Guidelines on performing Mendelian randomization investigations written by an all-star line-up of MR researchers are now available on Wellcome Open Research: https://t.co/tdg3JHX58K - represents a consensus statement after 12+ months of deliberation. Comments welcome!

Thanks to @hwang_seongwon for leading the project, to @jeffreypullin for performing code review, and to @chr1swallace and John Whittaker for co-supervising - has been a fun project so far, and look forward to getting feedback from the community!

However, like all statistical methods, it has limitations, and results should not be thought of as unquestionable truth. It is likely that the differences between datasets in other applications are similar or stronger than those we considered here.

In conclusion, while all methods were well-calibrated in the baseline scenario, they struggled to declare colocalization to different degrees when the datasets varied in terms of platform and population. Colocalization can be a valuable tool for triaging and prioritizing.

This was not intended to be a fair comparison - fairness is impossible to achieve. For example, coloc-SuSiE was judged to support colocalization if there was high PP.H4 for any pair of credible sets. Rather, we wanted to compare methods as they would typically be used.

We acknowledge that there are many legitimate reasons why we may observe non-colocalization for the same protein when using estimates from different platforms / populations. Also, we acknowledge that different methods use different standards of evidence.

Enumeration methods tended to outperform proportional methods in most scenarios. However, no single approach dominated in all scenarios, with coloc-SuSiE reporting the highest rate of colocalization in Case 1, Case 2B, and Case 4; colocPropTest in Case 2F; and coloc in Case 3.

In these cases, results were more mixed. We observed frequent disagreement between methods as to whether there was colocalization, non-colocalization, or insufficient evidence. In the worst-case scenario, colocalization was only agreed by all four methods for 20% of proteins.

We then consider associations with the same protein, but measured on different platforms (Olink vs SomaLogic in British [Case 2B] and Finnish [Case 2F] populations), and measured in different populations (British vs Finnish for Olink [Case 3] and SomaLogic [Case 4]).

In the baseline context, we split the UK Biobank Pharma Proteomics Project in two at random, and tested associations for the same protein in one half of the data versus the other half of the data (Case 1). Unsurprisingly, all methods performed well in this context.

We perform colocalization for protein-coding gene regions with ≥1 pQTL across four datasets using four colocalization methods: coloc, coloc-SuSiE, prop.coloc, and colocPropTest in a range of contexts.

New pre-print: "Systematic comparison of colocalization methods using protein quantitative trait loci" led by @hwang_seongwon at https://t.co/zCHfakHxmO. Which method does best? Find out!

Big thanks to all co-authors for contributing to this: @amymariemason, @VerenaZuber, @explodecomputer, Elena, @IamYuXu, Amanda, @BarWoolf, @eliasallara, @dpsg108, and @OpeSoremekun. Feedback would be very welcome!

Critical is what we can assume is shared between populations, and what is different - are we clear what we are assuming can be borrowed? And is it reasonable to borrow that information?

When analysing non-European data, there is often a compromise between only including the most relevant data to the target population, and including all available data from any population - we describe some approaches to this taken in the literature.

The green dashed arrows indicate potential mechanisms that would lead to heterogeneity and hence differences in MR estimates between populations - examples of each are given in Table 1.

There are many reasons why an MR estimate (or any epidemiological estimate) may differ between populations. We would opine that a true biological difference between population groups is rarely the most likely explanation for a difference.

The target population will likely depend on the question. For environmental exposures, geographic definitions may be best. For social patterned exposures, cultural (ethnic) definitions. For genetically-influenced exposures, ancestral definitions.

For instance, if we say "South Asians are at elevated risk of COVID-19", do we mean individuals living in South Asia? Do we mean individuals with South Asian ancestral heritage? Or do we mean individuals following South Asian cultural practices? These overlap, but are distinct.

Step 1 is to think carefully what population our datasets represent, and what population we want our analysis to represent. There are many ways to define a population of interest.

A common question on our MR course is "How to perform Mendelian randomization with non-European data?". This manuscript is our answer to that question.