Abstract
Recent advances in genomic technology and widespread adoption of electronic health records (EHRs) have accelerated the development of genomic medicine, bringing promising research findings from genome science into clinical practice. Genomic and phenomic data, accrued across large populations through biobanks linked to EHRs, have enabled the study of genetic variation at a phenome-wide scale. Through new quantitative techniques, pleiotropy can be explored with phenome-wide association studies, the occurrence of common complex diseases can be predicted using the cumulative influence of many genetic variants (polygenic risk scores), and undiagnosed Mendelian syndromes can be identified using EHR-based phenotypic signatures (phenotype risk scores). In this review, we trace the role of EHRs from the development of genome-wide analytic techniques to translational efforts to test these new interventions to the clinic. Throughout, we describe the challenges that remain when combining EHRs with genetics to improve clinical care.
Highlights
Genomic medicine is an emerging multidisciplinary specialty that aims to improve human health through the application of genomic research findings to clinical care [63, 119]. It is the component of precision medicine that is most salient to clinical practice, as it builds upon the decades-long field of medical genetics and leverages well-established and increasingly affordable laboratory technologies to provide clinical-grade sequencing at the point of care
The dramatic reduction in the cost of sequencing has enabled the study of genetic variation at the population level; the rate-limiting resource is the availability of the large populations of diverse, well-phenotyped individuals that are needed to unravel the associations between complex disease and genomic variation
Description Billing claims data used for diagnosis and procedures; examples include International Classification of Diseases (ICD), phecodes, and Current Procedural Terminology (CPT) Age, sex/gender, race, ethnicity, date of birth, date of death Terms may be mapped to SNOMED-CT and the Human Phenotype Ontology (HPO) Problem lists, family history, flow sheets, radiology, pathology, procedures, cytology reports Admission–discharge–transfer, provider and clinic assignments Laboratory name, value, unit, date; standardized by Logical Observation Identifiers Names and Codes (LOINC) in some electronic health record (EHR) Medication name, dosing, frequency, route, duration, form, strength standardized to RxNorm standard Organization (e.g., North American Association of Central Cancer Registries) for cancer registry data across public and private organizations for standardization Blood pressure, body mass index (BMI), height, weight, temperature
Summary
Genomic medicine is an emerging multidisciplinary specialty that aims to improve human health through the application of genomic research findings to clinical care [63, 119]. The dramatic reduction in the cost of sequencing has enabled the study of genetic variation at the population level; the rate-limiting resource is the availability of the large populations of diverse, well-phenotyped individuals that are needed to unravel the associations between complex disease and genomic variation. It is not surprising, that central to the emergence of genomic medicine is the marriage of genetic data to rich sources of phenotypic data, comprehensive electronic health records (EHRs) [1]. The development of these techniques, including phenome-wide association studies (PheWASs) [23, 24], genome-wide association studies (GWASs) [38, 43, 56, 74], and electronic phenotyping (e-phenotyping), is the subject of this review, along with the derivative translational methods of phenotype risk scores (PheRSs) and polygenic risk scores (PRSs), which are promising new interventions that may further influence clinical practice
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