Jill M. Pulley

Development of a Large-Scale De-Identified DNA Biobank to Enable Personalized Medicine

Our objective was to develop a DNA biobank linked to phenotypic data derived from an electronic medical record (EMR) system. An “opt‐out” model was implemented after significant review and revision. The plan included (i) development and maintenance of a de‐identified mirror image of the EMR, namely, the “synthetic derivative” (SD) and (ii) DNA extracted from discarded blood samples and linked to the SD. Surveys of patients indicated general acceptance of the concept, with only a minority (∼5%) opposing it. As a result, mechanisms to facilitate opt‐out included publicity and revision of a standard “consent to treatment” form. Algorithms for sample handling and procedures for de‐identification were developed and validated in order to ensure acceptable error rates (<0.3 and <0.1%, respectively). The rate of sample accrual is 700–900 samples/week. The advantages of this approach are the rate of sample acquisition and the diversity of phenotypes based on EMRs.

PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations

Motivation: Emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association scans (PheWAS) for disease–gene associations. We propose a novel method to scan phenomic data for genetic associations using International Classification of Disease (ICD9) billing codes, which are available in most EMR systems. We have developed a code translation table to automatically define 776 different disease populations and their controls using prevalent ICD9 codes derived from EMR data. As a proof of concept of this algorithm, we genotyped the first 6005 European–Americans accrued into BioVU, Vanderbilts DNA biobank, at five single nucleotide polymorphisms (SNPs) with previously reported disease associations: atrial fibrillation, Crohns disease, carotid artery stenosis, coronary artery disease, multiple sclerosis, systemic lupus erythematosus and rheumatoid arthritis. The PheWAS software generated cases and control populations across all ICD9 code groups for each of these five SNPs, and disease-SNP associations were analyzed. The primary outcome of this study was replication of seven previously known SNP–disease associations for these SNPs. Results: Four of seven known SNP–disease associations using the PheWAS algorithm were replicated with P-values between 2.8 × 10−6 and 0.011. The PheWAS algorithm also identified 19 previously unknown statistical associations between these SNPs and diseases at P < 0.01. This study indicates that PheWAS analysis is a feasible method to investigate SNP–disease associations. Further evaluation is needed to determine the validity of these associations and the appropriate statistical thresholds for clinical significance. Availability:The PheWAS software and code translation table are freely available at http://knowledgemap.mc.vanderbilt.edu/research. Contact: josh.denny@vanderbilt.edu

Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data

Candidate gene and genome-wide association studies (GWAS) have identified genetic variants that modulate risk for human disease; many of these associations require further study to replicate the results. Here we report the first large-scale application of the phenome-wide association study (PheWAS) paradigm within electronic medical records (EMRs), an unbiased approach to replication and discovery that interrogates relationships between targeted genotypes and multiple phenotypes. We scanned for associations between 3,144 single-nucleotide polymorphisms (previously implicated by GWAS as mediators of human traits) and 1,358 EMR-derived phenotypes in 13,835 individuals of European ancestry. This PheWAS replicated 66% (51/77) of sufficiently powered prior GWAS associations and revealed 63 potentially pleiotropic associations with P < 4.6 × 10−6 (false discovery rate < 0.1); the strongest of these novel associations were replicated in an independent cohort (n = 7,406). These findings validate PheWAS as a tool to allow unbiased interrogation across multiple phenotypes in EMR-based cohorts and to enhance analysis of the genomic basis of human disease.

Operational Implementation of Prospective Genotyping for Personalized Medicine: The Design of the Vanderbilt PREDICT Project

The promise of “personalized medicine” guided by an understanding of each individuals genome has been fostered by increasingly powerful and economical methods to acquire clinically relevant information. We describe the operational implementation of prospective genotyping linked to an advanced clinical decision‐support system to guide individualized health care in a large academic health center. This approach to personalized medicine entails engagement between patient and health‐care provider, identification of relevant genetic variations for implementation, assay reliability, point‐of‐care decision support, and necessary institutional investments. In one year, approximately 3,000 patients, most of whom were scheduled for cardiac catheterization, were genotyped on a multiplexed platform that included genotyping for CYP2C19 variants that modulate response to the widely used antiplatelet drug clopidogrel. These data are deposited into the electronic medical record (EMR), and point‐of‐care decision support is deployed when clopidogrel is prescribed for those with variant genotypes. The establishment of programs such as this is a first step toward implementing and evaluating strategies for personalized medicine.

