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Genomic selection using low density marker panels with application to a sire line in pigs.

Wellmann R, Preuß S, Tholen E, Heinkel J, Wimmers K, Bennewitz J - Genet. Sel. Evol. (2013)

Bottom Line: The imputation method was developed for a situation in which selection candidates are genotyped with an SNP panel of reduced density but have high-density genotyped sires.The estimated decrease in the accuracy of genomic breeding values due to imputation errors was 3% for the 384 marker panel and negligible for larger panels, provided that at least one parent of the selection candidates was genotyped at high-density.Genomic breeding values predicted from deregressed breeding values with low reliabilities were more strongly correlated with the estimated BLUP breeding values than with the true breeding values.A panel size of 384 markers can be recommended for selection candidates of a pig breeding program if at least one parent is genotyped at high-density, but this appears to be the lower bound.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Animal Husbandry and Animal Breeding, University of Hohenheim, D-70599 Stuttgart, Germany. r.wellmann@uni-hohenheim.de

ABSTRACT

Background: Genomic selection has become a standard tool in dairy cattle breeding. However, for other animal species, implementation of this technology is hindered by the high cost of genotyping. One way to reduce the routine costs is to genotype selection candidates with an SNP (single nucleotide polymorphism) panel of reduced density. This strategy is investigated in the present paper. Methods are proposed for the approximation of SNP positions, for selection of SNPs to be included in the low-density panel, for genotype imputation, and for the estimation of the accuracy of genomic breeding values. The imputation method was developed for a situation in which selection candidates are genotyped with an SNP panel of reduced density but have high-density genotyped sires. The dams of selection candidates are not genotyped. The methods were applied to a sire line pig population with 895 German Piétrain boars genotyped with the PorcineSNP60 BeadChip.

Results: Genotype imputation error rates were 0.133 for a 384 marker panel, 0.079 for a 768 marker panel, and 0.022 for a 3000 marker panel. Error rates for markers with approximated positions were slightly larger. Availability of high-density genotypes for close relatives of the selection candidates reduced the imputation error rate. The estimated decrease in the accuracy of genomic breeding values due to imputation errors was 3% for the 384 marker panel and negligible for larger panels, provided that at least one parent of the selection candidates was genotyped at high-density.Genomic breeding values predicted from deregressed breeding values with low reliabilities were more strongly correlated with the estimated BLUP breeding values than with the true breeding values. This was not the case when a shortened pedigree was used to predict BLUP breeding values, in which the parents of the individuals genotyped at high-density were considered unknown.

Conclusions: Genomic selection with imputation from very low- to high-density marker panels is a promising strategy for the implementation of genomic selection at acceptable costs. A panel size of 384 markers can be recommended for selection candidates of a pig breeding program if at least one parent is genotyped at high-density, but this appears to be the lower bound.

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Correlation between direct genomic values (DGV) and BLUP estimated breeding values (EBV). The regression lines show how the correlation between DGV and EBV depends on the accuracies of the EBV in the validation set; the solid line corresponds to the situation in which complete pedigrees were used for the calculation of EBV; for the dotted line, shortened pedigrees were used.
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Figure 3: Correlation between direct genomic values (DGV) and BLUP estimated breeding values (EBV). The regression lines show how the correlation between DGV and EBV depends on the accuracies of the EBV in the validation set; the solid line corresponds to the situation in which complete pedigrees were used for the calculation of EBV; for the dotted line, shortened pedigrees were used.

Mentions: Figure 3 visualizes the results of the regression approach. Each point in the scatter plot corresponds to one trait. The regression lines show how the correlation between DGV and EBV depends on the accuracies of the EBV in the validation set. The solid line shows the regression function when the EBV were calculated using complete pedigrees, where is the average accuracy of the EBV in the training set. The dotted line shows the regression function that was obtained when the parents of the sires genotyped at high-density were considered unknown in the calculation of EBV (i.e. the shortened pedigrees were used). To calculate the dotted line, the three outlier traits with EBV with the lowest accuracies in the validation set were omitted.


