Genomic prediction of genetic merit using LD-based haplotypes in the Nordic Holstein population.

Beatriz Cd Cuyabano,Guosheng Su,Mogens S Lund

doi:10.1186/1471-2164-15-1171

Abstract

BackgroundA haplotype approach to genomic prediction using high density data in dairy cattle as an alternative to single-marker methods is presented. With the assumption that haplotypes are in stronger linkage disequilibrium (LD) with quantitative trait loci (QTL) than single markers, this study focuses on the use of haplotype blocks (haploblocks) as explanatory variables for genomic prediction. Haploblocks were built based on the LD between markers, which allowed variable reduction. The haploblocks were then used to predict three economically important traits (milk protein, fertility and mastitis) in the Nordic Holstein population.ResultsThe haploblock approach improved prediction accuracy compared with the commonly used individual single nucleotide polymorphism (SNP) approach. Furthermore, using an average LD threshold to define the haploblocks (LD≥0.45 between any two markers) increased the prediction accuracies for all three traits, although the improvement was most significant for milk protein (up to 3.1 % improvement in prediction accuracy, compared with the individual SNP approach). Hotelling’s t-tests were performed, confirming the improvement in prediction accuracy for milk protein. Because the phenotypic values were in the form of de-regressed proofs, the improved accuracy for milk protein may be due to higher reliability of the data for this trait compared with the reliability of the mastitis and fertility data. Comparisons between best linear unbiased prediction (BLUP) and Bayesian mixture models also indicated that the Bayesian model produced the most accurate predictions in every scenario for the milk protein trait, and in some scenarios for fertility.ConclusionsThe haploblock approach to genomic prediction is a promising method for genomic selection in animal breeding. Building haploblocks based on LD reduced the number of variables without the loss of information. This method may play an important role in the future genomic prediction involving while genome sequences.

Highlights

A haplotype approach to genomic prediction using high density data in dairy cattle as an alternative to single-marker methods is presented
16,812 haploblocks that were built. These findings indicate that the use of haploblocks with high density (HD) data can reduce the number of explanatory variables in the two models by up to 30% (D ≥ 0.25)
The prediction reliabilities rG2 estimated breeding values (EBV) for the three traits of interest were compared for both the best linear unbiased prediction (BLUP) and mixture models, using the HD marker data for both the individual single nucleotide polymorphism (SNP) and haploblock approaches and comparing the different D thresholds (Figures 2, 3 and 4)

Summary

Introduction

A haplotype approach to genomic prediction using high density data in dairy cattle as an alternative to single-marker methods is presented. When high density (HD) marker data (770 k) became available, the accuracy of genomic prediction was expected to improve [2] as a result of an increased degree of linkage disequilibrium (LD) between the SNPs and the underlying quantitative trait loci (QTL) associated with the genetic variation in the traits of interest. This expectation has not been realized, because predictions using HD data have not shown very significant improvements [3,4,5] over similar predictions based on moderate density data. A further challenge is to process the large number of variables so that genomic predictions can be performed as efficiently as possible

Objectives

Methods

Results

Conclusion