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1.  A Pharmacogenetic versus a Clinical Algorithm for Warfarin Dosing 
The New England journal of medicine  2013;369(24):2283-2293.
BACKGROUND
The clinical utility of genotype-guided (pharmacogenetically based) dosing of warfarin has been tested only in small clinical trials or observational studies, with equivocal results.
METHODS
We randomly assigned 1015 patients to receive doses of warfarin during the first 5 days of therapy that were determined according to a dosing algorithm that included both clinical variables and genotype data or to one that included clinical variables only. All patients and clinicians were unaware of the dose of warfarin during the first 4 weeks of therapy. The primary outcome was the percentage of time that the international normalized ratio (INR) was in the therapeutic range from day 4 or 5 through day 28 of therapy.
RESULTS
At 4 weeks, the mean percentage of time in the therapeutic range was 45.2% in the genotype-guided group and 45.4% in the clinically guided group (adjusted mean difference, [genotype-guided group minus clinically guided group], −0.2; 95% confidence interval, −3.4 to 3.1; P=0.91). There also was no significant between-group difference among patients with a predicted dose difference between the two algorithms of 1 mg per day or more. There was, however, a significant interaction between dosing strategy and race (P=0.003). Among black patients, the mean percentage of time in the therapeutic range was less in the genotype-guided group than in the clinically guided group. The rates of the combined outcome of any INR of 4 or more, major bleeding, or thromboembolism did not differ significantly according to dosing strategy.
CONCLUSIONS
Genotype-guided dosing of warfarin did not improve anticoagulation control during the first 4 weeks of therapy. (Funded by the National Heart, Lung, and Blood Institute and others; COAG ClinicalTrials.gov number, NCT00839657.)
doi:10.1056/NEJMoa1310669
PMCID: PMC3942158  PMID: 24251361
3.  Prevalence and correlates of low medication adherence in apparent treatment resistant hypertension 
Low medication adherence may explain part of the high prevalence of apparent treatment resistant hypertension (aTRH). We assessed medication adherence and aTRH among 4,026 participants taking ≥ 3 classes of antihypertensive medication in the population-based REGARDS Study using the 4-item Morisky Medication Adherence Scale (MMAS). Low adherence was defined as a MMAS score ≥ 2. Overall, 66% of participants taking ≥ 3 classes of antihypertensive medication had aTRH. Perfect adherence on the MMAS was reported by 67.8% and 70.9% of participants with and without aTRH, respectively. Low adherence was present among 8.1% of participants with aTRH and 5.0% of those without aTRH (p<0.001). Among those with aTRH, female gender, residence outside the US stroke belt or stroke buckle, physical inactivity, elevated depressive symptoms, and a history of coronary heart disease were associated with low adherence. In the current study, a small percentage of participants with aTRH had low adherence.
doi:10.1111/j.1751-7176.2012.00690.x
PMCID: PMC3464920  PMID: 23031147
Hypertension; Treatment Resistant Hypertension; Medication adherence; Risk Factors
4.  Genetic variants associated with warfarin dose in African-American individuals: a genome-wide association study 
Lancet  2013;382(9894):790-796.
Summary
Background
VKORC1 and CYP2C9 are important contributors to warfarin dose variability, but explain less variability for individuals of African descent than for those of European or Asian descent. We aimed to identify additional variants contributing to warfarin dose requirements in African Americans.
Methods
We did a genome-wide association study of discovery and replication cohorts. Samples from African-American adults (aged ≥18 years) who were taking a stable maintenance dose of warfarin were obtained at International Warfarin Pharmacogenetics Consortium (IWPC) sites and the University of Alabama at Birmingham (Birmingham, AL, USA). Patients enrolled at IWPC sites but who were not used for discovery made up the independent replication cohort. All participants were genotyped. We did a stepwise conditional analysis, conditioning first for VKORC1 −1639G→A, followed by the composite genotype of CYP2C9*2 and CYP2C9*3. We prespecified a genome-wide significance threshold of p<5×10−8 in the discovery cohort and p<0·0038 in the replication cohort.
