Combinatorial pharmacogenetics

Synergistic Epistasis and Systems Biology Approaches to Uncover a Pharmacogenomic Map Linked to Pain, Anti-Inflammatory and Immunomodulating Agents (PAIma) in a Healthy Cohort

The global public health addiction crisis has been stark, with over 932,400 deaths in the USA and Canada from opioid overdose since 1999-2020, surpassing the mortality rates at the top of the HIV/AIDS epidemic. Both nations exhibit opioid consumption rates significantly above the norm for developed countries. Analgesic type of opioids present both therapeutic benefits and substantial health risks, necessitating balanced drug regulation, careful prescribing, and dedicated opioid stewardship. The role of the cytochrome P450 2D6 (CYP2D6) system (Enzymatic functions) in metabolizing opioids highlights the potential of genotype-guided analgesia. By integrating Pharmacogenomics (PGx), this approach aims to optimize pain management, enhance safety, and reduce addiction risks. This understanding prompted the utilization of multifactor dimensionality reduction (MDR) to explore a range of phenotypes including PGx and gene-gene interactions (GGI) in a healthy cohort, thereby personalizing pain management strategies. The study sampled 100 unrelated healthy Western Iranians and 100 individuals from the 1000 Genome Project. Pre-testing involved searching for PGx annotations (variants associated with drug-gene-diseases) related to pain sensitivity and inflammation using the PharmGKB database, which identified 128 relevant genes. A questionnaire helped select 100 participants who had never used potent opioids but also other psychoactive agents (e.g., nicotine, amphetamines, etc.) and disease-related drugs. Whole-exome sequencing (WES) was then employed to analyze these genes in an Iranian cohort. Further analyses included MDR for identifying synergistic gene annotations and GGI for exploring complex gene interactions through the Visualization of Statistical Epistasis Networks (ViSEN). The study identified a Pain, Anti-Inflammatory, and Immunomodulating agents (PAIma) panel from the 128 genes, resulting in 55,590 annotations across 21 curated pathways. After filtering, 54 significant structural or regulatory variants were identified. This research also highlighted novel gene relationships involving the CYP3A5 gene, hsa-miR-355-5p, Paliperidone, and CYP2D6, which warrant further investigation. This study offers a novel pharmacogenetic framework that could potentially transform opioid prescribing practices to mitigate misuse and enhance personalized pain management. Further validation of these findings from multi countries and ethnic groups could guide clinicians in implementing DNA-based opioid prescribing, aligning treatment more closely with individual genetic profiles.

Rationale and design for a pragmatic randomized trial to assess gene-based prescribing for SSRIs in the treatment of depression

Specific selective serotonin reuptake inhibitors (SSRIs) metabolism is strongly influenced by two pharmacogenes, CYP2D6 and CYP2C19. However, the effectiveness of prospectively using pharmacogenetic variants to select or dose SSRIs for depression is uncertain in routine clinical practice. The objective of this prospective, multicenter, pragmatic randomized controlled trial is to determine the effectiveness of genotype-guided selection and dosing of antidepressants on control of depression in participants who are 8 years or older with ≥3 months of depressive symptoms who require new or revised therapy. Those randomized to the intervention arm undergo pharmacogenetic testing at baseline and receive a pharmacy consult and/or automated clinical decision support intervention based on an actionable phenotype, while those randomized to the control arm have pharmacogenetic testing at the end of 6-months. In both groups, depression and drug tolerability outcomes are assessed at baseline, 1 month, 3 months (primary), and 6 months. The primary end point is defined by change in Patient-Reported Outcomes Measurement Information System (PROMIS) Depression score assessed at 3 months versus baseline. Secondary end points include change inpatient health questionnaire (PHQ-8) measure of depression severity, remission rates defined by PROMIS score < 16, medication adherence, and medication side effects. The primary analysis will compare the PROMIS score difference between trial arms among those with an actionable CYP2D6 or CYP2C19 genetic result or a CYP2D6 drug-drug interaction. The trial has completed accrual of 1461 participants, of which 562 were found to have an actionable phenotype to date, and follow-up will be complete in April of 2024.

Unlocking the Potential of Therapy-Induced Cytokine Responses: Illuminating New Pathways in Cancer Precision Medicine

Precision cancer medicine primarily aims to identify individual patient genomic variations and exploit vulnerabilities in cancer cells to select suitable patients for specific drugs. These genomic features are commonly determined by gene sequencing prior to therapy, to identify individuals who would be most responsive. This precision approach in cancer therapeutics remains a powerful tool that benefits a smaller pool of patients, sparing others from unnecessary treatments. A limitation of this approach is that proteins, not genes, are the ultimate effectors of biological functions, and therefore the targets of therapeutics. An additional dimension in precision medicine that considers an individual's cytokine response to cancer therapeutics is proposed. Cytokine responses to therapy are multifactorial and vary among individuals. Thus, precision is dictated by the nature and magnitude of cytokine responses in the tumor microenvironment exposed to therapy. This review highlights cytokine responses as modules for precision medicine in cancer therapy, including potential challenges. For solid tumors, both detectability of cytokines in tissue fluids and their being amenable to routine sensitive analyses could address the difficulty of specimen collection for diagnosis and monitoring. Therefore, in precision cancer medicine, cytokines offer rational targets that can be utilized to enhance the efficacy of cancer therapy.

