» Articles » PMID: 39891174

Multi-omics Approaches for Understanding Gene-environment Interactions in Noncommunicable Diseases: Techniques, Translation, and Equity Issues

Overview
Journal Hum Genomics
Publisher Biomed Central
Date 2025 Jan 31
PMID 39891174
Authors
Affiliations
Soon will be listed here.
Abstract

Non-communicable diseases (NCDs) such as cardiovascular diseases, chronic respiratory diseases, cancers, diabetes, and mental health disorders pose a significant global health challenge, accounting for the majority of fatalities and disability-adjusted life years worldwide. These diseases arise from the complex interactions between genetic, behavioral, and environmental factors, necessitating a thorough understanding of these dynamics to identify effective diagnostic strategies and interventions. Although recent advances in multi-omics technologies have greatly enhanced our ability to explore these interactions, several challenges remain. These challenges include the inherent complexity and heterogeneity of multi-omic datasets, limitations in analytical approaches, and severe underrepresentation of non-European genetic ancestries in most omics datasets, which restricts the generalizability of findings and exacerbates health disparities. This scoping review evaluates the global landscape of multi-omics data related to NCDs from 2000 to 2024, focusing on recent advancements in multi-omics data integration, translational applications, and equity considerations. We highlight the need for standardized protocols, harmonized data-sharing policies, and advanced approaches such as artificial intelligence/machine learning to integrate multi-omics data and study gene-environment interactions. We also explore challenges and opportunities in translating insights from gene-environment (GxE) research into precision medicine strategies. We underscore the potential of global multi-omics research in advancing our understanding of NCDs and enhancing patient outcomes across diverse and underserved populations, emphasizing the need for equity and fairness-centered research and strategic investments to build local capacities in underrepresented populations and regions.

References
1.
Chen C, Wang J, Pan D, Wang X, Xu Y, Yan J . Applications of multi-omics analysis in human diseases. MedComm (2020). 2023; 4(4):e315. PMC: 10390758. DOI: 10.1002/mco2.315. View

2.
Satam H, Joshi K, Mangrolia U, Waghoo S, Zaidi G, Rawool S . Next-Generation Sequencing Technology: Current Trends and Advancements. Biology (Basel). 2023; 12(7). PMC: 10376292. DOI: 10.3390/biology12070997. View

3.
Veller C, Coop G . Interpreting population- and family-based genome-wide association studies in the presence of confounding. PLoS Biol. 2024; 22(4):e3002511. PMC: 11008796. DOI: 10.1371/journal.pbio.3002511. View

4.
Visscher P, Wray N, Zhang Q, Sklar P, McCarthy M, Brown M . 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017; 101(1):5-22. PMC: 5501872. DOI: 10.1016/j.ajhg.2017.06.005. View

5.
Spanbauer C, Sparapani R . Nonparametric machine learning for precision medicine with longitudinal clinical trials and Bayesian additive regression trees with mixed models. Stat Med. 2021; 40(11):2665-2691. DOI: 10.1002/sim.8924. View