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Circulation. 2004;109:IV-47-IV-58
doi: 10.1161/01.CIR.0000133440.86427.26
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(Circulation. 2004;109:IV-47 – IV-58.)
© 2004 American Heart Association, Inc.

Markers of Malign Across the Cardiovascular Continuum: Interpretation and Application

Genetic Markers

Progress and Potential for Cardiovascular Disease

Gary H. Gibbons, MD; Choong Chin Liew, PhD; Mark O. Goodarzi, MD, PhD; Jerome I. Rotter, MD; Willa A. Hsueh, MD; Helmy M. Siragy, MD; Richard Pratt, PhD; Victor J. Dzau, MD

From the Cardiovascular Research Institute (G.H.G.), Morehouse School of Medicine, Atlanta, Ga; the Department of Medicine (C.C.L., R.P., V.J.D.), Brigham and Women’s Hospital, Boston, Mass; Medical Genetics Institute (M.O.G., J.I.R.), Departments of Pediatrics and Medicine, Steven Spielberg Pediatric Research Center, Cedars-Sinai Medical Center, and University of California, Los Angeles, Calif; the Division of Endocrinology, Diabetes, and Hypertension (M.O.G., W.A.H.), Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Calif; and the Department of Medicine (H.M.S.), University of Virginia, Charlottesville, Va.

Correspondence to Choong Chin Liew, PhD, Brigham & Women’s Hospital, 77 Louis Pasteur Avenue, NRB Room 0630K, Boston, MA 02115. E-mail cliew{at}rics.bwh.harvard.edu


Key Words: genes • cardiovascular diseases • heart defects, congenital


*    Introduction
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 *Introduction
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The recent completion of the Human Genome Project has provided an unprecedented opportunity for researchers to identify high-risk patients and improve human health through the use of technologies that integrate the entire genome. In the past, disorders that yielded their secrets to genetic investigations tended to be rare, single-gene conditions (eg, Brugada syndrome, Liddle syndrome). Today, attention is increasingly focused on elucidating genetic susceptibility to the common multifactorial diseases that clinicians encounter on a daily basis.1

A significant portion of current medical research is devoted to the pursuit of genetic variants that can be used to identify disease. These variants are not necessarily the cause of the illness, but markers that will help improve diagnosis and risk assessment. The level of expression of certain genes (ie, the amount of corresponding RNA or proteins produced) may signify a disease state. If these genes are consistently overexpressed or suppressed in a certain clinical context, they also may be considered biomarkers.

Two approaches are used when pursuing genetic markers: researchers can conduct candidate gene studies (which focus on single genes) or genomic studies (which examine the entire genome.) Some diseases are monogenic (ie, caused by defects in only one gene). In such cases, genetic studies make clinical diagnosis straightforward. Mutations can be assessed by patient genotyping, and the expression of single genes can be assessed using techniques such as real-time reverse-transcription polymerized chain reaction (RT-PCR) or Northern blot. However, for more common diseases, it has been more difficult to identify genetic markers, because most common diseases are polygenic. This genetic "web" of multiple genes may be very large and act in complex ways to induce a disease state. In addition, these conditions are often triggered by an interaction of genetic, environmental, and physiological factors, making it difficult for researchers to narrow their focus to a single gene. This is particularly true for many common cardiovascular disorders such as heart failure.

In these cases, a "genomic" approach that examines the entire genome may be valuable. To expedite the search for genes associated with polygenic diseases, researchers use the complementary approaches of whole genome scans and microarray gene profiling, in combination with real-time RT-PCR, to identify and validate clusters of relevant genes. These gene clusters or expression patterns may be used as markers to distinguish among different disease states. Thus, gene profiling can be performed on tissue biopsy samples or circulating blood cells. Some genetic markers associated with cardiovascular disease risk are listed in Table 1.2–99 This review focuses on biomarkers identified in human subjects; however, it is also noteworthy that genetic and genomic studies have made extensive use of animal models to further characterize the cardiovascular system.100


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  		TABLE 1. Selected Genes Associated With Cardiovascular Disease, Classified by Phenotype

It has long been known that some cardiovascular disorders are more prevalent among families that share genetic and environmental factors. Genetic studies involving twins and well-characterized pedigrees have established that the cardiovascular risk profile includes a substantial heritable component. It is clear that genetic factors influence quantitative traits (eg, levels of low-density lipoprotein [LDL] cholesterol and high-density lipoprotein [HDL] cholesterol, blood pressure, adiposity, and left ventricular mass). It is therefore not surprising that the causative basis of complex cardiovascular disorders (eg, atherothrombosis) involves a dynamic interplay among multiple genes in addition to gene–environment interactions. Our present challenge is to determine whether the genomic variations uncovered by the Human Genome Project are a useful addition to the clinician’s traditional assessment of family history and the application of standard risk factor profiles such as the Framingham Risk Score. We propose that the continued analysis and characterization of genomic variations will identify genetic markers that enhance the ability of clinicians to identify high-risk individuals with increased susceptibility to atherothrombotic vascular complications, as well as those individuals who are most likely to benefit from targeted therapeutic intervention.

Genetic Markers: Approaches to Defining Cardiovascular Disease Susceptibility
Genetic markers are variants in the DNA code (known as alleles) that, alone or in combination, are associated with a specific disease phenotype. Markers whose presence confers a high level of probability of disease (a "high predictive value") would be most useful as diagnostic tools or as predictors of prognosis or response to therapy. Even markers whose effects are modest may provide important clues to disease pathophysiology or suggest new avenues of therapeutic intervention. A marker may have functional consequences, such as altering the expression or function of a gene that directly contributes to development of disease. Alternatively, a marker may have no functional consequences but may be located near a functional variant such that both the marker and variant tend to be inherited together in the population at large; this is known as linkage disequilibrium. The latter type of marker has usefulness not only in disease prediction but also for eventual isolation of the direct functional variant. Single nucleotide polymorphisms (SNPs, or variants at a single DNA base pair) have received much attention as potential genetic markers. They have the advantage of a high frequency in the human genome (1 occurs every 1000 nucleotides, on average) and are relatively easy to genotype using current technologies.

Novel genetic markers for common cardiovascular diseases are reported on a regular basis. In time, this information will contribute to an understanding of the inheritance and pathophysiology of these conditions, allowing for improved prognosis, prevention, and treatment (Table 2). Association studies aim to demonstrate that a particular allele or genetic marker (typically an SNP) is a significant risk factor for a phenotype of interest (Table 3). An advantage of association studies is that they can be performed using either family or case-control material. To date, most association studies have operated under a specific hypothesis; in other words, they test for a predetermined gene or set of genes (referred to as candidate genes). A problem with many published association studies is that a positive association observed in one report is often not reproduced in subsequent studies. Contributing factors include inconsistently defined phenotypes, small sample sizes, studies conducted in different ethnic groups (SNP allele frequencies can differ widely in different populations), false-positive and false-negative associations, and population stratification.