Genomic selection using low density marker panels with application to a sire line in pigs.

Wellmann R, Preuß S, Tholen E, Heinkel J, Wimmers K, Bennewitz J - Genet. Sel. Evol. (2013)

Correlation between direct genomic values (DGV) and BLUP estimated breeding values (EBV). The regression lines show how the correlation between DGV and EBV depends on the accuracies of the EBV in the validation set; the solid line corresponds to the situation in which complete pedigrees were used for the calculation of EBV; for the dotted line, shortened pedigrees were used.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750593&req=5

Figure 3: Correlation between direct genomic values (DGV) and BLUP estimated breeding values (EBV). The regression lines show how the correlation between DGV and EBV depends on the accuracies of the EBV in the validation set; the solid line corresponds to the situation in which complete pedigrees were used for the calculation of EBV; for the dotted line, shortened pedigrees were used.
Mentions: Figure 3 visualizes the results of the regression approach. Each point in the scatter plot corresponds to one trait. The regression lines show how the correlation between DGV and EBV depends on the accuracies of the EBV in the validation set. The solid line shows the regression function when the EBV were calculated using complete pedigrees, where is the average accuracy of the EBV in the training set. The dotted line shows the regression function that was obtained when the parents of the sires genotyped at high-density were considered unknown in the calculation of EBV (i.e. the shortened pedigrees were used). To calculate the dotted line, the three outlier traits with EBV with the lowest accuracies in the validation set were omitted.

Bottom Line: The imputation method was developed for a situation in which selection candidates are genotyped with an SNP panel of reduced density but have high-density genotyped sires.The estimated decrease in the accuracy of genomic breeding values due to imputation errors was 3% for the 384 marker panel and negligible for larger panels, provided that at least one parent of the selection candidates was genotyped at high-density.Genomic breeding values predicted from deregressed breeding values with low reliabilities were more strongly correlated with the estimated BLUP breeding values than with the true breeding values.A panel size of 384 markers can be recommended for selection candidates of a pig breeding program if at least one parent is genotyped at high-density, but this appears to be the lower bound.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Animal Husbandry and Animal Breeding, University of Hohenheim, D-70599 Stuttgart, Germany. r.wellmann@uni-hohenheim.de

ABSTRACT

Background: Genomic selection has become a standard tool in dairy cattle breeding. However, for other animal species, implementation of this technology is hindered by the high cost of genotyping. One way to reduce the routine costs is to genotype selection candidates with an SNP (single nucleotide polymorphism) panel of reduced density. This strategy is investigated in the present paper. Methods are proposed for the approximation of SNP positions, for selection of SNPs to be included in the low-density panel, for genotype imputation, and for the estimation of the accuracy of genomic breeding values. The imputation method was developed for a situation in which selection candidates are genotyped with an SNP panel of reduced density but have high-density genotyped sires. The dams of selection candidates are not genotyped. The methods were applied to a sire line pig population with 895 German Piétrain boars genotyped with the PorcineSNP60 BeadChip.

Results: Genotype imputation error rates were 0.133 for a 384 marker panel, 0.079 for a 768 marker panel, and 0.022 for a 3000 marker panel. Error rates for markers with approximated positions were slightly larger. Availability of high-density genotypes for close relatives of the selection candidates reduced the imputation error rate. The estimated decrease in the accuracy of genomic breeding values due to imputation errors was 3% for the 384 marker panel and negligible for larger panels, provided that at least one parent of the selection candidates was genotyped at high-density.Genomic breeding values predicted from deregressed breeding values with low reliabilities were more strongly correlated with the estimated BLUP breeding values than with the true breeding values. This was not the case when a shortened pedigree was used to predict BLUP breeding values, in which the parents of the individuals genotyped at high-density were considered unknown.

Conclusions: Genomic selection with imputation from very low- to high-density marker panels is a promising strategy for the implementation of genomic selection at acceptable costs. A panel size of 384 markers can be recommended for selection candidates of a pig breeding program if at least one parent is genotyped at high-density, but this appears to be the lower bound.

Show MeSH
Related in: MedlinePlus