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Nature Clinical Practice Cardiovascular Medicine (2007) 4, 558-569

Received 15 January 2007 | Accepted 8 June 2007

Mechanisms of Disease: the genetic basis of coronary heartdisease

Iftikhar J Kullo* and Keyue Ding

Correspondence *Division of Cardiovascular Diseases, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905,USA

Email kullo.iftikhar@mayo.edu

SUMMARY

Since completion of the human genome sequence, considerable progress has been madein determining the genetic basis of human diseases. Understanding the genetic basis of 

coronary heart disease (CHD), the leading cause of mortality in developed countries, is apriority. Here we provide an update on the genetic basis of CHD, focusing mainly on theclinical manifestations rather than the risk factors, most of which are heritable and also

influenced by genetic factors. The challenges faced when identifying clinically relevantgenetic determinants of CHD include phenotypic and genetic heterogeneity, and gene–

gene and gene–environment interactions. In addition, the etiologic spectrum includescommon genetic variants with small effects, as well as rare genetic variants with large

effects. Advances such as the cataloging of human genetic variation, new statisticalapproaches for analyzing massive amounts of genetic data, and the development of high-throughput single-nucleotide polymorphism genotyping platforms, will increase thelikelihood of success in the search for genetic determinants of CHD. Such knowledgecould refine cardiovascular risk stratification and facilitate the development of new

therapies.

Keywords:

association, complex diseases, coronary heart disease, genetics, linkage

Medscape Continuing Medical Education online

Medscape, LLC is pleased to provide online continuing medical education (CME) for this journal article, allowing clinicians the opportunity to earn CME credit. Medscape, LLC isaccredited by the Accreditation Council for Continuing Medical Education (ACCME) toprovide CME for physicians. Medscape, LLC designates this educational activity for a

maximum of 1.0 AMA PRA Category 1 Credits™

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Learning objectives

Upon completion of this activity, participants should be able to:

1. Describe the epidemiology of genetic factors in coronary heart disease (CHD).2. Identify results of linkage studies of genetic factors in CHD.3. Compare linkage studies with association studies relating complex diseases to

genomic factors.4. Specify limitations of studies linking genetic factors to complex diseases.

5. Recognize possible contributions of genetic studies to the clinical care of patientsat risk for CHD.

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INTRODUCTION

In 2003 in the US alone, there were an estimated 1.2 million cases of coronary heartdisease (CHD), resulting in 479,000 deaths.1 Although recognition and treatment of established risk factors for CHD will reduce the disease burden considerably,

simultaneous efforts aimed at unraveling the genetic basis of CHD are important for thedevelopment of novel diagnostic and therapeutic methods.

Considerable progress has been made in determining the genetic basis of humandiseases since the human genome has been sequenced. The genetic determinants of 

more than 1,600 Mendelian diseases are now known, and discovery of genomic regionsand genetic polymorphisms that influence susceptibility to common 'complex' diseases isaccelerating. In this Review, we discuss the challenges of elucidating the genetic basis of CHD, focusing mainly on its clinical manifestations rather than its risk factors, andsummarize the studies that have yielded insights into the genetic basis of this common,complex disease. We also discuss how recent advances in this field will increase thelikelihood of identifying genetic determinants of CHD.

THE CURRENT STATE OF KNOWLEDGE

Several challenges exist in identifying the genetic determinants of common, complexdiseases such as CHD (Table 1). These include phenotypic and genetic heterogeneity,gene–gene and gene–environment interactions, and the fact that the etiologic spectrum

ranges from common genetic variants with small effects to rare genetic variants withlarge effects. Below, we attempt to summarize the current state of knowledge about the

genetic basis of CHD. A glossary of some common genetic terms used in this Review canbe found in Box 1.

Table 1 Challenges in identifying genetic determinants of coronary heartdisease.

Challenge Comment

Abbreviation: CHD, coronary heart disease.

Phenotypicheterogeneity

CHD can manifest as several clinical phenotypes, including chronicstable angina, acute coronary syndrome, myocardial infarction,

sudden cardiac death, and history of coronary revascularization.Measures of coronary atherosclerotic burden such as coronary arterycalcium or angiographic coronary artery disease are objective andquantitative, unlike the dichotomous characterization (presence orabsence) of a history of a cardiovascular event. Atheroscleroticdisease burden and cardiovascular events such as myocardialinfarction are distinct phenotypes, however, the latter being related

more to plaque instability and rupture rather than plaque burden.