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  		TABLE 2. Use of Genetic Markers


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  		TABLE 3. Association Versus Linkage Studies

Thus, our current challenge is to use SNPs more effectively as genetic markers. One approach is to identify groups of markers traveling together in families and populations. These groups are known as haplotypes (a set of variants occurring together on one chromosome).101 The human genome can be organized into regions of limited diversity, termed haplotype blocks.102 Haplotype blocks typically extend for several thousand nucleotides and are thought to encompass global variation across genes. Only a few SNPs are needed to characterize the most common haplotypes occurring within each haplotype block; these are termed haplotype tag-SNPs.103 Thus, association studies would ideally be performed using haplotypes that characterize the majority of chromosomes in a population. At present though, haplotype tag-SNPs have only been discovered for a small number of genes. In the future, because projects such as HAP-MAP and the National Heart, Lung, and Blood Institute (NHLBI) Programs in Genomic Applications (PGA) delineate these tag-SNPs, haplotype-based association analysis is likely to replace single SNP-based analysis. It is anticipated that as high-throughput genotyping continues to undergo technological advances that lower the cost per genotype and improve efficiency, it will become feasible to move beyond single candidate gene analyses to conduct genome-wide association studies using tag-SNPs. The potential usefulness of this approach was recently demonstrated by a study defining genes that increase susceptibility to myocardial infarction (MI).104

Linkage studies are often used in analyses of the entire genome to identify chromosomal regions (referred to as loci) that may harbor genes responsible for a particular disease (Table 3). They differ from association studies in that they require family data to detect chromosomal regions that are inherited nonrandomly in relation to the disease of interest. Linkage studies have the advantage that no a priori knowledge is required of the underlying genetic determinants of a disease. However, the regions identified by linkage studies typically span millions of base pairs or hundreds of genes; association studies are then used to determine whether the genes under a positive linkage signal are associated with the disease being studied. This is a laborious process, but one that has been successful in a few cases. Genome-wide association studies have been proposed as well, although because they rely on hundreds of thousands of markers, cost may be prohibitive even when the markers become available. It is hoped that identification of haplotype tag-SNPs for the whole genome will make this type of study feasible.103

An important determinant of the success of linkage and association studies is the characterization of the disease or trait (ie, the phenotype). This is particularly relevant for common diseases. For example, hypertension is arbitrarily defined as a blood pressure >140/90 mm Hg, but this is a rather crude phenotype likely to include substantial heterogeneity in cause and disease course and may confound genetic analysis. Genome scans of linkage for hypertension have been relatively unilluminating to date.105 A rigorous, reproducible definition of the study phenotype is critical; this may be achieved via detailed physiological testing. For example, insulin sensitivity or insulin resistance is often estimated using fasting glucose, fasting insulin, or calculations based on these measurements, which are convenient to obtain in population samples and have been successfully used to reveal novel hypertension loci.106 However, even these simple measurements are less reliable than the direct quantification of insulin sensitivity, using physiological studies such as the frequently sampled intravenous glucose tolerance test or the euglycemic clamp.107 This suggests that the precision of the physiological study is of key importance when measuring the phenotype. Such detailed phenotyping can elucidate physiological relationships, such as between blood pressure and insulin resistance.108 For example, use of the euglycemic clamp combined with the haplotype approach described has enabled identification of the lipoprotein lipase gene as a gene for insulin resistance.62 Other detailed phenotypes include the aldosterone response to angiotensin II infusion (the modulating/nonmodulating phenotype) in hypertension49 and LDL particle size in dyslipidemia.40,41

Twin studies have traditionally been considered the "gold standard" of human genetic analyses. These studies may consider either identical ("monozygotic") twins, who share an identical genotype, or fraternal ("dizygotic") twins, who share half of their genes, on average. Phenotypic differences in identical twins indicate causation by environmental factors. Similarly, fraternal twins can be used to study small differences in genetic factors, because they are often exposed to similar environmental influences (especially as children). Twin studies have yielded important insights into the genetic basis of human cardiovascular disorders. This method first demonstrated that blood pressure is heritable; twin studies were also used to examine specific angiotensin-converting enzyme (ACE) polymorphisms in cardiac hypertrophy and MI, and to explore the role of peroxisome proliferator-activated receptor (PPAR) and other genes in human obesity.109

Another advance is the use of intermediate phenotypes, which are early, subclinical phenotypes that occur over the natural course of disease progression.1 Detailed physiological assessments can serve as intermediate phenotypes. For example, endothelial dysfunction is an intermediate phenotype for atherosclerosis. The analysis of intermediate stages of disease pathogenesis is advantageous because such stages can be detected in younger, healthy subjects who are not yet experiencing clinical symptoms. In addition, the disease process itself often induces secondary changes in phenotype that can obscure linkage or association. Thus, use of intermediate phenotypes brings us further along the pathophysiologic pathway toward the discovery of causative genes.

Lessons From Monogenic Cardiovascular Diseases
Substantial progress has been made in the genetic characterization of uncommon, monogenic cardiovascular disorders that result in hyperlipidemia, hypertension, ventricular hypertrophy, and sudden death.110–113 Discovering the molecular basis of these rare but dramatic examples of heritable cardiovascular disease has provided new insights into the overall pathogenesis of cardiovascular disease.

The seminal work of Brown and Goldstein114 in characterizing the molecular basis of familial hypercholesterolemia was fundamental in establishing the pathogenic link between perturbations in cholesterol metabolism, atherosclerosis, and coronary heart disease. The identification of mutations in the LDL cholesterol receptor as the basis of this disorder led to the characterization of HMG CoA reductase as a key rate-limiting step in the regulation of cholesterol metabolism. This insight set the stage for the development of HMG CoA reductase inhibitors (statins) that have transformed our treatment of atherosclerotic vascular disease.115 Moreover, the ongoing characterization of various monogenic dyslipidemias (eg, Tangier disease, sitosterolemia) has further advanced our understanding of various aspects of cholesterol transport and metabolism.116,117 The pathogenic descriptions of key metabolic pathways provided by the study of monogenic lipid disorders should enable the definition of genetic markers that are relevant to the more common, polygenic forms of dyslipidemia.

Characterization of the molecular basis of familial hypertrophic cardiomyopathy has also yielded important insights into the fundamental pathogenesis of cardiac hypertrophy.118 Although the clinical characterization of the phenotype focuses on a common feature of ventricular hypertrophy, it is now clear that the same clinical end point can result from dozens of different mutations in 10 different sarcomeric proteins. Conversely, a single sarcomere mutation can trigger a variety of different clinical phenotypes among different kindreds.119 These observations suggest that other modifier genes, as well as environmental cues, influence the clinical phenotype of monogenic disorders or acquired comorbidities. This discovery provides important lessons for appreciating the underlying genetic heterogeneity of an apparently simple clinical phenotype such as ventricular hypertrophy. In addition, it underscores the significance of the complex gene–gene and gene–environment interactions as additional determinants of the clinical manifestations of a genetic factor.

The analysis of monogenic forms of hypertension has also yielded important pathogenic insights.120 The systematic analysis of families with either elevated or decreased blood pressure has defined at least 10 single-gene disorders. It is intriguing that the molecular characterization of these disorders supports the longstanding Guytonian model of blood pressure regulation and sodium homeostasis that focuses on the kidney as a central pathophysiologic element. In general, most monogenic disorders that perturb blood pressure homeostasis involve ion channels in the kidney that mediate sodium reabsorption or humoral pathways, which in turn modulate the expression or activity of mineralocorticoids that modulate sodium reabsorption via renal tubular ion channels. These findings reinforce the concept that genetic markers of hypertension susceptibility may include candidate genes involved in the physiological pathways of sodium homeostasis, such as the renin-angiotensin-aldosterone system or ion transport pathways.

