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Clinical Review | Clinician's Corner

Association Between 9p21 Genomic Markers and Heart Disease:  A Meta-analysis FREE

Glenn E. Palomaki, BS; Stephanie Melillo, MPH; Linda A. Bradley, PhD
[+] Author Affiliations

Author Affiliations: Women and Infants Hospital/Alpert Medical School of Brown University, Providence, Rhode Island (Mr Palomaki); Office of Public Health Genomics, Centers for Disease Control and Prevention (Ms Melillo and Dr Bradley), and McKing Consulting Corporation (Ms Melillo), Atlanta, Georgia. Dr Bradley is currently with the Women and Infants Hospital/Alpert Medical School of Brown University.


JAMA. 2010;303(7):648-656. doi:10.1001/jama.2010.118.
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Published online

Context Associations between chromosome 9p21 single-nucleotide polymorphisms (SNPs) and heart disease have been reported and replicated. If testing improves risk assessments using traditional factors, it may provide opportunities to improve public health.

Objectives To perform a targeted systematic review of published literature for effect size, heterogeneity, publication bias, and strength of evidence and to consider whether testing might provide clinical utility.

Data Sources Electronic search via HuGE Navigator through January 2009 and review of reference lists from included articles.

Study Selection English-language articles that tested for 9p21 SNPs with coronary heart/artery disease or myocardial infarction as primary outcomes. Included articles also provided race, numbers of participants, and data to compute an odds ratio (OR). Articles were excluded if reporting only intermediate outcomes (eg, atherosclerosis) or if all participants had existing disease. Twenty-five articles were initially identified and 16 were included. A follow-up search identified 6 additional articles.

Data Extraction Independent extraction was performed by 2 reviewers and consensus was reached. Credibility of evidence was assessed using published Venice criteria.

Data Synthesis Forty-seven distinct data sets from the 22 articles were analyzed, including 35 872 cases and 95 837 controls. The summary OR for heart disease among individuals with 2 vs 1 at-risk alleles was 1.25 (95% confidence interval [CI], 1.21-1.29), with low to moderate heterogeneity. Age at disease diagnosis was a significant covariate, with ORs of 1.35 (95% CI, 1.30-1.40) for age 55 years or younger and 1.21 (95% CI, 1.16-1.25) for age 75 years or younger. For a 65-year-old man, the 10-year heart disease risk for 2 vs 1 at-risk alleles would be 13.2% vs 11%. For a 40-year-old woman, the 10-year heart disease risk for 2 vs 1 at-risk alleles would be 2.4% vs 2.0%. Nearly identical but inverse results were found when comparing 1 vs 0 at-risk alleles. Three studies showed net reclassification indexes ranging from −0.1% to 4.8%.

Conclusion We found a statistically significant association between 9p21 SNPs and heart disease that varied by age at disease onset, but the magnitude of the association was small.

Figures in this Article

Preventing and managing cardiovascular disease (CVD) presents a challenge for health care and public health.1,2Quiz Ref IDNonmodifiable risk factors include increasing age, male sex, and heredity. Modifiable risk factors include smoking, hypertension, dyslipidemia, obesity, physical inactivity, and diabetes.35 Among men, the annual rate of initial CVD events increases from 3 to 74 per 1000 from ages 35 to 44 years to ages 85 to 94 years, respectively. Similar increases occur among women a decade later.6 Biomarkers (eg, C-reactive protein) have been combined with traditional risk factors to predict CVD events,7 and molecular markers hold further promise. In 2007, genome-wide association studies on CVD identified a series of associated single-nucleotide polymorphisms (SNPs) in an intergenic region of chromosome 9p21, near the CDKN2A (NM_000077) and CDKN2B (NM_004936) genes.8,9

Currently, no comprehensive compilation of the 9p21 literature uses formal methods to estimate the strength of the association with heart disease (eg, effect size, heterogeneity, publication bias, credibility of cumulative evidence) and examine clinical utility. This analysis is part of a targeted systematic review on existing cardiogenomic panels that included 28 genes in addition to the 9p21 SNPs. That review was commissioned by the Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative and overseen by the EGAPP Working Group (EWG).10

The review team included an experienced consultant to the EWG (G.E.P.) contracted by the Office of Public Health Genomics (OPHG) at the Centers for Disease Control and Prevention, Atlanta, Georgia, an OPHG researcher (S.M.), and a clinical geneticist (L.A.B.). Standard methods for evaluating clinical validity included systematic literature searches, preset inclusion/exclusion criteria, data abstraction, meta-analysis, and grading of studies and cumulative evidence.1113 Expert guidance on literature searches and analytic methods was provided by the Technical Expert Panel. Electronic searches used the HuGE Navigator (version 1.3)14,15 with search term (9p21[Text+Mesh]). Previous validations had cross-checked HuGE Navigator and PubMed16 search results for selected gene-disease associations and found the HuGE Navigator searches equally sensitive but more specific (data available from the author on request). Limiting key questions and truncating search strategies are 2 common methods in rapid reviews17 that we chose to use. The Web site of the laboratory offering 9p21 testing was reviewed for references or gray data. Reference lists of included articles were hand searched. Two team members (G.E.P., S.M.) reviewed included articles on clinical validity and extracted the raw data and demographic information into spreadsheets; discrepancies were resolved through discussion.

Included articles were published in English; contained primary outcomes of coronary heart disease (CHD), myocardial infarction (MI), or coronary artery disease (CAD); tested for 9p21 SNPs; reported race and numbers of affected and unaffected participants; and provided the odds ratio (OR) with confidence intervals (CIs) or data sufficient to compute it. If more than 1 outcome was reported, the best-described phenotype was chosen (eg, MI rather than CAD). The authors' definition of phenotype was not an exclusion criterion. Excluded articles reported only on stroke, intermediate outcomes (eg, atherosclerosis), or subgroups (eg, patients with diabetes). Several included articles reported consortium results with multiple independent populations. These populations were listed as separate data sets.

