Advances in DNA sequencing technologies have made it possible to rapidly, accurately and affordably sequence entire individual human genomes. As impressive as this ability seems, however, it will not likely to amount to much if one cannot extract meaningful information from individual sequence data. Annotating variations within individual genomes and providing information about their biological or phenotypic impact will thus be crucially important in moving individual sequencing projects forward, especially in the context of the clinical use of sequence information. In this paper we consider the various ways in which one might annotate individual sequence variations and point out limitations in the available methods for doing so. It is arguable that, in the foreseeable future, DNA sequencing of individual genomes will become routine for clinical, research, forensic, and personal purposes. We therefore also consider directions and areas for further research in annotating genomic variants.
Sequencing; functional analysis; computer modeling; genomic variation
Personalized medicine is increasingly being employed across many areas of clinical practice, as genes associated with specific diseases are discovered and targeted therapies are developed. Mobile apps are also beginning to be used in medicine with the aim of providing a personalized approach to disease management. In some areas of medicine, patient-tailored risk prediction and treatment are applied routinely in the clinic, whereas in other fields, more work is required to translate scientific advances into individualized treatment. In this forum article, we asked specialists in oncology, neurology, endocrinology and mobile health technology to discuss where we are in terms of personalized medicine, and address their visions for the future and the challenges that remain in their respective fields.
Diabetes; Genetics; Mobile health; Oncology; Personalized medicine; Smartphone; Stroke; Targeted therapy
Our previous studies identified a functional SNP, R952Q in the LRP8 gene, that was associated with increased platelet activation and familial and early-onset coronary artery disease (CAD) and myocardial infarction (MI) in American and Italian Caucasian populations. In this study, we analyzed four additional SNPs near R952Q (rs7546246, rs2297660, rs3737983, rs5177) to identify a specific LRP8 SNP haplotype that is associated with familial and early-onset CAD and MI. We employed a case–control association design involving 381 premature CAD and MI probands and 560 controls in GeneQuest, 441 individuals from 22 large pedigrees in GeneQuest II, and 248 MI patients with family history and 308 controls in an Italian cohort. Like R952Q, LRP8 SNPs rs7546246, rs2297660, rs3737983, and rs5177 were significantly associated with early-onset CAD/MI in both population-based and family-based association studies in GeneQuest. The results were replicated in the GeneQuest II family-based population and the Italian population. We then carried out a haplotype analysis for all five SNPs including R952Q. One common haplotype (TCCGC) was significantly associated with CAD (P = 4.0 × 10−11) and MI (P = 6.5 × 10−12) in GeneQuest with odds ratios of 0.53 and 0.42, respectively. The results were replicated in the Italian cohort (P = 0.004, OR = 0.71). The sib-TDT analysis also showed significant association between the TCCGC haplotype and CAD in GeneQuest II (P = 0.001). These results suggest that a common LRP8 haplotype TCCGC confers a significant protective effect on the development of familial, early-onset CAD and/or MI.
LRP8; Haplotype; SNPs; Association study
Genome-wide association studies (GWAS) of responses to drugs, including clopidogrel, pegylated-interferon and carbamazepine, have led to the identification of specific patient subgroups that benefit from therapy. However, the identification and replication of common sequence variants that are associated with either efficacy or safety for most prescription medications at odds ratios (ORs) >3.0 (equivalent to >300% increased efficacy or safety) has yet to be translated to clinical practice. Although some of the studies have been completed, the results have not been incorporated into therapy, and a large number of commonly used medications have not been subject to proper pharmacogenomic analysis. Adoption of GWAS, exome or whole genome sequencing by drug development and treatment programs is the most striking near-term opportunity for improving the drug candidate pipeline and boosting the efficacy of medications already in use.
The use of direct-to-consumer genomewide profiling to assess disease risk is controversial, and little is known about the effect of this technology on consumers. We examined the psychological, behavioral, and clinical effects of risk scanning with the Navigenics Health Compass, a commercially available test of uncertain clinical validity and utility.
We recruited subjects from health and technology companies who elected to purchase the Health Compass at a discounted rate. Subjects reported any changes in symptoms of anxiety, intake of dietary fat, and exercise behavior at a mean (±SD) of 5.6±2.4 months after testing, as compared with baseline, along with any test-related distress and the use of health-screening tests.