The expanding role of HLA gene tests for predicting drug side effects

Adverse drug reactions can be either dose-dependent (Type A) or idiosyncratic (Type B). Type B adverse drug reactions tend to be extremely rare and difficult to predict. They are usually immune-mediated. Examples include severe skin reactions and drug-induced liver injury. For many commonly prescribed drugs (such as antibiotics), the risk of developing an idiosyncratic adverse drug reaction is influenced by variability in the human leukocyte antigen (HLA) genes. Because these HLA-mediated adverse drug reactions can be lethal, there is growing interest in defining which specific drug-gene relationships might benefit from pre-emptive HLA genotyping and automated clinical decision support. This review summarizes the literature for HLA-mediated adverse reactions linked to common drugs.

Influence of Metabolic, Transporter, and Pathogenic Genes on Pharmacogenetics and DNA Methylation in Neurological Disorders

Pharmacogenetics and DNA methylation influence therapeutic outcomes and provide insights into potential therapeutic targets for brain-related disorders. To understand the effect of genetic polymorphisms on drug response and disease risk, we analyzed the relationship between global DNA methylation, drug-metabolizing enzymes, transport genes, and pathogenic gene phenotypes in serum samples from two groups of patients: Group A, which showed increased 5-methylcytosine (5mC) levels during clinical follow-up, and Group B, which exhibited no discernible change in 5mC levels. We identified specific SNPs in several metabolizing genes, including CYP1A2, CYP2C9, CYP4F2, GSTP1, and NAT2, that were associated with differential drug responses. Specific SNPs in CYP had a significant impact on enzyme activity, leading to changes in phenotypic distribution between the two patient groups. Group B, which contained a lower frequency of normal metabolizers and a higher frequency of ultra-rapid metabolizers compared to patients in Group A, did not show an improvement in 5mC levels during follow-up. Furthermore, there were significant differences in phenotype distribution between patient Groups A and B for several SNPs associated with transporter genes (ABCB1, ABCC2, SLC2A9, SLC39A8, and SLCO1B1) and pathogenic genes (APOE, NBEA, and PTGS2). These findings appear to suggest that the interplay between pharmacogenomics and DNA methylation has important implications for improving treatment outcomes in patients with brain-related disorders.

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

SLCO1B1 genotype is known to influence patient adherence to statin therapy, in part by increasing the risk for statin-associated musculoskeletal symptoms (SAMS). The SLCO1B1*5 allele has previously been associated with simvastatin discontinuation and SAMS. 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 SAMS in a large observational cohort using electronic medical record (EMR) 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 SAMS. 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 [C.I.]: 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, C.I. 1.1 - 1.4, p = 0.004). Additional time-to-event analyses also revealed that SCLO1B1*5 was associated with SAMS (Hazard Ratio 1.4, C.I. 1.1 - 1.7, p = 0.02). Atorvastatin discontinuation was associated with SAMS (Odds Ratio 1.67, p = 0.0001) in our cohort.

Genophenotypic Factors and Pharmacogenomics in Adverse Drug Reactions

Adverse drug reactions (ADRs) rank as one of the top 10 leading causes of death and illness in developed countries. ADRs show differential features depending upon genotype, age, sex, race, pathology, drug category, route of administration, and drug–drug interactions. Pharmacogenomics (PGx) provides the physician effective clues for optimizing drug efficacy and safety in major problems of health such as cardiovascular disease and associated disorders, cancer and brain disorders. Important aspects to be considered are also the impact of immunopharmacogenomics in cutaneous ADRs as well as the influence of genomic factors associated with COVID-19 and vaccination strategies. Major limitations for the routine use of PGx procedures for ADRs prevention are the lack of education and training in physicians and pharmacists, poor characterization of drug-related PGx, unspecific biomarkers of drug efficacy and toxicity, cost-effectiveness, administrative problems in health organizations, and insufficient regulation for the generalized use of PGx in the clinical setting. The implementation of PGx requires: (i) education of physicians and all other parties involved in the use and benefits of PGx; (ii) prospective studies to demonstrate the benefits of PGx genotyping; (iii) standardization of PGx procedures and development of clinical guidelines; (iv) NGS and microarrays to cover genes with high PGx potential; and (v) new regulations for PGx-related drug development and PGx drug labelling.

Potential Use of Pharmacogenetics to Reduce Drug-Induced Syndrome of Inappropriate Antidiuretic Hormone (SIADH)

Syndrome of inappropriate antidiuretic hormone (SIADH) is a common cause of hyponatremia, and many cases represent adverse reactions to drugs that alter ion channel conductance within the peptidergic nerve terminals of the posterior pituitary. The frequency of drug-induced SIADH increases with age; as many as 20% of patients residing in nursing homes have serum sodium levels below 135 mEq/L. Mild hyponatremia is associated with cognitive changes, gait instability, and falls. Severe hyponatremia is associated with cerebral edema, seizures, permanent disability, and/or death. Although pharmacogenetic tests are now being deployed for some drugs capable of causing SIADH (e.g., antidepressants, antipsychotics, and opioid analgesics), the implementation of these tests has been based upon the prior known association of these drugs with other serious adverse drug reactions (e.g., electrocardiographic abnormalities). Work is needed in large observational cohorts to quantify the strength of association between pharmacogene variants and drug-induced SIADH so that decision support can be developed to identify patients at high risk.

