Genome-wide association and pathway analysis of left ventricular function after anthracycline exposure in adults.

BACKGROUND: Anthracyclines are important chemotherapeutic agents, but their use is limited by cardiotoxicity. Candidate gene and genome-wide studies have identified putative risk loci for overt cardiotoxicity and heart failure, but there has been no comprehensive assessment of genomic variation influencing the intermediate phenotype of anthracycline-related changes in left ventricular (LV) function. The purpose of this study was to identify genetic factors influencing changes in LV function after anthracycline chemotherapy. METHODS: We conducted a genome-wide association study (GWAS) of change in LV function after anthracycline exposure in 385 patients identified from BioVU, a resource linking DNA samples to de-identified electronic medical record data. Variants with P values less than 1×10 were independently tested for replication in a cohort of 181 anthracycline-exposed patients from a prospective clinical trial. Pathway analysis was performed to assess combined effects of multiple genetic variants. RESULTS: Both cohorts were middle-aged adults of predominantly European descent. Among 11 candidate loci identified in discovery GWAS, one single nucleotide polymorphism near PR domain containing 2, with ZNF domain (PRDM2), rs7542939, had a combined P value of 6.5×10 in meta-analysis. Eighteen Kyoto Encyclopedia of Gene and Genomes pathways showed strong enrichment for variants associated with the primary outcome. Identified pathways related to DNA repair, cellular metabolism, and cardiac remodeling. CONCLUSION: Using genome-wide association we identified a novel candidate susceptibility locus near PRDM2. Variation in genes belonging to pathways related to DNA repair, metabolism, and cardiac remodeling may influence changes in LV function after anthracycline exposure.

Genetic determinants of variability in warfarin response after the dose-titration phase.

OBJECTIVES: Genetic factors contribute considerably toward variability in warfarin dose requirements and are important in the dose-titration phase; their effects on the stability of anticoagulation later in therapy are not known. METHODS: Using deidentified electronic medical records linked to a DNA-biobank, we studied 140 African-Americans and 943 European-Americans after the warfarin dose-titration phase. We genotyped 12 single nucleotide polymorphisms in genes (CYP2C9, VKORC1, CYP4F2, GGCX, EPHX1, CALU) associated with altered warfarin dose requirements and tested their associations with international normalized ratio variability (INRVAR) and percent time in therapeutic range in European-Americans and African-Americans. RESULTS: One allele copy of rs2108622 in CYP4F2 was associated with a 15% [95% confidence interval (CI): 1-26, P=0.03] decrease in the median INRVAR in European-Americans. In African-Americans, GGCX variants rs11676382 and rs699664 were associated with 4.16-fold (95% CI: 1.45-11.97, P=0.009) and 1.50-fold (95% CI: 1.07-2.08, P=0.02) changes in the median INRVAR per variant allele copy, respectively; rs11676382 was also significantly associated with a 23.19% (95% CI: 5.89-40.48, P=0.01) decrease in time in therapeutic range. The total variation in INRVAR explained by both clinical factors and rs2108622 was 5.2% for European-Americans. In African-Americans, the inclusion of GGCX variants rs11676382 and rs699664, and the CYP2C9*8 variant rs7900194 explained ∼29% of the variation in INRVAR. CONCLUSION: The stability of anticoagulation after the warfarin dose-titration phase is differentially affected by variants in CYP4F2 in European-Americans and GGCX loci in African-Americans.

A prognostic model based on readily available clinical data enriched a pre-emptive pharmacogenetic testing program.

OBJECTIVES: We describe the development, implementation, and evaluation of a model to pre-emptively select patients for genotyping based on medication exposure risk. STUDY DESIGN AND SETTING: Using deidentified electronic health records, we derived a prognostic model for the prescription of statins, warfarin, or clopidogrel. The model was implemented into a clinical decision support (CDS) tool to recommend pre-emptive genotyping for patients exceeding a prescription risk threshold. We evaluated the rule on an independent validation cohort and on an implementation cohort, representing the population in which the CDS tool was deployed. RESULTS: The model exhibited moderate discrimination with area under the receiver operator characteristic curves ranging from 0.68 to 0.75 at 1 and 2 years after index dates. Risk estimates tended to underestimate true risk. The cumulative incidences of medication prescriptions at 1 and 2 years were 0.35 and 0.48, respectively, among 1,673 patients flagged by the model. The cumulative incidences in the same number of randomly sampled subjects were 0.12 and 0.19, and in patients over 50 years with the highest body mass indices, they were 0.22 and 0.34. CONCLUSION: We demonstrate that prognostic algorithms can guide pre-emptive pharmacogenetic testing toward those likely to benefit from it.

