GWAS.utils
is an R package with basic helper functions for manipulating GWAS data, including two GWAS datasets.
To get started, please refer to this vignette.
remotes::install_github("sinarueeger/GWAS.utils")
Install including vignettes:
remotes::install_github("sinarueeger/GWAS.utils", build = TRUE,
build_opts = c("--no-resave-data", "--no-manual"))
vignette("gwas-utils-package")
Note: The vignette needs to have the Rmpfr
package installed. Use an old version if you have not updated R in a while.
There are six functions:
eaf2maf(eaf = eaf_vec)
: Transforms effect allele frequencies into minor allele frequencies.
inv_normal(x = vec)
: Inverse normal transformation of a variable.
z2p(z = z_vec)
: Transforms Z-statistics to P-values, includes an option for very large Z-statistics.
eff_nbr_tests(mat = genotype_df)
: Calculates the effective number of tests of a GWAS, based on the correlation between the SNPs.
QQplot(p = p_vec)
: Q-Q-plot of P-values (uniformly distributed under the null).
genomic_inflation(Z = z_vec)
: Calculates genomic inflation, with either P-values or Z-statistics.
Most of the functions are just handy and trivial helper functions. For QQplot()
and genomic_inflation()
there are a number of other packages with similar functions, e.g. GenABEL
or qqman
. Our genomic_inflation
function takes two types of summary statistics as input by making an assumption about the P-value origin. And QQplot
can inlcude the number of effective tests.
And two datasets:
giant
: Summary statistics of 10'000 SNPs from a GWAS in human body height.opensnp
data: Genotype data and human body height of 784 individuals from the publicly accessible openSNP database. Please note that the 'GWAS.utils' project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
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