An empirical Bayes mixture model for SNP detection in pooled sequencing data.

Abstract

Detecting single-nucleotide polymorphism (SNP) in pooled sequencing data is more challenging than in individual sequencing because of sampling variations across pools. To effectively differentiate SNP signal from sequencing error, appropriate estimation of the sequencing error is necessary. In this article, we propose an empirical Bayes mixture (EBM) model for SNP detection and allele frequency estimation in pooled sequencing data. The proposed model reliably learns the error distribution by pooling information across pools and genomic positions. In addition, the proposed EBM model builds in characteristics unique to the pooled sequencing data, boosting the sensitivity of SNP detection. For large-scale inference in SNP detection, the EBM model provides a flexible and robust way for estimation and control of local false discovery rate. We demonstrate the performance of the proposed method through simulation studies and real data application. Implementation of this method is available at https://sites.google.com/site/zhouby98.