Description
Assessing Predisposition Between Phenotypes using Polygenic Scores.
Description
Using polygenic scores (PGS, or PRS/GRS for binary outcomes), this package allows to investigate shared predisposition between different conditions, and do fast association analysis, export plots and views of the PGS distribution using 'ggplot2' object.
README.md
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")
| PGS | Phenotype | Phenotype_type | Statistical_method | Covar | N_cases | N_controls | N | Effect | SE | lower_CI | upper_CI | P_value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| t2d_PGS | t2d | Cases/Controls | Binary logistic regression | NA | 730 | 9270 | 10000 | 1.688258 | NA | 1.561821 | 1.824931 | 0 |
# 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
| PGS | Phenotype | Phenotype_type | Statistical_method | Covar | N_cases | N_controls | N | Effect | SE | lower_CI | upper_CI | P_value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | t2d_PGS | ethnicity 1 ~ 2 | Categorical | Multinomial logistic regression | age+sex+gen_array | 2142 | 6381 | 8523 | 0.9814174 | NA | 0.9345150 | 1.0306739 | 0.4528020 |
| 3 | t2d_PGS | ethnicity 1 ~ 3 | Categorical | Multinomial logistic regression | age+sex+gen_array | 1205 | 6381 | 7586 | 1.0178971 | NA | 0.9570931 | 1.0825640 | 0.5724292 |
| 4 | t2d_PGS | ethnicity 1 ~ 4 | Categorical | Multinomial logistic regression | age+sex+gen_array | 272 | 6381 | 6653 | 0.9434640 | NA | 0.8355980 | 1.0652542 | 0.3474694 |
| 21 | ldl_PGS | ethnicity 1 ~ 2 | Categorical | Multinomial logistic regression | age+sex+gen_array | 2142 | 6381 | 8523 | 0.9925623 | NA | 0.9451678 | 1.0423334 | 0.7648927 |
| 31 | ldl_PGS | ethnicity 1 ~ 3 | Categorical | Multinomial logistic regression | age+sex+gen_array | 1205 | 6381 | 7586 | 1.0083869 | NA | 0.9481215 | 1.0724830 | 0.7905175 |
| 41 | ldl_PGS | ethnicity 1 ~ 4 | Categorical | Multinomial logistic regression | age+sex+gen_array | 272 | 6381 | 6653 | 0.9760204 | NA | 0.8647226 | 1.1016433 | 0.6943783 |
| 1 | t2d_PGS | brc | Cases/Controls | Binary logistic regression | age+sex+gen_array | 402 | 5041 | 5443 | 1.0061678 | NA | 0.9087543 | 1.1140235 | 0.9057882 |
| 11 | ldl_PGS | brc | Cases/Controls | Binary logistic regression | age+sex+gen_array | 402 | 5041 | 5443 | 1.1037106 | NA | 0.9956370 | 1.2235153 | 0.0605407 |
| 12 | t2d_PGS | t2d | Cases/Controls | Binary logistic regression | age+sex+gen_array | 730 | 9270 | 10000 | 1.7359738 | NA | 1.6029867 | 1.8799938 | 0.0000000 |
| 13 | ldl_PGS | t2d | Cases/Controls | Binary logistic regression | age+sex+gen_array | 730 | 9270 | 10000 | 0.9823272 | NA | 0.9102411 | 1.0601223 | 0.6465580 |
| 14 | t2d_PGS | log_ldl | Continuous | Linear regression | age+sex+gen_array | NA | NA | 10000 | 0.0059961 | 0.0022747 | 0.0015378 | 0.0104544 | 0.0084010 |
| 15 | ldl_PGS | log_ldl | Continuous | Linear regression | age+sex+gen_array | NA | NA | 10000 | 0.0828545 | 0.0021183 | 0.0787027 | 0.0870064 | 0.0000000 |
| 16 | t2d_PGS | sbp_cat | Ordered Categorical | Ordinal logistic regression | age+sex+gen_array | NA | NA | 10000 | 1.0628744 | NA | 1.0236044 | 1.1036509 | 0.0015002 |
| 17 | ldl_PGS | sbp_cat | Ordered Categorical | Ordinal logistic regression | age+sex+gen_array | NA | NA | 10000 | 1.0078855 | NA | 0.9707330 | 1.0464598 | 0.6818849 |
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.