title: A cross-sample statistical model for SNP detection in short-read sequencing data.
abstract: Highly multiplex DNA sequencers have greatly expanded our ability to survey human genomes for previously unknown single nucleotide polymorphisms (SNPs). However, sequencing and mapping errors, though rare, contribute substantially to the number of false discoveries in current SNP callers. We demonstrate that we can significantly reduce the number of false positive SNP calls by pooling information across samples. Although many studies prepare and sequence multiple samples with the same protocol, most existing SNP callers ignore cross-sample information. In contrast, we propose an empirical Bayes method that uses cross-sample information to learn the error properties of the data. This error information lets us call SNPs with a lower false discovery rate than existing methods.
authors: Department of Statistics, Stanford University, 390 Serra Mall, Stanford, CA, 94305, USA.
authors: Omkar
authors: O
authors: Muralidharan
authors: Georges
authors: G
authors: Natsoulis
authors: John
authors: J
authors: Bell
authors: Daniel
authors: D
authors: Newburger
authors: Hua
authors: H
authors: Xu
authors: Itai
authors: I
authors: Kela
authors: Hanlee
authors: H
authors: Ji
authors: Nancy
authors: N
authors: Zhang
mesh_terms: D000483:Alleles; D060005:Genotyping Techniques; D059014:High-Throughput Nucleotide Sequencing; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA / Q000379:methods*