Contemporary Reviews in Cardiovascular Medicine
Genetics of Cardiovascular Diseases
From Single Mutations to the Whole Genome
François Cambien, MD; Laurence Tiret, PhD
W
Downloaded from http://circ.ahajournals.org/ by guest on June 15, 2017
being covered by introns and regulatory regions located
upstream (5⬘) and downstream (3⬘) of the coding sequence.
The most common type of human sequence variation
consists of differences in individual base pairs termed single
nucleotide polymorphisms (SNPs). Other sequence variations
comprise variable numbers of short or long repetitions of the
same motif in tandem such as mini- and microsatellites,6
insertions or deletions of various lengths, and structural
variants that affect large chromosomal regions.7 The vast
majority of these sequence variations are located in nonfunctional regions of the genome and have no phenotypic impact;
these are said to be neutral and are usually termed markers.
However, when variations occur within coding sequences or
regulatory regions, they may affect the protein sequence or
the level of gene expression and translate into observable
phenotypic effects.
hen the search for genes that predispose to cardiovascular diseases (CVD) started ⬎20 years ago, it was
anticipated that genetic polymorphisms might be analogous
to the already known CVD risk factors and could be incorporated in a risk model such as the Framingham score1 to
assess the risk of an individual and adopt preventive or
therapeutic measures accordingly. However, despite years of
intensive research, not a single genetic risk factor is used for
risk assessment. The new strategy of genome-wide association (GWA) studies (for example, see http://www.wtccc.
org.uk/) coupled with the availability of very large cohorts of
patients2 is starting to reveal novel genetic factors that
contribute to disease risk. Whether these variants will be
clinically more useful than those that were derived from the
study of candidate genes still needs to be demonstrated. As
time passes, the interest for genetic research on common
CVD moves progressively from the direct expectation of risk
stratification to the more fundamental understanding of disease origins and pathophysiology and their indirect diagnostic
and therapeutic implications.
The objective of the present review is not to provide an
exhaustive account of the numerous studies conducted on the
genetics of CVD (eg, Arnett et al3), but to introduce a few basic
notions required to understand the language of genetics and
genomics (see Appendix) and illustrate with a limited number of
examples the important insights provided by genetic research
into the causes and mechanisms of CVD. We will also discuss
the new GWA strategy and why this approach is likely to have
a considerable impact on biomedicine and human disease
understanding. Finally, we will try to explain the unsuccessful
search for genetic markers of risk and why phenotypic biomarkers are likely to be clinically more useful.
Mendelian Versus Complex Inheritance
The spectrum of the genetic variants that predispose to CVD
spans from rare, highly deleterious mutations responsible for
Mendelian diseases to common polymorphisms with weak
effects that, alone or in combination, modulate the risk of
common diseases (the “common variant–weak effect– common disease” model). In this latter case, the term “complex
disease” is often used to denote the fact that the pattern of
familial aggregation differs from that of Mendelian inheritance of a single genetic defect.
From an epidemiological perspective, rare deleterious mutations (eg, those that cause familial hypercholesterolemia
[FH]) confer an important risk of coronary heart disease
(CHD) in mutation carriers, but their impact at the population
level is low. Conversely, polymorphisms such as the apolipoprotein E (APOE) polymorphism, because they are frequent, may have a population impact that is far from
negligible despite a weak effect at the individual level. This
duality, which relates to the epidemiological notions of
absolute, relative, and attributable risks, has important medical and public health implications but is less crucial when the
interest lies in the identification of pathophysiological
pathways.
The Basis of Genetic Variation
During the past decade, considerable progress has been
achieved in the knowledge of the human genome and the
characterization of its natural variability.4,5 The 20 000 to
25 000 protein coding genes that the human genome comprises represent only 30% of its sequence, the remainder
being intergenic sequences that may contain important elements for the regulation of gene expression. In a typical
human gene, 5% of the sequence is composed of coding
exons that are in part translated into a protein, the remainder
Mutations Responsible for Mendelian Diseases
Mutations are usually identified by linkage analysis conducted in families with several affected members over differ-
From INSERM UMR S 525 and Université Pierre et Marie Curie, Paris, France.
Correspondence to Dr François Cambien, INSERM U525, Faculté de Médecine Pitié-Salpêtrière, 91 blvd de l’Hôpital, 75634 Paris cedex 13, France.
E-mail [email protected]
(Circulation. 2007;116:1714-1724.)
© 2007 American Heart Association, Inc.
Circulation is available at http://circ.ahajournals.org
DOI: 10.1161/CIRCULATIONAHA.106.661751
1714
Cambien and Tiret
Genetics of Cardiovascular Diseases
1715
Figure 1. Principle of genetic association studies. The schema represents a genomic region
that contains 12 SNPs. The 3 SNPs in black
are genotyped directly (these are the tag
SNPs). The 6 SNPs in gray are captured
through linkage disequilibrium (LD) with the tag
SNPs (as denoted by arrows). The 3 SNPs in
M1
S1
white are neither genotyped nor captured by
tag SNPs (uncaptured SNPs), and so disease
association with any of these uncaptured SNPs
M2
S1
Haplotypes
would be missed. The gray star represents a
SNP causally associated with disease. It has 2
M2
S2
alleles (S1 and S2) and is in LD with a tag SNP
that has 2 alleles (M1 and M2). The LD is
reflected by the fact that the 2 SNPs generate
only 3 haplotypes instead of the 4 possible
because the haplotype M1S2 is never
observed. As a consequence of this LD, the
Genotyped SNPs
SNPs captured by proxy
Uncaptured SNPs
Functional SNP
association of the causal SNP with disease
could be detected through an indirect association with the tag SNP. Adapted from Kruglyak,12 with permission from the publisher. Copyright © 2005, Nature Publishing Group.
Downloaded from http://circ.ahajournals.org/ by guest on June 15, 2017
ent generations. Regions that potentially harbor a diseasecausing gene are identified by testing of the cosegregation of
the disease with genetic markers that tag specific regions of
the genome. This strategy uses genetic markers (ie, panels
of microsatellites or large sets of SNPs regularly spaced
throughout the genome) and tests whether particular alleles
are cotransmitted with the disease at a higher frequency than
expected by chance. The success of linkage studies depends
on the availability of phenotypically well-characterized families that include a sufficiently large number of informative
affected individuals. When a disease-linked region of the
genome has been successfully mapped by linkage analysis,
finding the responsible gene and sequence variation is not
trivial because the region may sometimes encompass tens or
hundreds of genes. However, thanks to the improved annotation of the human genome sequence and the possible design
of dense SNP arrays that target the regions of interest, the
discovery of the responsible sequence mutation may be
accelerated by linkage disequilibrium (LD) mapping.8 Although exceptions exist (eg, within isolated populations
derived from a small number of founders), mutations that are
associated with Mendelian diseases are rare (much ⬍1%) and
their origin is recent. This explains why their presence may be
restricted to some groups of individuals only (population
isolates, families). In that case, they are said to be “private
mutations”.
with rare deleterious mutations, is that they have an ancient
origin. This explains why they are usually found in most
human populations albeit often with different allele
frequencies.
Because complex diseases do not follow a clear pattern of
Mendelian inheritance, the strategy used to identify their
genes of predisposition is usually not based on family studies
but on a radically different approach called “genetic association” analysis. This approach relies on the existence of LD
among physically close polymorphic sites in the genome,
which implies that even if a polymorphism causally involved
in the disease process is not directly observed, its association
may be captured by a measured proxy polymorphism in LD
with it. This is the basis of association studies that test the
statistical association between genetic markers (the term
“marker” denotes that no a priori causal role is assumed) and
the disease in the population. The principle of genetic
association studies is described in Figure 1. Initially, association studies focused on markers of candidate genes. Thanks
to various initiatives, in particular the “HapMap” Project,13
increasingly dense genome-wide panels of common SNPs are
now available that provide a powerful resource of markers (or
tag SNPs) (Figure 1) for association studies. Contemporary
association studies often encompass sets of genes that encode
components of biological systems, chromosome regions, or
even the whole genome.
Polymorphisms Involved in Complex Diseases
The HapMap (Haplotype Map) Project
At the other end of the frequency spectrum of genetic
variants, common polymorphisms (minor allele frequency
⬎1%) are the focus of most contemporary genetic studies that
target complex diseases. Common SNPs are estimated to
number ⬎10 million in the human genome.9 Because polymorphisms have common alleles, numerous combinations of
susceptibility alleles at several loci in a particular individual
are possible, and some of them may affect the risk of CVD in
a way that cannot be predicted from the separate effect of
each variant. This is the major obstacle to the characterization
of the genetics of complex traits and the rationale for the
proposal to explore systems of genes rather than single
genes.10,11 An important feature of polymorphisms, compared
The primary goal of the International HapMap Project13
(http://www.hapmap.org/) was to create a public resource of
common SNPs to capture most of the common human
genome sequence variability. A second objective was to
characterize the LD structure of the genome on the basis of
the analysis of these SNPs. Because of the strong LD
displayed by most regions of the genome, the combination of
alleles at neighboring SNPs, called haplotypes, generates
much less diversity than would be expected if they were
uncorrelated. Recent studies have shown that the human
genome is organized into a succession of distinct haplotype
blocks that are ancestrally conserved.14 –17 By resequencing
the genome of 270 individuals from populations with African,
1716
Circulation
October 9, 2007
Asian, and European ancestry, the HapMap Project has
identified a set of SNPs that tag most of the common
haplotypes in the human genome.18,19 This resource is used to
search for polymorphisms associated with susceptibility to
common diseases. For this purpose, genotyping arrays built
with tag SNPs that encompass the whole genome or specific
regions of interest are used; Figure 1 explains the principle.