Genetics of glucocorticoid-associated osteonecrosis in children with acute lymphoblastic leukemia.

Glucocorticoids are important therapy for acute lymphoblastic leukemia (ALL) and their major adverse effect is osteonecrosis. Our goal was to identify genetic and nongenetic risk factors for osteonecrosis. We performed a genome-wide association study of single nucleotide polymorphisms (SNPs) in a discovery cohort comprising 2285 children with ALL, treated on the Children's Oncology Group AALL0232 protocol (NCT00075725), adjusting for covariates. The minor allele at SNP rs10989692 (near the glutamate receptor GRIN3A locus) was associated with osteonecrosis (hazard ratio = 2.03; P = 3.59 × 10(-7)). The association was supported by 2 replication cohorts, including 361 children with ALL on St. Jude's Total XV protocol (NCT00137111) and 309 non-ALL patients from Vanderbilt University's BioVU repository treated with glucocorticoids (odds ratio [OR] = 1.87 and 2.26; P = .063 and .0074, respectively). In a meta-analysis, rs10989692 was also highest ranked (P = 2.68 × 10(-8)), and the glutamate pathway was the top ranked pathway (P = 9.8 × 10(-4)). Osteonecrosis-associated glutamate receptor variants were also associated with other vascular phenotypes including cerebral ischemia (OR = 1.64; P = 2.5 × 10(-3)), and arterial embolism and thrombosis (OR = 1.88; P = 4.2 × 10(-3)). In conclusion, osteonecrosis was associated with inherited variations near glutamate receptor genes. Further understanding this association may allow interventions to decrease osteonecrosis. These trials are registered at www.clinicaltrials.gov as #NCT00075725 and #NCT00137111.

Genome-Wide Association Study of Serum Creatinine Levels during Vancomycin Therapy.

Vancomycin, a commonly used antibiotic, can be nephrotoxic. Known risk factors such as age, creatinine clearance, vancomycin dose / dosing interval, and concurrent nephrotoxic medications fail to accurately predict nephrotoxicity. To identify potential genomic risk factors, we performed a genome-wide association study (GWAS) of serum creatinine levels while on vancomycin in 489 European American individuals and validated findings in three independent cohorts totaling 439 European American individuals. In primary analyses, the chromosome 6q22.31 locus was associated with increased serum creatinine levels while on vancomycin therapy (most significant variant rs2789047, risk allele A, ß = -0.06, p = 1.1 x 10(-7)). SNPs in this region had consistent directions of effect in the validation cohorts, with a meta-p of 1.1 x 10(-7). Variation in this region on chromosome 6, which includes the genes TBC1D32/C6orf170 and GJA1 (encoding connexin43), may modulate risk of vancomycin-induced kidney injury.

Phenotype-Driven Plasma Biobanking Strategies and Methods.

Biobank development and integration with clinical data from electronic medical record (EMR) databases have enabled recent strides in genomic research and personalized medicine. BioVU, Vanderbilt's DNA biorepository linked to de-identified clinical EMRs, has proven fruitful in its capacity to extensively appeal to numerous areas of biomedical and clinical research, supporting the discovery of genotype-phenotype interactions. Expanding on experiences in BioVU creation and development, we have recently embarked on a parallel effort to collect plasma in addition to DNA from blood specimens leftover after routine clinical testing at Vanderbilt. This initiative offers expanded utility of BioVU by combining proteomic and metabolomic approaches with genomics and/or clinical outcomes, widening the breadth for potential research and subsequent future impact on clinical care. Here, we describe the considerations and components involved in implementing a plasma biobank program from a feasibility assessment through pilot sample collection.

Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action.

A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin's antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets.

Examining rare and low-frequency genetic variants previously associated with lone or familial forms of atrial fibrillation in an electronic medical record system: a cautionary note.

