The Multi-Omics Revolution and Precision Medicine

Many common medical problems such as coronary heart disease, type 2 diabetes or obesity are most likely caused by strong genetic component in combination with many contributing lifestyle and environmental factors. These complex disorders urgently demand a better understanding of their etiology and more efficient therapeutic strategies [1]. Traditional medicine used symptoms to diagnose a disease and applied drugs to treat the symptoms. However, it has become increasingly clear that genetic variation affects the drug response, including susceptibility to adverse drug reactions, in patients [2,3]. This suggests that the one-size-fits-all strategy needs to be replaced by a more customized healthcare for an individual [4]. By identifying the factors that predispose a person to a particular disease and the molecular mechanisms that cause the condition, not only it would be possible to identify drugs and drug combinations optimized for each individual’s unique genetic background [4], but already the prevention strategies could be tailored to each individual, which is the great promise of the so called precision medicine.

Currently, genomics studies contribute the vast majority of precision medicine-based data, e.g. DNA sequencing is already being used to identify genetic variants that drive specific cancers [5]. Moreover, recent technological advances in other high-throughput omics technologies allow the retrieval of comprehensive and holistic data, including transcriptome, proteome, metabolome and even microbiome [6]. Thus opening up the amazing opportunity to capture the whole picture of biological systems in a hypothesis-free and unbiased mode and pawing the way for the next-generation diagnostics, involving the discovery of more complex biomarkers that more precisely predict the individual disease risk and the development of more efficient drugs [4].

However the translation of these future visions and strategies into clinically actionable tools has been slow, thus far [4]. This is partly due to the fact that, in human studies, it is difficult to collect large enough sample sizes (e.g. if human tissues such as liver, brain or vascular tissues are required) per se and under standardized conditions due to a multitude of confounding factors that are difficult or even impossible to control for (e.g., diet, medications) [7]. In addition, although each individual molecular layer can be profiled rather accurately and comprehensively, these measurements are restricted to the functional roles the respective omics domain plays in a biological system [4]. What remains a challenging task is the integration of multi omics data with clinical information into patient-centric models [4]. As a consequence, multi-dimensional data integration is currently a very active field of research and different computational solutions from multi-staged (e.g. associations between the genetic variation and other omics markers - the so called quantitative trait loci (QTL) mapping) to meta-dimensional analysis strategies (e.g., using machine learning and dimension reduction methods) are beginning to emerge [4,6].

On top of that, for complex disorders, the many contributing lifestyle and environmental factors have to be considered, as well. In fact, according to Genetic Liability Threshold Model, the later in life a multifactorial disorder develops the more it is dependent on these factors (i.e., the lower the heritability). To assess and model this contribution, more large-scale detailed population assessments like the UK Biobank initiative will be required, comprehensively collecting both molecular data as well as information on, for example, individual’s socioeconomical factors, physical activity and diet [8], ideally in prospective cohorts.

Clearly, the development of appropriate and efficient data storage, processing, integration and modelling strategies will stay a mandatory clinical informatics, bioinformatics and statistics task in the near future, in order to successfully extract the hidden information, thereby allowing its translation into actionable precision medicine tools [4]. Moreover, this information will need to be made readily accessible to clinicians including user-friendly visualization possibilities for insightful interpretation [4]. Furthermore, in the current big data era, as such repositories keep growing in volume, velocity and data variety, a strong IT infrastructure will be essential to embrace the promises of precision medicine - improved healthcare derived from personal data and tailored to individual needs [4].


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3. Bachtiar, M. & Lee, C. G. Genetics of population differences in drug response. Current Genetic Medicine Reports 1, 162–170 (2013).
4. Tebani, A., Afonso, C., Marret, S. & Bekri, S. Omics-based strategies in precision medicine: toward a paradigm shift in inborn errors of metabolism investigations. International journal of molecular sciences 17, 1555 (2016).
5. Grainger, D. The multi-omics revolution. The Journal of Precision Medicine (2016).
6. Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A. & Kim, D. Methods of integrating data to uncover genotype–phenotype interactions. Nature Reviews Genetics 16, 85 (2015).
7. Hasin, Y., Seldin, M. & Lusis, A. Multi-omics approaches to disease. Genome biology 18,83 (2017).
8. Bycroft, C. et al. Genome-wide genetic data on ̃ 500,000 uk biobank participants. bioRxiv 166298 (2017).

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