Polygenic risk scores (PRS) estimate an individual’s inherited likelihood of developing specific diseases or possessing certain traits. This calculation analyzes numerous genetic variations across a person’s DNA, providing a numerical summary of their estimated genetic predisposition. The score indicates how genetics might influence health outcomes, without accounting for environmental factors.
Defining a Polygenic Risk Score
A polygenic risk score is a single value that quantifies an individual’s common genetic liability to a particular trait or disease. The term “polygenic” signifies that many genes contribute to the trait or disease, each typically having a small effect. This contrasts with “monogenic” conditions, such as cystic fibrosis, which are primarily caused by a mutation in a single gene.
Think of polygenic traits like the collective success of a sports team, where many players each contribute small, individual strengths that add up to the team’s overall performance. No single player determines the outcome alone; rather, it’s the combined effort of the entire roster. Similarly, polygenic traits like height, blood pressure, or the risk for complex conditions like heart disease are influenced by hundreds or even thousands of genetic variants working together.
A polygenic risk score summarizes the estimated effect of these many genetic variants on an individual’s phenotype. It indicates a predisposition or statistical risk, not a definitive diagnosis. It helps to stratify individuals based on their predicted genetic risk.
The Calculation Process
Polygenic risk score calculation involves two main phases. The first is the “discovery” phase, which relies on large-scale Genome-Wide Association Studies (GWAS). In a GWAS, scientists scan the genomes of hundreds of thousands to millions of individuals to identify single-nucleotide polymorphisms (SNPs), common genetic variations associated with a particular disease or trait.
These studies compare the genetic profiles of individuals with a condition (cases) to those without it (controls) to find SNPs that occur more frequently in one group. For each SNP, the GWAS estimates an “effect size,” representing the strength of its association with the trait, along with a statistical significance value. This process identifies thousands of genetic markers, each contributing a small, quantifiable influence on the risk.
The second phase is the “application” phase, where an individual’s DNA is analyzed for these specific markers. The polygenic risk score is then calculated as a weighted sum of the risk-modifying alleles an individual possesses across many SNPs. Each SNP’s contribution is multiplied by its corresponding effect size. The total sum generates the individual’s final score.
Interpreting and Using Your Score
A polygenic risk score is a relative measure, comparing an individual’s genetic predisposition to a reference population. Scores are often presented as a percentile, indicating where an individual’s genetic risk falls compared to others in that group. For example, a score in the 90th percentile means an individual’s genetic risk is higher than 90% of the reference population.
In a clinical setting, a high PRS could inform personalized prevention strategies. For instance, individuals with a high PRS for certain cancers, like breast cancer, might be recommended for earlier or more frequent screenings than standard guidelines suggest. Similarly, a high score for heart disease could prompt more aggressive lifestyle changes, like dietary modifications and increased physical activity, or even inform medication choices to mitigate the inherited risk.
A PRS is only one piece of a comprehensive health assessment. Healthcare providers consider it alongside other factors, including family medical history, personal lifestyle choices, environmental exposures, and existing clinical markers. The score provides additional context, allowing for a more tailored approach to health management and disease prevention.
Accuracy and Population Differences
The accuracy of polygenic risk scores is influenced by several factors, including their probabilistic nature. A PRS provides a statistical estimate of genetic predisposition and cannot perfectly predict who will or will not develop a disease. It does not account for all genetic influences, nor does it incorporate environmental factors or lifestyle choices, which play a significant role in disease development. For example, a high genetic risk for type 2 diabetes can be mitigated by maintaining a healthy weight and engaging in regular physical activity.
A notable limitation concerns ancestry bias, as the majority of large-scale Genome-Wide Association Studies (GWAS) have historically been conducted on individuals of European descent. As of April 2022, approximately 79% of all GWAS participants were of European ancestry, meaning PRS models are most accurate for individuals from these populations. This disparity significantly reduces the predictive performance and generalizability of PRSs for individuals of African, Asian, Hispanic, or other non-European ancestries.
Differences in genetic ancestry can lead to varied polygenic risk score distributions across populations, making direct comparisons and risk assessments challenging. Researchers are actively working to improve the diversity of genetic datasets to enhance the accuracy and applicability of PRSs across all global populations. This ongoing effort aims to ensure that the benefits of polygenic risk scoring can be equitably realized for everyone.