The Role of Electronic Health Records in Advancing Genomic Medicine

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.

Keywords: GWAS; PheRS; PheWAS; electronic health records; phenome; translational genomics.

Figure 1

Advancing translational genomics relies on research across the genome and phenome. Progress relies both on enabling resources and on analytic methods and tools to capitalize on those resources. Discovery research utilizing new technologies built off large-scale EHR and genomic data has led to clinical translation and implementation and to eventual changes in clinical practice. Abbreviations: EHR, electronic health record; e-phenotyping, electronic phenotyping; GWAS, genome-wide association study; PheRS, phenotype risk score; PheWAS, phenome-wide association study; PRS, polygenic risk score.

Figure 2

Milestones enabling translational research. EHR (top) and genomic data (bottom) technologies facilitated advancements in medical genomics, increasing the understanding of common complex diseases. Abbreviations: EHR, electronic health record; e-phenotyping, electronic phenotyping; GWAS, genome-wide association study; HITECH, Health Information Technology for Economic and Clinical Health; HLA, human leukocyte antigen; PheRS, phenotype risk score; PheWAS, phenome-wide association study.

Figure 3

Creating a PheRS (see also the sidebar titled Creating a Phenotype Risk Score). (a) Summing the weights of each feature present in an EHR to calculate the PheRS. (b) An abbreviated version of OMIM’s clinical description for cystic fibrosis. (c) An example PheRS plot for a patient diagnosed with cystic fibrosis late in life. Before the diagnosis, this patient had a cystic fibrosis PheRS in the 99th percentile. Abbreviations: EHR, electronic health record; HPO, Human Phenotype Ontology; OMIM, Online Mendelian Inheritance in Man; PheRS, phenotype risk score.

Figure 4

Cumulative number of publications that included terms for common large-scale analytic methods (GWAS, genome-wide association study; PheWAS, phenome-wide association study, phenome wide; GRS, genomic risk score, genetic risk score, polygenic risk score) in the title, abstract, or MeSH term. Enabling methods such as GWAS and PheWAS in combination with the availability of large-scale EHR data laid the foundation for translational research such as PRSs and GRSs. Abbreviations: EHR, electronic health record; GRS, genomic risk score; GWAS, genome-wide association study; MeSH, Medical Subject Headings; PheWAS, phenome-wide association study; PRS, polygenic risk score.