Evidence map›Paper›PMID 26828719›Full record

ArticlePLoS genetics2016

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

Thomas F Cooke, Muh-Ching Yee, Marina Muzzio, Alexandra Sockell, Ryan Bell, Omar E Cornejo, Joanna L Kelley, Graciela Bailliet, Claudio M Bravi, Carlos D Bustamante and 1 more

Abstract read
In one paragraph

Article in PLoS genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Thomas F CookeDepartment of Genetics, Stanford University, Stanford, California, United States of America.
Muh-Ching YeeCarnegie Institution for Science, Department of Plant Biology, Stanford, California, United States of America.
Marina MuzzioDepartment of Genetics, Stanford University, Stanford, California, United States of America.
Alexandra SockellDepartment of Genetics, Stanford University, Stanford, California, United States of America.
Ryan BellDepartment of Genetics, Stanford University, Stanford, California, United States of America.
Omar E CornejoDepartment of Genetics, Stanford University, Stanford, California, United States of America.
Joanna L KelleyDepartment of Genetics, Stanford University, Stanford, California, United States of America.
Graciela BaillietInstituto Multidisciplinario de Biología Celular (CCT La Plata-CONICET, CICPBA), La Plata, Argentina.
Claudio M BraviFacultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata, La Plata, Argentina.
Carlos D BustamanteDepartment of Genetics, Stanford University, Stanford, California, United States of America.
Eimear E KennyDepartment of Genetics, Stanford University, Stanford, California, United States of America.

Funding

Supplement to Enhance Wellness and Resiliency in the Graduate EnvironmentT32GM007276 · NIGMS · STANFORD UNIVERSITY · PI MORRISON, ASHBY J. · 1985 to 2023
$32.9M
INSTITUTIONAL TRAINING GRANT IN GENOME SCIENCET32HG000044 · NHGRI · STANFORD UNIVERSITY · PI MICHAEL P. SNYDER · 1995 to 2026
$32.2M
NHGRI NIH HHS T32 HG000044NIGMS NIH HHS T32 GM007276
6 · The paper itself

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.

Indexed as

AllelesGenotyping TechniquesSoftwareStatistics as TopicGenetics, PopulationHigh-Throughput Nucleotide SequencingHumansPolymorphism, Single Nucleotide

Identifiers

PMID26828719
PMCPMC4734769

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.