Genetic testing is widely used to diagnose monogenic diseases that are caused by mutations in a single gene, such as cystic fibrosis or sickle cell disease. However, common diseases, such as type 2 diabetes and many neurodegenerative diseases, tend to be polygenic — influenced by a large number of genetic variants scattered throughout the genome.
New genomic technologies have allowed researchers to rapidly and inexpensively sequence large gene panels. While a genome-wide association study (GWAS) can identify such variation, a variants’ individual contribution to a disease may actually be negligible. Polygenic risk scores (PRS) determine the cumulative effect of millions of small genetic variations on disease risk.
At the Festival of Genomics and Biodata 2022, Sowmiya Moorthie (Senior Policy Analyst, PHG Foundation) discussed PHG’s work on evaluating the use of PRS in clinical settings. To investigate this, her team treated PRS as biomarkers, and applied existing frameworks to evaluate polygenic score analysis like any other medical test.
Their key questions included: How accurately can genetic markers predict risk? Does incorporation of PRS add value in a clinical setting? What are the organisational, ethical, and social implications?
Using a PHG report on cardiovascular disease in 2019 Sowmiya pointed out that while there are a multitude of polygenic models out there, they are all generated using different datasets and validated to different degrees. In addition, although there is a broad agreement that these models could improve the accuracy of risk stratification, more evidence is needed to produce clinical grade assays, and to more reliably understand the true value that these polygenic scores will add to patients.
Sowmiya cited a subsequent 2021 PHG report that aimed to consider both the implications of incorporating PGS (polygenic score) analysis into each step of CVD (cardiovascular disease) risk assessment within the NHS, and also the clinical utility of this approach.
It was concluded in these reports that PGS for CVD could be implemented within 3-5 years with only modest changes to the clinician-patient interaction.
As for clinical utility, there were broad and differing views on the utility of PGS analysis. A key point made by Sowmiya is that the added value of information from PRS is likely to vary in different use cases.
Use cases may include: informing disease screening programmes, informing choice of intervention, aiding in disease diagnosis, and for personal utility (like encouraging healthy lifestyle choice, or to inform on financial planning).
Another point that runs parallel to considering different use cases, is defining the difference between an assay and a test. A PRS may be a very good assay in isolation, but the broader context that this assay sits in, and the use case for the assay, can turn it from an assay into a test. The evaluation of an assay is different to the evaluation of a test, and different views from multiple decision makers (patients, healthcare organisers, clinicians) all place a differing emphasis on the clinical utility of tests.
Additionally, while PRS models have been validated in external datasets, these datasets are often from older cohorts of above 40 years of age and are primarily of European ancestry. This raises questions relating to their utility for diverse geographical and ethnic backgrounds.
Moving PRS into clinical settings isn’t going to happen overnight, and it doesn’t appear to be as easy as simply laying them on top of the processes that already exist. Instead, they will have to be woven into current clinical settings on a case-by-case basis to truly extract their full value.
Watch Sowmiya’s full presentation from the Festival of Genomics and Biodata 2022 by clicking here.
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