Researchers develop a new genetic framework to predict the risk of respiratory diseases

UTHealth Houston School of Public Health researchers developed a new genetic framework model to improve the prediction of respiratory diseases among multi-ancestry populations, according to a new study.

Researchers conducted one of the most expansive genome wide association studies by analyzing genetic data across populations. The authors identified 25 new loci, location of DNA markers, associated with lung function by in populations with East Asian ancestry from this new genetic model.

Yixuan He, PhD, assistant professor in Epidemiology, served as lead author of the recently published study in Nature Genetics.

Globally, respiratory diseases, which affect the airways and lungs, are leading causes of morbidity and may lead to extensive complications. While chronic obstructive pulmonary disease (COPD), asthma, and lung cancer cases are diagnosed worldwide, studies remain focused on genetic risk factors linked to solely European-ancestry populations, which ignore the multi-ancestry makeup outside of this group.

By limiting the variety of genetic makeup in prediction models, this can reduce the ability[HY3]  to apply to broad populations and exacerbate health care disparities across populations. To address this research gap, He and her team analyzed genetic data across multiple ancestry groups, including East Asian, African, Admixed American, and European populations.

The loci, specific markers in DNA, analyzed in this study influence how well one’s lungs work and their risk of developing respiratory diseases. Researchers identified several loci associated with immune and inflammatory pathways, which are key drivers of lung disease. By placing the specific locations in DNA that can increase respiratory risk, researchers can predict which populations are most at risk and account for genetic correlations across traits and ancestries.

 Some of the newly discovered loci were linked to broader health risks, such as white blood cell count, systolic blood pressure, and type 2 diabetes, showcasing how each is closely involved with lung function traits.

The traditional polygenic risk score (PRS) is constructed for a single trait. It uses a person’s genetic makeup to assess their risk of developing a medical condition, based on whether a genetic variant is present or absent. He and the team of researchers remodeled this to developed a cross trait and cross ancestry PRS (PRSxtra), which more comprehensively models genetic variants across multiple traits and ancestries.

The PRSxtra opened a new avenue of analyzing traits to identify genetic risks of COPD, asthma, lung cancer, pulmonary function, and smoking status and intensity.

“The PRSxtra leverages pleiotropic genetic effects shared across lung diseases and related traits such as lung function and smoking behaviors. It also combines information from multiple diverse populations to improve the transferability across ancestry groups,” said He.

The authors conducted a multi-populational analysis with the PRSxtra to identify individuals with higher risk of lung disease using data from the All of Us Research Program.

PRSxtra predicted asthma, COPD, and lung cancer more accurately than PRS in a multi-ancestry cohort.

“Understanding the genetic component of respiratory disease risk helps identify people at higher disease risk which can guide screening and prevention strategies,” He said on the importance of predicting disease risk. “It also helps reveal biological pathways involved in disease development, which can inform new treatments or targeted interventions.”

Results from the study suggest that using multi-trait and multi-ancestry approaches in respiratory disease analysis improves prediction and reveals that lung disease is shaped not only by lifestyle factors such as smoking, but also by genetic and biological pathways.

The study was in collaboration with Harvard University and the Broad Institute.