Accuracy of genomic breeding values in multi-breed dairy cattle populations

Predictive ability of models for litter size in swine on the basis of different sources of genetic information was investigated. Data represented average litter size on 2598, 1604 and 1897 60K genotyped sows from two purebred and one crossbred line, respectively. The average correlation (r) between observed and predicted phenotypes in a 10-fold cross-validation was used to assess predictive ability. Models were: pedigree-based mixed-effects model (PED), Bayesian ridge regression (BRR), Bayesian LASSO (BL), genomic BLUP (GBLUP), reproducing kernel Hilbert spaces regression (RKHS), Bayesian regularized neural networks (BRNN) and radial basis function neural networks (RBFNN). BRR and BL used the marker matrix or its principal component scores matrix (UD) as covariates; RKHS employed a Gaussian kernel with additive codes for markers whereas neural networks employed the additive genomic relationship matrix (G) or UD as inputs. The non-parametric models (RKHS, BRNN, RNFNN) gave similar predictions to the parametric counterparts (average r ranged from 0.15 to 0.23); most of the genome-based models outperformed PED (r 5 0.16). Predictive abilities of linear models and RKHS were similar over lines, but BRNN varied markedly, giving the best prediction (r 5 0.31) when G was used in crossbreds, but the worst (r 5 0.02) when the G matrix was used in one of the purebred lines. The r values for RBFNN ranged from 0.16 to 0.23. Predictive ability was better in crossbreds (0.26) than in purebreds (0.15 to 0.22). This may be related to family structure in the purebred lines.

Section: Resultsmentioning

confidence: 99%

Genome-enabled methods for predicting litter size in pigs: a comparison

Tusell

Pérez‐Rodríguez

Forni

et al. 2013

“…Although several studies on GEBV accuracy/reliability estimated from real data have been reported in the literature for cattle with GEBV reliabilities ranging from 18% to 78% (Harris et al, 2009;VanRaden et al, 2009;Hayes et al, 2009b), fewer are reported for sheep. Our GEBV accuracies are similar to others obtained using a medium-density markers chip of 15% to 79% for wool traits in Merino sheep (Daetwyler et al, 2010a), and 7% to 31% for carcass and meat-quality traits in multi-breed sheep data (Daetwyler et al, 2012b).…”

Section: Discussionmentioning

confidence: 99%

“…At present, the accuracy of GEBV has been evaluated in experiments involving several livestock species, such as dairy (Harris et al, 2009;Hayes et al, 2009b) and beef (Saatchi et al, 2011) cattle populations, chicken (González-Recio et al, 2009) and sheep (Daetwyler et al, 2010a(Daetwyler et al, , 2012a(Daetwyler et al, , 2012bDuchemin et al, 2012). Apart from the study by Kemper et al (2011), the use of high-density genomic information to select for nematode resistance in sheep has received less attention.…”

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.

“…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%

Reliabilities of genomic estimated breeding values in Danish Jersey

Thomasen

Guldbrandtsen

et al. 2012

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.