# Individual Level Data Colocalization

ColocBoost provides a flexible interface for individual-level colocalization analysis across multiple formats. We recommend using individual level genotype and phenotype data when available, to gain both sensitivity and precision compared to summary statistics-based approaches.

This vignette demonstrates how to perform multi-trait colocalization analysis using individual level data in ColocBoost, specifically focusing on the `Ind_5traits` dataset included in the package.

    library(colocboost)

# 1. The `Ind_5traits` Dataset

The `Ind_5traits` dataset contains 5 simulated phenotypes alongside corresponding genotype matrices. The dataset is specifically designed to evaluate and demonstrate the capabilities of ColocBoost in multi-trait colocalization analysis with individual-level data.

- `X`: A list of genotype matrices for different outcomes.
- `Y`: A list of phenotype vectors for different outcomes.
- `true_effect_variants`: True effect variants indices for each trait.

### Causal variant structure

The dataset features two causal variants with indices 194 and 589.

- Causal variant 194 is associated with traits 1, 2, 3, and 4.
- Causal variant 589 is associated with traits 2, 3, and 5.

This structure creates a realistic scenario where multiple traits are influenced by different but overlapping sets of genetic variants.

    # Loading the Dataset
    data(Ind_5traits)
    names(Ind_5traits)
    #> [1] "X"                    "Y"                    "true_effect_variants"
    Ind_5traits$true_effect_variants
    #> $Outcome_1
    #> [1] 194
    #>
    #> $Outcome_2
    #> [1] 194 589
    #>
    #> $Outcome_3
    #> [1] 194 589
    #>
    #> $Outcome_4
    #> [1] 194
    #>
    #> $Outcome_5
    #> [1] 589

Due to the file size limitation of CRAN release, this is a subset of simulated data. See full dataset in colocboost paper repo.

# 2. Matched individual level input \(X\) and \(Y\)

The preferred format for colocalization analysis in ColocBoost using individual level data is where genotype (\(X\)) and phenotype (\(Y\)) data are properly matched.

- Basic format: `X` and `Y` are organized as lists, matched by trait index,
  - `(X[1], Y[1])` contains individual level data for trait 1,
  - `(X[2], Y[2])` contains individual level data for trait 2,
  - And so on for each trait under analysis.
- Cross-trait flexibility:
  - There is no requirement for the same individuals across different traits. This allows for the analysis of traits with different sample sizes.
  - This is particularly useful when you have a large dataset with many traits and want to focus on specific individuals for each trait.

This function requires specifying genotypes `X` and phenotypes `Y` from the dataset:

    # Extract genotype (X) and phenotype (Y) data
    X <- Ind_5traits$X
    Y <- Ind_5traits$Y

    # Run colocboost with matched data
    res <- colocboost(X = X, Y = Y)
    #> Validating input data.
    #> Starting gradient boosting algorithm.
    #> Gradient boosting for outcome 4 converged after 40 iterations!
    #> Gradient boosting for outcome 5 converged after 59 iterations!
    #> Gradient boosting for outcome 1 converged after 61 iterations!
    #> Gradient boosting for outcome 3 converged after 91 iterations!
    #> Gradient boosting for outcome 2 converged after 94 iterations!
    #> Performing inference on colocalization events.

    # Identified CoS
    res$cos_details$cos$cos_index
    #> $`cos1:y1_y2_y3_y4`
    #> [1] 186 194 168 205
    #>
    #> $`cos2:y2_y3_y5`
    #> [1] 589 593

    # Plotting the results
    colocboost_plot(res)

### Results Interpretation

For comprehensive tutorials on result interpretation and advanced visualization techniques, please visit our tutorials portal at Visualization of ColocBoost Results and Interpret ColocBoost Output.

# 3. Other structures of individual level data

## 3.1. Single genotype matrix

When studying multiple traits with a common genotype matrix, such as gene expression in different tissues or cell types, we provide the interface for one single genotype matrix with multiple phenotypes. This is particularly useful when the same individuals are used for different traits, allowing for efficient analysis without redundancy.

- Input Format:
  - `X` is a single matrix containing genotype data for all individuals.
  - `Y` can be i) a matrix with \(N \times L\) dimension; ii) a list of phenotype vectors for \(L\) traits.

