Populations from under‐represented backgrounds are not adequately represented in clinical databases

Personalized medicine (PM) is a field in medicine that leverages information about a patient's genes, environment and behavior to prevent, diagnose, and treat disease. Health care providers rely on publicly available genetic databases for recommendations for implementing PM. For example, the Clinical Pharmacogenetics Implementation Consortium (CPIC) makes recommendations for clinical implementation of pharmacogenetics tests by creating, curating, and sharing peer‐reviewed, evidence‐based and updated drug‐gene pair clinical practice guidelines. While most drugs with CPIC guidelines have at least some evidence based off of non‐European data, none have been exclusively developed for a specific ancestral group. Similarly, ClinVar, an NIH supported, freely accessible archive, reports relationships between genetic variation and disease phenotypes. However, these databases reflect a measurable bias toward genetic data based on European ancestry, owed in large part to the underrepresentation of non‐white populations in federally funded and published research programs, including genome wide association (GWAS) and whole genome sequencing studies, which in turn limits the ability to identify variants associated with disease and response to certain therapeutics. Inadequate representation further exacerbates existing health disparities. After over a decade of GWAS, non‐European, non‐Asian groups combined account for <4% of individuals represented in the international GWAS catalog. Previously the role that ancestry plays in variant prioritization approaches often implemented in genomic medicine ( e.g ., Human Gene Mutation Database, ClinVar) was explored and it was determined these databases are missing considerable African‐specific pathogenicity data. Recent developments through national and international initiatives have provided an opportunity to classify new genetic variants and reassess prior variant classifications and deposit this information into public databases. Approximately half of the NHLBI supported Trans‐Omics for Precision Medicine (TOPMed) program's samples represent non‐European samples. Similarly, the NHGRI Population Architecture using Genomics and Epidemiology (PAGE) study aims to better characterize how genetic factors influence susceptibility to disease in a multi‐ethnic dataset. Researchers representing both TOPMed and PAGE previously designed a commercially available multi‐ethnic genotyping array that was enriched with genetic variants representing underrepresented minority populations, which is being widely used, including institutional biobanks. Together, these developments promise to improve genomic diversity and subsequently benefit underrepresented minorities through personalized medicine. Support or Funding Information NIH grant #HL104608 This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .