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Although genotyping microarrays produce some missing data, GBS produces an extremely sparse genotype matrix with mostly missing data: often hundreds of thousands of SNPs are discovered, but only a small fraction of the SNPs (e.g., <10%) pass missing data thresholds (Gardner et al. 2014).
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(A small proportion of out of area registered patients could not be classified, mostly because of missing data (3.7%)).
Due to the unbalanced nature of the data, mostly due to missing data from the second phase of the study, an exchangeable correlation structure and robust standard error estimation was applied.
Of these 308 participants, 62 did not participate at follow-up while another 54 were excluded (mostly because of missing data on the diagnostic interview, due to language problems), resulting in a final sample of 192 children.
The overall genotype call rate was 95.5% and when possible, missing data (mostly due to missing DNA samples) were inferred from the genotypes of parents/offspring.
This matrix was mostly complete with ~7%percentt missing data.
Again, this matrix was mostly complete with ~7% missing data.
Not all mother-offspring pairs were included in analyses because of missing data, mostly outcome data.
A further 2,036 were excluded due to missing data, mostly socioeconomic status.
All other items had fewer missing data, mostly between 0 and 4 among both mothers and fathers.
The number of participants was somewhat smaller (n = 704) for this analysis due to missing data (mostly BMI measurements).
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