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The Scientific World Journal
Volume 2013 (2013), Article ID 895496, 9 pages
Research Article

Evaluation of Allele Frequency Estimation Using Pooled Sequencing Data Simulation

1Vanderbilt Ingram Cancer Center, Center for Quantitative Sciences, Nashville, TN, USA
2Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, TN, USA
3VANTAGE, Vanderbilt University, Nashville, TN, USA

Received 15 November 2012; Accepted 30 December 2012

Academic Editors: L. Han, X. Li, and Z. Su

Copyright © 2013 Yan Guo 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.


Next-generation sequencing (NGS) technology has provided researchers with opportunities to study the genome in unprecedented detail. In particular, NGS is applied to disease association studies. Unlike genotyping chips, NGS is not limited to a fixed set of SNPs. Prices for NGS are now comparable to the SNP chip, although for large studies the cost can be substantial. Pooling techniques are often used to reduce the overall cost of large-scale studies. In this study, we designed a rigorous simulation model to test the practicability of estimating allele frequency from pooled sequencing data. We took crucial factors into consideration, including pool size, overall depth, average depth per sample, pooling variation, and sampling variation. We used real data to demonstrate and measure reference allele preference in DNAseq data and implemented this bias in our simulation model. We found that pooled sequencing data can introduce high levels of relative error rate (defined as error rate divided by targeted allele frequency) and that the error rate is more severe for low minor allele frequency SNPs than for high minor allele frequency SNPs. In order to overcome the error introduced by pooling, we recommend a large pool size and high average depth per sample.