2022
DOI: 10.1101/2022.01.25.474274
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hudson: A User-Friendly R Package to Extend Manhattan Plots

Abstract: The Manhattan plot is one of the most widely used visualization techniques when plotting summary statistics from genome-wide or phenome-wide association studies. While there are a number of existing tools to create these plots, there is room for extending their utility to satisfy increasingly complex and comprehensive analyses as well as the need for comparisons between different sets of results or between discovery and replication datasets. The R package presented here, hudson, provides user-friendly plotting… Show more

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Cited by 9 publications
(6 citation statements)
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“…In GWAS stratified by MF type (Supplemental Table 1), variants were filtered based on a more stringent control minor allele frequency (>5%) to remove potential spurious associations arising due to small sample size. Manhattan plots for results visualization were generated using the "qqman" and "hudson" R packages 66,67 .…”
Section: Genome-wide Association Studymentioning
confidence: 99%
“…In GWAS stratified by MF type (Supplemental Table 1), variants were filtered based on a more stringent control minor allele frequency (>5%) to remove potential spurious associations arising due to small sample size. Manhattan plots for results visualization were generated using the "qqman" and "hudson" R packages 66,67 .…”
Section: Genome-wide Association Studymentioning
confidence: 99%
“…Miami plots were generated, to display signi cantly associated SNPs in associated regions, using the Hudson package in R 71 . Genomic control and quantile-quantile (Q-Q) plots were conducted as a QC check, to re-evaluate genetic in ation and confounding biases such as cryptic relatedness and population strati cation (with the assumption that the regional groupings will be independent of each other).…”
Section: Visualization and Interpretation Of Genetic Associationsmentioning
confidence: 99%
“…The AUC quantified the models' ability to distinguish between presence and absence, while the Boyce index measured the agreement between observed and predicted values. We used several R packages, including "raster", "rgdal", "terra", "dismo", "modEvA", "dplyr", "gmp", "Metrics", "CalibratR", "caret", "ecospat" and "tidyr" 4,16,17,18,19,20,21,22,23,24,25,26 .…”
Section: Modelling Process Evaluation and Analysismentioning
confidence: 99%