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colocboost  

Multi-Context Colocalization Analysis for QTL and GWAS Studies
View on CRAN: Click here


Download and install colocboost package within the R console
Install from CRAN:
install.packages("colocboost")

Install from Github:
library("remotes")
install_github("cran/colocboost")

Install by package version:
library("remotes")
install_version("colocboost", "1.0.4")



Attach the package and use:
library("colocboost")
Maintained by
Xuewei Cao
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-05-02
Latest Update: 2025-05-02
Description:
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>.
How to cite:
Xuewei Cao (2025). colocboost: Multi-Context Colocalization Analysis for QTL and GWAS Studies. R package version 1.0.4, https://cran.r-project.org/web/packages/colocboost. Accessed 08 May. 2025.
Previous versions and publish date:
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Complete documentation for colocboost
Functions, R codes and Examples using the colocboost R package
Full colocboost package functions and examples
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