Active Genetics & Molecular Biology Brain & Nervous System

Mendelian Randomisation

In plain English

AI plain-English summary

Mendelian randomization uses genetic data as a natural experiment to determine whether one thing actually causes another—for example, whether low vitamin D causes depression, or whether inflammation drives heart disease. The problem is that standard observational studies often confuse correlation with causation. Two things may appear linked—people who exercise more live longer—but the real cause could be something else, like income or diet. Mendelian randomization sidesteps this by using genetic variants as stand-ins for the exposure, because genes are assigned randomly at conception and aren’t altered by lifestyle or environment. However, as genetic studies balloon to millions of samples and new data types emerge, the statistical methods haven’t kept pace. Flawed methods can produce misleading causal claims. This project will develop better statistical tools to handle these massive datasets, including family-based data and genetic influences that change with age. It will also create new approaches to model the molecular causes of disease, feeding directly into pharmaceutical collaborations for drug development. If successful, the work could identify time-critical windows for disease prevention and reveal factors that influence prognosis and survival—not just disease onset—opening routes to novel therapies.

View original technical description
Mendelian randomization (MR) is a statistical technique that uses genetically characterised studies to understand cause-and-effect relationships among complex traits and diseases. Its wide use reflects the growing availability of genetic data and advancements in analytical tools and methods. Exciting developments in genetic data in terms of study designs, sample sizes, tissues genomically analysed and breadth of phenotyping are expanding the scope of causal inference questions, but these new data and questions must be served by statistical methods and tools that enable reliable causal inference. We will examine the complexities arising from rapid growth of genetic studies into millions of samples for MR, and determine how to use such resources, including family-based data, to most effectively improve causal inference. We will expand genetic studies by leading a new large international collaboration to systematically identify genetic influences that change across the lifecourse, and use such factors to identify time-critical disease development processes. We will develop new approaches to better model the molecular causes of disease, which will feed into our pharma collaborations for drug development. We will continue developing methods and data resources that expand epidemiological analysis of disease from prioritising causal factors of disease onset, to identify factors that influence prognosis and survival, creating the potential to identify novel therapies.

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Researchers

George Davey Smith (Principal Investigator)Gibran Hemani (Principal Investigator)

Related Research

Grants with similar aims, by meaning.

Statistical approaches for causal analysis in genetics data
New horizons in Mendelian randomization
Mendelian randomization to hypothesis-free causal inference
Improving Mendelian randomisation based on meta-GWAS summary statistics.
Developing cross-population Mendelian randomization for generalizing evidence on drug targets: the MRC Cross-Population Mendelian Randomization Network

Original classification

Intramural

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