Leukemia Stem Cell Drug Discovery

The relative survival of cancer patients, when considering the tumoral stage at diagnosis, has not changed significantly in the last three decades, in spite of our increasingly detailed knowledge of the molecular alterations occurring in human tumors. In parallel, despite a growing number of clinical trials being conducted, the absolute number of drugs that are effective in humans is declining, and many new drugs move into the market without having enough evidence of their benefit on survival or quality of life. In part, this failure is due to the discordance between the results from preclinical and clinical trial phases, therefore leading to a high percentage of apparently promising lead compounds being abandoned in the transfer to the clinic. This discordance is caused, to a large degree, by the use of inappropriate animal models in the first stages of drug development. In this chapter, we discuss how the development of cancer therapies needs to be redesigned in order to achieve cancer cure, and how this redesign must involve the generation of better animal models, based on the tenets of the cancer stem cell theory, and capable of recapitulating all the aspects of human cancer. The use of such improved models should increase the likelihood of success in drug development, reducing the number of agents that go into trial, and the amount of patients undergoing useless trials.

CYP2C19 genotype-guided antiplatelet therapy: promises and pitfalls

Pharmacogenetic variants can alter the mechanism of action (pharmacodynamic gene variants) or kinetic processes such as absorption, distribution, metabolism and elimination (pharmacokinetic gene variants). Many initial successes in precision medicine occurred in the context of genes encoding the cytochromes P450 (CYP enzymes). CYP2C19 activates the antiplatelet drug clopidogrel, and polymorphisms in the CYP2C19 gene are known to alter the outcome for patients taking clopidogrel in the context of cardiovascular disease. CYP2C19 loss-of-function alleles are specifically associated with increased risk for coronary stent thrombosis and major adverse cardiovascular events in patients taking clopidogrel following percutaneous coronary intervention. We explore successes and challenges encountered as the clinical and scientific communities advance CYP2C19 genotyping in the context of routine patient care.

Pharmacogenomic network analysis of the gene-drug interaction landscape underlying drug disposition

In recent decades the identification of pharmacogenomic gene-drug associations has evolved tremendously. Despite this progress, a major fraction of the heritable inter-individual variability remains elusive. Higher-dimensional phenomena, such as gene-gene-drug interactions, in which variability in multiple genes synergizes to precipitate an observable phenotype have been suggested to account at least for part of this missing heritability. However, the identification of such intricate relationships remains difficult partly because of analytical challenges associated with the complexity explosion of the problem. To facilitate the identification of such combinatorial pharmacogenetic associations, we here propose a network analysis strategy. Specifically, we analyzed the landscape of drug metabolizing enzymes and transporters for 100 top selling drugs as well as all compounds with pharmacogenetic germline labels or dosing guidelines. Based on this data, we calculated the posterior probabilities that gene i is involved in metabolism, transport or toxicity of a given drug under the condition that another gene j is involved for all pharmacogene pairs (i, j). Interestingly, these analyses revealed significant patterns between individual genes and across pharmacogene families that provide insights into metabolic interactions. To visualize the gene-drug interaction landscape, we use multidimensional scaling to collapse this similarity matrix into a two-dimensional network. We suggest that Euclidian distance between nodes can inform about the likelihood of epistatic interactions and thus might provide a useful tool to reduce the search space and facilitate the identification of combinatorial pharmacogenomic associations.

Natural Product Medicines for Honey Bees: Perspective and Protocols

The western honey bee remains the most important pollinator for agricultural crops. Disease and stressors threaten honey bee populations and productivity during winter- and summertime, creating costs for beekeepers and negative impacts on agriculture. To combat diseases and improve overall bee health, researchers are constantly developing honey bee medicines using the tools of microbiology, molecular biology and chemistry. Below, we present a manifesto alongside standardized protocols that outline the development and a systematic approach to test natural products as ‘bee medicines’. These will be accomplished in both artificial rearing conditions and in colonies situated in the field. Output will be scored by gene expression data of host immunity, bee survivorship, reduction in pathogen titers, and more subjective merits of the compound in question. Natural products, some of which are already encountered by bees in the form of plant resins and nectar compounds, provide promising low-cost candidates for safe prophylaxis or treatment of bee diseases.

Implementation of wide-scale pharmacogenetic testing in primary care

The convergence of translational genomics and biomedical informatics has changed healthcare delivery. Institutional consortia have begun implementing lab testing and decision support for drug–gene interactions. Aggregate datasets are now revealing the impact of clinical decision support for drug–gene interactions. Given the pleiotropic nature of pharmacogenes, interdisciplinary teams and robust clinical decision support tools must exist within an informatics framework built to be flexible and capable of cross-talk between clinical specialties. Navigation of the challenges presented with the implementation of five steps to build a genetics program infrastructure requires the expertise of multiple healthcare professionals. Ultimately, this manuscript describes our efforts to place pharmacogenomics in the hands of the primary care provider integrating this information into a patient’s healthcare over their lifetime.

SLCO1B1 Polymorphisms are Associated With Drug Intolerance in Childhood Leukemia Maintenance Therapy

BACKGROUND

Therapy discontinuations and toxicities occur because of significant interindividual variations in 6-mercaptopurine (6-MP) and methotrexate (MTX) response during maintenance therapy of childhood acute lymphoblastic leukemia (ALL). 6-MP/MTX intolerance in some of the patients cannot be explained by thiopurine S-methyl transferase (TPMT) gene variants. In this study, we aimed to investigate candidate pharmacogenetic determinants of 6-MP and MTX intolerance in Turkish ALL children.

METHODS

In total, 48 children with ALL who had completed or were receiving maintenance therapy according to Children's Oncology Group (COG) protocols were enrolled. Fifteen single-nucleotide polymorphisms in 8 candidate genes that were related to drug toxicity or had a role in the 6-MP/MTX metabolism (TPMT, ITPA, MTHFR, IMPDH2, PACSIN2, SLCO1B1, ABCC4, and PYGL) were genotyped by competitive allele-specific PCR (KASP). Drug doses during maintenance therapy were modified according to the protocol.

