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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

Ratio between total number of haploblock variables used in the prediction models and total number of haploblocks containing the main SNP effects. 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 ratio (haploblock variables)/haploblocks
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Fig6: Ratio between total number of haploblock variables used in the prediction models and total number of haploblocks containing the main SNP effects. 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 ratio (haploblock variables)/haploblocks

Mentions: One interesting point about the selection of QTL-haploblocks was the relationship between the number of QTL-haploblocks selected by the main SNPs (Table 2) and the number of haploblock variables (Table 3). Figure 6 shows the average number of “alleles” per haploblock, for selection of QTL-haploblocks using 1000 to 10 000 main SNPs. For selection of QTL-haploblocks to predict fertility, the average number of “alleles” per haploblock was greater when using the Bayesian mixture model, than when using Bayesian BLUP. This difference was more accentuated at small numbers of main SNPs (1000 to 2000) to select QTL-haploblocks, and remained almost unchanged up to 6000 main SNPs. For 7000 main SNPs and more, the average number of “alleles” per haploblock became similar, for both the Bayesian BLUP and the Bayesian mixture models. For mastitis, the number of “alleles” per haploblock is also greater when using the Bayesian mixture model up to 6000 main SNPs, except when using 2000 main SNPs, for which the average was slightly greater when using Bayesian BLUP. This difference is more accentuated when using 3000 to 5000 of main SNPs to select QTL-haploblocks. For 7000 main SNPs and more, the difference started to decrease until the ratios converged to the same value. For protein yield, the scenario observed was different, except when using 1000 main SNPs to select QTL-haploblocks, the number of “alleles” per haploblock was greater when using the Bayesian BLUP model, this was most pronounced up to 4000 main SNPs, then converged to the same value.Fig. 6


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

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

Ratio between total number of haploblock variables used in the prediction models and total number of haploblocks containing the main SNP effects. 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 ratio (haploblock variables)/haploblocks
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig6: Ratio between total number of haploblock variables used in the prediction models and total number of haploblocks containing the main SNP effects. 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 ratio (haploblock variables)/haploblocks
Mentions: One interesting point about the selection of QTL-haploblocks was the relationship between the number of QTL-haploblocks selected by the main SNPs (Table 2) and the number of haploblock variables (Table 3). Figure 6 shows the average number of “alleles” per haploblock, for selection of QTL-haploblocks using 1000 to 10 000 main SNPs. For selection of QTL-haploblocks to predict fertility, the average number of “alleles” per haploblock was greater when using the Bayesian mixture model, than when using Bayesian BLUP. This difference was more accentuated at small numbers of main SNPs (1000 to 2000) to select QTL-haploblocks, and remained almost unchanged up to 6000 main SNPs. For 7000 main SNPs and more, the average number of “alleles” per haploblock became similar, for both the Bayesian BLUP and the Bayesian mixture models. For mastitis, the number of “alleles” per haploblock is also greater when using the Bayesian mixture model up to 6000 main SNPs, except when using 2000 main SNPs, for which the average was slightly greater when using Bayesian BLUP. This difference is more accentuated when using 3000 to 5000 of main SNPs to select QTL-haploblocks. For 7000 main SNPs and more, the difference started to decrease until the ratios converged to the same value. For protein yield, the scenario observed was different, except when using 1000 main SNPs to select QTL-haploblocks, the number of “alleles” per haploblock was greater when using the Bayesian BLUP model, this was most pronounced up to 4000 main SNPs, then converged to the same value.Fig. 6

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