Multiomics has a profound influence on clinical genetics; by integrating various omics fields such as genomics, transcriptomics, proteomics, and metabolomics we can advance clinical genetic practices. Dhavendra Kumar, an Honorary Clinical Professor at Queen Mary University of London, gave a quick overview of the human genome project and the evolution of genomics over the past 20 years.
The central dogma of DNA -> RNA-> protein, has transformed into a broader omics framework. Kumar suggested that multiomics bridges the gap between genomic diagnostics and conventional lab tests (e.g., biochemical and immunological). Now, NHS genomic labs frequently use omics-based diagnostics. Furthermore, integrating various omics fields impacts oncology, particularly when it is applied to patient stratification and personalised therapies. Multiomics moves away from a one-size-fits-all approach.
Kumar explained that multiomics supports the 6 Ps of modern medicine: personalised, precision, predictive, preventive, pre-emptive and participatory. He stated that multiomics has extensive applicability and is essential for understanding both rare and common diseases and linking mechanisms across conditions like diabetes, cancer, and congenital disorders.
Next, Kumar introduced an oncology case study that integrates pathology, imaging, and omics for personalised treatment. He also briefly touched on a study published in the Journal of Molecular Endocrinology that addressed how to use omics models for the prediction of health and disease. A study published in Orphanet examined how we can use omics for critical care medicine. The paper argued that multiomic strategies can uncover host-pathogen interactions, thus facilitating treatment management and predicting patient response.
Looking ahead it is clear that AI/ML is becoming increasingly essential for integrating and interpreting multiomics data. With rare diseases as a hot topic in the life sciences field, the general consensus is that multiomics will enable deeper understanding and better diagnostics of these complicated diseases.
