Polygenic risk scores offer personalized disease risk assessment based on cumulative genetic variants.
Expanding genetic diversity is crucial for improving accuracy and inclusivity of polygenic risk scores.
Deep dives
Evolution of Understanding Genetic Contribution to Diseases
Our knowledge of the genetic contribution to diseases has evolved over time, ranging from diseases where genetics is the main factor to common chronic diseases influenced by multiple genetic variants. Genetic risk factors for diseases like diabetes and heart disease involve numerous small effects across the genome, resulting in personalized risk profiles. Understanding this complex interplay has paved the way for tools like polygenic risk scores to enhance preventative medicine and population health.
Significance of Polygenic Risk Scores in Healthcare
Polygenic risk scores, representing the cumulative effect of multiple genetic variants on disease risk, provide a revolutionary approach to assessing individual susceptibility to various conditions. These scores, derived from large-scale genome-wide association studies, offer insights into personalized risk profiles for diseases like breast cancer, enabling tailored interventions and screenings based on an individual's unique genetic makeup.
Integration of Genetics into Clinical Practice
Current genetic testing in clinical practice predominantly focuses on identifying rare mutations with significant impacts on disease risk, such as BRCA1 or BRCA2 mutations in breast cancer. Despite the known genetic contributions to common diseases, mainstream genetic testing remains limited to detecting major-effect variants. The transition towards utilizing polygenic risk scores offers a paradigm shift in healthcare towards predictive and preventative measures.
Expanding Diversity and Improving Polygenic Risk Scores
To enhance the effectiveness of polygenic risk scores, increasing genetic data diversity is essential to ensure accurate risk predictions across diverse populations. Although current scores are most predictive in populations of European ancestry, efforts are underway to expand genetic studies to encompass broader ethnic backgrounds. Advanced methodologies and ongoing data accumulation aim to refine and optimize polygenic risk scores for a more inclusive and precise risk assessment.
Genetic testing is on the cusp of a major revolution, which has the potential to shift not just how we understand our risk for disease, but how we practice healthcare. In the clinic today, genetic testing is used only in cases where we know that mutations have big impact on physiology (BRCA mutations in breast cancer, for example). But our knowledge of how our genetics influences our risk for disease has evolved, and we now know that many (tens of thousands to even millions) small changes in our genes, each of which individually has a tiny effect, combine to influence our risk profile. This new appreciation — coupled with powerful statistical methods and massive datasets — has fueled the creation of a new tool to quantify the risk of a broad range of common diseases: the polygenic risk score. On this episode, which originally aired on January 18, 2021, host Lauren Richardson (@lr_bio) is joined by Peter Donnelly, (@genemodeller Professor of Statistical Science at the University of Oxford and the CEO of Genomics PLC,) and Vineeta Agarwala, (@vintweeta physician-scientist and general partner at a16z), to discuss these scores and how they can reshape healthcare, away from a paradigm of treating illness and towards prevention and maintenance of health.
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