Unraveling the Complexity of Gene-Environment Interactions in Noncommunicable Diseases through Multi-Omics Research
Published in Protocols & Methods, Computational Sciences, and Genetics & Genomics
By Dr. Robel Alemu (Lead Author), Associate Professor Azmeraw Amare (Senior Author), Associate Professor Tesfaye Mersha (Senior Author) and Collaborators
Understanding the Genetic and Environmental Complexity of Chronic Diseases
Noncommunicable diseases (NCDs), such as cardiovascular diseases, diabetes, cancers, and chronic respiratory conditions, are the leading causes of mortality worldwide, accounting for over 74% of global deaths
This intricate biological interplay means that individuals carrying the same genetic variant may experience different disease outcomes depending on their environment. For instance, certain genetic variants increase the risk of Parkinson’s disease in individuals exposed to pesticides
How Multi-Omics is Transforming Our Understanding of Disease Mechanisms
Genomic research, particularly genome-wide association studies (GWAS), has identified thousands of genetic variants linked to NCDs
By integrating multi-omics data with exposome data—which includes lifestyle, environmental, and social determinants of health—researchers can map how environmental influences shape biological pathways that drive disease
Expanding Global Representation in Omics Research
One of the most pressing challenges in genetic and multi-omics research is the underrepresentation of non-European populations. More than 85% of GWAS participants are of European ancestry
However, this diversity gap is not limited to genomic data—the disparities are even more pronounced in other omic layers, such as epigenomics, proteomics, and metabolomics
AI and Machine Learning in Multi-Omics Research: Addressing GxE Challenges
Gene-environment (GxE) interaction studies face persistent challenges, particularly limited sample sizes and the burden of multiple testing when analyzing high-dimensional biological data
AI and ML are transforming multi-omics research by enabling the integration of large, complex datasets and identifying hidden patterns that traditional methods often miss. Various computational techniques—including unsupervised learning methods like Principal Component Analysis (PCA) and t-SNE, as well as supervised approaches such as Support Vector Machines (SVMs), Random Forests, and deep learning—help uncover key biological insights by linking genetic and environmental factors to disease phenotypes
For example, deep learning models such as those developed by Wu et al. (2023) simultaneously estimate main effects and GxE interactions, overcoming hierarchical constraints in conventional regression-based models
However, AI-driven approaches are not without challenges. Bias in training datasets remains a major concern, as underrepresentation of certain populations can lead to skewed model performance and exacerbate disparities in GxE research. Additionally, the “black box” nature of deep learning models makes it difficult to interpret findings, raising concerns about clinical transparency and trust. Ethical considerations, such as data privacy and responsible AI implementation, must also be addressed to ensure equitable, reliable, and scalable applications of AI in multi-omics research.
Real-World Applications of Multi-Omics Research in Medicine
Multi-omics approaches are already shaping clinical decision-making and treatment strategies in various ways. For example, recent studies have identified genes that protect neurons from oxidative stress, a major contributor to neurodegenerative diseases like Alzheimer’s and Parkinson’s. In pharmacogenomics, multi-omics research has enabled personalized drug dosing, such as tailoring warfarin prescriptions based on CYP2C9 and VKORC1 genetic variants
These advances underscore the potential of multi-omics research to revolutionize medicine, moving us closer to individualized prevention and treatment strategies that consider both genetic makeup and environmental influences.
A Global Call to Action
To fully realize the potential of multi-omics research, scientists, policymakers, and funding agencies must prioritize global inclusivity and data-sharing initiatives. Expanding research in underrepresented populations, strengthening research infrastructure in low- and middle-income countries, improving analytical techniques for dissecting GxE interactions, and establishing global standards for data sharing and integration will be key to accelerating scientific discoveries and improving health outcomes worldwide.
As lead author Dr. Robel Alemu emphasizes, “Our review highlights the transformative power of multi-omics research in revealing the biological mechanisms behind chronic diseases. However, this potential will remain unrealized unless we address the significant equity gaps in omics research and ensure that these advancements benefit all populations.”
Join the conversation! How do you see multi-omics shaping the future of precision medicine? Let us know your thoughts in the comments! 🚀
Robel Alemu (Ph.D.)
Postdoctoral Researcher, University of California Los Angeles
Broad Institute of MIT and Harvard; The University of Adelaide Medical School
Email: robel.alemu@anderson.ucla.edu; ralemu@broadinstitute.org
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Human Genomics
Human Genomics is a peer-reviewed, open access journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics.
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Integrating Genome and Exposome: Advancing Translational Exposomics for Public Health
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