Statistics, TU Dortmund University, Dortmund, Germany.
J Toxicol Environ Health A. 2012;75(8-10):438-46. doi: 10.1080/15287394.2012.674910.
Missing values are a common problem in genetic association studies concerned with single-nucleotide polymorphisms (SNPs). Since many statistical methods cannot handle missing values, such values need to be removed prior to the actual analysis. Considering only complete observations, however, often leads to an immense loss of information. Therefore, procedures are required that can be used to impute such missing values. In this study, an imputation procedure based on a weighted k nearest neighbors algorithm is presented. This approach, called KNNcatImpute, searches for the k SNPs that are most similar to the SNP whose missing values need to be replaced and uses these k SNPs to impute the missing values. Alternatively, KNNcatImpute can search for the k nearest subjects. In this situation, the missing values of an individual are imputed by considering subjects showing a DNA pattern similar to the one of this individual. In a comparison to other imputation approaches, KNNcatImpute shows the lowest rates of falsely imputed genotypes when applied to the SNP data from the GENICA study, a candidate SNP study dedicated to the identification of genetic and gene-environment interactions associated with sporadic breast cancer. Moreover, KNNcatImpute can also be applied to data from genome-wide association studies, as an application to a subset of the HapMap data demonstrates.
在关注单核苷酸多态性 (SNP) 的遗传关联研究中，缺失值是一个常见问题。由于许多统计方法无法处理缺失值，因此需要在实际分析之前将其删除。然而，仅考虑完整的观察结果通常会导致大量信息丢失。因此，需要使用可以用来推断这些缺失值的程序。在这项研究中，提出了一种基于加权 k 最近邻算法的推断程序。这种方法称为 KNNcatImpute，它会搜索与需要替换的缺失值的 SNP 最相似的 k 个 SNP，并使用这些 k 个 SNP 来推断缺失值。或者，KNNcatImpute 可以搜索 k 个最近的对象。在这种情况下，通过考虑与该个体的 DNA 模式相似的个体来推断个体的缺失值。与其他推断方法相比，当应用于专门用于识别与散发性乳腺癌相关的遗传和基因-环境相互作用的候选 SNP 研究 GENICA 研究的 SNP 数据时，KNNcatImpute 显示出最低的假推断基因型率。此外，KNNcatImpute 还可以应用于全基因组关联研究的数据，对 HapMap 数据子集的应用证明了这一点。
