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BioMed Research International
Volume 2013 (2013), Article ID 813912, 7 pages
Research Article

Simpute: An Efficient Solution for Dense Genotypic Data

1Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
2Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan
3Institute of Statistics, National Tsing Hua University, Hsinchu, Taiwan

Received 27 November 2012; Accepted 4 January 2013

Academic Editor: Hao-Teng Chang

Copyright © 2013 Yen-Jen Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Single nucleotide polymorphism (SNP) data derived from array-based technology or massive parallel sequencing are often flawed with missing data. Missing SNPs can bias the results of association analyses. To maximize information usage, imputation is often adopted to compensate for the missing data by filling in the most probable values. To better understand the available tools for this purpose, we compare the imputation performances among BEAGLE, IMPUTE, BIMBAM, SNPMStat, MACH, and PLINK with data generated by randomly masking the genotype data from the International HapMap Phase III project. In addition, we propose a new algorithm called simple imputation (Simpute) that benefits from the high resolution of the SNPs in the array platform. Simpute does not require any reference data. The best feature of Simpute is its computational efficiency with complexity of order , where is the number of missing SNPs, is the number of the positions of the missing SNPs, and is the number of people considered. Simpute is suitable for regular screening of the large-scale SNP genotyping particularly when the sample size is large, and efficiency is a major concern in the analysis.