Not all researchers use the same 9p21 SNPs, and most articles reported results for multiple SNPs (uniquely identified by their rs number). We extracted data for all SNPs used by at least 2 of the 22 included articles, but we report herein 3 common SNPs (rs1333049, rs10757274, and rs2383207) that were included in all but 3 articles.1820 These SNPs are in high linkage disequilibrium (D′ = 1.00; r2>0.85).8,2125 The remaining 3 articles used 2 additional SNPs, rs289116820 and rs10757278.18,19 These were also in high linkage disequilibrium.19 One additional SNP (rs2383206) was reported in at least 2 included articles. When possible, we restricted results in each data set to a single race. Information about age at diagnosis and race/ethnicity was also collected.

As previously reported,26,27 individuals with 1 at-risk SNP allele (heterozygotes) were designated as the reference group. Heterozygotes comprise about 50% of the white population. For each data set, the observed genotype frequencies in controls were compared with expected frequencies based on Hardy-Weinberg equilibrium (χ2 test with 2 degrees of freedom). All P values are 2-sided at the P = .05 level.

Summary ORs and corresponding 95% CIs were derived (by reanalysis when possible) and summarized using random-effects modeling weighted by each data set's total variance (Comprehensive Meta-Analysis, version 2, Biostat Inc, Englewood, New Jersey).28 Subgroup differences were compared using the Q test for heterogeneity for each covariate separately. A fixed-effects meta-regression was performed for the OR vs age at diagnosis.28 Studies that did not report a value for any covariate were excluded. Publication bias was examined by performing a cumulative effects analysis.28 Wider ranges of these summary ORs indicate potential for publication bias.

Quality of individual studies (levels 1-4) and overall quality of evidence for clinical validity (convincing, adequate, or inadequate) were evaluated with EWG methods,11 consistent with other grading systems.29,30 Assessment of cumulative evidence used a consensus evaluation guideline (Venice criteria) specific to gene-disease association studies12 and focused on amount of evidence, replication of evidence, and protection from bias. Cumulative evidence was strong for 3 “A” grades, moderate for “B” grades but no “C” grades, and weak if it received at least 1 “C” grade.

The net reclassification index (NRI)31 was used as an intermediate measure of potential clinical utility. To compute the NRI, the cases (events) and controls (nonevents) in a population were cross-classified by risk assessment using traditional factors with and without 9p21 SNP results. The proportions of all cases reclassified into higher (correct) or lower (incorrect) risk categories were then computed and added together. A similar computation was made for controls. The sum of the 2 proportions (expressed as a percentage) is the NRI, with positive results indicating improved risk prediction.

Twenty-five citations were identified through the original literature search through January 2009; none were meta-analyses. Ten citations were excluded: 2 related to other diseases,32,33 3 addressed subgroups of the population,3436 3 addressed intermediate outcomes,3739 and 2 addressed stroke.40,41 The remaining 15 articles underwent a full review along with 4 additional citations identified through hand-searching reference lists.20,25,42,43 Three of these 19 articles were excluded: 2 due to inadequate or inconsistent data44,45 and 1 had availability of only an electronic abstract at that time.46 The 16 remaining articles were included.8,9,2025,42,43,4752 In December 2009, the search was repeated using the same methods, and 6 additional articles satisfied the inclusion criteria.18,19,46,5355 The analysis is based on data sets from all 22. In 1 article,46 9 data sets were reported; only 5 were included. The remaining 4 data sets were excluded because at least some data from these sets were in published studies already included in our analysis (written communication, Nilesh J. Samani, MD, January 15, 2010). Figure 1 provides a summary of the literature review.

Place holder to copy figure label and caption
Figure 1. Literature Search Results
Graphic Jump Location

Forty-seven distinct data sets were analyzed; most were case-control studies. Observed genotype frequencies were available from 33 data sets (70%), and all but one23 satisfied Hardy-Weinberg criteria (eTable 1). Consensus genotype frequencies in controls were 27% (range, 22%-36%), 50% (range, 47%-57%), and 23% (range, 17%-30%) for 0, 1, and 2 at-risk alleles, respectively. To demonstrate that the choice of SNP is relatively unimportant, we compared the ORs using 4 SNPs that the first 2 articles24,47 listed in the Table had in common (eFigure 1). Although the average ORs are different between the 2 articles (1.78 and 1.26), they are very similar within (eg, 4 SNPs47 provide nearly identical ORs of 1.78, 1.67, 1.75, and 1.72). This is expected, given the high linkage disequilibrium, and justifies reporting on a single SNP per data set.

Table Graphic Jump LocationTable. Summary of Evidence for 9p21 SNPs and Selected Cardiovascular Outcomes
Comparison of Individuals Having 2 At-Risk Alleles With Those Having 1 At-Risk Allele

Using a random-effects model, the summary OR was 1.25 (95% CI, 1.21-1.29, P < .001). Heterogeneity was low (Q = 51; I2 = 10%; P = .29). Figure 2 presents these data. The ORs were stratified by race, 9p21 SNP tested, heart disease outcome, and age cutoff at diagnosis for cases. No differences in ORs were found between Asians (8 data sets) and whites (39 data sets) (ORs, 1.32 and 1.24, respectively; P = .17); the SNPs used (18 data sets with rs1333049, 17 with rs10757274, 6 with rs2383207, 4 with rs2891168, and 2 with rs10757278) (ORs, 1.23, 1.24, 1.28, 1.29, and 1.27, respectively; P = .75); or the outcomes of CAD (19 data sets), MI (17 data sets), or CHD (9 data sets) (ORs, 1.27, 1.23, and 1.21, respectively; P = .45).

Place holder to copy figure label and caption
Figure 2. 9p21 Single-Nucleotide Polymorphism Markers and Heart Disease, Comparing Individuals With 2 At-Risk Alleles vs Those With 1 At-Risk Allele
Graphic Jump Location

The odds ratios (ORs) and 95% confidence intervals (CIs) are shown for the 37 data sets comparing individuals with 2 at-risk alleles vs those with 1 at-risk allele. The consensus (dashed vertical line) and no effect (dotted vertical line) are also shown. Heterogeneity is low.

