Sentence examples for missing data likelihood from inspiring English sources
Again, a missing data likelihood is used to allow for both haplotype phase uncertainty and missing SNP data.
When the data include some individuals with genotypes for all HLA and SNP markers, and others with genotypes for the HLA alleles only, the method uses a missing data likelihood to estimate the association for the SNPs of interest, using information from the HLA alleles when the SNP genotype is missing.
Association testing was performed in SNPTEST, allowing for uncertainty in the imputation in a missing data likelihood.
Association statistics for imputed SNPs were calculated in SNPTEST v1.1.5 (www.stats.ox.ac.uk/~marchini/software/gwas/snptest.html) using the ' proper' option, which is an additive model score test based on missing data likelihood, to allow for the uncertainty of imputed genotypes (9).
Data at 11 208 SNPs passing post-imputation QC (from an original 566377 imputed SNPs) were analysed in the program SNPTEST version 2.1.1 (46), using the '-method ml' option (a Newton Raphson algorithm that maximizes the missing data likelihood) to account for genotype uncertainty.
The genotyped data were imputed up to 1000 Genomes pilot reference panel (B36, June 2010) and the GWAS was performed again, using a missing data likelihood in a logistic regression model including a covariate representing the Australian and the UK strata, with the imputed data (N = 12.5 million SNPs).
Genome-wide analysis was performed using frequentist tests in SNPTEST calculated using missing data likelihoods.
However, few studies have addressed the costs and benefits of adding characters with missing data, especially for likelihood analyses of multiple loci.
Various simulation studies have shown that imputing missing data using maximum likelihood (ML) estimation produces better estimates than does listwise or pairwise deletion, or mean imputation [60], [61].
The presence of missing data reduces the likelihood of identifying true matches.
Low levels of missing data reduce the likelihood of there being systematic differences between those who complete questions and those who do not in a survey [ 19, 30].
