New tool better predicts COPD risk for people of non-European ancestry
UVA Health researchers and their collaborators have developed a better way to predict the risk of chronic obstructive pulmonary disease (COPD), a progressive and life-threatening form of lung inflammation, for people of non-European ancestry.
Early testing of the new, more inclusive tool found it is more effective at predicting COPD risk for African Americans and heavy smokers than existing models based on genetic information largely collected from people of European ancestry. The tool’s developers say their approach will allow doctors to better predict COPD risk for people of diverse ancestry in the United States and around the world.
“Our study demonstrates the possibility of learning from large-scale genetic studies performed primarily in European ancestry groups and then developing predictive models that can be used to predict genetic risk in other ancestry groups. ancestry,” said researcher Ani W. Manichaikul, PhD, of the University of Virginia School of Medicine. “While the current study focuses on COPD risk prediction, we are already looking to apply similar approaches to improve genetic risk prediction for other diseases.”
Although treatable, COPD is one of the leading causes of death in the United States and around the world. About 16 million Americans suffer from COPD, which is a group of lung conditions including emphysema and chronic bronchitis. The lung damage caused by COPD is irreversible and accumulates over time. This makes early detection and treatment particularly important.
In recent years, doctors have been able to predict patients’ genetic risk of developing COPD and other common diseases using what are called “polygenic risk scores” or PRS. These look at the total number of natural genetic variations in a person that predispose them to a disease – in this case, COPD. To date, most large-scale genetic studies available for the study of disease risk have limited representation of certain ancestry groups, including African Americans and Hispanics, resulting in a weaker prediction of disease risk. disease risk for these groups.
Manichaikul and his collaborators sought to improve the ability to predict COPD by better reflecting global genetic diversity. To do this, they overlaid genetic measures with other molecular measures of a diverse ancestry group that included a combination of European ancestry, African American and Hispanic individuals from the United States. Building on these resources, they developed what they call “the PrediXcan-derived Polygenic Transcriptome Risk Score,” or PTRS. This new approach incorporates much more information about the cumulative effects of genetic variations in different groups of people. The result is a model that “has a more direct link to underlying disease biology than standard PRS approaches,” the researchers report in a new scientific paper.
Scientists put their new tool to the test by analyzing its ability to predict COPD in tens of thousands of participants in studies conducted by the Trans-Omics for Precision Medicine (TOPMed) program sponsored by the National Heart, Lung and Blood from the National Institutes of Health. Institute (NHLBI).
They found that the PTRS was better at predicting COPD in African Americans and better at predicting moderate to severe COPD in longtime heavy smokers. Perhaps unsurprisingly (given that it was developed to better reflect non-European populations), the PTRS was less effective than the PRS in predicting COPD in people of European ancestry. But the availability of multiple “crystal balls” to predict COPD in different populations brings us closer to true precision medicine – medicine tailored to each individual.
“So far, we have shown that by relying on genomic data combined with gene expression data from individuals of diverse ancestry, we can improve the prediction of genetic risk for certain individuals,” said Manichaikul, from the Center for Public Health Genomics and the AVU Department of Public Health. Sciences. “Going forward, we are excited to consider how we can build on other collections of molecular data from individuals of diverse ancestry and continue to work on improved approaches for predicting genetic risk for other diseases.
The work was supported by NHLBI grants R01 HL131565, R01 HL153248, R01 HL135142, R01 HL137927, R01 HL089856, R01 HL147148, and K01-HL129039.