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We sought to replicate the association between the kinesin-like protein 6 (KIF6) Trp719Arg polymorphism (rs20455) and clinical coronary artery disease (CAD).
Recent prospective studies suggest that carriers of the 719Arg allele in KIF6 are at increased risk of clinical CAD compared with non-carriers.
The KIF6 Trp719Arg polymorphism (rs20455) was genotyped in nineteen case-control studies of non-fatal CAD either as part of a genome-wide association study or in a formal attempt to replicate the initial positive reports.
Over 17 000 cases and 39 000 controls of European descent as well as a modest number of South Asians, African Americans, Hispanics, East Asians, and admixed cases and controls were successfully genotyped. None of the nineteen studies demonstrated an increased risk of CAD in carriers of the 719Arg allele compared with non-carriers. Regression analyses and fixed effect meta-analyses ruled out with high degree of confidence an increase of ≥2% in the risk of CAD among European 719Arg carriers. We also observed no increase in the risk of CAD among 719Arg carriers in the subset of Europeans with early onset disease (<50 years of age for males and <60 years for females) compared with similarly aged controls as well as all non-European subgroups.
The KIF6 Trp719Arg polymorphism was not associated with the risk of clinical CAD in this large replication study.
Recent prospective observational studies suggest an association between the Trp719Arg SNP in kinesin-like protein 6 (rs20455) and the development of clinical coronary artery disease (CAD)(1–5). Carriers of the 719Arg alleles were found to have a modest increase in the risk in the Atherosclerosis Risk in Communities study (ARIC) (Hazard Ratio and 95% CI for log additive model: 1.11, 1.02–1.21, p = 0.02)(1), the Cardiovascular Health Study (CHS) (HR and 95% CI for dominant model: 1.29, 1.1–1.52, p = 0.005)(5), and the Women’s Health Study (WHS)(HR and 95% CI for dominant model: 1.24, 1.04–1.46, p = 0.01)(4).
An increase in the risk of CAD was also observed in the placebo arm of two statin trials: the West of Scotland Coronary Prevention Study (WOSCOPS) (Odds Ratio for incident CAD events and 95% CI for dominant model: 1.55, 1.14–2.09, p = 0.005) and the Cholesterol and Recurrent Events (CARE) trial (HR for recurrent myocardial infarction and 95% CI for dominant model: 1.50 (1.05–2.15), p = 0.03) (3). Curiously, carriers of the 719Arg allele were not at increased risk of CAD or recurrent MI in the pravastatin arms of these two trials. Furthermore, in the Pravastatin or Atorvastatin Evaluation and Infection Therapy Thrombolysis in Myocardial Infarction 22 (PROVE IT-TIMI 22) trial, carriers in the pravastatin arm were also not at increased risk of CAD while carriers in the atorvastatin arm had a decreased risk compared with non-carriers (adjusted HR 0.65, 95% CI 0.48 to 0.88; p = 0.005)(2). The discrepant results between the two arms of these three statin trials were deemed a consequence of a differential effect of genotype on the benefit derived from the use of statins with carriers benefiting to a larger degree from statin therapy than non-carriers (2,3). Because carriers randomized to the more potent statin were actually at decreased risk compared with non-carriers in the PROVE IT- TIMI 22 trial, it was hypothesized that the degree of incremental benefit from statin use among carriers was a function of the intensity of lipid lowering therapy(2).
The results of these initial studies have been used to justify the development of a KIF6 Trp719Arg variant pharmacogenomic test (“Statincheck”)(6). This test is currently being marketed to health care professionals as an aid to identifying subjects at high risk of incident or recurrent CAD events who stand to gain the most from the use of statins(6,7). However, in recent GWAS for CAD and/or MI, SNPs at the KIF6 locus were not among the associations reaching genome wide significance(8–12). Furthermore, in the ongoing Ottawa Heart Genomics Study, no association was found between the KIF6 Trp719Arg SNP and angiographically defined CAD in a subset of 1540 cases and 1455 controls(13). These discrepant results demand examination of this association in additional populations to substantiate the use of the KIF6 test in the management of subjects at risk of clinical CAD.
