Original Article
Polymorphisms Associated with Cholesterol and Risk of Cardiovascular Events
N Engl J Med 2008; 358:1240-1249March 20, 2008DOI: 10.1056/NEJMoa0706728
- Abstract
Background
Common single-nucleotide polymorphisms (SNPs) that are associated with blood low-density lipoprotein (LDL) or high-density lipoprotein (HDL) cholesterol modestly affect lipid levels. We tested the hypothesis that a combination of such SNPs contributes to the risk of cardiovascular disease.
Methods
We studied SNPs at nine loci in 5414 subjects from the cardiovascular cohort of the Malmö Diet and Cancer Study. We first validated the association between SNPs and either LDL or HDL cholesterol and subsequently created a genotype score on the basis of the number of unfavorable alleles. We used Cox proportional-hazards models to determine the time to the first cardiovascular event in relation to the genotype score.
Results
All nine SNPs showed replication of an association with levels of either LDL or HDL cholesterol. With increasing genotype scores, the level of LDL cholesterol increased from 152 mg to 171 mg per deciliter (3.9 to 4.4 mmol per liter), whereas HDL cholesterol decreased from 60 mg to 51 mg per deciliter (1.6 to 1.3 mmol per liter). During follow-up (median, 10.6 years), 238 subjects had a first cardiovascular event. The genotype score was associated with incident cardiovascular disease in models adjusted for covariates including baseline lipid levels (P<0.001). The use of the genotype score did not improve the clinical risk prediction, as assessed by the C statistic. However, there was a significant improvement in risk classification with the use of models that included the genotype score, as compared with those that did not include the genotype score.
Conclusions
A genotype score of nine validated SNPs that are associated with modulation in levels of LDL or HDL cholesterol was an independent risk factor for incident cardiovascular disease. The score did not improve risk discrimination but did modestly improve clinical risk reclassification for individual subjects beyond standard clinical factors.
Media in This Article
Figure 1
Predicted Cumulative Freedom from Myocardial Infarction, Ischemic Stroke, or Death from Coronary Heart Disease, According to Genotype Score.Figure 2
Receiver-Operating-Characteristic (ROC) Curves for Incident Myocardial Infarction, Ischemic Stroke, or Death from Coronary Heart Disease during 10-Year Follow-up.Article Activity
- Article
Plasma levels of low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol are associated with a future risk of cardiovascular disease.1,2 It has been estimated that roughly 50% of variation in LDL and HDL cholesterol levels is heritable.3 Several common DNA sequence variants have been related to blood LDL or HDL cholesterol levels.4-11 In light of the increased practicality of conducting genomewide association studies, it is likely that additional polymorphisms associated with lipid levels will be identified.
These observations suggest two hypotheses. First, a DNA sequence variant that is related to blood lipoprotein levels may influence the risk of cardiovascular disease. Second, since DNA sequence variants represent an index of lifelong exposure to altered lipoprotein levels,12 whereas plasma measurements vary, DNA sequence variants may add predictive information beyond a single measurement of blood lipids.
The potential of such genetic testing is limited by the fact that each sequence variant that has been discovered to date explains only a modest fraction of the variance (2% or less) in lipid levels.13 However, the combination of several lipid-related polymorphisms could improve risk prediction. Accordingly, we sought first to validate the association between single-nucleotide polymorphisms (SNPs) at nine loci with the level of LDL or HDL cholesterol and then to evaluate the ability of a panel of validated lipid-modulating SNPs to predict a first cardiovascular event in a large, community-based cohort.
Methods
Study Sample and Risk Factors
The Malmö Diet and Cancer Study (MDCS) is a community-based prospective epidemiologic cohort of 28,449 subjects who were recruited for baseline examination between 1991 and 1996.14 From this cohort, 6103 subjects were randomly selected to participate in a cardiovascular cohort (MDCS-CC), which seeks to investigate risk factors for cardiovascular disease. The MDCS-CC study protocols were approved by the ethics committee at Lund University. All subjects provided written informed consent.
All subjects underwent a review of their medical history, a physical examination, and a laboratory assessment of cardiovascular risk factors. The status of cigarette smoking was elicited by a self-administered questionnaire and was coded as never, former, or current. Current cigarette smoking was defined as any smoking within the past year. A parental or sibling history of myocardial infarction (regardless of the age at onset) was also elicited by questionnaire. Blood pressure was measured once after 10 minutes of rest in the supine position. Diabetes mellitus was defined by self-report on the basis of a physician's diagnosis, a fasting blood glucose level of more than 6.0 mmol per liter (108 mg per deciliter), or the use of antidiabetes medication. C-reactive protein was measured by a high-sensitivity assay (Tina-quant CRP, Roche Diagnostics). In fasting venous blood samples, we measured total cholesterol, HDL cholesterol, and triglycerides according to standard procedures at the Department of Clinical Chemistry, University Hospital Malmö. Levels of LDL cholesterol were calculated according to Friedewald's formula, with the assignment of a missing value to subjects with a triglyceride level of more than 400 mg per deciliter (4.5 mmol per liter). The use of lipid-lowering and antihypertensive therapy was recorded.
SNP Selection and Genotyping
We selected for study 11 SNPs in 9 genes on the basis of literature reports of individual associations and from a genomewide association study. We selected six SNPs from six loci on the basis of their association with levels of LDL or HDL cholesterol in at least one previous study. These six SNPs were, for association with LDL cholesterol, APOB (apolipoprotein B, rs693),4 PCSK9 (proprotein convertase subtilisin/kexin type 9, rs11591147),6 and LDLR (low-density lipoprotein receptor, rs688)5; and for association with HDL cholesterol, CETP (cholesteryl ester transfer protein, rs1800775),8 LIPC (hepatic lipase, rs1800588),9 and LPL (lipoprotein lipase, rs328).15 We selected five SNPs from a genomewide association study of levels of blood LDL and HDL cholesterol in 2931 subjects from Sweden and Finland (a sample distinct from the subjects in the MDCS-CC).13 In the genomewide association study, we found SNPs in APOB (rs7575840), APOE cluster (apolipoprotein E, rs4420638), HMGCR (3-hydroxy-3-methylglutaryl-coenzyme A reductase, rs12654264), and LDLR (rs1529729) to be nominally associated with LDL cholesterol (P=1×10−4, P=3.4×10−13, P=4×10−4, and P=0.002, respectively) and ABCA1 (ATP-binding cassette subfamily A member 1, rs3890182) with HDL cholesterol (P=3.3×10−5).16
Genotyping was performed with the use of matrix-assisted laser desorption–ionization time-of-flight mass spectrometry on a MassARRAY platform (Sequenom), as described previously.17 All SNPs were in Hardy–Weinberg equilibrium (P>0.001). The genotyping success rate was 96%. Using 15 samples analyzed in quadruplicate, we found the genotyping error rate to be <0.7%.
Outcomes
Cardiovascular events were ascertained through linkage of the 10-digit personal identification number of each Swedish citizen with three registries: the Swedish Hospital Discharge Register, the Swedish Cause of Death Register, and the Stroke Register of Malmö.18 The prespecified composite end point of cardiovascular events was defined as myocardial infarction, ischemic stroke, and death from coronary heart disease. Myocardial infarction was defined on the basis of codes 410 and I21 in the International Classification of Diseases, 9th Revision and 10th Revision (ICD-9 and ICD-10), respectively. Ischemic stroke was defined on the basis of codes 434 or 436 (ICD-9) and I63 or I64 (ICD-10). Before inclusion, ischemic stroke events in the Swedish Hospital Discharge Register were validated with the use of the Stroke Register of Malmö, in which original medical records (including imaging studies, when available) were examined.18 We considered only ischemic strokes and excluded cases of intracerebral or subarachnoid hemorrhage. Death from coronary heart disease was defined on the basis of codes 412 and 414 (ICD-9) or I22–I23 and I25 (ICD-10) in the Swedish Cause of Death Register. Follow-up extended to December 31, 2003.
Statistical Analysis
For each subject, levels of LDL or HDL cholesterol were adjusted for age, sex, and diabetes status to create a multivariable-adjusted residual level of LDL or HDL cholesterol. The multivariable-adjusted residual lipid level for each subject served as the phenotype in genotype–phenotype association analyses. We conducted multivariable linear regression analyses to test the null hypothesis that LDL or HDL cholesterol residuals did not differ according to an increasing copy number in the minor allele. Study subjects who were receiving lipid-lowering medication at the time of the baseline examination were excluded from these analyses.
On the basis of the results of the genotype–phenotype association analyses, we identified nine SNPs that were independently associated with lipid levels. Two SNPs that we initially tested were correlated with other SNPs in the panel and thus excluded.
We assumed that an individual SNP with a modest effect on lipid levels would have limited power to show an effect on incident cardiovascular events. We therefore constructed a genotype score on the basis of the number of unfavorable alleles (those associated with higher LDL cholesterol levels or lower HDL cholesterol levels) that were carried by each subject for each of the nine SNPs. We calculated crude incidence rates of first cardiovascular events according to strata of genotype scores.
We constructed multivariable proportional-hazards models to examine the association between the genotype score and the time to the first cardiovascular event, excluding subjects who had had a previous myocardial infarction or ischemic stroke. We first confirmed that the proportional-hazards assumption was met.19 The hazard ratio for the genotype score as a continuous measure was estimated in a model adjusting for all 14 available baseline covariates. Cumulative incidence curves were constructed according to the genotype score with the use of Cox regression analysis.
To evaluate the ability of the genotype score to classify risk, we plotted receiver-operating-characteristic (ROC) curves for the 14 baseline covariates with or without the genotype score.20 We used the incidence of cardiovascular events at 10 years as the outcome. The C statistic, a measure of the area under the ROC curve, was calculated with and without the genotype score. We also evaluated the ability of the genotype score to reclassify risk across categories outlined by the Adult Treatment Panel III of the National Cholesterol Education Program21 with the use of two separate approaches suggested by Cook22 and Pencina et al.23
All genetic association analyses were performed with the use of SPSS software, version 14.0. The tests for the proportional-hazards assumption were performed with the use of the survival package for R; the ROC curves and C statistics were generated with the use of the ROCR package for R (www.r-project.org).
Results
Baseline Characteristics
Of the 6103 MDCS-CC subjects, 563 did not provide a baseline plasma sample, 56 did not have data regarding levels of either LDL or HDL cholesterol, 18 did not have DNA available, and 52 did not have genotypes for at least one SNP.24 This left 5414 subjects who were eligible for the present investigation.
Baseline characteristics of the subjects are shown in Table 1Table 1
Baseline Characteristics of the Subjects.. The subjects were all of self-reported European ancestry. The mean (±SD) age was 58±6 years, and 59% were women. A small percentage of the subjects were receiving lipid-lowering therapy (2.3%) or had had a prevalent myocardial infarction or stroke (2.3%).SNP–Lipid Associations and Genotype Score
For validation of the association between SNPs and lipid levels, 5287 subjects who were not receiving lipid-lowering therapy at baseline were available. We validated the association between each of 11 SNPs with levels of either LDL cholesterol or HDL cholesterol (Table 2Table 2
Association between Single-Nucleotide Polymorphisms (SNPs) and Low-Density Lipoprotein (LDL) or High-Density Lipoprotein (HDL) Cholesterol.). The proportion of residual trait variance that was explained by each SNP was small, ranging from 0.1 to 2.5%. Among the SNPs that were associated with LDL cholesterol levels, the difference in LDL cholesterol level between homozygote classes ranged from 3 mg to 72 mg per deciliter (0.1 to 1.9 mmol per liter). Among the SNPs related to HDL cholesterol, the difference ranged from 3 mg to 5 mg per deciliter.For the APOB and LDLR loci, there were two significant correlated SNPs at each locus. We selected the SNP with the stronger evidence for association at each locus (rs693 and rs1529729, respectively) for inclusion in the genotype score. Thus, the analyses relating genotypes with cardiovascular-event prediction consisted of nine SNPs from nine loci (Table 2).
