Help for package comorbidPGS

Title:

Assessing Predisposition Between Phenotypes using Polygenic Scores

Version:

1.0.0

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.

Depends:

R (≥ 3.5.0)

License:

GPL (≥ 3)

Encoding:

UTF-8

RoxygenNote:

7.2.3

Imports:

ggplot2, stats, utils, MASS, nnet, parallel, ivreg

LazyData:

true

Suggests:

testthat (≥ 3.0.0)

Config/testthat/edition:

NeedsCompilation:

Packaged:

2025-07-15 14:26:52 UTC; vincentp

Author:

Vincent Pascat

[aut, cre]

Maintainer:

Vincent Pascat <vincent.pascat@univ-lille.fr>

Repository:

CRAN

Date/Publication:

2025-07-15 14:40:02 UTC

Association of a PGS distribution with a Phenotype

Description

assoc() takes a distribution of PGS, a Phenotype and eventual Confounders. Returns a data frame showing the association of PGS on the Phenotype

Usage

assoc(
  df = NULL,
  prs_col = "SCORESUM",
  phenotype_col = "Phenotype",
  scale = TRUE,
  covar_col = NA,
  verbose = TRUE,
  log = ""
)

Arguments

df

a dataframe with individuals on each row, and at least the following columns:

one ID column,
one PGS column, with numerical continuous values following a normal distribution,
one Phenotype column, can be numeric (Continuous Phenotype), character, boolean or factors (Discrete Phenotype)

prs_col

a character specifying the PGS column name

phenotype_col

a character specifying the Phenotype column name

scale

a boolean specifying if scaling of PGS should be done before testing

covar_col

a character vector specifying the covariate column names (facultative)

verbose

a boolean (TRUE by default) to write in the console/log messages.

log

a connection, or a character string naming the file to print to. If "" (by default), it prints to the standard output connection, the console unless redirected by sink.

Value

return a data frame showing the association of the PGS on the Phenotype with the following columns:

PGS: the name of the PGS
Phenotype: the name of Phenotype
Phenotype_type: either 'Continuous', 'Ordered Categorical', 'Categorical' or 'Cases/Controls'
Stat_method: association function detects what is the phenotype type and what is the best way to analyse it, either 'Linear regression', 'Binary logistic regression', 'Ordinal logistic regression' or 'Multinomial logistic regression'
Covar: list all the covariates used for this association
N_cases: if Phenotype_type is Cases/Controls, gives the number of cases
N_controls: if Phenotype_type is Cases/Controls, gives the number of controls
N: the number of individuals/samples
Effect: if Phenotype_type is Continuous, it represents the Beta coefficient of linear regression; Otherwise, it is the OR of logistic regression
SE: standard error of the Beta coefficient (if Phenotype_type is Continuous)
lower_CI: lower confidence interval of the related Effect (Beta or OR)
upper_CI: upper confidence interval of the related Effect (Beta or OR)
P_value: associated P-value

Examples

results <- assoc(
  df = comorbidData,
  prs_col = "ldl_PGS",
  phenotype_col = "log_ldl",
  scale = TRUE,
  covar_col = c("age", "sex", "gen_array")
)
print(results)

Multiple PGS Associations Plot

Description

assocplot() takes a data frame of associations. Returns plot of the associations from assoc() (ggplot2 object or list of ggplot object)

Usage

assocplot(score_table = NULL, axis = "vertical", pval = FALSE)

Arguments

score_table

a dataframe with association results with at least the following columns:

PGS: the name of the PGS
Phenotype: the name of Phenotype
Phenotype_type: either 'Continuous', 'Ordered Categorical', 'Categorical' or 'Cases/Controls'
Effect: if Phenotype_type is Continuous, it represents the Beta coefficient of linear regression, OR of logistic regression otherwise
lower_CI: lower confidence interval of the related Effect (Beta or OR)
upper_CI: upper confidence interval of the related Effect (Beta or OR)
P_value: associated P-value

axis

a character, 'horizontal' or "vertical" (the default) specifying the rotation of the plot

pval

a parameter specifying information on how to display P-value

if pval is FALSE, P-value does not appear on the plot
if pval is TRUE, P-value always appears next to the signal
if pval is a number, P-value will appear if the P-value is inferior to this given number.