Findings
The discovery cohort contained 533 participants and the replication cohort 432 participants. After the prespecified conditioning in the discovery cohort, we identified an association between a novel single nucleotide polymorphism in the CYP2C cluster on chromosome 10 (rs12777823) and warfarin dose requirement that reached genome-wide significance (p=1·51×10−8). This association was confirmed in the replication cohort (p=5·04×10−5); analysis of the two cohorts together produced a p value of 4·5×10−12. Individuals heterozygous for the rs12777823 A allele need a dose reduction of 6·92 mg/week and those homozygous 9·34 mg/week. Regression analysis showed that the inclusion of rs12777823 significantly improves warfarin dose variability explained by the IWPC dosing algorithm (21% relative improvement).
Interpretation
A novel CYP2C single nucleotide polymorphism exerts a clinically relevant effect on warfarin dose in African Americans, independent of CYP2C9*2 and CYP2C9*3. Incorporation of this variant into pharmacogenetic dosing algorithms could improve warfarin dose prediction in this population.
Funding
National Institutes of Health, American Heart Association, Howard Hughes Medical Institute, Wisconsin Network for Health Research, and the Wellcome Trust.
doi:10.1016/S0140-6736(13)60681-9
PMCID: PMC3759580  PMID: 23755828
5.  Pathway analysis of genome-wide data improves warfarin dose prediction 
BMC Genomics  2013;14(Suppl 3):S11.
Background
Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be difficult. Moreover, multiple variations in a gene or a set of genes within a pathway may all contribute to the phenotype, suggesting that the aggregation of variations found over the gene or pathway may be useful for improving the power to detect associations.
Results
Here, we present a method for aggregating single nucleotide polymorphisms (SNPs) along biologically relevant pathways in order to seek genetic associations with phenotypes. Our method uses all available genetic variants and does not remove those in linkage disequilibrium (LD). Instead, it uses a novel SNP weighting scheme to down-weight the contributions of correlated SNPs. We apply our method to three cohorts of patients taking warfarin: two European descent cohorts and an African American cohort. Although the clinical covariates and key pharmacogenetic loci for warfarin have been characterized, our association metric identifies a significant association with mutations distributed throughout the pathway of warfarin metabolism. We improve dose prediction after using all known clinical covariates and pharmacogenetic variants in VKORC1 and CYP2C9. In particular, we find that at least 1% of the missing heritability in warfarin dose may be due to the aggregated effects of variations in the warfarin metabolic pathway, even though the SNPs do not individually show a significant association.
Conclusions
Our method allows researchers to study aggregative SNP effects in an unbiased manner by not preselecting SNPs. It retains all the available information by accounting for LD-structure through weighting, which eliminates the need for LD pruning.
doi:10.1186/1471-2164-14-S3-S11
PMCID: PMC3829086  PMID: 23819817
6.  Warfarin Pharmacogenetics: Challenges and Opportunities for Clinical Translation 
doi:10.3389/fphar.2012.00183
PMCID: PMC3490409  PMID: 23133417
7.  High-dimensional pharmacogenetic prediction of a continuous trait using machine learning techniques with application to warfarin dose prediction in African Americans 
Bioinformatics  2011;27(10):1384-1389.
Motivation: With complex traits and diseases having potential genetic contributions of thousands of genetic factors, and with current genotyping arrays consisting of millions of single nucleotide polymorphisms (SNPs), powerful high-dimensional statistical techniques are needed to comprehensively model the genetic variance. Machine learning techniques have many advantages including lack of parametric assumptions, and high power and flexibility.