We then constructed a genotype score for each subject by counting the number of unfavorable alleles carried by that subject for each SNP. With zero, one, or two unfavorable alleles possible for each of nine SNPs, the potential genotype score for each subject ranged from 0 to 18. Since only a small number of subjects had a score of 6 or less, these groups were a priori collapsed into one group for the purposes of data display and analysis. In a similar way, subjects with a score of 13 or more were collapsed into one group.
SNPs and Cardiovascular Events
Complete data for all covariates in the multivariable Cox regression analyses were available for 4232 subjects who had not had prevalent myocardial infarction or stroke. During a median of 10.6 years of follow-up, 137 men and 101 women in this cohort had an incident cardiovascular event, prespecified as myocardial infarction (131 subjects), ischemic stroke (96), or death from coronary heart disease (11).
Table 3Table 3
Lipid Levels and Crude Incidence Rates of Cardiovascular Disease, According to Genotype Score. shows the lipid levels and crude incidence rates for the first cardiovascular event stratified according to genotype score. LDL cholesterol levels increased from 152 mg per deciliter (3.9 mmol per liter) for those with a score of 6 or less to 171 mg per deciliter (4.4 mmol per liter) for those with a score of 13 or more (P for trend, 2×10−18). HDL cholesterol levels decreased from 60 mg per deciliter (1.6 mmol per liter) for those with a score of 6 or less to 51 mg per deciliter (1.3 mmol per liter) for those with a score of 13 or more (P for trend, 3×10−24). There was an increase in the crude incidence rate of first cardiovascular events according to genotype score from 3.1 per 1000 person-years (among those with a score ≤6) to 11.0 per 1000 person-years (among those with a score ≥13).Multivariable Analysis of Outcome
After accounting for baseline lipid levels and a full set of available risk factors, we found that the genotype score remained associated with the first cardiovascular event (adjusted hazard ratio, 1.15 per copy of an unfavorable allele; 95% confidence interval [CI], 1.07 to 1.24; P=3×10−4 by multivariable analysis) (Table 4Table 4
Multivariable Analysis of the Association between Genotype Score and the Time to First Cardiovascular Event.). As compared with subjects with a genotype score of 9 or less (43.2% of the sample), subjects with a score of 11 or more (33.6%) had an increased risk of a cardiovascular event by a factor of 1.63 (95% CI, 1.21 to 2.19; P=0.001). The predicted cumulative freedom from cardiovascular events according to the genotype score, with adjustment for baseline covariates, is shown in Figure 1Figure 1Predicted Cumulative Freedom from Myocardial Infarction, Ischemic Stroke, or Death from Coronary Heart Disease, According to Genotype Score..We constructed ROC curves for the model incorporating established risk factors with and without inclusion of the genotype score, using the incidence of cardiovascular events at 10 years as the outcome (Figure 2Figure 2
Receiver-Operating-Characteristic (ROC) Curves for Incident Myocardial Infarction, Ischemic Stroke, or Death from Coronary Heart Disease during 10-Year Follow-up.). The C statistic (area under the ROC curve) for total cardiovascular events was 0.80 for the risk model either with or without the genotype score.Among subjects in the Adult Treatment Panel III intermediate-risk category (9% of the study sample), 26% were reclassified into a higher or lower risk category in the risk model containing the genotype score, as compared with the model without the genotype score (Table 1 of the Supplementary Appendix, available with the full text of this article at www.nejm.org). When we used the net reclassification index23 to account for the correct movement in categories (higher risk for subjects in whom cardiovascular disease subsequently developed and lower risk for subjects free of incident cardiovascular disease), there was a significant improvement in risk classification according to a model that incorporated the genotype score, as compared with one that did not (P=0.01) (Table 2 of the Supplementary Appendix). We also estimated a measure of improvement in model performance that did not depend on the choice of categories, called the integrated discrimination index.23 A model with the genotype score showed a greater integrated discrimination index than did a model without the genotype score (P=0.02).
To further characterize the relationship between genotype and cardiovascular events, we performed several additional exploratory analyses. These included analyses comparing the incremental predictive value of the SNPs combined in the genotype score with that of the SNPs modeled as a set, analyses of the incremental predictive value of the genotype score for individual components of the cardiovascular event end point, analyses of whether specific SNPs that comprised the score appeared to drive the association with cardiovascular disease, and analyses of the separate predictive value of the SNPs related to LDL cholesterol levels and those related to HDL cholesterol levels. All the additional analyses are discussed in the Supplementary Appendix.
Discussion
In a sample of approximately 5400 subjects from a community-based cohort, we observed that SNPs from nine loci were associated with the baseline level of LDL or HDL cholesterol; four of these associations represented a first replication. During more than 10 years of follow-up, we found that a panel of nine lipid-associated polymorphisms also predicted risk for a first cardiovascular event and provided independent information beyond that obtained from baseline lipid levels and other established risk factors. Nevertheless, the genotype score did not improve the accuracy of the clinical risk prediction, as assessed by the C statistic. Among subjects in the intermediate Adult Treatment Panel III risk category, 26% were reclassified into a higher or lower risk category when the genotype score was added to the other covariates.
These results highlight several important principles. First, for continuous, normally distributed quantitative traits like LDL and HDL cholesterol levels, multiple common alleles influence trait variation, with each allele conferring a modest effect.25 Second, although each SNP exerts a modest effect, a combination of SNPs, in aggregate, can have a substantial influence on lipid levels. Third, lipid-associated SNPs may provide incremental information about cardiovascular risk beyond lipid levels.
Why might a panel of SNPs associated with levels of LDL or HDL cholesterol help predict cardiovascular outcomes even after accounting for baseline lipid levels? It has been proposed that a single measurement of blood lipids may inadequately reflect the lifetime exposure to altered lipid levels.12,26 Lipid measurements may be affected by dietary, diurnal, and random factors.27 By contrast, genotypes are invariant over time, can be precisely measured on a single assay, and can exert an effect on lipid levels over a lifetime. An alternative possibility is that SNPs may influence atherogenesis through mechanisms unrelated to either LDL or HDL cholesterol.
When added to conventional risk factors, the current SNP panel did not improve the ability to determine which patients will have cardiovascular disease and which will remain disease-free, although it did modestly improve risk prediction, as measured by clinical risk reclassification. It remains possible that with the addition of more SNPs related to LDL or HDL cholesterol or SNPs directly related to the risk of myocardial infarction (e.g., the chromosome 9p21 SNPs recently associated with coronary artery disease and myocardial infarction28-30), a panel of SNPs could prove useful in risk discrimination or targeting therapies.
Several limitations of our study deserve mention. Since this analysis is among the first in the genetic-association literature to report on a panel of lipid-modulating SNPs,31 we may have overestimated the effect size per unfavorable allele. Additional studies should clarify the true effect size per unfavorable allele.
The specific SNP set in this panel may need refinement. We selected a set of loci and SNPs on the basis of previous studies and a strong biologic rationale. However, we did not test other loci meeting these criteria. At each of the selected loci, the particular SNPs that we tested may not actually be causal but rather correlated to the causal SNP.
In addition, due to the rapid progress of sequencing and genomewide association approaches, a flood of new association discoveries is expected in the near future.32-34 It will be important to systematically test the contribution of such newly identified loci to future risk-prediction models.
Our study sample was entirely of European ancestry, and some of the SNPs we studied are not polymorphic in other ethnic groups (e.g., rs11591147 at PCSK9 is monomorphic in persons of African ancestry).12 This factor may limit the generalizability of our findings. However, several of the SNPs (including the LPL and LIPC variants) have been shown to relate to levels of LDL or HDL cholesterol in a similar fashion in both whites and nonwhites.35
Moreover, in the interest of simplicity, we assigned an equal weight in our genotype score to each unfavorable allele, regardless of the strength of the effect on cholesterol. However, the effect on risk may be more accurately estimated by weighting the contribution of an allele to the score on the basis of its contribution to levels of LDL or HDL cholesterol and on the relationship of such levels to cardiovascular events. In addition, the effect of each SNP on the risk of cardiovascular disease may not relate to its effect on cholesterol in a linear fashion.
Finally, reclassification analysis may be affected in important ways by the choice of risk categories. For example, end points predicted in Adult Treatment Panel III differed from those in our study, so alternative risk-categorization schemes may be appropriate. To overcome this limitation, we also assessed the integrated discrimination index, a metric that is not dependent on the choice of categories.
In summary, in our analysis, nine SNPs from nine genes were found to be associated with levels of either LDL or HDL cholesterol. This panel of polymorphisms was independently associated with a risk of first myocardial infarction, ischemic stroke, or death from coronary heart disease. The SNP panel did not improve risk discrimination but did modestly improve clinical risk reclassification for individual subjects beyond standard clinical factors.
Supported by a Doris Duke Charitable Foundation Clinical Scientist Development Award, the Fannie E. Rippel Foundation, the Donovan Family Foundation, and a grant (K23-HL083102) from the National Institutes of Health (NIH), all to Dr. Kathiresan; by the Swedish Medical Society, the Swedish Heart and Lung Foundation, the Ernhold Lundströms Research Foundation, the Mossfelt Foundation, and the Region Skane, all to Dr. Melander; by the Swedish Medical Research Council, the Medical Faculty of Lund University, Malmö University Hospital, the Albert Påhlsson Research Foundation, and the Crafoord Foundation, all to Drs. Melander and Orho-Melander; by the Sigrid Juselius Foundation, the Finnish Diabetes Research Foundation, the Folkhalsan Research Foundation, the Clinical Research Institute of Helsinki University Central Hospital, and a Linné grant from the Swedish Research Council, all to Dr. Groop; by a Burroughs Wellcome Fund Clinical Scholar in Translational Research award and a Distinguished Clinical Scholar award from the Doris Duke Charitable Foundation, both to Dr. Altshuler; and by a Doris Duke Charitable Foundation Clinical Scientist Development Award, a Burroughs Wellcome Fund Career Award for Medical Scientists, and a grant (K23-HL080025) from the NIH, all to Dr. Newton-Cheh.
Dr. Kathiresan reports receiving consulting fees from Novartis and Pfizer; Dr. Hirschhorn, having an equity interest in and receiving consulting fees from Correlagen Diagnostics; Dr. Groop, receiving consulting fees from Tethys, lecture fees from Lilly and GlaxoSmithKline, and grant support from Novartis; and Dr. Altshuler, receiving consulting fees from Merck and MPM Capital and grant support from Novartis and having an equity interest in MPM Capital. No other potential conflict of interest relevant to this article was reported.
Drs. Kathiresan and Orho-Melander contributed equally to this article.
Source Information
From the Cardiology Division (S.K., C.N.-C.) and the Center for Human Genetic Research and Department of Molecular Biology (D.M.A.), Massachusetts General Hospital; the Departments of Medicine (S.K., D.M.A., C.N.-C.), Pediatrics (J.N.H.), and Genetics (J.N.H., D.M.A.), Harvard Medical School; and the Division of Endocrinology and Genetics, Children's Hospital (J.N.H.) — all in Boston; the Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA (S.K., C.G., N.P.B., J.N.H., D.M.A., C.N.-C.); the Departments of Clinical Sciences, Hypertension, and Cardiovascular Diseases (O.M.), Diabetes and Endocrinology (D.A., C.R., L.G., M.O.-M.), Internal Medicine (G.B.), and Epidemiological Research (B.H.), University Hospital Malmö, Lund University, Malmö, and Chalmers University of Technology, Göteborg (D.A.) — both in Sweden; and Helsinki University Hospital, Helsinki (L.G.).
