An international team of researchers has explored how multi-omics—the integration of molecular data across different biological layers—can enhance our understanding of the way genetic and environmental factors interact to influence chronic diseases.
Their review, recently published in Human Genomics, highlights how advancements in multi-omics technologies are helping to uncover the biological mechanisms driving non-communicable diseases (NCDs), which account for more than 74% of global deaths. These include cardiovascular diseases, cancers, diabetes, and chronic respiratory conditions.
Exploring Gene-Environment Interactions
The scoping review, co-led by Dr. Robel Alemu, a Visiting Research Fellow at the University of Adelaide Medical School (postdoctoral researcher at UCLA), and NHMRC Emerging Leadership Fellow Associate Professor Azmeraw Amare, examines extensive literature in the field. It assesses how multi-omics techniques are advancing research into these diseases, the challenges in integrating complex datasets, and the urgent need for greater diversity in genomic and biomedical research.
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“Non-communicable diseases are driven by a combination of genetic predispositions and environmental exposures, such as diet, pollution, and physical activity. How these factors function together is referred to as gene-environment (GxE) interactions, which play a significant role in determining disease risk and treatment responses,” said Dr. Alemu.
“In some cases, a person’s genetic makeup can alter how environmental exposures impact disease risk—for example, certain genetic variations are associated with an increased risk of Parkinson’s in people exposed to specific pesticides.”
“In other cases, environmental exposures influence which genetic factors contribute to disease risk. For instance, the impact of the FTO gene on body mass index varies depending on lifestyle factors.”
Multi-Omics: A Holistic Approach to Precision Medicine
By integrating multi-omics datasets—such as genomics (DNA), epigenomics (molecular markers that regulate gene activity), proteomics (proteins), and metabolomics (biochemical processes)—scientists can develop a more complete picture of how biological systems interact to influence health and disease.
Multi-omics approaches are transforming precision medicine, helping researchers develop more targeted treatments and prevention strategies. For example:
- Recent studies have identified specific genes that protect brain cells from oxidative stress, a key factor in neurodegenerative diseases.
- In pharmacogenomics, multi-omics research is enabling personalized medicine, such as using BRCA1/2 genetic testing to guide treatment decisions for breast cancer patients who may benefit from targeted therapies.
Challenges in Multi-Omics Research
Despite its promise, multi-omics research faces several challenges:
- Lack of Diversity in Biomedical Research – A major limitation is the underrepresentation of non-European populations. “For instance, 85% of genome-wide association studies (GWAS) primarily involve individuals of European ancestry, leading to significant disparities in polygenic score (PGS) predictive accuracy for other genetic ancestries,” Dr. Alemu said.
- Expanding genomic diversity can help address these inequities and lead to the discovery of clinically important variants, as seen in research on African ancestry uncovering critical insights into kidney disease and cholesterol regulation.
- High Costs and Computational Complexity – The integration of multi-omics data requires advanced bioinformatics tools, significant computing power, and specialized expertise.
- Need for Standardized Data Sharing – The complexity of integrating large, diverse datasets highlights the necessity for global standards in data sharing and accessibility.
The Future of Multi-Omics: AI and Global Collaboration
The review calls for international collaborations and the development of equity-centered computational methods to enhance data integration and ensure that scientific advances benefit all populations.
“Recent advances in AI and machine learning have the potential to revolutionize multi-omics research by integrating large-scale, complex datasets and uncovering novel biological insights,” said Associate Professor Amare.
“However, challenges such as data bias, lack of model transparency, and privacy concerns must be addressed to ensure responsible and effective use. By developing AI-driven approaches that are equitable and transparent, we can unlock new possibilities for personalized medicine and disease prevention.”
The authors advocate for:
- Expanding multi-omics research in underrepresented populations.
- Strengthening local research infrastructure in low- and middle-income countries.
- Developing global standards for data sharing and integration to accelerate discoveries and improve health outcomes.
As multi-omics continues to evolve, its potential to transform healthcare and enhance our understanding of chronic diseases is becoming increasingly evident. With the right investments in diversity, technology, and collaboration, researchers hope to unlock new frontiers in disease prevention and treatment.
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