Results: We have applied three machine learning approaches: Random Forest Regression (RFR), Boosted Regression Tree (BRT) and Support Vector Regression (SVR) to the prediction of warfarin maintenance dose in a cohort of African Americans. We have developed a multi-step approach that selects SNPs, builds prediction models with different subsets of selected SNPs along with known associated genetic and environmental variables and tests the discovered models in a cross-validation framework. Preliminary results indicate that our modeling approach gives much higher accuracy than previous models for warfarin dose prediction. A model size of 200 SNPs (in addition to the known genetic and environmental variables) gives the best accuracy. The R2 between the predicted and actual square root of warfarin dose in this model was on average 66.4% for RFR, 57.8% for SVR and 56.9% for BRT. Thus RFR had the best accuracy, but all three techniques achieved better performance than the current published R2 of 43% in a sample of mixed ethnicity, and 27% in an African American sample. In summary, machine learning approaches for high-dimensional pharmacogenetic prediction, and for prediction of clinical continuous traits of interest, hold great promise and warrant further research.
Contact: cduarte@uab.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btr159
PMCID: PMC3087957  PMID: 21450715
8.  Pharmacogenetic Warfarin Dose Refinements Remain Significantly Influenced by Genetic Factors after One Week of Therapy 
Thrombosis and Haemostasis  2011;107(2):232-240.
Summary
Introduction
By guiding initial warfarin dose, pharmacogenetic (PGx) algorithms may improve the safety of warfarin initiation. However, once INR response is known, the contribution of PGx to dose refinements is uncertain. This study sought to develop and validate clinical and PGx dosing algorithms for warfarin dose refinement on days 6–11 after therapy initiation.
Materials and Methods
An international sample of 2,022 patients at 13 medical centers on 3 continents provided clinical, INR, and genetic data at treatment days 6–11 to predict therapeutic warfarin dose. Independent derivation and retrospective validation samples were composed by randomly dividing the population (80%/20%). Prior warfarin doses were weighted by their expected effect on S-warfarin concentrations using an exponential-decay pharmacokinetic model. The INR divided by that “effective” dose constituted a treatment response index.
Results
Treatment response index, age, amiodarone, body surface area, warfarin indication, and target INR were associated with dose in the derivation sample. A clinical algorithm based on these factors was remarkably accurate: in the retrospective validation cohort its R2 was 61.2% and median absolute error (MAE) was 5.0 mg/week. Accuracy and safety was confirmed in a prospective cohort (N=43). CYP2C9 variants and VKORC1-1639 G→A were significant dose predictors in both the derivation and validation samples. In the retrospective validation cohort, the PGx algorithm had: R2= 69.1% (P<0.05 vs. clinical algorithm), MAE= 4.7 mg/week.
Conclusions
A pharmacogenetic warfarin dose-refinement algorithm based on clinical, INR, and genetic factors can explain at least 69.1% of therapeutic warfarin dose variability after about one week of therapy.
doi:10.1160/TH11-06-0388
PMCID: PMC3292349  PMID: 22186998
warfarin; VKORC1; CYP2C9; pharmacogenetic
9.  Practical Consideration of Genotype Imputation: Sample Size, Window Size, Reference Choice, and Untyped Rate 
Statistics and its interface  2011;4(3):339-352.
Imputation offers a promising way to infer the missing and/or untyped genotypes in genetic studies. In practice, however, many factors may affect the quality of imputation. In this study, we evaluated the influence of untyped rate, sizes of the study sample and the reference sample, window size, and reference choice (for admixed population), as the factors affecting the quality of imputation. The results show that in order to obtain good imputation quality, it is necessary to have an untyped rate less than 50%, a reference sample size greater than 50, and a window size of greater than 500 SNPs (roughly 1 MB in base pairs). Compared with the whole-region imputation, piecewise imputation with large-enough window sizes provides improved efficacy. For an admixed study sample, if only an external reference panel is used, it should include samples from the ancestral populations that represent the admixed population under investigation. Internal references are strongly recommended. When internal references are limited, however, augmentation by external references should be used carefully. More specifically, augmentation with samples from the major source populations of the admixture can lower the quality of imputation; augmentation with seemingly genetically unrelated cohorts may improve the quality of imputation.