BACKGROUND: Studies in individuals or small kindreds have implicated rare variants in 25 different genes in lone and familial atrial fibrillation (AF) using linkage and segregation analysis, functional characterization, and rarity in public databases. Here, we used a cohort of 20 204 patients of European or African ancestry with electronic medical records and exome chip data to compare the frequency of AF among carriers and noncarriers of these rare variants. METHODS AND RESULTS: The exome chip included 19 of 115 rare variants, in 9 genes, previously associated with lone or familial AF. Using validated algorithms querying a combination of clinical notes, structured billing codes, ECG reports, and procedure codes, we identified 1056 AF cases (>18 years) and 19 148 non-AF controls (>50 years) with available genotype data on the Illumina HumanExome BeadChip v.1.0 in the Vanderbilt electronic medical record-linked DNA repository, BioVU. Known correlations between AF and common variants at 4q25 were replicated. None of the 19 variants previously associated with AF were over-represented among AF cases (P>0.1 for all), and the frequency of variant carriers among non-AF controls was >0.1% for 14 of 19. Repeat analyses using non-AF controls aged >60 (n=14 904), >70 (n=9670), and >80 (n=4729) years did not influence these findings. CONCLUSIONS: Rare variants previously implicated in lone or familial forms of AF present on the exome chip are detected at low frequencies in a general population but are not associated with AF. These findings emphasize the need for caution when ascribing variants as pathogenic or causative.

A genome-wide association study of heparin-induced thrombocytopenia using an electronic medical record.

Heparin-induced thrombocytopenia (HIT) is an unpredictable, potentially catastrophic adverse effect of heparin treatment resulting from an immune response to platelet factor 4 (PF4)/heparin complexes. No genome-wide evaluations have been performed to identify potential genetic influences on HIT. Here, we performed a genome-wide association study (GWAS) and candidate gene study using HIT cases and controls identified using electronic medical records (EMRs) coupled to a DNA biobank and attempted to replicate GWAS associations in an independent cohort. We subsequently investigated influences of GWAS-associated single nucleotide polymorphisms (SNPs) on PF4/heparin antibodies in non-heparin treated individuals. In a recessive model, we observed significant SNP associations (odds ratio [OR] 18.52; 95% confidence interval [CI] 6.33-54.23; p=3.18×10(-9)) with HIT near the T-Cell Death-Associated Gene 8 (TDAG8). These SNPs are in linkage disequilibrium with a missense TDAG8 SNP. TDAG8 SNPs trended toward an association with HIT in replication analysis (OR 5.71; 0.47-69.22; p=0.17), and the missense SNP was associated with PF4/heparin antibody levels and positive PF4/heparin antibodies in non-heparin treated patients (OR 3.09; 1.14-8.13; p=0.02). In the candidate gene study, SNPs at HLA-DRA were nominally associated with HIT (OR 0.25; 0.15-0.44; p=2.06×10(-6)). Further study of TDAG8 and HLA-DRA SNPs is warranted to assess their influence on the risk of developing HIT.

Genotype and risk of major bleeding during warfarin treatment.

AIM: To determine whether genetic variants associated with warfarin dose variability were associated with increased risk of major bleeding during warfarin therapy. MATERIALS & METHODS: Using Vanderbilt's DNA biobank we compared the prevalence of CYP2C9, VKORC1 and CYP4F2 variants in 250 cases with major bleeding and 259 controls during warfarin therapy. RESULTS: CYP2C9*3 was the only allele that differed significantly among cases (14.2%) and controls (7.8%; p = 0.022). In the 214 (85.6%) cases with a major bleed 30 or more days after warfarin initiation, CYP2C9*3 was the only variant associated with bleeding (adjusted odds ratio: 2.05; 95% CI: 1.04, 4.04). CONCLUSION: The CYP2C9*3 allele may double the risk of major bleeding among patients taking warfarin for 30 or more days.

Biobanks and electronic medical records: enabling cost-effective research.

The use of electronic medical record data linked to biological specimens in health care settings is expected to enable cost-effective and rapid genomic analyses. Here, we present a model that highlights potential advantages for genomic discovery and describe the operational infrastructure that facilitated multiple simultaneous discovery efforts.

Using systems approaches to address challenges for clinical implementation of pharmacogenomics.

Many genetic variants have been shown to affect drug response through changes in drug efficacy and likelihood of adverse effects. Much of pharmacogenomic science has focused on discovering and clinically implementing single gene variants with large effect sizes. Given the increasing complexities of drug responses and their variability, a systems approach may be enabling for discovery of new biology in this area. Further, systems approaches may be useful in addressing challenges in moving these data to clinical implementation, including creation of predictive models of drug response phenotypes, improved clinical decision-making through complex biological models, improving strategies for integrating genomics into clinical practice, and evaluating the impact of implementation programs on public health.

Utilization of an EMR-biorepository to identify the genetic predictors of calcineurin-inhibitor toxicity in heart transplant recipients.

