Although individual -omics analysis such as genome-wide association studies (GWAS), can effectively map genetic variants correlated with noncommunicable diseases (NCDs), the majority of the heritability and the functional relevance of discovered variants are not explained or known by the identified variants. The limited success of singular approaches underscores the need for holistic and integrated approaches to investigate complex phenotypes using trans-omics data integration strategies. Integration of omics layers (e.g., genome, epigenome, transcriptome, proteome, metabolome, lipidome, exposome, microbiome), which often have complementary and synergistic effects, offer the opportunity to comprehensively understand the flow of information that underlines the biological basis of NCDs. To unravel the complexities of the genomic basis of human disease, and in particular the influence of the environment, it is imperative that we fully integrate multiple layers of genomic data. Gene-environment interactions play key roles in NCD pathogenesis. Therefore, better understanding of gene-environment interactions in NCDs can improve both clinical medicine and public health. However, significant gaps remain in defining and characterizing extent and mechanisms of gene-environment interactions in several NCDs. Importantly, evaluations of gene-environment interactions have lagged the fast discovery pace of characterizing genetic variations and improvements in exposure measurement due to limitations in methods. In addition, application of knowledge on gene-environment interactions towards improving human health, through translational research (e.g. precision medicine) has been limited. In this Collection, we aim to gather articles that address the broad area of gene-environment interactions in NCDs, with particular emphasis on articles that address gaps including multi-omics integration.
The purpose of this Collection is to identify and describe developments, challenges and opportunities related to the integration of environmental exposure data with -omics data in population studies.
• To understand the developments, challenges and opportunities in integrating environmental data with -omics.
• To identify the possible approaches and best practices for integration of multi-omics and environmental data.
• To identify special considerations for using environmental and multi-omic data for noncommunicable diseases.
• To determine how experimental models integrating environmental exposures and -omics data effects can be used to inform knowledge on noncommunicable diseases.
• To describe integration of environmental and -omics data in translational research to address noncommunicable diseases.