Address reprint requests to Dr. Kathiresan at the Cardiovascular Disease Prevention Center, Cardiology Division, Massachusetts General Hospital, 25 New Chardon St., Suite 301, Boston, MA 02114, or at skathiresan@partners.org.
- References
References
1
Kannel WB ,Dawber TR ,Kagan A ,Revotskie N ,Stokes J III . Factors of risk in the development of coronary heart disease -- six year follow-up experience: the Framingham Study. Ann Intern Med 1961;55:33-50
Web of Science | Medline2
Miller NE ,Miller GJ . High-density lipoprotein and atherosclerosis. Lancet 1975;1:1033-1033
CrossRef | Web of Science | Medline3
Namboodiri KK ,Kaplan EB ,Heuch I , et al. The Collaborative Lipid Research Clinics Family Study: biological and cultural determinants of familial resemblance for plasma lipids and lipoproteins. Genet Epidemiol 1985;2:227-254
CrossRef | Medline4
Benn M ,Nordestgaard BG ,Jensen JS ,Grande P ,Sillesen H ,Tybjaerg-Hansen A . Polymorphism in APOB associated with increased low-density lipoprotein levels in both genders in the general population. J Clin Endocrinol Metab 2005;90:5797-5803
CrossRef | Web of Science | Medline5
Boright AP ,Connelly PW ,Brunt JH ,Morgan K ,Hegele RA . Association and linkage of LDLR gene variation with variation in plasma low density lipoprotein cholesterol. J Hum Genet 1998;43:153-159
CrossRef | Web of Science | Medline6
Kotowski IK ,Pertsemlidis A ,Luke A , et al. A spectrum of PCSK9 alleles contributes to plasma levels of low-density lipoprotein cholesterol. Am J Hum Genet 2006;78:410-422
CrossRef | Web of Science | Medline7
Frikke-Schmidt R ,Nordestgaard BG ,Jensen GB ,Tybjaerg-Hansen A . Genetic variation in ABC transporter A1 contributes to HDL cholesterol in the general population. J Clin Invest 2004;114:1343-1353
Web of Science | Medline8
Boekholdt SM ,Sacks FM ,Jukema JW , et al. Cholesteryl ester transfer protein TaqIB variant, high-density lipoprotein cholesterol levels, cardiovascular risk, and efficacy of pravastatin treatment: individual patient meta-analysis of 13,677 subjects. Circulation 2005;111:278-287
CrossRef | Web of Science | Medline9
Guerra R ,Wang J ,Grundy SM ,Cohen JC . A hepatic lipase (LIPC) allele associated with high plasma concentrations of high density lipoprotein cholesterol. Proc Natl Acad Sci U S A 1997;94:4532-4537
CrossRef | Web of Science | Medline10
Wittrup HH ,Andersen RV ,Tybjaerg-Hansen A ,Jensen GB ,Nordestgaard BG . Combined analysis of six lipoprotein lipase genetic variants on triglycerides, high-density lipoprotein, and ischemic heart disease: cross-sectional, prospective, and case-control studies from the Copenhagen City Heart Study. J Clin Endocrinol Metab 2006;91:1438-1445
CrossRef | Web of Science | Medline11
Sing CF ,Davignon J . Role of the apolipoprotein E polymorphism in determining normal plasma lipid and lipoprotein variation. Am J Hum Genet 1985;37:268-285
Web of Science | Medline12
Cohen JC ,Boerwinkle E ,Mosley TH Jr ,Hobbs HH . Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N Engl J Med 2006;354:1264-1272
Free Full Text | Web of Science | Medline13
Saxena R ,Voight BF ,Lyssenko V , et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 2007;316:1331-1336
CrossRef | Web of Science | Medline14
Berglund G ,Elmstahl S ,Janzon L ,Larsson SA . The Malmo Diet and Cancer Study: design and feasibility. J Intern Med 1993;233:45-51
CrossRef | Web of Science | Medline15
Rip J ,Nierman MC ,Ross CJ , et al. Lipoprotein lipase S447X: a naturally occurring gain-of-function mutation. Arterioscler Thromb Vasc Biol 2006;26:1236-1245
CrossRef | Web of Science | Medline16
Altshuler D, Groop L, Hughes T. Whole genome scan for type 2 diabetes in a Scandinavian cohort. Cambridge, MA: Broad Institute, 2007. (Accessed February 12, 2008, at http://www.broad.mit.edu/diabetes/scandinavs/index.html.)
17
Gabriel SB ,Schaffner SF ,Nguyen H , et al. The structure of haplotype blocks in the human genome. Science 2002;296:2225-2229
CrossRef | Web of Science | Medline18
Jerntorp P ,Berglund G . Stroke registry in Malmö, Sweden. Stroke 1992;23:357-361
CrossRef | Web of Science | Medline19
Grambsch PM ,Therneau TM . Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 1994;81:515-526
CrossRef | Web of Science20
Sing T ,Sander O ,Beerenwinkel N ,Lengauer T . ROCR: visualizing classifier performance in R. Bioinformatics 2005;21:3940-3941
CrossRef | Web of Science | Medline21
Executive summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001;285:2486-2497
CrossRef | Web of Science22
Cook NR . Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007;115:928-935
CrossRef | Web of Science | Medline23
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;27:157-172
CrossRef | Web of Science | Medline24
Pero RW ,Olsson A ,Berglund G ,Janzon L ,Larsson SA ,Elmstahl S . The Malmö biological bank. J Intern Med 1993;233:63-67
CrossRef | Web of Science | Medline25
Fisher RA . The correlation between relatives on the supposition of Mendelian inheritance. Trans R Soc Edinburgh 1918;52:399-43326
Brown MS ,Goldstein JL . Biomedicine: lowering LDL -- not only how low, but how long? Science 2006;311:1721-1723
CrossRef | Web of Science | Medline27
Cooper GR ,Myers GL ,Smith SJ ,Schlant RC . Blood lipid measurements: variations and practical utility. JAMA 1992;267:1652-1660[Erratum, JAMA 1997;278:478.]
CrossRef | Web of Science | Medline28
Helgadottir A ,Thorleifsson G ,Manolescu A , et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 2007;316:1491-1493
CrossRef | Web of Science | Medline29
McPherson R ,Pertsemlidis A ,Kavaslar N , et al. A common allele on chromosome 9 associated with coronary heart disease. Science 2007;316:1488-1491
CrossRef | Web of Science | Medline30
Samani NJ ,Erdmann J ,Hall AS , et al. Genomewide association analysis of coronary artery disease. N Engl J Med 2007;357:443-453
Free Full Text | Web of Science | Medline31
Humphries SE ,Cooper JA ,Talmud PJ ,Miller GJ . Candidate gene genotypes, along with conventional risk factor assessment, improve estimation of coronary heart disease risk in healthy UK men. Clin Chem 2007;53:8-16
CrossRef | Web of Science | Medline32
Romeo S ,Pennacchio LA ,Fu Y , et al. Population-based resequencing of ANGPTL4 uncovers variations that reduce triglycerides and increase HDL. Nat Genet 2007;39:513-516
CrossRef | Web of Science | Medline33
Kathiresan S ,Melander O ,Guiducci C , et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, or triglycerides in humans. Nat Genet 2008;40:189-197
CrossRef | Web of Science | Medline34
Willer CJ ,Sanna S ,Jackson AU , et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet 2008;40:161-169
CrossRef | Web of Science | Medline35
Nettleton JA ,Steffen LM ,Ballantyne CM ,Boerwinkle E ,Folsom AR . Associations between HDL-cholesterol and polymorphisms in hepatic lipase and lipoprotein lipase genes are modified by dietary fat intake in African American and White adults. Atherosclerosis 2007;194:e131-e140
CrossRef | Medline
- Citing Articles (173)
Citing Articles
1
Roy H. Perlis. (2013) A Clinical Risk Stratification Tool for Predicting Treatment Resistance in Major Depressive Disorder. Biological Psychiatry 74:1, 7-14
2
Muin J. Khoury, A. Cecile J.W. Janssens, David F. Ransohoff. (2013) How can polygenic inheritance be used in population screening for common diseases?. Genetics in Medicine 15:6, 437-443
3
Lee D. Roberts, Robert E. Gerszten. (2013) Toward New Biomarkers of Cardiometabolic Diseases. Cell Metabolism
4
Tabish Hussain, Ashwin Kotnis, Rajiv Sarin, Rita Mulherkar. (2013) Genetic susceptibility to multiple primary neoplasms in the upper aero-digestive tract: Genotype Score and phenotype correlation. Cancer Letters 332:1, 46-54
5
S. Shah, J. P. Casas, T. R. Gaunt, J. Cooper, F. Drenos, D. Zabaneh, D. I. Swerdlow, T. Shah, R. Sofat, J. Palmen, M. Kumari, M. Kivimaki, S. Ebrahim, G. D. Smith, D. A. Lawlor, P. J. Talmud, J. Whittaker, I. N. M. Day, A. D. Hingorani, S. E. Humphries. (2013) Influence of common genetic variation on blood lipid levels, cardiovascular risk, and coronary events in two British prospective cohort studies. European Heart Journal 34:13, 972-981
6
C. Yang, Z. Y. Chen, S. L. Wong, J. Liu, Y. T. Liang, C. W. Lau, H. K. Lee, Y. Huang, S. Y. Tsang. (2013) -Sitosterol oxidation products attenuate vasorelaxation by increasing reactive oxygen species and cyclooxygenase-2. Cardiovascular Research 97:3, 520-532
7
Gunther Marsche, Marcus D. Saemann, Akos Heinemann, Michael Holzer. (2013) Inflammation alters HDL composition and function: Implications for HDL-raising therapies. Pharmacology & Therapeutics 137:3, 341-351
8
Peter W.F. Wilson. (2013) Lipids and Vascular Disease: A Framingham Perspective. Global Heart 8:1, 25-33
9
Roberto Elosua, Gavin Lucas, Carla Lluis-Ganella. (2013) Genetics and Cardiovascular Risk Prediction: A Step Toward Personalized Medicine?. Current Cardiovascular Risk Reports 7:1, 33-45
10
Jose M. de Miguel-Yanes, Bianca Porneala, Michael J. Pencina, Caroline S. Fox, Jose C. Florez, David S. Siscovick, Josée Dupuis, James B. Meigs. (2013) Lack of interaction of beta-cell-function-associated variants with hypertension on change in fasting glucose and diabetes risk. Journal of Hypertension1
11
Jiayin Wang, Zhongmeng Zhao, Zhi Cao, Aiyuan Yang, Jin Zhang. (2013) A probabilistic method for identifying rare variants underlying complex traits. BMC Genomics 14:Suppl 1, S11
12
Jacqueline S. Danik, Nina P. Paynter, Paul M. Ridker. Genomic Biomarkers in Human Population Studies. In: Genomic and Personalized Medicine. Elsevier, 2013:247-262.
13
Sekar Kathiresan, Daniel J. Rader. Lipoprotein Disorders. In: Genomic and Personalized Medicine. Elsevier, 2013:501-515.