In this study, we report association analyses for the KIF6 Trp719Arg SNP in 19 case-control studies of CAD that have recently genotyped this SNP using various genotyping platforms either as part of a GWA study or in a formal attempt to replicate the initial positive reports (1–5).
We included subjects participating in 19 different case-control studies of CAD conducted around the world: the Atherosclerotic Disease, VAscular functioN, and genetiC Epidemiology study (ADVANCE)(14) of northern California, USA, the AMI Gene Study/Dortmund Health (AMI Gene)(15) in Germany, the CATHGEN Research Project (CATHGEN)(16) in North Carolina, USA, the deCODE CAD study (deCODE)(10) in Iceland, the National Finrisk study (FINRISK)(17) in Finland, the sibling Genetic Study of Atherosclerosis Risk (GeneSTAR)(18) in Maryland, USA, the German Myocardial Infarction Family studies (GerMIFS I and GerMIFS II)(8) in Germany, the Heart Attack Risk in Puget Sound (HARPS)(19) study in Washington state, USA, the international INTERHEART study (INTERHEART)(20,21) coordinated by Population Health Research Institute of McMaster University in Hamilton, Ontario, the Irish Family Study (IFS)(22) in Ireland, the Malmo Diet and Cancer study (MDC)(23) in Malmo, Sweden, the Medstar/Washington Hospital Center (MEDSTAR)(10) study in Washington, D.C., the Massachusetts General Hospital Premature CAD study (MGH PCAD)(24) in Boston, USA, the Mid-America Heart Institute study (MAHI)(25,26) in Kansas City, Missouri, the University of Pennsylvania Medical Center Cardiac catheterization cohort study (PennCATH)(10) in Philadelphia, the Registre Gironı´ del Cor (REGICOR)(27) study in Gerona, Spain, the Verona Heart Study (VHS)(28) in Verona, Italy, and the Wellcome Trust Case Control Consortium study of CAD (WTCCC CAD) (11) in the United Kingdom. All participants gave written informed consent and local ethics committees approved all studies.
Investigators from several of these 19 studies are members of consortia with a primary interest of identifying novel genetic determinants of CAD through the use of GWAS technology. The consortia include the PennCATH/Medstar consortium comprised of the PennCATH and Medstar studies, the Myocardial Infarction Genetics consortium (MIGen)(12) comprised of the HARPS, REGICOR, MGH PCAD, FINRISK, and MDC studies, the Cardiogenics consortium(8) comprised of the WTCCC, GerMIFS I, GerMIFS II studies, and the Coronary ARtery Disease Genome-wide Replication And Meta-analysis consortium (CARDIoGRAM) comprised of the ADVANCE study, the deCODE study, and the Penn/Medstar, MIGen, and CARDIOGENICS consortia.
Each study used standard criteria to identify cases with myocardial infarction (MI) established by international organizations during the 1990s or more recently. While some of these studies restricted their enrollment of cases to subjects with at least one MI, others included cases diagnosed with clinically significant coronary atherosclerosis without MI. Elevation of cardiac markers (CKMB or troponin) accompanied with symptoms and/or ECG suggestive of cardiac ischemia were typical criteria used to identify MI cases. Non-MI cases included subjects with angina and confirmatory tests for ischemia, unstable angina based on symptoms and ECG changes without elevation of cardiac enzymes, or revascularization procedures that may have occurred in the presence or absence of symptoms. For the current analyses, both cases with and without a diagnosis of MI were considered in order to emulate the outcome used in the majority of the prospective studies published to date (1–5).
Most of the studies either only enrolled subjects of European descent or restricted their genotyping efforts to Europeans. However, in addition to Europeans, the ADVANCE study genotyped a modest number of African Americans, Hispanics, East Asians, and admixed individuals, the CATHGEN and GENESTAR studies genotyped a modest number of African Americans, and the INTERHEART study genotyped a large number of South Asians.