Variants of “Intermediate” to Low Frequency
Associated With Non-Mendelian Traits
Downloaded from http://circ.ahajournals.org/ by guest on June 15, 2017
Between the rare mutations responsible for Mendelian diseases and identified by family studies and the common
polymorphisms targeted in current association studies, genetic variants that have a low frequency (⬍1%) but a sizeable
individual effect (eg, relative risk ⬎3) probably exist in
significant numbers. These variants are presently difficult to
characterize because they do not generate evident familial
patterns of disease that would make them identifiable by
linkage studies, and they are missed in the current candidate
gene or genome-wide sequencing strategies, which use a
limited number of individuals for polymorphism screening.
Rare functional variants are difficult to tag with common
markers such as SNPs. Their systematic characterization is
therefore out of the scope of studies that rely on LD such as
GWA studies and will depend on the availability of new
high-throughput sequencing technologies and large DNA
banks of patients and controls. Rare variants associated with
non-Mendelian traits may prove to be clinically important as
they may confer a significant increase in risk and therefore
constitute potential diagnostic and prognostic tools. Interest
for these variants has recently grown after the discovery of a
number of them in the PCSK9 and ABCA1 genes.
Some Examples Related to Lipoproteins That
Illustrate the Strength of Genetics to Unravel
Mechanisms of Disease
Genetic studies have been instrumental in the understanding
of the mechanisms involved in the regulation of plasma
lipoproteins. We will focus on a few examples that illustrate
the broad range of frequency and effects of gene variants that
affect lipid metabolism.
The APOE Gene
The heritability of plasma low-density lipoprotein (LDL)–
cholesterol (LDLc) has been estimated to be ⬎50%.20 Epidemiological data show a striking parallel between plasma
LDLc levels and the risk of CHD that is observed over a wide
range of LDLc levels. This is why common polymorphisms
that affect plasma LDLc may contribute to the risk of CHD.
Such associations have been reported for several genes
involved in lipid metabolism,21 the best example being
APOE22; apoE plays an important role in the transport of
lipids to tissue and cells. It is present in several lipoproteins
and binds with high affinity to the LDL receptor. The APOE
gene is polymorphic with 2 common nonsynonymous (amino
acid changing) polymorphisms that generate 3 alleles (haplotypes) termed ⑀2, ⑀3, and ⑀4. These 3 alleles have variable
frequencies across populations; ⑀3 is the most common and
⑀2 is the least common.23 The 3 corresponding encoded
isoforms of the protein, E2, E3, and E4, have different
functional properties; the E2 isoform is associated with lower,
and the E4 isoform with higher, LDLc levels than E3. In a
recent metaanalysis, E4 carriers, who represent ⬎20% of the
population, were shown to have a 40% higher risk of CHD
compared with E3E3 homozygotes, whereas the relationship
between E2 and risk was less obvious.24 This is an example
of genetic variation that has an important effect at the
population level but has little relevance in the assessment of
individual risk, at least when considered alone.
LDL Receptor Gene
Despite the relatively low frequency of FH compared with the
common forms of hyperlipidemias, its study has provided
important insights into the mechanisms of cholesterol metabolism and opened new perspectives for the prevention of
CHD.25 Mutations in the coding sequence of the LDL
receptor gene (LDLR) may considerably reduce or abolish the
function of the LDL receptor and lead to an important rise of
circulating LDLc, which in turn is associated with a commensurate increase in CHD risk. More than 700 different
mutations of LDLR responsible for FH have been reported,
some of them clustered in particular populations.26 Mutations
affect the function of the receptor in various ways according
to their type and their position in the protein sequence, and an
important heterogeneity is present in clinical manifestations
even in individuals who carry the same mutation as a
consequence of differences in genetic and environmental
backgrounds. Currently, the clinical diagnosis of FH is based
on personal and family history, physical examination, and
laboratory findings. However, it has been suggested that the
diagnosis of FH should be based on the identification of the
genetic defect because statin therapy needs to be initiated in
young carriers of a LDLR mutation even if their plasma LDLc
is normal.27 However, no general agreement exists on this
approach because the risk of CHD is about the same in
phenotypically defined FH patients with or without mutation
in the LDLR gene.28 The clinical benefit of the genetic
diagnosis over the careful monitoring of LDLc levels, which
is required anyway, is therefore questionable.
Familial Defective ApoB100
Familial defective apoB100 is another form of FH in which
LDL binds defectively to the LDL receptor, which results in
increased circulating LDLc levels and premature atherosclerosis.29 In contrast with the myriad of LDLR mutations that
cause FH, the molecular defect responsible for familial
defective apoB100 is a single mutation (R3500Q) in the gene
encoding apoB, the main apolipoprotein in LDL that binds to
the LDL receptor.30 Although the molecular diagnosis of
familial defective apoB100 is theoretically easier than diagnosis of LDLR mutations that cause FH because a single
variant is responsible for the trait, it is still the direct
measurement of LDLc that appears the most appropriate to
evaluate the risk of CHD and monitor the drug response in
familial defective apoB100 patients.
Proprotein Convertase Subtilisin/Kexin 9
Recently, the careful study of families with several members
affected by dominant forms of hypercholesterolemia despite
Cambien and Tiret
Downloaded from http://circ.ahajournals.org/ by guest on June 15, 2017
absence of mutation in the LDLR gene and lack of the
APOB3500 variant led to the mapping of a locus on chromosome 1p32 and the subsequent identification of missense
mutations in the proprotein convertase subtilisin/kexin 9
(PCSK9) gene.31 PCSK9 was subsequently found to play a
major role in the LDL/LDLR pathway, even if the exact
mechanism of its influence remains incompletely understood.
Mice in which the PCSK9 gene has been inactivated exhibit
an increased hepatic LDLR level, accelerated LDL clearance,
and an important reduction of plasma LDLc.32 The PCSK9
mutations associated with FH are gain-of-function mutations
(variants that confer an increased or extra functionality) that
possibly affect the autocatalytic property of the pro-PCSK9
protein and promote the degradation of LDL receptors in
hepatocytes. In addition to these extremely rare mutations,
several more frequent nonsynonymous variants of the PCSK9
gene are associated with an impaired function of the protein
that results in a reduction of plasma LDLc caused by
accelerated LDL clearance. These variants of “intermediate”
frequency have a substantial impact on plasma LDLc and
CHD risk. For example, it has been estimated from the
Atherosclerosis Risk in Communities (ARIC) study that 3%
of African Americans were carriers of PCSK9 nonsynonymous variants, which were associated with a mean reduction
of 30% of LDLc and a parallel significant reduction of CHD
risk.33 This effect is comparable to the lowering effect of
statins on LDLc. The PCSK9 gene also carries common
noncoding polymorphisms that affect plasma LDLc; their
effect at the individual level is much weaker than that of the
coding variants of “intermediate” frequency just discussed,34
but their impact at the population level may be nonnegligible.
ATP-Binding Cassette Transporter 1
Another striking example of a Mendelian disorder that has
contributed to the discovery of new processes involved in
lipid metabolism and atherosclerosis is Tangier disease, a
very rare recessive deficit of high-density lipoprotein– cholesterol (HDLc) metabolism caused by mutations in the
ATP-binding cassette transporter 1 (ABCA1) gene.35–37
ABCA1 encodes a protein that regulates the cellular efflux of
cholesterol and phospholipids to an apolipoprotein transporter. Several mutations responsible for Tangier disease
have been identified, all of which result in a complete or
partial loss of function that leads to an accumulation of
cellular cholesterol, low plasma HDLc levels, and increased
risk of CHD. Apart from these very rare mutations, numerous
coding variants of “intermediate” to low frequency in the
ABCA1 gene may contribute to a significant fraction of the
low HDLc levels in the population. In the Dallas Heart Study,
20 of 128 individuals in the bottom 5% of the HDLc
distribution were carriers of nonsynonymous variants in the
ABCA1 gene (unknown before as common SNPs) versus only
2 of 128 individuals in the top 5% of the HDL distribution.38
This finding was replicated in an independent study, and
biochemical studies indicated that most of the variants associated with low HDLc were functionally important.38 The
results that pertain to variants of “intermediate” or low
frequency and the similar results for PCSK9 raise the interesting possibility that the contribution of rare variants to
Genetics of Cardiovascular Diseases
1717
common traits may be more important than initially thought.
Common polymorphisms in the ABCA1 gene, including
several nonsynonymous changes, have also been identified by
systematically resequencing the gene in a limited number of
individuals, and some of these polymorphisms have been
shown to be associated with plasma HDLc or apoA1 in the
population at large.39
Gene–Environment Interaction
The phenotypic expression of a genotype is dependent on a
host of factors that include genetic background, the stage of
development of the organism, age, gender, physiological and
pathological conditions, the intake of food and drugs, and
physical activity.40,41 The importance of these interactions
considerably mitigates the concept of genetic determinism
and provides perspectives for interference with the pejorative
impact of genetics on disease susceptibility through modifiable factors.
From a research perspective, the presence of interaction
complicates the detection of relevant associations that may be
masked if they are not investigated in appropriate conditions.
Except in the domain of pharmacogenetics, very little
progress has been made in our understanding of gene–
environment interaction. This is partly related to the difficulty
of accurate measurement of most environmental factors (drug
intake is a clear exception) as compared with genetic factors,
and to the generally low power of studies to analyze combinations of factors in presence of interaction. Prospective
studies might be more appropriate than case-control studies to
investigate gene– environment interactions because they are
less prone to biases as a result of modifications in environmental exposure induced by disease onset.42 Lack of appropriate accounting for gene– environment interactions may
explain some of the failure to replicate genetic associations.
Whether the recently initiated projects of huge biobanks such
as the UK Biobank (http://www.ukbiobank.ac.uk/) will help
resolve the pending issues of gene– gene and gene– environment interaction remains to be shown. Actually, the pattern of
interactions among factors that affect disease risk may be so
complex that completely different approaches such as system
genetics may be more helpful.10,11
Pharmacogenetics
The response to a drug is a phenotype that is under the
influence of both genetic and nongenetic factors. For reasons
that we shall attempt to explain below, many common genetic
variants have a strong influence on drug efficacy and toxicity.
