Polygenic Scores Help Reduce Racial Disparities in Predictive Accuracy of Automated Type 1 Diabetes Classification Algorithms
In a study, led by CGM PI, Miriam Udler, and her colleagues they implemented an automated clinical algorithm and a type 1 diabetes polygenic score to identify individuals with type 1 diabetes in two large biobanks: MGB Biobank and BioMe (Mt. Sinai). They assessed the accuracy of the clinical algorithm compared to a gold standard of clinician diagnosis on chart review. The authors found that the clinical algorithm more accurately predicted type 1 diabetes status for self-reported White individuals, compared to other race/ethnicity groups. However, after updating the clinical algorithm to incorporate type 1 diabetes polygenic scores, the accuracy improved for all individuals, and the racial/ethnic disparity was reduced. These results demonstrate the potential for polygenic scores to aid in clinical phenotyping and to help reduce health disparities.
Read more in Diabetes Care and a podcast here.
The Center for Genomic Medicine (CGM) comprises one of the largest and most vibrant hubs of genomic medicine research in the world. The CGM includes 46 faculty collaborating to define the ‘genomic medicine cycle’ – which envisages a genomics community seeking to advance research from basic genomics research to ultimately using genome information for diagnostics and targeted therapeutics. All of our investigators are faculty at Harvard Medical School, and many members of our community are also investigators at the Broad Institute of Harvard and MIT.
