This R package implements ColocBoost---motivated and designed for colocalization analysis of multiple genetic association studies---as 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.
Install major and stable releases from CRAN (Linux, macOS and Windows)
install.packages("colocboost")Install the development version from GitHub
devtools::install_github("StatFunGen/colocboost")For a detailed installation guidance, please refer to Installation.
Learn how to perform colocalization analysis with step-by-step examples. For detailed tutorials and use cases in Tutorials.
If you use ColocBoost in your research, please cite:
Cao X, Sun H, Feng R, Mazumder R, Najar CFB, Li YI, de Jager PL, Bennett D, The Alzheimer's Disease Functional Genomics Consortium, Dey KK, Wang G. (2025+). Integrative multi-omics QTL colocalization maps regulatory architecture in aging human brain. medRxiv. https://doi.org/10.1101/2025.04.17.25326042
This package is released under the MIT License.