iiinova — where your digital self keeps evolving

Stephen Burgess @stevesphd.bsky.social · 14/09/2026

Video replay officials in football (soccer) have to perform counterfactual reasoning when giving an offside decision. If the offside player [Fernandez] had not attempted to play the ball, would the defending player [Martinez] have made a different decision? from www.bbc.co.uk/sport/footba...

010

Stephen Burgess @stevesphd.bsky.social · 13/07/2026

We tested whether these variants associated with different head and neck cancers. We saw positive associations for all cancer types, with significant estimates for oral cavity squamous cell carcinoma (OCSCC) and HPV-positive oropharynx squamous cell carcinoma (OPSCC).

100

Stephen Burgess @stevesphd.bsky.social · 30/06/2026

...and 4) if none of these approaches are possible, perform a naive analysis and estimate the likely magnitude of bias that could occur under realistically severe index event bias.

100

Stephen Burgess @stevesphd.bsky.social · 28/06/2026

Thanks to @andy_grant1 for leading this work, and to Ash Patel for supporting. Any feedback is welcome. Hope this method is useful to everypony!

100

Stephen Burgess @stevesphd.bsky.social · 13/04/2026

Cis-MR studies are not intrinsically superior to genome-wide MR studies, and algorithmically-performed cis-MR analyses will rarely be optimal. But when performed with care, cis-MR is a powerful tool to inform about putative causal effects.

123

Stephen Burgess @stevesphd.bsky.social · 13/04/2026

In particular, molecular exposure biomarkers measured in bulk tissues (usually blood) may reflect mechanisms in irrelevant tissues, whereas downstream biomarkers may be more specific.

110

Stephen Burgess @stevesphd.bsky.social · 13/04/2026

The exposure biomarker reflects the causal mechanism of interest. It is not necessarily the causal risk factor itself, and often choosing a downstream trait is better - if water has reached a proximal downstream station, then it must have passed though the causal mechanism.

100

Stephen Burgess @stevesphd.bsky.social · 13/04/2026

Important steps are: 1) defining the analysis question, 2) choosing an appropriate gene region, 3) choosing an appropriate biomarker of the causal mechanism, 4) choosing the optimal variant(s) in the selected gene region(s), 5) validating the variants.

100

Stephen Burgess @stevesphd.bsky.social · 16/02/2026

We threw MR with alcohol as an exposure at a large number of exposures. Most came out supporting harmful effects, particularly for neurologic and behavioural, circulatory, and liver outcomes. Potential protective effects were for migraines and urinary calculus.

100

Stephen Burgess @stevesphd.bsky.social · 16/02/2026

Using MSE, with lots of confounding (\rho>0.3), IV outperforms OLS at F lower than 10. With minimal confounding (\rho~0.1), the F threshold is higher. Using an F statistic to determine your analysis strategy is a bad idea in any case, but that's a story for another day.

100

Stephen Burgess @stevesphd.bsky.social · 26/01/2026

This makes a substantial difference to estimates for LDL-cholesterol, and a detectable but much smaller difference to estimates for BMI and vitamin D. The obvious limitation is this only holds for GxE interactions we can measure and account for.

100

Stephen Burgess @stevesphd.bsky.social · 26/01/2026

For instance, genetic associations with 25(OH)D levels (a biomarker of vitamin D status) are larger in the summer and smaller in the winter, and genetic associations with several traits differ between men and women, and with socioeconomic markers.

100

Stephen Burgess @stevesphd.bsky.social · 21/01/2026

We present various methods that can be used in this setting depending on the format of data available (individual-level or summarized), who you have data on (both parents or one parent), and the assumptions (is assortative mating likely?).

100

Stephen Burgess @stevesphd.bsky.social · 21/01/2026

The idea of this work is not only to exploit randomness in whether you inherit a genetic variant, but also in whether you do not inherit a genetic variant from a parent. This enables not only the estimation of the effect of an exposure, but the direct effect of an exposure.

110

Stephen Burgess @stevesphd.bsky.social · 16/12/2025

For other cancers, we saw no consistent evidence for a harmful effect, including breast cancer, which had a null estimate even for 50k+ biobank cases and 130k+ consortium cases. However, we did see evidence for an effect of alcohol consumption on cancer mortality.

100

Stephen Burgess @stevesphd.bsky.social · 16/12/2025

For several cancers with strong prior evidence, we observed evidence supporting a harmful effect of alcohol consumption: combined head/neck, colorectal, and oesophageal. For liver cancer, we only saw evidence in the Million Veteran Program (high alcohol consumption cohort).

100

Stephen Burgess @stevesphd.bsky.social · 08/11/2025

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.

110

Stephen Burgess @stevesphd.bsky.social · 27/10/2025

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.

DAG with dashed arrows sources of potential heterogeneity

110

Stephen Burgess @stevesphd.bsky.social · 23/09/2025

4) PAH prelevance in the All of US dataset based on electronic health records is far higher than expected, and many identified "cases" do not have corresponding medication prescription consistent with PAH.

110

Stephen Burgess @stevesphd.bsky.social · 22/07/2025

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.

100

Stephen Burgess @stevesphd.bsky.social · 22/07/2025

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).

100

Stephen Burgess @stevesphd.bsky.social · 22/07/2025

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.

100

Stephen Burgess @stevesphd.bsky.social · 18/07/2025

Plots below represent the association of moderate periodontitis with cardiovascular disease (left) and cognitive function (right) for 393,216 different covariate adjustment options. The presence of positive and negative estimates is sometimes known as a Janus effect.

100

Stephen Burgess @stevesphd.bsky.social · 12/05/2025

Mendelian randomization is becoming a widely-used tool in all areas of epidemiology (including perio). However, its ease of use does not mean its use is always appropriate. We divide investigations into 3 categories: plausible, questionable, and implausible.

100

Stephen Burgess @stevesphd.bsky.social · 12/02/2025

This suggests the possibility of developing more targeted statins - achieving the CAD lowering effect but not the T2D increasing effect. We tried to use tissue-specific gene expression to resolve the signal to a body compartment, but we were not able.

100

Stephen Burgess @stevesphd.bsky.social · 12/02/2025

We show that the signals for LDL-cholesterol and body mass index (BMI) also do not colocalize, and in an MVMR model using variants in the HMGCR gene region only, LDL-cholesterol and BMI are causal risk factors for CAD, and only BMI is a causal risk factor for T2D.

100

Stephen Burgess @stevesphd.bsky.social · 08/01/2025

We show that immortal time bias can arise from confounding by survival until exposure allocation or selection bias from selecting on survival until eligibility.

121