comorbidPGS (version 1.0.0)

mr_2sls: 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 = ""
)

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

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.

Examples

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