A real data example includes an ambiguous colocalization between eQTL and GWAS

Description

An example result from one of our real data applications, which shows an ambiguous colocalization between eQTL and GWAS.

Usage

Ambiguous_Colocalization

Format

Ambiguous_Colocalization

A list with 2 elements

ColocBoost_Results

A colocboost output object

SuSiE_Results

Two susie output object for eQTL and GWAS

COLOC_V5_Results

A coloc output object

Source

The Ambiguous_Colocalization dataset contains a real data example from one of our real data applications, which shows an ambiguous colocalization between eQTL and GWAS. The dataset is specifically designed for evaluating and demonstrating the capabilities of ColocBoost in real data applications. See details in tutorial vignette https://statfungen.github.io/colocboost/articles/index.html.

See Also

Other colocboost_data: Heterogeneous_Effect, Ind_5traits, Non_Causal_Strongest_Marginal, Sumstat_5traits, Weaker_GWAS_Effect

Individual level data for 2 traits and 2 causal variants with heterogeneous effects

Description

An example dataset with simulated genotypes and traits for 2 traits and 2 common causal variants with heterogeneous effects

Usage

Heterogeneous_Effect

Format

Heterogeneous_Effect

A list with 3 elements

X

List of genotype matrices

Y

List of traits

variant

indices of two causal variants

Source

The Heterogeneous_Effect dataset contains 2 simulated phenotypes alongside corresponding genotype matrices. There are two causal variants, both of which have heterogeneous effects on two traits. Due to the file size limitation of CRAN release, this is a subset of simulated data to generate Figure 2b in Cao etc. 2025. See full dataset in colocboost paper repo https://github.com/StatFunGen/colocboost-paper.

See Also

Other colocboost_data: Ambiguous_Colocalization, Ind_5traits, Non_Causal_Strongest_Marginal, Sumstat_5traits, Weaker_GWAS_Effect

Individual level data for 5 traits

Description

An example dataset with simulated genotypes and traits for 5 traits

Usage

Ind_5traits

Format

Ind_5traits

A list with 3 elements

X

List of genotype matrices

Y

List of traits

true_effect_variants

List of causal variants

Source

The Ind_5traits dataset contains 5 simulated phenotypes alongside corresponding genotype matrices. The dataset is specifically designed for evaluating and demonstrating the capabilities of ColocBoost in multi-trait colocalization analysis with individual-level data. See Cao etc. 2025 for details. Due to the file size limitation of CRAN release, this is a subset of simulated data. See full dataset in colocboost paper repo https://github.com/StatFunGen/colocboost-paper.

See Also

Other colocboost_data: Ambiguous_Colocalization, Heterogeneous_Effect, Non_Causal_Strongest_Marginal, Sumstat_5traits, Weaker_GWAS_Effect

Individual level data for 2 traits and 2 causal variants, but the strongest marginal association is not causal

Description

An example dataset with simulated genotypes and traits for 2 traits and 2 common causal variants, but the strongest marginal association is not causal variant.

Usage

Non_Causal_Strongest_Marginal

Format

Non_Causal_Strongest_Marginal

A list with 3 elements

X

List of genotype matrices

Y

List of traits

variant

indices of two causal variants

Source

The Non_Causal_Strongest_Marginal dataset contains 2 simulated phenotypes alongside corresponding genotype matrices. There are two causal variants, but the strongest marginal association is not a causal variant. Due to the file size limitation of CRAN release, this is a subset of simulated data to generate Figure 2b in Cao etc. 2025. See full dataset in colocboost paper repo https://github.com/StatFunGen/colocboost-paper.

See Also

Other colocboost_data: Ambiguous_Colocalization, Heterogeneous_Effect, Ind_5traits, Sumstat_5traits, Weaker_GWAS_Effect

Summary level data for 5 traits

Description

An example dataset with simulated statistics for 5 traits

Usage

Sumstat_5traits

Format

Sumstat_5traits

A list with 2 elements

sumstat

Summary statistics for 5 traits

true_effect_variants

List of causal variants

Source

The Sumstat_5traits dataset contains 5 simulated summary statistics, where it is directly derived from the Ind_5traits dataset using marginal association. The dataset is specifically designed for evaluating and demonstrating the capabilities of ColocBoost in multi-trait colocalization analysis with summary association data. See Cao etc. 2025 for details. Due to the file size limitation of CRAN release, this is a subset of simulated data. See full dataset in colocboost paper repo https://github.com/StatFunGen/colocboost-paper.