Robust Replication of Genotype-Phenotype Associations across Multiple Diseases in an Electronic Medical Record

Large-scale DNA databanks linked to electronic medical record (EMR) systems have been proposed as an approach for rapidly generating large, diverse cohorts for discovery and replication of genotype-phenotype associations. However, the extent to which such resources are capable of delivering on this promise is unknown. We studied whether an EMR-linked DNA biorepository can be used to detect known genotype-phenotype associations for five diseases. Twenty-one SNPs previously implicated as common variants predisposing to atrial fibrillation, Crohn disease, multiple sclerosis, rheumatoid arthritis, or type 2 diabetes were successfully genotyped in 9483 samples accrued over 4 mo into BioVU, the Vanderbilt University Medical Center DNA biobank. Previously reported odds ratios (OR(PR)) ranged from 1.14 to 2.36. For each phenotype, natural language processing techniques and billing-code queries were used to identify cases (n = 70-698) and controls (n = 808-3818) from deidentified health records. Each of the 21 tests of association yielded point estimates in the expected direction. Previous genotype-phenotype associations were replicated (p < 0.05) in 8/14 cases when the OR(PR) was > 1.25, and in 0/7 with lower OR(PR). Statistically significant associations were detected in all analyses that were adequately powered. In each of the five diseases studied, at least one previously reported association was replicated. These data demonstrate that phenotypes representing clinical diagnoses can be extracted from EMR systems, and they support the use of DNA resources coupled to EMR systems as tools for rapid generation of large data sets required for replication of associations found in research cohorts and for discovery in genome science.

Principles of Human Subjects Protections Applied in an Opt-Out, De-identified Biobank

BioVU, the Vanderbilt DNA Databank, is one of few biobanks that qualifies as non‐human subjects research as determined by the local IRB and the federal Office of Human Research Protections (OHRP). BioVU accrues DNA samples extracted from leftover blood remaining from routine clinical testing. The resource is linked to a de‐identified version of data extracted from an Electronic Medical Record (EMR) system, termed the Synthetic Device (SD), in which all personal identifiers have been removed. Thus, there is no identifiable private information attached to the records. The Belmont Report enumerates the importance of the boundary between practice and research, and three principles: Respect for Persons, Beneficence, and Justice, which constitute the essential ethical framework by which IRBs and ethics committees judge the risks and benefi ts of research involving human subjects. BioVU was developed by designing and implementing new procedures, for which there were no previously established methods, which are consistent with the principles of the Belmont Report. These included special oversight and governance, new informatics technologies, provisions to accommodate patients’ preferences, as well as an extensive public education and communications component. Considerations of core principles and protections in the practical implementation of BioVU is the focus of this paper. Clin Trans Sci 2010; Volume #: 1–7

Identification of Genomic Predictors of Atrioventricular Conduction: Using Electronic Medical Records as a Tool for Genome Science

Background— Recent genome-wide association studies in which selected community populations are used have identified genomic signals in SCN10A influencing PR duration. The extent to which this can be demonstrated in cohorts derived from electronic medical records is unknown. Methods and Results— We performed a genome-wide association study on 2334 European American patients with normal ECGs without evidence of prior heart disease from the Vanderbilt DNA databank, BioVU, which accrues subjects from routine patient care. Subjects were identified by combinations of natural language processing, laboratory queries, and billing code queries of deidentified medical record data. Subjects were 58% female, of mean (±SD) age 54±15 years, and had mean PR intervals of 158±18 ms. Genotyping was performed with the use of the Illumina Human660W-Quad platform. Our results identify 4 single nucleotide polymorphisms (rs6800541, rs6795970, rs6798015, rs7430477) linked to SCN10A associated with PR interval (P=5.73×10−7 to 1.78×10−6). Conclusions— This genome-wide association study confirms a gene heretofore not implicated in cardiac pathophysiology as a modulator of PR interval in humans. This study is one of the first replication genome-wide association studies performed with the use of an electronic medical records–derived cohort, supporting their further use for genotype-phenotype analyses.Background— Recent genome-wide association studies in which selected community populations are used have identified genomic signals in SCN10A influencing PR duration. The extent to which this can be demonstrated in cohorts derived from electronic medical records is unknown. Methods and Results— We performed a genome-wide association study on 2334 European American patients with normal ECGs without evidence of prior heart disease from the Vanderbilt DNA databank, BioVU, which accrues subjects from routine patient care. Subjects were identified by combinations of natural language processing, laboratory queries, and billing code queries of deidentified medical record data. Subjects were 58% female, of mean (±SD) age 54±15 years, and had mean PR intervals of 158±18 ms. Genotyping was performed with the use of the Illumina Human660W-Quad platform. Our results identify 4 single nucleotide polymorphisms (rs6800541, rs6795970, rs6798015, rs7430477) linked to SCN10A associated with PR interval ( P =5.73×10−7 to 1.78×10−6). Conclusions— This genome-wide association study confirms a gene heretofore not implicated in cardiac pathophysiology as a modulator of PR interval in humans. This study is one of the first replication genome-wide association studies performed with the use of an electronic medical records–derived cohort, supporting their further use for genotype-phenotype analyses. # Clinical Perspective {#article-title-26}

