Circulation. 2006;113:e450-e455
doi: 10.1161/CIRCULATIONAHA.105.560151
(Circulation. 2006;113:e450-e455.)
© 2006 American Heart Association, Inc.
Cardiovascular Genomics
Marc S. Sabatine, MD, MPH;
Jonathan G. Seidman, PhD;
Christine E. Seidman, MD
From the Cardiovascular Division, Brigham and Women’s Hospital (M.S.S.), Department of Genetics, Harvard Medical School (J.G.S.), and Howard Hughes Medical Institute, Department of Genetics and Medicine, Brigham and Women’s Hospital and Harvard Medical School (C.E.S.), Boston, Mass.
Correspondence to Marc S. Sabatine, MD, MPH, Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA 02115. E-mail msabatine{at}partners.org
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Introduction
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Case presentation: A 46-year-old man presents with an ST-elevation
myocardial infarction (MI). He has neither hypertension nor
diabetes, does not smoke, and has a total cholesterol level
of 161 mg/dL with a high-density lipoprotein level of 43 mg/dL
and a low-density lipoprotein level of 92 mg/dL. Emergency coronary
angiography reveals an occluded left anterior descending artery
as well as moderate atherosclerotic disease in both the right
and left circumflex coronary arteries. The patient undergoes
stenting of his left anterior descending artery with a good
angiographic result. The next day, the patient asks, "Why did
this happen to me? I don’t smoke, I watch what I eat,
and I exercise every day. This same thing happened to my father
when he was my age and to my older brother just last year."
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Background
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Most physicians are familiar with simple mendelian genetics
wherein a rare mutation (by definition, one that occurs in <1%
of the population) in a gene causes a dramatic change in protein
concentration or function that is usually both necessary and
sufficient to cause the disease. These heritable diseases are
disproportionately concentrated within families that carry the
mutation, and environmental factors typically play a small or
nonexistent role. Examples of cardiovascular mendelian disorders
include familial hypercholesterolemia, familial hypertrophic
cardiomyopathy, Marfan syndrome, and congenital long-QT syndrome.
Unfortunately, but perhaps not unexpectedly, most of the common diseases in cardiology do not obey traditional mendelian genetics. These complex genetic diseases result from the combination of multiple heritable and environmental factors (Table 1). The associated genetic variants tend to be more common (>1% prevalence) and by convention are called polymorphisms (often single nucleotide polymorphisms or SNPs) rather than mutations. Moreover, the effect of each SNP on an individual’s phenotype tends to be far more modest and may not be necessary or sufficient to cause the disease (Figure 1).
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Figure 1. Gaussian distribution of traits. Although disease states are treated as discrete outcomes, to understand complex genetic disorders, it may be easier to conceptualize the genetic effect as influencing an intermediate quantitative trait with a gaussian distribution. The presence of the variant allele causes a shift in the distribution of the trait toward the disease threshold. In the case of a simple mendelian disorder (eg, sickle cell anemia), a rare mutation (substitution of valine for glutamic acid in the ß-globin chain) causes a very large shift in the trait (solubility of deoxygenated hemoglobin), which is sufficient to cause the disease. In contrast, for a complex disorder (eg, MI), a polymorphism (eg, in a gene encoding a platelet receptor) causes a more subtle shift in the trait (platelet aggregability) that increases the risk of the disease but alone is neither necessary nor sufficient, and the majority of individuals with this polymorphism will never develop the disease. Adapted with permission from Nature. 2000;405:847–856.
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Identifying Gene Variants That Contribute to Disease
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Genetic epidemiology studies typically rely on the coinheritance
of known polymorphic DNA markers with nearby but unknown disease-causing
variants. Traditionally, inheritance studies, which are performed
in families and utilize linkage analysis, have been used to
investigate monogenic, mendelian disorders.
1 DNA markers close
to the disease-causing gene are less likely to be separated
by a recombination event than are markers far away from the
disease-causing gene and therefore are more likely to be transmitted
together from parent to offspring (
Figure 2A). By gathering
data on the inheritance patterns of markers and disease states
within families, the distance between markers and the disease-causing
gene can be estimated and thus the location of the disease-causing
gene inferred. Although this approach has led to the discovery
of many genes that are linked to human diseases, it has proven
less useful in complex genetic disorders,
2 in part because of
reduced power to detect frequently occurring markers that convey
only modest effects and in part because of phenocopies.
