Polygenic Risk Scores (PRS) in Genetics

Definition and Calculation of Polygenic Risk Scores

Polygenic risk scores (PRS) are a quantitative measure that summarizes an individual's genetic liability to a disorder or trait, based on genome-wide genotype data. PRS are calculated using summary statistics from genome-wide association studies (GWAS), by summing the number of risk alleles carried by an individual, weighted by the effect size from the discovery GWAS.

• Key Point 1: PRS aggregates the effects of thousands to millions of common genetic variants, each with modest effect sizes.

• Key Point 2: PRS is often standardized (mean = 0, standard deviation = 1) for easy interpretation and comparison across individuals.

• Formula: The PRS for an individual is calculated as: where is the genotype (number of risk alleles) at variant , and is the effect size from GWAS.

• Example: In breast cancer, PRS can stratify risk and inform screening recommendations based on genetic risk quantiles.

Clinical Applications of PRS

PRS is used in research to predict case-control status or continuous traits in independent studies. In clinical settings, PRS can identify individuals at high genetic risk for diseases, potentially guiding screening, lifestyle modifications, or therapeutic interventions.

• Key Point 1: PRS can be used to stratify populations for disease risk, such as identifying the top 5% at risk for breast cancer who may benefit from earlier screening.

• Key Point 2: The average PRS in cases is expected to be higher than in controls, but the difference is often small, limiting predictive power for most individuals.

• Example: Women in the top 5% of breast cancer PRS reach the risk threshold for screening earlier than the general population.

PRS in Neuropsychiatric Disorders

PRS constructed from large GWAS of neuropsychiatric disorders, such as schizophrenia and autism, are significantly associated with disease status. However, the proportion of variation explained is modest.

• Key Point 1: In schizophrenia, genome-wide significant loci explain 3.4% of liability, increasing to 7% with expanded SNP sets.

• Key Point 2: PRS can be combined with rare variants (e.g., copy number variants, CNVs) to improve risk prediction.

• Example: Schizophrenia PRS helps differentiate first episode psychosis patients who develop schizophrenia from those who do not, explaining 9% of variance.

• Additional info: PRS for multiple traits (multivariate PRS) can improve prediction for complex traits like BMI by incorporating genetic information from correlated traits.

Challenges in Clinical Translation of PRS

Despite its potential, several challenges must be addressed before PRS can be widely used in precision medicine.

• Key Point 1: PRS requires a shift from rare variant genetics (binary risk) to continuous genetic liability.

• Key Point 2: Predictive ability of PRS is limited in non-European ancestry populations due to differences in allele frequencies and linkage disequilibrium.

• Key Point 3: Education and resources for clinicians and the public are needed to improve genetic literacy and understanding of polygenic risk.

• Example: Initiatives are underway to increase genetic data collection from diverse populations to improve PRS applicability.

Conclusions and Future Prospects

PRS captures important information about genetic risk for disease, but its utility as a single measure is limited. Combining PRS with environmental risk factors or high-risk variants may enhance prediction. PRS may be most useful at the extremes of the distribution (top and bottom deciles) for risk stratification and therapeutic planning.

• Key Point 1: PRS is likely to play a role in risk prediction, prognosis, and therapeutic stratification in a data-driven healthcare system.

• Key Point 2: Planning for 'polygenic medicine' is necessary as technology and genetic data collection advance.

Abbreviations

• BMI: Body mass index

• CNV: Copy number variant

• GWAS: Genome-wide association study

• PRS: Polygenic risk score