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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

Correlations between GBVs. Correlations (r) between GBVs estimated by different models and for different SNP data sets. Models are indicated in parentheses, followed by the number of SNPs used.
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Figure 2: Correlations between GBVs. Correlations (r) between GBVs estimated by different models and for different SNP data sets. Models are indicated in parentheses, followed by the number of SNPs used.

Mentions: A general overview of correlations between different GBVs is given in Figure 2. Correlations vary considerable from 0.99 between GBVSNP3328 for model 1 and model 2, as well as between GBVSNP1200 also for models 1 and 2 to as low as 0.47 between GBVSNP6000 for model 1 and GBVSNP300 for model 2. In general correlations between predicted GBVs resulting from models using the same number of SNPs are relatively high exceeding 0.80 (except two correlations involving GBVSNP3328 for model 3). Correlations between GBV estimates obtained from the same model, but using different NSNP are lower, generally falling under 0.70 for models 1 and 2 and somewhat higher – from 0.97 to 0.85 for model 3.


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

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

Correlations between GBVs. Correlations (r) between GBVs estimated by different models and for different SNP data sets. Models are indicated in parentheses, followed by the number of SNPs used.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Correlations between GBVs. Correlations (r) between GBVs estimated by different models and for different SNP data sets. Models are indicated in parentheses, followed by the number of SNPs used.
Mentions: A general overview of correlations between different GBVs is given in Figure 2. Correlations vary considerable from 0.99 between GBVSNP3328 for model 1 and model 2, as well as between GBVSNP1200 also for models 1 and 2 to as low as 0.47 between GBVSNP6000 for model 1 and GBVSNP300 for model 2. In general correlations between predicted GBVs resulting from models using the same number of SNPs are relatively high exceeding 0.80 (except two correlations involving GBVSNP3328 for model 3). Correlations between GBV estimates obtained from the same model, but using different NSNP are lower, generally falling under 0.70 for models 1 and 2 and somewhat higher – from 0.97 to 0.85 for model 3.

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