Calcineurin-inhibitors CI are immunosuppressive agents prescribed to patients after solid organ transplant to prevent rejection. Although these drugs have been transformative for allograft survival, long-term use is complicated by side effects including nephrotoxicity. Given the narrow therapeutic index of CI, therapeutic drug monitoring is used to prevent acute rejection from underdosing and acute toxicity from overdosing, but drug monitoring does not alleviate long-term side effects. Patients on calcineurin-inhibitors for long periods almost universally experience declines in renal function, and a subpopulation of transplant recipients ultimately develop chronic kidney disease that may progress to end stage renal disease attributable to calcineurin inhibitor toxicity (CNIT). Pharmacogenomics has the potential to identify patients who are at high risk for developing advanced chronic kidney disease caused by CNIT and providing them with existing alternate immunosuppressive therapy. In this study we utilized BioVU, Vanderbilt University Medical Center's DNA biorepository linked to de-identified electronic medical records to identify a cohort of 115 heart transplant recipients prescribed calcineurin-inhibitors to identify genetic risk factors for CNIT We identified 37 cases of nephrotoxicity in our cohort, defining nephrotoxicity as a monthly median estimated glomerular filtration rate (eGFR)<30 mL/min/1.73 m2 at least six months post-transplant for at least three consecutive months. All heart transplant patients were genotyped on the Illumina ADME Core Panel, a pharmacogenomic genotyping platform that assays 184 variants across 34 genes. In Cox regression analysis adjusting for age at transplant, pre-transplant chronic kidney disease, pre-transplant diabetes, and the three most significant principal components (PCAs), we did not identify any markers that met our multiple-testing threshold. As a secondary analysis we also modeled post-transplant eGFR directly with linear mixed models adjusted for age at transplant, cyclosporine use, median BMI, and the three most significant principal components. While no SNPs met our threshold for significance, a SNP previously identified in genetic studies of the dosing of tacrolimus CYP34A rs776746, replicated in an adjusted analysis at an uncorrected p-value of 0.02 (coeff(S.E.)=14.60(6.41)). While larger independent studies will be required to further validate this finding, this study underscores the EMRs usefulness as a resource for longitudinal pharmacogenetic study designs.

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.

Electronic health record design and implementation for pharmacogenomics: a local perspective.

PURPOSE: The design of electronic health records to translate genomic medicine into clinical care is crucial to successful introduction of new genomic services, yet there are few published guides to implementation. METHODS: The design, implemented features, and evolution of a locally developed electronic health record that supports a large pharmacogenomics program at a tertiary-care academic medical center was tracked over a 4-year development period. RESULTS: Developers and program staff created electronic health record mechanisms for ordering a pharmacogenomics panel in advance of clinical need (preemptive genotyping) and in response to a specific drug indication. Genetic data from panel-based genotyping were sequestered from the electronic health record until drug-gene interactions met evidentiary standards and deemed clinically actionable. A service to translate genotype to predicted drug-response phenotype populated a summary of drug-gene interactions, triggered inpatient and outpatient clinical decision support, updated laboratory records, and created gene results within online personal health records. CONCLUSION: The design of a locally developed electronic health record supporting pharmacogenomics has generalizable utility. The challenge of representing genomic data in a comprehensible and clinically actionable format is discussed along with reflection on the scalability of the model to larger sets of genomic data.

Stakeholder engagement: a key component of integrating genomic information into electronic health records.

Integrating genomic information into clinical care and the electronic health record can facilitate personalized medicine through genetically guided clinical decision support. Stakeholder involvement is critical to the success of these implementation efforts. Prior work on implementation of clinical information systems provides broad guidance to inform effective engagement strategies. We add to this evidence-based recommendations that are specific to issues at the intersection of genomics and the electronic health record. We describe stakeholder engagement strategies employed by the Electronic Medical Records and Genomics Network, a national consortium of US research institutions funded by the National Human Genome Research Institute to develop, disseminate, and apply approaches that combine genomic and electronic health record data. Through select examples drawn from sites of the Electronic Medical Records and Genomics Network, we illustrate a continuum of engagement strategies to inform genomic integration into commercial and homegrown electronic health records across a range of health-care settings. We frame engagement as activities to consult, involve, and partner with key stakeholder groups throughout specific phases of health information technology implementation. Our aim is to provide insights into engagement strategies to guide genomic integration based on our unique network experiences and lessons learned within the broader context of implementation research in biomedical informatics. On the basis of our collective experience, we describe key stakeholder practices, challenges, and considerations for successful genomic integration to support personalized medicine.