Finally, we evaluated age at CAD diagnosis. Some articles reported only on early onset disease47,51 or included data sets restricted to early onset disease.9,23,42,46,47 Others enrolled cases in wider age ranges.25,48 Some provided an upper age cutoff (eg, MI occurring by age 50 years) while others provided mean age (and standard deviation). Missing age cutoffs were estimated using information from 11 data sets that provided both (Table).8,9,25,42,50,51 Fourteen data sets not reporting this covariate were excluded.8,9,18,19,43,46,49,53,54 A meta-regression performed on the remaining 33 ORs vs the upper age cutoff level showed a significant association between higher ORs at earlier ages of disease onset (P < .001; slope and intercept of −0.00558 and 0.60881) (Figure 3). The regressed OR was 1.35 (95% CI, 1.30-1.40) for an upper age cutoff of 55 years and 1.21 (95% CI, 1.16-1.25) for 75 years; remaining heterogeneity was reduced (Q = 29; I2 = 0%; P = .58). Consistent results were found using the mean age at onset (eFigure 2). One way to estimate the OR for all adults is to use the 14 data sets with upper age cutoff levels of greater than 70 years. In this group, the summary OR was 1.19 (95% CI, 1.13-1.25; P < .001), with low heterogeneity (Q = 13; I2 = 1%; P = .44).

Place holder to copy figure label and caption
Figure 3. Meta-regression of 9p21 Single-Nucleotide Polymorphism Markers and Heart Disease, Comparing Individuals With 2 At-Risk Alleles vs Those With 1 At-Risk Allele
Graphic Jump Location

The upper age cutoff for the occurrence (eg, myocardial infarction at age ≤50 years) is plotted vs the odds ratio on a logarithmic scale. Larger symbols indicate more precise estimates (standard errors of the log odds ratios of <0.1, 0.1-0.14, 0.15-0.19, and ≥0.20). Although 47 data sets are included, only the 33 data sets reporting upper age cutoff levels were used in the regression.

Comparison of Individuals Having No At-Risk Alleles With Those Having 1 At-Risk Allele

The summary OR was 0.80 (95% CI, 0.77-0.82; P < .001), with moderate heterogeneity (Q = 65; I2 = 29%; P = .04) (Figure 4). No differences in ORs were found between Asians and whites (ORs, 0.81 and 0.79, respectively; P = .79); the SNP used (ORs, 0.82, 0.77, 0.84, 0.78, and 0.70; P = .22); or the outcomes of CAD, CHD, and MI (ORs, 0.79, 0.79, and 0.81; P = .87). Meta-regression again showed a significant slope (P = .001), with lower ORs associated with earlier ages of onset (slope and intercept of 0.00500 and −0.57854) (Figure 5). The regressed OR was 0.74 (95% CI, 0.71-0.77) f or an upper age cutoff of 55 years and 0.82 (95% CI, 0.79-0.85) for 75 years; heterogeneity was lower after fitting the model (Q = 40; I2 = 22%; P = .39). Results were similar when mean age was used (eFigure 2). When restricted to the 14 data sets with an upper age cutoff levels of 70 years or higher, the summary OR was 0.83 (95% CI, 0.78-0.89; P < .001). However, heterogeneity was moderate (Q = 21; I2 = 37%; P = .08). With the most discrepant finding removed (OR, 1.16),48 this summary OR became 0.82 (95% CI, 0.78-0.87) and heterogeneity was reduced (Q = 13; I2 = 7%; P = .37).

Place holder to copy figure label and caption
Figure 4. 9p21 Single-Nucleotide Polymorphism Markers and Heart Disease, Comparing Individuals With No At-Risk Alleles vs Those With 1 At-Risk Allele
Graphic Jump Location

The odds ratios (ORs) and 95% confidence intervals (CIs) are shown for the 37 data sets comparing individuals with no at-risk alleles vs those with 1 at-risk allele. The consensus (dashed vertical line) and no effect (dotted vertical line) are also shown. Heterogeneity is moderate.

Place holder to copy figure label and caption
Figure 5. Meta-regression of 9p21 Single-Nucleotide Polymorphism Markers and Heart Disease, Comparing Individuals With No At-Risk Alleles vs Those With 1 At-Risk Allele
Graphic Jump Location

The upper age cutoff for the occurrence (eg, myocardial infarction at age ≤50 years) is plotted vs the odds ratio on a logarithmic scale. Larger symbols indicate more precise estimates (standard errors of the log odds ratios of <0.1, 0.1-0.14, 0.15-0.19, and ≥0.20). Although 47 data sets are included, only the 33 data sets reporting upper age cutoff levels were used in the regression.

Several included articles reported the effect size as an allele-specific OR,8,9,21,22,46,4951,53 where an equivalent “dose” of risk was conferred per allele. That would imply that the reciprocal of our low OR should be similar to the high OR (eg, 1/0.80 = 1.25; the high OR is 1.25).

Assessment of Evidence for 9p21 and Heart Disease

Three cohort studies (Table) were deemed level 1 designs and the remaining case-control studies were level 2.11 Based on this and internal validity assessments, the overall quality of evidence for clinical validity was convincing to adequate. After accounting for age at onset, the Venice grading12 was A for amount of evidence (>1000 cases/controls with the least common genotype) and A for replication (low heterogeneity after meta-regression and for the 12 data sets with later ages of onset). Bias required examining phenotype definition, genotyping, population stratification, and selective reporting using the predefined “typical” effect size category (ORs, 1.15-1.80).12 The use of widely agreed-on definitions for CAD, CHD, and MI (eg, CHD defined as requiring coronary artery bypass graft surgery) in the data sets made the likelihood of phenotype bias low or none. Nearly all data sets were tested using commercial or well-described genotyping platforms, making the likelihood of genotyping bias low. Most case-control studies matched on race/ethnicity, and, where possible, we restricted results to a single race. We assigned the likelihood of stratification bias to be low, resulting in an overall protection from bias grade of A.

