Academic journal article Genetics

Accuracy of Genomic Selection Using Different Methods to Define Haplotypes

Academic journal article Genetics

Accuracy of Genomic Selection Using Different Methods to Define Haplotypes

Article excerpt


Genomic selection uses total breeding values for juvenile animals, predicted from a large number of estimated marker haplotype effects across the whole genome. In this study the accuracy of predicting breeding values is compared for four different models including a large number of markers, at different marker densities for traits with heritabilities of 50 and 10%. The models estimated the effect of (1) each single-marker allele [single-nucleotide polymorphism (SNP)1], (2) haplotypes constructed from two adjacent marker alleles (SNP2), and (3) haplotypes constructed from 2 or 10 markers, including the covariance between haplotypes by combining linkage disequilibrium and linkage analysis (HAP_IBD2 and HAP_IBD10). Between 119 and 2343 polymorphic SNPs were simulated on a 3-M genome. For the trait with a heritability of 10%, the differences between models were small and none of them yielded the highest accuracies across all marker densities. For the trait with a heritability of 50%, the HAP_IBD10 model yielded the highest accuracies of estimated total breeding values for juvenile and phenotyped animals at all marker densities. It was concluded that genomic selection is considerably more accurate than traditional selection, especially for a low-heritability trait.

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THE availability of many thousands of singlenucleotide polymorphisms (SNPs) spread across the genome for different livestock species opens up possibilities to include genomewide marker information in prediction of total breeding values, to perform genomic selection. Compared to traditional breeding practice, including genomic information yields a considerable increase in selection responses for juvenile animals that do not have phenotypic records (Meuwissen et al. 2001) and potentially can reduce the costs of a breeding program up to 90% (Schaeffer^sup 2^006).

Genomic selection as described by Meuwissen et al. (2001) predicts total breeding values on the basis of a large number of marker haplotypes across the entire genome. The underlying assumption of genomic selection is that haplotypes at some loci are in linkage disequilibrium (LD) with QTL alleles that affect the traits that are subject to selection. Different ways of deriving haplotypes of combinations of marker alleles, and the relationship between haplotypes at a locus, have been described. One method (SNP1) is to consider each different marker allele at a single locus to be a different haplotype, considering no relationships between different haplotypes, and thus breeding values are estimated directly for the marker alleles (Xu 2003). A second method is to construct haplotypes from two alleles at adjacent markers, assuming a zero relation between haplotypes at the same locus (SNP2) (Meuwissen et al. 2001). A third method is to construct haplotypes (HAP_IBD) using two or more surrounding marker alleles and derive identical-by-descent (IBD) probabilities between the different haplotypes at the same locus (Meuwissen and Goddard 2001).

The SNP1 model considers only two haplotypes at a locus and therefore may be suited for applications in, for instance, double-haploid populations with only two segregating genotypes at each locus (Xu 2003). For outbred populations, where the association between markers and QTL might be different in different families, the SNP1 model is perhaps less well suited. The advantage of the SNP1 approach is that determining the linkage phase of the haplotypes is not required and the markers do not need to be mapped. A disadvantage of the SNP1 model is that no new haplotypes arise as a result of recombination, while such an event actually might change the linkage between the marker and the QTL alleles. SNP1 and SNP2 do not make a distinction between haplotypes that are alike-in-state (AIS) due to a common ancestor (i.e., IBD) or simply due to chance. The benefit from the HAP_IBD approach is that the common background of haplotypes, and thus the probability that different haplotypes are associated with the same QTL allele, is modeled more accurately. …

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