Genes responsible for monogenic syndromes may also play a role in more common forms of cardiovascular disease. In combination with other alleles, subtle mutations in these genes may cause susceptibility to polygenic diseases and promote disease development. Moreover, defining these pathogenic pathways may uncover additional genes that contribute to the phenotypes of polygenic cardiovascular diseases.

Genetic Markers in Complex Multigenic Cardiovascular Disease
Progress in defining the genetic basis of complex disease traits such as hypertension, dyslipidemia, and atherosclerosis is hampered by the complexity of the pathobiology and the relative insensitivity of the methods used in classic genetics to identify monogenic disorders. As noted, there have been >1000 published associations using 1 or 2 SNPs in 1 or 2 candidate genes to assess an association between a genetic variant and a cardiovascular end point such as hypertension, lipid levels, ventricular hypertrophy, or coronary heart disease. Unfortunately, many of these studies had substantial design flaws, lacked adequate statistical power, and were often not reproducible in subsequent studies, which failed to confirm the putative association.121 There are some constraints when attempting to detect a significant correlation between a given SNP and a specific disease state; the association must be strong and/or the allele of interest must occur with relatively high frequency. Rare SNPs would need to represent very strong disease "risk factors" to be detected, but these types of alleles would typically already have been detected using linkage analysis. Therefore, common SNPs are more useful to a candidate gene study, but this has resulted in the omission of severe yet uncommon variants. Furthermore, many studies used 1 or 2 SNPs rather than haplotypes defined on the basis of linkage disequilibrium, with the result that the study failed to adequately assess the contribution of that particular candidate gene locus. In addition, it should be expected that for complex traits such as atherothrombotic disease, the relative contribution to disease susceptibility of a set of genetic markers at a candidate locus is likely to be modest. Therefore, it is not surprising that many studies to date have yielded only equivocal results.

As the field evolves, investigators will likely begin to benefit from the lessons learned from the first generation of association studies of complex traits. The future lies in studies involving clinical networks with shared phenotyping protocols that will allow for large-scale, case-control studies, with well-defined populations, using new high-throughput genotyping technologies. The development of tagged haplotypes for candidate loci across the genome, along with advances in biostatistics and bioinformatics, will facilitate the assessment of the relative contribution of genetic markers to disease susceptibility in complex multigenic cardiovascular disease.

Recent studies are beginning to explore this experimental approach in cardiovascular medicine. For example, investigators in Japan genotyped 112 polymorphisms from 71 candidate genes in 2819 patients with MI and 2242 unrelated controls.122 Variants in 3 genes (connexin 37, plasminogen-activator inhibitor 1, and stromelysin-1) were associated with increased risk of MI. The authors suggested that genotyping these variants could prove useful in risk stratification for preventive therapy. A different study examining 62 candidate genes in premature MI identified variants in 3 members of the thrombospondin gene family as risk markers.123 As a significant body of reproducible data accumulates, these genetic markers may find their way to clinical application for assessment of cardiovascular disease risk (Table 1).

Pharmacogenetics: Genetic Markers That Guide Therapeutic Strategies
The markers discussed predict disease risk. In contrast, pharmacogenetics involves the study of genetic variants that alter response to drug therapy. Functional variants in genes that encode drug-metabolizing enzymes, drug transporters, and drug targets are capable of influencing the pharmacodynamics of a drug and can account for clinically evident variations in drug response among individual patients. In addition, genetic variation can influence the pattern of expression or functionality of proteins that affect the disease process such that the therapeutic impact of an intervention is different in various patient subsets. For example, variants in adrenergic receptors may influence the extent of neurohumoral activation that occurs in congestive heart failure124 as well as modulate the patient’s responsiveness to ß-receptor blockade.125 Although in the past much of the focus has been on drug toxicity, an emerging goal of pharmacogenetic studies is to elucidate genetic variants that affect drug efficacy, allowing more rational selection of patients for specific drug therapies. The overwhelming prevalence of cardiovascular disease in Western nations points to the potential importance of pharmacogenetics. With millions of people at risk, pharmacogenetic information has the potential to provide great benefit in terms of maximizing drug efficacy and decreasing drug-related morbidity, which can be substantial with cardiovascular agents.

In the past several years, studies have been conducted to address the pharmacogenetics of cardiovascular disease and risk factor modification. An example is that of lipid-lowering therapy with statins, to which many subjects do not adequately respond. For example, a Taq1 polymorphism in intron 1 of the cholesterol ester transfer protein gene has been observed to alter the coronary angiographic response to pravastatin.126 The Lipoprotein and Coronary Atherosclerosis Study demonstrated that subjects homozygous for the deletion (D) allele of the intron 6 insertion/deletion (I/D) polymorphism in the ACE gene exhibited a more favorable lipid response and angiographic regression of coronary atherosclerosis in response to treatment with fluvastatin.127 In another study, homozygosity for the D allele of the ACE gene was associated with an improved antihypertensive response to ACE inhibitor therapy.128 The I/D polymorphism also has been shown to influence blood pressure response to hydrochlorothiazide.129 Another example is the estrogen receptor-{alpha} gene, in which an intronic polymorphism was associated with differential response of HDL cholesterol and soluble E-selectin levels, 2 factors that would be expected to affect atherogenesis.130,131 Several studies have also shown that apolipoprotein E polymorphisms influence lipid responses.132,133 In coming years, the combination of genetic markers that predict disease risk with those that predict treatment response will have potentially great impact on the prevention and treatment of cardiovascular disease and thus become an integral component of cardiovascular medicine.

Genomics, Proteomics, and Molecular Signatures of Cardiovascular Disease
The complete sequencing of the genomes of multiple species, from yeast to earthworms to humans, has alerted researchers to the substantial degree of genomic variation in certain chromosomal regions and the striking cross-species similarity of other regions. However, the functional significance of this genomic variation remains to be defined. This information has also allowed researchers to conduct in-depth quantitative and statistical analysis of genomic data, as well as to classify genes by putative function. Expressed sequence tag (EST) technology has been used to compile a comprehensive, annotated inventory of gene expression in the human cardiovascular system,134 illustrating the potential of genomic studies to detect biomarkers of human cardiovascular disease. Subsequent studies established that up to 27 000 distinct genes are expressed in this system135 and that cardiovascular-related genes form clusters at specific chromosomal locations.136 Furthermore, various methods are now available to quantitate and analyze mRNA expression on a genome-wide basis, such as DNA microarrays and serial analysis of gene expression (SAGE). The coding sequences of the entire human genome can be arrayed on one chip the size of a credit card for rapid, high-throughput analysis. Global analysis of gene expression represents a quantum leap beyond the traditional 1-molecule-at-a-time approach used by molecular biologists as recently as a decade ago. This fundamental shift in experimental approach provides the means to elucidate how scores of genes comprise "networks" that orchestrate complex biological processes within the organism. This strategy has already yielded new insights into metabolic pathways of simple organisms such as yeast.137 In addition, global characterization of gene expression profiles has revealed subtleties in the molecular phenotypes of diseases, which has enabled differentiation of various forms of cardiomyopathy,138 better characterization of tumor subtypes, and reclassification of certain leukemias according to different prognostic implications,139 among others.

