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The impact of single nucleotide polymorphism selection on prediction of genomewide breeding values.

Zukowski K, Suchocki T, Gontarek A, Szyda J - BMC Proc (2009)

Bottom Line: Differences between models are expressed by comparing the ranking of individuals based on EBV and on GBV and by correlations.The highest correlation between GBV and EBV amounts to 0.787 and is observed for model 3 with 3,328 SNPs selected based on their minor allele frequency, the lowest correlation of 0.519 is attributed to model 2 with 300 SNPs.Correlations between GBV estimates obtained from different models with the same number of SNPs range between 0.916 and 0. 998, whereas correlations between different SNP data sets using the same model fall under 0.850.These results indicate that successful application of high throughoutput SNP genotyping technologies for prediction of breeding values is a very promising approach, but before the method can be routinely applied further methodological improvements regarding model construction and SNP selection are required.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Animal Genetics, Wroclaw University of Life and Environmental Sciences, Wroclaw, Poland. kacper.zukowski@up.wroc.pl

ABSTRACT
The study focuses on the impact of different sets of single nucleotide polymorphisms (SNPs) selected from the available data set on prediction of genomewide breeding values (GBVs) of animals. Correlations between breeding values estimated as additive polygenic effects (EBVs) and GBVs as well as correlations between true breeding values (TBVs) and GBVs are used as major criteria for the comparison of different SNP selection schemes and GBV estimation models.The analysed data is the simulated data set from the XII QTL Workshop. In the analysis five different SNP data sets are considered. For prediction of EBVs a standard mixed animal model is applied, whereas GBVs are defined as the sum of additive effects of SNPs estimated for the different SNP data sets using model 1 with fixed SNPs effects, model 2 with fixed SNPs effects and a random additive polygenic effect, model 3 with a random effects of uncorrelated SNP genotypes.The additive polygenic and residual variance components estimated by the EBV model amount to 1.36 and 3.12, respectively. Differences between models are expressed by comparing the ranking of individuals based on EBV and on GBV and by correlations. Among 100 individuals with the highest EBVs, depending on a model and a data set, there are only between 11 and 37 individuals with the highest GBVs. The highest correlation between GBV and EBV amounts to 0.787 and is observed for model 3 with 3,328 SNPs selected based on their minor allele frequency, the lowest correlation of 0.519 is attributed to model 2 with 300 SNPs. Correlations between GBV estimates obtained from different models with the same number of SNPs range between 0.916 and 0. 998, whereas correlations between different SNP data sets using the same model fall under 0.850.These results indicate that successful application of high throughoutput SNP genotyping technologies for prediction of breeding values is a very promising approach, but before the method can be routinely applied further methodological improvements regarding model construction and SNP selection are required.

No MeSH data available.


Related in: MedlinePlus

Differences in ranking of individuals based on EBV and on GBVs. Individual differences in ranks based on EBV and different GBV models and for different SNP data sets, calculated for animals from the first four generations and sorted in ascending order. Model 1 is represented by black curves, model 2 – by red curves, and model 3 – by green curves. The best (lowest differences) and the worst (highest differences) models are represented by dashed curves.
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Figure 1: Differences in ranking of individuals based on EBV and on GBVs. Individual differences in ranks based on EBV and different GBV models and for different SNP data sets, calculated for animals from the first four generations and sorted in ascending order. Model 1 is represented by black curves, model 2 – by red curves, and model 3 – by green curves. The best (lowest differences) and the worst (highest differences) models are represented by dashed curves.

Mentions: Differences between the models expressed in the similarity in ranking of 100 individuals with the highest GBV are summarised in Table 1. When the ranking based on EBV is treated as a basis, the highest ranking similarity is observed for GBVSNP6000 of model 1 which has 41% correspondence with the 100 individuals with the highest rank based on EBV. The lowest similarity of 11% is observed for GBVSNP300 of model 2. In general, for a given number of SNPs model 2 has mostly the lowest number of individuals in the top 100 ranking based on EBV, while model 3 – mostly the highest. Consequently, when differences in ranking are compared on an individual level, the smallest differences are observed for model 3 with 3328 SNPs and highest differences – for model 2 and 300 SNPs (Figure 1). However in general, individual differences in ranks are similar across models and SNP data sets.


