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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 the Bayesian mixture model with QTL-haploblocks and random selected haploblocks as covariates for protein yield. 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. The grey shaded area shows the range (minimum to maximum prediction reliabilities) and the black lines indicate the mean reliabilities obtained of the models using the randomly selected haploblocks
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Fig5: Prediction reliabilities obtained using the Bayesian mixture model with QTL-haploblocks and random selected haploblocks as covariates for protein yield. 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. The grey shaded area shows the range (minimum to maximum prediction reliabilities) and the black lines indicate the mean reliabilities obtained of the models using the randomly selected haploblocks

Mentions: Figures 4 and 5 compare the results obtained using QTL-haploblocks (blue lines) and randomly selected haploblocks (shaded areas and black lines). Figure 4 shows the results using Bayesian BLUP and Fig. 5 shows the results using the Bayesian mixture model. The random subset of haploblocks was repeated 10 times for each number of SNPs used to select haploblocks (1000 to 50 000), and predictions were performed for each subset. For both Bayesian BLUP and Bayesian mixture models, the mean reliability of the randomly selected haploblocks was lower than those achieved by the QTL-haploblocks, and most of the shaded area is below the blue lines. This confirmed that QTL-haploblocks are better explanatory variables for genomic prediction than haploblocks selected by a random subset of SNPs. It was expected that an advantage of QTL-haploblocks over randomly selected haploblocks would be observed, based on the use of selected individual SNPs for genomic prediction. As shown in Figs. 4 and 5, when a group of individual SNPs were selected based on their estimated effects, the genomic prediction obtained using this group was superior than would be observed if using a randomly selected group of individual SNPs.Fig. 4


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 the Bayesian mixture model with QTL-haploblocks and random selected haploblocks as covariates for protein yield. 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. The grey shaded area shows the range (minimum to maximum prediction reliabilities) and the black lines indicate the mean reliabilities obtained of the models using the randomly selected haploblocks
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig5: Prediction reliabilities obtained using the Bayesian mixture model with QTL-haploblocks and random selected haploblocks as covariates for protein yield. 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. The grey shaded area shows the range (minimum to maximum prediction reliabilities) and the black lines indicate the mean reliabilities obtained of the models using the randomly selected haploblocks
Mentions: Figures 4 and 5 compare the results obtained using QTL-haploblocks (blue lines) and randomly selected haploblocks (shaded areas and black lines). Figure 4 shows the results using Bayesian BLUP and Fig. 5 shows the results using the Bayesian mixture model. The random subset of haploblocks was repeated 10 times for each number of SNPs used to select haploblocks (1000 to 50 000), and predictions were performed for each subset. For both Bayesian BLUP and Bayesian mixture models, the mean reliability of the randomly selected haploblocks was lower than those achieved by the QTL-haploblocks, and most of the shaded area is below the blue lines. This confirmed that QTL-haploblocks are better explanatory variables for genomic prediction than haploblocks selected by a random subset of SNPs. It was expected that an advantage of QTL-haploblocks over randomly selected haploblocks would be observed, based on the use of selected individual SNPs for genomic prediction. As shown in Figs. 4 and 5, when a group of individual SNPs were selected based on their estimated effects, the genomic prediction obtained using this group was superior than would be observed if using a randomly selected group of individual SNPs.Fig. 4

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