From a cohort of 3639 enrolled subjects, 2037 completed follow-up. Primary analyses showed no significant differences between baseline and follow-up in anxiety symptoms (P = 0.80), dietary fat intake (P = 0.89), or exercise behavior (P = 0.61). Secondary analyses revealed that test-related distress was positively correlated with the average estimated lifetime risk among all the assessed conditions (β = 0.117, P<0.001). However, 90.3% of subjects who completed follow-up had scores indicating no test-related distress. There was no significant increase in the rate of use of screening tests associated with genomewide profiling, most of which are not considered appropriate for screening asymptomatic persons in any case.
In a selected sample of subjects who completed follow-up after undergoing consumer genomewide testing, such testing did not result in any measurable short-term changes in psychological health, diet or exercise behavior, or use of screening tests. Potential effects of this type of genetic testing on the population at large are not known. (Funded by the National Institutes of Health and Scripps Health.)
The lack of plasticity of the medical profession and health care system in the face of new technology and information is about to be challenged on two major fronts in digital medicine: wireless technologies and genomics. These two areas have been characterized by unprecedented innovation and discovery at a breakneck pace. Whereas the 2000s saw the introduction of digital life-style devices, the 2010s will probably be known as the era of digital medical devices. These devices have exceptional promise for changing the future of medicine because of their ability to produce exquisitely detailed individual biological and physiological data.
Contemporary sequencing studies often ignore the diploid nature of the human genome because they do not routinely separate or ‘phase’ maternally and paternally derived sequence information. However, many findings — both from recent studies and in the more established medical genetics literature — indicate that relationships between human DNA sequence and phenotype, including disease, can be more fully understood with phase information. Thus, the existing technological impediments to obtaining phase information must be overcome if human genomics is to reach its full potential.
A new miniature high-resolution pocket-mobile echocardiographic (PME) device has become available to clinicians, but there are no data available comparing this technology with standard transthoracic echo (TTE) examination.
To assess the potential validity of PME imaging as a quick assessment of cardiovascular disease by direct comparison to standard TTE.
Ultrasonographers attempted to acquire seven standard echocardiography views with the PME prior to performing comprehensive standard TTEs. In blinded fashion, images from the two modalities were compared by two experienced echocardiographers and two cardiology fellows.
PRIMARY FUNDING SOURCE
This work was funded in part by Scripps Health and the NIH UL1 RR025774 (Scripps Translational Science Institute, Clinical and Translational Science Award).
Scripps Clinic/Green Hospital
97 consecutive unselected patients
Comparisons were made in regards to ejection fraction (EF), segmental wall motion abnormalities (WMA), left ventricular end-diastolic dimension (LVEDD), inferior vena cava (IVC) size, aortic and mitral valve pathology, and pericardial effusion.
PME images were adequate for interpretation of EF in 95% of the studies, LVEDD 95%, mitral valve 90%, WMA 83%, aortic valve 83%, and IVC 75%. Compared to standard TTE, PME interpretation by attendings and fellows had an accuracy of 97% and 93% for EF, respectively. Likewise, accuracy for WMA was 90% and 87% ; LVEDD 94% and 91%; aortic stenosis 97% and 95%; mitral abnormality 88% and 82%; and IVC size 81% and 74%.
As this was a validation study of imaging alone, further evaluation with clinician image acquisition is needed.
PME images obtained rapidly by skilled ultrasonographers provide excellent visualization in the vast majority of patients and correlate well with standard, comprehensive TTE. Such validation needs to be extended to untrained clinicians in larger and diverse patient populations before broad dissemination of this technology can be recommended.