Geneticheterogeneity

Genetic heterogeneity is likely, given the multiple causal pathwaysthat lead to CHD. For example, low plasma levels of HDL cholesterolcan result from variation in genes from multiple metabolic pathways.Similarly, the transition from a stable coronary atherosclerotic plaqueto an unstable inflamed plaque could be the result of genetic variationin multiple genes that participate in the inflammatory cascade andmatrix degradation.

Small gene effects

A single genetic variant can constitute only a small proportion (e.g.

1– 2%) of the total genetic contribution towards complex diseasephenotypes, and an odds ratio of 1.1–1.5 is typical for a susceptibilityvariant of a candidate gene.86 Uncovering such small effects requireslarge sample sizes, and recognition of this fact has motivatedassembly of the so-called Biobanks in several European countries,including Iceland, England and Estonia.87

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Challenge Comment

Gene–gene andgene–environmentinteractions

Identification of gene–gene and gene–environment interactions isessential for identification of genes responsible for complex diseases,and will require large sample sizes and adequate computationalresources.

Rare variants

causing complexdisease

Most current genetic epidemiology studies of complex diseases such

as CHD assume that common variants (i.e. minor allele frequency5%) account for much of the susceptibility to the disease. Bothcommon and rare variants, however, probably influence CHDsusceptibility. To uncover rare variants that influence susceptibility toCHD, resequencing or very large sample sizes will be required.

FAMILIAL CLUSTERING OF CORONARY HEART DISEASE

Twin and family studies have established that CHD aggregates in families and in fact

family history of early-onset CHD has long been considered a risk factor for the disease. 2 

Although a contentious issue, the familial clustering of CHD could be partly explained byheritable quantitative variation in known CHD risk factors. Evidence suggests that familyhistory contributes to an increased risk of CHD independently of the known risk factors.3,

4 High-risk families make up a considerable proportion of early CHD cases in the generalpopulation. In one study, families with a history of early CHD represented only 14% of the general population but accounted for 72% of early CHD cases (men aged <55 years,women aged <65 years) and 48% of CHD at all ages.5 A history of early CHD in a first-

degree relative approximately doubles the risk of CHD, although the reported relative riskranges from 1.3–11.3.4, 6, 7, 8, 9, 10 The highest relative hazard of CHD-related death can be

seen in monozygotic twins, when one twin dies of early-onset CHD.4 Furthermore, siblinghistory of myocardial infarction seems to be a greater risk factor than parental history of early-onset CHD.11 A proposed family risk score for CHD evaluates the ratio of observed

CHD events to expected events in an individual's first-degree relatives, adjusted for ageand sex at the onset of the first event.12 A higher family risk score is associated withgreater CHD risk.

MENDELIAN DISORDERS ASSOCIATED WITH CORONARY HEART DISEASE

Mendelian disorders associated with CHD, such as familial hypercholesterolemia,

comprise single-gene traits that are transmitted in an autosomal dominant, recessive orX-linked manner. For example, mutations in the LDL receptor gene (LDLR), the ligand-

binding domain of apolipoprotein B100 ( APOB), and proprotein convertasesubtilisin/kexin type 9 gene (PCSK9) result in familial hypercholesterolemia transmitted

in an autosomal dominant manner. The examination of disease pathophysiology andgene function in such Mendelian disorders might increase our understanding of theetiology of complex traits.13 Additionally, common variation in genes implicated inMendelian disorders could be used to determine disease susceptibility in the generalpopulation. Several Mendelian disorders of lipid metabolism are associated with increasedCHD risk and have yielded novel insights into the mechanisms of CHD. Investigation of the molecular basis of the rare disorder familial homozygous hypercholesterolemia led tothe discovery of the pathways of LDL cholesterol metabolism and the subsequent

development of statins.14 Rare allelic variants of three candidate genes that influence HDLcholesterol metabolism ( ABCA1 [ATP-binding cassette A1],  APOA1 [apolipoprotein A-1],

and LCAT [lecithin-cholesterol acyltransferase]) are associated with low HDL-cholesterollevel syndromes but are also found in individuals from the community with low HDLcholesterol levels.15, 16