This technology holds great promise for further clarifying the molecular pathways involved in the pathogenesis of cardiovascular disease and will facilitate the identification of novel drug targets and expand the future therapeutic armamentarium. For example, it is hypothesized that angiotensin-II activates several signaling pathways that promote atherothrombosis and end-stage coronary heart disease. It is now possible to begin to identify novel angiotensin-II target genes that play a critical role in mediating its "cardiotoxic" effects.140 Similarly, recent studies have used genomic analyses to distinguish between various forms of congestive heart failure as well as characterize the distinctive molecular features of a restenosis lesion relative to a normal coronary artery.141,142 In another approach, a zebrafish model and a zebrafish-specific microarray based mostly on cardiac genes was used to study gene expression at various stages of embryonic development and in response to hypoxia.143,144

If many genes are monitored in parallel (eg, using microarray technology), their combined expression patterns may constitute a "signature" that identifies a specific disease (ie, clusters of genes are overexpressed or underexpressed). Using this approach, researchers may choose to study the entire genome, or they may wish to scan a subset of genes (eg, apoptotic pathways) that are particularly relevant to the disease, using customized microarrays containing these genes. These arrays have been used to study RNA derived from tissue biopsy samples or circulating blood cells. Bioinformatic analysis of microarray data can reveal clusters of biomarker genes that signify a healthy or diseased state, and the results can then be validated by real-time RT-PCR and/or Taqman analysis, as well as proteomic techniques. Since the introduction of microarrays in the late 1990s, this procedure has been used to identify biomarkers and expression profiles for a wide variety of diseases. We constructed a customized cardiac-specific "Cardiochip" microarray, based on >10 000 distinct transcripts derived from cardiac cDNA libraries,145 that was subsequently used to compile gene expression profiles of dilated and hypertrophic cardiomyopathy.138,141

These genomic analyses of mRNA expression provide an unbiased, hypothesis-generating approach for the discovery of novel pathogenic mechanisms of disease. This analysis of global patterns of gene expression complements the linkage analyses and association studies described earlier. Indeed, it is conceivable that new candidate genes will be defined initially on the basis of their differential expression in a disease state and subsequently be determined to be new genetic susceptibility markers. This approach is particularly suited to the study of complex polygenic diseases, because it allows researchers to take a genomic "snapshot" at each progressive stage of the disease. For example, end-stage heart failure is a multifaceted disorder that can be induced by a wide variety of initial events that may be inflammatory or extramyocardial in nature; it can also occur secondary to diabetes.146 By tracking the progression of heart failure from the initial event (eg, cardiomyopathy) to end-stage failure using genomic techniques, researchers can develop a better understanding of the molecular processes involved. Using this approach, we have proposed more than a dozen biomarkers of dilated cardiomyopathy (ANP, lumican, ITP 3-kinase, etc), hypertrophic cardiomyopathy (CD59, HSP90, calpain, etc), and end-stage heart failure (desmin, dynamitin, nucleolin, etc), respectively.138,141,145 This combined approach has also been successfully used in animal models of hypertension and has significant implications for the study of human disease.147

Another intriguing application of genomic technology is the ability to assay molecular signatures of peripheral cells (circulating inflammatory cells or progenitor cells) that may play a pathogenic role in plaque rupture or tissue repair in acute ischemic syndromes.148 As major elements of the body’s defense and system of tissue repair, they are the logical nexus and barometer of the body’s homeostasis as well as its response to noxious environmental stimuli.149,150 It is conceivable that this strategy may reveal new pathobiological pathways, therapeutic targets, or novel biomarkers of disease prognosis.

Most recently, studies have shown that circulating peripheral blood cells, which come into contact with all human tissues and contribute to homeostasis, are valuable sources of information on the state of health or disease in the body. Thus, researchers have begun using RNA derived from blood cells (rather than invasive biopsy samples) for the genomic analysis of polygenetic cardiovascular disorders. Coronary artery disease was characterized using blood cell transcripts,151 and this technique was further endorsed by 2 separate studies on cancer. Whitney et al,152 in their study of the expression profiles of blood cell RNA, found that gene expression variations among healthy individuals were significantly less prominent than the variations between healthy and cancerous patients. DePrimo et al153 used microarray technology to pinpoint 4 blood cell-derived biomarkers for colorectal cancer. The use of blood cell-derived RNA may represent an important advancement for genomic research, because it would enable larger sample sizes (including more "normal controls"), better matching of patients, more standardized collection procedures, and high-throughput analysis.

Although great attention has been paid to the genome and its functional characterization by assays of gene expression, the ultimate mediator of biological function resides at the protein level. Moreover, it is clear that although there are {approx}30 000 genes in the human genome, there are probably >120 000 proteins expressed within our bodies. Furthermore, the functionality of each protein can be dramatically altered by posttranslational modifications such as phosphorylation, oxidation, glycation, ubiquination, or nitrosylation. The functional significance of a protein is not only affected by its level of expression but also highly dependent on protein–protein interactions as well as its localization within specific subcellular domains. Thus, the analysis of the proteome represents the next logical quantum leap beyond functional genomics. Although the technology for assessing the proteome is in its infancy relative to genome technology, new advances in mass spectroscopy and molecular imaging have great potential to further characterize the molecular networks that maintain normal cellular homeostasis or become perturbed in the context of human disease. The capacity to identify protein markers, coupled with the increasing sophistication of clinical imaging modalities such as magnetic resonance imaging (MRI) or positron emission technology (PET), could provide exciting new opportunities to diagnose the molecular signature of a plaque and assess its vulnerability for vascular complications. The integration of high-throughput systems biology has the potential to translate insights gained from DNA microarrays and proteomic analysis to new diagnostic tools relevant to cardiovascular medicine in the 21st century. We predict that within the next decade, biomarkers identifying high-risk cardiovascular patients will be discovered in clinical and epidemiologic studies that make use of these new genomic and proteomic platforms.