cardiovascular disease; echocardiography; imaging
A number of recent genome-wide association (GWA) studies have identified unequivocal statistical associations between inherited genetic variations, mostly single nucleotide polymorphisms (SNPs), and common complex diseases such as diabetes, cardiovascular disease, and obesity. Genotyping individuals for these variations has the potential to help redefine how pharmacologic agents undergo clinical development. By identifying carriers of known genomic variants that contribute to susceptibility, a high risk population can be defined as well as individuals with potential for a better response to a drug. We evaluated the potential utility that selecting individuals for a trial on the basis of genotype identified in contemporary GWA studies would have had on recently described clinical trials. We pursued this by constraining both the risks of a disease outcome associated with particular genotypes and overall drug responses to those actually observed in genetic association and clinical trial studies, respectively. We pursued these evaluations in the context of clinical trials investigating drugs for macular degeneration, obesity, heart disease, type II diabetes, prostate cancer and Alzheimer’s disease. We show that the increase in incidence of outcomes in trials restricted to individuals with specific genotypic profiles can result in substantial reductions in requisite sample sizes for such trials. In addition, we also derive realistic bounds for samples sizes for clinical trials investigating pharmacogenetic effects that leverage genetic variations identified in recent association studies.
Polymorphism; Translational medicine; Drug validation; DNA sequencing; Study Design
Acute myocardial infarction (MI), which involves the rupture of existing atheromatous plaque, remains highly unpredictable despite recent advances in the diagnosis and treatment of coronary artery disease. Accordingly, a biomarker that can predict an impending MI is desperately needed. Here, we characterize circulating endothelial cells (CECs) using the first automated and clinically feasible CEC 3-channel fluorescence microscopy assay in 50 consecutive patients with ST-elevation myocardial infarction (STEMI) and 44 consecutive healthy controls. CEC counts were significantly elevated in MI cases versus controls with median numbers of 19 and 4 cells/ml respectively (p = 1.1 × 10−10). A receiver-operating characteristic (ROC) curve analysis demonstrated an area under the ROC curve of 0.95, suggesting near dichotomization of MI cases versus controls. We observed no correlation between CECs and typical markers of myocardial necrosis (ρ=0.02, CK-MB; ρ=−0.03, troponin). Morphologic analysis of the microscopy images of CECs revealed a 2.5-fold increase (P<0.0001) in cellular area and 2-fold increase (P<0.0001) in nuclear area of MI CECs versus healthy control, age-matched CECs, as well as CECs obtained from patients with preexisting peripheral vascular disease. The distribution of CEC images containing from 2 up to 10 nuclei demonstrates that MI patients are the only group to contain more than 3 nuclei/image, indicating that multi-cellular and multi-nuclear clusters are specific for acute MI. These data indicate that CECs may serve as promising biomarkers for the prediction of atherosclerotic plaque rupture events.
Proton pump inhibitors (PPIs) may interfere with the metabolic activation of clopidogrel via inhibition of cytochrome P450 2C19, but the clinical implications remain unclear.
Methods and Results
The impact of PPI use on the 1‐year primary end point (ischemic stroke, myocardial infarction [MI], or vascular death) in the Clopidogrel versus Aspirin in Patients at Risk of Ischemic Events (CAPRIE) trial and the 28‐day (all‐cause death, MI, or urgent target vessel revascularization) and 1‐year (all‐cause death, MI, or stroke) primary end points in the Clopidogrel for Reduction of Events During Observation (CREDO) trial were examined. Clopidogrel appeared to elevate risk for the primary end point in CAPRIE among PPI users (estimated hazard ratio [EHR] 2.66, 95% CI 0.94 to 7.50) while lowering it for non‐PPI users (EHR 0.90, 95% CI 0.83 to 0.99, interaction P=0.047). Moreover, PPI use was associated with worse outcomes in patients receiving clopidogrel (EHR 2.39, 95% CI 1.74 to 3.28) but not aspirin (EHR 1.04, 95% CI 0.70 to 1.57, interaction P=0.001). Clopidogrel did not significantly alter risk for the 1‐year primary end point in CREDO among PPI users (EHR 0.82, 95% CI 0.48 to 1.40) while lowering it for non‐PPI users (EHR 0.71, 95% CI 0.52 to 0.98, interaction P=0.682). Also, PPI use was associated with worse outcomes in both patients receiving clopidogrel (EHR 1.67, 95% CI 1.06 to 2.64) and those receiving placebo (EHR 1.56, 95% CI 1.06 to 2.30, interaction P=0.811).
In CREDO, the efficacy of clopidogrel was not significantly affected by PPI use. However, in CAPRIE, clopidogrel was beneficial to non‐PPI users while apparently harmful to PPI users. Whether this negative interaction is clinically important for patients receiving clopidogrel without aspirin needs further study.
