Electronic Health Records in the Genomics Era: Impact of Clinical Genomics

Abstract

The human genome project has created a huge opportunity to move personalized genomic-based healthcare to the clinic [8-10]. This is greatly facilitated by the plummeting cost of DNA sequencing to about $1,000 a genome. Just this February, the US Federal Drug Administration approved a direct-to-consumer (DTC) genetic test for Bloom syndrome carrier status [11] by the company 23 and Me. The 1000 genome project [12] and more recently the 100,000 Genomes (Genomics England Project) have been generating a large amount of data on genetic variations, the Single Nucleotide Polymorphisms (SNPs) from individu-als around the globe. The Genome-Wide Association Studies (GWAS) are beginning to provide valuable clues to risk of disease development and phenotypic association for diverse diseases [13-19]. The clinical significance of the SNPs is incorporated in the Clinical Variations (ClinVar) database [20] from the National Center for Biotechnology Information (NCBI).Basic and translational researchers are well into the post genome era, but the current generations of physicians (with the exception of medical geneticists) are lagging far behind [21,22]. The Medical schools around the globe have been slow to incorporate modules on clinical genomics. Although use of genetic counselors has been the traditional medical approach, the field of genomics is highly complex and its datasets continuously evolving. Thus, a major gap exists between the advances in the genomics field and the physician interface with patients [23]. Furthermore, the EHR systems that are in wide use today offer little support for incorporation of genomics data. In a recent survey, only a small percentage of EHR specialists, primary care clinicians, medical geneticists, and genetic counselors surveyed (9%), felt that the EHR had an impact on genomics medicine [24]. There is a strong need for incorporating the genomics data into the EHR, if the advances in genetics are to help clinicians make the best-informed treatment decisions. In the past, genet-ic information was gathered, and patients counseled about the findings, only in families with a history of a particular disease or disorder. To begin to address this, the Electronic Medical Records and Genomics (eMERGE) Network, a National Human Genome Research Institute (NHGRI)-funded consortium was created. The eMERGE Network was tasked with developing methods and best practices for the utilization of the EHR as a tool for genomic research [25-27]. The network currently incor-porates nine geographically distinct groups around the US. The initial focus of the eMERGE phenotypes included cataract and High Density Lipoprotein (HDL), dementia, electrocardiograph-ic QRS duration, peripheral arterial disease, type 2 diabetes and hypothyroidism [27]. The eMERGE is now in its sixth year and second funding cycle (eMERGE II) and continues to make ad-vances in the field of genomics and health-care informatics. The first cycle of eMERGE had three major aims: (i) use EHR data for electronic phenotyping, (ii) conduct GWAS using the phenotypes and (iii) explore the ethical, legal, and social implications (ELSI) associated with EHR-based GWAS and wide-scale data sharing [25]. A Phenotype KnowledgeBase (PheKB) effort was created to develop phenotypic algorithms across the eMERGE network to facilitate mining the large-scale data.Integration of the genomics data into the EHR system faces considerable hurdles [28-30]. At the present time, no commercial EHR system is available that incorporates genomic data. Furthermore, the pharmacogenetic information, which could have a major impact in reducing adverse drug effects and efficacy, is still rarely used. This is despite strong evidence for clinical validity and utility for the pharmacogenetics use in the clinic [31-33].Other challenges for integration include the amount of the big data the GWAS studies generate (in terabytes), the rapidly expanding SNP variations dataset and the difficulty of establishing meaningful interpretations from these vast amounts of genetic data. The Version 1.0 of the eMERGE data encompassed more than 13 million SNPs from 42,000 samples [26]. In addition, there are a variety of EHR systems becoming available and a standard for handling the vast genomics information is lacking.