Article Impact Level: HIGH Data Quality: STRONG Summary of Human Genomics, 19(1), 8. https://doi.org/10.1186/s40246-025-00718-9 Dr. Robel Alemu et al.
Points
- The review highlights how integrating genomics, epigenomics, proteomics, and metabolomics can enhance understanding of non-communicable diseases (NCDs) by uncovering gene-environment (GxE) interactions that influence disease risk and treatment responses.
- Most genome-wide association studies (GWAS) focus on European ancestry, limiting generalizability. Expanding research to include diverse populations has led to breakthroughs, such as insights into kidney disease and cholesterol regulation.
- Underrepresentation of non-European ancestries has led to disparities in polygenic risk scores (PGS), highlighting the need for more diverse datasets to improve predictive accuracy across populations.
- AI can enhance multi-omics integration, advancing precision medicine through personalized treatment strategies. However, data bias, privacy, and model transparency remain significant challenges.
- The review emphasizes the need for international partnerships, improved research infrastructure in low- and middle-income countries, and standardized data-sharing practices to ensure equitable health outcomes worldwide.
Summary
This scoping review examines the current state of multi-omics data related to non-communicable diseases (NCDs) and the advancements in research, focusing on integrating genomics, epigenomics, proteomics, and metabolomics to understand the complex interactions between genetic predispositions and environmental exposures. These interactions, called gene-environment (GxE) interactions, are critical in determining disease risk and treatment responses. The review highlights that NCDs, which include cardiovascular diseases, cancers, and diabetes, account for more than 74% of global deaths, and integrating multi-omics data offers a more comprehensive approach to understanding the biological mechanisms behind these diseases. Despite the promise of these technologies, challenges in data integration, computational complexity, and the need for more diverse representation in genomic research remain significant barriers.
The review underscores the importance of diversity in multi-omics research, particularly in addressing health disparities. Current genomic studies, including 85% of genome-wide association studies (GWAS) that primarily focus on individuals of European ancestry, have limited the generalizability of findings to other populations. For instance, expanding genomic research to include more African ancestry data has led to breakthroughs in understanding kidney disease and cholesterol regulation. However, disparities in predictive accuracy for polygenic scores (PGS) persist due to the underrepresentation of non-European ancestries. To address these inequities, the review advocates for expanding the diversity of biomedical research and developing computational tools that integrate large, diverse datasets to ensure more inclusive and equitable health outcomes.
The authors call for using artificial intelligence (AI) and machine learning to enhance multi-omics data integration, which could revolutionize precision medicine by providing personalized treatment and prevention strategies. While AI has the potential to uncover novel biological insights, challenges such as data bias, privacy concerns, and model transparency must be addressed. To further these advancements, international collaborations, strengthening research infrastructure in low- and middle-income countries, and developing global data-sharing standards are essential to accelerate discoveries and improve health outcomes for all populations.
Link to the article: https://humgenomics.biomedcentral.com/articles/10.1186/s40246-025-00718-9
References Alemu, R., Sharew, N. T., Arsano, Y. Y., Ahmed, M., Tekola-Ayele, F., Mersha, T. B., & Amare, A. T. (2025). Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: Techniques, translation, and equity issues. Human Genomics, 19(1), 8. https://doi.org/10.1186/s40246-025-00718-9