CAPRIE; clopidogrel; CREDO; drug–drug interaction; proton pump inhibitors
The ongoing controversy surrounding direct-to-consumer (DTC) personal genomic tests intensified last year when the U.S. Government Accountability Office (GAO) released results of an undercover investigation of four companies that offer such testing. Among their findings, they reported that some of their donors received DNA-based predictions that conflicted with their actual medical histories. We aimed to more rigorously evaluate the relationship between DTC genomic risk estimates and self-reported disease by leveraging data from the Scripps Genomic Health Initiative (SGHI). We prospectively collected self-reported personal and family health history data for 3,416 individuals who went on to purchase a commercially available DTC genomic test. For 5 out of 15 total conditions studied, we found that risk estimates from the test were significantly associated with self-reported family and/or personal health history. The 5 conditions, included Graves’ disease, Type 2 Diabetes, Lupus, Alzheimer’s disease, and Restless Leg Syndrome. To further investigate these findings, we ranked each of the 15 conditions based on published heritability estimates and conducted post-hoc power analyses based on the number of individuals in our sample who reported significant histories of each condition. We found that high heritability, coupled with high prevalence in our sample and thus adequate statistical power, explained the pattern of associations observed. Our study represents one of the first evaluations of the relationship between risk estimates from a commercially available DTC personal genomic test and self-reported health histories in the consumers of that test.
direct-to-consumer; genetic testing; genetic risk estimates; clinical validity; consumer genomics
There have been a number of recent successes in the use of whole genome sequencing and sophisticated bioinformatics techniques to identify pathogenic DNA sequence variants responsible for individual idiopathic congenital conditions. However, the success of this identification process is heavily influenced by the ancestry or genetic background of a patient with an idiopathic condition. This is so because potential pathogenic variants in a patient’s genome must be contrasted with variants in a reference set of genomes made up of other individuals’ genomes of the same ancestry as the patient. We explored the effect of ignoring the ancestries of both an individual patient and the individuals used to construct reference genomes. We pursued this exploration in two major steps. We first considered variation in the per-genome number and rates of likely functional derived (i.e., non-ancestral, based on the chimp genome) single nucleotide variants and small indels in 52 individual whole human genomes sampled from 10 different global populations. We took advantage of a suite of computational and bioinformatics techniques to predict the functional effect of over 24 million genomic variants, both coding and non-coding, across these genomes. We found that the typical human genome harbors ∼5.5–6.1 million total derived variants, of which ∼12,000 are likely to have a functional effect (∼5000 coding and ∼7000 non-coding). We also found that the rates of functional genotypes per the total number of genotypes in individual whole genomes differ dramatically between human populations. We then created tables showing how the use of comparator or reference genome panels comprised of genomes from individuals that do not have the same ancestral background as a patient can negatively impact pathogenic variant identification. Our results have important implications for clinical sequencing initiatives.
clinical sequencing; congenital disease; whole genome sequencing; population genetics
Over the past 18 months, there have been notable developments in the direct-to-consumer (DTC) genomic testing arena, in particular with regard to issues surrounding governmental regulation in the USA. While commentaries continue to proliferate on this topic, actual empirical research remains relatively scant. In terms of DTC genomic testing for disease susceptibility, most of the research has centered on uptake, perceptions and attitudes toward testing among health care professionals and consumers. Only a few available studies have examined actual behavioral response among consumers, and we are not aware of any studies that have examined response to DTC genetic testing for ancestry or for drug response. We propose that further research in this area is desperately needed, despite challenges in designing appropriate studies given the rapid pace at which the field is evolving. Ultimately, DTC genomic testing for common markers and conditions is only a precursor to the eventual cost-effectiveness and wide availability of whole genome sequencing of individuals, although it remains unclear whether DTC genomic information will still be attainable. Either way, however, current knowledge needs to be extended and enhanced with respect to the delivery, impact and use of increasingly accurate and comprehensive individualized genomic data.
Community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) is a significant bacterial pathogen that poses considerable clinical and public health challenges. The majority of the CA-MRSA disease burden consists of skin and soft tissue infections (SSTI) not associated with significant morbidity; however, CA-MRSA also causes severe, invasive infections resulting in significant morbidity and mortality. The broad range of disease severity may be influenced by bacterial genetic variation.
