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Even when subject-level genotypes are available, due to the substantial computational cost of the typical genotype imputation, there is a need for faster imputation methods.
Therefore, our study confirms the two-step approach as the preferable imputation strategy, because it provides flexibility and faster imputation times, while providing an overly similar imputation performance to the single-step approach.
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In this study an accurate and fast imputation method was presented.
The strength of the Single_Step* method is its simplicity and ease of programming, which enables very fast imputation.
Summary statistics-based imputation methods offer fast imputation with great accuracy (Lee et al., 2013; Pasaniuc et al., 2014; Pickrell, 2014).
Thus, the proposed application is useful for a fast imputation of summary results for (i) studies of unrelated subjects, which (a) do not provide subject-level genotypes or (b) have a large size and (ii) family association studies.
Second, we apply a fast data imputation algorithm: a piecewise exponential distributed event times which can easily sampled from piecewise constant hazard functions.
"Faster, faster, faster".
The approximations outlined above do not harm the robustness as shown by a comparison between the rows 6 and 7 of Tables 2 and 3, but this implementation is faster because GD imputation is more efficient than MAP and the computation of the posterior probabilities are simplified.
These methods are simpler and faster, but their imputation accuracy is generally lower than the accuracy of HMM-based methods (Marchini and Howie, 2008, 2010).
Such a model could enable faster predictions of imputation accuracy across a wider range of study designs than use of the empirical method.
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