More details on the design of each study including the precise criteria used by each of the 19 studies to ascertain cases and controls can be found in the Supplementary Appendix and related references.
The Trp719Arg polymorphism in kinesin-like protein 6 (rs20455) was directly genotyped in all studies and no imputation of the genotype was necessary. For the GerMIFS I and WTCCC CAD studies, the genotyping data were extracted from the Affymetrix 500k array(29). For the deCODE, ADVANCE, and GeneSTAR studies, the genotyping data were extracted from the Illumina Infinium HumanHap317/370, 550, and 1M chip arrays, respectively(29). For the GerMIFS II, FINRISK, HARPS, MDC, MGH PCAD, PennCATH, Medstar, and REGICOR studies, the genotyping data were extracted from the Affymetrix 6.0 array(29). For the AMI Gene, IFS, INTERHEART, and MAHI studies, the SNP was genotyped using the iPLEX MassARRAY platform (Sequenom) platform(29). Finally, for CATHGEN and VHS, the SNP was genotyped using the Centaurus platform(29). All genotype data generated from the various platforms passed extensive quality control measures including tests of Hardy Weinberg Equilibrium (p > 0.001 in controls).
The age of onset of clinical CAD (for cases) and sex for cases and controls were documented in all studies. The presence of other traditional risk factors was documented in most but not all studies. In the deCODE study, risk factor data other than age, sex, and BMI were not collected. Furthermore, the presence of other traditional risk factors was defined in various ways and the timing of enrollment of cases hampered the accurate measurement of these risk factors in some studies. For example, several studies enrolled cases weeks to months (and sometimes years) after their initial CAD event, making it difficult to reliably discern whether certain medications being taken by a participant at the time of enrollment were prescribed to treat risk factors present prior to their first ever clinical manifestation of CAD vs. for secondary prevention reasons or to treat CAD symptoms (e.g. beta blockers, calcium channel blockers, ACE inhibitors, and statins). No attempt was made to standardize risk factor definitions across all studies.
We first calculated the crude Odds Ratios (OR) and 95% confidence interval (95% CI) based on the 2×2 table of KIF6 Trp719Arg allele-by-trait counts for each case control study. In case control studies with more than one race/ethnic group, we calculated an OR for each race/ethnic group. We then compared the distribution of the raw genotype counts in each case-control stratum with Armitage’s trend test(30). Next, we calculated OR and 95% CI adjusted for age of onset of CAD and sex using standard unconditional logistic regression. Because of the difficulties in ascertaining risk factors other than age and sex, we did not adjust OR for any additional CAD risk factors. Log additive and dominant OR were calculated as both modes of inheritance were reported to date and the 719Trp allele served as the reference allele given the 719Arg allele was identified as the high risk allele in prior publications. Analyses were repeated after excluding cases with an age of onset of CAD ≥50 years for males and ≥60 years for females. Controls for this subgroup analysis were also restricted to those in the same sex specific age range as cases at the time of enrollment. The GeneSTAR family study analyses were performed using generalized linear latent and mixed models (GLLAMM) with the logistic link and family membership as the random effect in order to account for family structure while the Irish Family Study used likelihood based association statistics incorporated in the UNPHASED software package(31) to account for family structure. In the latter family study, only the crude OR for the additive model using all subjects could be calculated.
Lastly, we combined crude and adjusted OR, 95% CI, and p values using a Mantel-Haenszel model in which the groups were allowed to have different population frequencies for genotypes but were assumed to have common relative risks(32). We weighed the results from each study by the standard error of the effect derived from the p value of the OR (see Supplementary Appendix for details). Using this meta-analytic approach, we calculated the overall combined OR, 95% CI, and p value separately for all case control studies of Europeans and all case-control studies of African Americans. The meta-analyses were repeated for the subgroup of cases in each study with early onset disease and their respective controls. For each meta-analysis, we also performed two standard heterogeneity tests (Cohran’s Q and Higgin’s I2) to assess the appropriateness of a fixed effects model over a random effects model(33).
