The Clinical Pharmacogenetics Implementation Consortium Guideline for SLCO1B1, ABCG2, and CYP2C9 genotypes and Statin-Associated Musculoskeletal Symptoms.

Statins reduce cholesterol, prevent cardiovascular disease, and are among the most commonly prescribed medications in the world. Statin-associated musculoskeletal symptoms (SAMS) impact statin adherence and ultimately can impede the long-term effectiveness of statin therapy. There are several identified pharmacogenetic variants that impact statin disposition and adverse events during statin therapy. SLCO1B1 encodes a transporter (SLCO1B1; alternative names include OATP1B1 or OATP-C) that facilitates the hepatic uptake of all statins. ABCG2 encodes an efflux transporter (BCRP) that modulates the absorption and disposition of rosuvastatin. CYP2C9 encodes a phase I drug metabolizing enzyme responsible for the oxidation of some statins. Genetic variation in each of these genes alters systemic exposure to statins (i.e., simvastatin, rosuvastatin, pravastatin, pitavastatin, atorvastatin, fluvastatin, lovastatin), which can increase the risk for SAMS. We summarize the literature supporting these associations and provide therapeutic recommendations for statins based on SLCO1B1, ABCG2, and CYP2C9 genotype with the goal of improving the overall safety, adherence, and effectiveness of statin therapy. This document replaces the 2012 and 2014 Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for SLCO1B1 and simvastatin-induced myopathy.

SLCO1B1*5 Allele Is Associated With Atorvastatin Discontinuation and Adverse Muscle Symptoms in the Context of Routine Care.

The SLCO1B1 genotype is known to influence patient adherence to statin therapy, in part by increasing the risk for statin-associated musculoskeletal symptoms (SAMSs). The SLCO1B1*5 allele has previously been associated with simvastatin discontinuation and SAMSs. Prior analyses of the relationship between SLCO1B1*5 and atorvastatin muscle side effects have been inconclusive due to insufficient power. We now quantify the impact of SLCO1B1*5 on atorvastatin discontinuation and SAMSs in a large observational cohort using electronic medical record data from a single health care system. In our study cohort (n = 1,627 patients exposed to atorvastatin during the course of routine clinical care), 56% (n = 912 of 1,627 patients) discontinued atorvastatin and 18% (n = 303 of 1,627 patients) developed SAMSs. A univariate model revealed that SLCO1B1*5 increased the likelihood that patients would stop atorvastatin during routine care (odds ratio 1.2; 95% confidence interval (CI), 1.1-1.5; P = 0.04). A multivariate Cox proportional hazards model further demonstrated that this same variant was associated with time to atorvastatin discontinuation (hazard ratio 1.2; 95% CI, 1.1-1.4; P = 0.004). Additional time-to-event analyses also revealed that SCLO1B1*5 was associated with SAMSs (hazard ratio 1.4; 95% CI, 1.1-1.7; P = 0.02). Atorvastatin discontinuation was associated with SAMSs (odds ratio 1.67; P = 0.0001) in our cohort.

Clinical and pharmacogenetic predictors of circulating atorvastatin and rosuvastatin concentrations in routine clinical care.

BACKGROUND: A barrier to statin therapy is myopathy associated with elevated systemic drug exposure. Our objective was to examine the association between clinical and pharmacogenetic variables and statin concentrations in patients. METHODS AND RESULTS: In total, 299 patients taking atorvastatin or rosuvastatin were prospectively recruited at an outpatient referral center. The contribution of clinical variables and transporter gene polymorphisms to statin concentration was assessed using multiple linear regression. We observed 45-fold variation in statin concentration among patients taking the same dose. After adjustment for sex, age, body mass index, ethnicity, dose, and time from last dose, SLCO1B1 c.521T>C (P<0.001) and ABCG2 c.421C>A (P<0.01) were important to rosuvastatin concentration (adjusted R(2)=0.56 for the final model). Atorvastatin concentration was associated with SLCO1B1 c.388A>G (P<0.01) and c.521T>C (P<0.05) and 4ß-hydroxycholesterol, a CYP3A activity marker (adjusted R(2)=0.47). A second cohort of 579 patients from primary and specialty care databases were retrospectively genotyped. In this cohort, genotypes associated with statin concentration were not differently distributed among dosing groups, implying providers had not yet optimized each patient's risk-benefit ratio. Nearly 50% of patients in routine practice taking the highest doses were predicted to have statin concentrations greater than the 90th percentile. CONCLUSIONS: Interindividual variability in statin exposure in patients is associated with uptake and efflux transporter polymorphisms. An algorithm incorporating genomic and clinical variables to avoid high atorvastatin and rosuvastatin levels is described; further study will determine whether this approach reduces incidence of statin myopathy.

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.

Individualized risk for statin-induced myopathy: current knowledge, emerging challenges and potential solutions.