See Also

Other colocboost_data: Ambiguous_Colocalization, Heterogeneous_Effect, Ind_5traits, Non_Causal_Strongest_Marginal, Weaker_GWAS_Effect

Individual level data for 2 traits and 2 causal variants with weaker effects for focal trait

Description

An example dataset with simulated genotypes and traits for 2 traits and 2 common causal variants with heterogeneous effects

Usage

Weaker_GWAS_Effect

Format

Weaker_GWAS_Effect

A list with 3 elements

X

List of genotype matrices

Y

List of traits

variant

indices of two causal variants

Source

The Weaker_GWAS_Effect dataset contains 2 simulated phenotypes alongside corresponding genotype matrices. There are two causal variants, one of which has a weaker effect on the focal trait compared to the other trait. Due to the file size limitation of CRAN release, this is a subset of simulated data to generate Figure 2b in Cao etc. 2025. See full dataset in colocboost paper repo https://github.com/StatFunGen/colocboost-paper.

See Also

Other colocboost_data: Ambiguous_Colocalization, Heterogeneous_Effect, Ind_5traits, Non_Causal_Strongest_Marginal, Sumstat_5traits

ColocBoost: A gradient boosting informed multi-omics xQTL colocalization method

Description

colocboost implements a proximity adaptive smoothing gradient boosting approach for multi-trait colocalization at gene loci, accommodating multiple causal variants. This method, introduced by Cao etc. (2025), is particularly suited for scaling to large datasets involving numerous molecular quantitative traits and disease traits. In brief, this function fits a multiple linear regression model Y = XB + E in matrix form. ColocBoost can be generally used in multi-task variable selection regression problem.

Usage

colocboost(
  X = NULL,
  Y = NULL,
  sumstat = NULL,
  LD = NULL,
  dict_YX = NULL,
  dict_sumstatLD = NULL,
  outcome_names = NULL,
  focal_outcome_idx = NULL,
  focal_outcome_variables = TRUE,
  overlap_variables = FALSE,
  intercept = TRUE,
  standardize = TRUE,
  effect_est = NULL,
  effect_se = NULL,
  effect_n = NULL,
  M = 500,
  stop_thresh = 1e-06,
  tau = 0.01,
  learning_rate_init = 0.01,
  learning_rate_decay = 1,
  dynamic_learning_rate = TRUE,
  prioritize_jkstar = TRUE,
  func_compare = "min_max",
  jk_equiv_corr = 0.8,
  jk_equiv_loglik = 1,
  coloc_thresh = 0.1,
  lambda = 0.5,
  lambda_focal_outcome = 1,
  func_simplex = "LD_z2z",
  func_multi_test = "lfdr",
  stop_null = 1,
  multi_test_max = 1,
  multi_test_thresh = 1,
  ash_prior = "normal",
  p.adjust.methods = "fdr",
  residual_correlation = NULL,
  coverage = 0.95,
  min_cluster_corr = 0.8,
  dedup = TRUE,
  overlap = TRUE,
  n_purity = 100,
  min_abs_corr = 0.5,
  median_abs_corr = NULL,
  median_cos_abs_corr = 0.8,
  tol = 1e-09,
  merge_cos = TRUE,
  sec_coverage_thresh = 0.8,
  weight_fudge_factor = 1.5,
  check_null = 0.1,
  check_null_method = "profile",
  check_null_max = 0.025,
  weaker_effect = TRUE,
  LD_free = FALSE,
  output_level = 1
)

Arguments

Details

The function colocboost implements the proximity smoothed gradient boosting method from Cao etc (2025). There is an additional step to help merge the confidence sets with small between_putiry (default is 0.8) but within the same locus. This step addresses potential instabilities in linkage disequilibrium (LD) estimation that may arise from small sample sizes or discrepancies in minor allele frequencies (MAF) across different confidence sets.

Value

A "colocboost" object with some or all of the following elements:

Source

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

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)
res$cos_details$cos$cos_index

Plot visualization plot from a ColocBoost output.

Description

colocboost_plot generates visualization plots for colocalization events from a ColocBoost analysis.