We sequenced the complete genomes of 36 CA-MRSA clinical isolates from the predominant North American community acquired clonal type USA300 (18 SSTI and 18 severe infection-associated isolates). While all 36 isolates shared remarkable genetic similarity, we found greater overall time-dependent sequence diversity among SSTI isolates. In addition, pathway analysis of non-synonymous variations revealed increased sequence diversity in the putative virulence genes of SSTI isolates.
Here we report the first whole genome survey of diverse clinical isolates of the USA300 lineage and describe the evolution of the pathogen over time within a defined geographic area. The results demonstrate the close relatedness of clinically independent CA-MRSA isolates, which carry implications for understanding CA-MRSA epidemiology and combating its spread.
Individuals can now obtain their personal genomic information via direct-to-consumer genetic testing, but what, if any, impact will this have on their lifestyle and health? A recent longitudinal cohort study of individuals who underwent consumer genome scanning found minimal impacts of testing on risk-reducing lifestyle behaviors, such as diet and exercise. These results raise an important question: is personal genomic information likely to beneficially impact public health through motivation of lifestyle behavioral change? In this article, we review the literature on lifestyle behavioral change in response to genetic testing for common disease susceptibility variants. We find that only a few studies have been carried out, and that those that have been done have yielded little evidence to suggest that the mere provision of genetic information alone results in widespread changes in lifestyle health behaviors. We suggest that further study of this issue is needed, in particular studies that examine response to multiplex testing for multiple genetic markers and conditions. This will be critical as we anticipate the wide availability of whole-genome sequencing and more comprehensive phenotyping of individuals. We also note that while simple communication of genomic information and disease susceptibility may be sufficient to catalyze lifestyle changes in some highly motivated groups of individuals, for others, additional strategies may be required to prompt changes, including more sophisticated means of risk communication (e.g., in the context of social norm feedback) either alone or in combination with other promising interventions (e.g., real-time wireless health monitoring devices).
behavioral intervention; consumer genomics; direct-to-consumer; genetic risk; genetic testing; nudging; personalized medicine; social norm feedback; wireless monitoring
The majority of first-time angiography patients are without obstructive coronary artery disease (CAD). A blood gene expression score (GES) for obstructive CAD likelihood was validated in the PREDICT study, but its relation to major adverse cardiovascular events (MACE) and revascularization was not assessed. Patients (N = 1,160) were followed up for MACE and revascularization 1 year post-index angiography and GES, with 1,116 completing follow-up. The 30-day event rate was 23% and a further 2.2% at 12 months. The GES was associated with MACE/revascularizations (p < 0.001) and added to clinical risk scores. Patients with GES >15 trended towards increased >30 days MACE/revascularization likelihood (odds ratio = 2.59, 95% confidence interval = 0.89–9.14, p = 0.082). MACE incidence overall was 1.5% (17 of 1,116) and 3 of 17 patients had GES ≤15. For the total low GES group (N = 396), negative predictive value was 90% for MACE/revascularization and >99% for MACE alone, identifying a group of patients without obstructive CAD and highly unlikely to suffer MACE within 12 months.
Electronic supplementary material
The online version of this article (doi:10.1007/s12265-012-9353-z) contains supplementary material, which is available to authorized users.
Coronary artery disease; Peripheral blood gene expression; Genomics; Angiography; Coronary interventions; MACE
N-of-1 or single subject clinical trials consider an individual patient as the sole unit of observation in a study investigating the efficacy or side-effect profiles of different interventions. The ultimate goal of an n-of-1 trial is to determine the optimal or best intervention for an individual patient using objective data-driven criteria. Such trials can leverage study design and statistical techniques associated with standard population-based clinical trials, including randomization, washout and crossover periods, as well as placebo controls. Despite their obvious appeal and wide use in educational settings, n-of-1 trials have been used sparingly in medical and general clinical settings. We briefly review the history, motivation and design of n-of-1 trials and emphasize the great utility of modern wireless medical monitoring devices in their execution. We ultimately argue that n-of-1 trials demand serious attention among the health research and clinical care communities given the contemporary focus on individualized medicine.