PMCID: PMC3269888  PMID: 22308193
genotype imputation; genetic study; admixed population; untyped rate; window size; reference
10.  Controlling Population Structure in Human Genetic Association Studies with Samples of Unrelated Individuals 
Statistics and its interface  2011;4(3):317-326.
In genetic studies, associations between genotypes and phenotypes may be confounded by unrecognized population structure and/or admixture. Studies have shown that even in European populations, which are thought to be relatively homogeneous, population stratification exists and can affect the validity of association studies. A number of methods have been proposed to address this issue in recent years. Among them, the mixed-model based approach and the principal component-based approach have several advantages over other methods. However, these approaches have not been thoroughly evaluated on large human datasets. The objectives of this study are to (1) evaluate and compare the performance of the mixed-model approach and the principal component-based approach for genetic association mapping using human data consisting of unrelated individuals, and (2) understand the relationship between these two approaches. To achieve these goals, we simulate datasets based on the HapMap data under various scenarios. Our results indicate that the mixed-model approach performs well in controlling for population structure/admixture. It has similar performance as that based on principal component analysis. However, the approach combining mixed-model and principal component analysis does not perform as well as either method itself.
PMCID: PMC3269890  PMID: 22308192
Mixed-effects Model; Principal Component Analysis; Population Structure/Admixture; Genetic Association Analysis
11.  Warfarin Dosing in Patients With Impaired Kidney Function 
Background
Warfarin, a drug primarily metabolized by the cytochrome P450 system, is initiated at similar doses and managed similarly in patients with kidney impairment as in the general medical population. Unfortunately, few data exist to guide dose adjustment in patients with reduced kidney function. Herein we determine the degree of warfarin dose reduction associated with kidney impairment and make recommendations for warfarin dosing.
Study Design
Cross-sectional analysis.
Setting & Participants
Chronic warfarin users followed at anticoagulation clinics (n=980); 708 participants from the University of Alabama (UAB) and 272 participants from the University of Chicago (UIC).
Predictor
No/mild (eGFR≥60ml/min/1.73 m2), moderate (eGFR=30–59ml/min/1.73 m2) and severe (eGFR<30ml/min/1.73 m2) kidney impairment, CYP2C9 and VKORC1 genotype, age, race, gender, body mass, socio-demographic factors, smoking status, alcohol, vitamin K intake, comorbid conditions (e.g. CHF, etc.) and drug interactions (e.g. amiodarone, statins, etc.).
Outcome & Measurement
Warfarin dose (mg/day) was evaluated using linear regression after adjustment for clinical demographic and genetic factors.
Results
The prevalence of moderate kidney impairment (31.8% and 27.6%) and severe kidney impairment (8.9% and 6.6%) was similar in the UAB and UIC cohorts. Warfarin dose requirements were significantly lower in patients with moderate and severe kidney impairment compared to those with none/mild kidney impairment in the UAB (p<0.001) and UIC (p<0.001) cohorts. Compared to patients with no/mild kidney impairment, patients with moderate kidney impairment required 9.5% lower doses (p<0.001) and patients with severe kidney impairment required 19% lower doses (p<0.001).
Limitations
No measurement of warfarin, serum albumin, vitamin K and clotting factor levels, no evaluation of other markers (e.g. cystatin).
Conclusion
Moderate and severe kidney impairment were associated with a reduction in warfarin dose requirements.
doi:10.1053/j.ajkd.2010.05.023
PMCID: PMC2963672  PMID: 20709439
Warfarin; Dose adjustment; Pharmacogenetics; CYP2C9; VKORC1; Kidney impairment
12.  Effect of epilepsy magnetic source imaging on intracranial electrode placement 
Annals of neurology  2009;65(6):716-723.