RESULTS

The median drug dose intensity was 50% (28% to 92%) for 6-MP and 58% (27% to 99%) for MTX in the first year of maintenance therapy, which were lower than that scheduled in all patients. Among the analyzed polymorphisms, variant alleles in SLCO1B1 rs4149056 and rs11045879 were found to be associated with lower 6-MP/MTX tolerance.

CONCLUSIONS

SLCO1B1 rs4149056 and rs11045879 polymorphisms may be important genetic markers to individualize 6-MP/MTX doses.

Association between SLCO1B1 -521T>C and -388A>G polymorphisms and risk of statin-induced adverse drug reactions: A meta-analysis

An increasing number of studies have investigated the association between SLCO1B1 -521T>C and -388A>G polymorphisms and the risk of statin-induced adverse drug reactions (ADRs), but the results have been inconsistent. This meta-analysis was performed to gain more insight into the relationship. PubMed, Embase, Cochrane Library and Web of Science were searched for relevant articles published before March 5th, 2015. The quality of included studies was evaluated by the Newcastle-Ottawa Quality scale. Pooled effect estimates (odds ratios [ORs] or hazard ratios [HRs) and corresponding 95 % confidence intervals (CIs) were calculated to assess the association in overall and subgroup analyses for various genetic models. Begg's rank correlation test and Egger's linear regression test were used to examine the publication bias. A total of nine cohort and four case-control studies involving 11, 246 statin users, of whom 2, 355 developing ADRs were included in the analysis. Combined analysis revealed a significant association between the SLCO1B1-521T>C polymorphism and increased risk for ADRs caused by various statins, but the synthesis heterogeneity was generally large (dominant model: pooled effect estimate = 1.85, 95 % CI 1.20-2.85, P = 0.005; I (2) = 80.70 %, Pheterogeneity < 0.001). Subgroup analysis by statin type showed that the ADRs risk was significantly elevated among simvastatin users (dominant model: pooled effect estimate = 3.43, 95 % CI 1.80-6.52, P = 0.001; I (2) = 59.60 %, Pheterogeneity = 0.060), but not among atorvastatin users. No significant relationship was found between the -388A>G polymorphism and ADRs caused by various statins (dominant model: pooled effect estimate = 0.94, 95 % CI 0.79-1.13, P = 0.526; I (2) = 40.10 %, Pheterogeneity = 0.196). The meta-analysis suggests that SLCO1B1 -521T>C polymorphism may be a risk factor for statin-induced ADRs, especially in simvastatin therapy. Conversely, there may be no significant association for -388A>G polymorphism.

Genetic interaction of P2X7 receptor and VEGFR-2 polymorphisms identifies a favorable prognostic profile in prostate cancer patients

VEGFR-2 and P2X₇ receptor (P2X₇R) have been described to stimulate the angiogenesis and inflammatory processes of prostate cancer. The present study has been performed to investigate the genetic interactions among VEGFR-2 and P2X₇R SNPs and their correlation with overall survival (OS) in a population of metastatic prostate cancer patients. Analyses were performed on germline DNA obtained from blood samples and SNPs were investigated by real-time PCR technique. The survival dimensionality reduction (SDR) methodology was applied to investigate the genetic interaction between SNPs. One hundred patients were enrolled. The SDR software provided two genetic interaction profiles consisting of the combination between specific VEGFR-2 (rs2071559, rs11133360) and P2X₇R (rs3751143, rs208294) genotypes. The median OS was 126 months (95% CI, 115.94–152.96) and 65.65 months (95% CI, 52.95–76.53) for the favorable and the unfavorable genetic profile, respectively (p < 0.0001). The genetic statistical interaction between VEGFR-2 (rs2071559, rs11133360) and P2X₇R (rs3751143, rs208294) genotypes may identify a population of prostate cancer patients with a better prognosis.

Navigating pleiotropy in precision medicine: pharmacogenes from trauma to behavioral health

A strong emerging principle in the field of precision medicine is that variation in any one pharmacogene may impact clinical outcome for more than one drug. Variants tested in the acute care setting often have downstream implications for other drugs impacting chronic disease management. A flexible framework is needed as clinicians and scientists move toward deploying automated decision support for gene-based drug dosing in electronic medical records.

Pharmacogenetic interaction analysis of VEGFR-2 and IL-8 polymorphisms in advanced breast cancer patients treated with paclitaxel and bevacizumab

Aim: To investigate pharmacogenetic interactions among VEGF-A, VEGFR-2, IL-8, HIF-1α, EPAS-1 and TSP-1 SNPs and their role on progression-free survival in a population of metastatic breast cancer patients treated with bevacizumab in combination with first-line paclitaxel. Patients & methods: Analyses were performed on germline DNA obtained from blood samples and SNPs were investigated by real-time polymerase chain reaction technique. The multifactor dimensionality reduction methodology was applied to investigate the interaction between SNPs. Results: One hundred and thirteen patients were enrolled from eight Italian Oncology Units ( clinicaltrial.gov : NCT01935102). The multifactor dimensionality reduction software provided two pharmacogenetic interaction profiles consisting of the combination between specific VEGFR-2 rs11133360 and IL-8 rs4073 genotypes. The median progression-free survival was 14.1 months (95% CI: 11.4–16.8) and 10.2 months (95% CI: 8.8–11.5) for the favorable and the unfavorable genetic profile, respectively (HR: 0.44, 95% CI: 0.29–0.66, p < 0.0001). Conclusion: The pharmacogenetic statistical interaction between VEGFR-2 rs11133360 and IL-8 rs4073 genotypes may identify a population of patients with a better outcome.

