Abstract
BackgroundUsing 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.ResultsThe 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).ConclusionsHaplotype 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.
Highlights
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
The imputed data was edited by removing SNPs with a minor allele frequency (MAF) less than 0.01 and SNPs that were in complete linkage disequilibrium (LD) with adjacent ones [13]
Because the selection of haploblocks was based on the SNP effects obtained from two models that included all individual SNPs, the haploblocks selected differed by trait and model
Summary
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. A previous study based on simulated data showed that the use of haplotypes leads to higher prediction reliabilities than individual marker predictors [3]. Using haplotype blocks (haploblocks) based on LD, from a highdensity (HD) marker data in the Nordic Holstein population, reliability of genomic prediction for economically important traits was increased by 3 % when compared to predictions using individual SNPs [4]. Prediction accuracies of breeding values depend strongly on the number of phenotyped and genotyped relatives within the population [6] This method using IBD relationships aims at decreasing marker density to reduce genotyping cost, whereas the method based on haploblocks in the current study aims at reducing prediction variables from HD marker data. The LD-based haploblocks were preferred in this study
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