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Comparison of statistical procedures for estimating polygenic effects using dense genome-wide marker data.

Pimentel EC, König S, Schenkel FS, Simianer H - BMC Proc (2009)

Bottom Line: In this study we compared different statistical procedures for estimating SNP effects using the simulated data set from the XII QTL-MAS workshop.Correlations between these and the true breeding values were also moderate.We concluded that the ridge regression procedures applied in this study did not outperform the simple use of a ratio of variances in a mixed model method, both providing moderate accuracies of predicted genomic breeding values.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Animal Breeding and Genetics, University of Göttingen, Göttingen, 37075, Germany. epiment@gwdg.de

ABSTRACT
In this study we compared different statistical procedures for estimating SNP effects using the simulated data set from the XII QTL-MAS workshop. Five procedures were considered and tested in a reference population, i.e., the first four generations, from which phenotypes and genotypes were available. The procedures can be interpreted as variants of ridge regression, with different ways for defining the shrinkage parameter. Comparisons were made with respect to the correlation between genomic and conventional estimated breeding values. Moderate correlations were obtained from all methods. Two of them were used to predict genomic breeding values in the last three generations. Correlations between these and the true breeding values were also moderate. We concluded that the ridge regression procedures applied in this study did not outperform the simple use of a ratio of variances in a mixed model method, both providing moderate accuracies of predicted genomic breeding values.

No MeSH data available.


Related in: MedlinePlus

Marker effects, estimated from alternate BLUP procedures, against position (cM) on the genome.
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Figure 1: Marker effects, estimated from alternate BLUP procedures, against position (cM) on the genome.

Mentions: The means (± SD) of the GEBV and correlations between EBV and GEBV, in the validation sample, from all procedures are presented in Table 1. Estimates of regression coefficients against the marker position on the genome for the first four methods are presented in Figures 1 and 2.


Comparison of statistical procedures for estimating polygenic effects using dense genome-wide marker data.

Pimentel EC, König S, Schenkel FS, Simianer H - BMC Proc (2009)

Marker effects, estimated from alternate BLUP procedures, against position (cM) on the genome.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654493&req=5

Figure 1: Marker effects, estimated from alternate BLUP procedures, against position (cM) on the genome.
Mentions: The means (± SD) of the GEBV and correlations between EBV and GEBV, in the validation sample, from all procedures are presented in Table 1. Estimates of regression coefficients against the marker position on the genome for the first four methods are presented in Figures 1 and 2.

Bottom Line: In this study we compared different statistical procedures for estimating SNP effects using the simulated data set from the XII QTL-MAS workshop.Correlations between these and the true breeding values were also moderate.We concluded that the ridge regression procedures applied in this study did not outperform the simple use of a ratio of variances in a mixed model method, both providing moderate accuracies of predicted genomic breeding values.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Animal Breeding and Genetics, University of Göttingen, Göttingen, 37075, Germany. epiment@gwdg.de

ABSTRACT
In this study we compared different statistical procedures for estimating SNP effects using the simulated data set from the XII QTL-MAS workshop. Five procedures were considered and tested in a reference population, i.e., the first four generations, from which phenotypes and genotypes were available. The procedures can be interpreted as variants of ridge regression, with different ways for defining the shrinkage parameter. Comparisons were made with respect to the correlation between genomic and conventional estimated breeding values. Moderate correlations were obtained from all methods. Two of them were used to predict genomic breeding values in the last three generations. Correlations between these and the true breeding values were also moderate. We concluded that the ridge regression procedures applied in this study did not outperform the simple use of a ratio of variances in a mixed model method, both providing moderate accuracies of predicted genomic breeding values.

No MeSH data available.


Related in: MedlinePlus