2012
DOI: 10.1093/bioinformatics/bts610
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GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies

Abstract: GWASTools is an R/Bioconductor package for quality control and analysis of genome-wide association studies (GWAS). GWASTools brings the interactive capability and extensive statistical libraries of R to GWAS. Data are stored in NetCDF format to accommodate extremely large datasets that cannot fit within R's memory limits. The documentation includes instructions for converting data from multiple formats, including variants called from sequencing. GWASTools provides a convenient interface for linking genotypes a… Show more

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Cited by 175 publications
(127 citation statements)
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“…CNVs were determined algorithmically from Illumina HumanHap 550k BeadChip (for the CHD cases) and Illumina 610k-Quad BeadChip data (for the controls) using the programs PennCNV 13 and GWASTools 14 . In brief, CNVs were considered potentially pathogenic after filtering for size (>300kb), PennCNV quality score >100, overlap with genes, and novelty, as previously reported by Carey et al 6 .…”
Section: Methodsmentioning
confidence: 99%
“…CNVs were determined algorithmically from Illumina HumanHap 550k BeadChip (for the CHD cases) and Illumina 610k-Quad BeadChip data (for the controls) using the programs PennCNV 13 and GWASTools 14 . In brief, CNVs were considered potentially pathogenic after filtering for size (>300kb), PennCNV quality score >100, overlap with genes, and novelty, as previously reported by Carey et al 6 .…”
Section: Methodsmentioning
confidence: 99%
“…Ancestry was classified as Black (African-American), White (non-Hispanic Caucasians), Hispanic (Hispanic Caucasian), and others including Asian and multi-racial using GWASTools 12 to generate eigenvalues for the entire dataset.…”
Section: Methodsmentioning
confidence: 99%
“…The logistic regression model for glioma risk was modeled with a SNP, Sex, SNP × Sex interaction, biospecimen (saliva/blood) and cohort (WU/UTAH/TORONTO/NYU), as well as the first 4 principal components from principal component analysis (PCA) using linkage disequilibrium (LD) pruned SNPs to control for population stratification. The bioConductor package “SNPRelate” (10) was used for LD pruning (the maximum basepairs in the sliding window=10e06, LD threshold=0.2 and the “composite” method was adopted for LD metrics) and PCA analysis and “GWASTools” (11) was used for genome wide association analysis using logistic regression modeling under the dominant genetic model. The odds ratios (ORs) of male, female, ratio of the ORs for glioma risk between males and females (the SNP × Sex interaction) and the likelihood ratio (LR) P-values on the ratio which was obtained by comparing the full logistic regression model to the model leaving the interaction out were reported.…”
Section: Methodsmentioning
confidence: 99%