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colocboost: Multi-Context Colocalization Analysis for QTL and GWAS Studies
A multi-task learning approach to variable selection regression with highly correlated predictors and sparse effects, based on frequentist statistical inference. It provides statistical evidence to identify which subsets of predictors have non-zero effects on which subsets of response variables, motivated and designed for colocalization analysis across genome-wide association studies (GWAS) and quantitative trait loci (QTL) studies. The ColocBoost model is described in Cao et. al. (2025) <doi:10.1101/2025.04.17.25326042>.
Getting started
Ambiguous Colocalization from Trait-Specific Effects
Animation Example: Proximity Gradient Boosting Algorithm
Bioinformatics Pipeline for ColocBoost
Handling partial overlapping variants across traits in ColocBoost
Individual Level Data Colocalization
Input Data Format
Installation
Interpret ColocBoost Output
LD mismatch and LD-free Colocalization
Mixed Data-type and Disease Prioritized Colocalization
News
Pairwise Colocalization with Flexible Input Formats
Single-trait Fine-mapping with FineBoost
Summary Statistics Data Colocalization
Visualization of ColocBoost Results
Browse package contents
Package details |
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| Author | Xuewei Cao [cre, aut, cph], Haochen Sun [aut, cph], Ru Feng [aut, cph], Daniel Nachun [aut, cph], Kushal Dey [aut, cph], Gao Wang [aut, cph] |
| Maintainer | Xuewei Cao <xc2270@cumc.columbia.edu> |
| License | MIT + file LICENSE |
| Version | 1.0.4 |
| URL | https://github.com/StatFunGen/colocboost |
| Package repository | View on CRAN |
| Installation |
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