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Single-trait Fine-mapping with FineBoost

This vignette demonstrates how to perform single-trait fine-mapping analysis using FineBoost, a specialized single-trait version of ColocBoost, with both individual-level data and summary statistics. Specifically focusing on the 2nd trait with 2 causal variants (194 and 589) from the Ind_5traits and Sumstat_5traits datasets included in the package.

library(colocboost)

1. Fine-mapping with individual-level data

In this section, we demonstrate how to perform fine-mapping using individual-level genotype (X) and phenotype (Y) data. This approach uses raw data directly to identify causal variants.

# Load example data
data(Ind_5traits)
X <- Ind_5traits$X[[2]]
Y <- Ind_5traits$Y[[2]]

res <- colocboost(X = X, Y = Y)
#> Validating input data.
#> Starting gradient boosting algorithm.
#> Gradient boosting for outcome 1 converged after 44 iterations!
#> Performing inference on colocalization events.
#> No colocalization results in this region!
#> Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
#> For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
colocboost_plot(res)

2. Fine-mapping with summary statistics

This section demonstrates fine-mapping analysis using summary statistics along with a proper LD matrix.

# Load example data
data(Sumstat_5traits) 
sumstat <- Sumstat_5traits$sumstat[[2]]
LD <- get_cormat(Ind_5traits$X[[2]])

res <- colocboost(sumstat = sumstat, LD = LD)
#> Validating input data.
#> Starting gradient boosting algorithm.
#> Gradient boosting for outcome 1 converged after 44 iterations!
#> Performing inference on colocalization events.
#> No colocalization results in this region!
#> Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
#> For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
colocboost_plot(res)

3. LD-free fine-mapping with one causal variant assumption

In scenarios where LD information is unavailable, FineBoost can still perform fine-mapping under the assumption that there is a single causal variant. This approach is less computationally intensive but assumes that only one variant within a region is causal.

# Load example data
res <- colocboost(sumstat = sumstat)
#> Validating input data.
#> Warning in colocboost_validate_input_data(X = X, Y = Y, sumstat = sumstat, :
#> Providing the LD or X_ref for summary statistics data is highly recommended.
#> Without LD, only a single iteration will be performed under the assumption of
#> one causal variable per outcome. Additionally, the purity of CoS cannot be
#> evaluated!
#> Starting gradient boosting algorithm.
#> Running ColocBoost with assumption of one causal per outcome per region!
#> Performing inference on colocalization events.
#> No colocalization results in this region!
#> Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05.
#> Keep only uCoS with pvalue of variants for the outcome < 1e-05.
colocboost_plot(res)

Note: Weak learners SEL in FineBoost may capture noise as putative signals, potentially introducing false positives to our findings. To identify and filter spurious signals, we use a fine-tuned threshold of \(\Delta L_l\) based on extensive simulations to balance sensitivity and specificity. This threshold is set to 0.025 by default for ColocBoost when detect the colocalization, but we suggested a less conservative threshold of 0.015 for FineBoost when performing single-trait fine-mapping analysis (check_null_max = 0.015 as we suggested).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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