*    References
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 up arrowIntroduction
 *References
 

  1. King RA, Rotter JI, Motulsky AG, eds. The Genetic Basis of Common Diseases. 2nd ed. New York: Oxford University Press, Inc; 2002.
  2. Strauss E, Waliszewski K, Gabriel M, et al. Increased risk of the abdominal aortic aneurysm in carriers of the MTHFR 677T allele. J Appl Genet. 2003; 44: 85–93.[Medline] [Order article via Infotrieve]
  3. Schillinger M, Exner M, Mlekusch W, et al. Heme oxygenase-1 gene promoter polymorphism is associated with abdominal aortic aneurysm. Thromb Res. 2002; 106: 131–136.[CrossRef][Medline] [Order article via Infotrieve]
  4. Gardeman NA, Stricker J, Humme J, et al. Angiotensinogen T174M and M235T gene polymorphisms are associated with the extent of coronary atherosclerosis. Atherosclerosis. 1999; 145: 309–314.[CrossRef][Medline] [Order article via Infotrieve]
  5. Uzui H, Harpf A, Liu M, et al. Increased expression of membrane type 3-matrix metalloproteinase in human atherosclerotic plaque: role of activated macrophages and inflammatory cytokines. Circulation. 2002; 106: 3024–3030.[Abstract/Free Full Text]
  6. Yoon S, Kuivaniemi H, Gatalica Z, et al. MMP13 promoter polymorphism is associated with atherosclerosis in the abdominal aorta of young black males. Matrix Biol. 2002; 21: 487–498.[CrossRef][Medline] [Order article via Infotrieve]
  7. Davignon J, Gregg RE, Sing CF. Apolipoprotein E polymorphism and atherosclerosis. Arteriosclerosis. 1988; 8: 1–21.[Abstract/Free Full Text]
  8. Mackness B, Hunt R, Durrington PN, et al. Increased immunolocalization of paraoxonase, clusterin, and apolipoprotein A-I in the human artery wall with the progression of atherosclerosis. Arterioscler Thromb Vasc Biol. 1997; 17: 1233–1238.[Abstract/Free Full Text]
  9. Haddy N, Sass C, Droesch S, et al. IL-6, TNF-alpha and atherosclerosis risk indicators in a healthy family population: the STANISLAS cohort. Atherosclerosis. 2003; 170: 277–283.[CrossRef][Medline] [Order article via Infotrieve]
  10. Stavljenic-Rukavina A. Genetics of cardiovascular disease. J Internat Fed Clin Chem. Available at http://www.ifcc.org/ejifcc/vol14no2/140206200305n.htm.
  11. Morange PE, Juhan-Vague I, Scarabin PY, et al. Association between TAFI antigen and Ala147Thr polymorphism of the TAFI gene and the angina pectoris incidence. The PRIME Study (Prospective Epidemiological Study of MI). Thromb Haemost. 2003; 89: 554–560.[Medline] [Order article via Infotrieve]
  12. Rook MB, Bezzina Alshinawi C, Groenewegen WA, et al. Human SCN5A gene mutations alter cardiac sodium channel kinetics and are associated with the Brugada syndrome. Cardiovasc Res. 1999; 44: 507–517.[Abstract/Free Full Text]
  13. Ortlepp JR, Hoffmann R, Ohme F, et al. The vitamin D receptor genotype predisposes to the development of calcific aortic valve stenosis. Heart. 2001; 85: 635–638.[Abstract/Free Full Text]
  14. Wang Q, Shen J, Splawski I, et al. SCN5A mutations associated with an inherited cardiac arrhythmia, long QT syndrome. Cell. 1995; 80: 805–811.[CrossRef][Medline] [Order article via Infotrieve]
  15. Patel R, Lim DS, Reddy D, et al. Variants of trophic factors and expression of cardiac hypertrophy in patients with hypertrophic cardiomyopathy. J Mol Cell Cardiol. 2000; 32: 2369–2377.[CrossRef][Medline] [Order article via Infotrieve]
  16. Rauramaa R, Vaisanen SB, Luong LA, et al. Stromelysin-1 and interleukin-6 gene promoter polymorphisms are determinants of asymptomatic carotid artery atherosclerosis. Arterioscler Thromb Vasc Biol. 2000; 20: 2657–2662.[Abstract/Free Full Text]
  17. Reis MM, Higuchi Mde L, Benvenuti LA, et al. An in situ quantitative immunohistochemical study of cytokines and IL-2R+ in chronic human chagasic myocarditis: correlation with the presence of myocardial Trypanosoma cruzi antigens. Clin Immunol Immunopathol. 1997; 83: 165–172.[CrossRef][Medline] [Order article via Infotrieve]
  18. Towbin JA, Bowles NE. The failing heart. Nature. 2002; 415: 227–233.[CrossRef][Medline] [Order article via Infotrieve]
  19. Niimura H, Patton KK, McKenna WJ, et al. Sarcomere protein gene mutations in hypertrophic cardiomyopathy of the elderly. Circulation. 2002; 105: 446–451.[Abstract/Free Full Text]
  20. Pereira AC, Cuoco MA, Mota GF, et al. Hemochromatosis gene variants in patients with cardiomyopathy. Am J Cardiol. 2001; 88: 388–391.[CrossRef][Medline] [Order article via Infotrieve]
  21. Tiago AD, Badenhorst D, Skudicky D, et al. An aldosterone synthase gene variant is associated with improvement in left ventricular ejection fraction in dilated cardiomyopathy. Cardiovasc Res. 2002; 54: 584–589.[Abstract/Free Full Text]
  22. Arimura T, Nakamura T, Hiroi S, et al. Characterization of the human nebulette gene: a polymorphism in an actin-binding motif is associated with nonfamilial idiopathic dilated cardiomyopathy. Hum Genet. 2000; 107: 440–451.[CrossRef][Medline] [Order article via Infotrieve]
  23. Yamada Y, Ichihara S, Izawa H, et al. Association of a G994 –> T (Val279 –> Phe) polymorphism of the plasma platelet-activating factor acetylhydrolase gene with myocardial damage in Japanese patients with nonfamilial hypertrophic cardiomyopathy. J Hum Genet. 2001; 46: 436–441.[CrossRef][Medline] [Order article via Infotrieve]
  24. Wang XL, Greco M, Sim AS, et al. Effect of CYP1A1 MspI polymorphism on cigarette smoking related coronary artery disease and diabetes. Atherosclerosis. 2002; 162: 391–397.[CrossRef][Medline] [Order article via Infotrieve]
  25. von Beckerath N, Schusterschitz Y, Koch W, et al. G protein beta 3 subunit 825T allele carriage and risk of coronary artery disease. Atherosclerosis. 2003; 167: 135–139.[CrossRef][Medline] [Order article via Infotrieve]
  26. Lin RC, Wang XL, Morris BJ. Association of coronary artery disease with glucocorticoid receptor N363S variant. Hypertension. 2003; 41: 404–407.[Abstract/Free Full Text]
  27. Chen Q, Reis SE, Kammerer CM, et al. Association between the severity of angiographic coronary artery disease and paraoxonase gene polymorphisms in the National Heart, Lung, and Blood Institute-sponsored Women’s Ischemia Syndrome Evaluation (WISE) study. Am J Hum Genet. 2003; 72: 13–22.[CrossRef][Medline] [Order article via Infotrieve]
  28. Nassar BA, Bevin LD, Johnstone DE, et al. Relationship of the Glu298Asp polymorphism of the endothelial nitric oxide synthase gene and early-onset coronary artery disease. Am Heart J. 2001; 142: 586–589.[CrossRef][Medline] [Order article via Infotrieve]