From previous work, we know that one of the two admixed race/ethnic strata in the ADVANCE study, the admixed non-Hispanics, has significant differences in the degree of admixture between cases and controls(14). Specifically, cases in this stratum are, on average, significantly more European (and less African American) than controls. Therefore, we included a covariate in the multivariate regression model indicating the proportion of white ancestry for each individual estimated by the program STRUCTURE (34,35) in this stratum. The other admixed stratum in the ADVANCE study, the admixed Hispanics, did not show differences in degree of admixture between cases and controls. Therefore, no additional covariates were added to the regression model for this stratum.
Some Icelandic affected individuals and controls are related, both within and between groups, causing the chi square test statistic to have a mean >1 and a median larger than 0.6752. The inflation factor, lambda (λ), was estimated at 1.21 using a method of genomic control(36) by calculating the average of the observed chi statistics for the genome-wide SNP set, which accounts for relatedness and for potential population stratification. The 95% CI and p values presented for deCODE are based on adjusting the chi square statistic by dividing it by this inflation factor. The inflation factor was also found to be high in the GerMIFS I study (λ ~1.27), therefore p values and 95% CI for this study were also adjusted in the same manner. For all other studies with genome wide data, a genomic control correction was not applied to the data as the inflation factor was found to be very low (λ ≤1.05).
We performed sensitivity analyses in Europeans only by repeating all analyses described above after first excluding non-MI cases in the subset of case-control studies which included both MI and non-MI cases of CAD.
Table 1 summarizes the ascertainment scheme and the inclusion criteria for phenotype, age, sex, and race stratified by study and case-control status. The distribution and frequency of all traditional risk factors of CAD stratified by study and case-control status can be found in the Supplementary appendix (Table 1S). Out of the 19 case control studies, 12 enrolled only cases with MI and 14 also restricted enrollment or genotyping to early onset disease (when considering the traditional age cutoffs of ≤ 65 years for females and ≤ 55 years for males at the time of first onset of disease). However, the largest study (deCODE) enrolled both MI and non MI cases of CAD and included CAD cases with any age of onset. The proportion of cases that were female varied from study to study and was influenced by the ascertainment scheme and matching (either one to one or frequency matching). The overall weighted average age of onset of CAD was 55.7 years.
Table 2 summarizes the genotype counts stratified by study, case control status, and race/ethnic group as well as the adjusted OR, 95% CI, and p values for the association between KIF6 rs20455 SNP and CAD. Crude ORs were not materially different from their respective ORs adjusted for age and sex. Therefore, only ORs adjusted for age and sex are presented. Among Europeans, only one out of the 19 studies (deCODE) demonstrated a nominally significant association between the KIF6 polymorphism and CAD but in the opposite direction of the published literature (the 719Arg allele inversely associated with risk: log additive OR of 0.93, 95% CI 0.88–0.99, log dominant OR of 0.91, 0.85–0.99). The meta-analysis produced a point estimate of the log additive OR near unity with very tight confidence intervals (log additive OR 0.98, 95% CI 0.95–1.02, log dominant OR 0.97, 0.93–1.01). We also found no significant association between the KIF6 rs20455 SNP and CAD among South Asian participants in the INTERHEART study (log additive OR 1.02, 95% CI 0.91–1.14, log dominant OR 1.04, 0.87– 1.24), African American participants in the ADVANCE, CATHGEN, and GENESTAR studies (fixed effects meta analysis log additive OR 0.91, 95% CI 0.73–1.13, log dominant OR 0.81, 0.42–1.55), and a smaller number of Hispanic, East Asian, and admixed individuals participating in the ADVANCE study (see Table for details).
Table 3 shows genotype counts and association analyses restricted to the subgroup of cases with very early onset disease (<50 years for males and <60 years for females) and controls within the same age range at the time of enrollment. Among this subgroup, we also observed no increase in the risk of CAD in either the European carriers of the 719Arg allele compared with non-carriers (fixed effects meta-analysis log additive OR 0.99, 95% CI 0.95–1.04, log dominant OR 1.01, 0.95–1.08) or the non-European subjects (see Table for details).