Data sets restricted to early onset heart disease are likely to be smaller in size (since these events are uncommon) and have a larger effect size (Figure 3). This might appear to be positive publication bias. To avoid confounding, each of the high ORs (Table) was divided by the expected OR given that data set's age at onset (Figure 3). These age-adjusted ORs (median, 1.000) could then be evaluated for publication bias. The cumulative effect size analysis indicated a trivial 4% change from the most precise to the least precise cumulative estimates (0.96 for the 6 largest data sets to 1.00 for all 33 data sets).

The OR associated with an individual's 9p21 test result can be used to modify heart disease risk based on traditional factors. For example, a 65-year-old man with no other traditional risk factors would have a 10-year heart disease risk of 11%,56 while a 40-year-old woman with no other risk factors would have a 10-year risk of 2%. If both were to have 2 at-risk 9p21 alleles, risk estimates would increase to about 13.2% (11 × 1.2) and 2.4% (2 × 1.2), respectively, compared with an individual with 1 at-risk allele. Having no at-risk alleles would result in reduced risk estimates of 9.2% and 1.7%, respectively, compared with an individual with 1 at-risk allele. Comparing no at-risk alleles with 2 at-risk alleles could modify risk estimates by 1.44-fold.

Net Reclassification

Three studies that included 1721 cases and 34 797 controls also provided information to compute the NRI.25,43,57 Four NRI estimates were made because 1 study43 reported 2 risk algorithms. For the 4 data sets, the proportions of cases reclassified were 0.5%, 0.7%, 2.5%, and −0.1% (P = .65, .79, .03, and .87), respectively; proportions of controls reclassified were 0.3%, 4.2%, −0.1%, and 0% (P = .36, <.001, .52, and .87), respectively. The corresponding NRIs were 0.8%, 4.9%, 2.5%, and −0.2% (P = .51, .09, .03, and .96), respectively. Details are given in eTable 2.

Based on EWG and Venice criteria, evidence for the association between heart disease and the 9p21 SNP markers has strong credibility. The 9p21 markers have been identified through genome-wide association studies that were not hypothesis-driven8,9,51 and appear independent of traditional risk factors or family history.24,57 The pathophysiological mechanism is not yet understood.24,25,57 Risk alleles are more strongly associated with heart disease events in younger persons (OR, 1.35) than with heart disease in general (OR, 1.21). One company offering 9p21 SNP testing and interpretation uses an OR of 1.3 in its clinical reports for “MI at any age” and 1.6 for “MI at a younger age.”27

Most data sets are limited to white populations, usually of European descent, so the results for other racial/ethnic groups might differ. In 8 studies among Asians, however, the association appears similar. We did not include data for black populations, but limited evidence suggests a smaller effect.9,23 Rather than adjusting ORs for age/sex, we based our OR on raw data when possible, because adjusted ORs appear to be close to raw estimates. For example, 1 article43 reported per-allele hazard ratios that were 1.14 when unadjusted, 1.15 after adjustment for age, and 1.12 after adjustment for age, blood pressure, lipid measurements, smoking status, diabetes, and antihypertensive use. These differences were not statistically significant. Finally, we removed 2 articles from the meta-analysis for which we could not generate reliable ORs. Both were small (202 cases of CAD/MI44 and 118 individuals with MI/coronary insufficiency)45 and inclusion would be unlikely to influence the results reported using larger sample sets.

A test has clinical utility when its results lead to a measurable improvement in health outcomes. Intervention trials to establish the clinical utility of adding 9p21 testing were lacking.5860 There is an expectation that genetic susceptibility information could increase motivation for long-term lifestyle changes (eg, improvement in risk-reducing behaviors, treatment adherence).61,62 However, measuring behavioral change is challenging. Quiz Ref IDCommunicating genetic information to patients has resulted in encouraging reports of short-term positive effects63,64 and has not shown reduced adherence or fatalistic thinking.64,65

Achievement of more accurate risk by adding 9p21 to traditional risk factors could be considered an intermediate measure of clinical utility. The computed NRIs ranged from a 0.2% decrease to a 4.9% improvement. The study showing the largest NRI25 achieved most of the risk reclassification because of reduced risk in individuals without events and, therefore, would have little chance of improving outcomes. The study reporting 2 traditional risk factor models43 showed that adding 9p21 testing to the Adult Treatment Panel III model showed significant improvement (NRI, 2.5%), while adding testing to the Reynolds Risk Score (RRS) showed no improvement (NRI, −0.2%). One main difference between the 2 models was inclusion of a family history of myocardial infarction in the RRS. None of the 3 studies modified treatment protocols based on addition of 9p21 or examined long-term health or behavioral outcomes. Quiz Ref IDHow best to analyze the effect of adding genomic markers to traditional risk factors (eg, area under the curve, NRI, integrated discrimination improvement) remains an active research area.31,6668

The incidence of MI can be reduced by drugs that lower cholesterol/low-density lipoproteins and blood pressure in individuals with risk factors.69 Improved risk assessment might influence decision making about effective interventions and behavioral change. However, only 37% of US physicians reported regular use of a heart disease risk score.70Quiz Ref IDA systematic review found preliminary evidence that CHD risk scores may translate into modest benefits (eg, increased drug treatment, short-term blood pressure reduction) without clinical harms.71 However, the need for higher-quality evidence on long-term outcomes, and for replication of the results in different clinical settings, was emphasized.71 Other complicating factors may be patient adherence to lipid-lowering medications (about half reach target lipid levels and one-quarter continue long-term drug treatment)72 and access to medical care and medications. Therefore, the clinical utility of adding 9p21 markers to traditional risk factors cannot be assumed, even if risk assessment is improved. One proposal suggests that heart disease may be more effectively prevented by implementing an inexpensive, standardized, multidrug intervention (ie, the polypill) in all persons aged 55 years or older, regardless of individual risk levels.69

In summary, showing that a genetic test has clinical validity does not necessarily lead to improved health. Quiz Ref IDClinical trials need to demonstrate that use of the test is associated with changes in physician management decisions, patient motivation and long-term behavioral changes, improved health outcomes, and/or reduced costs to the health care system. Using genomic tests to improve existing risk models would likely require the inclusion of many markers like 9p21.73,74 Such risk assessments may be more accurate but may not result in more appropriate treatments until the underlying mechanisms are known. Uncovering these mechanisms may provide insights into new or improved treatments and prevention activities.