This may obviously have an important impact on patient care.
Pharmacogenetics has 3 major specificities when compared
with most other areas of gene– environment studies: the direct
medical relevance, the relative ease of exposure measurement, the strength of the genetic effects.
CYP2D6 as an Example
For many drug-metabolizing enzymes, phenotyping tests
were available prior to the possibility to directly assess their
genetic variability at the molecular level. CYP2D6, with the
extensive metabolizer and poor metabolizer inherited phenotypes, is an example. The poor metabolizer phenotype is
1718
Circulation
October 9, 2007
Downloaded from http://circ.ahajournals.org/ by guest on June 15, 2017
associated with a considerable increase in the maximum
concentration and area under the curve for a large number of
drugs. These include the ␤-blockers metoprolol, timolol, and
propranolol, for which the same dose leads to a greater
lowering of heart rate and blood pressure in subjects with the
poor metabolizer phenotype. The genetic variability of
CYP2D6 is under the influence of a large number of genetic
variants, some of them common, that may be simultaneously
present in an individual and whose distribution may considerably vary across ethnic groups.43 Many metabolizing enzymes exhibit a similar pattern of genetic variability.44
Because CYP2D6 polymorphisms affect the metabolism of
so many drugs, a tendency currently exists in the pharmaceutical industry to stop the development of therapeutic agents
that are metabolized by CYP2D6. One concern is that the
generalization of this attitude to other drug-metabolizing
enzymes might lead to the rejection of a large number of
drugs that would be efficient and safe in subgroups of patients
identified by genetic testing. A striking example is provided
by the pharmacogenetics of warfarin, a drug that has been in
use since the 1950s but would probably have been abandoned
in early development nowadays as a consequence of its
pharmacogenetic features.
The Pharmacogenetics of Warfarin
Warfarin and, more generally, vitamin K antagonists are
widely used oral anticoagulants whose prescription is complicated by their narrow and highly variable therapeutic
range. The dose requirement and risk of bleeding are influenced by intake of vitamin K, illness, age, gender, concurrent
medication, body surface, and genetics. Besides the possible
or demonstrated influence of a large number of genes,45
warfarin’s effect is influenced by 2 major genes, one involved
in its biotransformation (CYP2C9) and the other involved in
its mechanism of action (VKORC1). The gene that encodes
CYP2C9, the main metabolizing enzyme of warfarin, is
highly polymorphic with many alleles that exhibit different
functional properties and different frequencies across populations. In individuals of European descent, CYP2C9*1 is the
most common allele, whereas CYP2C9*2 and CYP2C9*3
have a frequency of 12% and 8%, respectively, and a reduced
activity relative to CYP2C9*1, which implies that carriers of
the CYP2C9*2 or CYP2C9*3 form (⬇40% of the Europeans)
treated by warfarin would normally require a lower dose of
the drug.46 In individuals of African and Asian origins, the
CYP2C9*2 and CYP2C9*3 alleles are less frequent than in
Europeans, but other functional alleles are found predominantly in these 2 ethnic groups that also affect the drug
response. VKORC1, the other major gene that influences
warfarin metabolism, is the vitamin K cycle enzyme that
controls regeneration of reduced vitamin K. Warfarin exerts
its pharmacological effect by inhibition of VKORC1. The
VKORC1 gene carries several common polymorphisms in its
regulatory regions, such as the ⫺1639G/A polymorphism (or
similarly ⫺1173T/C, which is in strong LD with it), which
strongly correlate with warfarin response. A regression model
that incorporates polymorphisms of the 2 genes as well as
age, height, and gender has been proposed that accounts for
⬎50% of the variability of warfarin response in Europeans
and may be used as a dosing algorithm in this population.47
Large-Scale Genotyping of
Drug-Metabolizing Enzymes
It is now possible to design genotyping devices that allow the
simultaneous testing of a large number of variants that affect
drug metabolism.48 Such tools may be very useful in the early
development of drugs, and no major technological obstacle
exists to improving them to a point where they will allow
testing of most SNPs that affect drug metabolism. However,
some limitations may reduce the clinical applicability of such
tools. First, SNPs only represent a part of the genetic
variation that affect drug metabolism. Variable number of
tandem repeat or structural polymorphisms may not be easily
tagged by SNPs. Second, a major gene effect (where a single
variant dominates all other effects) cannot always be assumed, and it may be difficult to translate a complex pattern
of variation that involves many different SNPs into an
accurate prediction of drug response. Third, the previous
point is further complicated by the possible influence of
nongenetic cofactors.
Evolutionary Aspects of
Drug-Metabolizing Enzymes
The conjunction of a strong effect and a high frequency that
distinguishes the variants that affect drug metabolism from
most of those that affect disease phenotypes is likely to have
an evolutionary explanation. Many drug-metabolizing enzymes are highly genetically polymorphic within and across
species. A good example is offered by the CYP2D gene
family. In mice, 9 CYP2D genes exist. In humans, only 1
CYP2D gene (CYP2D6) is present, and it is highly polymorphic. Because CYP2D enzymes have a high affinity for
plant toxins, it has been proposed that they are essential for
the survival of mice in their specific dietary environment.
During hominization on the other hand, as a consequence of
changes in food selection, the detoxifying potential of
CYP2D enzymes became less essential for survival, and, with
no selection pressure applied on CYP2D gene products,
accumulation of mutations resulted in a high degree of
polymorphism and ultimately in the degradation and loss of
function of most CYP2D genes.43
Going Further With GWA Studies
Traditionally, genetic association studies focused on candidate genes selected on the basis of their biological function.
After the recent availability of panels of SNPs that tag the
whole genome and their incorporation into high-density
genotyping microarrays, it is now possible to conduct GWA
studies to investigate the genetic component of common
diseases and quantitative traits without relying on any prior
biological hypothesis. The rationale of this approach is that, if
unknown disease-predisposing variants are present somewhere in the genome, they may be detected through their LD
with tagging SNPs represented on the genotyping array
(Figure 1). This approach offers a great potential for the
discovery of new causes and mechanisms of disease.49 The
GWA strategy has already demonstrated its power to identify
Cambien and Tiret
Downloaded from http://circ.ahajournals.org/ by guest on June 15, 2017
novel regions that contain genes that contribute to CHD. Two
recent reports have identified a common region on chromosome 9p21 associated with the risk of CHD in several
populations of European descent.50,51 The SNPs of the region
were in strong LD and defined a haplotype associated with a
15% to 20% increase in risk in the 50% of individuals who
were heterozygous for the haplotype and a 30% to 40%
increase in the 25% who were homozygous.50 It is particularly striking that the genomic region of interest contains no
annotated genes and that the SNPs that tag this region are not
associated with any established risk factor for CHD. The
association with the chromosome 9p21 region was further
replicated in larger groups in the Wellcome Trust Case
Control Consortium (WTCCC) study52 and the Cardiogenics
study,53 which both identified several additional regions that
harbor potential disease genes. Similarly, several GWA
studies of type 2 diabetes mellitus conducted in large groups
of patients and controls have reported, in addition to the
already well-established TCF7L2 gene,54,55 a series of
genomic regions close to the genes SLC30A8, IGFBP2,
CDKAL1, HHEX, and CDKN2A/CDKN2B that were consistently associated with risk of type 2 diabetes mellitus.56 – 60 As
for CHD, in most of these locations no obvious candidate
gene for type 2 diabetes mellitus appeared based on prior
biological knowledge. GWA studies for obesity, another
CHD risk factor, have also been reported. Whereas an
association with the FTO gene, whose function is unknown,
has been replicated in a large number of studies,61 an
association between the INSIG2 gene and adult and childhood
obesity, which had been initially found in 4 populations,62
was further replicated in some but not all studies conducted
so far.63 The possibility of a lack of complete consistency
across studies is not unexpected in presence of a weak
association, it could be the consequence of random fluctuation, population heterogeneity, or some other unknown confounder. Notwithstanding, the examples given above clearly
show that the GWA strategy has the capacity to generate new
findings that are robust to replication. The design of GWA
studies is evolving very rapidly in terms of sample size,
density of SNP arrays, and power, as exemplified by 2 recent
studies conducted in CHD (Figure 2).
The application of GWA studies to quantitative traits
provides a powerful way to explore the genetics of various
risk factors measured in epidemiological studies. For example, in a GWA study of 1464 type 2 diabetes patients and
1467 controls, associations of LDLc, HDLc, triglycerides,
and apoA1 and apoB plasma levels with loci already known
to influence these traits were found again, but a novel
association of triglycerides with the gene encoding the
glucokinase regulatory protein (GCKR) was also identified.57
In large population studies, quantitative traits can also be
dichotomized (eg, by comparison of extremes of the distribution of the trait). An interesting illustration of this approach
was recently provided with the discovery of a gene that
influences the QT interval (QTi) diagnosed on the ECG. A
QTi prolongation is an indicator of delayed ventricular
repolarization that may become clinically manifest with the
occurrence of syncopes and ventricular arrhythmias such as
torsades de pointe, which may lead to sudden death. The QTi
Genetics of Cardiovascular Diseases
1719
length has a heritability of 30%, and short as well as long
QTis are associated with an increased risk of cardiovascular
morbidity and mortality (see Dekker et al66). Recently, a
GWA study was performed to identify genes whose variability may contribute to the QTi variability in the population at
large.67 A multistage design was used, which started with the
comparison of the 2 extremes of the QTi distribution in
females followed by a step that refined the best regions of
interest and finally replicated the most interesting result in the
whole initial population and 2 independent studies. This
strategy resulted in the identification of frequent polymorphisms in the nitric oxide synthase 1 adaptor protein
(NOS1AP) gene that were consistently associated with QTi
both in men and women. NOS1AP is a regulator of the
neuronal nitric oxide synthase that had not been previously
suspected to be involved in cardiac repolarization. Its genetic
variability accounts for ⬇1.5% of the variance of the QTi in
the population at large. The finding, which has now been
replicated in other studies,68,69 demonstrates the potential of
the GWA approach applied to quantitative traits and opens a
new area of research for the prevention of sudden death.