Skeletal muscle toxicity is the primary adverse effect of statins. In this review, we summarize current knowledge regarding the genetic and nongenetic determinants of risk for statin induced myopathy. Many genetic factors were initially identified through candidate gene association studies limited to pharmacokinetic (PK) targets. Through genome-wide association studies, it has become clear that SLCO1B1 is among the strongest PK predictors of myopathy risk. Genome-wide association studies have also expanded our understanding of pharmacodynamic candidate genes, including RYR2. It is anticipated that deep resequencing efforts will define new loci with rare variants that also contribute, and sophisticated computational approaches will be needed to characterize gene-gene and gene-environment interactions. Beyond environment, race is a critical covariate, and its influence is only partly explained by geographic differences in the frequency of known pharmacodynamic and PK variants. As such, admixture analyses will be essential for a full understanding of statin-induced myopathy.

Lack of association between polymorphisms in the prostaglandin F2α receptor and solute carrier organic anion transporter family 2A1 genes and intraocular pressure response to prostaglandin analogs.

PURPOSE: To evaluate the association between variants in the prostaglandin F(α) receptor (PTGFR) and solute carrier organic anion transporter family 2A1 (SLCO2A1) genes and intraocular pressure (IOP) response to prostaglandin analogs. METHODS: The medical records of subjects with previously diagnosed open angle glaucoma or ocular hypertension were searched for intraocular pressure measurements before and after prescriptions of prostaglandin analogs. Stored DNA samples were genotyped for the following SNPs: rs3753380 (promoter region) and rs3766355 (intronic region) of the prostaglandin F(2α) receptor gene, and rs34550074 (Ala396Thr) of SLCO2A1. The mean change in IOP by genotype was measured. RESULTS: Prostaglandin analogs were prescribed to 267 subjects; 242 (204 right eyes, 205 left eyes) met the inclusion/exclusion criteria for the current study. There was no significant association between genotype and IOP response to prostaglandin analogs (p = 0.48, p = 0.54, p = 0.90). CONCLUSION: In summary, we found no indication for an association between SNPs in the prostaglandin F(2α) receptor gene or SLCO2A1 and IOP response to prostaglandin analogs in a population of European descent.

Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies.

We repurposed existing genotypes in DNA biobanks across the Electronic Medical Records and Genomics network to perform a genome-wide association study for primary hypothyroidism, the most common thyroid disease. Electronic selection algorithms incorporating billing codes, laboratory values, text queries, and medication records identified 1317 cases and 5053 controls of European ancestry within five electronic medical records (EMRs); the algorithms' positive predictive values were 92.4% and 98.5% for cases and controls, respectively. Four single-nucleotide polymorphisms (SNPs) in linkage disequilibrium at 9q22 near FOXE1 were associated with hypothyroidism at genome-wide significance, the strongest being rs7850258 (odds ratio [OR] 0.74, p = 3.96 × 10(-9)). This association was replicated in a set of 263 cases and 1616 controls (OR = 0.60, p = 5.7 × 10(-6)). A phenome-wide association study (PheWAS) that was performed on this locus with 13,617 individuals and more than 200,000 patient-years of billing data identified associations with additional phenotypes: thyroiditis (OR = 0.58, p = 1.4 × 10(-5)), nodular (OR = 0.76, p = 3.1 × 10(-5)) and multinodular (OR = 0.69, p = 3.9 × 10(-5)) goiters, and thyrotoxicosis (OR = 0.76, p = 1.5 × 10(-3)), but not Graves disease (OR = 1.03, p = 0.82). Thyroid cancer, previously associated with this locus, was not significantly associated in the PheWAS (OR = 1.29, p = 0.09). The strongest association in the PheWAS was hypothyroidism (OR = 0.76, p = 2.7 × 10(-13)), which had an odds ratio that was nearly identical to that of the curated case-control population in the primary analysis, providing further validation of the PheWAS method. Our findings indicate that EMR-linked genomic data could allow discovery of genes associated with many diseases without additional genotyping cost.

Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks.

Genome-wide association studies (GWAS) are routinely being used to examine the genetic contribution to complex human traits, such as high-density lipoprotein cholesterol (HDL-C). Although HDL-C levels are highly heritable (h(2)∼0.7), the genetic determinants identified through GWAS contribute to a small fraction of the variance in this trait. Reasons for this discrepancy may include rare variants, structural variants, gene-environment (GxE) interactions, and gene-gene (GxG) interactions. Clinical practice-based biobanks now allow investigators to address these challenges by conducting GWAS in the context of comprehensive electronic medical records (EMRs). Here we apply an EMR-based phenotyping approach, within the context of routine care, to replicate several known associations between HDL-C and previously characterized genetic variants: CETP (rs3764261, p = 1.22e-25), LIPC (rs11855284, p = 3.92e-14), LPL (rs12678919, p = 1.99e-7), and the APOA1/C3/A4/A5 locus (rs964184, p = 1.06e-5), all adjusted for age, gender, body mass index (BMI), and smoking status. By using a novel approach which censors data based on relevant co-morbidities and lipid modifying medications to construct a more rigorous HDL-C phenotype, we identified an association between HDL-C and TRIB1, a gene which previously resisted identification in studies with larger sample sizes. Through the application of additional analytical strategies incorporating biological knowledge, we further identified 11 significant GxG interaction models in our discovery cohort, 8 of which show evidence of replication in a second biobank cohort. The strongest predictive model included a pairwise interaction between LPL (which modulates the incorporation of triglyceride into HDL) and ABCA1 (which modulates the incorporation of free cholesterol into HDL). These results demonstrate that gene-gene interactions modulate complex human traits, including HDL cholesterol.