Corresponding Author: Glenn E. Palomaki, BS, Women and Infants Hospital, 70 Elm St, Second Floor, Providence, RI 02903 (gpalomaki@ipmms.org).

Author Contributions: Mr Palomaki had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Palomaki, Bradley.

Acquisition of data: Palomaki, Melillo.

Analysis and interpretation of data: Palomaki, Melillo.

Drafting of the manuscript: Palomaki, Melillo, Bradley.

Critical revision of the manuscript for important intellectual content: Palomaki, Melillo, Bradley.

Statistical analysis: Palomaki, Melillo.

Obtained funding: Palomaki.

Administrative, technical, or material support: Palomaki, Melillo, Bradley.

Study supervision: Palomaki.

Financial Disclosures: None reported.

Funding/Support: This study was funded by the OPHG, Centers for Disease Control and Prevention (contract 200-2003-01396-0128 to McKing Consulting Corp Inc).

Role of the Sponsor: The OPHG provided support for Ms Melillo and Dr Bradley but had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Additional Contributions: We thank the Technical Expert Panel members for their contributions (Ned Calonge, MD, MPH, Colorado Department of Public Health and Environment; Celia Kaye, MD, PhD; University of Colorado Denver School of Medicine; Carolyn Sue Richards, PhD, Oregon Health and Science University; Joan A. Scott, MS, CGC, Johns Hopkins University; and consultant Christopher J. O’Donnell, MD, MPH, National Heart, Lung, and Blood Institute/NHLBI Framingham Heart Study [all unpaid]). We also thank A. Cecile J. W. Janssens, PhD, Erasmus MC University Medical Center, Rotterdam, the Netherlands, who provided guidance and comments (Dr Janssens received travel reimbursement for EGAPP meetings).