Usage

colocboost_plot(
  cb_output,
  y = "log10p",
  grange = NULL,
  plot_cos_idx = NULL,
  outcome_idx = NULL,
  plot_all_outcome = FALSE,
  plot_focal_only = FALSE,
  plot_focal_cos_outcome_only = FALSE,
  points_color = "grey80",
  cos_color = NULL,
  add_vertical = FALSE,
  add_vertical_idx = NULL,
  outcome_names = NULL,
  plot_cols = 2,
  variant_coord = FALSE,
  show_top_variables = FALSE,
  show_cos_to_uncoloc = FALSE,
  show_cos_to_uncoloc_idx = NULL,
  show_cos_to_uncoloc_outcome = NULL,
  plot_ucos = FALSE,
  plot_ucos_idx = NULL,
  title_specific = NULL,
  ylim_each = TRUE,
  outcome_legend_pos = "top",
  outcome_legend_size = 1.8,
  cos_legend_pos = c(0.05, 0.4),
  show_variable = FALSE,
  lab_style = c(2, 1),
  axis_style = c(2, 1),
  title_style = c(2.5, 2),
  ...
)

Arguments

Value

Visualization plot for each colocalization event.

Source

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

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)
colocboost_plot(res, plot_cols = 1)
colocboost_plot(res, plot_cols = 1, outcome_idx = 1:3)

Get ambiguous colocalization events from trait-specific (uncolocalized) effects.

Description

get_ambiguous_colocalization get the colocalization by discarding the weaker colocalization events or colocalized outcomes

Usage

get_ambiguous_colocalization(
  cb_output,
  min_abs_corr_between_ucos = 0.5,
  median_abs_corr_between_ucos = 0.8,
  tol = 1e-09
)

Arguments

Value

A "colocboost" object of colocboost output with additional elements:

Source

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

See Also

Other colocboost_inference: get_colocboost_summary(), get_robust_colocalization()

Examples

data(Ambiguous_Colocalization)
test_colocboost_results <- Ambiguous_Colocalization$ColocBoost_Results
res <- get_ambiguous_colocalization(test_colocboost_results)
names(res$ambiguous_cos)

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

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)

A fast function to calculate correlation matrix (LD matrix) from individual level data

Description

This function calculates the correlation matrix (LD matrix) from individual level data.

Usage

get_cormat(X, intercepte = TRUE)

Arguments

Value

A correlation matrix (LD matrix).

See Also

Other colocboost_utilities: get_cos(), get_cos_purity(), get_cos_summary(), get_hierarchical_clusters(), get_ucos_summary()

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)
cormat <- get_cormat(X)

Extract CoS at different coverage

Description

get_cos extracts colocalization confidence sets (CoS) at different coverage levels from ColocBoost results. When genotype data (X) or correlation matrix (Xcorr) is provided, it can also calculate and filter CoS based on purity statistics, ensuring that variants within each CoS are sufficiently correlated.

Usage

get_cos(
  cb_output,
  coverage = 0.95,
  X = NULL,
  Xcorr = NULL,
  n_purity = 100,
  min_abs_corr = 0.5,
  median_abs_corr = NULL
)

Arguments

Value

A list of indices of variables in each CoS.

See Also

Other colocboost_utilities: get_cormat(), get_cos_purity(), get_cos_summary(), get_hierarchical_clusters(), get_ucos_summary()

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_cos(res, coverage = 0.99, X = X)
get_cos(res, coverage = 0.99, X = X, min_abs_corr = 0.95)

Calculate purity within and in-between CoS

Description

Calculate purity statistics between all pairs of colocalization confidence sets (CoS)

Usage

get_cos_purity(cos, X = NULL, Xcorr = NULL, n_purity = 100)

Arguments

Value

A list containing three matrices (min_abs_cor, max_abs_cor, median_abs_cor) with purity statistics for all pairs of CoS. Diagonal elements represent within-CoS purity.

See Also

Other colocboost_utilities: get_cormat(), get_cos(), get_cos_summary(), get_hierarchical_clusters(), get_ucos_summary()

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 
true_beta[10, 2] <- 0.4 
true_beta[50, 2] <- 0.3 
true_beta[80, 3] <- 0.6 
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)
cos_res <- get_cos(res, coverage = 0.8)
get_cos_purity(cos_res$cos, X = X)

Get colocalization summary table from a ColocBoost output.

Description

get_cos_summary get the colocalization summary table with or without the outcomes of interest.

Usage

get_cos_summary(
  cb_output,
  outcome_names = NULL,
  interest_outcome = NULL,
  region_name = NULL
)

Arguments

Value

A summary table for colocalization events with the following columns:

Source

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

See Also

Other colocboost_utilities: get_cormat(), get_cos(), get_cos_purity(), get_hierarchical_clusters(), get_ucos_summary()

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_cos_summary(res)

Perform modularity-based hierarchical clustering for a correlation matrix

Description

This function performs a modularity-based hierarchical clustering approach to identify clusters from a correlation matrix.

