Using electronic health records to drive discovery in disease genomics

Key Points

  • The recently projected sample-size requirements for genomic studies for common variants of modest effect size, and rare variants of larger effect size, are rapidly outpacing the capabilities and budgets of most investigators and organizations.

  • The ongoing substantial investment in electronic health records (EHRs) for clinical care can be leveraged to cost-effectively accelerate population-scale genomic research by at least an order of magnitude and to reduce costs by at least an order of magnitude.

  • EHR-driven genomic research (EDGR) uses both the codified data available in the EHR and the phenotypic characterizations buried in the narrative text of the record by means of natural language processing (NLP).

  • Among the advantages of EDGR versus conventional cohort studies are timely clinical relevance and cost-effective scalability.

  • Biobanking and existing cohort studies will increasingly use EHR-derived data to augment the phenotypic characterizations obtained.

  • EDGR studies have already shown they can reproduce conventionally run genome-wide association (GWA) studies and extend the findings of those prior GWA studies to additional, often underrepresented populations.

  • EDGR can also enable studies that would be difficult to conduct otherwise, such as calculating the effect size of a genetic variant not for one disease or trait but for all diseases and traits captured in the EHR (a so-called phenome-wide association study).

  • A thorny challenge is the broad and international adoption of a standardized consent model or regulatory framework. If unaddressed, this challenge may impede further rapid adoption of EDGR. The patchy implementation of EHRs and their very large costs also will slow adoption of EDGR techniques.

Abstract

If genomic studies are to be a clinically relevant and timely reflection of the relationship between genetics and health status — whether for common or rare variants — cost-effective ways must be found to measure both the genetic variation and the phenotypic characteristics of large populations, including the comprehensive and up-to-date record of their medical treatment. The adoption of electronic health records, used by clinicians to document clinical care, is becoming widespread and recent studies demonstrate that they can be effectively employed for genetic studies using the informational and biological 'by-products' of health-care delivery while maintaining patient privacy.

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Figure 1: From clinical notes to structured phenotypes.
Figure 2: Two archetypal workflows in electronic health record-driven genomic research.

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Acknowledgements

The following were kind enough to share insights into their respective EDGR-related efforts: S. Brunak, J. Kim, J. Terdiman, L. Walter, J. Starren, J. Vilo, D. Masys, D. Roden, N. Stimson, L. Bry and S. Churchill. Any errors in communicating these insights are the sole responsibility of the author. The author was supported in part by US National Institutes of Health funding for the US National Centers for Biomedical Computing, U54 LM008748.

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FURTHER INFORMATION

Isaac S. Kohane's homepage

Biobank Japan Project

Danish National Biobank

Database of Genotypes and Phenotypes (dbGaP)

eMERGE Network

Estonian Genome Center

Genome.gov DNA sequencing costs

i2b2

Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH)

Marshfield Clinic Personalized Medicine Research Project (PMRP)

UK Biobank

Vanderbilt BioVU

Glossary

Biorepository

A biological materials repository that collects, processes, stores and distributes biospecimens to support future scientific investigation.

Natural language processing

(NLP). A field of computer science and linguistics concerned with the interactions between computers and human (natural) languages. NLP techniques allow the text in electronic medical records to be transformed from a clinical narrative to a set of codified terms or tags that are more readily subject to computational and statistical analysis.

Biobank

A cryogenic storage facility used to archive biological samples for use in research and experiments. Ranging in size from individual refrigerators to warehouses, biobanks are maintained by institutions such as hospitals, universities, non-profit organizations, pharmaceutical companies and national biorepositories. More recently, the term biobank has been used to signify a population cohort study with stored biological samples.

Population stratification

The presence of a systematic difference in allele frequencies between subpopulations from a larger population, possibly owing to different ancestry, especially in the context of association studies. (Population stratification is also referred to as population structure in this context.) If not properly accounted for in association studies, population stratification can lead to spurious associations.

Controlled vocabularies

A controlled vocabulary only includes terms that have been selected by the group that created the vocabulary. The goal of such a vocabulary is to standardize and simplify the organization of data and knowledge in a particular domain.

Phenome

The set of all phenotypes expressed by a cell, tissue, organ, organism or species.

Datamart

The entire stored data of an enterprise (for example, a health-care centre) is often termed the data warehouse. For a specified purpose (for example, a disease-specific study), a subset of the data warehouse, called the datamart, is extracted for a group of analysts.

Metagenomics

The study of metagenomes, which consist of genetic material recovered directly from environmental samples. Increasingly, it is used to describe the shotgun sequencing and analysis of the microbial genomes found in the milieu of the human body and its waste products.

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Kohane, I. Using electronic health records to drive discovery in disease genomics. Nat Rev Genet 12, 417–428 (2011). https://doi.org/10.1038/nrg2999

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