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Figure 2. Linkage vs association studies. Panel A, In linkage analysis, members of a family are genotyped at known polymorphic markers (in this case, 3 biallelic markers A/a, B/b, and D/d). The location of the disease-causing variant (shown here in red with an associated asterisk) is unknown to the investigator. Markers that are close to the disease-causing variant are unlikely to be separated by recombination during meiosis and therefore tend to be transmitted together from parents to offspring. In this case, in the first generation, alleles A, B, and D are associated with the disease state (filled symbol). In the second generation, among the affected individuals, because of recombination, 2 carry A and B (but not D) and 1 carries B and D (but not A). In the third generation, among the affected individuals 1 carries A and B, 1 carries B, and 1 carries D (because of recombination between B and the disease-causing variant). From these data, one can infer that the disease-causing variant is likely closest to marker B. Panel B, In an association study, one studies unrelated individuals, genotyping either known potentially direct causal variants or polymorphic markers that one hopes are in linkage disequilibrium across the population with the true disease-causing variant. In this case, a genetic polymorphism (inner red symbol) is found in 6 of 16 cases but in only 1 of 6 controls.
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In contrast, association studies investigate complex genetic disorders using unrelated individuals, testing for nonindependence between a genotype and the disease phenotype: if a genotype is truly related to a disease, it will be found more frequently in individuals with the disease than in those without the disease (Figure 2B). Within association studies, one can opt to genotype either putative causal variants (ie, direct association) or polymorphic markers that one hopes are close to the true disease-causing variant (indirect association).3 In the former case, the investigator typically selects functional SNPs in biologically relevant genes. Although intuitively appealing, such an approach is constrained by the current biological knowledge base and involves many assumptions. For that reason, others have advocated employing a comprehensive genome-wide scan using random DNA markers that blanket the entire genome. The cataloguing of 
10 million SNPs4 and advances in high-throughput genotyping, including high-density DNA microarrays,5 make such genome-wide scans possible, albeit daunting. Akin to linkage analysis, a disease-causing variant that arose many generations ago in close proximity to a DNA marker will be coinherited with that marker so strongly and persistently that it ultimately leads to an association at the population level (termed linkage disequilibrium) due to shared common ancestry.6
Of note, haplotypes are linear arrangements of adjacent SNPs on the same chromosome and offer the potential to improve both genetic precision and genotyping efficiency. In association studies, recombination between the marker SNP and the disease-causing variant will erode the linkage disequilibrium in some patients and lead to confusing results. In contrast, a haplotype that is defined by the presence of 2 SNPs flanking the disease-causing variant would be more likely to remain linked with the disease-causing variant than would either SNP alone. Moreover, although many SNPs may fall within a haplotype, because of linkage disequilibrium it is necessary to genotype only a few SNPs (so-called haplotype tagging SNPs) to uniquely identify the haplotype. This approach permits a substantial reduction in genotyping effort7 and consequently reduces costs and the number of independent variables analyzed, thereby profoundly affecting the statistical analyses. To that end, the International HapMap project has reported the construction of a genome-wide map of linkage disequilibrium in multiple populations.8
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Potential Pitfalls in Genetic Studies
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Genetic epidemiology studies have several special limitations
(
Table 2), including genetic heterogeneity (different genotypes
leading to the same phenotype), phenotypic heterogeneity (variability
in expressivity or clinical manifestations of a genetic disease),
phenotypic imprecision (combining phenotypes with potentially
different genetic contributions), phenocopy (a phenotype that
mimics the genetically caused phenotype of interest), and population
stratification. The latter is a special type of confounding
that can occur whenever a non–ethnically homogeneous population
is studied. If an ethnic group has a higher frequency of a disease
for nongenetic reasons, any genetic variant that occurs more
frequently in that ethnic group, even if it is not causally
linked to the disease, will spuriously appear to be associated
with the disease.
