Genetic Markers
2004; Lippincott Williams & Wilkins; Volume: 109; Issue: 25_suppl_1 Linguagem: Inglês
10.1161/01.cir.0000133440.86427.26
ISSN1524-4539
AutoresGary H. Gibbons, Choong Chin Liew, Mark O. Goodarzi, Jerome I. Rotter, Willa A. Hsueh, Helmy M. Siragy, Richard E. Pratt, Victor J. Dzau,
Tópico(s)Genetics and Neurodevelopmental Disorders
ResumoHomeCirculationVol. 109, No. 25_suppl_1Genetic Markers Free AccessResearch ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessResearch ArticlePDF/EPUBGenetic MarkersProgress and Potential for Cardiovascular Disease Gary H. Gibbons, Choong Chin Liew, Mark O. Goodarzi, Jerome I. Rotter, Willa A. Hsueh, Helmy M. Siragy, Richard Pratt and Victor J. Dzau Gary H. GibbonsGary H. Gibbons 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. , Choong Chin LiewChoong Chin Liew 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. , Mark O. GoodarziMark O. Goodarzi 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. , Jerome I. RotterJerome I. Rotter 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. , Willa A. HsuehWilla A. Hsueh 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. , Helmy M. SiragyHelmy M. Siragy 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. , Richard PrattRichard Pratt 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. and Victor J. DzauVictor J. Dzau 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. Originally published29 Jun 2004https://doi.org/10.1161/01.CIR.0000133440.86427.26Circulation. 2004;109:IV-47–IV-58The 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.1A 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.100TABLE 1. Selected Genes Associated With Cardiovascular Disease, Classified by PhenotypePhenotypeGene SymbolGene NameReferenceAMP indicates adenosine monophosphate; LDL, low-density lipoprotein; N/A, not available.Abdominal aortic aneurysmMTHFRMethylenetetrahydrofolate reductase (NADPH)2HMOX1Heme oxygenase (decycling) 13AtherosclerosisAGTAngiotensinogen4MMP3Matrix metalloproteinase 3 (stromelysin 1)5MMP13Matrix metalloproteinase 136APOEApolipoprotein E7PON1Paraoxonase8MTHFRMethylenetetrahydrofolate reductase4IL-6Interleukin 69APOBApolipoprotein B10APOC3Apolipoprotein C310CETPCholesterol ester transfer protein10LPLLipoprotein lipase10ACEAngiotensin I converting enzyme 110ATIIR1Angiotensin II receptor type 110CBSCystathionine-synthase10GPIIIAGlycoprotein IIIa10FGAFibrinogen10ELAMEndothelial leukocyte adhesion molecule-110Atherothrombosis (angina pectoris)TAFIThrombin activatable fibrinolysis inhibitor11Brugada syndromeSCN5ASodium channel voltage-gated12Calcific aortic valve stenosisVDRVitamin D receptor13Cardiac arrhythmia, inheritedSCN5ASodium channel voltage-gated14Cardiac hypertrophyIL-6Interleukin 615Carotid artery atherosclerosisMMP3Matrix metalloproteinase 3 (stromelysin 1)16Cardiomyopathy (with Chagas disease)TNFAIP2Tumor necrosis factor a17IFNGInterferon-γ17IL-6Interleukin-617IL-4Interleukin-417Cardiomyopathy (dilated, with conduction disease)LMNALamin a/c18Cardiomyopathy (familial dilated)DMDDystrophin18TAZTafazzin18ACTCCardiac actin18DESDesmin18SGCDDelta-sarcoglycan18TNNT2Cardiac troponin T18MYH7β-Cardiac myosin heavy chain18N/AMitochondrial respiratory chain18TPM1a-Tropomyosin18Cardiomyopathy (familial hypertrophic)CMYBPCCardiac myosin binding protein C19TNTICardiac troponin I19TNNT2Cardiac troponin T19MYH7β-Cardiac myosin heavy chain19TPM1a-Tropomyosin19ACTCCardiac actin19AMHCa-Cardiac myosin heavy chain18TTNTitin18MYL3Myosin light chain18Cardiomyopathy (hypertrophic, with Wolff-Parkinson-White syndrome)AMPKProtein kinase, AMP-activated18N/AMitochondrial respiratory chain18Cardiomyopathy (ischemic)HFEHemochromatosis20Cardiomyopathy (nonfamilial dilated)CYP11B2Aldosterone21NEBLNebulette22Cardiomyopathy (nonfamilial hypertrophic)PAFAHPhospholipase A2 group VII23Cigarette smoking, coronary artery disease, and diabetesCYP1A1Cytochrome P450, subfamily I (aromatic compound-inducible) polypeptide 124Coronary artery diseaseGNB3Guanine nucleotide binding protein (G protein) β polypeptide 325NR3C1Glucocorticoid receptor26PON2Paraoxonase 227PON1Paraoxonase 127NOS3Nitric oxide synthase 328LPLLipoprotein lipase protein29APOBApolipoprotein B29ACEAngiotensin I converting enzyme 130PAI1Plasminogen activator inhibitor 131LRPLipoprotein receptor related protein"PPAR APeroxisome proliferative activated receptor a32PAFAHPhospholipase A2 group VII33IL-6Interleukin 634CRPC-reactive protein34HPHaptoglobin35HFEHemochromatosis36AGTAngiotensinogen37ACEAngiotensin