Value

return either:

a ggplot object representing the association results.
a list of two ggplot objects, accessible by $continuous_phenotype and $discrete_phenotype, if there are both Continuous Phenotypes and Discrete Phenotypes (i.e. "Categorical" or "Cases/Controls")

Centiles Plot from a PGS Association

Description

centileplot() takes a distribution of PGS, a Phenotype and eventual Confounders. Returns a plot (ggplot2 object) with centiles (or deciles if not enough individuals) of PGS in x and Prevalence/Median/Mean of the Phenotype in y

Usage

centileplot(
  df = NULL,
  prs_col = "SCORESUM",
  phenotype_col = "Phenotype",
  decile = FALSE,
  continuous_metric = NA
)

Arguments

df

a dataframe with individuals on each row, and at least the following columns:

one ID column,
one PGS column, with numerical continuous values following a normal distribution,
one Phenotype column, can be numeric (Continuous Phenotype), character, boolean or factors (Discrete Phenotype)

prs_col

a character specifying the PGS column name

phenotype_col

a character specifying the Phenotype column name

decile

a boolean specifying if centiles or deciles should be used

continuous_metric

a facultative character specifying what metric to use for continuous Phenotype, only three options: NA, "median" or "mean"

Value

return a figure of results in the format ggplot2 object

Mock dataset for comorbidPGS package

Description

A dataset with sets of PGSs, Phenotypes and Covariates to demo the comorbidPGS package

Usage

comorbidData

Format

`who`

A data frame with 10,000 rows (individuals) and 16 columns:

ID: Individual's identifier, characters
sex: Sex of the individuals, binary numeric values
age: Age of the individuals, numeric value
gen_array: The genotypic array used for those individuals, factor values
ethnicity: The ethnicity of individuals, can be also used as Categorical Phenotype, factor values
brc_PGS, t2d_PGS, ldl_PGS: Three distributions of PGS for Breast Cancer, Type 2 Diabetes and Hypertension respectively; numeric values
brc, t2d, hypertension: Three Cases/Controls Phenotypes, representing Breast Cancer, Type 2 Diabetes and Hypertension respectively; binary values
ldl, bmi, sbp: Three Continuous Phenotypes, representing low-density lipoprotein, body-mass index, and systolic blood pressure respectively; numeric values
log_ldl: A continuous Phenotype, based on log(ldl) to have a normal distribution; numeric values
sbp_cat: An Ordered Categorical Phenotype, with 3 possible outcomes: low, normal or high systolic blood pressure; factor values

Source

https://github.com/VP-biostat/comorbidPGS

Deciles BoxPlot from a PGS Association with a Continuous Phenotype

Description

decileboxplot() takes a distribution of PGS, a Continuous Phenotype. Returns a plot with deciles of PGS in x and Boxplot of the Phenotype in y

Usage

decileboxplot(df = NULL, prs_col = "SCORESUM", phenotype_col = "Phenotype")

Arguments

df

a dataframe with individuals on each row, and at least the following columns:

one ID column,
one PGS column, with numerical continuous values following a normal distribution,
one Phenotype column, can be numeric (Continuous Phenotype), character, boolean or factors (Discrete Phenotype)

prs_col

a character specifying the PGS column name

phenotype_col

a character specifying the Continuous Phenotype column name

Value

return a ggplot object (ggplot2)

Density Plot from a PGS Association

Description

densityplot() takes a distribution of PGS, a Phenotype and eventual Confounders. Returns a plot with density of PGS in x by Categories of the Phenotype

Usage

densityplot(
  df = NULL,
  prs_col = "SCORESUM",
  phenotype_col = "Phenotype",
  scale = TRUE,
  threshold = NA
)

Arguments

df

a dataframe with individuals on each row, and at least the following columns:

one ID column,
one PGS column, with numerical continuous values following a normal distribution,
one Phenotype column, can be numeric (Continuous Phenotype), character, boolean or factors (Discrete Phenotype)

prs_col

a character specifying the PGS column name

phenotype_col

a character specifying the Phenotype column name

scale

a boolean specifying if scaling of PGS should be done before plotting

threshold

a facultative numeric specifying for Continuous Phenotype the Threshold to consider individuals as Cases/Controls as following:

Phenotype > Threshold = Case
Phenotype < Threshold = Control

Value

return a ggplot object (ggplot2)

Mendelian Randomization Two-Stage Least Square (2SLS) method with external PGS

Description

mr_2sls() takes a distribution of PGS, an Exposure (Phenotype), an Outcome (Phenotype). Returns a data frame of the result of the Mendelian Randomization 2SLS methods using PGS

