In a groundbreaking meta-analysis of over 150,000 individuals with asthma, 49 new genetic loci were identified.
Asthma affects millions of people worldwide, but its genetic underpinnings are not well understood. The prevalence of asthma is variable in different parts of the world and in different populations within countries, but the heterogeneity cannot be explained solely by environmental factors like air pollution. Complex interactions between genetic predisposition and environmental triggers complicate our understanding of asthma’s genetic architecture.
In a meta-analysis published in Cell Genomics that included 22 biobanks and more than 150,000 people with asthma from different ancestral backgrounds, scientists conducted a genome-wide association study (GWAS) in the hopes of uncovering genetic signatures of asthma. The sample contained data from individuals of European, African, admixed American, East Asian, Middle Eastern, and Central and South Asian ancestry.
Genetic Overlap Between Asthma and Comorbid Diseases Was Found
As a polygenic disease, asthma is associated with clusters of genetic variants, rather than being defined by a single mutation. The GWAS identified 49 novel asthma-associated loci, and the genetic effects were mostly consistent across biobanks and ancestries. There was also genetic overlap between asthma and comorbidities, such as allergies, psychiatric disorders, and obesity, and between age-of-onset subtypes.
To demonstrate the benefit of large and diverse datasets, the researchers compared these loci with an analysis using only European-descent cohorts. The inclusion of diverse ancestries facilitated the detection of more loci, many of which would not have been discovered with a less diverse sample.
Improving Sample Diversity Improves the Power of Genome-Wide Association Studies
There is currently a disproportionate representation of European ancestries in medical research, including the biobanks used in this study. The greater diversity used in this meta-analysis proved to be more powerful for detecting genome-wide associations of asthma. Continuing data-sharing and prioritizing underrepresented groups in research will accelerate gene discovery, improve risk prediction, and advance our understanding of asthma.
Tsuo, K., Zhou, W., Wang, Y., Kanai, M., Namba, S., Gupta, R., Majara, L., Nkambule, L. L., Morisaki, T., Okada, Y., Neale, B. M., Daly, M. J., & Martin, A. R. (2022). Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity. Cell Genom, 2(12), 100212. https://doi.org/10.1016/j.xgen.2022.100212