Reducing Bias of Allele Frequency Estimates by Modeling SNP Genotype Data with Informative Missingness - Lin, Wan-Yu; Liu, Nianjun (Frontiers in Genetics, 2012)
The presence of missing single-nucleotide polymorphism (SNP) genotypes is common in genetic data. For studies with low-density SNPs, the most commonly used approach to deal with genotype missingness is to simply remove the observations with missing genotypes from the analyses. This naïve method is straightforward but is appropriate only when the missingness is random. However, a given assay often has a different capability in genotyping heterozygotes and homozygotes, causing the phenomenon of ‘differential dropout’ in the sense that the missing rates of heterozygotes and homozygotes are different. In practice, differential dropout among genotypes exists in even carefully designed studies, such as the data from the HapMap project and the Wellcome Trust Case Control Consortium. In this study, we propose a statistical method to model the differential dropout among different genotypes. Compared with the naïve method, our method provides more accurate allele frequency estimates when the differential dropout is present. To demonstrate its practical use, we further apply our method to the HapMap data and a scleroderma data set.