A large study of people whose traits or health outcomes do not match expectations based on their genetic risk score has found that the disparity can often be explained by rare genetic variants.
The study was carried out by researchers from the Big Data Institute at Oxford Population Health and the Department of Statistics, University of Oxford and is published in The American Journal of Human Genetics.
Scientists are increasingly using polygenic scores, which combine the effects of many common genetic variants, to predict traits like height and BMI (body mass index) as well as risk of diseases such as diabetes and heart disease. But while most people have traits and conditions that align with their polygenic score, some do not.
These so-called ‘misaligned’ individuals were the focus of the study. Among 400,000 individuals in the UK Biobank, the researchers identified a small subset whose physical characteristics were substantially different from those forecast by their polygenic score. They found that such individuals often carry rare, high-impact variants in genes linked with severe genetic disorders.
For example, someone who was taller or shorter than expected, often had rare variants in genes linked with growth disorders, while someone with higher or lower than expected LDL cholesterol often had variants associated with extreme lipid disorders. The researchers looked at rare variants associated with several other traits including bone density and age at menopause.
They found a similar pattern for diseases such as type 2 diabetes, osteoporosis, and heart disease. Some people carried rare variants that caused them to develop diabetes despite having genetic scores suggesting they were at low risk. Others carried rare variants that were protective for heart disease. This meant that they stayed healthy despite having a high genetic risk.
Dr Nikolas Baya, a researcher at the Big Data Institute and lead author of the study, said:
We wanted to show that when you remove the common variant signal, what’s left is a clearer picture of the rare variant effects. That’s important because you can use that misalignment to prioritise people when screening for rare disorders.
Baya noted that applying the same method to a larger and more diverse dataset would allow researchers to look at more traits and potentially uncover more variants associated with rare diseases. Participants in the UK Biobank tend to be healthier than the general population and mainly come from European ancestry.
The researchers found that not all of the differences in misaligned individuals were caused by genetics. Some were the result of other factors such as socioeconomic status, medication use, and lifestyle choices such as smoking and exercise.
The results nevertheless point to a potential new tool for improving the diagnosis of rare diseases and disorders. Further research into rare variants that have a protective effect on some diseases or conditions could also point to new treatments that mimic those effects.