David Evans

Sometimes Correlation DOES Equal Causation: Developing Statistical Methods to Determine Causality Using Genetic Data

There is a well-known mantra that correlation does not necessarily equal causation. This is why randomized controlled trials in which participants are physically randomized into treatment and placebo groups are the gold standard for assessing causality in epidemiological investigations. However, what is less appreciated is that strong evidence for causality can sometimes be obtained using observational data only. In particular, genotypes are randomly transmitted from parents to their offspring independent of the environment and other confounding factors, meaning that genotypes associated with particular traits can be used like natural “randomized controlled trials” to examine whether these traits causally affect risk of disease.

The aim of this PhD project is to develop statistical methods to assess causality using observational data alone. The successful candidate will gain experience across a wide range of advanced statistical genetics methodologies including Mendelian randomization (a way of using genetic variants to investigate putatively causal relationships), structural equation modelling, genome-wide association analysis (GWAS), genetic restricted maximum likelihood (G-REML) analysis of genome-wide data which can be used to partition variation in phenotypes into genetic and environmental sources of variation, and instrumental variables analysis (using natural “experiments” to obtain information on causality from observational data). The candidate will apply the new statistical methods that they develop to large genetically informative datasets like the UK Biobank (500,000 individuals with genome-wide SNP data).

Using Genetics To Unravel The Developmental Origins of Health and Disease (DOHaD)

During the final winter of the Second World War, a German blockade of food supplies to the occupied Netherlands precipitated a severe famine known as the Dutch Hunger Winter of 1944–45. This tragic episode created an unprecedented natural experiment for studying how severe nutritional deprivation during pregnancy can influence the long term health of the offspring of these mothers across their life course.

Genetic approaches such as Mendelian randomization provide another powerful natural experiment, using inherited genetic variants as proxies for potentially causal exposures. This allows researchers to investigate whether factors experienced during development or later in life have a causal influence on health and disease, helping to disentangle causation from correlation.

The aim of this PhD project is to develop and apply statistical genetic methods to assess the impact of adverse maternal exposures during pregnancy on increased risk of disease in offspring.

Understanding the Causes and Consequences of Gestational Diabetes

Gestational diabetes mellitus (GDM) is a form of diabetes that develops during pregnancy, arising when the body cannot produce enough insulin to meet the increased demands of pregnancy and resulting in elevated blood glucose levels. Women who experience GDM have a substantially increased risk of developing type 2 diabetes later in life, with estimates suggesting a 7–10-fold higher risk compared with women without GDM.

This PhD project will use Mendelian randomization and large scale genetic studies to investigate the causes and consequences of gestational diabetes, identifying potentially modifiable risk factors and clarifying its longer-term impact on the health of mothers and their children. The project will harness genetic data to help disentangle causal relationships from correlation and uncover new insights into the developmental and lifelong pathways linking gestational diabetes with later disease.