A major limitation of GWA studies is that they are very
costly and time-consuming when applied to studies of large
sample size. One proposed solution to circumvent this limitation is to conduct GWA studies on pooled DNA samples.70
DNA Pooling and GWA Studies
The general principle of SNP microarray technology is to
produce a quantitative signal proportional to the number of
copies of a given allele in the DNA sample analyzed. When
the DNA sample is that of an individual, the signal is used to
assign a genotype according to the number of copies (0, 1, or
2) of a given SNP allele. When samples correspond to pooled
DNAs, the signal is proportional to the number of copies of a
given allele in the pool. By judicious composition of the pools
(eg, grouping cases and controls in distinct pools), it is
possible to compare allele frequencies between pools. This
economical approach to the estimation of allele frequencies in
GWA studies has been shown to be efficient.71–76 All studies
of sufficient sample size in which appropriate phenotypes and
DNA are available could benefit from this approach, the
major restriction being the ability to construct pools of high
quality. Because of the considerably reduced cost as a result
of DNA pooling, several complementary genotyping arrays
may be used in the same study, and the new genotyping
arrays that will become available in the future with more and
better markers will be usable in the same study with no major
cost restriction. DNA pooling results in some loss of sensitivity that may be reduced but not completely eliminated by
multiplying the number of pools and hybridizing each pool to
several arrays. However, the possibility to combine the
results of numerous studies should compensate for this loss of
precision and facilitate replication analysis. The most important limitation of the pooling strategy is that it restricts the
analyses to the hypotheses that were prespecified through the
design of the pools.
Overall, with the accumulation of results from GWA studies,
we are witnessing a true revolution that will not only impact our
understanding of the genetics of common CVD but will, through
1720
Circulation
October 9, 2007
B Cardiogenics Project
A Ottawa Heart Study (OHS)
GWA : 100,000 SNPs
SNPs with p<0.025
OHS-1-retrospective
322
patients
with CHD
at age<60
GWA: 500,000 SNPs
312
controls
with no
CHD
WTCCC – retrospective
1,926 patients
with early
onset of
familial CHD
2,938
controls
German – retrospective
Replication : 2,582 SNPs
OHS-2-retrospective
311
patients
875 patients
with early
onset of
familial CHD
326
Controls
1,644
controls
SNPs with p<0.025
Replication : 50 SNPs
ARIC - prospective
Downloaded from http://circ.ahajournals.org/ by guest on June 15, 2017
1,347
cases
Validation of 2 SNPs
on chromosome 9p21
9,054
controls
CCHS, DHS, OHS-3
2,297
cases
Replication: up to 500
regions
10,362
controls
Retrospective and
prospective studies
> 5000
cases
Resequencing of 50
regions, identification of
responsible variants
> 5000
controls
Functional studies: up
to 20 regions
Figure 2. Evolution of GWA studies in terms of sample size and power. A, The Ottawa Heart Study (OHS)50 used a 3-step design for
discovery of regions associated with disease. The initial GWA study was performed in a limited sample of cases and controls highly
contrasted for CHD risk (OHS-1) with a custom oligonucleotide array that comprised 100 000 SNPs. The best SNPs from this first step
were genotyped in an independent limited case/control sample (OHS-2) with the same criteria as OHS-1. Fifty SNPs associated with
CHD at a nominal significance threshold of 0.025 were further tested in a large prospective study of CHD risk (ARIC). Two SNPs
located close to each other on chromosome 9p21 were replicated and further validated in 3 independent studies. This strategy progressively filtered out the associations that were likely to be spurious to finally restrict the finding to a small reproducible set. However,
the sample size of the initial study (OHS-1) was relatively small, which resulted in a low sensitivity to identify weak signals. This is
why the most recent studies often comprise much larger samples of patients and controls for the discovery phase. B, As a typical
example, the Cardiogenics European Integrated Project53 combines the results of 2 GWA studies performed with the Affymetrix
GeneChip® 500K Mapping Array Set in 2 large case/control samples from the United Kingdom and Germany. Besides the large sample
size, the power of the project to identify loci associated with CHD was further increased by the strong familial component and the low
age at onset of disease in both studies.64,65 It is not surprising that this project delivered numerous replicated signals, such as that of
the chromosome 9p21 region. CCHS indicates Copenhagen City Heart Study ; DHS, Dallas Heart Study; and WTCCC, Wellcome Trust
Case Control Consortium.
the discovery of the implication of completely unsuspected
genetic sequences, undoubtedly affect our understanding of the
causes and pathophysiology of these diseases and open new
directions for their prevention and treatment.
Genotypic Versus Phenotypic Biomarkers
A large number of associations between candidate gene polymorphisms and common CVD have been reported, but relatively
few of these associations have been replicated, and those that
have demonstrated some robustness according to meta-analyses
were actually very weak, with odds ratios generally ⬍1.3.3
Assuming odds ratios of this size, it becomes obvious that most
of the genetic case-control studies conducted so far comprised
too few subjects. As a logical consequence of this lack of power
and of the multiplication of studies and markers tested, it is likely
that a number of true associations were missed whereas numerous spurious ones were reported. These methodological problems aside, it is important to realize that common CVD are
multifactorial diseases: Because they involve several genetic and
nongenetic factors, which possibly interact among each other
and have different population frequencies, we can expect some
degree of heterogeneity across studies. This heterogeneity may
also account for part of the inconsistent results in the literature.
Phenotypic biomarkers such as LDLc, blood glucose,
blood pressure, brain natriuretic peptide, C-reactive protein,
or several pharmacogenetic tests integrate a large number of
genetic and nongenetic influences. This is why they are very
informative and convenient to use in a medical context.
Conversely, the weak increase in individual risk conveyed by
single genetic polymorphisms explains why they are not
useful risk indicators or biomarkers for common CVD.
Information from several polymorphisms would have to be
integrated to become clinically useful, but such integration is
not trivial in the presence of weak nonadditive effects and
multiplicity of possible combinations of genotypes that increase the risk. Because most of these combinations may be
Cambien and Tiret
Downloaded from http://circ.ahajournals.org/ by guest on June 15, 2017
rarely observed, even in the largest studies, assessment of
their relationship with the disease is quite challenging.
On the other hand, genetics can help elucidate whether a
molecule, such as a protein measured in the blood, is involved
in the origin of disease. For example, circulating levels of
inflammatory biomarkers such as C-reactive protein or various cytokines are known to be increased in the course of
atherosclerosis and to predict future cardiovascular events. If
the level of a molecule in the circulation reflects the evolution
or the extension of atherosclerosis, it can be a very useful
biomarker but its elevation may not reflect a causal mechanism. Genetic studies may help elucidate whether the elevation is primary or secondary to the disease process. Indeed, an
association of genetic variants with both circulating levels of
the biomarker and disease risk is an argument for a causal
relationship. An investigation based on this reasoning failed
to support a causal association between plasma C-reactive
protein and the metabolic syndrome.77 Conversely, a causal
role of interleukin-18 in atherosclerosis and its complications
was recently suggested by this approach.78
Conclusion
The main objective of human genetic research is to discover new
mechanisms of disease. In recent years, genetics has profoundly
changed our understanding of several CVD and the novel GWA
strategy should further widen our understanding of the pathophysiology of these disorders. The discovery of new drug targets
as a consequence of genetic research may considerably modify
the therapeutic approach of cardiovascular disorders in the
middle and long terms. In addition, even if it is not the case
presently, it cannot be excluded that in some “simple” situations
genetic testing (genotyping) may be useful to help the clinician
in the diagnostic and prognostic assessment of patients. It is
probably in the area of pharmacogenetics that the benefit of
genotyping for the patient will be the most important. Microarrays that assay a large set of functional polymorphisms may be
used for that purpose. In many instances, however, phenotypic
biomarkers that naturally integrate multiple genetic and nongenetic influences are likely to be preferred to genetic biomarkers
because the integration of genotypic information and its translation into medical decision will be very challenging. Finally,
although the topic is outside the scope of the present review, it
is important for clinicians and scientists to be aware of the
ethical issues associated with genetic research and its contemporary realizations.79,80
Glossary of Terms Used in Genetics
Alleles: variant forms of the DNA sequence at a specified
locus. For example, alleles at a SNP are characterized by
the nucleotide that is changing. Combination of 2 alleles at
a locus forms a genotype.
Candidate genes: genes suspected of involvement in the
disease process.
Complex disease: a disease whose pattern of familial aggregation differs from that expected from the Mendelian
inheritance of a single genetic defect.
Epigenetics: the study of heritable changes in gene activity that
occur without a change in the sequence of the genetic
material.
Genetics of Cardiovascular Diseases
1721
Gene– environment interaction: the effect of environmental
factors on an association between a genotype and a
phenotype. This can also be expressed in the reverse way
(eg, in the case of pharmacogenetics where the focus is on
how genetic factors affect the effects of a drug).
Genome-wide association (GWA) study: a study that investigates the statistical associations between a phenotype and
a very large number of genetic markers supposed to inform
on the global variability of the genome. Because GWA
studies do not rely on a priori knowledge, they may lead to
the discovery of new causes of disease.
Genotype: the genetic constitution of an organism, which it
has inherited from its parents. More specifically, the
genotype refers to the particular combination of alleles at
specified loci present in an organism. At a specified locus
(eg, a single base pair on homologous chromosomes),
homozygous individuals have identical alleles and heterozygous individuals have different alleles.
