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PLoS Genet. 2016 Feb 1;12(2):e1005631. doi: 10.1371/journal.pgen.1005631. eCollection 2016 Feb.

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

Author information

1
Department of Genetics, Stanford University, Stanford, California, United States of America.
2
Carnegie Institution for Science, Department of Plant Biology, Stanford, California, United States of America.
3
Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.
4
Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata, La Plata, Argentina.
5
School of Biological Sciences, Washington State University, Pullman, Washington, United States of America.
6
Instituto Multidisciplinario de Biología Celular (CCT La Plata-CONICET, CICPBA), La Plata, Argentina.
7
Department of Genetics and Genome Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.
8
Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.
9
Center of Statistical Genetics, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.

Abstract

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.

PMID:
26828719
PMCID:
PMC4734769
DOI:
10.1371/journal.pgen.1005631
[Indexed for MEDLINE]
Free PMC Article

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