14
Neena Theresa Kumar, Knut Liestøl, Else Marit Løberg, Henrik Mikael Reims, Sverre-Henning Brorson, Jan Mæhlen. (2012) The apolipoprotein E polymorphism and cardiovascular diseases—an autopsy study. Cardiovascular Pathology 21:6, 461-469
15
M Garaulet, A Esteban Tardido, Y-C Lee, C E Smith, L D Parnell, J M Ordovás. (2012) SIRT1 and CLOCK 3111T>C combined genotype is associated with evening preference and weight loss resistance in a behavioral therapy treatment for obesity. International Journal of Obesity 36:11, 1436-1441
16
Y. Ge, T. J. Wang. (2012) Identifying novel biomarkers for cardiovascular disease risk prediction. Journal of Internal Medicine 272:5, 430-439
17
Celia G. Walker, Susan A. Jebb. (2012) Gene–Diet Interactions on Lipid Levels: Current Knowledge in the Era of Genome-Wide Association Studies. Current Nutrition Reports 1:3, 123-131
18
Cristiano Fava, Martina Montagnana, Gian Cesare Guidi, Olle Melander. (2012) From circulating biomarkers to genomics and imaging in the prediction of cardiovascular events in the general population. Annals of Medicine 44:5, 433-447
19
X. CHEN, S. LI, Y. YANG, X. YANG, Y. LIU, Y. LIU, W. HU, L. JIN, X. WANG. (2012) Genome-wide association study validation identifies novel loci for atherosclerotic cardiovascular disease. Journal of Thrombosis and Haemostasis 10:8, 1508-1514
20
Benjamin F Voight, Gina M Peloso, Marju Orho-Melander, Ruth Frikke-Schmidt, Maja Barbalic, Majken K Jensen, George Hindy, Hilma Hólm, Eric L Ding, Toby Johnson, Heribert Schunkert, Nilesh J Samani, Robert Clarke, Jemma C Hopewell, John F Thompson, Mingyao Li, Gudmar Thorleifsson, Christopher Newton-Cheh, Kiran Musunuru, James P Pirruccello, Danish Saleheen, Li Chen, Alexandre FR Stewart, Arne Schillert, Unnur Thorsteinsdottir, Gudmundur Thorgeirsson, Sonia Anand, James C Engert, Thomas Morgan, John Spertus, Monika Stoll, Klaus Berger, Nicola Martinelli, Domenico Girelli, Pascal P McKeown, Christopher C Patterson, Stephen E Epstein, Joseph Devaney, Mary-Susan Burnett, Vincent Mooser, Samuli Ripatti, Ida Surakka, Markku S Nieminen, Juha Sinisalo, Marja-Liisa Lokki, Markus Perola, Aki Havulinna, Ulf de Faire, Bruna Gigante, Erik Ingelsson, Tanja Zeller, Philipp Wild, Paul I W de Bakker, Olaf H Klungel, Anke-Hilse Maitland-van der Zee, Bas J M Peters, Anthonius de Boer, Diederick E Grobbee, Pieter W Kamphuisen, Vera H M Deneer, Clara C Elbers, N Charlotte Onland-Moret, Marten H Hofker, Cisca Wijmenga, WM Monique Verschuren, Jolanda MA Boer, Yvonne T van der Schouw, Asif Rasheed, Philippe Frossard, Serkalem Demissie, Cristen Willer, Ron Do, Jose M Ordovas, Gonçalo R Abecasis, Michael Boehnke, Karen L Mohlke, Mark J Daly, Candace Guiducci, Noël P Burtt, Aarti Surti, Elena Gonzalez, Shaun Purcell, Stacey Gabriel, Jaume Marrugat, John Peden, Jeanette Erdmann, Patrick Diemert, Christina Willenborg, Inke R König, Marcus Fischer, Christian Hengstenberg, Andreas Ziegler, Ian Buysschaert, Diether Lambrechts, Frans Van de Werf, Keith A Fox, Nour Eddine El Mokhtari, Diana Rubin, Jürgen Schrezenmeir, Stefan Schreiber, Arne Schäfer, John Danesh, Stefan Blankenberg, Robert Roberts, Ruth McPherson, Hugh Watkins, Alistair S Hall, Kim Overvad, Eric Rimm, Eric Boerwinkle, Anne Tybjaerg-Hansen, L Adrienne Cupples, Muredach P Reilly, Olle Melander, Pier M Mannucci, Diego Ardissino, David Siscovick, Roberto Elosua, Kari Stefansson, Christopher J O'Donnell, Veikko Salomaa, Daniel J Rader, Leena Peltonen, Stephen M Schwartz, David Altshuler, Sekar Kathiresan. (2012) Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. The Lancet 380:9841, 572-580
21
Ariel Brautbar, Lisa A. Pompeii, Abbas Dehghan, Julius S. Ngwa, Vijay Nambi, Salim S. Virani, Fernando Rivadeneira, André G. Uitterlinden, Albert Hofman, Jacqueline C.M. Witteman, Michael J. Pencina, Aaron R. Folsom, L. Adrienne Cupples, Christie M. Ballantyne, Eric Boerwinkle. (2012) A genetic risk score based on direct associations with coronary heart disease improves coronary heart disease risk prediction in the Atherosclerosis Risk in Communities (ARIC), but not in the Rotterdam and Framingham Offspring, Studies. Atherosclerosis 223:2, 421-426
22
Maria Grazia Andreassi, Daniel Adlerstein, Clara Carpeggiani, Erlet Shehi, Sonia Fantinato, Elisa Ghezzi, Nicoletta Botto, Michele Coceani, Antonio L'Abbate. (2012) Individual and summed effects of high-risk genetic polymorphisms on recurrent cardiovascular events following ischemic heart disease. Atherosclerosis 223:2, 409-415
23
K. Salari, H. Watkins, E. A. Ashley. (2012) Personalized medicine: hope or hype?. European Heart Journal 33:13, 1564-1570
24
Sony Tuteja, Daniel J Rader. (2012) Genomic medicine in the prevention and treatment of atherosclerotic cardiovascular disease. Personalized Medicine 9:4, 395-404
25
Nora Pashayan, Paul Pharoah. (2012) Population-based screening in the era of genomics. Personalized Medicine 9:4, 451-455
26
Carla Lluis-Ganella, Isaac Subirana, Gavin Lucas, Marta Tomás, Daniel Muñoz, Mariano Sentí, Eduardo Salas, Joan Sala, Rafel Ramos, Jose M. Ordovas, Jaume Marrugat, Roberto Elosua. (2012) Assessment of the value of a genetic risk score in improving the estimation of coronary risk. Atherosclerosis 222:2, 456-463
27
Christina L. Wassel, Claudia Lamina, Vijay Nambi, Stefan Coassin, Kenneth J. Mukamal, Santhi K. Ganesh, David R. Jacobs, Nora Franceschini, George J. Papanicolaou, Quince Gibson, Lisa R. Yanek, Pim van der Harst, Jane F. Ferguson, Dana C. Crawford, Lindsay L. Waite, Matthew A. Allison, Michael H. Criqui, Mary M. McDermott, Reena Mehra, L. Adrienne Cupples, Shih-Jen Hwang, Susan Redline, Robert C. Kaplan, Gerardo Heiss, Jerome I. Rotter, Eric Boerwinkle, Herman A. Taylor, Luis H. Eraso, Margot Haun, Mingyao Li, Christa Meisinger, Jeffrey R. O’Connell, Alan R. Shuldiner, Anne Tybjærg-Hansen, Ruth Frikke-Schmidt, Barbara Kollerits, Barbara Rantner, Benjamin Dieplinger, Marietta Stadler, Thomas Mueller, Meinhard Haltmayer, Peter Klein-Weigel, Monika Summerer, H.-Erich Wichmann, Folkert W. Asselbergs, Gerjan Navis, Irene Mateo Leach, Kristin Brown-Gentry, Robert Goodloe, Themistocles L. Assimes, Diane M. Becker, John P. Cooke, Devin M. Absher, Jeffrey W. Olin, Braxton D. Mitchell, Muredach P. Reilly, Emile R. Mohler, Kari E. North, Alexander P. Reiner, Florian Kronenberg, Joanne M. Murabito. (2012) Genetic determinants of the ankle-brachial index: A meta-analysis of a cardiovascular candidate gene 50K SNP panel in the candidate gene association resource (CARe) consortium. Atherosclerosis 222:1, 138-147
28
N. Yiannakouris, M. Katsoulis, V. Dilis, L.D. Parnell, D. Trichopoulos, J.M. Ordovas, A. Trichopoulou. (2012) Genetic predisposition to coronary heart disease and stroke using an additive genetic risk score: A population-based study in Greece. Atherosclerosis 222:1, 175-179
29
Douglas G. Johns, Joseph Duffy, Timothy Fisher, Brian K. Hubbard, Michael J. Forrest. (2012) On- and Off-Target Pharmacology of Torcetrapib. Drugs 72:4, 491-507
30
Amy I. Lynch, John H. Eckfeldt, Barry R. Davis, Charles E. Ford, Eric Boerwinkle, Catherine Leiendecker-Foster, Donna K. Arnett. (2012) Gene panels to help identify subgroups at high and low risk of coronary heart disease among those randomized to antihypertensive treatment. Pharmacogenetics and Genomics1
31
Martin Magnusson, Philippe Burri, Olle Melander. (2012) A clinically confirmed family history for early myocardial infarction is associated with increased risk of obesity, insulin resistance and metabolic syndrome. Journal of Hypertension1
32
Guan Xing, Chang-Yun Lin, Stephen P. Wooding, Chao Xing. (2012) Blindly Using Wald's Test Can Miss Rare Disease-Causal Variants in Case-Control Association Studies. Annals of Human Geneticsno-no
33
Jung-A Pyun, Sunshin Kim, KyungChae Park, Inkyung Baik, Nam H Cho, InSong Koh, Jong-Young Lee, Yoon Shin Cho, Young Jin Kim, Min Jin Go, Eugene Shim, KyuBum Kwack, Chol Shin. (2012) Interaction Effects of Lipoprotein Lipase Polymorphisms with Lifestyle on Lipid Levels in a Korean Population: A Cross-sectional Study. Genomics & Informatics 10:2, 88
34
Paul M Ridker, Peter Libby. Risk Markers for Atherothrombotic Disease. In: Braunwald's Heart Disease: A Textbook of Cardiovascular Medicine. Elsevier, 2012:914-934.
35
Bruna Gigante, Karin Leander, Max Vikström, Shu Ye, Ulf de Faire. (2012) Chromosome 1p13 genetic variants antagonize the risk of myocardial infarction associated with high ApoB serum levels. BMC Cardiovascular Disorders 12:1, 90
36
Ting-Ting Yan, Rui-Xing Yin, Qing Li, Ping Huang, Xiao-Na Zeng, Ke-Ke Huang, Dong-Feng Wu, Lynn Htet Aung. (2012) Association of MYLIP rs3757354 SNP and several environmental factors with serum lipid levels in the Guangxi Bai Ku Yao and Han populations. Lipids in Health and Disease 11:1, 141
37
Zhijie Xiao, Juan Wang, Weirong Chen, Peng Wang, Houlin Zeng, Weixi Chen. (2012) Association studies of several cholesterol-related genes (ABCA1, CETP and LIPC) with serum lipids and risk of Alzheimer’s disease. Lipids in Health and Disease 11:1, 163
38
Genetics. In: High-Density Lipoproteins. John Wiley & Sons, Inc., 2011:161-191.