Haplotype: a combination of alleles that are located at closely
linked loci and tend to be inherited together.
Linkage analysis: a statistical method that aims to locate a gene
causally related to a disease by identifying genetic markers of
known chromosomal location that are coinherited with the
trait of interest. Linkage analyses are conducted in families
with several affected members and may use sets of markers
that encompass the whole genome or specific regions.
Linkage disequilibrium (LD): polymorphisms in the human
genome are often not independent of one another. When a
mutation arises, it is associated with particular variants
present on the same chromosome. Recombination subsequently acts to erode this association, but for physically
close polymorphisms (eg, within a gene), the correlation,
known as LD, persists over time. For this and other reasons
related to population history and selection, strong statistical
associations between nearby polymorphisms are often
observed at the population level.
Locus: a specific region within a DNA sequence.
Mendelian disease: a disease with a pattern of familial
aggregation that reflects the inheritance of a single genetic
defect.
Mini- or microsatellite: a variable locus where alleles are
characterized by different numbers of repetitions of the
same motif in tandem. Mini- and microsatellites differ by
the length of the repeated motif (sequence of 2 to 5
nucleotides for microsatellites, ⬎10 nucleotides for minisatellites). Mini- and microsatellites are very polymorphic
(a large number of alleles exist in the population) and
therefore constitute excellent genetic markers for linkage
analysis.
Mutation: in the field of genetic epidemiology, a mutation is
a genetic variation with a very low allele frequency (eg,
mutations responsible for Mendelian diseases).
Phenotype: the phenotype is the actual appearance of an
organism or, more specifically in the research context, of a
trait of interest. It may be a binary trait (eg, the presence/
absence of a disease) or a quantitative trait (eg, the level of
blood pressure).
Polymorphism: any variation in the sequence of DNA among
individuals. In the field of genetic epidemiology, a polymorphism is a common genetic variation (allele frequency
⬎1%).
Single nucleotide polymorphism (SNP): the most common
type of polymorphism, in which the alleles differ at the
level of a single nucleotide.
1722
Circulation
October 9, 2007
Structural variants: structural rearrangements present in the
genome, such as copy-number variants (CNV), segmental
duplications, large insertions and deletions, inversions,
translocations, or large tandem repeats.
Disclosures
None.
References
Downloaded from http://circ.ahajournals.org/ by guest on June 15, 2017
1. Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H,
Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837–1847.
2. Evans A, Salomaa V, Kulathinal S, Asplund K, Cambien F, Ferrario M,
Perola M, Peltonen L, Shields D, Tunstall-Pedoe H, Kuulasmaa K.
MORGAM (an international pooling of cardiovascular cohorts). Int J
Epidemiol. 2005;34:21–27.
3. Arnett DK, Baird AE, Barkley RA, Basson CT, Boerwinkle E, Ganesh
SK, Herrington DM, Hong Y, Jaquish C, McDermott DA, O’Donnell CJ.
Relevance of genetics and genomics for prevention and treatment of
cardiovascular disease. A scientific statement from the American Heart
Association Council on Epidemiology and Prevention, the Stroke
Council, and the Functional Genomics and Translational Biology Interdisciplinary Working Group. Circulation. 2007;115:2878 –2901.
4. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon
K, Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A,
Howland J, Kann L, Lehoczky J, LeVine R, McEwan P, McKernan K,
Meldrim J, Mesirov JP, Miranda C, Morris W, Naylor J, Raymond C, Rosetti
M, Santos R, Sheridan A, Sougnez C, Stange-Thomann N, Stojanovic N,
Subramanian A, Wyman D, Rogers J, Sulston J, Ainscough R, Beck S,
Bentley D, Burton J, Clee C, Carter N, Coulson A, Deadman R, Deloukas P,
Dunham A, Dunham I, Durbin R, French L, Grafham D, Gregory S, Hubbard
T, Humphray S, Hunt A, Jones M, Lloyd C, McMurray A, Matthews L,
Mercer S, Milne S, Mullikin JC, Mungall A, Plumb R, Ross M, Shownkeen
R, Sims S, Waterston RH, Wilson RK, Hillier LW, McPherson JD, Marra
MA, Mardis ER, Fulton LA, Chinwalla AT, Pepin KH, Gish WR, Chissoe
SL, Wendl MC, Delehaunty KD, Miner TL, Delehaunty A, Kramer JB, Cook
LL, Fulton RS, Johnson DL, Minx PJ, Clifton SW, Hawkins T, Branscomb
E, Predki P, Richardson P, Wenning S, Slezak T, Doggett N, Cheng JF,
Olsen A, Lucas S, Elkin C, Uberbacher E, Frazier M, Gibbs RA, Muzny DM,
Scherer SE, Bouck JB, Sodergren EJ, Worley KC, Rives CM, Gorrell JH,
Metzker ML, Naylor SL, Kucherlapati RS, Nelson DL, Weinstock GM,
Sakaki Y, Fujiyama A, Hattori M, Yada T, Toyoda A, Itoh T, Kawagoe C,
Watanabe H, Totoki Y, Taylor T, Weissenbach J, Heilig R, Saurin W,
Artiguenave F, Brottier P, Bruls T, Pelletier E, Robert C, Wincker P, Smith
DR, Doucette-Stamm L, Rubenfield M, Weinstock K, Lee HM, Dubois J,
Rosenthal A, Platzer M, Nyakatura G, Taudien S, Rump A, Yang H, Yu J,
Wang J, Huang G, Gu J, Hood L, Rowen L, Madan A, Qin S, Davis RW,
Federspiel NA, Abola AP, Proctor MJ, Myers RM, Schmutz J, Dickson M,
Grimwood J, Cox DR, Olson MV, Kaul R, Raymond C, Shimizu N,
Kawasaki K, Minoshima S, Evans GA, Athanasiou M, Schultz R, Roe BA,
Chen F, Pan H, Ramser J, Lehrach H, Reinhardt R, McCombie WR, de la
Bastide M, Dedhia N, Blocker H, Hornischer K, Nordsiek G, Agarwala R,
Aravind L, Bailey JA, Bateman A, Batzoglou S, Birney E, Bork P, Brown
DG, Burge CB, Cerutti L, Chen HC, Church D, Clamp M, Copley RR,
Doerks T, Eddy SR, Eichler EE, Furey TS, Galagan J, Gilbert JG, Harmon
C, Hayashizaki Y, Haussler D, Hermjakob H, Hokamp K, Jang W, Johnson
LS, Jones TA, Kasif S, Kaspryzk A, Kennedy S, Kent WJ, Kitts P, Koonin
EV, Korf I, Kulp D, Lancet D, Lowe TM, McLysaght A, Mikkelsen T,
Moran JV, Mulder N, Pollara VJ, Ponting CP, Schuler G, Schultz J, Slater G,
Smit AF, Stupka E, Szustakowski J, Thierry-Mieg D, Thierry-Mieg J,
Wagner L, Wallis J, Wheeler R, Williams A, Wolf YI, Wolfe KH, Yang SP,
Yeh RF, Collins F, Guyer MS, Peterson J, Felsenfeld A, Wetterstrand KA,
Patrinos A, Morgan MJ, de Jong P, Catanese JJ, Osoegawa K, Shizuya H,
Choi S, Chen YJ; International Human Genome Sequencing Consortium.
Initial sequencing and analysis of the human genome. Nature. 2001;409:
860–921.
5. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith
HO, Yandell M, Evans CA, Holt RA, Gocayne JD, Amanatides P, Ballew
RM, Huson DH, Wortman JR, Zhang Q, Kodira CD, Zheng XH, Chen L,
Skupski M, Subramanian G, Thomas PD, Zhang J, Gabor Miklos GL,
Nelson C, Broder S, Clark AG, Nadeau J, McKusick VA, Zinder N,
Levine AJ, Roberts RJ, Simon M, Slayman C, Hunkapiller M, Bolanos R,
Delcher A, Dew I, Fasulo D, Flanigan M, Florea L, Halpern A, Han-
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
nenhalli S, Kravitz S, Levy S, Mobarry C, Reinert K, Remington K,
Abu-Threideh J, Beasley E, Biddick K, Bonazzi V, Brandon R, Cargill M,
Chandramouliswaran I, Charlab R, Chaturvedi K, Deng Z, Di Francesco
V, Dunn P, Eilbeck K, Evangelista C, Gabrielian AE, Gan W, Ge W,
Gong F, Gu Z, Guan P, Heiman TJ, Higgins ME, Ji RR, Ke Z, Ketchum
KA, Lai Z, Lei Y, Li Z, Li J, Liang Y, Lin X, Lu F, Merkulov GV,
Milshina N, Moore HM, Naik AK, Narayan VA, Neelam B, Nusskern D,
Rusch DB, Salzberg S, Shao W, Shue B, Sun J, Wang Z, Wang A, Wang
X, Wang J, Wei M, Wides R, Xiao C, Yan C, Yao A, Ye J, Zhan M,
Zhang W, Zhang H, Zhao Q, Zheng L, Zhong F, Zhong W, Zhu S, Zhao
S, Gilbert D, Baumhueter S, Spier G, Carter C, Cravchik A, Woodage T,
Ali F, An H, Awe A, Baldwin D, Baden H, Barnstead M, Barrow I,
Beeson K, Busam D, Carver A, Center A, Cheng ML, Curry L, Danaher
S, Davenport L, Desilets R, Dietz S, Dodson K, Doup L, Ferriera S, Garg
N, Gluecksmann A, Hart B, Haynes J, Haynes C, Heiner C, Hladun S,
Hostin D, Houck J, Howland T, Ibegwam C, Johnson J, Kalush F, Kline
L, Koduru S, Love A, Mann F, May D, McCawley S, McIntosh T,
McMullen I, Moy M, Moy L, Murphy B, Nelson K, Pfannkoch C, Pratts
E, Puri V, Qureshi H, Reardon M, Rodriguez R, Rogers YH, Romblad
D, Ruhfel B, Scott R, Sitter C, Smallwood M, Stewart E, Strong R, Suh
E, Thomas R, Tint NN, Tse S, Vech C, Wang G, Wetter J, Williams S,
Williams M, Windsor S, Winn-Deen E, Wolfe K, Zaveri J, Zaveri K,
Abril JF, Guigo R, Campbell MJ, Sjolander KV, Karlak B, Kejariwal
A, Mi H, Lazareva B, Hatton T, Narechania A, Diemer K, Muruganujan
A, Guo N, Sato S, Bafna V, Istrail S, Lippert R, Schwartz R, Walenz B,
Yooseph S, Allen D, Basu A, Baxendale J, Blick L, Caminha M,
Carnes-Stine J, Caulk P, Chiang YH, Coyne M, Dahlke C, Mays A,
Dombroski M, Donnelly M, Ely D, Esparham S, Fosler C, Gire H,
Glanowski S, Glasser K, Glodek A, Gorokhov M, Graham K, Gropman
B, Harris M, Heil J, Henderson S, Hoover J, Jennings D, Jordan C, Jordan
J, Kasha J, Kagan L, Kraft C, Levitsky A, Lewis M, Liu X, Lopez J, Ma
D, Majoros W, McDaniel J, Murphy S, Newman M, Nguyen T, Nguyen
N, Nodell M, Pan S, Peck J, Peterson M, Rowe W, Sanders R, Scott J,
Simpson M, Smith T, Sprague A, Stockwell T, Turner R, Venter E, Wang
M, Wen M, Wu D, Wu M, Xia A, Zandieh A, Zhu X. The sequence of
the human genome. Science. 2001;291:1304 –1351.