clinical equipoise; early-phase trials; individualized medicine; n-of-1; remote phenotyping; single patient trial; treatment repositioning; wireless health
Genome wide association studies (GWAS) have identified SNPs in the 9p21 gene desert associated with coronary artery disease (CAD)1–4 and Type 2 diabetes (T2D)5–7. Despite evidence for a role of the associated interval in neighboring gene regulation8–10, the biological underpinnings of these genetic associations to CAD or T2D have not yet been explained. Here we identify 33 enhancers in 9p21; the interval is the second densest gene-desert for predicted enhancers and 6 times denser than the whole genome (p<6.55 10−33). The CAD risk alleles of SNPs rs10811656/rs10757278 are located in one of these enhancers and disrupt a binding site for STAT1. Lymphoblastoid cell lines (LCL) homozygous for the CAD risk haplotype exhibit no binding of STAT1, and in LCL homozygous for the CAD non-risk haplotype binding of STAT1 inhibits CDKN2BAS expression, which is reversed by siRNA knock-down of STAT1. Using a new, open-ended approach to detect long-distance interactions (3D-DSL), we find that in human vascular endothelium cells (HUVEC) the enhancer interval containing the CAD locus physically interacts with the CDKN2A/B locus, the MTAP gene and an interval downstream of INFA21. In HUVEC, IFNγ activation strongly affects the structure of the chromatin and the transcriptional regulation in the 9p21 locus, including STAT1 binding, long-range enhancer interactions and altered expression of neighboring genes. Our findings establish a link between CAD genetic susceptibility and the response to inflammatory signaling in a vascular cell type and thus demonstrate the utility of GWAS findings to direct studies to novel genomic loci and biological processes important for disease etiology.
Paraoxonase 1 (PON1) is reported to have antioxidant and cardioprotective properties. The relationship between PON1 genotypes and functional activity with systemic measures of oxidative stress and cardiovascular disease (CVD) risk in humans has not been systematically investigated.
To investigate the relationship of genetic and biochemical determinants of PON1 activity with systemic measures of oxidative stress and CVD risk in humans.
Design, Setting, and Participants
The association between systemic PON1 activity measures and a functional polymorphism (Q192R) resulting in high PON1 activity with prevalent CVD and future major adverse cardiac events (myocardial infarction, stroke, or death) was evaluated in 1399 sequential consenting patients undergoing diagnostic coronary angiography between September 2002 and November 2003 at the Cleveland Clinic. Patients were followed up until December 2006. Systemic levels of multiple structurally defined fatty acid oxidation products were also measured by mass spectrometry in 150 age-, sex-, and race-matched patients and compared with regard to PON1 genotype and activity.
Main Outcome Measures
Relationship between a functional PON1 polymorphism and PON1 activity with global indices of systemic oxidative stress and risk of CVD.
The PON1 genotype demonstrated significant dose-dependent associations (QQ192>QR192>RR192) with decreased levels of serum PON1 activity and with increased levels of systemic indices of oxidative stress. Compared with participants with either the PON1 RR192 or QR192 genotype, participants with the QQ192 genotype demonstrated an increased risk of all-cause mortality (43/681 deaths [6.75%] in RR192 and QR192 and 62/584 deaths [11.1%] in QQ192; adjusted hazard ratio, 2.05; 95% confidence interval [CI], 1.32–3.18) and of major adverse cardiac events (88/681 events [13.6%] in RR192 and QR192 and 102/584 events [18.0%] in QQ192; adjusted hazard ratio, 1.48; 95% CI, 1.09–2.03; P=.01). The incidence of major adverse cardiac events was significantly lower in participants in the highest PON1 activity quartile (23/315 [7.3%]) and 235/324 [7.7%] for paraoxonase and arylesterase, respectively) compared with those in the lowest activity quartile (78/311 [25.1%] and 75/319 [23.5%]; P<.001 for paraoxonase and arylesterase, respectively). The adjusted hazard ratios for major adverse cardiac events between the highest and lowest PON1 activity quartiles were, for paraoxonase, 3.4 (95% CI, 2.1–5.5; P<.001) and for arylesterase, 2.9 (95% CI, 1.8–4.7; P<.001) and remained independent in multivariate analysis.
