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Protocols

GWASTools specifications

Information


Unique identifier OMICS_11020
Name GWASTools
Software type Framework/Library
Interface Command line interface
Restrictions to use None
Input data Plain text, PLINK, Variant Call Format, imputed genotypes (IMPUTE2, BEAGLE, MaCH)
Output data PLINK, snpStats objects
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License Artistic License version 2.0
Computer skills Advanced
Version 1.26.1
First release date 2012
Stability Stable
Requirements
methods, stats, graphics, BiocGenerics, Biobase, IRanges, GenomicRanges, DNAcopy, utils, S4Vectors, Biostrings, RUnit, survival, snpStats, RSQLite, DBI, VariantAnnotation, quantsmooth, ncdf4, gdsfmt, GWASExactHW, sandwich, lmtest, logistf, GWASdata, SNPRelate
Maintained Yes

Versioning


No version available

Documentation


Maintainer


  • person_outline Stephanie Gogarten

Publication for GWASTools

GWASTools citations

 (14)
call_split

Inbreeding estimates in human populations: Applying new approaches to an admixed Brazilian isolate

2018
PLoS One
PMCID: 5916862
PMID: 29689090
DOI: 10.1371/journal.pone.0196360
call_split See protocol

[…] x/Thermo Fisher Scientific); (2) all markers with significant differences in missing data proportions between groups (defined by sex, experimental batch, and subpopulation status) using the R package GWASTools v.3.5 []; (3) all genotyped loci with more than 10% of missing values; (4) all data from loci with extreme deviations from Hardy-Weinberg proportions (P ≤ 10−8), using the asymptotic exact t […]

call_split

Genome Wide Association Mapping Uncovers Fw1, a Dominant Gene Conferring Resistance to Fusarium Wilt in Strawberry

2018
PMCID: 5940171
PMID: 29602808
DOI: 10.1534/g3.118.200129
call_split See protocol

[…] -package ‘SNPRelate’ () were utilized to filter SNPs; 14,408 SNPs with high-quality bi-allelic clusters and < 5% missing data were selected for subsequent analyses. The R-packages ‘SNPRelate’ () and ‘GWASTools’ () were used to generate genotypic input files for GWAS from raw genotyping reads. […]

library_books

msgbsR: An R package for analysing methylation sensitive restriction enzyme sequencing data

2018
Sci Rep
PMCID: 5794748
PMID: 29391490
DOI: 10.1038/s41598-018-19655-w

[…] correctly and to determine if there are any differences in read counts between groups, allowing it to be used in conjunction with other Bioconductor packages for assessing genetic variation, such as GWAStools.Differential DNA methylation can be performed using msgbsR which contains a wrapper function using edgeR. We choose to make a wrapper function of edgeR since MRE-seq experiments typically co […]

library_books

Identifying genetic variants that affect viability in large cohorts

2017
PLoS Biol
PMCID: 5584811
PMID: 28873088
DOI: 10.1371/journal.pbio.2002458

[…] re marked as alive. For the category of ages at death > 75 years, only parents who survived beyond 75 years were considered.All Manhattan and quantile-quantile plots were generated using qqman [] and GWASTools [] packages. […]

call_split

A Preliminary Genome Wide Association Study of Pain Related Fear: Implications for Orofacial Pain

2017
PMCID: 5494109
PMID: 28701861
DOI: 10.1155/2017/7375468
call_split See protocol

[…] han genome-wide significance; thus, the threshold for significance in these analyses was p < 5 × 10−7. The Manhattan and quantile-quantile plots used to visualize the results were generated using the GWASTools package in R (R Foundation for Statistical Computing, Vienna, Austria) []. LocusZoom [] was used to create regional association plots. […]

call_split

Genome wide approach identifies a novel gene maternal pre pregnancy BMI interaction on preterm birth

2017
Nat Commun
PMCID: 5472707
PMID: 28598419
DOI: 10.1038/ncomms15608
call_split See protocol

[…] eam at the University of Washington Genetics Coordinating Center (UWGCC) performed GWAS data cleaning on the combined samples according to the protocol described by Laurie et al. using the R packages GWASTools and SNPRelate. Briefly, the following parameters were examined: (1) CIDR technical filters; (2) missing call rate per SNP, per chromosome and per sample; (3) the reproducibility rate among t […]

Citations

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GWASTools institution(s)
Department of Biostatistics, University of Washington, Seattle, WA, USA; Department of Bioinformatics and Computational Biology, Genentech Inc, South San Francisco, CA, USA; Department of Health Services, University of Washington, Seattle, WA, USA; Department of Statistics, University of Auckland, Auckland, New Zealand
GWASTools funding source(s)
Supported by National Institutes of Health, GENEVA Coordinating Center (U01 HG 004446); GARNET Coordinating Center (U01 HG 005157).

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