Data Application

Data Application#

This section contains the data applications discussed in the ColocBoost paper. Below, we outline key components and protocols for analyzing data using the colocboost R package and related methodologies.

1. Vignettes for colocboost#

This subsection provides a vignette demonstrating how to use the colocboost::colocboost() function and other related functions to analyze data. The tutorials contains:

  • Input data format: Standard input data formats to perform colocboost.

  • Running colocboost: Example workflows for multi-trait colocalization analysis using individual level data and summary statistics.

  • Interpreting results: Guidance on interpreting and visualization the output of colocboost.

2. xQTL Protocols#

This subsection outlines protocols for xQTL analysis, including:

a. Bioinformatics pipeline for ColocBoost#

  • Tutorial of bioinformatics pipeline for ColocBoost tutorial link.

  • Steps to perform multi-trait colocalization using ColocBoost and data preparation protocol link.

b. Comparison with fine-mapping using SuSiE#

  • Protocol of fine-mapping analysis using SuSiE protocol link.

  • Example workflows for identifying causal variants.

c. Enrichment Analysis#

  • Steps to perform enrichment analysis using xQTL data protocol link.

  • Integration with external datasets for functional annotation.

d. SLDSC Analysis#

  • Protocol for stratified linkage disequilibrium score regression (SLDSC) protocol link.

  • Example use cases for partitioning heritability.

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