The impact of single nucleotide polymorphism selection on prediction of genomewide breeding values.

Zukowski K, Suchocki T, Gontarek A, Szyda J - BMC Proc (2009)

Differences in ranking of individuals based on EBV and on GBVs. Individual differences in ranks based on EBV and different GBV models and for different SNP data sets, calculated for animals from the first four generations and sorted in ascending order. Model 1 is represented by black curves, model 2 – by red curves, and model 3 – by green curves. The best (lowest differences) and the worst (highest differences) models are represented by dashed curves.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654494&req=5

Figure 1: Differences in ranking of individuals based on EBV and on GBVs. Individual differences in ranks based on EBV and different GBV models and for different SNP data sets, calculated for animals from the first four generations and sorted in ascending order. Model 1 is represented by black curves, model 2 – by red curves, and model 3 – by green curves. The best (lowest differences) and the worst (highest differences) models are represented by dashed curves.
Mentions: Differences between the models expressed in the similarity in ranking of 100 individuals with the highest GBV are summarised in Table 1. When the ranking based on EBV is treated as a basis, the highest ranking similarity is observed for GBVSNP6000 of model 1 which has 41% correspondence with the 100 individuals with the highest rank based on EBV. The lowest similarity of 11% is observed for GBVSNP300 of model 2. In general, for a given number of SNPs model 2 has mostly the lowest number of individuals in the top 100 ranking based on EBV, while model 3 – mostly the highest. Consequently, when differences in ranking are compared on an individual level, the smallest differences are observed for model 3 with 3328 SNPs and highest differences – for model 2 and 300 SNPs (Figure 1). However in general, individual differences in ranks are similar across models and SNP data sets.

Bottom Line: Differences between models are expressed by comparing the ranking of individuals based on EBV and on GBV and by correlations.The highest correlation between GBV and EBV amounts to 0.787 and is observed for model 3 with 3,328 SNPs selected based on their minor allele frequency, the lowest correlation of 0.519 is attributed to model 2 with 300 SNPs.Correlations between GBV estimates obtained from different models with the same number of SNPs range between 0.916 and 0. 998, whereas correlations between different SNP data sets using the same model fall under 0.850.These results indicate that successful application of high throughoutput SNP genotyping technologies for prediction of breeding values is a very promising approach, but before the method can be routinely applied further methodological improvements regarding model construction and SNP selection are required.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Animal Genetics, Wroclaw University of Life and Environmental Sciences, Wroclaw, Poland. kacper.zukowski@up.wroc.pl

ABSTRACT
The study focuses on the impact of different sets of single nucleotide polymorphisms (SNPs) selected from the available data set on prediction of genomewide breeding values (GBVs) of animals. Correlations between breeding values estimated as additive polygenic effects (EBVs) and GBVs as well as correlations between true breeding values (TBVs) and GBVs are used as major criteria for the comparison of different SNP selection schemes and GBV estimation models.The analysed data is the simulated data set from the XII QTL Workshop. In the analysis five different SNP data sets are considered. For prediction of EBVs a standard mixed animal model is applied, whereas GBVs are defined as the sum of additive effects of SNPs estimated for the different SNP data sets using model 1 with fixed SNPs effects, model 2 with fixed SNPs effects and a random additive polygenic effect, model 3 with a random effects of uncorrelated SNP genotypes.The additive polygenic and residual variance components estimated by the EBV model amount to 1.36 and 3.12, respectively. Differences between models are expressed by comparing the ranking of individuals based on EBV and on GBV and by correlations. Among 100 individuals with the highest EBVs, depending on a model and a data set, there are only between 11 and 37 individuals with the highest GBVs. The highest correlation between GBV and EBV amounts to 0.787 and is observed for model 3 with 3,328 SNPs selected based on their minor allele frequency, the lowest correlation of 0.519 is attributed to model 2 with 300 SNPs. Correlations between GBV estimates obtained from different models with the same number of SNPs range between 0.916 and 0. 998, whereas correlations between different SNP data sets using the same model fall under 0.850.These results indicate that successful application of high throughoutput SNP genotyping technologies for prediction of breeding values is a very promising approach, but before the method can be routinely applied further methodological improvements regarding model construction and SNP selection are required.

No MeSH data available.


Related in: MedlinePlus