# Interpreting associated studies: colocalisation analysis

**URL:** <https://community.opentargets.org/t/interpreting-associated-studies-colocalisation-analysis/1146>\
**Category:** General\
**Tags:** genetics-portal\
**Created:** [12 July 2023 10:35 UTC](https://community.opentargets.org/t/interpreting-associated-studies-colocalisation-analysis/1146 "2023-07-12T10:35:43Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![kamalika\_ray](https://dub1.discourse-cdn.com/flex017/user_avatar/community.opentargets.org/kamalika_ray/32/310_2.png) [@kamalika\_ray](https://community.opentargets.org/u/kamalika_ray)\
**Post date:** [12 July 2023 10:35 UTC](https://community.opentargets.org/t/interpreting-associated-studies-colocalisation-analysis/1146/1 "2023-07-12T10:35:43Z")

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Hi,

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:

 ![image](https://europe1.discourse-cdn.com/flex017/uploads/opentargets/original/1X/8175d0d94c3b2df6f1ba98683ad76177fe84e554.png)

Could you please tell me how do I interpret H4 score here, which is the trait 1, trait 2 and the shared SNP?

Thanks a lot!

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**Author:** ![irene](https://dub1.discourse-cdn.com/flex017/user_avatar/community.opentargets.org/irene/32/50_2.png) [@irene](https://community.opentargets.org/u/irene)\
**Post date:** [12 July 2023 13:00 UTC](https://community.opentargets.org/t/interpreting-associated-studies-colocalisation-analysis/1146/2 "2023-07-12T13:00:34Z")

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Hi @kamalika_ray,

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](https://genetics.opentargets.org/Gene/ENSG00000128573/associations), you’ll find other traits that are driven by a common genetic base.

Best,  
Irene