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Accuracy of whole-genome prediction using a genetic architecture-enhanced variance-covariance matrix.

Zhang Z, Erbe M, He J, Ober U, Gao N, Zhang H, Simianer H, Li J - G3 (Bethesda) (2015)

Bottom Line: Predictive ability of BLUP/GA was validated with three model traits in a dairy cattle dataset and 11 traits in three public datasets with a variety of genetic architectures and compared with GBLUP and other approaches.Further analyses showed that the difference of accuracies for BLUP/GA and GBLUP significantly correlate with the distance between the T: and G: matrices.Applying BLUP/GA in WGP would ease the burden of model selection.

View Article: PubMed Central - PubMed

Affiliation: National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China Department of Animal Sciences, Animal Breeding and Genetics Group, Georg-August-Universität Göttingen, Göttingen 37075, Germany.

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Heat maps of the realized relationship matrix (G) and three trait-specific relationship matrices (S) in dairy cattle dataset. The G matrix was built with all markers (A), and S matrices were built with top 1% SNPs for fat% (B), milk yield (C), and somatic cell score (D), respectively. These matrices were calculated with the genotypes of 1000 randomly selected bulls, and these bulls were sorted by their genotypes of the SNP with the largest marker effects for each trait.
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fig5: Heat maps of the realized relationship matrix (G) and three trait-specific relationship matrices (S) in dairy cattle dataset. The G matrix was built with all markers (A), and S matrices were built with top 1% SNPs for fat% (B), milk yield (C), and somatic cell score (D), respectively. These matrices were calculated with the genotypes of 1000 randomly selected bulls, and these bulls were sorted by their genotypes of the SNP with the largest marker effects for each trait.

Mentions: To investigate the causes of the differential predictive ability of GBLUP and BLUP/GA, we plotted heat maps of the G matrix and the three S matrices (component of T matrix; see the section Materials and Methods for details) built for the three traits in the dairy cattle dataset (Figure 5). Individuals in all S matrices were ordered by the genotypes of SNP with the largest estimated effect on fat%. The S matrix for FP and MY showed apparent blocks (Figure 5, B and C), which is reflecting the three DGAT1 genotypes, and was distinct from the G matrix (Figure 5A), whereas the difference between S and G for somatic cell score (Figure 5D) was only marginal.


Accuracy of whole-genome prediction using a genetic architecture-enhanced variance-covariance matrix.

Zhang Z, Erbe M, He J, Ober U, Gao N, Zhang H, Simianer H, Li J - G3 (Bethesda) (2015)

Heat maps of the realized relationship matrix (G) and three trait-specific relationship matrices (S) in dairy cattle dataset. The G matrix was built with all markers (A), and S matrices were built with top 1% SNPs for fat% (B), milk yield (C), and somatic cell score (D), respectively. These matrices were calculated with the genotypes of 1000 randomly selected bulls, and these bulls were sorted by their genotypes of the SNP with the largest marker effects for each trait.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4390577&req=5

fig5: Heat maps of the realized relationship matrix (G) and three trait-specific relationship matrices (S) in dairy cattle dataset. The G matrix was built with all markers (A), and S matrices were built with top 1% SNPs for fat% (B), milk yield (C), and somatic cell score (D), respectively. These matrices were calculated with the genotypes of 1000 randomly selected bulls, and these bulls were sorted by their genotypes of the SNP with the largest marker effects for each trait.
Mentions: To investigate the causes of the differential predictive ability of GBLUP and BLUP/GA, we plotted heat maps of the G matrix and the three S matrices (component of T matrix; see the section Materials and Methods for details) built for the three traits in the dairy cattle dataset (Figure 5). Individuals in all S matrices were ordered by the genotypes of SNP with the largest estimated effect on fat%. The S matrix for FP and MY showed apparent blocks (Figure 5, B and C), which is reflecting the three DGAT1 genotypes, and was distinct from the G matrix (Figure 5A), whereas the difference between S and G for somatic cell score (Figure 5D) was only marginal.

Bottom Line: Predictive ability of BLUP/GA was validated with three model traits in a dairy cattle dataset and 11 traits in three public datasets with a variety of genetic architectures and compared with GBLUP and other approaches.Further analyses showed that the difference of accuracies for BLUP/GA and GBLUP significantly correlate with the distance between the T: and G: matrices.Applying BLUP/GA in WGP would ease the burden of model selection.

View Article: PubMed Central - PubMed

Affiliation: National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China Department of Animal Sciences, Animal Breeding and Genetics Group, Georg-August-Universität Göttingen, Göttingen 37075, Germany.

Show MeSH
Related in: MedlinePlus