Lastly, our sensitivity analyses in Europeans restricting cases to those defined on the basis of an MI in all 19 case control studies also revealed no increased risk of CAD in European carriers of the 719Arg allele compared with non-carriers both for the overall analysis (fixed effects meta-analysis log additive OR 0.99, 95% CI 0.96–1.03, log dominant OR 0.98(0.94–1.02) and the subgroup analysis of early onset MI cases (fixed effects meta-analysis log additive OR 1.03, 95% CI 0.98–1.09, log dominant OR 1.03, 0.97–1.11). Additional details for these analyses are provided in Table 2S and 3S of the Supplementary Appendix.
None of the Q tests revealed heterogeneity in our meta-analyses (lowest p value = 0.252) and all meta-analyses in Europeans had an I2 value of zero percent suggesting that the variability in effect sizes in Europeans was due entirely to sampling error within the studies (see Table 4S in Supplementary Appendix). Thus, performing random effects model meta-analyses was not necessary in Europeans as the results would be identical to the fixed-effects model. Only the log dominant model for CAD overall among African Americans demonstrated an I2 value > 0%. For this stratum, the random effect model (DerSimonian and Laird method) revealed an OR that was similar to the fixed effects model but with wider confidence intervals (Table 4S).
The principal finding of this study was the uniform lack of elevated risk of clinical CAD among carriers of the KIF6 719Arg allele compared with non-carriers in 19 case control studies performed around the world. Our meta-analysis involving a very large number of subjects with European ancestry (over 17000 cases and 39000 controls) suggests that the risk of CAD among European carriers of the 719Arg allele is unlikely to be increased by more than 2% compared with non-carriers.
Our study has two strengths in addition to the very high power conferred by studying a large number of European subjects. First, we studied a large number of early onset CAD cases (<50 years of age for males and <60 years of age for females) but observed no association with KIF6 Trp719Arg and CAD despite the expectation that susceptibility alleles would be more prevalent in this subgroup(37). In this subgroup with early onset disease, we were able to rule out an increase in risk of ≥8% in Europeans. Second, this is the only study to date that examines several non-European racial/ethnic groups. In each of non-European case-control strata, we also found no significant associations between the KIF6 Trp719Arg polymorphism and CAD. However. the point estimates of the OR for these non-European subgroups have substantially wider confidence intervals than those derived in Europeans due to relatively small sample sizes (particularly in our Hispanic, East Asians, admixed Hispanic, and admixed non Hispanics groups). Thus, further study of all of these non-European race/ethnic groups is needed to rule out more modest effects on risk.
Our study has three important limitations related to the case-control design. The first limitation is the potential selection bias by studying only non-fatal cases of CAD. If the 719Arg allele increases the risk of incident fatal CAD more than the risk of incident non-fatal CAD, the exclusion of incident fatal CAD cases could conceivably bias the OR towards the null (i.e. OR = 1). However, the difference in relative risk between these two subgroups of cases would have to be quite large to result in a substantially biased OR in our study given the majority of incident CAD events are not fatal especially among subjects with early onset disease (e.g. in the ARIC surveillance study 20 to 35% of subjects aged 60 years or greater and 10 to 20% of subjects under the age of 60 years died as a consequence of a complication of their initial presentation of CAD(38)). The second potential limitation is our inability to measure traditional risk factors as robustly as they are measured in prospective studies. We therefore made no attempt to fully adjust ORs for all traditional risk factors of CAD. However, prospective studies published to date suggest that traditional risk factors are not correlated with the KIF6 Trp719Arg polymorphism making adjustment unnecessary (1–5). Lastly, our study does not allow us to explore whether statin use modifies the effect of the 719Arg allele on risk as was done in the WOSCOPS, CARE, and PROVE IT TIMI 22 trials as reliable information on the use of statins in relation to the incident event was not available for most studies. However, we believe it unlikely that our null results are a consequence of a high prevalence of the use of statins at the time of the event in cases. In fact, we suspect that the overall prevalence of the use of statins in our set of 19 case control studies is actually lower than the prevalence of use observed at the last follow up for participants in the ARIC, CHS, and WHS studies(39–41) because a majority of our case-control studies restricted their recruitment and/or genotyping efforts to very early onset cases and young controls. For example, in the ADVANCE study, which focused on very early onset CAD (<45 years for males, <55 years for females), access to the electronic pharmacy records confirmed that only a small proportion of cases (~14.4%) and controls (~4.7%) were on statins during the appropriate time window of exposure.