  29. Stepanov VA, Puzyrev VP, Karpov RS, et al. Genetic markers in coronary artery disease in a Russian population. Hum Biol. 1998; 70: 47–57.[Medline] [Order article via Infotrieve]
  30. Alvarez R, Gonzalez P, Batalla A, et al. Association between the NOS3 (-786 T/C) and the ACE (I/D) DNA genotypes and early coronary artery disease. Nitric Oxide. 2001; 5: 343–348.[CrossRef][Medline] [Order article via Infotrieve]
  31. Benes P, Muzik J, Benedik J, et al. The C766T low-density lipoprotein receptor related protein polymorphism and coronary artery disease, plasma lipoproteins, and longevity in the Czech population. J Mol Med. 2001; 79: 116–120.[CrossRef][Medline] [Order article via Infotrieve]
  32. Lacquemant C, Lepretre F, Pineda Torra I, et al. Mutation screening of the PPARalpha gene in type 2 diabetes associated with coronary heart disease. Diabetes Metab. 2000; 26: 393–401.[Medline] [Order article via Infotrieve]
  33. Packard CJ, O’Reilly DS, Caslake MJ, et al. Lipoprotein-associated phospholipase A2 as an independent predictor of coronary heart disease. West of Scotland Coronary Prevention Study Group. N Engl J Med. 2000; 343: 1148–1155.[Abstract/Free Full Text]
  34. Vickers MA, Green FR, Terry C, et al. Genotype at a promoter polymorphism of the interleukin-6 gene is associated with baseline levels of plasma C-reactive protein. Cardiovasc Res. 2002; 53: 1029–1034.[Abstract/Free Full Text]
  35. De Bacquer D, De Backer G, Langlois M, et al. Haptoglobin polymorphism as a risk factor for coronary heart disease mortality. Atherosclerosis. 2001; 157: 161–166.[CrossRef][Medline] [Order article via Infotrieve]
  36. Rasmussen ML, Folsom AR, Catellier DJ, et al. A prospective study of coronary heart disease and the hemochromatosis gene (HFE) C282Y mutation: the Atherosclerosis Risk in Communities (ARIC) study. Atherosclerosis. 2001; 154: 739–746.[CrossRef][Medline] [Order article via Infotrieve]
  37. Rodriguez-Perez JC, Rodriguez-Esparragon F, Hernandez-Perera O, et al. Association of angiotensinogen M235T and A(-6)G gene polymorphisms with coronary heart disease with independence of essential hypertension: the PROCAGENE study. Prospective Cardiac Gene. J Am Coll Cardiol. 2001; 37: 1536–1542.[Abstract/Free Full Text]
  38. Higashi K, Ishikawa T, Ito T, et al. Association of a genetic variation in the beta 3-adrenergic receptor gene with coronary heart disease among Japanese. Biochem Biophys Res Commun. 1997; 232: 728–730.[CrossRef][Medline] [Order article via Infotrieve]
  39. Morrison AC, Bray MS, Folsom AR, et al. ADD1 460W allele associated with cardiovascular disease in hypertensive individuals. Hypertension. 2002; 39: 1053–1057.[Abstract/Free Full Text]
  40. Rotter JI, Bu X, Cantor RM, et al. Multilocus genetic determinants of LDL particle size in coronary artery disease families. Am J Hum Genet. 1996; 58: 585–594.[Medline] [Order article via Infotrieve]
  41. Allayee H, Aouizerat BE, Cantor RM, et al. Families with familial combined hyperlipidemia and families enriched for coronary artery disease share genetic determinants for the atherogenic lipoprotein phenotype. Am J Hum Genet. 1998; 63: 577–585.[CrossRef][Medline] [Order article via Infotrieve]
  42. Ferrarotti I, Zorzetto M, Beccaria M, et al. Tumour necrosis factor family genes in a phenotype of COPD associated with emphysema. Eur Respir J. 2003; 21: 444–449.[Abstract/Free Full Text]
  43. Holweg CT, Baan CC, Niesters HG, et al. TGF-beta1 gene polymorphisms in patients with end-stage heart failure. J Heart Lung Transplant. 2001; 20: 979–984.[CrossRef][Medline] [Order article via Infotrieve]
  44. Cho Y, Somer BG, Amatya A. Natriuretic peptides and their therapeutic potential. Heart Dis. 1999; 1: 305–328.[Medline] [Order article via Infotrieve]
  45. Lovati E, Richard A, Frey BM, et al. Genetic polymorphisms of the renin-angiotensin-aldosterone system in end-stage renal disease. Kidney Int. 2001; 60: 46–54.[CrossRef][Medline] [Order article via Infotrieve]
  46. Rossi GP, Taddei S, Virdis A, et al. The T-786C and Glu298Asp polymorphisms of the endothelial nitric oxide gene affect the forearm blood flow responses of Caucasian hypertensive patients. J Am Coll Cardiol. 2003; 41: 938–945.[Abstract/Free Full Text]
  47. Zhu H, Sagnella GA, Dong Y, et al. Contrasting associations between aldosterone synthase gene polymorphisms and essential hypertension in blacks and in whites. J Hypertens. 2003; 21: 87–95.[CrossRef][Medline] [Order article via Infotrieve]
  48. Abe M, Nakura J, Yamamoto M, et al. Association of GNAS1 gene variant with hypertension depending on smoking status. Hypertension. 2002; 40: 261–265.[Abstract/Free Full Text]
  49. Kosachunhanun N, Hunt SC, Hopkins PN, et al. Genetic determinants of nonmodulating hypertension. Hypertension. 2003; 42: 901–908.[Abstract/Free Full Text]
  50. Tan KT, Dempsey A, Liew CC. Cardiac genes and gene databases for cardiovascular disease genetics. Curr Hypertens Rep. 1999; 1: 51–58.[Medline] [Order article via Infotrieve]
  51. Jia H, Hingorani AD, Sharma P, et al. Association of the G(s)alpha gene with essential hypertension and response to beta-blockade. Hypertension. 1999; 34: 8–14.[Abstract/Free Full Text]
  52. Chiang FT, Chiu KC, Tseng YZ, et al. Nucleotide(-258) G-to-A transition variant of the liver glucokinase gene is associated with essential hypertension. Am J Hypertens. 1997; 10: 1049–1052.[CrossRef][Medline] [Order article via Infotrieve]
  53. Baker EH, Dong YB, Sagnella GA, et al. Association of hypertension with T594M mutation in beta subunit of epithelial sodium channels in black people resident in London. Lancet. 1998; 351: 1388–1392.[CrossRef][Medline] [Order article via Infotrieve]
  54. Sharma P, Hingorani A, Jia H, et al. Positive association of tyrosine hydroxylase microsatellite marker to essential hypertension. Hypertension. 1998; 32: 676–682.[Abstract/Free Full Text]
  55. Sharma P, Hingorani A, Jia H, et al. Quantitative association between a newly identified molecular variant in the endothelin-2 gene and human essential hypertension. J Hypertens. 1999; 17: 1281–1287.[CrossRef][Medline] [Order article via Infotrieve]
  56. Stevens PA, Brown MJ. Genetic variability of the ET-1 and the ETA receptor genes in essential hypertension. J Cardiovasc Pharmacol. 1995; 26: S9–S12.
