Really excited to share our metabolite ratio QTL work Friday!

- 25% more findings from already existing QTL studies

- We proxy enzymatic activity that is otherwise unmeasured

- We implicate ABCG2 as a PFOS 'forever chemical' transporter

Come by the poster and say hi!

Sure! It’s mostly my own incompetence holding me back! But will do when I encounter a bug or really don’t know what to do!

Congratulations! I’ve been using the code already for some projects! Happy to see it being published!

This work wouldn't have been possible without @zkutalik.bsky.social nor Chiara Auwerx, Carolina Borges, Robert Warmerdam, Lude Franke, Urmo Võsa and the eQTLGen consortium!

Thanks for reading all the way, and if you have ANY questions, happy to interact!

So that's MR-link-2 in a nutshell!

There's a lot more little gems in the paper, so do have a read! www.nature.com/articles/s41...

Interested in running MR-link-2? take a look at the GitHub!
github.com/adriaan-vd-g...

All feedback is welcome! Happy to guide even total beginners!

And as a final ground truth we use the markergenes that are used to identify cell types in single cell experiments to the cell type proportions themselves.

Here, the cell type should influence gene expression, and not the other way around! MR-link-2 has very good discriminative ability.

We think MR-link-2 truly estimates pleiotropy!

When we correlate the MR-link-2 estimate of pleiotropy with the deviation (individual Q statistic contribution of each region) from meta-analysis, we find highly significant correlation

When we look at biomarker -- disease causality, we find that MR-link-2 identifies all of the true positives we tested.
All while having a median per locus false positive rate of 0.09 in the true negatives (compared to 0.15 of other methods): MR-link-2 has lower false positive rates

Our ground truth is set up to be quite stringent, but when we look at other significant results outside of the ground truth, MR-link-2 is the only method that finds the causal relationship between pyruvate and citrate, a key citric acid cycle link. As well as other very compelling examples.

And MR-link-2 is very competitive in discriminating between direct reactions and reactions that are super far away (Negative distance).

Here, we find that MR-link-2 is better at identifying the correct causal effect estimate between substrates and products (should be positive) than other MR-methods

Metabolic pathways

We used 4 metabolite QTL studies, and 3 metabolic pathway references to build a network of reactions that an MR method is supposed to detect.

Now, you may be wondering, sounds like something cool, but how ever would you prove that it works?

We spent a lot of time on to build ground truth in human causality. We compiled ground truth causality from metabolic pathways, biomarker-disease pairs and cell types and marker genes.

The trick is that we use all the SNPs in an associated region, compared to other methods that usually only use one SNP.

We developed MR-link-2, an MR method robust to pleiotropy. The unique bit is that it only requires a single associated region for it to work. Interested?

Have a look at the code and examples on the github!
github.com/adriaan-vd-g...