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Results of this study indicated that prediction based on imputed SNPs and/or inferred haplotypes does not significantly improve predictions based on nonimputed SNPs.
For each phenotype, the resulting estimate of h L 2 based on imputed SNPs was lower than that using only genotyped SNPs (Supplementary Table 3), a paradoxical result that demonstrates the importance of adjusting for tagging when performing heritability analysis.
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For other individuals (partly overlapping with the fingerprint-sample), SNP data were available based on imputed genome-wide SNP arrays.
In contrast, with imputation by either of the three map-dependent methods Beagle, FImpute, and IMPUTE2, the correlation between RD estimates based on imputed and original 90 SNP data was higher than the respective benchmark for all missing data levels.
Our objective was to investigate the reliability of genomic prediction based on imputed whole-genome sequence genotypes versus high-density SNP genotypes using real cattle data.
Results presented here are based on imputed data.
We therefore display results based on imputed data.
We only present results based on imputed datasets.
‡Treatment effects are ratios of means based on imputed data.
Since results of analyses based on imputed datasets were comparable to the results found in complete case analyses, we only show results based on imputed data.
Each study performed genome-wide association testing for age at menarche or menopause across ∼2.5 million imputed SNPs, based on linear regression under an additive genetic model.
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