Niha Zubair, Arivale Clinical Research Scientist, PhD
Most common diseases have a genetic component to them. Because of this, some clinicians now consider an individual’s genetics when identifying whether they’re at possible risk for developing certain diseases.
Typically, these assessments have focused on finding rare single-gene mutations that predispose individuals to have a significantly increased risk for disease. However, risk for many diseases has a polygenic – or multi-gene – component involving many common genetic variants that each have a small effect.
To address this, researchers have now begun creating polygenic scores for diseases that sum up the effects of all the common genetic variants implicated for a disease observed within an individual. Each individual has their own score, which tells them how genetically predisposed they are toward getting a disease.
So, why do clinicians typically consider single gene mutations and not polygenic scores when assessing disease risk? Monogenic mutations, while rare, typically present a high level of increased risk of disease. It’s unclear whether a polygenic score can identify individuals at an increased risk comparable to levels conferred by monogenic mutations.
Researchers examined whether a polygenic score could identify subgroups of individuals with disease risk equivalent or exceeding that of monogenic mutations. They examined five common diseases: coronary artery disease (CAD), atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer.
For each disease, researchers created 31 candidate polygenic scores based on information from recent large genome-wide association studies (GWAS) in participants of primarily European ancestry. Each score was created using a differing approach and varied in the variants included.
These 31 candidate polygenic scores were validated and tested in the UK Biobank, a large, long-term biobank study that collects genotype data and extensive phenotypic information (e.g. weight, height, disease history, etc.) on over 400,000 participants of British ancestry.
In the validation step, a subset of these UK Biobank participants was used to identify the “best” polygenic score for each disease based on its ability to predict who would or wouldn’t get the disease.
Next, in the testing step, the remaining UK Biobank participants were used to assess the performance of this “best” polygenic score for each disease.
After validating and testing, researchers found that each chosen polygenic score identified a relatively large proportion of the population – ranging from 1.5 percent (breast cancer) to 8 percent (CAD) – with a greater than threefold increased risk for developing disease. This risk is comparable to that seen from monogenic mutations.
Specifically for the CAD polygenic score, 8 percent of the population was identified as having three times the risk of developing CAD; 8 percent is a much higher proportion of the population (20 times higher) than the proportion of those that carry rare single mutations conferring comparable risk.
In summary, polygenic scores for common diseases identify individuals with risk equivalent to single gene mutations. The researchers propose that it’s time to contemplate the inclusion of polygenic risk prediction in clinical care.
At Arivale, we’re completely on board with the idea of including polygenic scores in clinical care. In fact, we’ve been implementing this in our program for a few years now!
We’ve created polygenic scores for wellness-related traits including BMI, LDL cholesterol, and triglycerides, among others. Like common diseases, these traits are influenced by many genetic variants; with this knowledge, we knew it would be less accurate to provide individuals with single genetic variants pertaining to these traits. Using some of the latest scientific research, we’ve designed polygenic scores to assess an individual’s genetic predisposition for a trait.
However, as the researchers of the study note, there are some limitations in the interpretation and use of polygenic scores.
First, it’s important to note the risk associated with a polygenic score may not reflect a single underlying biological mechanism, but rather the combined influence of multiple pathways. Nevertheless, prevention and detection strategies may have utility regardless of the underlying mechanism – statin therapy for CAD or intensified mammography screening for breast cancer, for example.
Second, there’s much work to be done in terms of communication and clinical recommendations if polygenic scores are to be used in clinical practice. Specifically, it will be important to accurately communicate risks to a patient and also decide what lifestyle modifications or disease screenings are needed.
Lastly, polygenic scores rely heavily on the state of the current science. The polygenic risk scores used in this study were derived and tested in individuals of primarily European ancestry, the group in which most genetic studies have been undertaken to date. Because genetic architecture varies with ancestry, these polygenic scores are not optimized for other ethnic groups. As the researchers note, “it will be important for the biomedical community to ensure that all ethnic groups have access to genetic risk prediction of comparable quality, which will require undertaking or expanding GWAS in non-European ethnic groups.”
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[Arivale Hot Topics address health stories currently in the news. The Arivale Clinical Team’s commentary on these news articles is not a review of the scientific evidence, nor an endorsement of a specific study, and is not meant as official medical opinion.]