Genotyping error detection in samples of unrelated individuals without replicate genotyping.
Genotyping error detection in samples of unrelated individuals without replicate genotyping.
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DOI：
10.1159/000181153
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发表时间：
2009
期刊：
影响因子：
1.8
通讯作者：
Zhao H
中科院分区：
文献类型：
--
作者：
Liu N;Zhang D;Zhao H
识别基因分型错误是遗传学研究中的一个重要问题，但在由无关个体组成的样本中研究相对较少。在这篇文章中，我们考虑了几种模型的基因分型错误，这是最初提出的系谱数据，为无关的人口样本与单核苷酸多态性（SNP）基因型数据。研究了检测基因分型错误的数学约束条件，而无需重复检测或基因分型亲属。对于各种建议的基因分型误差模型，我们揭示的条件下，参数是可识别的。这些结果通过应用于模拟和真实的SNP数据进行了验证。我们发现，在约束条件下，两个特定的模型提供了可识别的错误率和等位基因频率的SNP无关的人口数据。模拟研究表明，这两个模型提供了无偏估计的等位基因频率。其中一个模型还给出了基因分型错误率的无偏估计。虽然Hardy-Weinberg平衡检验可用于检测基因分型错误，但这些模型的关键优势是明确估计基因分型错误率和等位基因频率。这项工作可以帮助研究人员估计错误率，并在分析中使用估计值来增加功效和减少偏倚，而无需对家族成员或重复进行基因分型的额外工作。
Identifying genotyping errors is an important issue in genetic research, yet it has been relatively less studied in samples consisting of unrelated individuals. In this article, we consider several models of genotyping errors, which were originally proposed for pedigree data, for unrelated population samples with single nucleotide polymorphism (SNP) genotype data. The mathematical constraints are investigated for detecting genotyping errors without resampling replicates or genotyping relatives. For the various proposed genotyping error models, we unveil the conditions under which the parameters are identifiable. These results are verified through applications to simulated and real SNP data. We show that, with constraints, two particular models provide both identifiable error rate and allele frequencies of an SNP for unrelated population data. The simulation study shows that these two models present unbiased estimates for the allele frequencies. One of the models also gives an unbiased estimate for the genotyping error rate. While the Hardy-Weinberg equilibrium test can be used to detect genotyping errors, a key advantage of these models is the explicit estimates of genotyping error rates and allele frequencies. This work may help researchers to estimate error rates and to use the estimates in their analysis to increase power and decrease bias, without the extra work of genotyping family members or replicates.
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