LINKAGE STUDIES: IDENTIFICATION OF GENES RELATED TO CORONARY HEARTDISEASE

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Linkage studies are performed by using polymorphic DNA markers (Box 2 and Figure 1).Microsatellite markers (short tandem repeat DNA sequences that are dispersedthroughout the human genome) are typically used in linkage studies, although single-nucleotide polymorphisms (SNPs) can also be used (Box 1). Several genome-widelinkage studies for myocardial infarction and coronary artery disease have been reported(Table 2). The largest study, the British Heart Foundation Family Heart Study, included4,175 individuals with CHD from 1,933 families recruited throughout the UK. 17 Despite

the large sample size, a statistically significant logarithmic odds of linkage (LOD) score(i.e. 3) was not obtained for any of the cardiovascular end points studied. For coronaryartery disease (verified by exercise stress test or angiography), the highest LOD score

was 2.70 (chromosome 2 at 149 cM) in families (n = 1,698) with age at onset of 56years or less. For myocardial infarction, an overlapping peak with a LOD score of 2.1 (at119.3 cM) in families (n = 801) with age of at onset 59 years or less, was observed.Genomic regions identified in the published linkage studies as being correlated with CHDare largely non-overlapping, suggesting genetic heterogeneity, although phenotypicheterogeneity could also have contributed to the non-replicability of results. Farrall et al .attempted to replicate a genomic locus for CHD by performing linkage analysis in twoindependent samples of European whites.18 The investigators found evidence of 

replication for a locus on chromosome 17 (at 69 cM).

Figure 1. Different genetic markers used in linkage and association studies.

(A) A short tandem repeat consists of short sequences of DNA (normally 2–5 base pairs)that are repeated numerous times. A single-nucleotide polymorphism is a single

nucleotide change or variation that occurs in a DNA sequence. (B) A haplotype is thecombination of alleles (for different markers) that are located close together on the samechromosome and tend to be inherited together.

Table 2 Linkage studies of myocardial infarction and coronary artery disease.

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Study Population

Study sample

(number of 

participants)

AscertainmentLocus or

loci

Peak 

LOD

Candidate

gene(s)

Confirmed

by fine

mapping

Wang et al .88 Americana 428 multiplex

families (1,613)

Each family in the

cohort had at least

two affected

siblings with

 premature CAD or MI, and most

families also had

one unaffected

sibling

1p36 11.7 GJA4  No

Francke et 

al .89 Indo-Mauritian99 sibships

(535)

Proband with

CHD before 52

years of age and

additional siblings

with MI or 

diabetes

16p13.3 3.1SOCS1,

 ACSM3 No

Helgadottir 

et al .19 Icelandic

296 multiplex

families (713cases; 1,741

first-degree

relatives)

On the basis of MI13q12–13 2.5  ALOX5AP  Yes

Broeckel

et al .90 German513 sibships

(1,406)

At least two

siblings with MI

or CAD affected

at 59 years of 

age

14q32 3.9 Unknown Unknown

Wang et 

al .21

European-

Americans

1 family (25; 13

 patients)Proband with MI 15q26 4.2 MEF2A Yes

Samani et al .17 British 1,933 sibships

(4,175)

Each sibship had

at least twoavailable siblings

with CAD or MI

2q14.3– 21.2

1.98  IL1A, IL1B, PROC 

 No

Hauser et 

al .91

Predominantly

European-

Americans

438 sibships

(1,168)

Affected sibling

 pairs with onset of 

CAD at <51 years

of age for men and

<56 years of age

for women

3q13 3.5 Unknown Unknown

Pajukanta

et al .92 Finnish156 sibships

(526)

Each sibship had

at least two

siblings with

 premature CAD(>50% stenosis of 

at least two

coronary arteries)

Xq25 3.5  AGTR2  No

aIncluded: 2.3% African American, 0.8% Asian or Pacific Islander, 91.7% White, 1.4%

Hispanic, 1.7% Native American or Alaskan, 0.8% mixed ethnicity, and 1.3%

unknown ethnicity. Abbreviations:  ACSM3, acyl-CoA synthetase medium-chain

family member 3;  AGTR2, angiotensin II receptor, type 2;  ALOX5AP , arachidonate

5-lipoxygenase-activating protein; CAD, coronary artery disease; CHD, coronary

heart disease; GJA4, gap junction protein, alpha 4;  IL1A, interleukin 1 alpha;  IL1B,

interleukin 1 beta; LOD, logarithm of odds of linkage; MEF2A, MADS box

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transcription enhancer factor 2, polypeptide A; MI, myocardial infarction;  PROC ,

 protein C; SOCS1, suppressor of cytokine signaling 1.