Genetic variation in the UGT1A locus is associated with simvastatin efficacy in a clinical practice setting

Aim: Simvastatin is a lactone prodrug that exists in equilibrium with its active hydroxyacid through a process mediated by UGT1A enzymes. The UGT1A locus has been associated with simvastatin response and disposition in humans. Therefore, we fine-mapped the UGT1A locus to identify genetic variations contributing to simvastatin disposition and response variability. Methods: Using de-identified electronic medical records linked to a DNA biobank, we extracted information about dose and low-density lipo-protein cholesterol (LDL-C) concentrations for patients who received more than two different doses of simvastatin. Pharmacodynamic measures of simvastatin potency and efficacy were calculated from dose-response curves (E0 = baseline LDL-C, ED50 = dose yielding 50% maximum response, and Emax = maximum decrease in LDL-C) in 1100 patients. We selected 153 polymorphisms in UGT1A1 and UGT1A3 for genotyping and conducted genotype-phenotype associations using a prespecified additive model. Results: Two variants in UGT1A1 (rs2003569 and rs12052787) were associated with Emax (p = 0.0059 and 0.031, respectively; for rs2003569 the mean Emax was 59.3 ± 23.0, 62.0 ± 22.4, and 69.7 ± 24.8 mg/dl, for patients with 0, 1 or 2 copies of the minor A allele, respectively). When stratified by race, the difference in response was greater in African-Americans than in European Americans. Rs2003569 was also negatively associated with total serum bilirubin levels (p = 7.85 × 10-5). Four rare SNPs were nominally associated with E0 and ED50. Conclusion: We identified a UGT1A1 promoter variant (rs2003569) associated with simvastatin efficacy. Original submitted 26 March 2014; Revision submitted 26 August 2014.

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.

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 4β-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.

CYP3A4/5 combined genotype analysis for predicting statin dose requirement for optimal lipid control

BACKGROUND

Statins are indicated for prevention of atherosclerotic cardiovascular disease. Metabolism of certain statins involves the cytochrome P450 3A (CYP3A) enzymes, and CYP3A4*22 significantly influences the dose needed for achieving optimal lipid control for atorvastatin, simvastatin, and lovastatin. CYP3A4/5 combined genotype approaches have proved useful in some studies involving CYP3A substrates. We intend to compare a combined genotype analysis to our previously reported single gene CYP3A4 analysis.

METHODS

A total of 235 patients receiving stable statin doses were genotyped and grouped by CYP3A4/5 status.

RESULTS

The number and demographic composition of the patients categorized into the combined genotype groups were consistent with those reported for other cohorts. Dose requirement was significantly associated with the ordered combined-genotype grouping; median daily doses were nearly 40% greater for CYP3A4/5 intermediate metabolizers compared with poor metabolizers, and median daily doses were nearly double for extensive metabolizers compared with poor metabolizers. The combined-genotype approach, however, did not improve the genotype-dosage correlation p-values when compared with the previously-reported analysis; values changed from 0.129 to 0.166, 0.036 to 0.185, and 0.014 to 0.044 for atorvastatin, simvastatin, and the combined statin analysis, respectively.

CONCLUSIONS

The previously-reported single-gene approach was superior for predicting statin dose requirement in this cohort.

Molecular markers to predict outcome to antiangiogenic therapies in colorectal cancer: current evidence and future perspectives

Angiogenesis is a universal requirement for the growth of solid tumours beyond the limits of oxygen diffusion from the existing vasculature. The expression and function of proangiogenic and antiangiogenic factors are altered in solid malignancies to drive net neoangiogenesis. Vascular endothelial growth factor (VEGF) has been confirmed in several clinical trials as an important therapeutic target in colorectal cancer (CRC) treatment. However, given that the efficacy of antiangiogenic agents appears to be limited to a subset of patients, the identification of who will obtain the greater benefit from this therapy or suffer from specific toxicities and when or for how long they should be administered in the treatment algorithm are major open questions for clinicians and challenges for present and future research. Current evidence indicates some predictive value for particular circulating measures, such as an increase in VEGF, a decrease in vascular endothelial growth factor receptor 2 (VEGFR-2) or circulating endothelial cells, tissue biomarkers, microvessel density, KRAS and BRAF gene mutations or polymorphisms affecting components of the VEGF pathway. Many questions relating to these and other surrogate biomarkers, however, remain unanswered and their clinical usefulness has yet to be proven. This review will focus on the present status of knowledge and future perspectives for developing molecular tools to foresee and monitor antiangiogenic therapy activity in CRC patients.

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.

Gene-gene interactions in folate and adenosine biosynthesis pathways affect methotrexate efficacy and tolerability in rheumatoid arthritis

OBJECTIVE

As no single nucleotide polymorphism has emerged as pivotal to predict the lack of efficacy and dose-limiting toxicities to methotrexate (MTX), we evaluated the contribution of gene-gene interactions to the effects of this prodrug in rheumatoid arthritis.

METHODS

A total of 255 patients treated with MTX for at least 3 months were evaluated with efficacy assessed using the European League Against Rheumatism response criteria or a physician's assessment of patient's response to MTX visual analog scale. Gastrointestinal and neurological idiosyncrasies were recorded in 158 patients. Fourteen single nucleotide polymorphisms in folate and adenosine biosynthesis pathways were measured and detection of gene-gene interactions was performed using multifactor-dimensionality reduction, a method that reduces high-dimensional genetic data into a single dimension of predisposing or risk-genotype combinations.