  57. Stankovic A, Zivkovic M, Glisic S, et al. Angiotensin II type 1 receptor gene polymorphism and essential hypertension in Serbian population. Clin Chim Acta. 2003; 327: 181–185.[CrossRef][Medline] [Order article via Infotrieve]
  58. Koschinsky ML, Boffa MB, Nesheim ME, et al. Association of a single nucleotide polymorphism in CPB2 encoding the thrombin-activatable fibrinolysis inhibitor (TAF1) with blood pressure. Clin Genet. 2001; 60: 345–349.[CrossRef][Medline] [Order article via Infotrieve]
  59. Ogawa S, Emi M, Shiraki M, et al. Association of estrogen receptor beta (ESR2) gene polymorphism with blood pressure. J Hum Genet. 2000; 45: 327–330.[CrossRef][Medline] [Order article via Infotrieve]
  60. Rutherford S, Johnson MP, Curtain RP, et al. Chromosome 17 and the inducible nitric oxide synthase gene in human essential hypertension. Hum Genet. 2001; 109: 408–415.[CrossRef][Medline] [Order article via Infotrieve]
  61. Wascher TC, Paulweber B, Malaimare L, et al. Associations of a human G protein beta3 subunit dimorphism with insulin resistance and carotid atherosclerosis. Stroke. 2003; 34: 605–609.[Abstract/Free Full Text]
  62. Goodarzi MO, Guo X, Taylor KD, et al. Lipoprotein lipase is a gene for insulin resistance in Mexican-Americans. Diabetes. 2004; 53: 214–220.[Abstract/Free Full Text]
  63. Ryan AS, Nicklas BJ, Berman DM, et al. The insertion/deletion polymorphism of the ACE gene is related to insulin sensitivity in overweight women. Diabetes Care. 2001; 24: 1646–1652.[Abstract/Free Full Text]
  64. Kubaszek A, Pihlajamaki J, Karhapaa P, et al. The K121Q polymorphism of the PC-1 gene is associated with insulin resistance but not with dyslipidemia. Diabetes Care. 2003; 26: 464–467.[Abstract/Free Full Text]
  65. Ohtoshi K, Yamasaki Y, Gorogawa S, et al. Association of (-)786T-C mutation of endothelial nitric oxide synthase gene with insulin resistance. Diabetologia. 2002; 45: 1594–1601.[CrossRef][Medline] [Order article via Infotrieve]
  66. Tan MS, Chang SY, Chang DM, et al. Association of resistin gene 3'-untranslated region +62G–>A polymorphism with type 2 diabetes and hypertension in a Chinese population. J Clin Endocrinol Metab. 2003; 88: 1258–1263.[Abstract/Free Full Text]
  67. Biggart S, Chin D, Fauchon M, et al. Association of genetic polymorphisms in the ACE, ApoE, and TGF beta genes with early onset ischemic heart disease. Clin Cardiol. 1998; 21: 831–836.[Medline] [Order article via Infotrieve]
  68. Kanters JK, Larsen LA, Orholm M, et al. Novel donor splice site mutation in the KVLQT1 gene is associated with long QT syndrome. J Cardiovasc Electrophysiol. 1998; 9: 620–624.[Medline] [Order article via Infotrieve]
  69. Terashima M, Akita H, Kanazawa K, et al. Stromelysin promoter 5A/6A polymorphism is associated with acute myocardial infarction. Circulation. 1999; 99: 2717–2719.[Abstract/Free Full Text]
  70. Holmer SR, Hengstenberg C, Mayer B, et al. Lipoprotein lipase gene polymorphism, cholesterol subfractions and myocardial infarction in large samples of the general population. Cardiovasc Res. 2000; 47: 806–812.[Abstract/Free Full Text]
  71. Gonzalez P, Alvarez R, Batalla A, et al. Genetic variation at the chemokine receptors CCR5/CCR2 in myocardial infarction. Genes Immun. 2001; 2: 191–195.[CrossRef][Medline] [Order article via Infotrieve]
  72. Tybjaerg-Hansen A, Nordestgaard BG, Gerdes LU, et al. Variation of apolipoprotein B gene is associated with myocardial infarction and lipoprotein levels in Danes. Atherosclerosis. 1991; 89: 69–81.[CrossRef][Medline] [Order article via Infotrieve]
  73. Batalla A, Alvarez R, Reguero JR, et al. Synergistic effect between apolipoprotein E and angiotensinogen gene polymorphisms in the risk for early myocardial infarction. Clin Chem. 2000; 46: 1910–1915.[Abstract/Free Full Text]
  74. Margaglione M, Cappucci G, Colaizzo D, et al. C-reactive protein in offspring is associated with the occurrence of myocardial infarction in first-degree relatives. Arterioscler Thromb Vasc Biol. 2000; 20: 198–203.[Abstract/Free Full Text]
  75. Zito F, Di Castelnuovo A, Amore C, et al. Bcl I polymorphism in the fibrinogen beta-chain gene is associated with the risk of familial myocardial infarction by increasing plasma fibrinogen levels. A case-control study in a sample of GISSI-2 patients. Arterioscler Thromb Vasc Biol. 1997; 17: 3489–3494.[Abstract/Free Full Text]
  76. Ireland H, Kunz G, Kyriakoulis K, et al. Thrombomodulin gene mutations associated with myocardial infarction. Circulation. 1997; 96: 15–18.[Abstract/Free Full Text]
  77. Sasaoka T, Kimura A, Hohta SA, et al. Polymorphisms in the platelet-endothelial cell adhesion molecule-1 (PECAM-1) gene, Asn563Ser and Gly670Arg, associated with myocardial infarction in the Japanese. Ann N Y Acad Sci. 2001; 947: 259–269.[Abstract/Free Full Text]
  78. Bolla MK, Miller GJ, Yellon DM, et al. Analysis of the association of a heat shock protein70–1 gene promoter polymorphism with myocardial infarction and coronary risk traits. Dis Markers. 1998; 13: 227–235.[Medline] [Order article via Infotrieve]
  79. Tregouet DA, Barbaux S, Escolano S, et al. Specific haplotypes of the P-selectin gene are associated with myocardial infarction. Hum Mol Genet. 2002; 11: 2015–2023.[Abstract/Free Full Text]
  80. Unkelbach K, Gardemann A, Kostrzewa M, et al. A new promoter polymorphism in the gene of lipopolysaccharide receptor CD14 is associated with expired myocardial infarction in patients with low atherosclerotic risk profile. Arterioscler Thromb Vasc Biol. 1999; 19: 932–938.[Abstract/Free Full Text]
  81. Nikitin AG, Gorashko NM, Minushkina LO, et al. Association of the G7831A polymorphism of the ACE gene with myocardial ischemia in the Moscow population. Mol Biol (Mosk). 2003; 37: 54–56.
  82. Babunova NB, Minushkina LO, Zateishchikov DA, et al. Association of the T174M and M235T polymorphisms of the angiotensinogen gene with myocardial ischemia in the Russian population of the city of Moscow. Mol Biol (Mosk). 2003; 37: 57–60.
  83. Garenc C, Perusse L, Chagnon YC, et al. The alpha 2-adrenergic receptor gene and body fat content and distribution: the HERITAGE Family Study. Mol Med. 2002; 8: 88–94.[Medline] [Order article via Infotrieve]
  84. Mori Y, Kim-Motoyama H, Ito Y, et al. The Gln27Glu beta2-adrenergic receptor variant is associated with obesity due to subcutaneous fat accumulation in Japanese men. Biochem Biophys Res Commun. 1999; 258: 138–140.[CrossRef][Medline] [Order article via Infotrieve]
