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Interestingly, in the multibreed analysis there were more significant SNP located within genes than in single breed analysis (Table 5).
However, the more genetically diverse the populations are, the less accurate are the genomic predictions for across breed analysis.
SNPs in the region surrounding the ABCG2 locus [ 14] on BTA6 showed a much stronger signal in Holstein within breed analysis than the multibreed analysis, as there was no evidence that this mutation was segregating in our Jersey population.
As previously seen (and in QQ plots, Additional file 5: Figure S1), our within breed analysis actually reduced the number of significant effects suggesting combining the breeds is not leading to an increase in the number of false positives [ 6, 28].
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To further explore the intra-breed analysis, we analyzed haplotype diversity for each locus in each population, for each of the four breeds.
For the Tarentaise breed, imputation error rate was slightly higher in the multi-breed analysis than in the single-breed analysis.
There was evidence of LD at the QTL position in the combined-breed analysis.
So for large QTL that segregate in multiple breeds, there, appears to be more precision in the multi-breed analysis.
Then, to enlarge the training population, data from the three breeds were combined and a multi-breed analysis was performed.
However, Hayes et al. [ 23] showed that, in sheep breeds, accuracy of imputation in single-breed analyses was higher than accuracy of imputation in a multi-breed analysis.
For the dairy data, 17,425 Holstein and Jersey cattle were genotyped, with the GWAS results coming from a multi-breed analysis [ 32].
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