Objective
Intracranial electroencephalography (ICEEG) with chronically implanted electrodes is a costly invasive diagnostic procedure that remains necessary for a large proportion of patients who undergo evaluation for epilepsy surgery. This study was designed to evaluate whether magnetic source imaging (MSI), a non-invasive test based on magnetoencephalography source localization, can supplement ICEEG by affecting electrode placement to improve sampling of the seizure onset zone(s).
Methods
Of 298 consecutive epilepsy surgery candidates (between 2001-2006) 160 cases were prospectively enrolled on the basis of insufficient localization from seizure monitoring and MRI results. Prior to presenting MSI results, decisions were made as to whether to proceed with ICEEG, and if so, where to place electrodes such that the hypothetical seizure onset zone would be sampled. MSI results were then provided with allowance of changes to the original plan.
Results
MSI indicated additional electrode coverage in 18 of 77 (23%) ICEEG cases. In 39% percent (95% CI: 16.4, 61.4) seizure onset ICEEG patterns involved the additional electrodes indicated by MSI. Sixty-two patients underwent surgical resection based on ICEEG recording of seizures. Highly localized MSI was significantly associated with seizure-free outcome (mean=3.4 years, minimum > 1 year) for the entire surgical population (n=62).
Interpretation
MSI spike localization increases the chance that the seizure onset zone is sampled when patients undergo ICEEG for presurgical epilepsy evaluations. The clinical impact of this effect–-improving diagnostic yield of ICEEG–-should be considered in surgery candidates that do not have satisfactory indication of epilepsy localization from seizure semiology, EEG, and MRI.
doi:10.1002/ana.21660
PMCID: PMC2729691  PMID: 19557860
magnetic source imaging; epilepsy; surgery; magnetoencephalography
13.  VKORC1 polymorphisms, haplotypes and haplotype groups on warfarin dose among African–Americans and European–Americans 
Pharmacogenomics  2008;9(10):1445-1458.
Background:
Although the influence of VKORC1 and CYP2C9 polymorphisms on warfarin response has been studied, variability in dose explained by CYP2C9 and VKORC1 is lower among African–Americans compared with European–Americans. This has lead investigators to hypothesize that assessment of VKORC1 haplotypes may help capture a greater proportion of the variability in dose for this under-represented group. However, the inadequate representation of African–Americans and the assessment of a few VKORC1 polymorphisms have hindered this effort.
Methods:
To determine if VKORC1 haplotypes or haplotype groups explain a higher variability in warfarin dose, we comprehensively assessed VKORC1 polymorphisms in 273 African–Americans and 302 European–Americans. The influence of VKORC1 polymorphisms, race-specific haplotypes and haplotype groups on warfarin dose was evaluated in race-stratified multivariable analyses after accounting for CYP2C9 (*2, *3, *5, *6 and *11) and clinical covariates.
Results:
VKORC1 explained 18% (30% with CYP2C9) variability in warfarin dose among European–Americans and 5% (8% with CYP2C9) among African–Americans. Four common haplotypes in European–Americans and twelve in African–Americans were identified. In each race VKORC1 haplotypes emerged into two groups: low-dose (Group A) and high-dose (Group B). African–Americans had a lower frequency of Group A haplotype (10.6%) compared with European–Americans (35%, p < 0.0001).The variability in dose explained by VKORC1 haplotype or haplotype groups was similar to that of a single informative polymorphism.
Conclusions:
Our findings support the use of CYP2C9, VKORC1 polymorphisms (rs9934438 or rs9923231) and clinical covariates to predict warfarin dose in both African– and European–Americans. A uniform set of common polymorphisms in CYP2C9 and VKORC1, and limited clinical covariates can be used to improve warfarin dose prediction for a racially diverse population.
doi:10.2217/14622416.9.10.1445
PMCID: PMC2586955  PMID: 18855533
African–Americans; cohort study; CYP2C9; European–Americans; pharmacogenetics; VKORC1 haplotypes; warfarin

Results 1-13 (13)