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Comparison of analyses of the QTLMAS XII common dataset. I: Genomic selection.

Lund MS, Sahana G, de Koning DJ, Su G, Carlborg O - BMC Proc (2009)

Bottom Line: The best BLUP models as well as the best Bayesian models gave unbiased predictions.The BLUP models are clearly sensitive to the assumed SNP variance, because they do not estimate SNP variance, but take the specified variance as the true variance.On the other hand fixed effect type models are unlikely to provide any gain over traditional pedigree indexes for selection.

View Article: PubMed Central - HTML - PubMed

Affiliation: Aarhus University, Faculty of Agricultural Sciences, Department of Genetics & Biotechnology, Research Centre Foulum, DK-8830, Box 50, Tjele, Denmark. Mogens.lund@agrsci.dk

ABSTRACT
A dataset was simulated and distributed to participants of the QTLMAS XII workshop who were invited to develop genomic selection models. Each contributing group was asked to describe the model development and validation as well as to submit genomic predictions for three generations of individuals, for which they only knew the genotypes. The organisers used these genomic predictions to perform the final validation by comparison to the true breeding values, which were known only to the organisers. Methods used by the 5 groups fell in 3 classes 1) fixed effects models 2) BLUP models, and 3) Bayesian MCMC based models. The Bayesian analyses gave the highest accuracies, followed by the BLUP models, while the fixed effects models generally had low accuracies and large error variance. The best BLUP models as well as the best Bayesian models gave unbiased predictions. The BLUP models are clearly sensitive to the assumed SNP variance, because they do not estimate SNP variance, but take the specified variance as the true variance. The current comparison suggests that Bayesian analyses on haplotypes or SNPs are the most promising approach for Genomic selection although the BLUP models may provide a computationally attractive alternative with little loss of efficiency. On the other hand fixed effect type models are unlikely to provide any gain over traditional pedigree indexes for selection.

No MeSH data available.


Related in: MedlinePlus

Cumulative distribution of minor allele frequencies in the last 7 generations.
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Figure 2: Cumulative distribution of minor allele frequencies in the last 7 generations.

Mentions: In generations 1–7, the mean minor allele frequency (MAF) of markers was 0.298. The cumulative distribution of MAF in Figure 2 shows a rather equal distribution, which is a consequence of random drift over the 50 generations from the original starting values of 0.5 for each locus. For 38 marker loci one allele was fixed. The mean linkage disequilibrium (r2) between adjacent SNPs was 0.20 and the median was 0.11. This may be relatively low compared to other simulation studies that often use 1000 generations of random mating. On the other hand, the LD achieved in this paper seems very comparable to the realised values from real data analysis. For markers with a distance around 0.1 Mb the average R2 was 0.14 and 0.22 in different breeds and studies [8-10].


Comparison of analyses of the QTLMAS XII common dataset. I: Genomic selection.

Lund MS, Sahana G, de Koning DJ, Su G, Carlborg O - BMC Proc (2009)

Cumulative distribution of minor allele frequencies in the last 7 generations.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654490&req=5

Figure 2: Cumulative distribution of minor allele frequencies in the last 7 generations.
Mentions: In generations 1–7, the mean minor allele frequency (MAF) of markers was 0.298. The cumulative distribution of MAF in Figure 2 shows a rather equal distribution, which is a consequence of random drift over the 50 generations from the original starting values of 0.5 for each locus. For 38 marker loci one allele was fixed. The mean linkage disequilibrium (r2) between adjacent SNPs was 0.20 and the median was 0.11. This may be relatively low compared to other simulation studies that often use 1000 generations of random mating. On the other hand, the LD achieved in this paper seems very comparable to the realised values from real data analysis. For markers with a distance around 0.1 Mb the average R2 was 0.14 and 0.22 in different breeds and studies [8-10].

Bottom Line: The best BLUP models as well as the best Bayesian models gave unbiased predictions.The BLUP models are clearly sensitive to the assumed SNP variance, because they do not estimate SNP variance, but take the specified variance as the true variance.On the other hand fixed effect type models are unlikely to provide any gain over traditional pedigree indexes for selection.

View Article: PubMed Central - HTML - PubMed

Affiliation: Aarhus University, Faculty of Agricultural Sciences, Department of Genetics & Biotechnology, Research Centre Foulum, DK-8830, Box 50, Tjele, Denmark. Mogens.lund@agrsci.dk

ABSTRACT
A dataset was simulated and distributed to participants of the QTLMAS XII workshop who were invited to develop genomic selection models. Each contributing group was asked to describe the model development and validation as well as to submit genomic predictions for three generations of individuals, for which they only knew the genotypes. The organisers used these genomic predictions to perform the final validation by comparison to the true breeding values, which were known only to the organisers. Methods used by the 5 groups fell in 3 classes 1) fixed effects models 2) BLUP models, and 3) Bayesian MCMC based models. The Bayesian analyses gave the highest accuracies, followed by the BLUP models, while the fixed effects models generally had low accuracies and large error variance. The best BLUP models as well as the best Bayesian models gave unbiased predictions. The BLUP models are clearly sensitive to the assumed SNP variance, because they do not estimate SNP variance, but take the specified variance as the true variance. The current comparison suggests that Bayesian analyses on haplotypes or SNPs are the most promising approach for Genomic selection although the BLUP models may provide a computationally attractive alternative with little loss of efficiency. On the other hand fixed effect type models are unlikely to provide any gain over traditional pedigree indexes for selection.

No MeSH data available.


Related in: MedlinePlus