Study highlights need to move from populations to individuals in genomic medicine

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

The researchers’ analysis, published in Natureshows that the accuracy of polygenic scores (PGS) varies among individuals along a continuum of genetic ancestry, and this is true even in populations traditionally considered “homogeneous” (for example, Europeans), Pasaniuc said, main author of the article. .

PGS performance assessment has commonly been done at the “population” level, as in “Europeans,” grouping individuals of similar ancestry into a genetic ancestry group, the authors said.

“Imposing artificial limits on this continuum and ignoring diversity, or ‘heterogeneity,’ within groups can obscure variation within a group, hide similarities that may exist in individuals in different groups, and leave out individuals who do not. They fit perfectly into a particular group.” genetic ancestry,” said Yi Ding, a graduate student in bioinformatics at UCLA, a member of the Pasaniuc Laboratory and first author of the paper.

To provide a more accurate estimate of PGS accuracy, the researchers developed a method to assess PGS accuracy at the individual level. To test it, they applied PGS for 84 complex traits to data from more than 35,000 people in the UCLA ATLAS Precision Health Biobank, one of the most diverse biobanks in the world, in part because the Los Angeles area is home to one of the most ancestrally diverse populations. Worldwide.

The “training” data for the new tool came from a subset of people in the UK Biobank in the UK. As a surrogate for discrete genetic ancestry, a continuous “genetic distance” metric was used to establish each individual’s position in the ATLAS database on the genetic ancestry continuum, essentially showing how similar or different the genome of a given individual was. target individual (ATLAS). to that of the UK training population.

“We found that the more different, or genetically ‘distant’, a target individual’s genome was from the UK Biobank training data, the less accurate the PGS was,” Ding said.

The accuracy of the PGS decreased as genetic distance increased, even when the researchers looked specifically at groupings of genetic ancestry that were considered to be homogeneous, such as among individuals of European genetic ancestry. Conversely, some unidentified people with European ancestry might have higher levels of genetic similarity, demonstrating that PGS performance might differ between two people of the same ancestry but be comparable for two people of different ancestries, depending on their genetic similarity.

“Our genetic distance metric outperformed discrete clustering in identifying people who might benefit from PGS,” said Pasaniuc, an investigator at the UCLA David Geffen School of Medicine and UCLA Institute for Precision Health.

The research team identified several factors, topics for ongoing and future studies, that could affect the accuracy and usefulness of PGS, especially in people of “mixed” ancestry. These are generally defined as individuals with recent ancestry from two or more continental sources, such as African Americans and Latinos.

Pasaniuc, whose research focuses on improving genetic risk factor predictions for people of mixed ancestry, said these people have “mosaic” genomes, with segments of different continental ancestry in each region. With different portions contributed by different ancestries, it is extremely difficult to accurately classify these individuals using conventional ancestry labels.

“For PGS to be used equitably,” he said, “precision assessment of PGS must take into account the full spectrum of genetic diversity.”