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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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Cumulative proportion of genetic variance explained by single-nucleotide polymorphisms (SNPs). The top 1% (A), 10% (B) and 100% (C) SNPs were sorted by the size of estimated effects in decreasing order. Results for fat percentage, milk yield, and somatic cell score were plotted with blue solid lines, green dash lines and red dotted lines, respectively. The marker weights for genomic best linear unbiased prediction are shown by black solid lines.
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fig4: Cumulative proportion of genetic variance explained by single-nucleotide polymorphisms (SNPs). The top 1% (A), 10% (B) and 100% (C) SNPs were sorted by the size of estimated effects in decreasing order. Results for fat percentage, milk yield, and somatic cell score were plotted with blue solid lines, green dash lines and red dotted lines, respectively. The marker weights for genomic best linear unbiased prediction are shown by black solid lines.

Mentions: Results in Table 2 clearly show that BLUP/GA improved the accuracy for FP and MY, but not for SCS. To determine the feature of a trait on which the accuracy of WGP can be improved, we calculated the genetic variance explained by each marker as , where p and α are the allele frequency and the estimated allele substitution effect for the marker under consideration. Then, we sorted all markers by their size of estimated effects (/α/) in decreasing order, and finally plotted the cumulative proportion of genetic variance explained by the ordered SNPs for each scenario. The proportion of genetic variance explained by the top 1%, 10%, and 100% SNPs are shown in panels A, B, and C in Figure 4. Interestingly, the differences among the three curves occur mainly at the top SNPs, especially for the top ~0.1% SNPs (Figure 4A), and the curves are nearly parallel for the remaining part (Figure 4C). For fat percentage, more than one third of the genetic variance is explained by the top 1% SNPs; moreover, only the top 0.1% SNPs (43 SNPs) explain ~28% of the genetic variance. By contrast, the top 1% SNPs explain only ~13% of the genetic variance for somatic cell score (Figure 4A).


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)

Cumulative proportion of genetic variance explained by single-nucleotide polymorphisms (SNPs). The top 1% (A), 10% (B) and 100% (C) SNPs were sorted by the size of estimated effects in decreasing order. Results for fat percentage, milk yield, and somatic cell score were plotted with blue solid lines, green dash lines and red dotted lines, respectively. The marker weights for genomic best linear unbiased prediction are shown by black solid lines.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: Cumulative proportion of genetic variance explained by single-nucleotide polymorphisms (SNPs). The top 1% (A), 10% (B) and 100% (C) SNPs were sorted by the size of estimated effects in decreasing order. Results for fat percentage, milk yield, and somatic cell score were plotted with blue solid lines, green dash lines and red dotted lines, respectively. The marker weights for genomic best linear unbiased prediction are shown by black solid lines.
Mentions: Results in Table 2 clearly show that BLUP/GA improved the accuracy for FP and MY, but not for SCS. To determine the feature of a trait on which the accuracy of WGP can be improved, we calculated the genetic variance explained by each marker as , where p and α are the allele frequency and the estimated allele substitution effect for the marker under consideration. Then, we sorted all markers by their size of estimated effects (/α/) in decreasing order, and finally plotted the cumulative proportion of genetic variance explained by the ordered SNPs for each scenario. The proportion of genetic variance explained by the top 1%, 10%, and 100% SNPs are shown in panels A, B, and C in Figure 4. Interestingly, the differences among the three curves occur mainly at the top SNPs, especially for the top ~0.1% SNPs (Figure 4A), and the curves are nearly parallel for the remaining part (Figure 4C). For fat percentage, more than one third of the genetic variance is explained by the top 1% SNPs; moreover, only the top 0.1% SNPs (43 SNPs) explain ~28% of the genetic variance. By contrast, the top 1% SNPs explain only ~13% of the genetic variance for somatic cell score (Figure 4A).

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