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

RT @Rbn_Hfmstr: 🚨 Our parent-of-origin study is out in @Nature ! 🧬.Maternal and paternal alleles can have distinct — even opposite — effect….

What do you want to know?.

RT @CAMBRIDGE_CEU: 🎉 Huge congratulations to @amymariemason – voted Most Popular Mathematician in I’m a Mathematician 2025! 🏆.She’s taken….

Thanks to @amymariemason and @BarWoolf for working on this together, and to @ChatGPTapp for helping to get the ball rolling with the writing, even if we overruled you in many places!.

. but the initial text needed a lot of work - it struggled to synthesize the ideas, and the structure was not great. Maybe a better prompt? Some of the ideas we seeded in the prompt ended up less important in the eventual submission.

But it did cut down the overall writing time - I would estimate by around 50%. This is a topic that has been in my head for several years, and I don't think I would have got round to writing it otherwise. It was much better at writing the abstract and cover letter. .

To be honest, I was a bit disappointed with the draft - in particular, the simulation study was incorrect and quite limited in scope (we hoped it would do well with this). We ended up re-writing large chunks of text, although some vestiges remain in the final submission.

A subtext to this work is that it is the first manuscript I've written where the first draft was generated by ChatGPT - we used the Deep Research function. The AI prompt is in the appendix, and we will share the full machine-written draft (pre-edits) with the community.

as for context stratification, the subgroups differ based on other factors by definition - as they come from different centres. In conclusion, the idea may work in some cases, but even when it does, it is somewhat limited in scope and interpretation.

Additionally, differences in centre-stratified estimates may occur for a variety of reasons, including non-linearity, but also other differences between centres. The same is true for other stratification methods, but potentially worse for context stratification. .

However, the separation between mean exposure levels in centres is far less than between subgroups defined by the residual-based or doubly-ranked method, allowing us to consider non-linearity over a much narrower range.

We can perform MR analyses in each centre, obtaining context-stratified MR estimates that can be analysed using a heterogeneity test or trend test (i.e. meta-regression).

An alternative is to stratify on existing structure in the data, such as recruitment centres. For instance, in UK Biobank, average vitamin D levels differ across centres - higher in the south-west, lower in Scotland.

A naive approach is to stratify on the exposure directly. But this induces bias, as the exposure is a collider of the IV and exposure-outcome confounders. Alternative approaches can work (residual-based and doubly-ranked methods), but rely on untestable assumptions.

Several existing approaches for non-linear Mendelian randomization stratify the population into subgroups and perform separate analyses in these subgroups. But constructing subgroups such that the IV assumptions hold in the subgroups is tricky.

New pre-print online: "Context-stratified Mendelian randomization: exploiting regional exposure variation to explore causal effect heterogeneity and non-linearity", We propose an alternative approach to assess non-linearity in Mendelian randomization.

Thanks to Nasir and Eduardo for leading this work, and to @bar_woolf for contributing - was great to think together about the value of these multiverse analyses and how to interpret!.

Research like this cannot prove reliability of findings, but it can indicate consistency and sensitivity of findings, and potentially highlight covariates whose adjustment has a big effect on estimates - meaning we need to be confident in our adjustment choice for that covariate.

Worse still is when a Janus effect is combined with p-hacking. A Janus effect means that it is often possible to find a plausible sounding set of covariates that leads to whatever estimate you want - whether positive, null, or negative.

If associations are consistent in direction, this gives confidence in periodontitis as a risk factor - but not absolute proof. Similarly, if inconsistent, this does not rule out periodontitis as a risk factor - but it means any causal claim is sensitive to our assumptions.

Moderate periodontitis is consistently negatively associated with cognitive function, but its association with cardiovascular disease depends on the selection of covariates (mostly positive estimates, but some negative - Janus effect). How to interpret this?.