GBStools: A Statistical Method for Estimating Allelic Dropout in Reduced Representation Sequencing Data

COOKE TF; YEE M-C; MUZZIO M; SOCKELL A; BELL RYAN; CORNEJO OE; KELLEY JL; BAILLIET G; BRAVI CM; BUSTAMANTE C; KENNY EE

PLOS GENETICS

PUBLIC LIBRARY SCIENCE

Lugar: San Francisco; Año: 2016 vol. 12 p. 1 - 18

1553-7390

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 simulateddata 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 mostpronounced 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.