estimate_liability_multi {LTFHPlus}

Estimating the genetic or full liability for multiple phenotypes

Description

estimate_liability_multi estimates the genetic component of the full liability and/or the full liability for a number of individuals based on their family history for a variable number of phenotypes.

Usage

estimate_liability_multi(
  .tbl = NULL,
  family_graphs = NULL,
  h2_vec,
  genetic_corrmat,
  full_corrmat,
  phen_names = NULL,
  pid = "PID",
  fam_id = "fam_ID",
  role = "role",
  family_graphs_col = "fam_graph",
  out = c(1),
  tol = 0.01
)

Arguments

.tbl

A matrix, list or data frame that can be converted into a tibble. Must have at least seven columns that hold the family identifier, the personal identifier, the role and the lower and upper thresholds for all phenotypes of interest. Note that the role must be one of the following abbreviations

• g (Genetic component of full liability)

• o (Full liability)

• m (Mother)

• f (Father)

• c[0-9]*.[0-9]* (Children)

• mgm (Maternal grandmother)

• mgf (Maternal grandfather)

• pgm (Paternal grandmother)

• pgf (Paternal grandfather)

• s[0-9]* (Full siblings)

• mhs[0-9]* (Half-siblings - maternal side)

• phs[0-9]* (Half-siblings - paternal side)

• mau[0-9]* (Aunts/Uncles - maternal side)

• pau[0-9]* (Aunts/Uncles - paternal side). Defaults to NULL.

family_graphs

A tibble with columns pid and family_graph_col. See prepare_graph for construction of the graphs. The family graphs Defaults to NULL.

h2_vec

A numeric vector representing the liability-scale heritabilities for all phenotypes. All entries in h2_vec must be non-negative and at most 1.

genetic_corrmat

A numeric matrix holding the genetic correlations between the desired phenotypes. All diagonal entries must be equal to one, while all off-diagonal entries must be between -1 and 1. In addition, the matrix must be symmetric.

full_corrmat

A numeric matrix holding the full correlations between the desired phenotypes. All diagonal entries must be equal to one, while all off-diagonal entries must be between -1 and 1. In addition, the matrix must be symmetric.

phen_names

A character vector holding the phenotype names. These names will be used to create the row and column names for the covariance matrix. If it is not specified, the names will default to phenotype1, phenotype2, etc. Defaults to NULL.

pid

A string holding the name of the column in family and threshs that hold the personal identifier(s). Defaults to "PID".

fam_id

A string holding the name of the column in family that holds the family identifier. Defaults to "fam_ID".

role

A string holding the name of the column in .tbl that holds the role.Each role must be chosen from the following list of abbreviations

• g (Genetic component of full liability)

• o (Full liability)

• m (Mother)

• f (Father)

• c[0-9]*.[0-9]* (Children)

• mgm (Maternal grandmother)

• mgf (Maternal grandfather)

• pgm (Paternal grandmother)

• pgf (Paternal grandfather)

• s[0-9]* (Full siblings)

• mhs[0-9]* (Half-siblings - maternal side)

• phs[0-9]* (Half-siblings - paternal side)

• mau[0-9]* (Aunts/Uncles - maternal side)

• pau[0-9]* (Aunts/Uncles - paternal side). Defaults to "role".

family_graphs_col

Name of column with family graphs in family_graphs. Defaults to "fam_graph".

out

A character or numeric vector indicating whether the genetic component of the full liability, the full liability or both should be returned. If out = c(1) or out = c("genetic"), the genetic liability is estimated and returned. If out = c(2) or out = c("full"), the full liability is estimated and returned. If out = c(1,2) or out = c("genetic", "full"), both components are estimated and returned. Defaults to c(1).

tol

A number that is used as the convergence criterion for the Gibbs sampler. Equals the standard error of the mean. That is, a tolerance of 0.2 means that the standard error of the mean is below 0.2. Defaults to 0.01.

Details

This function can be used to estimate either the genetic component of the full liability, the full liability or both for a variable number of traits.

Value

If family and threshs are two matrices, lists or data frames that can be converted into tibbles, if family has two columns named like the strings represented in pid and fam_id, if threshs has a column named like the string given in pid as well as a column named "lower" and a column named "upper" and if the liability-scale heritabilities in h2_vec, genetic_corrmat, full_corrmat, out and tol are of the required form, then the function returns a tibble with at least six columns (depending on the length of out). The first two columns correspond to the columns fam_id and pid present in the tibble family. If out is equal to c(1) or c("genetic"), the third and fourth columns hold the estimated genetic liability as well as the corresponding standard error for the first phenotype, respectively. If out equals c(2) or c("full"), the third and fourth columns hold the estimated full liability as well as the corresponding standard error for the first phenotype, respectively. If out is equal to c(1,2) or c("genetic","full"), the third and fourth columns hold the estimated genetic liability as well as the corresponding standard error for the first phenotype, respectively, while the fifth and sixth columns hold the estimated full liability as well as the corresponding standard error for the first phenotype, respectively. The remaining columns hold the estimated genetic liabilities and/or the estimated full liabilities as well as the corresponding standard errors for the remaining phenotypes.

See Also

future_apply, estimate_liability_single, estimate_liability

Examples

genetic_corrmat <- matrix(0.4, 3, 3)
diag(genetic_corrmat) <- 1
full_corrmat <- matrix(0.6, 3, 3)
diag(full_corrmat) <- 1
#
sims <- simulate_under_LTM(fam_vec = c("m","f"), n_fam = NULL, add_ind = TRUE, 
genetic_corrmat = genetic_corrmat, full_corrmat = full_corrmat, h2 = rep(.5,3), 
n_sim = 1, pop_prev = rep(.1,3))
estimate_liability_multi(.tbl = sims$thresholds, h2_vec = rep(.5,3), 
genetic_corrmat = genetic_corrmat, full_corrmat = full_corrmat,
pid = "indiv_ID", fam_id = "fam_ID", role = "role", out = c(1), 
phen_names = paste0("phenotype", 1:3), tol = 0.01)