With the exponential growth of genomic data and analysis techniques we are seeing huge breakthroughs in use of polygenic risk scores to predict genetic risk of many common diseases, such as cancer, heart disease and diabetes. The study of genetic risk for common diseases is complex, as many genetic and environmental variants affect the disease risk. But following the success of genome-wide association studies (GWAS) in identifying the causal variants associated with the disease, polygenic risk scores (PRS) provide a way of aggregating all the variants carried by an individual into a single risk score.
Despite the surge of interest, polygenic risk scores are not a new concept. In fact, the first research exploring the predictive ability of genetic risk scores was published by B. Horne et al. in 2005. In 2013, N Chatterjee et al. projected that the “utility of future polygenic models will depend on achievable sample sizes, underlying genetic architecture and information on other risk-factors, including family history.” We now have the technical infrastructure to scale genotype studies to collect enough data and develop scoring algorithms to identify clinically meaningful increases in risk. In this blog, we explore the utility of PRS, drawing upon some of the latest research in personalised preventive care and treatments including therapeutic intervention, early disease screening and long-term healthcare planning.
A.Torkamani et al. defines PRS as “a weighted sum of the number of risk alleles carried by an individual, where the risk alleles and their weights are defined by the loci and their measured effects as detected by genome wide association studies.”
Genome wide association studies are required to collect the necessary data to calculate the polygenic risk scoring algorithms. Genome-wide association studies compare the genome of thousands of individuals who have a disease against the control group to identify all of the common genetic variants associated with the trait of interest. Following the GWAS, a threshold for significance is calculated to assess the association between a variant and the disease, and each variant is assigned a weight depending on their measured effect. This information is used to develop an algorithm that computes the sum of the weights of all the variants carried by an individual into a PRS. The resulting PRS provides insight into their relative risk compared with total study population.
Running GWAS on different ethnic populations is a priority for the research community. Research by the Broad Institute stresses the importance of diverse studies to develop “clinically meaningful risk predictors." Results show that the frequency of variants are different between populations, so applying a risk scoring algorithm based on data from people of European descent to a person in of Asian descent will not be accurate or clinically useful.
PRS enable the stratification of a population of patients into those at high risk, moderate risk, or low risk for different kinds of diseases. Researchers are using this stratification in three key ways: to improve therapeutic intervention, inform early detection strategies, and predict disease progression.
In patients infected with cancer, schizophrenia and coronary artery disease, PRS have been shown to improve therapeutic intervention . For example, Natarajan et al. found that “those at high genetic risk have a greater burden of subclinical atherosclerosis and derive greater relative and absolute benefit from statin therapy to prevent a first coronary heart disease event.” Individuals in the top quintile of the polygenic risk score are at a 30% increased risk for a coronary event, but achieve a 46% relative risk reduction upon initiation of preventative statin therapy. Individuals with an intermediate risk (in the second to fourth quintiles) achieve a 26% relative risk reduction with statin therapy.
Researchers have demonstrated the utility of PRS to make risk-based recommendations for disease screening, rather than relying on traditional age-based screening for breast and prostate cancer. For example, the US Preventive Services Task Force guidelines recommend biennial screening mammography for women aged 50 to 74 years, unless an individual risk assessment indicates screening should take place earlier. P.Maas et al. developed a risk-based model, based on PRS and other clinical risk factors, to provide stratification for a population of white women in the United States. The model identified 16% of the population with a genetic risk higher than an average 50-year-old. These individuals would benefit from more intensive risk-reduction strategies such as early screening plans to initiate at the age of 40 years. On the other hand, 32% of the population could delay screening, as their disease risk at 50 years was lower than that of an average 40-year-old.
As risk scores become integrated into the clinical practice, information on PRS may help high-risk individuals make more informed lifestyle decisions. For example, across four studies involving 55,685 participants, people with a high genetic risk could offset their relative risk of coronary artery disease by nearly 50% by following a healthy lifestyle. Another example is the use of PRS to predict the age of disease onset to help with long-term financial preparations and healthcare planning. R. Desikan et al. developed a PRS to classify the population into subgroups based on the age of Alzheimer's disease (AD) onset. The study showed that individuals in the top quartile had an average age of disease onset of 75 years, compared to 95 years for those in the lowest quartile. “From a disease management perspective, by providing an accurate probabilistic assessment regarding the likelihood of AD neurodegeneration, determining a genomic profile of AD may help initiate a dialogue on future planning. Finally, a similar genetic epidemiology framework may be useful for quantifying the risk associated with numerous other common diseases.”
This research is entering clinical trials for more well-researched diseases, such as breast cancer. For example, one study aims to demonstrate the actionability of risk scores for adjusting and stratifying screening recommendations. Studies like this are currently being undertaken, but by following the path of research into individualised medicine, in the near future, it is likely that polygenic risk scores will have clinical utility in personalised and preventive healthcare for individuals at risk of disease.