RESULTS

Efficacy to MTX (53% responders) was associated with high-order epistasis among variants in inosine-triphosphate pyrophosphatase, aminoimidazole-carboxamide ribonucleotide transformylase, and reduced folate carrier genes. In the absence of predisposing genotype combinations, a 3.8-fold lower likelihood of efficacy was observed (vs. in their presence, 95% confidence interval: 2.2-6.4; P<0.001). Increasing MTX polyglutamate concentrations tended to partially overcome this selective disadvantage. Idiosyncrasies occurred in 29% of patients. In the presence of risk-genotype combinations among variants in methylene tetrahydrofolate reductase, γ-glutamyl-hydrolase, thymidylate synthase, serine hydroxymethyltransferase, and inosine-triphosphate pyrophosphatase genes, an 8.9-fold higher likelihood to exhibit toxicities was observed (vs. in their absence, 95% confidence interval: 3.6-21.9; P<0.001). False-positive report probabilities were below 0.2, thereby indicating that true signals were likely detected in this cohort.

CONCLUSION

These data indicate that gene-gene interactions impact MTX efficacy and tolerability in rheumatoid arthritis.

The clinical pharmacogenomics implementation consortium: CPIC guideline for SLCO1B1 and simvastatin-induced myopathy

Cholesterol reduction from statin therapy has been one of the greatest public health successes in modern medicine. Simvastatin is among the most commonly used prescription medications. A non-synonymous coding single-nucleotide polymorphism (SNP), rs4149056, in SLCO1B1 markedly increases systemic exposure to simvastatin and the risk of muscle toxicity. This guideline explores the relationship between rs4149056 (c.521T>C, p.V174A) and clinical outcome for all statins. The strength of the evidence is high for myopathy with simvastatin. We limit our recommendations accordingly.

Cardiovascular pharmacogenomics

Patients vary in their responses to drug therapy, and some of that variability is genetically determined. This review outlines general approaches used to identify genetic variation that influences drug response. Examples from specific therapeutic areas are presented, such as cholesterol management, arrhythmias, heart failure, hypertension, warfarin anticoagulation, and antiplatelet agents. A brief view of potential pathways to implementation is presented.

Analysis of gene-gene interactions

The goal of this unit is to introduce gene-gene interactions (epistasis) as a significant complicating factor in the search for disease susceptibility genes. This unit begins with an overview of gene-gene interactions and why they are likely to be common. Then, it reviews several statistical and computational methods for detecting and characterizing genes with effects that are dependent on other genes. The focus of this unit is genetic association studies of discrete and quantitative traits because most of the methods for detecting gene-gene interactions have been developed specifically for these study designs.

Bioinformatics challenges for personalized medicine

MOTIVATION

Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.

RESULTS

This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice.

CONTACT

russ.altman@stanford.edu

The emerging role of electronic medical records in pharmacogenomics

Health-care information technology and genotyping technology are both advancing rapidly, creating new opportunities for medical and scientific discovery. The convergence of these two technologies is now facilitating genetic association studies of unprecedented size within the context of routine clinical care. As a result, the medical community will soon be presented with a number of novel opportunities to bring functional genomics to the bedside in the area of pharmacotherapy. By linking biological material to comprehensive medical records, large multi-institutional biobanks are now poised to advance the field of pharmacogenomics through three distinct mechanisms: (i) retrospective assessment of previously known findings in a clinical practice-based setting, (ii) discovery of new associations in huge observational cohorts, and (iii) prospective application in a setting capable of providing real-time decision support. This review explores each of these translational mechanisms within a historical framework.

High-density lipoprotein (HDL) cholesterol: leveraging practice-based biobank cohorts to characterize clinical and genetic predictors of treatment outcome

Over the past decade, large multicenter trials have unequivocally demonstrated that decreasing low-density lipoprotein (LDL) cholesterol can reduce both primary and secondary cardiovascular events in patients at risk. However, even in the context of maximal LDL lowering, there remains considerable residual cardiovascular risk. Some of this risk can be attributed to variability in high-density lipoprotein (HDL) cholesterol. As such, there is tremendous interest in defining determinants of HDL homeostasis. Risk prediction models are being constructed based upon (1) clinical contributors, (2) known molecular determinants and (3) the genetic architecture underlying HDL cholesterol levels. To date, however, no single resource has combined these factors within the context of a practice-based data set. Recently, a number of academic medical centers have begun constructing DNA biobanks linked to secure encrypted versions of their respective electronic medical record. As these biobanks combine resources, the clinical community is in a position to characterize lipid-related treatment outcome on an unprecedented scale.

Mapping genes that predict treatment outcome in admixed populations

There is great interest in characterizing the genetic architecture underlying drug response. For many drugs, gene-based dosing models explain a considerable amount of the overall variation in treatment outcome. As such, prescription drug labels are increasingly being modified to contain pharmacogenetic information. Genetic data must, however, be interpreted within the context of relevant clinical covariates. Even the most predictive models improve with the addition of data related to biogeographical ancestry. The current review explores analytical strategies that leverage population structure to more fully characterize genetic determinants of outcome in large clinical practice-based cohorts. The success of this approach will depend upon several key factors: (1) the availability of outcome data from groups of admixed individuals (that is, populations recombined over multiple generations), (2) a measurable difference in treatment outcome (that is, efficacy and toxicity end points), and (3) a measurable difference in allele frequency between the ancestral populations.

