Determining Disease Risks Based on Genetic Ancestry Can Counter Health Care Disparities, But Doesn’t Go Far Enough

When it comes to estimating risk of a disease that is either genetic or has a genetic component, ancestry of an individual plays an important role. That's because increased risk of a particular health condition may be associated with a gene variant (aka mutation) in one population, but not another. Someone from a group not represented in the data on which a clinical test is based could receive an incorrect risk assessment, or even prescribed a drug unlikely to work.

 

A team from the Johns Hopkins Bloomberg School of Public Health and the National Cancer Institute has developed a new algorithm for genetic risk-scoring for major diseases across diverse ancestral populations. Their findings are published in Nature Genetics.

 

Although the algorithm is a start, and takes a logical approach to address health care disparities, it doesn't go far enough. Considering large groups – like Latinos or Africans – doesn't parse humanity sufficiently to hold much predictive power for genetic diseases, or conditions with large genetic components.

 

Tools to Track Disease: Biobanks to AI

 

To continue reading, go to DNA Science, where this post first appeared.