Imputing Missing Genotypes with Weighted k Nearest Neighbors

Abstract

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.

Acknowledgments

The financial support of the Deutsche Forschungsgemeinschaft (Research Training Group “Statistical Modeling”) is gratefully acknowledged. The author also thanks all partners within the GENICA network for their cooperation and for providing the data set used in this article.