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From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators.

Pedrum Mohammadi-ShemiraniTushar SoodGuillaume Paré
Published in: Current atherosclerosis reports (2023)
Multi-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of 'omics data. Studies with multiple types of 'omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of 'omics data. For instance, disease-associated loci in the genome can be supplemented with other 'omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. As technologies improve, costs for 'omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of 'omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of 'omics data to provide insights greater than the sum of its parts.
Keyphrases
  • single cell
  • electronic health record
  • rna seq
  • big data
  • genome wide
  • gene expression
  • small molecule
  • dna methylation
  • copy number
  • transcription factor
  • deep learning
  • genome wide analysis