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Thus, (vi - wi) is not computed if vi and/or wi is a missing value To assess the influence of missing data rates and different replacement methods into clustering results (see Figure 1), we have analysed the co-associated genes of an original dataset (without MVs) compared to these genes location in a set with MVs.
We computed missing data rates for items, sub-scales and overall score.
Including studies with survival outcomes may influence missing data rates since participants are censored at dropout.
Because missing data rates were below 5% common EM-substitution was applied.
Data completeness was assessed by calculating missing data rates for each item.
Missing data was not imputed for the EQ-5D, SF-6D or DHP-18 as missing data rates were low.
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First, although the missing data rate for each SNP across 320 samples was maintained within 30%, the missing data rate for all SNPs in each sample was neglected in most studies, which may have resulted in errors.
A set of 37,128 high-quality SNPs, with a 30% missing data rate, was generated through tGBS.
The histograms presenting the average missing data rate (18.1%) and average number of reads (nine) per SNP site are provided in Supplementary Fig. S1.
We tested four evolutionary scenarios on a reduced data set (only loci with a missing data rate <3% were used, yielding 712 loci; to confirm that the reduced data set is representative a DAPC was performed, SI 5, Fig. S1).
Genotype likelihoods were calculated in ANGSD and loci were retained when the base quality phred-score was >20, the mapping quality phred-score >20, and the missing data rate was below 30%.
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