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Selection of haplotype variables from a high-density marker map for genomic prediction.

Cuyabano BC, Su G, Lund MS - Genet. Sel. Evol. (2015)

Bottom Line: Haplotype blocks used as predictors can improve the reliability of genomic prediction compared to the individual SNP model.Furthermore, the use of a subset of haplotype blocks that contains the main SNP effects from genomic data could be a feasible approach to genomic prediction in dairy cattle, given an increase in density of genotype data available.The predictive ability of the models that use a subset of haplotype blocks was similar to that obtained using either all haplotype blocks or all individual SNPs, with the benefit of having a much lower computational demand.

View Article: PubMed Central - PubMed

Affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark. beatriz.cuyabano@agrsci.dk.

ABSTRACT

Background: Using haplotype blocks as predictors rather than individual single nucleotide polymorphisms (SNPs) may improve genomic predictions, since haplotypes are in stronger linkage disequilibrium with the quantitative trait loci than are individual SNPs. It has also been hypothesized that an appropriate selection of a subset of haplotype blocks can result in similar or better predictive ability than when using the whole set of haplotype blocks. This study investigated genomic prediction using a set of haplotype blocks that contained the SNPs with large effects estimated from an individual SNP prediction model. We analyzed protein yield, fertility and mastitis of Nordic Holstein cattle, and used high-density markers (about 770k SNPs). To reach an optimum number of haplotype variables for genomic prediction, predictions were performed using subsets of haplotype blocks that contained a range of 1000 to 50 000 main SNPs.

Results: The use of haplotype blocks improved the prediction reliabilities, even when selection focused on only a group of haplotype blocks. In this case, the use of haplotype blocks that contained the 20 000 to 50 000 SNPs with the highest effect was sufficient to outperform the model that used all individual SNPs as predictors (up to 1.3 % improvement in prediction reliability for mastitis, compared to individual SNP approach), and the achieved reliabilities were similar to those using all haplotype blocks available in the genome data (from 0.6 % lower to 0.8 % higher reliability).

Conclusions: Haplotype blocks used as predictors can improve the reliability of genomic prediction compared to the individual SNP model. Furthermore, the use of a subset of haplotype blocks that contains the main SNP effects from genomic data could be a feasible approach to genomic prediction in dairy cattle, given an increase in density of genotype data available. The predictive ability of the models that use a subset of haplotype blocks was similar to that obtained using either all haplotype blocks or all individual SNPs, with the benefit of having a much lower computational demand.

No MeSH data available.


Related in: MedlinePlus

Prediction reliabilities obtained using models with QTL-haploblocks as covariates for fertility. The values on the x-axis are the number of main SNPs used to select QTL-haploblocks to perform genomic prediction, and the y-axis indicates the reliability of predictions
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Fig2: Prediction reliabilities obtained using models with QTL-haploblocks as covariates for fertility. The values on the x-axis are the number of main SNPs used to select QTL-haploblocks to perform genomic prediction, and the y-axis indicates the reliability of predictions

Mentions: Figures 1, 2 and 3 present the prediction reliabilities in graphs, for protein yield, fertility and mastitis, respectively. These figures show the fast increase of prediction reliabilities when using up to 10 000 main SNP effects to select QTL-haploblocks. Thereafter, the curves stabilize around the reliabilities obtained by the models that used all haploblocks.Fig. 1


Selection of haplotype variables from a high-density marker map for genomic prediction.

Cuyabano BC, Su G, Lund MS - Genet. Sel. Evol. (2015)

Prediction reliabilities obtained using models with QTL-haploblocks as covariates for fertility. The values on the x-axis are the number of main SNPs used to select QTL-haploblocks to perform genomic prediction, and the y-axis indicates the reliability of predictions
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4522081&req=5

Fig2: Prediction reliabilities obtained using models with QTL-haploblocks as covariates for fertility. The values on the x-axis are the number of main SNPs used to select QTL-haploblocks to perform genomic prediction, and the y-axis indicates the reliability of predictions
Mentions: Figures 1, 2 and 3 present the prediction reliabilities in graphs, for protein yield, fertility and mastitis, respectively. These figures show the fast increase of prediction reliabilities when using up to 10 000 main SNP effects to select QTL-haploblocks. Thereafter, the curves stabilize around the reliabilities obtained by the models that used all haploblocks.Fig. 1

Bottom Line: Haplotype blocks used as predictors can improve the reliability of genomic prediction compared to the individual SNP model.Furthermore, the use of a subset of haplotype blocks that contains the main SNP effects from genomic data could be a feasible approach to genomic prediction in dairy cattle, given an increase in density of genotype data available.The predictive ability of the models that use a subset of haplotype blocks was similar to that obtained using either all haplotype blocks or all individual SNPs, with the benefit of having a much lower computational demand.

View Article: PubMed Central - PubMed

Affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark. beatriz.cuyabano@agrsci.dk.

ABSTRACT

Background: Using haplotype blocks as predictors rather than individual single nucleotide polymorphisms (SNPs) may improve genomic predictions, since haplotypes are in stronger linkage disequilibrium with the quantitative trait loci than are individual SNPs. It has also been hypothesized that an appropriate selection of a subset of haplotype blocks can result in similar or better predictive ability than when using the whole set of haplotype blocks. This study investigated genomic prediction using a set of haplotype blocks that contained the SNPs with large effects estimated from an individual SNP prediction model. We analyzed protein yield, fertility and mastitis of Nordic Holstein cattle, and used high-density markers (about 770k SNPs). To reach an optimum number of haplotype variables for genomic prediction, predictions were performed using subsets of haplotype blocks that contained a range of 1000 to 50 000 main SNPs.

Results: The use of haplotype blocks improved the prediction reliabilities, even when selection focused on only a group of haplotype blocks. In this case, the use of haplotype blocks that contained the 20 000 to 50 000 SNPs with the highest effect was sufficient to outperform the model that used all individual SNPs as predictors (up to 1.3 % improvement in prediction reliability for mastitis, compared to individual SNP approach), and the achieved reliabilities were similar to those using all haplotype blocks available in the genome data (from 0.6 % lower to 0.8 % higher reliability).

Conclusions: Haplotype blocks used as predictors can improve the reliability of genomic prediction compared to the individual SNP model. Furthermore, the use of a subset of haplotype blocks that contains the main SNP effects from genomic data could be a feasible approach to genomic prediction in dairy cattle, given an increase in density of genotype data available. The predictive ability of the models that use a subset of haplotype blocks was similar to that obtained using either all haplotype blocks or all individual SNPs, with the benefit of having a much lower computational demand.

No MeSH data available.


Related in: MedlinePlus