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We used the imputed most likely genotypes for association analyses.
In this study, the most likely genotypes were set as the imputed genotypes, but it is also possible to infer imputed genotypes from a posterior distribution provided by certain methods (such as the one based on HMM, IMPUTE) [5].
Alternatively, it can run faster without much loss of accuracy by using a two-stage process (using a single set of estimates for the crossover and error rate map and, conditional on these, to find the most likely genotypes).
In both cases, the most likely genotypes were used for prediction.
Also for the SNP-specific imputation accuracy, genotype dosage probabilities were used, rather than the most likely genotypes.
The literature shows that for selection candidates, predictions differ when gene contents or the most likely genotypes are used [ 20].
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The most likely genotype is the one that is sampled most frequently.
Then the most likely genotype at untyped loci can be generated from final haplotype pairs.
We retained for analysis the 1 705 237 genotyped and imputed SNPs with, on average over individuals, probability ≥0.9 assigned to their most likely genotype.
The ABACUS algorithm uses a maximum likelihood model to determine the most likely genotype and provides a log10 quality score (QS) for each base call [18].
In addition to computing the concordance rates based on most likely genotype call, we also used the –mask option in MACH to compute quality measures for the imputed markers and estimated MAF based on the expected genotype (i.e., dosage).
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