Estrogen receptor genotype is associated with risk of venous thromboembolism during tamoxifen therapy.

Thromboembolism is a serious complication of tamoxifen therapy in women with breast cancer. Banked DNA from tamoxifen-treated individuals with breast cancer from the Marshfield Clinic Personalized Medicine Research Project, a population-based DNA repository, was tested for association between incidence of tamoxifen-associated thromboembolic events (TTE) and single nucleotide polymorphisms encoding the estrogen receptors 1,2 (ESR1, ESR2) or drug metabolism enzymes cytochrome P450 2D6 (CYP2D6) and aromatase (CYP19). TTE were experienced by 16/220 subjects with risk association noted for XbaI (rs9340799) genotype and ESR1 Xbal/PvuII diplotype (rs9340799 and rs2234693) (hazard ratio 3.47, 95% CI 0.97-12.44, P = 0.035). Association persisted after adjusting for classical risk factors including age at diagnosis and body mass index at enrollment. Initial evidence of association between increased risk for TTE and ESR1 genotype and ESR1 diplotype is presented. Determination of estrogen receptor genotype may identify a subset of women at increased risk for thromboembolism with tamoxifen exposure.

Marshfield Clinic Personalized Medicine Research Project (PMRP): design, methods and recruitment for a large population-based biobank.

OBJECTIVES: The objective of this paper is to summarize the planning for Phase I of the Marshfield Clinic Personalized Medicine Research Project (PMRP) and to describe the recruitment efforts in the first 2 years. METHODS: The purpose of Phase I of the PMRP was to develop a large population-based biobank with DNA, plasma and serum samples to facilitate genomics research. Planning and consultation was facilitated with three external boards: the Ethics and Security Advisory Board; the Scientific Advisory Board; and the Community Advisory Group. Commencing in September 2002, residents aged 18 and above who resided in 1 of 19 zip codes surrounding Marshfield, WI, USA, were invited to participate. After providing written informed consent, participants completed brief questionnaires that included questions about demographics, some environmental exposures, family history of disease, and adverse drug reactions, as well as family members living in the study area. Participants provided 50 ml of blood from which DNA was extracted and plasma and serum samples were stored. The informed consent document allowed access to electronic medical records and included language about non-disclosure of personal research results. A tick-off box was also included so that participants could either allow or decline subsequent recontact for future research studies. RESULTS: A total of 17,463 subjects were enrolled during the first 23 months of recruitment (44.3% of the residents who the Research Project Assistants were able to contact). The participants ranged in age from 18 to 98.5 years (mean = 48.9, median = 48); 57.2% (n = 9986) were female. Self-reported race in the study cohort was similar to the year 2000 census for Wood County, WI, USA, with the majority (98%) reporting themselves to be White Caucasian. The majority of subjects (n = 13,391, 76.7%) indicated that they had German ancestry. Only 142 participants (< 1%) opted out on the consent form for contact for future studies. The majority of the cohort reported that their current area of residence was a suburb, city or village (n = 10630, 60.87%); the remainder reported residence in a rural home or hobby farm (n = 5365, 30.72%), or a working farm or ranch (n = 1451, 8.31%). More than half the cohort (n = 9409, 53.88%) had lived on a working farm at some point in their life. CONCLUSION: The PMRP database will allow research in three areas: genetic epidemiology, pharmacogenetics, and population genetics. The size and the stability of the population as well as the relative ethnic homogeneity will help facilitate longitudinal studies with valid research results that are not biased by population stratification.

Methylmercury induces apoptosis in cultured rat dorsal root ganglion neurons.

Methylmercury is known to have devastating effects on the mammalian nervous system. In order to characterize the dose dependence of methylmercury-induced neurotoxicity, we first studied neurite outgrowth from rat dorsal root ganglia explants. In this model, methylmercury inhibited neurite outgrowth with a TD(50) of approximately 0.5 microM. We then used this relationship to optimize dosing for subsequent transcriptional profiling analyses in two independent neuronal model systems: dissociated sensory neurons and PC12 cells. As seen in previous studies, the expression of a number of genes associated with oxidative stress was altered following a 6h challenge with 1 microM methylmercury. When PC12 cells were subjected to a longer exposure (24h), a relative increase was noted in the representation of genes associated with cell cycling and apoptosis. To confirm the presence of apoptosis in cultured neurons, we then applied TUNEL staining and bis-benzimide staining techniques to primary cultures of dissociated sensory neurons. After 24h, 1 microM methylmercury increased both DNA end-labeling (P<0.01) and nuclear fragmentation (P<0.02). The latter effect appeared to be dose-dependent.