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PubMed   |  Link to Article
Broadbent HM, Peden JF, Lorkowski S,  et al; PROCARDIS Consortium.  Susceptibility to coronary artery disease and diabetes is encoded by distinct, tightly linked SNPs in the ANRIL locus on chromosome 9p.  Hum Mol Genet. 2008;17(6):806-814
PubMed   |  Link to Article
Shen GQ, Li L, Rao S,  et al.  Four SNPs on chromosome 9p21 in a South Korean population implicate a genetic locus that confers high cross-race risk for development of coronary artery disease.  Arterioscler Thromb Vasc Biol. 2008;28(2):360-365
PubMed   |  Link to Article
Shen GQ, Rao S, Martinelli N,  et al.  Association between four SNPs on chromosome 9p21 and myocardial infarction is replicated in an Italian population.  J Hum Genet. 2008;53(2):144-150
PubMed   |  Link to Article
Assimes TL, Knowles JW, Basu A,  et al.  Susceptibility locus for clinical and subclinical coronary artery disease at chromosome 9p21 in the multi-ethnic ADVANCE study.  Hum Mol Genet. 2008;17(15):2320-2328
PubMed   |  Link to Article
Anderson JL, Horne BD, Kolek MJ,  et al.  Genetic variation at the 9p21 locus predicts angiographic coronary artery disease prevalence but not extent and has clinical utility.  Am Heart J. 2008;156(6):1155-1162, e2
PubMed   |  Link to Article
Talmud PJ, Cooper JA, Palmen J,  et al.  Chromosome 9p21.3 coronary heart disease locus genotype and prospective risk of CHD in healthy middle-aged men.  Clin Chem. 2008;54(3):467-474
PubMed   |  Link to Article
Janssens AC, van Duijn CM. Five genetic variants associated with prostate cancer.  N Engl J Med. 2008;358(25):2739
PubMed
deCODE Diagnostic Laboratory.  Patient handout. http://www.decodediagnostics.com/PDF/MI%20Sample%20test%20report%20(High%20Risk).pdf. Accessed September 28, 2009
Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis. West Sussex, England: John Wiley & Sons Ltd; 2009
Stroup DF, Berlin JA, Morton SC,  et al.  Meta-analysis of observational studies in epidemiology: a proposal for reporting: Meta-analysis of Observational Studies in Epidemiology (MOOSE) Group.  JAMA. 2000;283(15):2008-2012
PubMed   |  Link to Article
Guyatt GH, Oxman AD, Vist GE,  et al; GRADE Working Group.  GRADE: an emerging consensus on rating quality of evidence and strength of recommendations.  BMJ. 2008;336(7650):924-926
PubMed   |  Link to Article
Pencina MJ, D'Agostino RB Sr, D’Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.  Stat Med. 2008;27(2):157-172
PubMed   |  Link to Article
Vignoli M, Scaini MC, Ghiorzo P,  et al.  Genomic rearrangements of the CDKN2A locus are infrequent in Italian malignant melanoma families without evidence of CDKN2A/CDK4 point mutations.  Melanoma Res. 2008;18(6):431-437
PubMed   |  Link to Article
Ohta S, Uemura H, Matsui Y,  et al.  Alterations of p16 and p14ARF genes and their 9p21 locus in oral squamous cell carcinoma.  Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2009;107(1):81-91
PubMed   |  Link to Article
Doria A, Wojcik J, Xu R,  et al.  Interaction between poor glycemic control and 9p21 locus on risk of coronary artery disease in type 2 diabetes.  JAMA. 2008;300(20):2389-2397
PubMed   |  Link to Article
O’Donnell CJ, Cupples LA, D’Agostino RB,  et al.  Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI's Framingham Heart Study.  BMC Med Genet. 2007;8:(suppl 1)  S4
PubMed   |  Link to Article
Wood GC, Still CD, Chu X,  et al.  Association of chromosome 9p21 SNPs with cardiovascular phenotypes in morbid obesity using electronic health record data.  Genomic Med. 2008;2(1-2):33-43
PubMed   |  Link to Article
Thompson AR, Drenos F, Hafez H, Humphries  SE. Candidate gene association studies in abdominal aortic aneurysm disease: a review and meta-analysis.  Eur J Vasc Endovasc Surg. 2008;35(1):19-30
PubMed   |  Link to Article
Ye S, Willeit J, Kronenberg F, Xu Q, Kiechl S. Association of genetic variation on chromosome 9p21 with susceptibility and progression of atherosclerosis: a population-based, prospective study.  J Am Coll Cardiol. 2008;52(5):378-384
PubMed   |  Link to Article
Chen SN, Ballantyne CM, Gotto AM Jr, Marian  AJ. The 9p21 susceptibility locus for coronary artery disease and the severity of coronary atherosclerosis.  BMC Cardiovasc Disord. 2009;9:3
PubMed   |  Link to Article
Helgadottir A, Thorleifsson G, Magnusson KP,  et al.  The same sequence variant on 9p21 associates with myocardial infarction, abdominal aortic aneurysm and intracranial aneurysm.  Nat Genet. 2008;40(2):217-224
PubMed   |  Link to Article
Matarin M, Brown WM, Singleton A, Hardy JA, Meschia JF.ISGS investigators.  Whole genome analyses suggest ischemic stroke and heart disease share an association with polymorphisms on chromosome 9p21.  Stroke. 2008;39(5):1586-1589
PubMed   |  Link to Article
Schunkert H, Gotz A, Braund P,  et al; Cardiogenics Consortium.  Repeated replication and a prospective meta-analysis of the association between chromosome 9p21.3 and coronary artery disease.  Circulation. 2008;117(13):1675-1684
PubMed   |  Link to Article
Paynter NP, Chasman DI, Buring JE, Shiffman D, Cook NR, Ridker PM. Cardiovascular disease risk prediction with and without knowledge of genetic variation at chromosome 9p21.3.  Ann Intern Med. 2009;150(2):65-72
PubMed   |  Link to Article
Chen Z, Qian Q, Ma G,  et al.  A common variant on chromosome 9p21 affects the risk of early-onset coronary artery disease.  Mol Biol Rep. 2009;36(5):889-893
PubMed   |  Link to Article
Larson MG, Atwood LD, Benjamin EJ,  et al.  Framingham Heart Study 100K project: genome-wide associations for cardiovascular disease outcomes.  BMC Med Genet. 2007;8:(suppl 1)  S5
PubMed   |  Link to Article
Samani NJ, Deloukas P, Erdmann J,  et al; Coronary Artery Disease Consortium.  Large scale association analysis of novel genetic loci for coronary artery disease.  Arterioscler Thromb Vasc Biol. 2009;29(5):774-780
PubMed   |  Link to Article
Abdullah KG, Li L, Shen GQ,  et al.  Four SNPS on chromosome 9p21 confer risk to premature, familial CAD and MI in an American Caucasian population (GeneQuest).  Ann Hum Genet. 2008;72(pt 5):654-657
PubMed   |  Link to Article
Dehghan A, van Hoek M, Sijbrands EJ,  et al.  Lack of association of two common polymorphisms on 9p21 with risk of coronary heart disease and myocardial infarction: results from a prospective cohort study.  BMC Med. 2008;6:30
PubMed   |  Link to Article
Hinohara K, Nakajima T, Takahashi M,  et al.  Replication of the association between a chromosome 9p21 polymorphism and coronary artery disease in Japanese and Korean populations.  J Hum Genet. 2008;53(4):357-359
PubMed   |  Link to Article
Hiura Y, Fukushima Y, Yuno M,  et al.  Validation of the association of genetic variants on chromosome 9p21 and 1q41 with myocardial infarction in a Japanese population.  Circ J. 2008;72(8):1213-1217
PubMed   |  Link to Article
Samani NJ, Erdmann J, Hall AS,  et al; WTCCC and Cardiogenics Consortium.  Genomewide association analysis of coronary artery disease.  N Engl J Med. 2007;357(5):443-453
PubMed   |  Link to Article
Zhou L, Zhang X, He M,  et al.  Associations between single nucleotide polymorphisms on chromosome 9p21 and risk of coronary heart disease in Chinese Han population.  Arterioscler Thromb Vasc Biol. 2008;28(11):2085-2089
PubMed   |  Link to Article
Newton-Cheh C, Cook NR, VanDenburgh M, Rimm EB, Ridker PM, Albert CM. A common variant at 9p21 is associated with sudden and arrhythmic cardiac death.  Circulation. 2009;120(21):2062-2068
PubMed   |  Link to Article
Peng WH, Lu L, Zhang Q,  et al.  Chromosome 9p21 polymorphism is associated with myocardial infarction but not with clinical outcome in Han Chinese.  Clin Chem Lab Med. 2009;47(8):917-922
PubMed   |  Link to Article
Zhang Q, Wang XF, Cheng SS,  et al.  Three SNPs on chromosome 9p21 confer increased risk of myocardial infarction in Chinese subjects.  Atherosclerosis. 2009;207(1):26-28
PubMed   |  Link to Article
Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories.  Circulation. 1998;97(18):1837-1847
PubMed   |  Link to Article
Brautbar A, Ballantyne CM, Lawson K,  et al.  Impact of adding a single allele in the 9p21 locus to traditional risk factors on reclassification of coronary heart disease risk and implications for lipid-modifying therapy in the Atherosclerosis Risk in Communities study.  Circ Cardiovasc Genet. 2009;2(3):279-285
PubMed   |  Link to Article
Khoury MJ, McBride CM, Schully SD,  et al; Centers for Disease Control and Prevention.  The Scientific Foundation for personal genomics: recommendations from a National Institutes of Health-Centers for Disease Control and Prevention multidisciplinary workshop.  Genet Med. 2009;11(8):559-567
PubMed   |  Link to Article
Ioannidis JP. Personalized genetic prediction: too limited, too expensive, or too soon?  Ann Intern Med. 2009;150(2):139-141
PubMed   |  Link to Article
Khoury MJ, Bertram L, Boffetta P,  et al.  Genome-wide association studies, field synopses, and the development of the knowledge base on genetic variation and human diseases.  Am J Epidemiol. 2009;170(3):269-279
PubMed   |  Link to Article
Marteau TM, Lerman C. Genetic risk and behavioural change.  BMJ. 2001;322(7293):1056-1059
PubMed   |  Link to Article
O’Neill SC, White DB, Sanderson SC,  et al.  The feasibility of online genetic testing for lung cancer susceptibility: uptake of a Web-based protocol and decision outcomes.  Genet Med. 2008;10(2):121-130
PubMed   |  Link to Article
McBride CM, Beplar G, Lipkus IM,  et al.  Incorporating genetic susceptibility feedback into a smoking cessation program for African-American smokers with low income.  Cancer Epidemiol Biomarkers Prev. 2002;11(6):521-528
PubMed
Pijl M, Timmermans DR, Claassen L,  et al.  Impact of communicating familial risk of diabetes on illness perceptions and self-reported behavioral outcomes: a randomized controlled trial.  Diabetes Care. 2009;32(4):597-599
PubMed   |  Link to Article
Marteau T, Senior V, Humphries SE,  et al; Genetic Risk Assessment for FH Trial Study Group.  Psychological impact of genetic testing for familial hypercholesterolemia within a previously aware population: a randomized controlled trial.  Am J Med Genet A. 2004;128A(3):285-293
PubMed   |  Link to Article
Hlatky MA, Greenland P, Arnett DK,  et al; American Heart Association Expert Panel on Subclinical Atherosclerotic Diseases and Emerging Risk Factors; Stroke Council.  Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association.  Circulation. 2009;119(17):2408-2416
PubMed   |  Link to Article
Cook NR. Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve.  Clin Chem. 2008;54(1):17-23
PubMed   |  Link to Article
Janssens AC, van Duijn CM. Genome-based prediction of common diseases: methodological considerations for future research.  Genome Med. 2009;1(2):20
PubMed   |  Link to Article
Wald NJ, Law MR. A strategy to reduce cardiovascular disease by more than 80%.  BMJ. 2003;326(7404):1419
PubMed   |  Link to Article
Sposito AC, Ramires JA, Jukema JW,  et al.  Physicians' attitudes and adherence to use of risk scores for primary prevention of cardiovascular disease: crosssectional survey in three world regions.  Curr Med Res Opin. 2009;25(5):1171-1178
PubMed   |  Link to Article
Sheridan SL, Crespo E. Does the routine use of global coronary heart disease risk scores translate into clinical benefits or harms? a systematic review of the literature.  BMC Health Serv Res. 2008;8:60
PubMed   |  Link to Article
Schedlbauer A, Schroeder K, Fahey T. How can adherence to lipid-lowering medication be improved? a systematic review of randomized controlled trials.  Fam Pract. 2007;24(4):380-387
PubMed   |  Link to Article
van der Net JB, Janssens AC, Sijbrands EJ, Steyerberg EW. Value of genetic profiling for the prediction of coronary heart disease.  Am Heart J. 2009;158(1):105-110
PubMed   |  Link to Article
Janssens AC, Gwinn M, Bradley LA, Oostra BA, van Duijn CM, Khoury MJ. A critical appraisal of the scientific basis of commercial genomic profiles used to assess health risks and personalize health interventions.  Am J Hum Genet. 2008;82(3):593-599
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1. Literature Search Results
Graphic Jump Location
Place holder to copy figure label and caption
Figure 2. 9p21 Single-Nucleotide Polymorphism Markers and Heart Disease, Comparing Individuals With 2 At-Risk Alleles vs Those With 1 At-Risk Allele
Graphic Jump Location

