Assessment of a pharmacogenomic marker panel in a polypharmacy population identified from electronic medical records.

BACKGROUND: The ADME Core Panel assays 184 variants across 34 pharmacogenes, many of which are difficult to accurately genotype with standard multiplexing methods. METHODS: We genotyped 326 frequently medicated individuals of European descent in Vanderbilt's biorepository linked to de-identified electronic medical records, BioVU, on the ADME Core Panel to assess quality and performance of the assay. We compared quality control metrics and determined the extent of direct and indirect marker overlap between the ADME Core Panel and the Illumina Omni1-Quad. RESULTS: We found the quality of the ADME Core Panel data to be high, with exceptions in select copy number variants and markers in certain genes (notably CYP2D6). Most of the common variants on the ADME panel are genotyped by the Omni1, but absent rare variants and copy number variants could not be accurately tagged by single markers. CONCLUSION: Our frequently medicated study population did not convincingly differ in allele frequency from reference populations, suggesting that heterogeneous clinical samples (with respect to medications) have similar allele frequency distributions in pharmacogenetics genes compared with reference populations.

Modulators of normal electrocardiographic intervals identified in a large electronic medical record.

BACKGROUND: Traditional electrocardiographic (ECG) reference ranges were derived from studies in communities or clinical trial populations. The distribution of ECG parameters in a large population presenting to a healthcare system has not been studied. OBJECTIVE: The purpose of this study was to define the contribution of age, race, gender, height, body mass index, and type 2 diabetes mellitus to normal ECG parameters in a population presenting to a healthcare system. METHODS: Study subjects were obtained from the Vanderbilt Synthetic Derivative, a de-identified image of the electronic medical record (EMR), containing more than 20 years of records on 1.7 million subjects. We identified 63,177 unique subjects with an ECG that was read as "normal" by the reviewing cardiologist. Using combinations of natural language processing and laboratory and billing code queries, we identified a subset of 32,949 subjects without cardiovascular disease, interfering medications, or abnormal electrolytes. The ethnic makeup was 77% Caucasian, 13% African American, 1% Hispanic, 1% Asian, and 8% unknown. RESULTS: The range that included 95% of normal PR intervals was 125-196 ms, QRS 69-103 ms, QT interval corrected with Bazett formula 365-458 ms, and heart rate 54-96 bpm. Linear regression modeling of patient characteristic effects reproduced known age and gender effects and identified novel associations with race, body mass index, and type 2 diabetes mellitus. A web-based application for patient-specific normal ranges is available online at http://biostat.mc.vanderbilt.edu/ECGPredictionInterval. CONCLUSION: Analysis of a large set of EMR-derived normal ECGs reproduced known associations, found new relationships, and established patient-specific normal ranges. Such knowledge informs clinical and genetic research and may improve understanding of normal cardiac physiology.

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, Vanderbilt's DNA biobank, at five single nucleotide polymorphisms (SNPs) with previously reported disease associations: atrial fibrillation, Crohn's 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 x 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.

Attitudes and perceptions of patients towards methods of establishing a DNA biobank.

BACKGROUND: This study aimed to assess patient attitudes as part of the planning process for a large-scale effort to collect genetic samples for research from excess clinical blood specimens ('DNA Databank' project). METHOD: A pre-tested, 38-item questionnaire was mailed to a random sample of 5,000 inpatients, outpatients, and emergency department patients. RESULTS: Approximately 20% of patients responded (n = 1003). Most were comfortable with anonymized genetic information being used for research (89.3%) and supported the potential benefits (98.7%). A binary logistic regression on the level of comfort with the DNA program shows that the variability in respondents' feelings about the program can best be explained by beliefs, age, and health status. Respondents were attitudinally segmented into 5 distinct categories. CONCLUSIONS: These data indicate general acceptance among respondents, but a subset of the population would be opposed to the program. This reinforces the need to broadly and continuously communicate with patients about the program and the ability to exclude a given sample. The effects of prior beliefs would benefit from further exploration.