Genetic risk scores are a powerful tool that can provide insights into a person’s likelihood of developing certain health conditions. These scores, which are based on variations in the genome called single-nucleotide polymorphisms (SNPs), are commonly used by researchers and DNA testing companies like 23andMe to assess potential health risks. However, a new study has shed light on the potential privacy risks associated with these scores.
Researchers at Columbia University in New York, Gamze GĂŒrsoy and Kirill Nikitin, have demonstrated that genetic risk scores can be reverse-engineered to reveal detailed information about an individual’s DNA. By analyzing a series of polygenic risk models using a small number of SNPs, GĂŒrsoy and Nikitin were able to reconstruct the genetic data of individuals with a high degree of accuracy.
The researchers found that even a limited number of SNPs could be used to identify an individual within a large dataset. In fact, they were able to predict 2450 SNPs per individual with 94.6% accuracy. This raises concerns about the potential for individuals to be re-identified using their genetic risk scores, especially those from underrepresented populations such as African and East Asian descent.
While the researchers acknowledge that the overall risk is low, they emphasize the importance of considering privacy implications when using genetic risk scores in research studies. It is essential to safeguard sensitive genetic information and ensure that individuals are informed about the potential risks associated with sharing their genetic data.
Ying Wang, a researcher at Massachusetts General Hospital, notes that existing data protections and computational limitations help mitigate the risk of genetic risk scores being exploited in this way. However, she cautions that small risk models should be treated as potentially sensitive data in clinical reporting and informed consent discussions.
Overall, the study highlights the need for careful consideration of privacy implications when using genetic risk scores in research and clinical settings. By understanding the potential risks associated with these scores, researchers and healthcare providers can take steps to protect individuals’ genetic information and ensure their privacy is safeguarded.
