In recent years, the potential of using human genetic data to predict disease risk has gained attention, with the development of polygenic risk scores (PRS) for various disorders such as diabetes, heart disease, and kidney disease. While the human genome is largely shared among individuals, subtle variations in DNA sequences can have varying biological impacts. Some variants are inconsequential, while others significantly influence disease susceptibility.
Researchers have made strides in evaluating and validating tests to determine PRS for ten common diseases. A crucial aspect of their work was ensuring the applicability of these scores to patients with diverse ancestries, addressing a limitation where many scores were initially developed using genetic data from individuals of European descent. The study, reported in Nature Medicine, involved the analysis of genetic and health data from 25,000 individuals, aiming to enhance the clinical utility of genetic information and its potential to positively impact patient outcomes.
Niall Lennon, the chief scientific officer of Broad Clinical Labs and the study’s first author, emphasized the importance of developing PRS that are actionable in clinical settings. The team sought scores derived from data representing two or more ancestries and linked to preventive strategies, be it lifestyle modifications or specific treatments, for the diseases they predicted. This approach aimed to ensure that individuals receiving high-risk classifications could take tangible actions to mitigate their disease susceptibility.
The focus diseases included asthma, atrial fibrillation, breast cancer, chronic kidney disease, coronary heart disease, hypercholesterolemia, prostate cancer, type 1 diabetes, obesity, and type 2 diabetes. While acknowledging that biases in the data cannot be completely eradicated, the researchers aimed to identify high-risk individuals irrespective of their ancestry.
Moving forward, the researchers plan to engage with the study participants to understand how this genetic information might influence their healthcare decisions. The goal is to integrate such information into preventive medicine, empowering individuals to take proactive measures to lower their disease risks based on personalized genetic insights.