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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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Manhattan plot of the marker effects estimated for fat percentage. The marker effects (gi) were estimated using ridge regression best linear unbiased prediction and rescaled so that the average marker effect was 1, in order to make the sizes of marker effect from different population sizes (N) or different traits comparable.
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fig2: Manhattan plot of the marker effects estimated for fat percentage. The marker effects (gi) were estimated using ridge regression best linear unbiased prediction and rescaled so that the average marker effect was 1, in order to make the sizes of marker effect from different population sizes (N) or different traits comparable.

Mentions: In addition, we compared the marker effects estimated from different population sizes for fat percentage (Figure 2), milk yield (Supporting Information, Figure S1), and somatic cell score (Figure S2), respectively. To ensure that the effects estimated from different traits and / or different population sizes were comparable, we rescaled the marker effects so that the average absolute value of marker effect was 1 for each scenario. It is clear that the marker with the highest estimated effect was found around DGAT1 (Diacylglycerol O-Acyltransferase 1, the peak in the left side of chromosome 14) for all scenarios in fat percentage (Figure 2) and milk yield (Figure S1). Although decreasing the population size (N) from 5024 to 125, the scaled peak value decreased from 60 to 9 for fat percentage (Figure 2), from 26 to 7 for milk yield (Figure S1), but no apparent decrease was observed for SCS (Figure S2). Similar effects on both the size and variance of SNP effects were reported in Liu et al. (2011) for a German Holstein dataset.


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)

Manhattan plot of the marker effects estimated for fat percentage. The marker effects (gi) were estimated using ridge regression best linear unbiased prediction and rescaled so that the average marker effect was 1, in order to make the sizes of marker effect from different population sizes (N) or different traits comparable.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Manhattan plot of the marker effects estimated for fat percentage. The marker effects (gi) were estimated using ridge regression best linear unbiased prediction and rescaled so that the average marker effect was 1, in order to make the sizes of marker effect from different population sizes (N) or different traits comparable.
Mentions: In addition, we compared the marker effects estimated from different population sizes for fat percentage (Figure 2), milk yield (Supporting Information, Figure S1), and somatic cell score (Figure S2), respectively. To ensure that the effects estimated from different traits and / or different population sizes were comparable, we rescaled the marker effects so that the average absolute value of marker effect was 1 for each scenario. It is clear that the marker with the highest estimated effect was found around DGAT1 (Diacylglycerol O-Acyltransferase 1, the peak in the left side of chromosome 14) for all scenarios in fat percentage (Figure 2) and milk yield (Figure S1). Although decreasing the population size (N) from 5024 to 125, the scaled peak value decreased from 60 to 9 for fat percentage (Figure 2), from 26 to 7 for milk yield (Figure S1), but no apparent decrease was observed for SCS (Figure S2). Similar effects on both the size and variance of SNP effects were reported in Liu et al. (2011) for a German Holstein dataset.

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