assoc {comorbidPGS}

Association of a PGS distribution with a Phenotype

Description

assoc() take a distribution of PGS, a Phenotype and eventual Confounders return 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)