39
Emil M. deGoma, Joshua W. Knowles, Fabio Angeli, Matthew J. Budoff, Daniel J. Rader. (2011) The Evolution and Refinement of Traditional Risk Factors for Cardiovascular Disease. Cardiology in Review1
40
R H Perlis. (2011) Translating biomarkers to clinical practice. Molecular Psychiatry 16:11, 1076-1087
41
Cesare R Sirtori. (2011) Investigational CETP antagonists for hyperlipidemia and atherosclerosis prevention. Expert Opinion on Investigational Drugs 20:11, 1543-1554
42
V. Hamrefors, B. Hedblad, G. Engström, P. Almgren, M. Sjögren, O. Melander. (2011) A myocardial infarction genetic risk score is associated with markers of carotid atherosclerosis. Journal of Internal Medicineno-no
43
Margarida C. Lopes, Eleftheria Zeggini, Kalliope Panoutsopoulou. (2011) Review: Do genome-wide association scans have potential for translation?. Clinical Chemistry and Laboratory Medicine---
44
J. F. Peden, M. Farrall. (2011) Thirty-five common variants for coronary artery disease: the fruits of much collaborative labour. Human Molecular Genetics 20:R2, R198-R205
45
L. Jostins, J. C. Barrett. (2011) Genetic risk prediction in complex disease. Human Molecular Genetics 20:R2, R182-R188
46
Toshiro Fukami, Toyofumi Suzuki, Ayyalusamy Ramamoorthy, Kazuo Tomono. Nanoparticle Processing of Cholesterol-Lowering Drug. In: Nanomedicine and the Cardiovascular System. Science Publishers, 2011:263-283.
47
Ruth Frikke-Schmidt. (2011) Genetic variation in ABCA1 and risk of cardiovascular disease. Atherosclerosis 218:2, 281-282
48
Ying Zhao, Marieke Pennings, Carlos L.J. Vrins, Laura Calpe-Berdiel, Menno Hoekstra, J. Kar Kruijt, Roelof Ottenhoff, Reeni B. Hildebrand, Ronald van der Sluis, Wendy Jessup, Wilfried Le Goff, M. John Chapman, Thierry Huby, Albert K. Groen, Theo J.C. Van Berkel, Miranda Van Eck. (2011) Hypocholesterolemia, foam cell accumulation, but no atherosclerosis in mice lacking ABC-transporter A1 and scavenger receptor BI. Atherosclerosis 218:2, 314-322
49
Jonatan R. Ruiz, Carmen Fiuza-Luces, Amaya Buxens, Amalia Cano-Nieto, Félix Gómez-Gallego, Catalina Santiago, Gabriel Rodríguez-Romo, Nuria Garatachea, José I. Lao, María Morán, Alejandro Lucia. (2011) Are centenarians genetically predisposed to lower disease risk?. AGE
50
A. Cecile. J. W. Janssens, John P. A. Ioannidis, Sara Bedrosian, Paolo Boffetta, Siobhan M. Dolan, Nicole Dowling, Isabel Fortier, Andrew N. Freedman, Jeremy M. Grimshaw, Jeffrey Gulcher, Marta Gwinn, Mark A. Hlatky, Holly Janes, Peter Kraft, Stephanie Melillo, Christopher J. O’Donnell, Michael J. Pencina, David Ransohoff, Sheri D. Schully, Daniela Seminara, Deborah M. Winn, Caroline F. Wright, Cornelia M. van Duijn, Julian Little, Muin J. Khoury. (2011) Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration. European Journal of Clinical Investigation 41:9, 1010-1035
51
Luca Járomi, Veronika Csöngei, Noémi Polgár, Gábor Rappai, Zoltán Szolnoki, Anita Maász, Katalin Horvatovich, Enikő Sáfrány, Csilla Sipeky, Lili Magyari, Béla Melegh. (2011) Triglyceride Level-Influencing Functional Variants of the ANGPTL3, CILP2, and TRIB1 Loci in Ischemic Stroke. NeuroMolecular Medicine 13:3, 179-186
52
Ndeye Coumba Ndiaye, Mohsen Azimi Nehzad, Said El Shamieh, Maria G. Stathopoulou, Sophie Visvikis-Siest. (2011) Cardiovascular diseases and genome-wide association studies. Clinica Chimica Acta 412:19-20, 1697-1701
53
Shuren Guo, Fang Zheng, Xueping Qiu, Na Yang. (2011) ChREBP gene polymorphisms are associated with coronary artery disease in Han population of Hubei province. Clinica Chimica Acta 412:19-20, 1854-1860
54
I. Tzoulaki, G. Liberopoulos, J. P. A. Ioannidis. (2011) Use of reclassification for assessment of improved prediction: an empirical evaluation. International Journal of Epidemiology 40:4, 1094-1105
55
A. Cecile J.W. Janssens, John P.A. Ioannidis, Sara Bedrosian, Paolo Boffetta, Siobhan M. Dolan, Nicole Dowling, Isabel Fortier, Andrew N. Freedman, Jeremy M. Grimshaw, Jeffrey Gulcher, Marta Gwinn, Mark A. Hlatky, Holly Janes, Peter Kraft, Stephanie Melillo, Christopher J. O’Donnell, Michael J. Pencina, David Ransohoff, Sheri D. Schully, Daniela Seminara, Deborah M. Winn, Caroline F. Wright, Cornelia M. van Duijn, Julian Little, Muin J. Khoury. (2011) Strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS): explanation and elaboration. Journal of Clinical Epidemiology 64:8, e1-e22
56
Emily Sonestedt, Elisabet Wirfält, Peter Wallström, Bo Gullberg, Marju Orho-Melander, Bo Hedblad. (2011) Dairy products and its association with incidence of cardiovascular disease: the Malmö diet and cancer cohort. European Journal of Epidemiology 26:8, 609-618
57
C Herder, M Karakas, W Koenig. (2011) Biomarkers for the Prediction of Type 2 Diabetes and Cardiovascular Disease. Clinical Pharmacology & Therapeutics 90:1, 52-66
58
Nora Pashayan, Paul Pharoah. (2011) Translating genomics into improved population screening: hype or hope?. Human Genetics 130:1, 19-21
59
Minori Koga, Aya Kawasaki, Ikue Ito, Takumi Furuya, Jun Ohashi, Chieko Kyogoku, Satoshi Ito, Taichi Hayashi, Isao Matsumoto, Makio Kusaoi, Yoshinari Takasaki, Hiroshi Hashimoto, Takayuki Sumida, Naoyuki Tsuchiya. (2011) Cumulative association of eight susceptibility genes with systemic lupus erythematosus in a Japanese female population. Journal of Human Genetics 56:7, 503-507
60
Paul E. Drawz, John R. Sedor. (2011) The genetics of common kidney disease: a pathway toward clinical relevance. Nature Reviews Nephrology 7:8, 458-468
61
T. A. Potapova, N. S. Yudin, I. V. Pilipenko, V. F. Kobsev, A. G. Romashchenko, E. V. Shakhtshneider, M. Yu. Ogarkov, M. I. Voevoda. (2011) Association of Cold Receptor TRPM8 Gene Polymorphism with Blood Lipid Indices and Anthropometric Parameters in Shorians. Bulletin of Experimental Biology and Medicine 151:2, 223-226
62
J. Katz, Y. Gong, D. Salmasinia, W. Hou, B. Burkley, P. Ferreira, O. Casanova, T.Y. Langaee, J.S. Moreb. (2011) Genetic polymorphisms and other risk factors associated with bisphosphonate induced osteonecrosis of the jaw. International Journal of Oral and Maxillofacial Surgery 40:6, 605-611
63
JOSEPH M. DEVANEY, PAUL D. THOMPSON, PAUL S. VISICH, WILLIAM A. SALTARELLI, PAUL M. GORDON, E. FUNDA ORKUNOGLU-SUER, HEATHER GORDISH-DRESSMAN, BRENNAN T. HARMON, MARGARET K. BRADBURY, KARUNA PANCHAPAKESAN, RAHUL KHIANEY, MONICA J. HUBAL, PRISCILLA M. CLARKSON, LINDA S. PESCATELLO, ROBERT F. ZOELLER, NIALL M. MOYNA, THEODORE J. ANGELOPOULOS, WILLIAM E. KRAUS, ERIC P. HOFFMAN. (2011) The 1p13.3 LDL (C)-Associated Locus Shows Large Effect Sizes in Young Populations. Pediatric Research 69:6, 538-543
64
A Cecile JW Janssens, John PA Ioannidis, Sara Bedrosian, Paolo Boffetta, Siobhan M Dolan, Nicole Dowling, Isabel Fortier, Andrew N Freedman, Jeremy M Grimshaw, Jeffrey Gulcher, Marta Gwinn, Mark A Hlatky, Holly Janes, Peter Kraft, Stephanie Melillo, Christopher J O'Donnell, Michael J Pencina, David Ransohoff, Sheri D Schully, Daniela Seminara, Deborah M Winn, Caroline F Wright, Cornelia M van Duijn, Julian Little, Muin J Khoury. (2011) Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration. European Journal of Human Genetics 19:5,
65
Yin Ge, Thomas J. Wang. (2011) Circulating, Imaging, and Genetic Biomarkers in Cardiovascular Risk Prediction. Trends in Cardiovascular Medicine 21:4, 105-112
66
David Evans, Jens Aberle, Frank-Ulrich Beil. (2011) The relative importance of common and rare genetic variants in the development of hypertriglyceridemia. Expert Review of Cardiovascular Therapy 9:5, 637-644
67
A. Cecile J. W. Janssens, John P. A. Ioannidis, Sara Bedrosian, Paolo Boffetta, Siobhan M. Dolan, Nicole Dowling, Isabel Fortier, Andrew N. Freedman, Jeremy M. Grimshaw, Jeffrey Gulcher, Marta Gwinn, Mark A. Hlatky, Holly Janes, Peter Kraft, Stephanie Melillo, Christopher J. O’Donnell, Michael J. Pencina, David Ransohoff, Sheri D. Schully, Daniela Seminara, Deborah M. Winn, Caroline F. Wright, Cornelia M. Duijn, Julian Little, Muin J. Khoury. (2011) Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration. European Journal of Epidemiology 26:4, 313-337
68
Maria Hughes, Frank Kee, Veikko Salomaa. (2011) Clinical Utility of Multiple Biomarker Panels for Cardiovascular Disease Risk Prediction. Current Cardiovascular Risk Reports 5:2, 165-173
69
Michael V. Holmes, Seamus Harrison, Philippa J. Talmud, Aroon D. Hingorani, Steve E. Humphries. (2011) Utility of genetic determinants of lipids and cardiovascular events in assessing risk. Nature Reviews Cardiology 8:4, 207-221
70
Edwin Ho, Ravinay Bhindi, Euan A. Ashley, Gemma A. Figtree. (2011) Genetic Analysis in Cardiovascular Disease. Cardiology in Review 19:2, 81-89
71
Hiroshi Yamanaka, Keiko Takeda. (2011) Practice of personalized primary prevention of lifestyle diseases: associated problems and issues in Japan. Personalized Medicine 8:2, 215-224
72
J. P. Evans, E. M. Meslin, T. M. Marteau, T. Caulfield. (2011) Deflating the Genomic Bubble. Science 331:6019, 861-862
73
A. C. J. W. Janssens, A. A. M. Wilde, I. M. Langen. (2011) The sense and nonsense of direct-to-consumer genetic testing for cardiovascular disease. Netherlands Heart Journal 19:2, 85-88
74
Khemanganee E. Liyanage, John R. Burnett, Amanda J. Hooper, Frank M. van Bockxmeer. (2011) Familial hypercholesterolemia: epidemiology, Neolithic origins and modern geographic distribution. Critical Reviews in Clinical Laboratory Sciences 48:1, 1-18
75
Christine Klein, Andreas Ziegler. (2011) From GWAS to clinical utility in Parkinson's disease. The Lancet 377:9766, 613-614
76
Bich NH Tran, Nguyen D Nguyen, Vinh X Nguyen, Jacqueline R Center, John A Eisman, Tuan V Nguyen. (2011) Genetic profiling and individualized prognosis of fracture. Journal of Bone and Mineral Research 26:2, 414-419
77
R. Lubomirov, S. Colombo, J. di Iulio, B. Ledergerber, R. Martinez, M. Cavassini, B. Hirschel, E. Bernasconi, L. Elzi, P. Vernazza, H. Furrer, H. F. Gunthard, A. Telenti, . (2011) Association of Pharmacogenetic Markers with Premature Discontinuation of First-line Anti-HIV Therapy: An Observational Cohort Study. Journal of Infectious Diseases 203:2, 246-257
78
Raluca Mihaescu, Ramal Moonesinghe, Muin J Khoury, A Janssens. (2011) Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability. Genome Medicine 3:7, 51
79
Keyue Ding, Kent R Bailey, Iftikhar J Kullo. (2011) Genotype-informed estimation of risk of coronary heart disease based on genome-wide association data linked to the electronic medical record. BMC Cardiovascular Disorders 11:1, 66
80
Qing Li, Rui-Xing Yin, Ting-Ting Yan, Lin Miao, Xiao-Li Cao, Xi-Jiang Hu, Lynn Htet Htet Aung, Dong-Feng Wu, Jin-Zhen Wu, Wei-Xiong Lin. (2011) Association of the GALNT2 gene polymorphisms and several environmental factors with serum lipid levels in the Mulao and Han populations. Lipids in Health and Disease 10:1, 160
81
Li Chen, Guoqiang Yu, Carl D Langefeld, David J Miller, Richard T Guy, Jayaram Raghuram, Xiguo Yuan, David M Herrington, Yue Wang. (2011) Comparative analysis of methods for detecting interacting loci. BMC Genomics 12:1, 344
82
Aseel AlSaleh, Gary S. Frost, Bruce A. Griffin, Julie A. Lovegrove, Susan A. Jebb, Thomas A.B. Sanders, Sandra D. O’Dell. (2011) PPARγ2 Gene Pro12Ala and PPARα Gene Leu162Val Single Nucleotide Polymorphisms Interact with Dietary Intake of Fat in Determination of Plasma Lipid Concentrations. Journal of Nutrigenetics and Nutrigenomics 4:6, 354-366
83
Kiran Musunuru, Sekar Kathiresan. Genetics of Cardiovascular Disease and Its Role in Risk Prediction. In: Preventive Cardiology: Companion to Braunwald's Heart Disease. Elsevier, 2011:46-53.