Usage

mr_2sls(
  df = NULL,
  prs_col = "SCORESUM",
  exposure_col = NA,
  outcome_col = NA,
  scale = TRUE,
  verbose = TRUE,
  log = ""
)

Arguments

df

a dataframe with individuals on each row, and at least the following columns:

one ID column,
one PGS column, with numerical continuous values following a normal distribution,
two Phenotype columns (for Exposure and Outcome), can be numeric (Continuous Phenotype), character, boolean or factors (Discrete Phenotype)

prs_col

a character specifying the PGS column name

exposure_col

a character specifying the Exposure (Phenotype) column name

outcome_col

a character specifying the Outcome (Phenotype) column name

scale

a boolean specifying if scaling of PGS should be done before testing

verbose

a boolean (TRUE by default) to write in the console/log messages.

log

a connection, or a character string naming the file to print to. If "" (by default), it prints to the standard output connection, the console unless redirected by sink.

Value

return a data frame with the Mendelian Randomization association result using 2SLS method with the following columns:

PGS: the name of the PGS used
Exposure: the name of Phenotype used as Exposure
Outcome: the name of Phenotype used as Outcome
Method: the MR method used (here 2SLS)
N_cases: if Phenotype_type is Cases/Controls, the number of cases
N_controls: if Phenotype_type is Cases/Controls, the number of controls
N: the number of individuals/samples
MR_estimate: the MR estimate (beta) using the ratio method
SE: the associated standard error (second order)
F_stat: the F-statistic of the Exposure ~ PGS association

Examples

result <- mr_2sls(
  df = comorbidData,
  prs_col = "ldl_PGS",
  exposure_col = "log_ldl",
  outcome_col = "bmi",
  scale = TRUE
)
print(result)

Mendelian Randomization ratio method with external PGS

Description

mr_ratio() takes a distribution of PGS, an Exposure (Phenotype), an Outcome (Phenotype). Returns a data frame showing the Mendelian Randomization ratio methods using PGS

Usage

mr_ratio(
  df = NULL,
  prs_col = "SCORESUM",
  exposure_col = NA,
  outcome_col = NA,
  scale = TRUE,
  verbose = TRUE,
  log = ""
)

Arguments

df

a dataframe with individuals on each row, and at least the following columns:

one ID column,
one PGS column, with numerical continuous values following a normal distribution,
two Phenotype columns (for Exposure and Outcome), can be numeric (Continuous Phenotype), character, boolean or factors (Discrete Phenotype)

prs_col

a character specifying the PGS column name

exposure_col

a character specifying the Exposure (Phenotype) column name

outcome_col

a character specifying the Outcome (Phenotype) column name

scale

a boolean specifying if scaling of PGS should be done before testing

verbose

a boolean (TRUE by default) to write in the console/log messages.

log

a connection, or a character string naming the file to print to. If "" (by default), it prints to the standard output connection, the console unless redirected by sink.

Value

return a data frame with the Mendelian Randomization association result using the ratio method with the following columns:

PGS: the name of the PGS used
Exposure: the name of Phenotype used as Exposure
Outcome: the name of Phenotype used as Outcome
Method: the MR method used (here Ratio)
N_cases: if Phenotype_type is Cases/Controls, the number of cases
N_controls: if Phenotype_type is Cases/Controls, the number of controls
N: the number of individuals/samples
MR_estimate: the MR estimate (beta) using the ratio method
SE: the associated standard error (second order)
F_stat: the F-statistic of the Exposure ~ PGS association

Examples

result <- mr_ratio(
  df = comorbidData,
  prs_col = "ldl_PGS",
  exposure_col = "log_ldl",
  outcome_col = "bmi",
  scale = TRUE
)
print(result)

Multiple PGS Associations from a Data Frame

Description

multiassoc() takes a data frame with distribution(s) of PGS and Phenotype(s), and a table of associations to make from this data frame.

