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. There are a few key references which this vignette will not duplicate (see below).
In brief, two approaches can be implemented. The proportional testing approach 1 2 has now been moved to its own package, coloc.prop.
This package implements the more population enumeration approach.
You can read about how to prepare your data in this vignette or read the vignettes listed in one of the sections below to understand how coloc works.
Claudia Giambartolomei and Vincent Plagnol proposed the enumeration
method, which makes use of Jon Wakefield’s work on determining
approximate Bayes Factors from p values
3 to generate
a colocalisation analysis 4, implemented in the function
coloc.abf(). By assuming there is at most one causal
variant per trait, every possible configuration can be individually
enumerated and evaluated, and aggregating over these allows us to gauge
the relative support for models which support colocalisation to those
You can see more about the enumeration approach on this blogpost.
See vignette: enumeration
As a Bayesian method, coloc.abf() requires the user to specify prior probabilities of SSNP causality and colocalisation. Post-hoc sensitivity analysis can be used to assess whether results are robust across a range of plausible priors.
See vignette: sensitivity
The single variant assumption can be relaxed through conditioning. We have implemented a conditioning step within coloc, which we hope will increase use of conditioning, and proposed an alternative, masking.
See vignette: conditioning/masking