Preliminary investigation on reliability of genomic estimated breeding values in the Danish Holstein population

Extensive genetic progress has been achieved in dairy cattle populations on many traits of economic importance because of efficient breeding programmes. Success of these programmes has relied on progeny testing of the best young males to accurately assess their genetic merit and hence their potential for breeding. Over the last few years, the integration of dense genomic information into statistical tools used to make selection decisions, commonly referred to as genomic selection, has enabled gains in predicting accuracy of breeding values for young animals without own performance. The possibility to select animals at an early stage allows defining new breeding strategies aimed at boosting genetic progress while reducing costs. The first objective of this article was to review methods used to model and optimize breeding schemes integrating genomic selection and to discuss their relative advantages and limitations. The second objective was to summarize the main results and perspectives on the use of genomic selection in practical breeding schemes, on the basis of the example of dairy cattle populations. Two main designs of breeding programmes integrating genomic selection were studied in dairy cattle. Genomic selection can be used either for pre-selecting males to be progeny tested or for selecting males to be used as active sires in the population. The first option produces moderate genetic gains without changing the structure of breeding programmes. The second option leads to large genetic gains, up to double those of conventional schemes because of a major reduction in the mean generation interval, but it requires greater changes in breeding programme structure. The literature suggests that genomic selection becomes more attractive when it is coupled with embryo transfer technologies to further increase selection intensity on the dam-to-sire pathway. The use of genomic information also offers new opportunities to improve preservation of genetic variation. However, recent simulation studies have shown that putting constraints on genomic inbreeding rates for defining optimal contributions of breeding animals could significantly reduce achievable genetic gain. Finally, the article summarizes the potential of genomic selection to include new traits in the breeding goal to meet societal demands regarding animal health and environmental efficiency in animal production.Keywords: genomic selection, breeding programme, dairy cattle, genetic gain, inbreeding rates ImplicationsThe practical use of genomic information to select animals, or genomic selection, is currently revolutionizing the organization of dairy cattle breeding schemes. The success of this new technology is because of increased genetic progress on both the bull and cow genetic pathways by reducing costs compared with conventional selection schemes, but also by the potential of using this rich source of information to manage genetic resources. Practical results of the implementation of genomic selection in dairy cattle breeding schemes are of great importa...

Section: Impact Of Genomic Selection On Selection Response and Its Vamentioning

confidence: 99%

Integrating genomic selection into dairy cattle breeding programmes: a review

Bouquet

Juga

2013

“…It is therefore important to evaluate the reliabilities of the genomic predictions in the same population from which breeding candidates are being selected. Reliabilities based on real data have been reported from Holstein populations (Hayes et al, 2009a;VanRaden et al, 2009;Harris and Johnson, 2010;Lund et al, 2010; -E-mail: jrt@vikinggenetics.com Su et al, 2010) and Jersey populations (Hayes et al, 2009b;Harris and Johnson, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…First, Su et al (2010) applied a cross-validation method to real data where subsets of proven bulls in turn are used as test bulls for predictions of DGV by omitting their estimates of genetic merit. Cross-validation has previously been used for validation on simulated data ).…”

Section: Introductionmentioning

confidence: 99%

Reliabilities of genomic estimated breeding values in Danish Jersey

Thomasen

Guldbrandtsen

et al. 2012

Self Cite

In order to optimize the use of genomic selection in breeding plans, it is essential to have reliable estimates of the genomic breeding values. This study investigated reliabilities of direct genomic values (DGVs) in the Jersey population estimated by three different methods. The validation methods were (i) fivefold cross-validation and (ii) validation on the most recent 3 years of bulls. The reliability of DGV was assessed using squared correlations between DGV and deregressed proofs (DRPs). In the recent 3-year validation model, estimated reliabilities were also used to assess the reliabilities of DGV. The data set consisted of 1003 Danish Jersey bulls with conventional estimated breeding values (EBVs) for 14 different traits included in the Nordic selection index. The bulls were genotyped for Single-nucleotide polymorphism (SNP) markers using the Illumina 54 K chip. A Bayesian method was used to estimate the SNP marker effects. The corrected squared correlations between DGV and DRP were on average across all traits 0.04 higher than the squared correlation between DRP and the pedigree index. This shows that there is a gain in accuracy due to incorporation of marker information compared with parent index pre-selection only. Averaged across traits, the estimates of reliability of DGVs ranged from 0.20 for validation on the most recent 3 years of bulls and up to 0.42 for expected reliabilities. Reliabilities from the cross-validation were on average 0.24. For the individual traits, the reliability varied from 0.12 (direct birth) to 0.39 (milk). Bulls whose sires were included in the reference group had an average reliability of 0.25, whereas the bulls whose sires were not included in the reference group had an average reliability that was 0.05 lower.

“…However, validation studies can also be conducted using separate phenotyped and genotyped populations (Hayes et al, 2009a;Luan et al, 2009;Su et al, 2010), with an accuracy that depends on the genetic relationship of the validation set to the TS (Habier et al, 2007(Habier et al, , 2010. This is possible because markers used in the statistical models to estimate marker effects also capture additive genetic relationships between individuals (Cockerham, 1969;Ritland, 1996); therefore, even if markers are not in LD with QTL, the accuracy of GEBV will still be non-zero.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of genomic prediction within and across populations for nematode resistance and body weight traits in sheep

Riggio

Abdel-Aziz

Matika

et al. 2014

Genomic prediction utilizes single nucleotide polymorphism (SNP) chip data to predict animal genetic merit. It has the advantage of potentially capturing the effects of the majority of loci that contribute to genetic variation in a trait, even when the effects of the individual loci are very small. To implement genomic prediction, marker effects are estimated with a training set, including individuals with marker genotypes and trait phenotypes; subsequently, genomic estimated breeding values (GEBV) for any genotyped individual in the population can be calculated using the estimated marker effects. In this study, we aimed to: (i) evaluate the potential of genomic prediction to predict GEBV for nematode resistance traits and BW in sheep, within and across populations; (ii) evaluate the accuracy of these predictions through within-population cross-validation; and (iii) explore the impact of population structure on the accuracy of prediction. Four data sets comprising 752 lambs from a Scottish Blackface population, 2371 from a Sarda × Lacaune backcross population, 1000 from a Martinik Black-Belly × Romane backcross population and 64 from a British Texel population were used in this study. Traits available for the analysis were faecal egg count for Nematodirus and Strongyles and BW at different ages or as average effect, depending on the population. Moreover, immunoglobulin A was also available for the Scottish Blackface population. Results show that GEBV had moderate to good within-population predictive accuracy, whereas across-population predictions had accuracies close to zero. This can be explained by our finding that in most cases the accuracy estimates were mostly because of additive genetic relatedness between animals, rather than linkage disequilibrium between SNP and quantitative trait loci. Therefore, our results suggest that genomic prediction for nematode resistance and BW may be of value in closely related animals, but that with the current SNP chip genomic predictions are unlikely to work across breeds.