Constructing covariance matrix from local family graph for multi trait analysis — graph_based_covariance_construction

Function that constructs the genetic covariance matrix given a graph around a proband and extracts the threshold information from the graph.

Usage

graph_based_covariance_construction_multi(
  fid,
  pid,
  cur_proband_id,
  cur_family_graph,
  h2_vec,
  genetic_corrmat,
  phen_names,
  useMixture = FALSE,
  add_ind = TRUE
)

Arguments

fid: Name of column with the family ID (typically the proband ID)
pid: Name of column of personal ID
cur_proband_id: id of proband
cur_family_graph: local graph of current proband
h2_vec: vector of liability scale heritabilities
genetic_corrmat: matrix with genetic correlations between considered phenotypes. Must have same order as h2_vec.
phen_names: Names of the phenotypes, as given in cur_family_graph.
useMixture: whether to return K_i and K_pop columns.
add_ind: whether to add genetic liability of the proband or not. Defaults to true.

Value

list with three elements. The first element is temp_tbl, which contains the id of the current proband, the family ID and the lower and upper thresholds for all phenotypes. The second element, cov, is the covariance matrix of the local graph centred on the current proband. The third element is newOrder, which is the order of ids from pid and phen_names pasted together, such that order can be enforced elsewhere too.

Examples

fam <- data.frame(
fam = c(1, 1, 1, 1),
id = c("pid", "mom", "dad", "pgf"),
dadcol = c("dad", 0, "pgf", 0),
momcol = c("mom", 0, 0, 0))

thresholds <- data.frame(
  id = c("pid", "mom", "dad", "pgf"),
  lower_1 = c(-Inf, -Inf, 0.8, 0.7),
  upper_1 = c(0.8, 0.8, 0.8, 0.7),
  lower_2 = c(-Inf, 0.3, -Inf, 0.2),
  upper_2 = c(0.3, 0.3, 0.3, 0.2))

graph <- prepare_graph(fam, icol = "id", fcol = "dadcol",
 mcol = "momcol", node_attributes = thresholds)

ntrait <- 2
genetic_corrmat <- matrix(0.2, ncol = ntrait, nrow = ntrait)
diag(genetic_corrmat) <- 1
full_corrmat <- matrix(0.3, ncol = ntrait, nrow = ntrait)
diag(full_corrmat) <- 1
h2_vec <- rep(0.6, ntrait)

graph_based_covariance_construction_multi(fid = "fam",
                                          pid = "id",
                                          cur_proband_id = "pid",
                                          cur_family_graph = graph,
                                          h2_vec = h2_vec,
                                          genetic_corrmat = genetic_corrmat,
                                          phen_names = c("1", "2"))
#> $temp_tbl
#> # A tibble: 10 × 4
#>    fam   id       lower upper
#>    <chr> <chr>    <dbl> <dbl>
#>  1 pid   pid_g_1 -Inf   Inf  
#>  2 pid   pid_1   -Inf     0.8
#>  3 pid   mom_1   -Inf     0.8
#>  4 pid   dad_1      0.8   0.8
#>  5 pid   pgf_1      0.7   0.7
#>  6 pid   pid_g_2 -Inf   Inf  
#>  7 pid   pid_2   -Inf     0.3
#>  8 pid   mom_2      0.3   0.3
#>  9 pid   dad_2   -Inf     0.3
#> 10 pid   pgf_2      0.2   0.2
#> 
#> $cov
#>         pid_g_1 pid_1 mom_1 dad_1 pgf_1 pid_g_2 pid_2 mom_2 dad_2 pgf_2
#> pid_g_1    0.60  0.60  0.30  0.30  0.15    0.12  0.12  0.06  0.06  0.03
#> pid_1      0.60  1.00  0.30  0.30  0.15    0.12  0.12  0.06  0.06  0.03
#> mom_1      0.30  0.30  1.00  0.00  0.00    0.06  0.06  0.12  0.00  0.00
#> dad_1      0.30  0.30  0.00  1.00  0.30    0.06  0.06  0.00  0.12  0.06
#> pgf_1      0.15  0.15  0.00  0.30  1.00    0.03  0.03  0.00  0.06  0.12
#> pid_g_2    0.12  0.12  0.06  0.06  0.03    0.60  0.60  0.30  0.30  0.15
#> pid_2      0.12  0.12  0.06  0.06  0.03    0.60  1.00  0.30  0.30  0.15
#> mom_2      0.06  0.06  0.12  0.00  0.00    0.30  0.30  1.00  0.00  0.00
#> dad_2      0.06  0.06  0.00  0.12  0.06    0.30  0.30  0.00  1.00  0.30
#> pgf_2      0.03  0.03  0.00  0.06  0.12    0.15  0.15  0.00  0.30  1.00
#> 
#> $newOrder
#>  [1] "pid_g_1" "pid_1"   "mom_1"   "dad_1"   "pgf_1"   "pid_g_2" "pid_2"  
#>  [8] "mom_2"   "dad_2"   "pgf_2"  
#>