Is it possible that other less obvious sources of bias are responsible for the differences in associations observed between case control and cohort studies of KIF6? While we cannot rule out this possibility, we also deem this scenario unlikely given such cryptic biases, if they exist, have not influenced associations between SNPs at the 9p21.3 locus and CAD in the same manner(9,10). In fact, many of the case-control studies included in this report have produced OR of disease for the 9p21.3 locus equivalent to or larger than the hazard ratios derived from cohort studies(9,10,12,14,42–44). These cohort studies include the two largest cohort studies to date (ARIC and WHS) reporting on the association between KIF6 and CAD. Thus, we are left with the real possibility that the initial reports falsely suggested an association between variation in KIF6 and CAD. The reasons for this are unclear but include the possibility of chance findings or inadequate correction for multiple testing(45–47). The lack of biologic studies implicating KIF6 in the pathogenesis of coronary atherosclerosis combined with the lack of consistent evidence of expression of this gene in relevant tissues such as the vasculature (48,49) could also argue for a false positive association. However, many recent population genetic studies of complex traits have clearly demonstrated that such mechanistic data are not necessary to validate highly significant genetic associations uncovered through GWAS (50).
Our findings question not only the usefulness of the KIF6 test in identifying subjects at increased risk of incident or recurrent CAD but also its usefulness in identifying subjects most likely to benefit from statins. Although we could not test the latter hypothesis directly, the previously reported interaction between genotype and benefit from statins is largely dependent on the validity of the association among subjects not on statins which could not be replicated in this study. We also call attention to the fact that the interaction term p value between genotype and statin use was only marginally significant in the WOSCOPS (p = 0.021) and PROVEIT-TIMI22 (p = 0.018) trials and not significant in the CARE trial (adjusted p = 0.39)(2,3). Despite these observations, additional high quality prospective cohort studies of the effect of the KIF6 variant on CAD risk among users and non users of statins are needed before any firm conclusions can be made regarding the validity of this interaction.
In conclusion, we were unable to confirm an increased risk of CAD in carriers of the KIF6 719Arg allele compared with non-carriers in a very large number of European subjects. We also observed no compelling evidence of an association between the KIF6 Trp719Arg SNP and CAD in multiple other race/ethnic groups and among subjects with early onset CAD. Our null results are unlikely to be a consequence of selection bias, a high prevalence of use of statins among cases, or other cryptic biases. These findings do not support the clinical utility of testing for the KIF6 Trp719Arg polymorphism in the primary prevention of CAD and indirectly question whether genotype information at this locus is able to identify subjects most likely to benefit from the use of statins.
The collection of clinical and sociodemographic data in the Dortmund Health Study was supported by the German Migraine & Headache Society (DMKG) and by unrestricted grants of equal share from Astra Zeneca, Berlin Chemie, Boots Healthcare, Glaxo-Smith-Kline, McNeil Pharma (former Woelm Pharma), MSD Sharp & Dohme and Pfizer to the University of Muenster. Recruitment of the Medstar sample was supported by a research grant from GlaxoSmithKline and genotyping of the PennCATH and Medstar samples was supported by GlaxoSmithKline. Four co-authors of this study are employees of deCODE genetics, a for-profit company that develops SNP based diagnostic tests for various diseases including coronary artery disease.