    # Extract a single SNP (as a vector)
    X_single <- X[[1]]  # First SNP for all individuals

    # Run colocboost
    res <- colocboost(X = X_single, Y = Y)
    #> Validating input data.
    #> Starting gradient boosting algorithm.
    #> Gradient boosting for outcome 4 converged after 40 iterations!
    #> Gradient boosting for outcome 5 converged after 59 iterations!
    #> Gradient boosting for outcome 1 converged after 61 iterations!
    #> Gradient boosting for outcome 3 converged after 91 iterations!
    #> Gradient boosting for outcome 2 converged after 94 iterations!
    #> Performing inference on colocalization events.

    # Identified CoS
    res$cos_details$cos$cos_index
    #> $`cos1:y1_y2_y3_y4`
    #> [1] 186 194 168 205
    #>
    #> $`cos2:y2_y3_y5`
    #> [1] 589 593

## 3.2. Genotype matrix is a superset of individuals across different phenotypes

When the genotype matrix includes a superset of individuals across different phenotypes, with Input Format:

- `X` is a matrix of genotype data for all individuals.
- `Y` is a list of phenotype vectors for different traits.
- Row names of both `X` and `Y` should be provided to match individuals - same format of individual id.
- It is better if `X` contain all individuals present in the phenotype vectors (optional).
- This is particularly useful when you have a large genotype matrix and want to use it for multiple phenotypes with different individuals. It allows for efficient analysis without redundancy.

    # Create phenotype with different samples - remove 50 samples trait 1 and trait 3.
    X_superset <- X[[1]]
    Y_remove <- Y
    Y_remove[[1]] <- Y[[1]][-sample(1:length(Y[[1]]),50), , drop=F]
    Y_remove[[3]] <- Y[[3]][-sample(1:length(Y[[3]]),50), , drop=F]

    # Run colocboost
    res <- colocboost(X = X_superset, Y = Y_remove)
    #> Validating input data.
    #> Starting gradient boosting algorithm.
    #> Gradient boosting for outcome 4 converged after 40 iterations!
    #> Gradient boosting for outcome 5 converged after 65 iterations!
    #> Gradient boosting for outcome 1 converged after 73 iterations!
    #> Gradient boosting for outcome 3 converged after 93 iterations!
    #> Gradient boosting for outcome 2 converged after 101 iterations!
    #> Performing inference on colocalization events.

    # Identified CoS
    res$cos_details$cos$cos_index
    #> $`cos1:y1_y2_y3_y4`
    #> [1] 186 194 168 205
    #>
    #> $`cos2:y2_y3_y5`
    #> [1] 589 593

## 3.3. Arbitrary input matrices with mapping dictionary provided

When studying multiple traits with arbitrary genotype matrices for different traits, we also provide the interface for arbitrary genotype matrices with multiple phenotypes. This particularly benefits meta-analysis across heterogeneous datasets where, for different subsets of traits, genotype data comes from different genotyping platforms or sequencing technologies.

- Input Format:
  - `X = list(X1, X3)` is a list of genotype matrices.
  - `Y = list(Y1, Y2, Y3, Y4, Y5)` is a list of phenotype vectors, where traits 1 and 2 matched to the 1st genotype matrix `X1`; traits 3,4,5 matched to 2nd genotype matrix `X3`.
  - `dict_YX` is a dictionary matrix that index of Y to index of X.

    # Create a simple dictionary for demonstration purposes
    X_arbitrary <- X[c(1,3)]
    dict_YX = cbind(c(1:5), c(1,1,2,2,2))

    # Display the dictionary
    dict_YX
    #>      [,1] [,2]
    #> [1,]    1    1
    #> [2,]    2    1
    #> [3,]    3    2
    #> [4,]    4    2
    #> [5,]    5    2

    # Run colocboost
    res <- colocboost(X = X_arbitrary, Y = Y, dict_YX = dict_YX)
    #> Validating input data.
    #> Starting gradient boosting algorithm.
    #> Gradient boosting for outcome 4 converged after 40 iterations!
    #> Gradient boosting for outcome 5 converged after 59 iterations!
    #> Gradient boosting for outcome 1 converged after 61 iterations!
    #> Gradient boosting for outcome 3 converged after 91 iterations!
    #> Gradient boosting for outcome 2 converged after 94 iterations!
    #> Performing inference on colocalization events.

    # Identified CoS
    res$cos_details$cos$cos_index
    #> $`cos1:y1_y2_y3_y4`
    #> [1] 186 194 168 205
    #>
    #> $`cos2:y2_y3_y5`
    #> [1] 589 593