Usage

get_hierarchical_clusters(cormat, min_cluster_corr = 0.8)

Arguments

Value

A list containing:

See Also

Other colocboost_utilities: get_cormat(), get_cos(), get_cos_purity(), get_cos_summary(), get_ucos_summary()

Examples

# Example usage
set.seed(1)
N <- 100
P <- 4
sigma <- matrix(0.2, nrow = P, ncol = P)
diag(sigma) <- 1
sigma[1:2, 1:2] <- 0.9
sigma[3:4, 3:4] <- 0.9
X <- MASS::mvrnorm(N, rep(0, P), sigma)
cormat <- get_cormat(X)
clusters <- get_hierarchical_clusters(cormat)
clusters$cluster
clusters$Q_modularity

Recalibrate and summarize robust colocalization events.

Description

get_robust_colocalization get the colocalization by discarding the weaker colocalization events or colocalized outcomes

Usage

get_robust_colocalization(
  cb_output,
  cos_npc_cutoff = 0.5,
  npc_outcome_cutoff = 0.2,
  pvalue_cutoff = NULL,
  weight_fudge_factor = 1.5,
  coverage = 0.95
)

Arguments

Value

A "colocboost" object with some or all of the following elements:

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_colocboost_summary()

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)
res$cos_details$cos$cos_index
filter_res <- get_robust_colocalization(res, cos_npc_cutoff = 0.5, npc_outcome_cutoff = 0.2)
filter_res$cos_details$cos$cos_index

Get trait-specific summary table from a ColocBoost output.

Description

get_ucos_summary produces a trait-specific summary table for uncolocalized (single-trait) associations from ColocBoost results. This is particularly useful for examining trait-specific signals or for summarizing results from single-trait FineBoost analyses.

Usage

get_ucos_summary(
  cb_output,
  outcome_names = NULL,
  region_name = NULL,
  ambiguous_cos = FALSE,
  min_abs_corr_between_ucos = 0.5,
  median_abs_corr_between_ucos = 0.8
)

Arguments

Value

A list containing:

• ucos_summary: A summary table for trait-specific, uncolocalized associations with the following columns:

  • outcomes: Outcome being analyzed

  • ucos_id: Unique identifier for trait-specific confidence sets

  • purity: Minimum absolute correlation of variables within trait-specific confidence sets

  • top_variable: The variable with highest variant-level probability of association (VPA)

  • top_variable_vpa: Variant-level probability of association (VPA) for the top variable

  • ucos_npc: Normalized probability of causal association for the trait-specific confidence set

  • n_variables: Number of variables in trait-specific confidence set

  • ucos_index: Indices of variables in the trait-specific confidence set

  • ucos_variables: List of variables in the trait-specific confidence set

  • ucos_variables_vpa: Variant-level probability of association (VPA) for all variables in the confidence set

  • region_name: Region name if provided through the region_name parameter

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

  • outcomes: Outcome in the ambiguous colocalization event

  • ucos_id: Unique identifiers for the ambiguous event

  • min_between_purity: Minimum absolute correlation between variables across trait-specific sets in the ambiguous event

  • median_between_purity: Median absolute correlation between variables across trait-specific sets in the ambiguous event

  • overlap_idx: Indices of variables that overlap between ambiguous trait-specific sets

  • overlap_variables: Names of variables that overlap between ambiguous trait-specific sets

  • n_recalibrated_variables: Number of variables in the recalibrated colocalization set from an ambiguous event

  • recalibrated_index: Indices of variables in the recalibrated colocalization set from an ambiguous event

  • recalibrated_variables: Names of variables in the recalibrated colocalization set from an ambiguous event

  • recalibrated_variables_vcp: Variant colocalization probabilities for recalibrated variables from an ambiguous event

  • region_name: Region name if provided through the region_name parameter

Source

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

See Also

Other colocboost_utilities: get_cormat(), get_cos(), get_cos_purity(), get_cos_summary(), get_hierarchical_clusters()

Examples

# colocboost example with single trait analysis
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 <- 1  # Only one trait for single-trait analysis
true_beta <- matrix(0, P, L)
true_beta[10, 1] <- 0.5 # SNP10 affects the trait
true_beta[80, 1] <- 0.2 # SNP11 also affects the trait but with lower effect
Y <- X %*% true_beta + rnorm(N, 0, 1)
res <- colocboost(X = X, Y = Y, output_level = 2)
# Get the trait-specifc effect summary
get_ucos_summary(res)