Genome- and Phenome-Wide Analyses of Cardiac Conduction Identifies Markers of Arrhythmia Risk

Background— ECG QRS duration, a measure of cardiac intraventricular conduction, varies ≈2-fold in individuals without cardiac disease. Slow conduction may promote re-entrant arrhythmias. Methods and Results— We performed a genome-wide association study to identify genomic markers of QRS duration in 5272 individuals without cardiac disease selected from electronic medical record algorithms at 5 sites in the Electronic Medical Records and Genomics (eMERGE) network. The most significant loci were evaluated within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium QRS genome-wide association study meta-analysis. Twenty-three single-nucleotide polymorphisms in 5 loci, previously described by CHARGE, were replicated in the eMERGE samples; 18 single-nucleotide polymorphisms were in the chromosome 3 SCN5A and SCN10A loci, where the most significant single-nucleotide polymorphisms were rs1805126 in SCN5A with P=1.2×10−8 (eMERGE) and P=2.5×10−20 (CHARGE) and rs6795970 in SCN10A with P=6×10−6 (eMERGE) and P=5×10−27 (CHARGE). The other loci were in NFIA, near CDKN1A, and near C6orf204. We then performed phenome-wide association studies on variants in these 5 loci in 13859 European Americans to search for diagnoses associated with these markers. Phenome-wide association study identified atrial fibrillation and cardiac arrhythmias as the most common associated diagnoses with SCN10A and SCN5A variants. SCN10A variants were also associated with subsequent development of atrial fibrillation and arrhythmia in the original 5272 “heart-healthy” study population. Conclusions— We conclude that DNA biobanks coupled to electronic medical records not only provide a platform for genome-wide association study but also may allow broad interrogation of the longitudinal incidence of disease associated with genetic variants. The phenome-wide association study approach implicated sodium channel variants modulating QRS duration in subjects without cardiac disease as predictors of subsequent arrhythmias.

Clinically Actionable Genotypes Among 10,000 Patients With Preemptive Pharmacogenomic Testing

Since September 2010, more than 10,000 patients have undergone preemptive, panel‐based pharmacogenomic testing through the Vanderbilt Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment program. Analysis of the genetic data from the first 9,589 individuals reveals that the frequency of genetic variants is concordant with published allele frequencies. Based on five currently implemented drug–gene interactions, the multiplexed test identified one or more actionable variants in 91% of the genotyped patients and in 96% of African American patients. Using medication exposure data from electronic medical records, we compared a theoretical “reactive,” prescription‐triggered, serial single‐gene testing strategy with our preemptive, multiplexed genotyping approach. Reactive genotyping would have generated 14,656 genetic tests. These data highlight three advantages of preemptive genotyping: (i) the vast majority of patients carry at least one pharmacogenetic variant; (ii) data are available at the point of care; and (iii) there is a substantial reduction in testing burden compared with a reactive strategy.

Optimizing Drug Outcomes Through Pharmacogenetics: A Case for Preemptive Genotyping

Routine integration of genotype data into drug decision making could improve patient safety, particularly if many relevant genetic variants can be assayed simultaneously before prescribing the target drug. The frequency of opportunities for pharmacogenetic prescribing and the potential adverse events (AEs) mitigated are unknown. We examined the frequency with which 56 medications with known outcomes influenced by variant alleles were prescribed in a cohort of 52,942 medical home patients at Vanderbilt University Medical Center (VUMC). Within a 5‐year window, we estimated that 64.8% (95% confidence interval (CI): 64.4–65.2%) of individuals were exposed to at least one medication with an established pharmacogenetic association. Using previously published results for six medications with severe, well‐characterized, genetically linked AEs, we estimated that 383 events (95% CI, 212–552) could have been prevented with an effective preemptive genotyping program. Our results suggest that multiplexed, preemptive genotyping may represent an efficient alternative approach to current single‐use (“reactive”) methods and may also improve safety.