84
Vijay Nambi, Ariel Brautbar, Christie M. Ballantyne. Novel Biomarkers and the Assessment of Cardiovascular Risk. In: Preventive Cardiology: Companion to Braunwald's Heart Disease. Elsevier, 2011:54-63.
85
Philip Greenland, Joseph S. Alpert, George A. Beller, Emelia J. Benjamin, Matthew J. Budoff, Zahi A. Fayad, Elyse Foster, Mark A. Hlatky, John McB. Hodgson, Frederick G. Kushner, Michael S. Lauer, Leslee J. Shaw, Sidney C. Smith, Allen J. Taylor, William S. Weintraub, Nanette K. Wenger. (2010) 2010 ACCF/AHA Guideline for Assessment of Cardiovascular Risk in Asymptomatic Adults. Journal of the American College of Cardiology 56:25, e50-e103
86
THS Dent. (2010) Predicting the risk of coronary heart disease. II: The role of novel molecular biomarkers and genetics in estimating risk, and the future of risk prediction. Atherosclerosis 213:2, 352-362
87
M. E. Mealiffe, R. P. Stokowski, B. K. Rhees, R. L. Prentice, M. Pettinger, D. A. Hinds. (2010) Assessment of Clinical Validity of a Breast Cancer Risk Model Combining Genetic and Clinical Information. JNCI Journal of the National Cancer Institute 102:21, 1618-1627
88
Yingchang Lu, Edith J.M. Feskens, Jolanda M.A. Boer, Sandra Imholz, W.M. Monique Verschuren, Cisca Wijmenga, Anika Vaarhorst, Eline Slagboom, Michael Müller, Martijn E.T. Dollé. (2010) Exploring genetic determinants of plasma total cholesterol levels and their predictive value in a longitudinal study. Atherosclerosis 213:1, 200-205
89
Ian J. Brown, Paul Elliott. (2010) Recent Findings from Mendelian Randomization Studies of Cardiovascular Disease. Current Cardiovascular Risk Reports 4:6, 429-436
90
Xin-chun Yang, Qi Zhang, Shu-juan Li, Xin-hong Wan, Guang-zhen Zhong, Wen-li Hu, Lei Li, Shun-zhang Yu, Li Jin, Xiao-feng Wang. (2010) Association study between three polymorphisms and myocardial infarction and ischemic stroke in Chinese Han population. Thrombosis Research 126:4, 292-294
91
Samuli Ripatti, Emmi Tikkanen, Marju Orho-Melander, Aki S Havulinna, Kaisa Silander, Amitabh Sharma, Candace Guiducci, Markus Perola, Antti Jula, Juha Sinisalo, Marja-Liisa Lokki, Markku S Nieminen, Olle Melander, Veikko Salomaa, Leena Peltonen, Sekar Kathiresan. (2010) A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. The Lancet 376:9750, 1393-1400
92
Christos Katsios, Odysseas Zoras, Dimitrios H Roukos. (2010) Cancer genome sequencing and potential application in oncology. Future Oncology 6:10, 1527-1531
93
Jerome Carayol, Frédéric Tores, Inke R. König, Jörg Hager, Andreas Ziegler. (2010) Evaluating diagnostic accuracy of genetic profiles in affected offspring families. Statistics in Medicine 29:22, 2359-2368
94
Wai Kwong Lee, Sandosh Padmanabhan, Anna F Dominiczak. Molecular Genetics of Cardiovascular Disease. In: Encyclopedia of Life Sciences. John Wiley & Sons, Ltd, 2010.
95
Kiran Musunuru, Sekar Kathiresan. (2010) Genetics of Coronary Artery Disease. Annual Review of Genomics and Human Genetics 11:1, 91-108
96
Alan R. Tall. (2010) Functions of cholesterol ester transfer protein and relationship to coronary artery disease risk. Journal of Clinical Lipidology 4:5, 389-393
97
L. B. Travis, C. Beard, J. M. Allan, A. A. Dahl, D. R. Feldman, J. Oldenburg, G. Daugaard, J. L. Kelly, M. E. Dolan, R. Hannigan, L. S. Constine, K. C. Oeffinger, P. Okunieff, G. Armstrong, D. Wiljer, R. C. Miller, J. A. Gietema, F. E. van Leeuwen, J. P. Williams, C. R. Nichols, L. H. Einhorn, S. D. Fossa. (2010) Testicular Cancer Survivorship: Research Strategies and Recommendations. JNCI Journal of the National Cancer Institute 102:15, 1114-1130
98
R. Mihaescu, M. van Zitteren, M. van Hoek, E. J. G. Sijbrands, A. G. Uitterlinden, J. C. M. Witteman, A. Hofman, M. G. M. Hunink, C. M. van Duijn, A. C. J. W. Janssens. (2010) Improvement of Risk Prediction by Genomic Profiling: Reclassification Measures Versus the Area Under the Receiver Operating Characteristic Curve. American Journal of Epidemiology 172:3, 353-361
99
Jeffrey L. Anderson, Benjamin D. Horne, Nicola J. Camp, Joseph B. Muhlestein, Paul N. Hopkins, Lisa A. Cannon-Albright, Chrissa P. Mower, James J. Park, Jessica L. Clarke, Zachary P. Nicholas, Jason T. McKinney, John F. Carlquist. (2010) Joint effects of common genetic variants from multiple genes and pathways on the risk of premature coronary artery disease. American Heart Journal 160:2, 250-256.e3
100
Carla Lluís-Ganella, Gavin Lucas, Isaac Subirana, Mariano Sentí, Jordi Jimenez-Conde, Jaume Marrugat, Marta Tomás, Roberto Elosua. (2010) Efecto aditivo de diferentes variantes genéticas en el riesgo de cardiopatía isquémica. Revista Española de Cardiología 63:8, 925-933
101
Dimitrios H Roukos. (2010) Next-generation, genome sequencing-based biomarkers: concerns and challenges for medical practice. Biomarkers in Medicine 4:4, 583-586
102
Manolio , Teri A. . (2010) Genomewide Association Studies and Assessment of the Risk of Disease. New England Journal of Medicine 363:2, 166-176
Free Full Text103
M. Juonala, J. S. A. Viikari, M. Kahonen, L. Taittonen, T. Laitinen, N. Hutri-Kahonen, T. Lehtimaki, A. Jula, M. Pietikainen, E. Jokinen, R. Telama, L. Rasanen, V. Mikkila, H. Helenius, M. Kivimaki, O. T. Raitakari. (2010) Life-time risk factors and progression of carotid atherosclerosis in young adults: the Cardiovascular Risk in Young Finns study. European Heart Journal 31:14, 1745-1751
104
HaiRong Wang, Mei Jiang, JianPing Qiu. (2010) Quantitative Assessment of the Effect of Hepatic Lipase Gene Polymorphism on the Risk of Coronary Heart Disease. Archives of Medical Research 41:5, 383-390
105
Christos Katsios, Dimitrios H Roukos. (2010) Individual genomes and personalized medicine: life diversity and complexity. Personalized Medicine 7:4, 347-350
106
Christopher T Johansen, Matthew B Lanktree, Robert A Hegele. (2010) Translating genomic analyses into improved management of coronary artery disease. Future Cardiology 6:4, 507-521
107
Gualberto Ruaño, Paul D Thompson, John P Kane, Clive R Pullinger, Andreas Windemuth, Richard L Seip, Mohan Kocherla, Theodore R Holford, Alan HB Wu. (2010) Physiogenomic analysis of statin-treated patients: domain-specific counter effects within the ACACB gene on low-density lipoprotein cholesterol?. Pharmacogenomics 11:7, 959-971
108
Aparna Amarendra Bhanushali, Bibhu R. Das. (2010) Genetic variants at the APOE, lipoprotein lipase (LpL), cholesteryl ester transfer protein (CETP), and endothelial nitric oxide (eNOS) genes and coronary artery disease (CAD): CETP Taq1 B2B2 associates with lower risk of CAD in Asian Indians. Journal of Community Genetics 1:2, 55-62
109
Peng Hu, Yuan Han Qin, Ling Lu, Bo Hu, Cheng Xue Jing, Feng Ying Lei, Ming Fang Li. (2010) Genetic variation of apolipoprotein E does not contribute to the lipid abnormalities secondary to childhood minimal change nephrotic syndrome. International Urology and Nephrology 42:2, 453-460
110
Marianne Benn, Børge G. Nordestgaard, Peer Grande, Peter Schnohr, Anne Tybjærg-Hansen. (2010) PCSK9 R46L, Low-Density Lipoprotein Cholesterol Levels, and Risk of Ischemic Heart Disease. Journal of the American College of Cardiology 55:25, 2833-2842
111
James Pirruccello, Sekar Kathiresan. (2010) Genetics of lipid disorders. Current Opinion in Cardiology 25:3, 238-242
112
Luca Járomi, Veronika Csöngei, Noémi Polgár, Zoltán Szolnoki, Anita Maász, Katalin Horvatovich, Bernadett Faragó, Csilla Sipeky, Enikő Sáfrány, Lili Magyari, Péter Kisfali, Márton Mohás, Ingrid Janicsek, Lilla Lakner, Béla Melegh. (2010) Functional Variants of Glucokinase Regulatory Protein and Apolipoprotein A5 Genes in Ischemic Stroke. Journal of Molecular Neuroscience 41:1, 121-128
113
Euan A Ashley, Atul J Butte, Matthew T Wheeler, Rong Chen, Teri E Klein, Frederick E Dewey, Joel T Dudley, Kelly E Ormond, Aleksandra Pavlovic, Alexander A Morgan, Dmitry Pushkarev, Norma F Neff, Louanne Hudgins, Li Gong, Laura M Hodges, Dorit S Berlin, Caroline F Thorn, Katrin Sangkuhl, Joan M Hebert, Mark Woon, Hersh Sagreiya, Ryan Whaley, Joshua W Knowles, Michael F Chou, Joseph V Thakuria, Abraham M Rosenbaum, Alexander Wait Zaranek, George M Church, Henry T Greely, Stephen R Quake, Russ B Altman. (2010) Clinical assessment incorporating a personal genome. The Lancet 375:9725, 1525-1535
114
E Shyong Tai, Jose M Ordovas. (2010) Genome-wide association studies for blood lipids. A great start but a long way to go. Current Opinion in Lipidology 21:2, 101-103
115
Philip E Tarr, Margalida Rotger, Amalio Telenti. (2010) Dyslipidemia in HIV-infected individuals: from pharmacogenetics to pharmacogenomics. Pharmacogenomics 11:4, 587-594
116
