Improving the Accuracy of Polygenic Risk Scores through Large-scale Multi-ethnic Studies

Timothy Goh^*
*Correspondence: Timothy Goh, Department of Urology, Columbia University, New York, USA, Email:

Introduction

The study of Polygenic Risk Scores (PRS) has become an essential area of research in genetic epidemiology, offering an innovative approach to understanding the genetic underpinnings of complex diseases. These scores are designed to estimate an individual's genetic predisposition to a given disease by aggregating the effects of numerous genetic variants distributed across the genome. Although the utility of PRS in predicting disease risk has been well-documented, their clinical application has often been limited by certain challenges. One such challenge is the lack of diversity in the populations used for the development of these scores. Historically, large-scale Genome-Wide Association Studies (GWAS) and PRS have been predominantly conducted on individuals of European descent, which has led to questions about the accuracy and generalizability of PRS in non-European populations. The underrepresentation of ethnic minorities in genomic research has resulted in polygenic risk scores that may not accurately capture the genetic variation present in diverse populations, limiting their effectiveness in predicting disease risk across different ethnic groups. As a result, improving the accuracy of polygenic risk scores through large-scale, multi-ethnic studies has become a critical priority in the field of genetic epidemiology [1].

Description

Polygenic risk scores are derived from GWAS, which identify genetic variants (typically single nucleotide polymorphisms or SNPs) associated with particular diseases or traits. In theory, these scores offer a comprehensive method of assessing genetic risk, as they aggregate the contributions of thousands or even millions of genetic variants. Each SNP in the score is weighted by its effect size, which is determined based on its association with the disease of interest in a large cohort. The more risk alleles an individual carries, the higher their PRS, indicating an increased genetic risk for developing the disease. For diseases such as cardiovascular conditions, diabetes, and various cancers, PRS have been used to predict individual risk and inform preventive measures, including lifestyle changes or targeted medical interventions. However, the effectiveness of these scores depends significantly on the population in which they are derived. When the reference population for a PRS is predominantly of European ancestry, the predictive power of the score tends to diminish in individuals of other ethnic backgrounds due to differences in the genetic architecture of the population [2]. The reasons for these disparities are multifaceted. One of the main issues lies in the genetic differences across populations. While human populations share a common genetic heritage, variations in allele frequencies, linkage disequilibrium, and gene-environment interactions contribute to differences in disease risk and genetic susceptibility. For example, genetic variants that are strongly associated with a disease in individuals of European descent may have different frequencies or effects in individuals of African, Asian, or Latin American descent. This means that a polygenic risk score developed in one population may not accurately reflect the genetic contributions to disease in another population, leading to discrepancies in risk prediction. Moreover, genetic data from underrepresented populations are often limited in size, resulting in lower statistical power to detect important genetic variants and construct accurate PRS for these groups [3].

To address these challenges, the focus of research has shifted toward increasing the diversity of genomic datasets used to develop polygenic risk scores. Large-scale, multi-ethnic studies are crucial for improving the accuracy of PRS, as they allow for the identification of population-specific genetic variants that may not be captured in studies focused on a single ethnic group. By including individuals from diverse ethnic backgrounds, researchers can better understand the genetic architecture of diseases across populations and develop more accurate and generalizable polygenic risk scores. Furthermore, multi-ethnic studies provide the opportunity to uncover shared genetic risk factors that may be common across populations, as well as unique genetic variations that contribute to disease susceptibility in particular ethnic groups [4].

In recent years, the inclusion of diverse populations in genomic research has increased, thanks in part to international collaborations and the growing recognition of the importance of diversity in biomedical research. Large-scale initiatives such as the All of Us Research Program in the United States, the UK Biobank, and the Global Biobank Meta-analysis Initiative have made significant strides in collecting genetic data from a wide range of ethnic groups. These studies have provided a more comprehensive understanding of the genetic factors contributing to common diseases and have helped improve the predictive power of polygenic risk scores. For instance, research has shown that multi-ethnic PRS can be more accurate than those derived from a single ethnic group, especially when genetic variants are shared across populations. However, the development of these scores is still in its early stages, and several challenges remain in improving their accuracy and applicability in diverse populations [5].

Conclusion

In conclusion, improving the accuracy of polygenic risk scores through large-scale, multi-ethnic studies is a critical step in advancing precision medicine. By increasing the diversity of genomic datasets, researchers can develop polygenic risk scores that are more accurate, generalizable, and applicable to a broader range of populations. While challenges remain in terms of sample size, computational methods, and the integration of environmental factors, the progress made in recent years demonstrates the potential for multi-ethnic PRS to revolutionize disease prediction and prevention. As genomic research continues to evolve and more diverse populations are included, polygenic risk scores will become increasingly valuable tools for identifying individuals at risk for complex diseases, enabling more personalized and effective healthcare strategies. Ultimately, the goal is to ensure that genetic risk assessments benefit all populations, helping to reduce health disparities and improve global health outcomes.

Acknowledgment

None.

Conflict of Interest

None.

References

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