Ethical and practical challenges to studying patients who opt out of large-scale biorepository research.

Large-scale biorepositories that couple biologic specimens with electronic health records containing documentation of phenotypic expression can accelerate scientific research and discovery. However, differences between those subjects who participate in biorepository-based research and the population from which they are drawn may influence research validity. While an opt-out approach to biorepository-based research enhances inclusiveness, empirical research evaluating voluntariness, risk, and the feasibility of an opt-out approach is sparse, and factors influencing patients' decisions to opt out are understudied. Determining why patients choose to opt out may help to improve voluntariness, however there may be ethical and logistical challenges to studying those who opt out. In this perspective paper, the authors explore what is known about research based on the opt-out model, describe a large-scale biorepository that leverages the opt-out model, and review specific ethical and logistical challenges to bridging the research gaps that remain.

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.

Attention deficit/hyperactivity disorder-derived coding variation in the dopamine transporter disrupts microdomain targeting and trafficking regulation.

Attention deficit/hyperactivity disorder (ADHD) is the most commonly diagnosed disorder of school-age children. Although genetic and brain-imaging studies suggest a contribution of altered dopamine (DA) signaling in ADHD, evidence of signaling perturbations contributing to risk is largely circumstantial. The presynaptic, cocaine- and amphetamine (AMPH)-sensitive DA transporter (DAT) constrains DA availability at presynaptic and postsynaptic receptors following vesicular release and is targeted by the most commonly prescribed ADHD therapeutics. Using polymorphism discovery approaches with an ADHD cohort, we identified a hDAT (human DAT) coding variant, R615C, located in the distal C terminus of the transporter, a region previously implicated in constitutive and regulated transporter trafficking. Here, we demonstrate that, whereas wild-type DAT proteins traffic in a highly regulated manner, DAT 615C proteins recycle constitutively and demonstrate insensitivity to the endocytic effects of AMPH and PKC (protein kinase C) activation. The disrupted regulation of DAT 615C parallels a redistribution of the transporter variant away from GM1 ganglioside- and flotillin1-enriched membranes, and is accompanied by altered CaMKII (calcium/calmodulin-dependent protein kinase II) and flotillin-1 interactions. Using C-terminal peptides derived from wild-type DAT and the R615C variant, we establish that the DAT 615C C terminus can act dominantly to preclude AMPH regulation of wild-type DAT. Mutagenesis of DAT C-terminal sequences suggests that phosphorylation of T613 may be important in sorting DAT between constitutive and regulated pathways. Together, our studies support a coupling of DAT microdomain localization with transporter regulation and provide evidence of perturbed DAT activity and DA signaling as a risk determinant for ADHD.

Predicting warfarin dosage in European-Americans and African-Americans using DNA samples linked to an electronic health record.

AIM: Warfarin pharmacogenomic algorithms reduce dosing error, but perform poorly in non-European-Americans. Electronic health record (EHR) systems linked to biobanks may allow for pharmacogenomic analysis, but they have not yet been used for this purpose. PATIENTS & METHODS: We used BioVU, the Vanderbilt EHR-linked DNA repository, to identify European-Americans (n = 1022) and African-Americans (n = 145) on stable warfarin therapy and evaluated the effect of 15 pharmacogenetic variants on stable warfarin dose. RESULTS: Associations between variants in VKORC1, CYP2C9 and CYP4F2 with weekly dose were observed in European-Americans as well as additional variants in CYP2C9 and CALU in African-Americans. Compared with traditional 5 mg/day dosing, implementing the US FDA recommendations or the International Warfarin Pharmacogenomics Consortium (IWPC) algorithm reduced error in weekly dose in European-Americans (13.5-12.4 and 9.5 mg/week, respectively) but less so in African-Americans (15.2-15.0 and 13.8 mg/week, respectively). By further incorporating associated variants specific for European-Americans and African-Americans in an expanded algorithm, dose-prediction error reduced to 9.1 mg/week (95% CI: 8.4-9.6) in European-Americans and 12.4 mg/week (95% CI: 10.0-13.2) in African-Americans. The expanded algorithm explained 41 and 53% of dose variation in African-Americans and European-Americans, respectively, compared with 29 and 50%, respectively, for the IWPC algorithm. Implementing these predictions via dispensable pill regimens similarly reduced dosing error. CONCLUSION: These results validate EHR-linked DNA biorepositories as real-world resources for pharmacogenomic validation and discovery.