The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

comorbidPGS

comorbidPGS is a tool for analysing an already computed Polygenic Score (PGS, also named PRS/GRS for binary outcomes) distribution to investigate shared genetic aetiology in multiple conditions.

comorbidPGS is under GPL-3 license, and is freely available for download.

Prerequisite

• R version 3.5 or higher with the following packages:
  • stats
  • utils
  • ggplot2

Installation

You can install the development version of comorbidPGS from GitHub with:

# install.packages("devtools")
devtools::install_github("VP-biostat/comorbidPGS")

Example

Building an Association Table

This is a basic example which shows you how to do basic association with the example dataset:

library(comorbidPGS)
#> 
#> Attachement du package : 'comorbidPGS'
#> L'objet suivant est masqué depuis 'package:graphics':
#> 
#>     assocplot

# use the demo dataset
dataset <- comorbidData
# NOTE: The dataset must have at least 3 different columns:
# - an ID column (the first one)
# - a PGS column (must be numeric, by default it is the column named "SCORESUM" or the second column if "SCORESUM" is not present)
# - a Phenotype column, can be factors, numbers or characters

# do an association of one PGS with one Phenotype
result_1 <- assoc(dataset, prs_col = "t2d_PGS", phenotype_col = "t2d")

# do multiple associations
assoc <- expand.grid(c("t2d_PGS", "ldl_PGS"), c("ethnicity","brc","t2d","log_ldl","sbp_cat"))
result_2 <- multiassoc(df = dataset, assoc_table = assoc, covar = c("age", "sex", "gen_array"))
#> Warning in phenotype_type(df = df, phenotype_col = phenotype_col): Phenotype
#> column log_ldl is continuous and not normal, please normalise prior association

#> Warning in phenotype_type(df = df, phenotype_col = phenotype_col): Phenotype
#> column log_ldl is continuous and not normal, please normalise prior association

Examples of plot

densityplot(dataset, prs_col = "ldl_PGS", phenotype_col = "sbp_cat")

# show multiple associations in a plot
assoplot <- assocplot(score_table = result_2)
assoplot$continuous_phenotype

assoplot$discrete_phenotype

NOTE: The score_table should have the assoc() output format

centileplot(dataset, prs_col = "brc_PGS", phenotype_col = "brc")
#> Warning in centileplot(dataset, prs_col = "brc_PGS", phenotype_col = "brc"):
#> The dataset has less than 10,000 individuals, centiles plot may not look good!
#> Use the argument decile = T to adapt to small datasets

As those graphical functions use ggplot2, you can fully customize your plot:

library(ggplot2)
centileplot(dataset, prs_col = "t2d_PGS", phenotype_col = "t2d") + 
  scale_color_gradient(low = "green", high = "red")

decileboxplot(dataset, prs_col = "ldl_PGS", phenotype_col = "ldl")

Citation

If you use comorbidPGS in any published work, please cite the following manuscript:

Pascat V (????). comorbidPGS: Assessing the shared predisposition between Phenotypes using Polygenic Scores (PGS, or PRS/GRS for binary outcomes). R package version 0.3.9000.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.