Wacholder , Sholom , Hartge , Patricia , Prentice , Ross , Garcia-Closas , Montserrat , Feigelson , Heather Spencer , Diver , W. Ryan , Thun , Michael J. , Cox , David G. , Hankinson , Susan E. , Kraft , Peter , Rosner , Bernard , Berg , Christine D. , Brinton , Louise A. , Lissowska , Jolanta , Sherman , Mark E. , Chlebowski , Rowan , Kooperberg , Charles , Jackson , Rebecca D. , Buckman , Dennis W. , Hui , Peter , Pfeiffer , Ruth , Jacobs , Kevin B. , Thomas , Gilles D. , Hoover , Robert N. , Gail , Mitchell H. , Chanock , Stephen J. , Hunter , David J. , . (2010) Performance of Common Genetic Variants in Breast-Cancer Risk Models. New England Journal of Medicine 362:11, 986-993
Free Full Text117
H. Schunkert, J. Erdmann, N. J. Samani. (2010) Genetics of myocardial infarction: a progress report. European Heart Journal
118
M. Junyent, K.L. Tucker, J. Shen, Y.-C. Lee, C.E. Smith, J. Mattei, C.-Q. Lai, L.D. Parnell, J.M. Ordovas. (2010) A composite scoring of genotypes discriminates coronary heart disease risk beyond conventional risk factors in the Boston Puerto Rican Health Study. Nutrition, Metabolism and Cardiovascular Diseases 20:3, 157-164
119
Rachel Kaiser, Lindsey A Criswell. (2010) Genetics research in systemic lupus erythematosus for clinicians: methodology, progress, and controversies. Current Opinion in Rheumatology 22:2, 119-125
120
Paul E. Drawz, John R. Sedor. (2010) Translating Associations Between Common Kidney Diseases and Genetic Variation Into the Clinic. Seminars in Nephrology 30:2, 195-202
121
Stephen A. Ramsey, Elizabeth S. Gold, Alan Aderem. (2010) A systems biology approach to understanding atherosclerosis. EMBO Molecular Medicine 2:3, 79-89
122
Ruth Frikke-Schmidt. (2010) Genetic variation in the ABCA1 gene, HDL cholesterol, and risk of ischemic heart disease in the general population. Atherosclerosis 208:2, 305-316
123
Michael V. Holmes, Sonia H. Shah, Aspasia Angelakopoulou, Tauseef Khan, Daniel Swerdlow, Karoline Kuchenbaecker, Reecha Sofat, Tina Shah. (2010) A report on the Genetics of Complex Diseases meeting of the British Atherosclerosis Society, Cambridge, UK, 17–18 September 2009. Atherosclerosis 208:2, 599-602
124
Sekar Kathiresan, Daniel J. Rader. Lipoprotein Disorders. In: Essentials of Genomic and Personalized Medicine. Elsevier, 2010:269-288.
125
M.C. Smart, G. Dedoussis, E. Louizou, M. Yannakoulia, F. Drenos, C. Papoutsakis, N. Maniatis, S.E. Humphries, P.J. Talmud. (2010) APOE, CETP and LPL genes show strong association with lipid levels in Greek children. Nutrition, Metabolism and Cardiovascular Diseases 20:1, 26-33
126
Tisha R Joy, Robert A Hegele. (2009) Lipid-altering gene variants and cardiovascular risk in the older population. Clinical Lipidology 4:6, 721-724
127
L. Groop, V. Lyssenko. (2009) Genetic basis of β-cell dysfunction in man. Diabetes, Obesity and Metabolism 11, 149-158
128
A. McClure, M. Lunt, S. Eyre, X. Ke, W. Thomson, A. Hinks, J. Bowes, L. Gibbons, D. Plant, A. G. Wilson, I. Marinou, A. W. Morgan, P. Emery, , S. Steer, L. J. Hocking, D. M. Reid, P. Wordsworth, P. Harrison, J. Worthington, A. Barton. (2009) Investigating the viability of genetic screening/testing for RA susceptibility using combinations of five confirmed risk loci. Rheumatology 48:11, 1369-1374
129
Deepak Voora, Geoffrey S. Ginsburg. (2009) Pharmacogenetics of the response to statins. Current Cardiovascular Risk Reports 3:6, 434-440
130
R. A. Hegele, M. R. Ban, N. Hsueh, B. A. Kennedy, H. Cao, G. Y. Zou, S. Anand, S. Yusuf, M. W. Huff, J. Wang. (2009) A polygenic basis for four classical Fredrickson hyperlipoproteinemia phenotypes that are characterized by hypertriglyceridemia. Human Molecular Genetics 18:21, 4189-4194
131
Jeanette Erdmann, Patrick Linsel-Nitschke, Heribert Schunkert. (2009) Genetic basis of myocardial infarction: Novel insights from genome-wide association studies. Current Cardiovascular Risk Reports 3:6, 426-433
132
Aart C. Strang, G. Kees Hovingh, Erik S.G. Stroes, John J.P. Kastelein. (2009) The Genetics of High-Density Lipoprotein Metabolism: Clinical Relevance for Therapeutic Approaches. The American Journal of Cardiology 104:10, 22E-31E
133
Arnika K. Wagner, Heribert Schunkert, Jeanette Erdmann. (2009) Genetik des Herzinfarktes. Der lange Weg von der positiven Familienanamnese zum Gen. Chemie in unserer Zeit 43:5, 288-295
134
, J. R. B. Perry, L. Ferrucci, S. Bandinelli, J. Guralnik, R. D. Semba, N. Rice, D. Melzer, R. Saxena, L. J. Scott, M. I. McCarthy, A. T. Hattersley, E. Zeggini, M. N. Weedon, T. M. Frayling. (2009) Circulating β-carotene levels and type 2 diabetes—cause or effect?. Diabetologia 52:10, 2117-2121
135
Vilmundur Gudnason. (2009) Lipoprotein(a): a causal independent risk factor for coronary heart disease?. Current Opinion in Cardiology 24:5, 490-495
136
Marianne Benn. (2009) Apolipoprotein B levels, APOB alleles, and risk of ischemic cardiovascular disease in the general population, a review. Atherosclerosis 206:1, 17-30
137
Amit V. Khera, Daniel J. Rader. (2009) Discovery and Validation of New Molecular Targets in Treating Dyslipidemia: The Role of Human Genetics. Trends in Cardiovascular Medicine 19:6, 195-201
138
A. Murray, C. Cluett, S. Bandinelli, A. M. Corsi, L. Ferrucci, J. Guralnik, A. Singleton, T. Frayling, D. Melzer. (2009) Common lipid-altering gene variants are associated with therapeutic intervention thresholds of lipid levels in older people. European Heart Journal 30:14, 1711-1719
139
Marisa Wong Medina, Ronald M. Krauss. (2009) The Role of HMGCR Alternative Splicing in Statin Efficacy. Trends in Cardiovascular Medicine 19:5, 173-177
140
Jeroen B. van der Net, A. Cecile J.W. Janssens, Eric J.G. Sijbrands, Ewout W. Steyerberg. (2009) Value of genetic profiling for the prediction of coronary heart disease. American Heart Journal 158:1, 105-110
141
F. Drenos, P. J. Talmud, J. P. Casas, L. Smeeth, J. Palmen, S. E. Humphries, A. D. Hingorani. (2009) Integrated associations of genotypes with multiple blood biomarkers linked to coronary heart disease risk. Human Molecular Genetics 18:12, 2305-2316
142
L. C. Tsoi, M. Boehnke, R. L. Klein, W. J. Zheng. (2009) Evaluation of genome-wide association study results through development of ontology fingerprints. Bioinformatics 25:10, 1314-1320
143
Peter Kraft, Sholom Wacholder, Marilyn C. Cornelis, Frank B. Hu, Richard B. Hayes, Gilles Thomas, Robert Hoover, David J. Hunter, Stephen Chanock. (2009) Beyond odds ratios — communicating disease risk based on genetic profiles. Nature Reviews Genetics 10:4, 264-269
144
Javier Sanz, Pedro R. Moreno, Valentin Fuster. (2009) The Year in Atherothrombosis. Journal of the American College of Cardiology 53:15, 1326-1337
145
Jan H van Ee. (2009) Soy constituents: modes of action in low-density lipoprotein management. Nutrition Reviews 67:4, 222-234
146
Valeriya Lyssenko, Leif Groop. (2009) Genome-wide association study for type 2 diabetes: clinical applications. Current Opinion in Lipidology 20:2, 87-91
147
Eva Boes, Stefan Coassin, Barbara Kollerits, Iris M. Heid, Florian Kronenberg. (2009) Genetic-epidemiological evidence on genes associated with HDL cholesterol levels: A systematic in-depth review. Experimental Gerontology 44:3, 136-160
148
N. FRANCESCHINI, H. MUALLEM, K. M. ROSE, E. BOERWINKLE, N. MAEDA. (2009) Low density lipoprotein receptor polymorphisms and the risk of coronary heart disease: the Atherosclerosis Risk in Communities Study. Journal of Thrombosis and Haemostasis 7:3, 496-498
149
