The use of electronic health records for psychiatric phenotyping and genomics

Abstract

The widespread adoption of electronic health record (EHRs) in healthcare systems has created a vast and continuously growing resource of clinical data and provides new opportunities for population-based research. In particular, the linking of EHRs to biospecimens and genomic data in biobanks may help address what has become a rate-limiting study for genetic research: the need for large sample sizes. The principal roadblock to capitalizing on these resources is the need to establish the validity of phenotypes extracted from the EHR. For psychiatric genetic research, this represents a particular challenge given that diagnosis is based on patient reports and clinician observations that may not be well-captured in billing codes or narrative records. This review addresses the opportunities and pitfalls in EHR-based phenotyping with a focus on their application to psychiatric genetic research. A growing number of studies have demonstrated that diagnostic algorithms with high positive predictive value can be derived from EHRs, especially when structured data are supplemented by text mining approaches. Such algorithms enable semi-automated phenotyping for large-scale case-control studies. In addition, the scale and scope of EHR databases have been used successfully to identify phenotypic subgroups and derive algorithms for longitudinal risk prediction. EHR-based genomics are particularly well-suited to rapid look-up replication of putative risk genes, studies of pleiotropy (phenomewide association studies or PheWAS), investigations of genetic networks and overlap across the phenome, and pharmacogenomic research. EHR phenotyping has been relatively under-utilized in psychiatric genomic research but may become a key component of efforts to advance precision psychiatry.

Genomic Characterization of Posttraumatic Stress Disorder in a Large US Military Veteran Sample

“…Moreover, we were able to examine simultaneously genomewide associations with several other PTSD phenotypes reflecting various aspects of symptom type and severity, respectively. These data highlight the tremendous scientific value of EHR-based biobanks and automated phenotyping for genomic research Smoller, 2018), and even more so, the value of collecting relevant self-report data such as the PCL. Biobanks have specific subject and ascertainment characteristics; the nature of the MVP makes it uniquely suitable for study of PTSD, a disorder that occurs at increased rates in military veterans.…”

“…Additional post-GWAS analyses included enrichment (tissue; pathway) and transcriptome-wide (Gamazon et al, 2015) analyses. Phenome-wide analysis (PheWAS) was conducted for the top GWAS hits against the VA-MVP EHR (Bush et al, 2016;Smoller, 2018) and possible drug candidates for repositioning were identified using publicly available drug-genomic databases.…”

eHealth as a Facilitator of Precision Medicine in Epilepsy

“…While not a primary aim for some of these efforts, they all facilitate an increase in the number of patients that obtain a molecular diagnosis, thus creating a new opportunity to develop an infrastructure that supports formalising clinician-driven patient outcome registries to maintain careful phenotyping and prospectively report treatment outcomes linked to individuals with secure molecular diagnoses. Such standardised registries will empower large-scale studies investigating the extent of genotype-phenotype correlations within individual epilepsy genes and the relationships between molecular diagnoses and existing/novel therapeutic compounds [26]. The expert curation of patient-ascertained presumed pathogenic variants will also be of major research value since a more complete knowledge of the full disease-associated allelic series in epilepsy genes will facilitate appropriate research-driven functional studies.…”

“…For example, an individual's health care may cross multiple organisational boundaries and hence involve multiple EHR instances. Furthermore, the validity of the phenotypes may be uncertain if the query used in the EHR-based case ascertainment is not appropriately defined [26]. These challenges are not insurmountable, but must be considered when exploiting EHR technology to enhance genomic medicine.…”

Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four healthcare systems

“…We recently established the PsycheMERGE consortium within the NIH-funded Electronic Medical Records and Genomics (eMERGE) Network 15,16 to leverage electronic health record (EHR) data linked to genomic data to facilitate psychiatric genetic research 17 . In this first report from PsycheMERGE, we evaluated the performance of a schizophrenia PRS generated from summary statistics published by the Psychiatric Genomics Consortium 14 using EHR data on more than 100,000 patients from four large healthcare systems (Geisinger Health System, Mount Sinai Health System, Partners Healthcare System, and Vanderbilt University Medical Center).…”