TY - JOUR
T1 - Multi-omics data integration methods and their applications in psychiatric disorders
AU - Sathyanarayanan, Anita
AU - Mueller, Tamara T.
AU - Ali Moni, Mohammad
AU - Schueler, Katja
AU - Dierssen, Mara
AU - Ebert, Bjarke
AU - Fabbri, Chiara
AU - Fusar-Poli, Paolo
AU - Gennarelli, Massimo
AU - Harmer, Catherine
AU - Howes, Oliver D.
AU - Janzing, Joost G.E.
AU - Maron, Eduard
AU - Minelli, Alessandra
AU - Nonell, Lara
AU - Pisanu, Claudia
AU - Potier, Marie Claude
AU - Rybakowski, Filip
AU - Serretti, Alessandro
AU - Sqassina, Alessio
AU - Stacey, David
AU - van Westrhenen, Roos
AU - Xicota, Laura
AU - Baune, Bernhard T.
AU - Lio, Pietro
AU - Mehta, Divya
AU - European College of Neuropsychopharmacology (ECNP) Pharmacogenomics & Transcriptomics Network
N1 - Funding Information:
No acknowledgements to declare.
Publisher Copyright:
© 2023 Elsevier B.V. and ECNP
PY - 2023/4/1
Y1 - 2023/4/1
N2 - To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.
AB - To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.
KW - Bench to bedside
KW - Genomics
KW - Machine learning
KW - Multi-omics
KW - Psychiatry
KW - Statistics
KW - Transcriptomics
U2 - 10.1016/j.euroneuro.2023.01.001
DO - 10.1016/j.euroneuro.2023.01.001
M3 - Article
C2 - 36706689
SN - 0924-977X
VL - 69
SP - 26
EP - 46
JO - European Neuropsychopharmacology
JF - European Neuropsychopharmacology
IS - 1
ER -