In the documentation, the description for colocalisation analysis is given as:
Colocalisation analysis is used to test whether two independent association signals at a locus are consistent with having a shared casual variant. If two traits share a causal variant (they are colocalised), this increases the evidence that they also share a causal mechanism.
H4: Association with trait 1 and trait 2, one shared SNP
I am trying to understand this table with respect to the definition given in the document:
in a colocalisation analysis, H4 measures the posterior probability that two traits are associated and that both share a causal variant.
In your example, if we look at the first record, we are reporting the colocalisation result between:
a GWAS study where 1_55029009_C_T was found to be associated with LDL levels,
and another study where 1_55029009_C_T is associated with a quantitative change in the abundance of PCSK9.
So the interpretation here is that there’s a high probability that the same genetic variant impacts both LDL cholesterol levels and the abundance of PCSK9.
This PCSK/LDL association is commonly known, but the widget is mostly useful to inform about correlated traits that might not seem so obvious. For example, FOXP2 is a gene known to be involved in speech disorders, and if you look at the colocalised studies, you’ll find other traits that are driven by a common genetic base.