Ellegren H. Microsatellites: simple sequences with complex evolution.
Nat Rev Genet. 2004;5:435– 445.
Feuk L, Carson AR, Scherer SW. Structural variation in the human
genome. Nat Rev Genet. 2006;7:85–97.
Helgadottir A, Manolescu A, Thorleifsson G, Gretarsdottir S, Jonsdottir
H, Thorsteinsdottir U, Samani NJ, Gudmundsson G, Grant SFA, Thorgeirsson G, Sveinbjornsdottir S, Valdimarsson EM, Matthiasson SE,
Johannsson H, Gudmundsdottir O, Gurney ME, Sainz J, Thorhallsdottir
M, Andresdottir M, Frigge ML, Topol EJ, Kong A, Gudnason V,
Hakonarson H, Gulcher JR, Stefansson K. The gene encoding 5-lipoxygenase activating protein confers risk of myocardial infarction and stroke.
Nature Genet. 2004;36:233–239.
Botstein D, Risch N. Discovering genotypes underlying human phenotypes: past successes for Mendelian disease, future approaches for
complex disease. Nat Genet. 2003;33(Suppl):228 –237.
Cambien F, Tiret L. Atherosclerosis: from genetic polymorphisms to
system genetics. Cardiovasc Toxicol. 2005;5:143–152.
Barbaux S, Tregouet D, Nicaud V, Poirier O, Perret C, Godefroy T,
Francomme C, Combadière C, Arveiler D, Luc G, Ruidavets J, Evans A,
Kee F, Morrison C, Tiret L, Brand-Herrmann S, Cambien F. Polymorphisms in 33 inflammatory genes and risk of myocardial infarction A system genetics approach. J Mol Med. In press.
Kruglyak L. Power tools for human genetics. Nature Genet. 2005;37:
1299 –1300.
The International HapMap Consortium. A haplotype map of the human
genome. Nature. 2005;437:1299 –1320.
Patil N, Berno AJ, Hinds DA, Barrett WA, Doshi JM, Hacker CR,
Kautzer CR, Lee DH, Marjoribanks C, McDonough DP, Nguyen BT,
Norris MC, Sheehan JB, Shen N, Stern D, Stokowski RP, Thomas DJ,
Trulson MO, Vyas KR, Frazer KA, Fodor SP, Cox DR. Blocks of limited
haplotype diversity revealed by high-resolution scanning of human chromosome 21. Science. 2001;294:1719 –1723.
Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B,
Higgins J, DeFelice M, Lochner A, Faggart M, Liu-Cordero SN, Rotimi
C, Adeyemo A, Cooper R, Ward R, Lander ES, Daly MJ, Altshuler D.
The structure of haplotype blocks in the human genome. Science. 2002;
296:2225–2229.
Wall JD, Pritchard JK. Haplotype blocks and linkage disequilibrium in
the human genome. Nat Rev Genet. 2003;4:587–597.
Cambien and Tiret
Downloaded from http://circ.ahajournals.org/ by guest on June 15, 2017
17. Conrad DF, Jakobsson M, Coop G, Wen X, Wall JD, Rosenberg NA,
Pritchard JK. A worldwide survey of haplotype variation and linkage
disequilibrium in the human genome. Nat Genet. 2006;38:1251–1260.
18. Zeggini E, Rayner W, Morris AP, Hattersley AT, Walker M, Hitman GA,
Deloukas P, Cardon LR, McCarthy MI. An evaluation of HapMap sample
size and tagging SNP performance in large-scale empirical and simulated
data sets. Nat Genet. 2005;37:1320 –1322.
19. de Bakker PI, Burtt NP, Graham RR, Guiducci C, Yelensky R, Drake JA,
Bersaglieri T, Penney KL, Butler J, Young S, Onofrio RC, Lyon HN,
Stram DO, Haiman CA, Freedman ML, Zhu X, Cooper R, Groop L,
Kolonel LN, Henderson BE, Daly MJ, Hirschhorn JN, Altshuler D.
Transferability of tag SNPs in genetic association studies in multiple
populations. Nat Genet. 2006;38:1298 –1303.
20. Coon H, Leppert MF, Kronenberg F, Province MA, Myers RH, Arnett
DK, Eckfeldt JH, Heiss G, Williams RR, Hunt SC. Evidence for a major
gene accounting for mild elevation in LDL cholesterol: the NHLBI
Family Heart Study. Ann Hum Genet. 1999;63:401– 412.
21. Cambien F. Coronary heart disease and polymorphisms in genes affecting
lipid metabolism and inflammation. Curr Atheroscler Rep. 2005;7:
188 –195.
22. Mahley RW, Rall SC. Apolipoprotein E: far more than a lipid transport
protein. Annu Rev Genomics Hum Genet. 2000;1:507–537.
23. Hallman DM, Boerwinkle E, Saha N, Sandholzer C, Menzel HJ, Csazar
A, Utermann G. The apolipoprotein E polymorphism: a comparison of
allele frequencies and effects in nine populations. Am J Hum Genet.
1991;49:338 –349.
24. Song Y, Stampfer MJ, Liu S. Meta-analysis: apolipoprotein E genotypes
and risk for coronary heart disease. Ann Intern Med. 2004;141:137–147.
25. Brown MS, Kovanen PT, Goldstein JL. Regulation of plasma cholesterol
by lipoprotein receptors. Science. 1981;212:628 – 635.
26. Austin MA, Hutter CM, Zimmern RL, Humphries SE. Genetic causes of
monogenic heterozygous familial hypercholesterolemia: a HuGE prevalence review. Am J Epidemiol. 2004;160:407– 420.
27. van Aalst-Cohen ES, Jansen ACM, Tanck MWT, Defesche JC, Trip MD,
Lansberg PJ, Stalenhoef AFH, Kastelein JJP. Diagnosing familial hypercholesterolaemia: the relevance of genetic testing. Eur Heart J. 2006;27:
2240 –2246.
28. Damgaard D, Larsen ML, Nissen PH, Jensen JM, Jensen HK, Soerensen
VR, Jensen LG, Faergeman O. The relationship of molecular genetic to
clinical diagnosis of familial hypercholesterolemia in a Danish population. Atherosclerosis. 2005;180:155–160.
29. Innerarity TL, Weisgraber KH, Arnold KS, Mahley RW, Krauss RM,
Vega GL, Grundy SM. Familial defective apolipoprotein B-100: low
density lipoproteins with abnormal receptor binding. Proc Natl Acad Sci
U S A. 1987;84:6919 – 6923.
30. Soria LF, Ludwig EH, Clarke HR, Vega GL, Grundy SM, McCarthy BJ.
Association between a specific apolipoprotein B mutation and familial
defective apolipoprotein B-100. Proc Natl Acad Sci U S A. 1989;86:
587–591.
31. Abifadel M, Varret M, Rabes J-P, Allard D, Ouguerram K, Devillers M,
Cruaud C, Benjannet S, Wickham L, Erlich D, Derre A, Villeger L,
Farnier M, Beucler I, Bruckert E, Chambaz J, Chanu B, Lecerf J-M, Luc
G, Moulin P, Weissenbach J, Prat A, Krempf M, Junien C, Seidah NG,
Boileau C. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat Genet. 2003;34:154 –156.
32. Rashid S, Curtis DE, Garuti R, Anderson NN, Bashmakov Y, Ho YK,
Hammer RE, Moon Y-A, Horton JD. Decreased plasma cholesterol and
hypersensitivity to statins in mice lacking Pcsk9. Proc Natl Acad Sci
U S A. 2005;102:5374 –5379.
33. Cohen JC, Boerwinkle E, Mosley TH, Hobbs HH. Sequence variations in
PCSK9, low LDL, and protection against coronary heart disease. N Engl
J Med. 2006;354:1264 –1272.
34. Kotowski IK, Pertsemlidis A, Luke A, Cooper RS, Vega GL, Cohen JC,
Hobbs HH. A spectrum of PCSK9 alleles contributes to plasma levels of
low-density lipoprotein cholesterol. Am J Hum Genet. 2006;78:410 – 422.
