Integrative omics for health and disease

Key Points

  • Genomics has already begun to enter clinical practice, diagnosing patients with rare disease and informing cancer treatments. As other high-throughput technologies are developing, multiple omics technologies will be used in a similar fashion. Here, we describe the use of integrative omics for rare and common germline diseases, as well as cancers, and describe the challenges therein.

  • Exome and genome sequencing have already been successful in aiding the diagnosis of patients with Mendelian diseases. Recently, RNA sequencing (RNA-seq) has been used to supplement such analyses by identifying transcriptomic aberrations that led to the identification of previously missed causal variants.

  • Identifying risk factors for common germline diseases remains a considerable challenge: omics technologies have been instrumental in elucidating molecular mechanisms of disease. Recently, the first examples of longitudinal integrative omics profiles in a single individual have been performed, which have suggested personalized therapies.

  • Integrative omics has been a useful process for identifying driver genes, as well as molecular signatures of cancers. In particular, such analyses have indicated prognoses, as well as targeted therapies.

  • Multiple analytical, technical and interpretational challenges remain in the path to clinical adoption of integrative omics. Large reference panels of such data sets, as well as clinical guidelines, will be necessary to complete this transition.

Abstract

Advances in omics technologies — such as genomics, transcriptomics, proteomics and metabolomics — have begun to enable personalized medicine at an extraordinarily detailed molecular level. Individually, these technologies have contributed medical advances that have begun to enter clinical practice. However, each technology individually cannot capture the entire biological complexity of most human diseases. Integration of multiple technologies has emerged as an approach to provide a more comprehensive view of biology and disease. In this Review, we discuss the potential for combining diverse types of data and the utility of this approach in human health and disease. We provide examples of data integration to understand, diagnose and inform treatment of diseases, including rare and common diseases as well as cancer and transplant biology. Finally, we discuss technical and other challenges to clinical implementation of integrative omics.

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Figure 1: Identifying a causal variant to diagnose a patient with a rare disease.
Figure 2: From genome-wide association studies to mechanism.
Figure 3: Finding the relevant tissue.

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Acknowledgements

K.J.K. is supported by the US National Institute of General Medical Sciences (NIGMS) Fellowship F32GM115208. M.P.S. is supported by grants from the National Institutes of Health (NIH).

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Both authors contributed to all aspects of the manuscript, including researching data, discussing content, writing and editing.

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Correspondence to Konrad J. Karczewski or Michael P. Snyder.

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Competing interests

M.P.S. is a cofounder of Personalis, SensOmics and Qbio and is on the scientific advisory board of Personalis, SensOmics, Qbio, Epinomics and Genapsys. K.J.K. declares no competing interests.

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FURTHER INFORMATION

Genome Aggregation Database

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Glossary

Actionability

The property of a molecular finding that would result in a specific medical recommendation that is expected to improve a disease outcome.

Mendelian diseases

Diseases caused by a single locus or gene and that follow Mendelian patterns of inheritance (for example, dominant or recessive).

Genetic aetiology

The genetic factors that cause a particular disease.

Expression quantitative trait loci

(eQTLs). Genetic variants that are statistically associated with gene expression.

Heritability

The fraction of phenotypic variability of a trait that can be attributed to additive genetic variation.

DNase hypersensitivity

A measure of openness of chromatin, as measured by its sensitivity to cleavage by DNase I.

Structural variants

A class of genetic variation that is typically 1 kb or larger, which includes copy number duplications, insertions or deletions, as well as translocations and inversions.

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Karczewski, K., Snyder, M. Integrative omics for health and disease. Nat Rev Genet 19, 299–310 (2018). https://doi.org/10.1038/nrg.2018.4

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