Racial diversity in the USA is increasing. Based on the latest census population projections, by 2045, individuals of European descent are expected to represent less than 50% of the American population, as Hispanic, African American, Asian, and multiracial populations continue to grow.
Despite this trend, racial minorities in the US continue to experience more disease and die earlier than white people. Diabetes, heart disease, obesity, asthma, autoimmune conditions, hepatitis C, HIV, and tuberculosis are all more common in racial minority groups compared to people with European ancestries.
As we’ve previously highlighted on our blog, racial health disparities now extend to genomic research and precision medicine. Here at Global Gene Corp, we are dedicated to ensuring that genomic datasets are fully representative of the global population, to bring precision healthcare to everyone.
That’s why we were so happy to read the results of a new study published in Nature last month and covered by several press outlets, which demonstrates the power of diversity in genomics research, not only for under-represented groups but for everyone.
The new study, known as PAGE–Population Architecture using Genomics and Epidemiology, was led by researchers from the Icahn School of Medicine at Mount Sinai, and the Fred Hutchinson Cancer Research Center in Seattle. The team investigated whether 26 genetic risk scores determined using genomic data from people of European ancestry could be extrapolated to racial minorities.
Current genetic risk scores are underperforming in non-Europeans
Some genetic variants do not appear to impact people’s health at all, while others can have a profound effect. For most common diseases, tiny changes in hundreds or thousands of genes can add up to an increased risk of a particular condition. Adding up the cumulative impact of these variations creates a figure known as a polygenic risk score for that disease.
“If you’re going to have next-generation medicine and derive polygenic risk scores, those risk scores should be equally accurate regardless of what an individual’s genetic ancestry is. And they’re not” said Dr Chris Carlson, Associate Member of the Public Health Sciences Division at Fred Hutchinson, and co-corresponding author of the publication
Polygenic risk scores are derived from genome-wide association studies (GWAS), where researchers study large groups of people to figure out whether certain genetic variations are more or less likely to be associated with diseases, conditions or traits.
“Because the availability of non-European genomic data is limited, much of the existing clinical therapies disproportionately benefit those of European descent – further widening the health disparities gap,” said Dr Eimear Kenny, Associate Professor of Medicine and Genetics at the Icahn School of Medicine at Mount Sinai and co-senior author of the publication.
Finding unknown genetic variations
“As we anticipated, by examining previously underrepresented populations, we found new ancestry-specific associations, which furthers our understanding of the genetic architecture of traits and underscores the importance of including diverse populations in these studies,” said Dr Ulrike Peters, associate director of Fred Hutchinson’s Public Health Sciences Division and a senior scientist on the PAGE project.
The PAGE team analysed a number of different traits in nearly 50,000 people with non-European ancestry to see how their genetic background affected each attribute, discovering 27 new trait-variant associations which had never been found before.
For example, the researchers identified genetic variations found in some Hispanic/Latino populations that reduce HbA1c production, a molecule that reflects blood sugar levels and is used to monitor people with diabetes. As a result, people with this genetic variant may display erroneously low HbA1c levels, leading their doctor to believe their blood sugar levels are under control when they may not be.
The team also looked at genetic variations associated with height. Previous studies conducted using genomic data from 250,000 people of European descent have identified four height-related variants, but when they added the genomic data of the 50,000 PAGE participants, only one variation was consistently involved with height across all groups.
This means that the other three are regional variations that aren’t truly linked to the trait – a powerful demonstration of how using diverse populations in genomics helps to pinpoint genuine genetic associations more accurately and rule out misleading matches.
In yet another example, one newly discovered genetic variation associated with smoking an increased number of cigarettes per day turned up in nearly one in five people with Native Hawaiian and Pacific Islander ancestry. If people of Native Hawaiian and Pacific Islander ancestry had not been included in the GWAS, this genetic association would still remain unknown.
Benefits for everyone
Historically, genomics research has used populations of similar people to make data processing easier. But advances in technology mean those limits can – and should – be lifted.
“Nowadays, we have computer methods that can handle immense complexity in your data, so it actually doesn’t always make a whole lot of sense to run separate GWAS for different populations,” said data scientist Genevieve Wojcik, first author on the PAGE study paper. “Pooling data from diverse groups actually enhances precision in disease risk predictions.”
At Global Gene Corp, we’re dedicated to building diverse genomic datasets and developing analytical tools to generate insights that are relevant for the health of all populations, no matter where they live or where they come from.
It’s all part of our vision to ensure that as many people as possible can benefit from the coming revolution in precision healthcare.
- Genetic analyses of diverse populations improves discovery for complex traits. Wojcik et al. Nature 570: 514–518 (2019) https://www.nature.com/articles/s41586-019-1310-4