Lin Wan-Yu, Liu Nianjun
Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University Taipei, Taiwan.
Front Genet. 2012 Jun 18;3:107. doi: 10.3389/fgene.2012.00107. eCollection 2012.
The presence of missing single-nucleotide polymorphism (SNP) genotypes is common in genetic studies. For studies with low-density SNPs, the most commonly used approach to dealing with genotype missingness is to simply remove the observations with missing genotypes from the analyses. This naïve method is straightforward but is valid 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. Under the assumption of Hardy-Weinberg equilibrium and no genotyping error, we here 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.
在基因研究中，单核苷酸多态性（SNP）基因型缺失的情况很常见。对于低密度SNP的研究，处理基因型缺失最常用的方法是在分析中简单地剔除基因型缺失的观测值。这种简单的方法很直接，但仅在缺失是随机的情况下才有效。然而，给定的检测方法在对杂合子和纯合子进行基因分型时往往具有不同的能力，从而导致“差异缺失”现象，即杂合子和纯合子的缺失率不同。实际上，即使在精心设计的研究中，如国际人类基因组单体型图计划（HapMap）项目和威康信托病例对照协会的数据，基因型之间也存在差异缺失。在哈迪-温伯格平衡和无基因分型错误的假设下，我们在此提出一种统计方法来模拟不同基因型之间的差异缺失。与简单方法相比，当存在差异缺失时，我们的方法能提供更准确的等位基因频率估计。为了展示其实际应用，我们进一步将我们的方法应用于HapMap数据和一个硬皮病数据集。
