get_colocboost_summary: Get summary tables from a ColocBoost output.

In colocboost: Multi-Context Colocalization Analysis for QTL and GWAS Studies

Get summary tables from a ColocBoost output.

Description

get_colocboost_summary get colocalization and trait-specific summary table with or without the outcomes of interest.

Usage

get_colocboost_summary(
  cb_output,
  summary_level = 1,
  outcome_names = NULL,
  interest_outcome = NULL,
  region_name = NULL,
  min_abs_corr_between_ucos = 0.5,
  median_abs_corr_between_ucos = 0.8
)

Arguments

cb_output	Output object from colocboost analysis
summary_level	When summary_level = 1, return basic summary table for colocalization results. See details in get_ucos_summary function when summary_level = 2.
outcome_names	Optional vector of names of outcomes, which has the same order as Y in the original analysis.
interest_outcome	Optional vector specifying a subset of outcomes from outcome_names to focus on. When provided, only colocalization events that include at least one of these outcomes will be returned.
region_name	Optional character string. When provided, adds a column with this gene name to the output table for easier filtering in downstream analyses.
min_abs_corr_between_ucos	Minimum absolute correlation for variants across two trait-specific (uncolocalized) effects to be considered colocalized. The default is 0.5.
median_abs_corr_between_ucos	Median absolute correlation for variants across two trait-specific (uncolocalized) effects to be considered colocalized. The default is 0.8.

Details

When summary_level = 1, additional details and examples are introduced in get_cos_summary. When summary_level = 2 or summary_level = 3, additional details for trait-specific effects and ambiguous colocalization events are included. See get_ucos_summary for details on these tables.

Value

A list containing results from the ColocBoost analysis:

• When summary_level = 1 (default):

  • cos_summary: A summary table for colocalization events with the following columns:

    • focal_outcome: The focal outcome being analyzed if exists. Otherwise, it is FALSE.

    • colocalized_outcomes: Colocalized outcomes for colocalization confidence set (CoS)

    • cos_id: Unique identifier for colocalization confidence set (CoS)

    • purity: Minimum absolute correlation of variables within colocalization confidence set (CoS)

    • top_variable: The variable with highest variant colocalization probability (VCP)

    • top_variable_vcp: Variant colocalization probability for the top variable

    • cos_npc: Normalized probability of colocalization

    • min_npc_outcome: Minimum normalized probability of colocalized traits

    • n_variables: Number of variables in colocalization confidence set (CoS)

    • colocalized_index: Indices of colocalized variables

    • colocalized_variables: List of colocalized variables

    • colocalized_variables_vcp: Variant colocalization probabilities for all colocalized variables

• When summary_level = 2:

  • cos_summary: As described above

  • ucos_summary: A summary table for trait-specific (uncolocalized) effects

• When summary_level = 3:

  • cos_summary: As described above

  • ucos_summary: A summary table for trait-specific (uncolocalized) effects

  • ambiguous_cos_summary: A summary table for ambiguous colocalization events from trait-specific effects

Source

See detailed instructions in our tutorial portal: https://statfungen.github.io/colocboost/articles/Interpret_ColocBoost_Output.html

See Also

Other colocboost_inference: get_ambiguous_colocalization(), get_robust_colocalization()

Examples

# colocboost example
set.seed(1)
N <- 1000
P <- 100
# Generate X with LD structure
sigma <- 0.9^abs(outer(1:P, 1:P, "-"))
X <- MASS::mvrnorm(N, rep(0, P), sigma)
colnames(X) <- paste0("SNP", 1:P)
L <- 3
true_beta <- matrix(0, P, L)
true_beta[10, 1] <- 0.5 # SNP10 affects trait 1
true_beta[10, 2] <- 0.4 # SNP10 also affects trait 2 (colocalized)
true_beta[50, 2] <- 0.3 # SNP50 only affects trait 2
true_beta[80, 3] <- 0.6 # SNP80 only affects trait 3
Y <- matrix(0, N, L)
for (l in 1:L) {
  Y[, l] <- X %*% true_beta[, l] + rnorm(N, 0, 1)
}
res <- colocboost(X = X, Y = Y)
get_colocboost_summary(res)

colocboost documentation built on June 8, 2025, 11:07 a.m.