Body weight is highly heritable so we can use genetic approaches to understand why some people gain more weight than others and how some people manage to stay thin. Our genetic studies are focussed on two cohorts recruited over several years with the help of many collaborators in the UK and worldwide.
GOOS (Genetics Of Obesity Study): includes over 7500 people with severe obesity from a young age.
STILTS (STudy Into Lean and Thin Subjects) cohort includes over 3000 thin people (body mass index< 18 kg/m2)(www.stilts.org.uk).
Early work that identified the first genetic obesity syndromes relied on sequencing of candidate genes. We now use a combination of exome arrays, whole exome sequencing and targeted resequencing to find genetic variants in obese and thin people. We compare the data from people in these cohorts to people with a normal weight who have been recruited into studies such as UK Biobank
To quantify the contribution of many different genetic variants to a person’s weight, researchers add up their effects to derive a risk score. BMI risk scores derived from hundreds of common variants are higher in obese people than in normal weight people and very low in extremely thin people (Human Gain-of-Function MC4R Variants Show Signaling Bias and Protect against Obesity).
Recently, colleagues in Boston, USA tapped into genetic data from 0.5 million people involved in UK Biobank. By adding up the contribution of 2 million variants they derived a risk score that for the first time predicts obesity from age 12 years onwards (Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood). Their work alongside ours, shows that the dice are loaded against people who develop obesity and in favour of others who carry variants that protect them from developing obesity and allow a subset of people to stay very thin.
Sometimes we find variants in genes that we already know can cause obesity when disrupted; for others, we have to undertake additional studies in the lab and in the Translational Research Facility (TRF) to test whether the variants are indeed contributing to a person’s weight problem. Sometimes this can be hard to prove.
As researchers learn more about genetics, we realise just how complicated it can be to prove a link between a gene and a condition. But new computational tools and technologies are emerging all the time opening up new possibilities for gene discovery.