Reducing bias of allele frequency estimates by modeling SNP genotype data with informative missingness.
Reducing bias of allele frequency estimates by modeling SNP genotype data with informative missingness.
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DOI：
10.3389/fgene.2012.00107
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发表时间：
2012
影响因子：
3.7
通讯作者：
Liu N
中科院分区：
文献类型：
--
作者：
Lin WY;Liu N
缺失单核苷酸多态(SNP)基因类型的存在在遗传学研究中很常见。对于低密度SNPs的研究，处理基因缺失最常用的方法是简单地从分析中删除缺失基因的观察结果。这种天真的方法简单明了，但只有在遗漏是随机的情况下才有效。然而，同一种方法对杂合子和纯合子进行基因分型的能力往往不同，导致了杂合子和纯合子的缺失率不同的“差别辍学”现象。在实践中，即使是精心设计的研究也存在不同的基因缺失，例如来自HapMap项目和惠康信托病例对照联合会的数据。在Hardy-Weinberg平衡和无基因分型错误的假设下，我们提出了一种统计方法来模拟不同基因型间的差异脱落。与朴素方法相比，当存在差异丢失时，我们的方法提供了更准确的等位基因频率估计。为了展示它的实际应用，我们进一步将我们的方法应用于HapMap数据和硬皮病数据集。
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.
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影响因子：
30.8
作者：
Marchini, Jonathan;Howie, Bryan;Donnelly, Peter
通讯作者：
Donnelly, Peter
DOI：
10.1080/03610920802696588
发表时间：
2009
期刊：
Communications in statistics: theory and methods
影响因子：
--
作者：
Liu N;Bucala R;Zhao H
通讯作者：
Zhao H
影响因子：
9.8
作者：
Allen, AS;Rathouz, PJ;Satten, GA
通讯作者：
Satten, GA
影响因子：
5
作者：
Baugh, JA;Chitnis, S;Bucala, R
通讯作者：
Bucala, R
影响因子：
1.8
作者：
Graffelman, Jan;Morales Camarena, Jair
通讯作者：
Morales Camarena, Jair