I converting enzyme 137ADRB3β adrenergic receptor38ADD1Adducin 1 (a)39DyslipidemiaARHLDL receptor40,41CETPCholesterol ester transfer protein40,41SOD2Manganese superoxide dismutase40,41APOC3Apolipoprotein C-III40,41APOAIVApolipoprotein A-IV40,41APOA1Apolipoprotein A-I40,41LCATLecithin cholesterol acyltransferase40,41EmphysemaTNFATumor necrosis factor a42Heart failure (end-stage)TGFB1Transforming growth factor β143ANPAtrial natriuretic peptide44BNPBrain natriuretic peptide44CNPC-type natriuretic peptide44End-stage renal diseaseACEAngiotensin I converting enzyme 145HypertensionNOS3Nitric oxide synthase 346CYP11B2Cytochrome P450, subfamily XIB (steroid 11-β-hydroxylase), polypeptide 247GNASGNAS complex locus48AGTAngiotensinogen49ACEAngiotensin I converting enzyme 149ADRA2a2 Adrenergic receptor50ADRB2β2 Adrenergic receptor50ADRB3β3 Adrenergic receptor50ADD1Adducin 1 (a)50AGTR1Angiotensin II receptor type 150GNB3G protein β3 subunit50RENRenin50INSRInsulin receptor50TGFB1Transforming growth factor β150GCGRGlucagon receptor50LPLLipoprotein lipase protein50GNAI1G protein a subunit51GCKGlucokinase52Hypertension (continued)SCNN1BEpithelial sodium channel β subunit53THTyrosine hydroxylase54END1Endothelin 155END2Endothelin 256ANPAtrial natriuretic peptide44BNPBrain natriuretic peptide44CNPC-type natriuretic peptide44AGTR1Angiotensin receptor 157TAFIThrombin activatable fibrinolysis inhibitor58ESR2Estrogen receptor β59NOS2ANitric oxide synthase 2A60Insulin resistanceGNB3Guanine nucleotide binding protein (G protein) β polypeptide 361LPLLipoprotein lipase protein62ACEAngiotensin I converting enzyme 163GP1BAPlasma glycoprotein 164NOS3Nitric oxide synthase 365RETNResistin66Ischemic heart disease (early onset)ACEAngiotensin I converting enzyme 167Left ventricular noncompactionTAZTafazzin18DTNAa Dystrobrevin18Myocardial infarctionMMP3Matrix metalloproteinase 369LPLLipoprotein lipase protein70CCR5Chemokine (C-C motif) receptor 571APOBApolipoprotein B72APOEApolipoprotein E73AGTR1Angiotensin II type 1 receptor73AGTAngiotensinogen73ACEAngiotensin I converting enzyme 173CRPC-reactive protein74FGBFibrinogen β chain75THBDThrombomodulin76PECAM1Platelet-endothelial cell adhesion molecule-177ANPAtrial natriuretic peptide44BNPBrain natriuretic peptide44CNPC-type natriuretic peptide44HSP70A1Heat shock protein 70-178SELPSelectin P79CD14CD1480Myocardial ischemiaACEAngiotensin I converting enzyme 181AGTAngiotensinogen82ObesityADRA2a2 Adrenergic receptor83ADRB2β2 Adrenergic receptor84LEPRLeptin receptor85Peripheral arterial diseaseADD1Adducin 1 (a)39Septal hypertrophyMYH7Cardiac β myosin heavy chain86StrokeLPLLipoprotein lipase protein39APOEApolipoprotein E39CD14CD1487NOS3Nitric oxide synthase 388PDE4DPhosphodiesterase 4D89MMP1Matrix metalloproteinase 190MMP3Matrix metalloproteinase 390FGBFibrinogen B β polypeptide91MTHFRMethylenetetrahydrofolate reductase92VWFvon Willebrand factor93Stroke (continued)AGTAngiotensinogen94Vascular diseaseP1A1Glycoprotein IIIa95MTHFRMethylenetetrahydrofolate reductase95ITGA2Integrin a2 Glycoprotein Ia95ACEAngiotensin I converting enzyme 195Vasospastic anginaACEAngiotensin I converting enzyme 196Wall thickness of the radial and/or carotid arteriesAGTR1Angiotensin II receptor type 197ACEAngiotensin I converting enzyme 197AGTAngiotensinogen98PON1Paraoxonase 199It 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 SusceptibilityGenetic 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. TABLE 2. Use of Genetic MarkersDisease Phenotype Identified by MarkerApplicationEtiologyPathophysiologyOccurrenceRisk assessmentSubtypesDiagnosisNatural historyPrognosisResponse: pharmacogeneticsOptimal therapyTABLE 3. Association Versus Linkage StudiesArea of StudyAssociationLinkageHuman materialFamilies or case-controlFamiliesGene effectGenes of small to modest effectGenes of modest to large effectCandidate genesMarkers must be in linkage disequilibrium; haplotype approach optimalAny marker, optimally highly polymorphicGenome-wideThousands to millions of genetic markers requiredHundreds of genetic markers requiredThus, 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).104Linkage 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.103An 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.40,41Twin 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.109Another 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 DiseasesSubstantial 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 devel
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