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
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