The odds ratios (ORs) and 95% confidence intervals (CIs) are shown for the 37 data sets comparing individuals with 2 at-risk alleles vs those with 1 at-risk allele. The consensus (dashed vertical line) and no effect (dotted vertical line) are also shown. Heterogeneity is low.

Place holder to copy figure label and caption
Figure 3. Meta-regression of 9p21 Single-Nucleotide Polymorphism Markers and Heart Disease, Comparing Individuals With 2 At-Risk Alleles vs Those With 1 At-Risk Allele
Graphic Jump Location

The upper age cutoff for the occurrence (eg, myocardial infarction at age ≤50 years) is plotted vs the odds ratio on a logarithmic scale. Larger symbols indicate more precise estimates (standard errors of the log odds ratios of <0.1, 0.1-0.14, 0.15-0.19, and ≥0.20). Although 47 data sets are included, only the 33 data sets reporting upper age cutoff levels were used in the regression.

Place holder to copy figure label and caption
Figure 4. 9p21 Single-Nucleotide Polymorphism Markers and Heart Disease, Comparing Individuals With No At-Risk Alleles vs Those With 1 At-Risk Allele
Graphic Jump Location

The odds ratios (ORs) and 95% confidence intervals (CIs) are shown for the 37 data sets comparing individuals with no at-risk alleles vs those with 1 at-risk allele. The consensus (dashed vertical line) and no effect (dotted vertical line) are also shown. Heterogeneity is moderate.

Place holder to copy figure label and caption
Figure 5. Meta-regression of 9p21 Single-Nucleotide Polymorphism Markers and Heart Disease, Comparing Individuals With No At-Risk Alleles vs Those With 1 At-Risk Allele
Graphic Jump Location

The upper age cutoff for the occurrence (eg, myocardial infarction at age ≤50 years) is plotted vs the odds ratio on a logarithmic scale. Larger symbols indicate more precise estimates (standard errors of the log odds ratios of <0.1, 0.1-0.14, 0.15-0.19, and ≥0.20). Although 47 data sets are included, only the 33 data sets reporting upper age cutoff levels were used in the regression.