Type I statistical errors (false-positives) are a major problem due to multiple testing. With 30 000 genes and 10 million SNPs, the number of possible association tests is enormous. Techniques have been developed to control experiment-wise error rates that include setting the false discovery rate and performing permutation testing. Replication of results in an independent population before proclaiming a confirmed association remains the best approach9 and one that is now required by leading journals. Type II statistical errors (false-negatives) are also a problem due to small sample size. The excess risk or benefit associated with a polymorphism may be modest in magnitude and therefore only detectable with large studies on the order of at least 1000 case-control pairs, although this conventional wisdom has recently been challenged.10
Finally, it is important to remember that statistical association is not proof of causality. A polymorphism associated with a disease is unlikely to be the causal variant but rather to be in strong linkage disequilibrium with the true causal variant within the same gene or even potentially in nearby genes. This can lead to inconsistencies between studies if the causal variants arose independently in different populations.
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Examples of Genetic Approaches to Coronary Artery Disease
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Linkage Analysis
In an attempt to apply traditional linkage analysis to the complex
genetic disorder of coronary artery disease, researchers performed
a genome-wide scan on a single family with an apparent autosomal
dominant pattern of coronary artery disease. They found significant
linkage with a marker in a region that contains the myocyte
enhancer factor-2 family (
MEF2A) gene.
11 However, a different
research group was not able to replicate this finding in individuals
with sporadic MI.
12 Other groups have used linkage analysis
to large numbers of multiplex families with a variety of coronary
heart disease phenotypes.
13–16 Each group has found significant
linkage but to different chromosomal regions. Whether the different
loci identified in each study reflect phenotypic imprecision
or false-positive results remains unclear.
Multilocus Candidate Gene Studies
In 2002, one of the largest genetic association studies for MI was published, involving 112 polymorphisms in 71 candidate genes in 5061 unrelated individuals.17 The investigators conducted a staged approach, setting a more modest threshold for identifying candidate SNPs in a small cohort and then a more stringent threshold for validating the associations in a second, larger cohort. In analyses stratified by gender, their approach yielded 1 SNP in men (in the connexin 37 gene) and, surprisingly, 2 other SNPs in women (in the PAI-1 and stromelysin genes). Several other, smaller multilocus studies have been published, but none of these studies has identified the same genetic variants as associated with MI. Of note, one of the groups that found linkage between the gene encoding 5-lipoxygenase activating protein and coronary heart disease16 has subsequently reported an association between a haplotype spanning the gene encoding leukotriene A4 hydrolase and MI.18 Although it is intriguing that these genes encode proteins within the same biochemical pathway, it is notable that the associations were found in different populations, one white and one black.
Genome-Wide Association Studies
A group in Japan examined 92 788 SNPs in a large case-control study involving 1133 cases with MI and 1006 controls.19 They identified 3 SNPs in the lymphotoxin-
gene that were strongly associated with MI. Subsequent molecular biology studies demonstrated that the variants were associated with transcriptional and functional changes in lymphotoxin-. Another group of researchers performed a genome-wide association study examining 11 053 SNPs in 6891 genes, using 3 sequential studies to validate their findings.20 They found variants in 4 genes, none previously implicated in MI, that were consistently associated with MI in all 3 stages. As with the multilocus studies, no 2 groups have identified the same variant.
Pharmacogenomics
Investigators have explored the interaction between genetic variants and response to cardiovascular drugs with the hope of more precisely defining efficacy and safety profiles. To that end, polymorphisms in the genes encoding HMG-CoA reductase,21 apolipoprotein E,22 and the ADAMTS-1 metalloproteinase23 appear to predict the magnitude of change in lipid levels and/or the reduction in adverse clinical outcomes in response to statin therapy. Polymorphisms in the genes that encode ß-adrenergic receptors have been associated not only with the risk of developing heart failure24 but also with improvement in ejection fraction in response to ß-blocker therapy.25 Finally, the anticoagulant effect of a dose of warfarin is affected by polymorphisms in the genes that encode CYP2C9 and vitamin K epoxide reductase complex 1.26,27
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Conclusions
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Learning from early forays, researchers are now designing better
genetic epidemiological studies to detect the subtle but likely
important contribution of genetic variation to common cardiovascular
diseases. The hallmarks of a good genetic association study
include accurate and comprehensive genotyping, evaluation of
a large number of carefully phenotyped patients, and replication
of significant associations. The requisite scope of such studies
underscores the need for a multidisciplinary and multicenter
collaborative approach. With the pace of advances into the molecular
basis for atherothrombosis, management of patients such as the
one described in our case study will, in the near future, likely
include genetic analysis to improve risk stratification and
tailor therapy appropriately.
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Introduction
Background