This study provides direct evidence for a mechanistic link between genetic determinants and activity of PON1 with systemic oxidative stress and prospective cardiovascular risk, indicating a potential mechanism for the atheroprotective function of PON1.
Smoking increases platelet aggregability, and the degree of platelet inhibition by clopidogrel on ex vivo platelet function tests. Whether smoking status affects the relationship between clopidogrel and clinical outcomes is unknown.
Methods and Results
We evaluated the relationship between smoking status (current smoker (CS), former smoker (FS), and never smoker (NS)) and treatment with clopidogrel on the risk of all-cause, cardiovascular, and cancer mortality among the 12,152 participants from the CHARISMA trial with established cardiovascular disease. Current smoking was associated with an increase in all-cause (adjusted hazard ratio [HR] 2.58, [1.85–3.60]), cardiovascular (HR 2.26, [1.48–3.45]), and cancer mortality (HR 4.16, [2.46–7.03]) compared to NS. The impact of clopidogrel and mortality differed by smoking status (P for interaction = 0.018 for current smokers). Among CS, clopidogrel was associated with a reduction in all-cause mortality (HR 0.68, [0.49–0.94]); clopidogrel did not reduce all cause mortality among FS (HR 0.95, [0.75–1.19]) or NS (HR 1.14, [0.83–1.58]). A similar pattern was noted for cardiovascular mortality. As expected, no relationship was observed between clopidogrel and cancer mortality by smoking status. The risk of bleeding seemed to differ according to smoking status; randomized clopidogrel was associated with a significantly increased hazard of severe or moderate bleeding (HR 1.62, P=0.04) among CS, but a smaller and nonsignificant increase among NS (HR 1.31, P=0.15).
Clopidogrel therapy may be more effective, but with a greater bleeding risk in CS than in patients who are not smokers. Further studies are needed to investigate this possibility.
Smoking; Clopidogrel; Mortality; Cardiovascular disease
Genome wide association (GWA) studies, which test for association between common genetic markers and a disease phenotype, have shown varying degrees of success. While many factors could potentially confound GWA studies, we focus on the possibility that multiple, rare variants (RVs) may act in concert to influence disease etiology. Here, we describe an algorithm for RV analysis, RareCover. The algorithm combines a disparate collection of RVs with low effect and modest penetrance. Further, it does not require the rare variants be adjacent in location. Extensive simulations over a range of assumed penetrance and population attributable risk (PAR) values illustrate the power of our approach over other published methods, including the collapsing and weighted-collapsing strategies. To showcase the method, we apply RareCover to re-sequencing data from a cohort of 289 individuals at the extremes of Body Mass Index distribution (NCT00263042). Individual samples were re-sequenced at two genes, FAAH and MGLL, known to be involved in endocannabinoid metabolism (187Kbp for 148 obese and 150 controls). The RareCover analysis identifies exactly one significantly associated region in each gene, each about 5 Kbp in the upstream regulatory regions. The data suggests that the RVs help disrupt the expression of the two genes, leading to lowered metabolism of the corresponding cannabinoids. Overall, our results point to the power of including RVs in measuring genetic associations.
We focus on the problem of detecting multiple rare variants (RVs) that act together to influence disease phenotypes. In considering this problem, we argue that the detection of causal rare variants must necessarily be different from typical single-marker analysis used for common variants and propose a novel algorithm, RareCover, to accomplish this analysis. RareCover combines a disparate collection of RVs, each with very low effect and modest penetrance. Extensive simulations over a range of values for penetrance and population attributable risk (PAR) illustrate the power of our approach over other published methods, including the collapsing and weighted-sum strategies. To showcase the method, we applied RareCover to data from 289 individuals at the extremes of Body Mass Index distribution (NCT00263042), sequenced around the FAAH and MGLL genes. RareCover analysis identified exactly one significantly associated region in each gene, each about 5Kbp in the upstream regulatory regions. The data suggests that the RVs help disrupt the expression of the two genes leading to lowered metabolism of the corresponding endocannabinoids previously linked with obesity. Overall, our results point to the power of including RVs in measuring genetic associations, and suggest that whole genome, DNA sequencing-based association studies investigating RV effects are feasible.