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For each significant SNP a functional proxy was searched with imputation, without improving the results.
Given that imputation is designed to infer unknown genotypes, one purpose of this paper was to use IQS to evaluate statistics that measure the quality of imputation without knowing the true genotype.
In our simulations, we found that using PMM was nearly always better than using passive imputation without PMM.
We carried out imputation (without prephasing) within the Gambian case/control samples, and within the cases and pseudocontrols derived from the Gambian case-parent trio samples, in the 4 Mb region around the known causal SNP (rs334) on chromosome 11.
The multiple imputation analyses are not presented as the main analyses as it was technically impossible to perform an imputation without comprehensive manipulation of the data, such as redefinition of the continuous variables into binary or ordinary variables and exclusion of the variable "academic problems" (important to calculate the propensity score) because of collinearity.
Further sensitivity testing of the effects of missing data for variables missing in more than 10% of the patients (BMI, weight loss, and cholesterol) consisted of imputation without these variables and without controlling for these variables and of complete case analyses excluding these variables.
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Figure 1b shows the same simulation after multiple imputations without any auxiliary variables, i.e. only Y, X1 and X2 are in the imputation model (MI+0).
A secondary goal of this paper is to determine whether there are ways to evaluate imputation quality without knowing the true genotypes.
The infant feeding data has previously been evaluated using a "multiple imputation procedure" without effect on the results [ 9].
Our approximation is based on the idea that, within a limited genomic region, allelic consistency between study and reference individuals can be used to quickly rule out unhelpful reference haplotypes, thereby making imputation faster without sacrificing accuracy.
If publication bias was suspected, results are shown without imputation and with "missing" studies imputed with Duval and Tweedie's trim and fill method.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com