The coloc package can be used to perform genetic colocalisation analysis of two potentially related phenotypes, to ask whether they share common genetic causal variant(s) in a given region.

Most of the questions I get relate to misunderstanding the assumptions behind coloc (dense genotypes across a single genomic region) and/or the data structures used. Please read vignette("a02_data",package="coloc") before starting an issue. The FAQ page may also be helpful.

To install the latest stable CRAN version of coloc run:

install.packages("coloc")

To install the development version of coloc from GitHub run:

if(!require("remotes"))
   install.packages("remotes") # if necessary
remotes::install_github("chr1swallace/coloc@main",build_vignettes=TRUE)

This update (version 6) adds the ability to use variant-specific priors in coloc. See

• Pullin JM, Wallace C (2025) Variant-specific priors clarify colocalisation analysis. PLoS Genet 21(5): e1011697. https://doi.org/10.1371/journal.pgen.1011697

for a description of the implementation and a comparison of different sources of prior information. If you use variant-specific priors in coloc please cite this paper.

More detail is also available in the vignette a07_variant_specific_priors.html accessible by

vignette("a07-variant_specific_priors",package="coloc")

Version 5 superseded previously published version 4 by introducing use of the SuSiE approach to deal with multiple causal variants rather than conditioning or masking. See

• Wang, G., Sarkar, A., Carbonetto, P., & Stephens, M. (2020). A simple new approach to variable selection in regression, with application to genetic fine mapping. Journal of the Royal Statistical Society: Series B (Statistical Methodology). https://doi.org/10.1111/rssb.12388

for the full SuSiE paper and

• Wallace (2021). A more accurate method for colocalisation analysis allowing for multiple causal variants. PLoS Genetics. https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1009440

for a description of its use in coloc.

Note that in all simulations, susie outperforms the earlier conditioning approach, so is recommended. To use susie with coloc please look at coloc.susie. It can take raw datasets, but the time consuming part is running SuSiE. coloc runs SuSiE and saves a little extra information using the runsusie function before running an adapted colocalisation on the results. So please look at the docs for runsusie too. A helpful recipe is

1. Run runsusie on dataset 1, storing the results
2. Run runsusie on dataset 2, storing the results
3. Run coloc.susie on the two outputs from above

More detail is available in the vignette a06_SuSiE.html accessible by

vignette("a06_SuSiE",package="coloc")

Please see the FAQ page if you have issues or questions.

For usage, please see the vignette at https://chr1swallace.github.io/coloc

Key previous references are:

• Original propostion of proportional colocalisation Plagnol et al (2009)

• Proportional colocalisation with type 1 error rate control Wallace et al (2013)

• Colocalisation by enumerating all the possible causal SNP configurations between two traits, assuming at most one causal variant per trait Giambartolomei et al (2013)

• Thoughts about priors in coloc are described in Wallace C (2020)