  85. Wauters M, Mertens I, Chagnon M, et al. Polymorphisms in the leptin receptor gene, body composition and fat distribution in overweight and obese women. Int J Obes Relat Metab Disord. 2001; 25: 714–720.[CrossRef][Medline] [Order article via Infotrieve]
  86. Havndrup O, Bundgaard H, Andersen PS, et al. A novel missense mutation, Leu390Val, in the cardiac beta-myosin heavy chain associated with pronounced septal hypertrophy in two families with hypertrophic cardiomyopathy. Scand Cardiovasc J. 2000; 34: 558–563.[CrossRef][Medline] [Order article via Infotrieve]
  87. Risley P, Jerrard-Dunne P, Sitzer M, et al. Promoter polymorphism in the endotoxin receptor (CD14) is associated with increased carotid atherosclerosis only in smokers: the Carotid Atherosclerosis Progression Study (CAPS). Stroke. 2003; 34: 600–604.[Abstract/Free Full Text]
  88. Hou L, Osei-Hyiaman D, Yu H, et al. Association of a 27-bp repeat polymorphism in ecNOS gene with ischemic stroke in Chinese patients. Neurology. 2001; 56: 490–496.[Abstract/Free Full Text]
  89. Gretarsdottir S, Thorleifsson G, Reynisdottir ST, et al. The gene encoding phosphodiesterase 4D confers risk of ischemic stroke. Nat Genet. 2003; 35: 131–138.[CrossRef][Medline] [Order article via Infotrieve]
  90. Ghilardi G, Biondi ML, DeMonti M, et al. Matrix metalloproteinase-1 and matrix metalloproteinase-3 gene promoter polymorphisms are associated with carotid artery stenosis. Stroke. 2002; 33: 2408–2412.[Abstract/Free Full Text]
  91. Nishiuma S, Kario K, Yakushijin K, et al. Genetic variation in the promoter region of the beta-fibrinogen gene is associated with ischemic stroke in a Japanese population. Blood Coagul Fibrinolysis. 1998; 9: 373–379.[Medline] [Order article via Infotrieve]
  92. Spence JD, Malinow MR, Barnett PA, et al. Plasma homocyst(e)ine concentration, but not MTHFR genotype, is associated with variation in carotid plaque area. Stroke. 1999; 30: 969–973.[Abstract/Free Full Text]
  93. Dai K, Gao W, Ruan C. The Sma I polymorphism in the von Willebrand factor gene associated with acute ischemic stroke. Thromb Res. 2001; 104: 389–395.[CrossRef][Medline] [Order article via Infotrieve]
  94. Tang DC, Prauner R, Liu W, et al. Polymorphisms within the angiotensinogen gene (GT-repeat) and the risk of stroke in pediatric patients with sickle cell disease: a case-control study. Am J Hematol. 2001; 68: 164–169.[CrossRef][Medline] [Order article via Infotrieve]
  95. Hessner MJ, Dinauer DM, Kwiatkowski R, et al. Age-dependent prevalence of vascular disease-associated polymorphisms among 2689 volunteer blood donors. Clin Chem. 2001; 47: 1879–1884.[Abstract/Free Full Text]
  96. Kuroki S, Ikeda U, Maeda Y, et al. Lack of association between the insertion/deletion polymorphism of the angiotensin-converting enzyme gene and vasospastic angina. Clin Cardiol. 1997; 20: 873–876.[Medline] [Order article via Infotrieve]
  97. Girerd X, Hanon O, Mourad JJ, et al. Lack of association between renin-angiotensin system, gene polymorphisms, and wall thickness of the radial and carotid arteries. Hypertension. 1998; 32: 579–583.[Abstract/Free Full Text]
  98. Chapman CM, Palmer LJ, McQuillan BM, et al. Polymorphisms in the angiotensinogen gene are associated with carotid intimal-medial thickening in females from a community-based population. Atherosclerosis. 2001; 159: 209–217.[CrossRef][Medline] [Order article via Infotrieve]
  99. Fortunato G, Rubba P, Panico S, et al. A paraoxonase gene polymorphism, PON 1 (55), as an independent risk factor for increased carotid intima-media thickness in middle-aged women. Atherosclerosis. 2003; 167: 141–148.[CrossRef][Medline] [Order article via Infotrieve]
  100. Dalloz F, Osinska H, Robbins J. Manipulating the contractile apparatus: genetically defined animal models of cardiovascular disease. J Mol Cell Cardiol. 2001; 33: 9–25.[CrossRef][Medline] [Order article via Infotrieve]
  101. Goodarzi MO, Guo X, Taylor KD, et al. Determination and use of haplotypes: ethnic comparison and association of the lipoprotein lipase gene and coronary artery disease in Mexican-Americans. Genet Med. 2003; 5: 322–327.[Medline] [Order article via Infotrieve]
  102. Gabriel SB, Schaffner SF, Nguyen H, et al. The structure of haplotype blocks in the human genome. Science. 2002; 296: 2225–2229.[Abstract/Free Full Text]
  103. Sebastiani P, Lazarus R, Weiss ST, et al. Minimal haplotype tagging. Proc Natl Acad Sci U S A. 2003; 100: 9900–9905.[Abstract/Free Full Text]
  104. Ozaki K, Ohnishi Y, Iida A, et al. Functional SNPs in the lymphotoxin-alpha gene that are associated with susceptibility to myocardial infarction. Nat Genet. 2002; 32: 650–654.[CrossRef][Medline] [Order article via Infotrieve]
  105. Province MA, Kardia SL, Ranade K, et al. A meta-analysis of genome-wide linkage scans for hypertension: the National Heart, Lung and Blood Institute Family Blood Pressure Program. Am J Hypertens. 2003; 16: 144–147.[CrossRef][Medline] [Order article via Infotrieve]
  106. Cheng LS, Davis RC, Raffel LJ, et al. Coincident linkage of fasting plasma insulin and blood pressure to chromosome 7q in hypertensive Hispanic families. Circulation. 2001; 104: 1255–1260.[Abstract/Free Full Text]
  107. Bergman RN, Zaccaro DJ, Watanabe RM, et al. Minimal model-based insulin sensitivity has greater heritability and a different genetic basis than homeostasis model assessment or fasting insulin. Diabetes. 2003; 52: 2168–2174.[Abstract/Free Full Text]
  108. Xiang AH, Azen SP, Raffel LJ, et al. Evidence for joint genetic control of insulin sensitivity and systolic blood pressure in Hispanic families with a hypertensive proband. Circulation. 2001; 103: 78–83.[Abstract/Free Full Text]
  109. Luft FC. Twins in cardiovascular genetic research. Hypertension. 2001; 37 (2 Part 2): 350–356.[Abstract/Free Full Text]
  110. Schunkert H. Molecular genetics of congestive heart failure. Scand Cardiovasc J Suppl. 1998; 47: 37–43.[Medline] [Order article via Infotrieve]
  111. Tybjaerg-Hansen A. Rare and common mutations in hyperlipidemia and atherosclerosis, with special reference to familial defective apolipoprotein B-100. Scand J Clin Lab Invest Suppl. 1995; 220: 57–76.[Medline] [Order article via Infotrieve]
  112. Crook ED. The genetics of human hypertension. Semin Nephrol. 2002;