RT Journal Article
SR Electronic
T1 GBStools: A Unified Approach for Reduced Representation Sequencing and Genotyping
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 030494
DO 10.1101/030494
A1 Thomas F Cooke
A1 Muh-Ching Yee
A1 Marina Muzzio
A1 Alexandra Sockell
A1 Ryan Bell
A1 Omar E Cornejo
A1 Joanna L. Kelley
A1 Graciela Bailliet
A1 Claudio M. Bravi
A1 Carlos D Bustamante
A1 Eimear E Kenny
YR 2015
UL http://biorxiv.org/content/early/2015/11/02/030494.abstract
AB Reduced representation sequencing methods such as genotyping-by-sequencing (GBS) enable low-cost measurement of genetic variation without the need for a reference genome assembly. These methods are widely used in genetic mapping and population genetics studies, especially with non-model organisms. Variant calling error rates, however, are higher in GBS than in standard sequencing, in particular due to restriction site polymorphisms, and few computational tools exist that specifically model and correct these errors. We developed a statistical method to remove errors caused by restriction site polymorphisms, implemented in the software package GBStools. We evaluated it in several simulated data sets, varying in number of samples, mean coverage and population mutation rate, and in two empirical human data sets (N = 8 and N = 63 samples). In our simulations, GBStools improved genotype accuracy more than commonly used filters such as Hardy-Weinberg equilibrium p-values. GBStools is most effective at removing genotype errors in data sets over 100 samples when coverage is 40X or higher, and the improvement is most pronounced in species with high genomic diversity. We also demonstrate the utility of GBS and GBStools for human population genetic inference in Argentine populations and reveal widely varying individual ancestry proportions and an excess of singletons, consistent with recent population growth.