Research At University Of California, Los Angeles Emphasizes To Focus On Individuals, Versus Populations, In Genomic Medicine

One way scientists can estimate a person’s risk for a wide range of diseases is a measure called a polygenic score. The score is based on the variants in that person’s genes and how closely those variants are associated with the risk for disease among large groups of people who have similar genetic traits.

The measure holds promise not only for identifying risk for disease but also for guiding personalized treatments. But a new UCLA study, published in the journal Nature, found that polygenic scores fail to account for the wide range of genetic diversity across all ancestries.

“Polygenic scores can estimate the likelihood of an individual having a certain trait by pulling together and analyzing the small effects of thousands to millions of common genetic variants into a single score, but their performance among individuals from diverse genetic backgrounds is limited,” said Bogdan Pasaniuc, a UCLA Health expert in statistical and computational methods for understanding genetic risk factors for common diseases.

The UCLA researchers developed a method to evaluate polygenic scores’ accuracy at the individual level. To test it, they applied polygenic scores for 84 complex traits to data from more than 35,000 people in the UCLA ATLAS Precision Health Biobank, one of the world’s most diverse biobanks.