Genetics and Genomicsvvv

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