RESUMO
Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.
Assuntos
Células-Tronco Embrionárias/citologia , Células-Tronco Embrionárias/transplante , Terapia a Laser/métodos , Camundongos Transgênicos/genética , Camundongos Transgênicos/cirurgia , Microinjeções/métodos , Microcirurgia/métodos , Transplante de Células-Tronco/métodos , Animais , Camundongos , Camundongos Endogâmicos BALB C , Camundongos Endogâmicos C57BL , Camundongos Mutantes , Camundongos Transgênicos/anatomia & histologiaRESUMO
Massively parallel signature sequencing (MPSS) is one of the newest tools available for conducting in-depth expression profiling. MPSS is an open-ended platform that analyses the level of expression of virtually all genes in a sample by counting the number of individual mRNA molecules produced from each gene. There is no requirement that genes be identified and characterised prior to conducting an experiment. MPSS has a routine sensitivity at a level of a few molecules of mRNA per cell, and the datasets are in a digital format that simplifies the management and analysis of the data. Therefore, of the various microarray and non-microarray technologies currently available, MPSS provides many advantages for generating the type of complete datasets that will help to facilitate hypothesis-driven experiments in the era of digital biology.