Ethnic minorities have typically been underrepresented in genomic databases, making it more difficult for clinicians to accurately diagnose and treat patients and for geneticists to identify disease variants. As a result, patients might not receive the correct medications or care they need, Heather Lindsey writes in the June issue of Clinical Laboratory News. 

Experts stress that “Clinical laboratory professionals need to consider the limitations of existing genomic data when analyzing patient samples,” Lindsey writes. 

When interpreting genetic variants, labs should be aware of the ancestry of the patients they’re testing and which specific populations are included in reference databases, Lawrence C. Brody, PhD, director of the Division of Genomics and Society and senior investigator at the National Human Genome Research Institute (NHGRI), tells CLN. 

Labs should also consider the differences between common and rare variants, Brody suggests. 

Some research organizations have taken steps to improve genomic diversity, and a University of Washington study indicates that genetic data now includes more Asian populations. But according to the study’s co-author, Stephanie Fullerton, DPhil, an associate professor of bioethics and humanities, under-representation remains an issue with Hispanics and blacks. 

Fullerton and other experts suggest that researchers move away from the Genome-Wide Association Studies (GWAS) Catalog—a compendium of all published GWAS that skews toward European populations—and focus more on whole genome and whole exome sequencing, which involves rarer variations that may be more clinically relevant and ancestry-specific. 

Whole exome sequencing, however, has lacked genetic diversity in study populations, Lindsey’s article acknowledges. 

“It is well recognized that various demographic groups have been poorly represented in past genomic studies and in control databases that have been used to identify causative genetic variants,” Arjun Manrai, PhD, a research fellow in the department of biomedical informatics at Harvard Medical School in Boston, tells CLN. 

Several large-scale data projects are seeking to improve genetic diversity. These include the genome Aggregation Database, whose sequencing data represents more than 140,000 individuals from ancestrally diverse populations, and the National Heart, Lung, and Blood Institute’s Trans-Omics for Precision Medicine project, which is looking at whole genome sequencing of diverse populations that previous research has underrepresented. 

NHGRI is involved in the “All of Us” project, which plans to enroll 1 million people from various backgrounds across the United States. Brody says the project may serve as a useful resource in interpreting U.S. genetic data. 

Community outreach is another strategy to ensure that genomic databases are ethnically diverse. But as Fullerton points out, it’s often a challenge to get underrepresented populations to participate in studies. “We also need to put genomic research in context—think about how it intersects with the provision of healthcare and the problem of return of research results,” Fullerton says. 

Pick up the June issue of CLN and learn more about the challenges labs and researchers face in promoting diversity in genomic databases.