35. Bodzioch M, Orso E, Klucken J, Langmann T, Bottcher A, Diederich W,
Drobnik W, Barlage S, Buchler C, Porsch-Ozcurumez M, Kaminski WE,
Hahmann HW, Oette K, Rothe G, Aslanidis C, Lackner KJ, Schmitz G.
The gene encoding ATP-binding cassette transporter 1 is mutated in
Tangier disease. Nat Genet. 1999;22:347–351.
36. Brooks-Wilson A, Marcil M, Clee SM, Zhang LH, Roomp K, van Dam M,
Yu L, Brewer C, Collins JA, Molhuizen HO, Loubser O, Ouelette BF,
Fichter K, Ashbourne-Excoffon KJ, Sensen CW, Scherer S, Mott S, Denis
M, Martindale D, Frohlich J, Morgan K, Koop B, Pimstone S, Kastelein JJ,
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
Genetics of Cardiovascular Diseases
1723
Genest J, Hayden MR. Mutations in ABC1 in Tangier disease and familial
high-density lipoprotein deficiency. Nat Genet. 1999;22:336–345.
Rust S, Rosier M, Funke H, Real J, Amoura Z, Piette JC, Deleuze JF,
Brewer HB, Duverger N, Denefle P, Assmann G. Tangier disease is
caused by mutations in the gene encoding ATP-binding cassette transporter 1. Nat Genet. 1999;22:352–355.
Cohen JC, Kiss RS, Pertsemlidis A, Marcel YL, McPherson R, Hobbs
HH. Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science. 2004;305:869 – 872.
Tregouet D-A, Ricard S, Nicaud V, Arnould I, Soubigou S, Rosier M,
Duverger N, Poirier O, Macé S, Kee F, Morrison C, Denefle P, Tiret L,
Evans A, Deleuze J-F, Cambien F. In-depth haplotype analysis of
ABCA1 gene polymorphisms in relation to plasma ApoA1 levels and
myocardial infarction. Arterioscler Thromb Vasc Biol. 2004;24:775–781.
Hemminki K, Lorenzo Bermejo J, Forsti A. The balance between heritable and environmental aetiology of human disease. Nat Rev Genet.
2006;7:958 –965.
Tiret L. Gene-environment interaction: a central concept in multifactorial
diseases. Proc Nutr Soc. 2002;61:457– 463.
Manolio TA, Bailey-Wilson JE, Collins FS. Genes, environment and the
value of prospective cohort studies. Nat Rev Genet. 2006;7:812– 820.
Ingelman-Sundberg M. Genetic polymorphisms of cytochrome P450 2D6
(CYP2D6): clinical consequences, evolutionary aspects and functional
diversity. Pharmacogenomics J. 2005;5:6 –13.
Andersson T, Flockhart DA, Goldstein DB, Huang SM, Kroetz DL, Milos
PM, Ratain MJ, Thummel K. Drug-metabolizing enzymes: evidence for
clinical utility of pharmacogenomic tests. Clin Pharmacol Ther. 2005;
78:559 –581.
Wadelius M, Pirmohamed M. Pharmacogenetics of warfarin: current
status and future challenges. Pharmacogenomics J. 2007;7:99 –111.
Rettie AE, Jones JP. Clinical and toxicological relevance of CYP2C9:
drug-drug interactions and pharmacogenetics. Annu Rev Pharmacol
Toxicol. 2005;45:477– 494.
Sconce EA, Khan TI, Wynne HA, Avery P, Monkhouse L, King BP,
Wood P, Kesteven P, Daly AK, Kamali F. The impact of CYP2C9 and
VKORC1 genetic polymorphism and patient characteristics upon
warfarin dose requirements: proposal for a new dosing regimen. Blood.
2005;106:2329 –2333.
Juran BD, Egan LJ, Lazaridis KN. The AmpliChip CYP450 test: principles, challenges, and future clinical utility in digestive disease. Clin
Gastroenterol Hepatol. 2006;4:822– 830.
Hirschhorn JN, Daly MJ. Genome-wide association studies for common
diseases and complex traits. Nat Rev Genet. 2005;6:95–108.
McPherson R, Pertsemlidis A, Kavaslar N, Stewart A, Roberts R, Cox
DR, Hinds DA, Pennacchio LA, Tybjaerg-Hansen A, Folsom AR, Boerwinkle E, Hobbs HH, Cohen JC. A Common Allele on Chromosome 9
Associated with Coronary Heart Disease. Science. 2007;316:1488 –1491.
Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T,
Jonasdottir A, Sigurdsson A, Baker A, Palsson A, Masson G, Gudbjartsson D, Magnusson KP, Andersen K, Levey AI, Backman VM, Matthiasdottir S, Jonsdottir T, Palsson S, Einarsdottir H, Gunnarsdottir S,
Gylfason A, Vaccarino V, Hooper WC, Reilly MP, Granger CB, Austin
H, Rader DJ, Shah SH, Quyyumi AA, Gulcher JR, Thorgeirsson G,
Thorsteinsdottir U, Kong A, Stefansson K. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science. 2007;
316:1491–1493.
The Wellcome Trust Case Control Consortium. Genome-wide association
study of 14,000 cases of seven common diseases and 3,000 shared
controls. Nature. 2007;447:661– 678.
Samani N, Erdmann J, Hall A, Hengstenberg C, Mangino M, Mayer B,
Dixon R, Meitinger T, Braund P, Wichmann H, Barrett J, König I,
Stevens S, Szymczak S, Tregouet D, Iles M, Pahlke F, Pollard H, Lieb W,
Cambien F, Fischer M, Ouwehand W, Blankenberg S, Balmforth A,
Baessler A, Ball S, Strom T, Braenne I, Gieger C, Deloukas P, Tobin M,
Ziegler A, Thompson J, Schunkert H. Analysis of two genome-wide
association studies identifies and validates novel gene loci for myocardial
infarction. N Engl J Med. 2007;357:443– 453.
Grant SFA, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu
A, Sainz J, Helgason A, Stefansson H, Emilsson V, Helgadottir A,
Styrkarsdottir U, Magnusson KP, Walters GB, Palsdottir E, Jonsdottir T,
Gudmundsdottir T, Gylfason A, Saemundsdottir J, Wilensky RL, Reilly
MP, Rader DJ, Bagger Y, Christiansen C, Gudnason V, Sigurdsson G,
Thorsteinsdottir U, Gulcher JR, Kong A, Stefansson K. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes.
Nat Genet. 2006;38:320 –323.
1724
Circulation
October 9, 2007
Downloaded from http://circ.ahajournals.org/ by guest on June 15, 2017
55. Cauchi S, El Achhab Y, Choquet H, Dina C, Krempler F, Weitgasser R,
Nejjari C, Patsch W, Chikri M, Meyre D, Froguel P. TCF7L2 is reproducibly associated with type 2 diabetes in various ethnic groups: a global
meta-analysis. J Mol Med. 2007;85:777–782.
56. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P, Vincent
D, Belisle A, Hadjadj S, Balkau B, Heude B, Charpentier G, Hudson TJ,
Montpetit A, Pshezhetsky AV, Prentki M, Posner BI, Balding DJ, Meyre D,
Polychronakos C, Froguel P. A genome-wide association study identifies
novel risk loci for type 2 diabetes. Nature. 2007;445:881–885.
57. Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H, Roix
JJ, Kathiresan S, Hirschhorn JN, Daly MJ, Hughes TE, Groop L, Altshuler D, Almgren P, Florez JC, Meyer J, Ardlie K, Bengtsson K, Isomaa
B, Lettre G, Lindblad U, Lyon HN, Melander O, Newton-Cheh C,
Nilsson P, Orho-Melander M, Rastam L, Speliotes EK, Taskinen MR,
Tuomi T, Guiducci C, Berglund A, Carlson J, Gianniny L, Hackett R,
Hall L, Holmkvist J, Laurila E, Sjogren M, Sterner M, Surti A, Svensson
M, Tewhey R, Blumenstiel B, Parkin M, Defelice M, Barry R, Brodeur
W, Camarata J, Chia N, Fava M, Gibbons J, Handsaker B, Healy C,
Nguyen K, Gates C, Sougnez C, Gage D, Nizzari M, Gabriel SB, Chirn
GW, Ma Q, Parikh H, Richardson D, Ricke D, Purcell S. Genome-wide
association analysis identifies loci for type 2 diabetes and triglyceride
levels. Science. 2007;316:1331–1336.
58. Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R,
Jonsdottir T, Walters GB, Styrkarsdottir U, Gretarsdottir S, Emilsson V,
Ghosh S, Baker A, Snorradottir S, Bjarnason H, Ng MC, Hansen T,
Bagger Y, Wilensky RL, Reilly MP, Adeyemo A, Chen Y, Zhou J,
Gudnason V, Chen G, Huang H, Lashley K, Doumatey A, So WY, Ma
RC, Andersen G, Borch-Johnsen K, Jorgensen T, van Vliet-Ostaptchouk
JV, Hofker MH, Wijmenga C, Christiansen C, Rader DJ, Rotimi C,
Gurney M, Chan JC, Pedersen O, Sigurdsson G, Gulcher JR, Thorsteinsdottir U, Kong A, Stefansson K. A variant in CDKAL1 influences insulin
response and risk of type 2 diabetes. Nat Genet. 2007;39:770 –775.
59. Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango
H, Timpson NJ, Perry JR, Rayner NW, Freathy RM, Barrett JC, Shields
B, Morris AP, Ellard S, Groves CJ, Harries LW, Marchini JL, Owen KR,
Knight B, Cardon LR, Walker M, Hitman GA, Morris AD, Doney AS,
McCarthy MI, Hattersley AT. Replication of Genome-Wide Association
Signals in U.K. Samples Reveals Risk Loci for Type 2 Diabetes. Science.
2007;316:1336 –1341.
60. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos
MR, Stringham HM, Chines PS, Jackson AU, Prokunina-Olsson L, Ding
CJ, Swift AJ, Narisu N, Hu T, Pruim R, Xiao R, Li XY, Conneely KN,
Riebow NL, Sprau AG, Tong M, White PP, Hetrick KN, Barnhart MW,
Bark CW, Goldstein JL, Watkins L, Xiang F, Saramies J, Buchanan TA,
Watanabe RM, Valle TT, Kinnunen L, Abecasis GR, Pugh EW, Doheny
KF, Bergman RN, Tuomilehto J, Collins FS, Boehnke M. A
Genome-Wide Association Study of Type 2 Diabetes in Finns Detects
Multiple Susceptibility Variants. Science. 2007;316:1341–1345.
61. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM,
Lindgren CM, Perry JR, Elliott KS, Lango H, Rayner NW, Shields B,
Harries LW, Barrett JC, Ellard S, Groves CJ, Knight B, Patch AM, Ness
AR, Ebrahim S, Lawlor DA, Ring SM, Ben-Shlomo Y, Jarvelin MR,
Sovio U, Bennett AJ, Melzer D, Ferrucci L, Loos RJ, Barroso I, Wareham
NJ, Karpe F, Owen KR, Cardon LR, Walker M, Hitman GA, Palmer CN,
Doney AS, Morris AD, Smith GD, Hattersley AT, McCarthy MI. A
common variant in the FTO gene is associated with body mass index and
predisposes to childhood and adult obesity. Science. 2007;316:889 – 894.
62. Herbert A, Gerry NP, McQueen MB, Heid IM, Pfeufer A, Illig T,
Wichmann HE, Meitinger T, Hunter D, Hu FB, Colditz G, Hinney A,
Hebebrand J, Koberwitz K, Zhu X, Cooper R, Ardlie K, Lyon H,
Hirschhorn JN, Laird NM, Lenburg ME, Lange C, Christman MF. A
common genetic variant is associated with adult and childhood obesity.
Science. 2006;312:279 –283.
63. Lyon HN, Emilsson V, Hinney A, Heid IM, Lasky-Su J, Zhu X, Thorleifsson G, Gunnarsdottir S, Walters GB, Thorsteinsdottir U, Kong A,
Gulcher J, Nguyen TT, Scherag A, Pfeufer A, Meitinger T, Bronner G,
Rief W, Soto-Quiros ME, Avila L, Klanderman B, Raby BA, Silverman
EK, Weiss ST, Laird N, Ding X, Groop L, Tuomi T, Isomaa B, Bengtsson
K, Butler JL, Cooper RS, Fox CS, O’Donnell CJ, Vollmert C, Celedon
JC, Wichmann HE, Hebebrand J, Stefansson K, Lange C, Hirschhorn JN.
The association of a SNP upstream of INSIG2 with body mass index is
reproduced in several but not all cohorts. PLoS Genet. 2007;3:627– 633.
64. Samani NJ, Burton P, Mangino M, Ball SG, Balmforth AJ, Barrett J,
Bishop T, Hall A. A genomewide linkage study of 1,933 families affected
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
by premature coronary artery disease: the British Heart Foundation (BHF)
Family Heart Study. Am J Hum Genet. 2005;77:1011–1020.
Fischer M, Broeckel U, Holmer S, Baessler A, Hengstenberg C, Mayer B,
Erdmann J, Klein G, Riegger G, Jacob HJ, Schunkert H. Distinct heritable
patterns of angiographic coronary artery disease in families with myocardial infarction. Circulation. 2005;111:855– 862.
Dekker JM, Crow RS, Hannan PJ, Schouten EG, Folsom AR. Heart
rate-corrected QT interval prolongation predicts risk of coronary heart
disease in black and white middle-aged men and women: the ARIC study.
J Am Coll Cardiol. 2004;43:565–571.
Arking DE, Pfeufer A, Post W, Kao WHL, Newton-Cheh C, Ikeda M,
West K, Kashuk C, Akyol M, Perz S, Jalilzadeh S, Illig T, Gieger C, Guo
C-Y, Larson MG, Wichmann HE, Marban E, O’Donnell CJ, Hirschhorn
JN, Kaab S, Spooner PM, Meitinger T, Chakravarti A. A common genetic
variant in the NOS1 regulator NOS1AP modulates cardiac repolarization.
Nat Genet. 2006;38:644 – 651.
Aarnoudse AJ, Newton-Cheh C, de Bakker PI, Straus SM, Kors JA,
Hofman A, Uitterlinden AG, Witteman JC, Stricker BH. Common
NOS1AP variants are associated with a prolonged QTc interval in the
Rotterdam Study. Circulation. 2007;116:10 –16.
Post W, Shen H, Damcott C, Arking DE, Kao WH, Sack PA, Ryan KA,
Chakravarti A, Mitchell BD, Shuldiner AR. Associations between genetic
variants in the NOS1AP (CAPON) gene and cardiac repolarization in the
old order Amish. Hum Hered. 2007;64:214 –219.
Sham P, Bader JS, Craig I, O’Donovan M, Owen M. DNA Pooling: a tool
for large-scale association studies. Nat Rev Genet. 2002;3:862– 671.
Meaburn E, Butcher LM, Schalkwyk LC, Plomin R. Genotyping pooled
DNA using 100K SNP microarrays: a step towards genomewide association scans. Nucleic Acids Res. 2006;34:e27.
Hanson RL, Craig DW, Millis MP, Yeatts KA, Kobes S, Pearson JV, Lee
AM, Knowler WC, Nelson RG, Wolford JK. Identification of PVT1 as a
candidate gene for end-stage renal disease in type 2 diabetes using a
pooling-based genome-wide single nucleotide polymorphism association
study. Diabetes. 2007;56:975–983.
Melquist S, Craig DW, Huentelman MJ, Crook R, Pearson JV, Baker M,
Zismann VL, Gass J, Adamson J, Szelinger S, Corneveaux J, Cannon A,
Coon KD, Lincoln S, Adler C, Tuite P, Calne DB, Bigio EH, Uitti RJ,
Wszolek ZK, Golbe LI, Caselli RJ, Graff-Radford N, Litvan I, Farrer MJ,
Dickson DW, Hutton M, Stephan DA. Identification of a novel risk locus
for progressive supranuclear palsy by a pooled genomewide scan of
500,288 single-nucleotide polymorphisms. Am J Hum Genet. 2007;80:
769 –778.
Wilkening S, Chen B, Wirtenberger M, Burwinkel B, Forsti A, Hemminki
K, Canzian F. Allelotyping of pooled DNA with 250 K SNP microarrays.
BMC Genomics. 2007;8:77.
Papassotiropoulos A, Stephan DA, Huentelman MJ, Hoerndli FJ, Craig
DW, Pearson JV, Huynh KD, Brunner F, Corneveaux J, Osborne D,
Wollmer MA, Aerni A, Coluccia D, Hanggi J, Mondadori CR, Buchmann
A, Reiman EM, Caselli RJ, Henke K, de Quervain DJ. Common Kibra
alleles are associated with human memory performance. Science. 2006;
314:475– 478.
Steer S, Abkevich V, Gutin A, Cordell HJ, Gendall KL, Merriman ME,
Rodger RA, Rowley KA, Chapman P, Gow P, Harrison AA, Highton J,
Jones PB, O’Donnell J, Stamp L, Fitzgerald L, Iliev D, Kouzmine A, Tran
T, Skolnick MH, Timms KM, Lanchbury JS, Merriman TR. Genomic
DNA pooling for whole-genome association scans in complex disease:
empirical demonstration of efficacy in rheumatoid arthritis. Genes
Immun. 2007;8:57– 68.
Timpson NJ, Lawlor DA, Harbord RM, Gaunt TR, Day INM, Palmer LJ,
Hattersley AT, Ebrahim S, Lowe GDO, Rumley A, Davey Smith G.
C-reactive protein and its role in metabolic syndrome: mendelian randomisation study. Lancet. 2005;366:1954 –1959.
Tiret L, Godefroy T, Lubos E, Nicaud V, Tregouet D-A, Barbaux S,
Schnabel R, Bickel C, Espinola-Klein C, Poirier O, Perret C, Menzel T,
Rupprecht H-J, Lackner K, Cambien F, Blankenberg S. Genetic analysis
of the interleukin-18 system highlights the role of the interleukin-18 gene
in cardiovascular disease. Circulation. 2005;112:643– 650.
Lillquist E, Sullivan CA. Legal regulation of the use of race in medical
research. J Law Med Ethics. 2006;34:535–551, 480.
Fulda KG, Lykens K. Ethical issues in predictive genetic testing: a public
health perspective. J Med Ethics. 2006;32:143–147.
KEY WORDS: cardiovascular diseases
䡲 lipoproteins 䡲 risk factors
䡲
epidemiology
䡲
genetics
Genetics of Cardiovascular Diseases: From Single Mutations to the Whole Genome
François Cambien and Laurence Tiret
Circulation. 2007;116:1714-1724
doi: 10.1161/CIRCULATIONAHA.106.661751
Downloaded from http://circ.ahajournals.org/ by guest on June 15, 2017
Circulation is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231
Copyright © 2007 American Heart Association, Inc. All rights reserved.
Print ISSN: 0009-7322. Online ISSN: 1524-4539
The online version of this article, along with updated information and services, is located on the
World Wide Web at:
http://circ.ahajournals.org/content/116/15/1714
Permissions: Requests for permissions to reproduce figures, tables, or portions of articles originally published
in Circulation can be obtained via RightsLink, a service of the Copyright Clearance Center, not the Editorial
Office. Once the online version of the published article for which permission is being requested is located,
click Request Permissions in the middle column of the Web page under Services. Further information about
this process is available in the Permissions and Rights Question and Answer document.
Reprints: Information about reprints can be found online at:
http://www.lww.com/reprints
Subscriptions: Information about subscribing to Circulation is online at:
http://circ.ahajournals.org//subscriptions/