Diversity in genetic data improves the ability to predict high cholesterol risk

Reviewed by Emily Henderson, B.Sc.Dec 13 2021

Including data from people of diverse ancestries substantially improves certain genetic risk predictions in all populations, according to a study that examined the genes responsible for regulating blood cholesterol levels.

Researchers have noted that a "polygenic risk score," a DNA-based tool that enhances our ability to predict the risk of common diseases, works well only for people of northern European ancestry, primarily because that's where much of the data has been collected.

The biggest weakness of using these risk scores in the clinic is that they have the potential to exacerbate health inequities because they perform much better in whites and perform the worst in African Americans."

Themistocles "Tim" Assimes, MD, PhD, associate professor of cardiovascular medicine, Stanford University School of Medicine

Assimes is a co-senior author of the study with Cristen Willer, PhD, a professor of internal medicine, human genetics, and computational medicine and bioinformatics at the University of Michigan. They and a team of more than 600 co-authors published their findings in Nature Dec. 8.  "We find that diversifying the populations under study, rather than simply increasing sample size, is the most effective approach to creating polygenic risk scores that work equally well in predicting high cholesterol in all populations," said Assimes, who is also a co-director of VA Palo Alto Epidemiology and Informatics Center for Genomics. "We hope that the same is observed for other health traits, but this remains to be seen."

The study, which examined the genetic variants associated with cholesterol levels for more than 1.65 million people, also showed that by including population samples from a wide variety of ancestry groups, the critical gene variants involved with bad cholesterol were found more quickly.

"The message couldn't be clearer: To have a fuller understanding of the effects of genomic variation on disease, we simply must include as many diverse groups as possible," said Charles Rotimi, PhD, a co-author on the paper and scientific director of the National Human Genome Research Institute.

The researchers combined a huge amount of data through a meta-analysis organized by the Global Lipids Genetics Consortium, which brought together genome-wide association data from more than 200 primary studies around the globe. Those analyses were performed by the many co-authors of the study.

The Million Veteran Program, one of the world's most diverse biobanks with approximately 850,000 U.S. veterans, was instrumental in increasing the diversity of the study, said Assimes, a senior investigator for the program. About one-quarter of the veterans are either Black or Hispanic.