Sekar Kathiresan, Benjamin F Voight, Shaun Purcell, Kiran Musunuru, Diego Ardissino, Pier M Mannucci, Sonia Anand, James C Engert, Nilesh J Samani, Heribert Schunkert, Jeanette Erdmann, Muredach P Reilly, Daniel J Rader, Thomas Morgan, John A Spertus, Monika Stoll, Domenico Girelli, Pascal P McKeown, Chris C Patterson, David S Siscovick, Christopher J O'Donnell, Roberto Elosua, Leena Peltonen, Veikko Salomaa, Stephen M Schwartz, Olle Melander, David Altshuler, Diego Ardissino, Pier Angelica Merlini, Carlo Berzuini, Luisa Bernardinelli, Flora Peyvandi, Marco Tubaro, Patrizia Celli, Maurizio Ferrario, Raffaela Fetiveau, Nicola Marziliano, Giorgio Casari, Michele Galli, Flavio Ribichini, Marco Rossi, Francesco Bernardi, Pietro Zonzin, Alberto Piazza, Pier M Mannucci, Stephen M Schwartz, David S Siscovick, Jean Yee, Yechiel Friedlander, Roberto Elosua, Jaume Marrugat, Gavin Lucas, Isaac Subirana, Joan Sala, Rafael Ramos, Sekar Kathiresan, James B Meigs, Gordon Williams, David M Nathan, Calum A MacRae, Christopher J O'Donnell, Veikko Salomaa, Aki S Havulinna, Leena Peltonen, Olle Melander, Goran Berglund, Benjamin F Voight, Sekar Kathiresan, Joel N Hirschhorn, Rosanna Asselta, Stefano Duga, Marta Spreafico, Kiran Musunuru, Mark J Daly, Shaun Purcell, Benjamin F Voight, Shaun Purcell, James Nemesh, Joshua M Korn, Steven A McCarroll, Stephen M Schwartz, Jean Yee, Sekar Kathiresan, Gavin Lucas, Isaac Subirana, Roberto Elosua, Aarti Surti, Candace Guiducci, Lauren Gianniny, Daniel Mirel, Melissa Parkin, Noel Burtt, Stacey B Gabriel, Nilesh J Samani, John R Thompson, Peter S Braund, Benjamin J Wright, Anthony J Balmforth, Stephen G Ball, Alistair S Hall, Heribert Schunkert, Jeanette Erdmann, Patrick Linsel-Nitschke, Wolfgang Lieb, Andreas Ziegler, Inke R König, Christian Hengstenberg, Marcus Fischer, Klaus Stark, Anika Grosshennig, Michael Preuss, H-Erich Wichmann, Stefan Schreiber, Heribert Schunkert, Nilesh J Samani, Jeanette Erdmann, Willem Ouwehand, Christian Hengstenberg, Panos Deloukas, Michael Scholz, Francois Cambien, Muredach P Reilly, Mingyao Li, Zhen Chen, Robert Wilensky, William Matthai, Atif Qasim, Hakon H Hakonarson, Joe Devaney, Mary-Susan Burnett, Augusto D Pichard, Kenneth M Kent, Lowell Satler, Joseph M Lindsay, Ron Waksman, Stephen E Epstein, Daniel J Rader, Thomas Scheffold, Klaus Berger, Monika Stoll, Andreas Huge, Domenico Girelli, Nicola Martinelli, Oliviero Olivieri, Roberto Corrocher, Thomas Morgan, John A Spertus, Pascal P McKeown, Chris C Patterson, Heribert Schunkert, Jeanette Erdmann, Patrick Linsel-Nitschke, Wolfgang Lieb, Andreas Ziegler, Inke R König, Christian Hengstenberg, Marcus Fischer, Klaus Stark, Anika Grosshennig, Michael Preuss, H-Erich Wichmann, Stefan Schreiber, Hilma Hólm, Gudmar Thorleifsson, Unnur Thorsteinsdottir, Kari Stefansson, James C Engert, Ron Do, Changchun Xie, Sonia Anand, Sekar Kathiresan, Diego Ardissino, Pier M Mannucci, David Siscovick, Christopher J O'Donnell, Nilesh J Samani, Olle Melander, Roberto Elosua, Leena Peltonen, Veikko Salomaa, Stephen M Schwartz, David Altshuler. (2009) Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nature Genetics 41:3, 334-341
150
Robert A. Hegele. (2009) Plasma lipoproteins: genetic influences and clinical implications. Nature Reviews Genetics 10:2, 109-121
151
Steven W.J. Lamberts, André G. Uitterlinden. (2009) Genetic Testing in Clinical Practice. Annual Review of Medicine 60:1, 431-442
152
Eva Fisher, Matthias B. Schulze, Norbert Stefan, Hans-Ulrich Häring, Frank Döring, Hans-Georg Joost, Hadi Al-Hasani, Heiner Boeing, Tobias Pischon. (2009) Association of the FTO rs9939609 Single Nucleotide Polymorphism With C-reactive Protein Levels. Obesity 17:2, 330-334
153
Sekar Kathiresan, Cristen J Willer, Gina M Peloso, Serkalem Demissie, Kiran Musunuru, Eric E Schadt, Lee Kaplan, Derrick Bennett, Yun Li, Toshiko Tanaka, Benjamin F Voight, Lori L Bonnycastle, Anne U Jackson, Gabriel Crawford, Aarti Surti, Candace Guiducci, Noel P Burtt, Sarah Parish, Robert Clarke, Diana Zelenika, Kari A Kubalanza, Mario A Morken, Laura J Scott, Heather M Stringham, Pilar Galan, Amy J Swift, Johanna Kuusisto, Richard N Bergman, Jouko Sundvall, Markku Laakso, Luigi Ferrucci, Paul Scheet, Serena Sanna, Manuela Uda, Qiong Yang, Kathryn L Lunetta, Josée Dupuis, Paul I W de Bakker, Christopher J O'Donnell, John C Chambers, Jaspal S Kooner, Serge Hercberg, Pierre Meneton, Edward G Lakatta, Angelo Scuteri, David Schlessinger, Jaakko Tuomilehto, Francis S Collins, Leif Groop, David Altshuler, Rory Collins, G Mark Lathrop, Olle Melander, Veikko Salomaa, Leena Peltonen, Marju Orho-Melander, Jose M Ordovas, Michael Boehnke, Gonçalo R Abecasis, Karen L Mohlke, L Adrienne Cupples. (2009) Common variants at 30 loci contribute to polygenic dyslipidemia. Nature Genetics 41:1, 56-65
154
Jen-Pei Liu, Jr-Rung Lin, Shein-Chung Chow. (2009) Inference on treatment effects for targeted clinical trials under enrichment design. Pharmaceutical Statisticsn/a-n/a
155
Jack W. Kent. (2009) Analysis of multiple phenotypes. Genetic Epidemiology 33:S1, S33-S39
156
Yurii S Aulchenko, Samuli Ripatti, Ida Lindqvist, Dorret Boomsma, Iris M Heid, Peter P Pramstaller, Brenda W J H Penninx, A Cecile J W Janssens, James F Wilson, Tim Spector, Nicholas G Martin, Nancy L Pedersen, Kirsten Ohm Kyvik, Jaakko Kaprio, Albert Hofman, Nelson B Freimer, Marjo-Riitta Jarvelin, Ulf Gyllensten, Harry Campbell, Igor Rudan, Åsa Johansson, Fabio Marroni, Caroline Hayward, Veronique Vitart, Inger Jonasson, Cristian Pattaro, Alan Wright, Nick Hastie, Irene Pichler, Andrew A Hicks, Mario Falchi, Gonneke Willemsen, Jouke-Jan Hottenga, Eco J C de Geus, Grant W Montgomery, John Whitfield, Patrik Magnusson, Juha Saharinen, Markus Perola, Kaisa Silander, Aaron Isaacs, Eric J G Sijbrands, Andre G Uitterlinden, Jacqueline C M Witteman, Ben A Oostra, Paul Elliott, Aimo Ruokonen, Chiara Sabatti, Christian Gieger, Thomas Meitinger, Florian Kronenberg, Angela Döring, H-Erich Wichmann, Johannes H Smit, Mark I McCarthy, Cornelia M van Duijn, Leena Peltonen. (2009) Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nature Genetics 41:1, 47-55
157
José M. Ordovas. (2009) Interacciones entre genes y entorno y factores de riesgo cardiovascular. Revista Española de Cardiología Suplementos 9:2, 39-51
158
Roberto Elosua, Carla Lluis, Gavin Lucas. (2009) Estudio del componente genético de la cardiopatía isquémica: de los estudios de ligamiento al genotipado integral del genoma. Revista Española de Cardiología Suplementos 9:2, 24-38
159
José M. Baena-Díez, Rafael Ramos, Jaume Marrugat. (2009) Capacidad predictiva de las funciones de riesgo cardiovascular: limitaciones y oportunidades. Revista Española de Cardiología Suplementos 9:2, 4-13
160
Christopher W. Knouff, Noha Lim, Kijoung Song, Xin Yuan, Max C. Walker, Ray Townsend, Gerard Waeber, Paul M. Matthews, Peter Vollenweider, Dawn M. Waterworth, Vincent Mooser. (2008) Pharmacological effects of lipid-lowering drugs recapitulate with a larger amplitude the phenotypic effects of common variants within their target genes. Pharmacogenetics and Genomics 18:12, 1051-1057
161
Matthew Lanktree, Jisun, Robert A. Hegele. (2008) Genetic testing for atherosclerosis risk: Inevitability or pipe dream?. Canadian Journal of Cardiology 24:11, 851-854
162
A. C. J.W. Janssens, C. M. van Duijn. (2008) Genome-based prediction of common diseases: advances and prospects. Human Molecular Genetics 17:R2, R166-R173
163
K. L. Mohlke, M. Boehnke, G. R. Abecasis. (2008) Metabolic and cardiovascular traits: an abundance of recently identified common genetic variants. Human Molecular Genetics 17:R2, R102-R108
164
L. Wang, R. M. Weinshilboum. (2008) Pharmacogenomics: candidate gene identification, functional validation and mechanisms. Human Molecular Genetics 17:R2, R174-R179
165
Iulia Iatan, Khalid Alrasadi, Isabelle Ruel, Khalid Alwaili, Jacques Genest. (2008) Effect of ABCA1 mutations on risk for myocardial infarction. Current Atherosclerosis Reports 10:5, 413-426
166
Peter Kraft. (2008) Curses—Winnerʼs and Otherwise—in Genetic Epidemiology. Epidemiology 19:5, 649-651
167
Heribert Schunkert, Inke R. König, Jeanette Erdmann. (2008) Molecular Signatures of Cardiovascular Disease Risk. Molecular Diagnosis & Therapy 12:5, 281-287
168
G. Lettre, V. G. Sankaran, M. A. C. Bezerra, A. S. Araujo, M. Uda, S. Sanna, A. Cao, D. Schlessinger, F. F. Costa, J. N. Hirschhorn, S. H. Orkin. (2008) From the Cover: DNA polymorphisms at the BCL11A, HBS1L-MYB, and -globin loci associate with fetal hemoglobin levels and pain crises in sickle cell disease. Proceedings of the National Academy of Sciences 105:33, 11869-11874
169
Adriaan G Holleboom, Menno Vergeer, G Kees Hovingh, John JP Kastelein, Jan Albert Kuivenhoven. (2008) The value of HDL genetics. Current Opinion in Lipidology 19:4, 385-394
170
(2008) Cholesterol Gene Polymorphisms and Cardiovascular Events. New England Journal of Medicine 359:1, 92-93
Free Full Text171
Sarah S. Murray, Eric J. Topol. (2008) Gaining Insights in Coronary Disease Genomics
Editorials published in the Journal of the American College of Cardiology reflect the views of the authors and do not necessarily represent the views of JACC or the American College of Cardiology.. Journal of the American College of Cardiology 52:5, 385-386
172
(2008) Genotype influences lipid profile and incident cardiovascular disease risk. Nature Clinical Practice Endocrinology & Metabolism 4:7, 362-362
173
J. Wang, M. R. Ban, G. Y. Zou, H. Cao, T. Lin, B. A. Kennedy, S. Anand, S. Yusuf, M. W. Huff, R. L. Pollex, R. A. Hegele. (2008) Polygenic determinants of severe hypertriglyceridemia. Human Molecular Genetics 17:18, 2894-2899
- Letters