Tables

Table Graphic Jump LocationTable. Summary of Evidence for 9p21 SNPs and Selected Cardiovascular Outcomes

References

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PubMed   |  Link to Article
Lemmens R, Abboud S, Robberecht W,  et al.  Variant on 9p21 strongly associates with coronary heart disease, but lacks association with common stroke.  Eur J Hum Genet. 2009;17(10):1287-1293
PubMed   |  Link to Article
Broadbent HM, Peden JF, Lorkowski S,  et al; PROCARDIS Consortium.  Susceptibility to coronary artery disease and diabetes is encoded by distinct, tightly linked SNPs in the ANRIL locus on chromosome 9p.  Hum Mol Genet. 2008;17(6):806-814
PubMed   |  Link to Article
Shen GQ, Li L, Rao S,  et al.  Four SNPs on chromosome 9p21 in a South Korean population implicate a genetic locus that confers high cross-race risk for development of coronary artery disease.  Arterioscler Thromb Vasc Biol. 2008;28(2):360-365
PubMed   |  Link to Article
Shen GQ, Rao S, Martinelli N,  et al.  Association between four SNPs on chromosome 9p21 and myocardial infarction is replicated in an Italian population.  J Hum Genet. 2008;53(2):144-150
PubMed   |  Link to Article
Assimes TL, Knowles JW, Basu A,  et al.  Susceptibility locus for clinical and subclinical coronary artery disease at chromosome 9p21 in the multi-ethnic ADVANCE study.  Hum Mol Genet. 2008;17(15):2320-2328
PubMed   |  Link to Article
Anderson JL, Horne BD, Kolek MJ,  et al.  Genetic variation at the 9p21 locus predicts angiographic coronary artery disease prevalence but not extent and has clinical utility.  Am Heart J. 2008;156(6):1155-1162, e2
PubMed   |  Link to Article
Talmud PJ, Cooper JA, Palmen J,  et al.  Chromosome 9p21.3 coronary heart disease locus genotype and prospective risk of CHD in healthy middle-aged men.  Clin Chem. 2008;54(3):467-474
PubMed   |  Link to Article
Janssens AC, van Duijn CM. Five genetic variants associated with prostate cancer.  N Engl J Med. 2008;358(25):2739
PubMed
deCODE Diagnostic Laboratory.  Patient handout. http://www.decodediagnostics.com/PDF/MI%20Sample%20test%20report%20(High%20Risk).pdf. Accessed September 28, 2009
Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis. West Sussex, England: John Wiley & Sons Ltd; 2009
Stroup DF, Berlin JA, Morton SC,  et al.  Meta-analysis of observational studies in epidemiology: a proposal for reporting: Meta-analysis of Observational Studies in Epidemiology (MOOSE) Group.  JAMA. 2000;283(15):2008-2012
PubMed   |  Link to Article
Guyatt GH, Oxman AD, Vist GE,  et al; GRADE Working Group.  GRADE: an emerging consensus on rating quality of evidence and strength of recommendations.  BMJ. 2008;336(7650):924-926
PubMed   |  Link to Article
Pencina MJ, D'Agostino RB Sr, D’Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.  Stat Med. 2008;27(2):157-172
PubMed   |  Link to Article
Vignoli M, Scaini MC, Ghiorzo P,  et al.  Genomic rearrangements of the CDKN2A locus are infrequent in Italian malignant melanoma families without evidence of CDKN2A/CDK4 point mutations.  Melanoma Res. 2008;18(6):431-437
PubMed   |  Link to Article
Ohta S, Uemura H, Matsui Y,  et al.  Alterations of p16 and p14ARF genes and their 9p21 locus in oral squamous cell carcinoma.  Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2009;107(1):81-91
PubMed   |  Link to Article
Doria A, Wojcik J, Xu R,  et al.  Interaction between poor glycemic control and 9p21 locus on risk of coronary artery disease in type 2 diabetes.  JAMA. 2008;300(20):2389-2397
PubMed   |  Link to Article
O’Donnell CJ, Cupples LA, D’Agostino RB,  et al.  Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI's Framingham Heart Study.  BMC Med Genet. 2007;8:(suppl 1)  S4
PubMed   |  Link to Article
Wood GC, Still CD, Chu X,  et al.  Association of chromosome 9p21 SNPs with cardiovascular phenotypes in morbid obesity using electronic health record data.  Genomic Med. 2008;2(1-2):33-43
PubMed   |  Link to Article
Thompson AR, Drenos F, Hafez H, Humphries  SE. Candidate gene association studies in abdominal aortic aneurysm disease: a review and meta-analysis.  Eur J Vasc Endovasc Surg. 2008;35(1):19-30
PubMed   |  Link to Article
Ye S, Willeit J, Kronenberg F, Xu Q, Kiechl S. Association of genetic variation on chromosome 9p21 with susceptibility and progression of atherosclerosis: a population-based, prospective study.  J Am Coll Cardiol. 2008;52(5):378-384
PubMed   |  Link to Article
Chen SN, Ballantyne CM, Gotto AM Jr, Marian  AJ. The 9p21 susceptibility locus for coronary artery disease and the severity of coronary atherosclerosis.  BMC Cardiovasc Disord. 2009;9:3
PubMed   |  Link to Article
Helgadottir A, Thorleifsson G, Magnusson KP,  et al.  The same sequence variant on 9p21 associates with myocardial infarction, abdominal aortic aneurysm and intracranial aneurysm.  Nat Genet. 2008;40(2):217-224
PubMed   |  Link to Article
Matarin M, Brown WM, Singleton A, Hardy JA, Meschia JF.ISGS investigators.  Whole genome analyses suggest ischemic stroke and heart disease share an association with polymorphisms on chromosome 9p21.  Stroke. 2008;39(5):1586-1589
PubMed   |  Link to Article
Schunkert H, Gotz A, Braund P,  et al; Cardiogenics Consortium.  Repeated replication and a prospective meta-analysis of the association between chromosome 9p21.3 and coronary artery disease.  Circulation. 2008;117(13):1675-1684
PubMed   |  Link to Article
Paynter NP, Chasman DI, Buring JE, Shiffman D, Cook NR, Ridker PM. Cardiovascular disease risk prediction with and without knowledge of genetic variation at chromosome 9p21.3.  Ann Intern Med. 2009;150(2):65-72
PubMed   |  Link to Article
Chen Z, Qian Q, Ma G,  et al.  A common variant on chromosome 9p21 affects the risk of early-onset coronary artery disease.  Mol Biol Rep. 2009;36(5):889-893
PubMed   |  Link to Article
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