Pharmacogenetic interaction analysis for the efficacy of systemic treatment in metastatic colorectal cancer

BACKGROUND

Pharmacogenetic markers related to drug metabolism and mechanisms of action could help to better select patients with metastatic colorectal cancer (mCRC) for treatment. Genetic interaction analysis is used as a rational tool to study the contribution of polygenic variation in relation to drug response.

PATIENTS AND METHODS

A selection of 17 polymorphisms in genes encoding drug targets, pathway molecules and detoxification enzymes was analyzed in 279 previously untreated mCRC patients treated with capecitabine, oxaliplatin and bevacizumab (CAPOX-B). Multifactor dimensionality reduction analysis was used to identify a genetic interaction profile for progression-free survival (PFS).

RESULTS

Median PFS was 10.9 [95% confidence interval (CI) 9.4-12.4] months. A genetic interaction profile consisting of the TYMS enhancer region and VEGF +405G>C polymorphisms was significantly associated with PFS. Median PFS was 13.3 (95% CI 11.4-15.3) and 9.7 (95% CI 7.6-11.8) months for the beneficial and unfavorable genetic profiles, respectively, corresponding to a hazards ratio for PFS of 1.58 (95% CI 1.14-2.19). None of the studied polymorphisms were individually associated with PFS.

CONCLUSIONS

Our results support a genetic interaction between the TYMS enhancer region and VEGF +405G>C polymorphisms as a predictor of the efficacy of CAPOX-B in mCRC patients.

Use of biological knowledge to inform the analysis of gene-gene interactions involved in modulating virologic failure with efavirenz-containing treatment regimens in ART-naïve ACTG clinical trials participants

Personalized medicine is a high priority for the future of health care. The idea of tailoring an individual's wellness plan to their unique genetic code is one which we hope to realize through the use of pharmacogenomics. There have been examples of tremendous success in pharmacogenomic associations however there are many such examples in which only a small proportion of trait variance has been explained by the genetic variation. Although the increased use of GWAS could help explain more of this variation, it is likely that a significant proportion of the genetic architecture of these pharmacogenomic traits are due to complex genetic effects such as epistasis, also known as gene-gene interactions, as well as gene-drug interactions. In this study, we utilize the Biofilter software package to look for candidate epistasis contributing to risk for virologic failure with efavirenz-containing antiretroviral therapy (ART) regimens in treatment-naïve participants of AIDS Clinical Trials Group (ACTG) randomized clinical trials. A total of 904 individuals from three ACTG trials with data on efavirenz treatment are analyzed after race-stratification into white, black, and Hispanic ethnic groups. Biofilter was run considering 245 candidate ADME (absorption, distribution, metabolism, and excretion) genes and using database knowledge of gene and protein interaction networks to produce approximately 2 million SNP-SNP interaction models within each ethnic group. These models were evaluated within the PLATO software package using pair wise logistic regression models. Although no interaction model remained significant after correction for multiple comparisons, an interaction between SNPs in the TAP1 and ABCC9 genes was one of the top models before correction. The TAP1 protein is responsible for intracellular transport of antigen to MHC class I molecules, while ABCC9 codes for a transporter which is part of the subfamily of ABC transporters associated with multi-drug resistance. This study demonstrates the utility of the Biofilter method to prioritize the search for gene-gene interactions in large-scale genomic datasets, although replication in a larger cohort is required to confirm the validity of this particular TAP1-ABCC9 finding.

Dexamethasone-induced FKBP51 expression in peripheral blood mononuclear cells could play a role in predicting the response of asthmatics to treatment with corticosteroids

BACKGROUND

Corticosteroids (CSs) are the preferred anti-inflammatory therapy for the treatment of asthma, but the responses of asthmatics to CSs are known to vary. It has thus become important to discover reliable markers in predicting responses to CSs.

METHODS

We performed time-series microarrays using a murine model of asthma after a single dose of dexamethasone, based on the assumption that the gene showing a greater change in response to CSs can also be a potential marker for that finding. We then evaluated the clinical meaning of the gene discovered in the microarray experiments.

RESULTS

We found that the expression of FK506 binding protein 51 gene (FKBP51) in lung tissue markedly increased after dexamethasone treatment in a murine model of asthma. We then measured dexamethasone-induced FKBP51 expression in peripheral blood mononuclear cells (PBMCs) in asthmatics. Dexamethasone-induced FKBP51 expression in PBMCs was significantly higher in severe asthmatics compared with mild-to-moderate asthmatics treated with inhaled CSs. In addition, we found that dexamethasone-induced FKBP51 expression in PBMCs was inversely correlated with improvement in lung function after treatment with orally administered prednisolone in six steroid-naive asthmatics.

CONCLUSION

Dexamethasone-induced FKBP51 expression in PBMCs may be a reliable and practical biomarker in predicting the response to CSs in asthmatics.

Biobanking and pharmacogenomics

The study of genetic determinants underlying drug outcome is rapidly advancing. Initial success was realized within the context of candidate pharmacokinetic genes and serious adverse drug reactions, particularly for drugs with narrow therapeutic indices. Although genetic predictors of outcome have proven useful in other contexts, effect size has typically been small. To address these challenges, the clinical and scientific communities have begun studying larger numbers of gene variants (often at the genome-wide level) in cohorts of increasing sample size. Electronic health records are being increasingly used for this purpose. Longitudinal data available in practice-based datasets will position investigators to characterize genetic factors with small but reproducible effects on drug outcome in the context of gene-environment interactions.