Returns a data frame showing the association results

Usage

multiassoc(
  df = NULL,
  assoc_table = NULL,
  scale = TRUE,
  covar_col = NA,
  verbose = TRUE,
  log = "",
  parallel = FALSE,
  num_cores = NA
)

Arguments

df

a dataframe with individuals on each row, and at least the following columns:

one ID column,
one PGS column, with numerical continuous values following a normal distribution,
one Phenotype column, can be numeric (Continuous Phenotype), character, boolean or factors (Discrete Phenotype)

assoc_table

a dataframe or matrix specifying the associations to make from df, with 2 columns: PGS and Phenotype (in this order)

scale

a boolean specifying if scaling of PGS should be done before testing

covar_col

a character vector specifying the covariate column names (facultative)

verbose

a boolean (TRUE by default) to write in the console/log messages.

log

a connection, or a character string naming the file to print to. If "" (by default), it prints to the standard output connection, the console unless redirected by sink. If parallel = TRUE, the log will be incomplete

parallel

a boolean, if TRUE, multiassoc() parallelise the association analysis to run it faster (no log available with this option, does not work with Windows machine) If FALSE (default), the association analysis will not be parallelised (useful for debugging process)

num_cores

an integer, if parallel = TRUE (default), multiassoc() parallelise the association analysis to run it faster using num_cores as the number of cores. If nothing is provided, it detects the number of cores of the machine and use num_cores-1

Value

return a data frame showing the association of the PGS(s) on the Phenotype(s) with the following columns:

PGS: the name of the PGS
Phenotype: the name of Phenotype
Phenotype_type: either 'Continuous', 'Ordered Categorical', 'Categorical' or 'Cases/Controls'
Stat_method: association function detects what is the phenotype type and what is the best way to analyse it, either 'Linear regression', 'Binary logistic regression', 'Ordinal logistic regression' or 'Multinomial logistic regression'
Covar: list all the covariates used for this association
N_cases: if Phenotype_type is Cases/Controls, gives the number of cases
N_controls: if Phenotype_type is Cases/Controls, gives the number of controls
N: the number of individuals/samples
Effect: if Phenotype_type is Continuous, it represents the Beta coefficient of linear regression, OR of logistic regression otherwise
SE: standard error of the related Effect (Beta or OR)
lower_CI: lower confidence interval of the related Effect (Beta or OR)
upper_CI: upper confidence interval of the related Effect (Beta or OR)
P_value: associated P-value

Examples

assoc_table <- expand.grid(
  c("t2d_PGS", "ldl_PGS"),
  c("ethnicity","brc","t2d","log_ldl","sbp_cat")
)
results <- multiassoc(
  df = comorbidData,
  assoc_table = assoc_table,
  covar_col = c("age", "sex", "gen_array"),
  parallel = FALSE,
  verbose = FALSE
)
print(results)

Multiple PGS Associations from different Phenotypes

Description

multiphenassoc() takes a distribution of PGS and multiple Phenotypes and eventual confounders. Returns a data frame showing the association results

Usage

multiphenassoc(
  df = NULL,
  prs_col = "SCORESUM",
  phenotype_col = "Phenotype",
  scale = TRUE,
  covar_col = NA,
  verbose = TRUE,
  log = ""
)

Arguments

df

a dataframe with individuals on each row, and at least the following columns:

one ID column,
one PGS column, with numerical continuous values following a normal distribution,
one Phenotype column, can be numeric (Continuous Phenotype), character, boolean or factors (Discrete Phenotype)

prs_col

a character specifying the PGS column name

phenotype_col

a character vector specifying the Phenotype column names

scale

a boolean specifying if scaling of PGS should be done before testing

covar_col

a character vector specifying the covariate column names (facultative)

verbose

a boolean (TRUE by default) to write in the console/log messages.

log

a connection, or a character string naming the file to print to. If "" (by default), it prints to the standard output connection, the console unless redirected by sink.

Value

return a data frame showing the association of the PGS on the Phenotypes with the following columns:

PGS: the name of the PGS
Phenotype: the name of Phenotype
Phenotype_type: either 'Continuous', 'Ordered Categorical', 'Categorical' or 'Cases/Controls'
Stat_method: association function detects what is the phenotype type and what is the best way to analyse it, either 'Linear regression', 'Binary logistic regression', 'Ordinal logistic regression' or 'Multinomial logistic regression'
Covar: list all the covariates used for this association
N_cases: if Phenotype_type is Cases/Controls, gives the number of cases
N_controls: if Phenotype_type is Cases/Controls, gives the number of controls
N: the number of individuals/samples
Effect: if Phenotype_type is Continuous, it represents the Beta coefficient of linear regression; Otherwise, it is the OR of logistic regression
SE: standard error of the Beta coefficient (if Phenotype_type is Continuous)
lower_CI: lower confidence interval of the related Effect (Beta or OR)
upper_CI: upper confidence interval of the related Effect (Beta or OR)
P_value: associated P-value