Historical approach of risk assessments
What is the best measure of predicting risk of major disease in an individual based on genetics? Previously, the go-to method for Direct-to-Consumer (D2C) genomics businesses has been to simply match the results of genome wide association studies (GWAS) with individual single nucleotide polymorphisms (SNPs) and then giving the costumer simplified insights into the specific discoveries that has been made on that SNP. The problem with this approach is that you can only derive meaningful information from the SNPs that have an uncommonly large impact on the development of the certain disease. In reality, however, the contribution of individual SNPs to the development of disease is often negligible. How do we then draw any meaningful conclusion from analysing these SNPs?
Accounting for multiple contributing SNPs
The way to deal with this issue is to consider all the small contributions from every single SNP that is involved in development of a certain disease and then calculating a total score. The score here is referred to as a polygenic risk score (PRS). By calculating the PRS, we can take much more information into account. Instead of focusing on individual SNPs with a big contribution, we can now make holistic assessments based on all the available scientific information about every contributory SNP.
How to calculate the polygenic risk score?
There are various ways to calculate the PRS. The most common method is to count the effect alleles and thereby deriving a risk score. The more risk alleles you have, the higher the score. The main drawback is, it does not account the effect size of each individual SNP. Moreover, we have to consider that each individual SNP can have a high or low contribution to a certain disease, both in positive (decreasing the risk for a certain disease) or negative (increasing the risk for a certain disease) manner.
At Pregenic, we try to incorporate as many contributing variables as possible, in order to give the most accurate prediction, while using state-of-the-art algorithms. We look at the effect size of each individual SNP, risk allele frequencies in the general population, count the effect alleles and many more aspects, with the view to give a relative risk score which is easy to interpret.
How to interpret the polygenic risk score
Like all other information based on your genetics, that is currently provided by D2C companies, the polygenic risk score is a relative term. It helps describe the risk of a person developing a certain disease, based on their genetics, compared to an average person. This kind of information should always be reflected upon when you receive it. If you e.g., have an increased risk of a certain disease, always pay attention to your family history regarding that disease. If two people in your family has developed the disease earlier, you have a good reason to pay extra attention to this result.
Where will this journey go?
Our vision is clear, we want to help people live longer and a healthier life. Everyone should have the right to understand their own genetics. Our ambitions are high and we want to provide even more accurate results so that you can take life changing actions. Therefore, we are constantly experimenting with different techniques and constantly trying to develop new methods.