# 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).