Why That Number Is Probably Misleading
In this article, Wayne Delport examines how polygenic risk scores (PRS) can sometimes generate misleading interpretations of disease risk — particularly for Type 2 Diabetes. Using a genetic report that suggested an “89% lifetime risk” result as a real-world example, the article explains the difference between relative risk and absolute risk, why multiplicative genetic models can exaggerate outcomes, and how population statistics are often misunderstood in consumer genomics reports.
The article explores the scientific limitations of current genomic risk prediction models, including ancestry bias, statistical overfitting, and the challenges of translating complex genetic associations into clinically meaningful insights. It also highlights the growing importance of responsible genomic interpretation in precision medicine, preventive healthcare, and AI-driven health analytics.
Rather than treating genetics as destiny, the article emphasizes that lifestyle, environment, metabolic health, and clinical context remain critical factors in determining actual disease outcomes. The piece provides an accessible overview of genetic risk modeling while encouraging more transparent, evidence-based communication in consumer genomics and digital health platforms.
Wayne Delport is a bioinformatics and genomics technology leader with more than two decades of experience building scalable computational biology platforms and advancing precision medicine. As Co-Founder and CTO of Simplify Genomics, he leads the development of AI-driven genomic interpretation and search technologies designed to accelerate clinical decision-making and make whole-genome data actionable in healthcare. Wayne has authored widely cited research in evolutionary biology, genomics, and computational methods, with thousands of academic citations across his work. His expertise spans cloud-based bioinformatics systems, genomic data infrastructure, and translating complex biological data into practical clinical applications.