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For both inversions and other structural variants, it has been anticipated that it would be possible to impute these variants from high-density SNP array data.
For some mCNVs, it may be possible to impute their alleles from flanking SNP haplotypes; in other words, using the genotypes of the surrounding SNPs, one may be able to estimate the copy number or structural allele present at the mCNV for a given individual [ 8, 66].
This project discovered 48.6 million high-quality SNPs, which must include many of the causal variants underlying quantitative variation in cattle, and it may be possible to impute genotypes at the resolution of the genome sequence (Daetwyler et al. 2011) in populations that have been genotyped with both assays.
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Using the haplotype phasing and imputation program, BEAGLE, it is possible to impute genotypes from low- to high-density (50K) in the Thoroughbred horse with reasonable to high accuracy.
Whilst it is possible to impute values between these years, there is no possibility of picking up genuine fluctuations in mortality on a more regular basis.
For incorrectly imputed genotypes it is possible to impute one or both alleles incorrectly.
With the resources from Hapmap, it is possible to impute up to 2.5 million SNPs in LD with the tag SNPs present in these GWAS SNP arrays.
The results of this study show that it is possible to impute genotypes from low- to high- density in Thoroughbred horses with reasonable to high accuracy.
This means that by using MGPs, it is possible to impute the missing data in between the metabolic measurements more efficiently.
Indeed, several studies [ 12- 14] have shown that it is possible to impute the genotypes of young animals for the missing SNPs with a very low error rate when both parents are genotyped with the denser beadchip.
Moreover, by using the extensive catalogues of variation in the founders (see Keane et al., 2011 [ 7]) it is possible to impute the variants into each CC lines and test for association [ 26].
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