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.
- Version1.0.4
- R versionR (≥ 4.0.0)
- LicenseMIT
- Needs compilation?No
- colocboost citation info
- Last release05/02/2025
Documentation
- VignetteAmbiguous Colocalization from Trait-Specific Effects
- VignetteAnimation Example: Proximity Gradient Boosting Algorithm
- VignetteBioinformatics Pipeline for ColocBoost
- VignetteMixed Data-type and Disease Prioritized Colocalization
- VignetteSingle-trait Fine-mapping with FineBoost
- VignetteIndividual Level Data Colocalization
- VignetteInput Data Format
- VignetteInterpret ColocBoost Output
- VignetteLD mismatch and LD-free Colocalization
- VignettePairwise Colocalization with Flexible Input Formats
- VignetteHandling partial overlapping variants across traits in ColocBoost
- VignetteSummary Statistics Data Colocalization
- VignetteVisualization of ColocBoost Results
- VignetteNews
- VignetteInstallation
Team
Xuewei Cao
MaintainerShow author detailsHaochen Sun
Show author detailsRolesAuthor, Copyright holderRu Feng
Show author detailsRolesAuthor, Copyright holderGao Wang
Show author detailsRolesAuthor, Copyright holderDaniel Nachun
Show author detailsRolesAuthor, Copyright holderKushal Dey
Show author detailsRolesAuthor, Copyright holder
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Dependencies
- Imports2 packages
- Suggests6 packages