Pharmacogenetics of tomorrow: the 1 + 1 = 3 principle

Disappointing results from replicating pharmacogenetic association studies have prompted the search for novel statistical techniques to analyze the data, while taking into account the biological complexity underlying drug response. Two of these techniques – multifactor dimensionality reduction and classification and regression tree analysis – will probably be applied in increasing numbers of future pharmacogenetic studies. In this article, we describe the concepts underlying both techniques and illustrate their application in a recent pharmacogenetic study.

Bioinformatics challenges for genome-wide association studies

Motivation: The sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype–phenotype relationship that is characterized by significant heterogeneity and gene–gene and gene–environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods.

Contact:jason.h.moore@dartmouth.edu

Epistasis and its implications for personal genetics

The widespread availability of high-throughput genotyping technology has opened the door to the era of personal genetics, which brings to consumers the promise of using genetic variations to predict individual susceptibility to common diseases. Despite easy access to commercial personal genetics services, our knowledge of the genetic architecture of common diseases is still very limited and has not yet fulfilled the promise of accurately predicting most people at risk. This is partly because of the complexity of the mapping relationship between genotype and phenotype that is a consequence of epistasis (gene-gene interaction) and other phenomena such as gene-environment interaction and locus heterogeneity. Unfortunately, these aspects of genetic architecture have not been addressed in most of the genetic association studies that provide the knowledge base for interpreting large-scale genetic association results. We provide here an introductory review of how epistasis can affect human health and disease and how it can be detected in population-based studies. We provide some thoughts on the implications of epistasis for personal genetics and some recommendations for improving personal genetics in light of this complexity.

Use of an electronic medical record to characterize cases of intermediate statin-induced muscle toxicity

Statin use can be accompanied by a variety of musculoskeletal complaints. The authors describe the clinical characteristics of case patients who experienced adverse statin-induced musculoskeletal symptoms within a large population-based cohort in Central Wisconsin. Case status was determined based on elevated serum creatine kinase (CK) levels and the presence of at least 1 physician note reflecting an increased index of suspicion for statin intolerance. From the medical records of nearly 2 million unique patients, the authors identified more than 20,000 potential study patients ( approximately 1%) having CK data and at least 1 exposure to a statin drug. Manual screening was completed on 2227 patients with CK levels in the upper 10th percentile. Of those screened, 267 met inclusion criteria (12.0% eligibility) and 218 agreed to participate in a retrospective study characterizing the risk determinants of statin-induced muscle toxicity. Three categoric pain variables were graded retrospectively (distribution, location, and severity of pain). The presenting complaints of the case patients were extremely heterogeneous. The number of patients with a compelling pain syndrome (diffuse, proximal muscle pain of high intensity) increased at higher serum CK levels; the number of patients with indeterminate pain variables decreased at higher serum CK levels. The lines reflecting these relationships cross at a CK level of approximately 1175 U/L, approximately half the threshold level needed to make a clinical diagnosis of "myopathy" (ie, CK >10-fold the upper limit of normal).

Systems biology in pharmacogenomic research: the way to personalized prescribing?

Response to pharmacotherapy can be highly variable amongst individuals. Pharmacogenomics may explain the interindividual variability in drug response due to genetic variation. However, besides genetics, many other factors can play a role in the response to pharmacotherapy, including disease severity, co-morbidity, environmental factors, therapy adherence and co-medication use. Better understanding of these factors and inter-relationships should bring about a much more effective approach to disease management. Systems biology that studies organisms as integrated and interacting networks of genes, proteins and biochemical reactions can contribute to this. Organisms are no longer studied part by part, but in a more integral manner. Integration of the genetic data with intermediate and end point phenotypic characterization may prove essential to define the inherent nature of drug effects. Therefore, in the future, a multidisciplinary systems-based approach will be necessary to deal with the bulk of the biological data that is available and, ultimately, to reach the goal of personalized prescribing.

Sensible Initialization Using Expert Knowledge for Genome-Wide Analysis of Epistasis Using Genetic Programming

In human genetics it is now possible to measure large numbers of DNA sequence variations across the human genome. Given current knowledge about biological networks and disease processes it seems likely that disease risk can best be modeled by interactions between biological components, which may be examined as interacting DNA sequence variations. The machine learning challenge is to effectively explore interactions in these datasets to identify combinations of variations which are predictive of common human diseases. Genetic programming is a promising approach to this problem. The goal of this study is to examine the role that an expert knowledge aware initializer can play in the framework of genetic programming. We show that this expert knowledge aware initializer outperforms both a random initializer and an enumerative initializer.

Genetic variation in cannabinoid receptor 1 (CNR1) is associated with derangements in lipid homeostasis, independent of body mass index

AIMS

In humans, genetic variation in endocannabinergic signaling has been associated with anthropometric measures of obesity. In randomized trials, pharmacological blockade at the level of the cannabinoid receptor 1 (CNR1) receptor not only facilitates weight reduction, but also improves insulin sensitivity and clinical measures of lipid homeostasis. We therefore tested the hypothesis that genetic variation in CNR1 is associated with common obesity-related metabolic disorders.

MATERIALS & METHODS

A total of six haplotype tagging SNPs were selected for CNR1, using data available within the Human HapMap (Centre d'Etude du Polymorphisme Humain population) these included: two promoter SNPs, three exonic SNPs, and a single SNP within the 3'-untranslated region. These tags were then genotyped in a rigorously phenotyped family-based collection of obese study subjects of Northern European origin.

RESULTS & CONCLUSIONS

A common CNR1 haplotype (H4; prevalence 0.132) is associated with abnormal lipid homeostasis. Additional statistical tests using single tagging SNPs revealed that these associations are partly independent of body mass index.