BOX 2 LINKAGE STUDIES VERSUS ASSOCIATION STUDIES.

Linkage studies require enrollment of families with the disease or phenotype of 

interest. Approximately 400 microsatellite markers throughout the genome are

genotyped, and statistical evidence of cotransmission of DNA markers with the disease or

phenotype of interest is sought. If significant evidence for cotransmission is found

between a marker and the disease, the marker is probably situated near the disease-

susceptibility gene of interest (within approximately 10 megabases). The figure (below)

shows the linkage analysis of a disease transmitted in a dominant fashion; the marker

allele A1 cosegregates with the disease. Once linkage is found, fine mapping using a

geographically narrower set of markers can identify the region linked to the disease more

precisely. Fine mapping can also be performed by analyses of haplotypes, in which

multiple DNA markers are analyzed together rather than individually. Alternatively,

polymorphisms in candidate genes can be tested to determine whether they associate

with disease, or the most likely candidate gene can be sequenced to identify a novelfunctional variant responsible for the disease phenotype.

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Association studies compare allele frequencies in cases and controls to assess the

contribution of genetic variants to phenotypes. The figure (below) shows that the markerallele A6 is present more often in cases than controls and is, therefore, associated with

disease. Association studies use a case–control design and they do not require the study

of families. Family-based association studies using the transmission disequilibrium test

are, however, useful in reducing confounding caused by population stratification.107 Most

commonly, association studies test putative functional single-nucleotide polymorphisms

within candidate genes and regions. Alternatively, the entire set of markers in the gene

can be genotyped, which reduces bias, but entails considerable cost and adds complexity

to the statistical genetic analysis because of the large number of single-nucleotide

polymorphisms that can be present in a gene.

Helgadottir and colleagues showed the utility of linkage analysis in identifying new genesfor CHD.19 They performed linkage analysis with 1,068 microsatellite markers and found

a linkage signal (LOD 2.86) on chromosome 13 for 296 Icelandic families (713individuals) enrolled on the basis of a history of myocardial infarction. The investigators

then genotyped an additional 120 microsatellite markers in this interval in 802 cases of myocardial infarction and 837 controls, and found that a 4-SNP haplotype spanning the

 ALOX5AP gene (encoding arachidonate 5-lipoxygenase-activating protein) was associatedwith a doubled risk of myocardial infarction. A subsequent study found that ALOX5AP wasassociated with CHD in an English population and associated with stroke in Icelandic andScottish populations.20 Another example of a novel gene identified by linkage analysis ina pedigree with several members affected by early-onset CHD is MEF2 (myocyteenhancer factor 2), a transcription factor expressed in coronary artery endothelium.21 The

results of these studies have not yet translated into specific genetic tests but may pointto novel drug targets; for example, an inhibitor of  ALOX5AP pathway is being

investigated for clinical use.22

Genome-wide linkage studies for quantitative measures of atherosclerotic burden,including coronary artery calcium levels, carotid intima-media thickness and ankle–brachial index, have also been reported.23, 24, 25, 26 Although genomic regions with LODscores greater than 3 have been linked to some of these traits, specific genes responsiblefor the linkage signals have yet to be identified.

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ASSOCIATION STUDIES: IDENTIFICATION OF GENES RELATED TO CORONARYHEART DISEASE

Association studies compare allele frequencies in cases and controls to assess thecontribution of genetic variants to phenotypes of interest (Box 2). In contrast to linkagestudies, association studies of complex diseases localize disease-related genomic regionsmore precisely and have greater statistical power for detecting small gene effects.27 A

major concern, however, is the considerable proportion of associations between geneticvariants and disease that are reported but not replicated.28 The difficulty encountered in

reproducing the results of genetic association studies could be attributable to severalissues common to epidemiologic risk factor studies,29, 30, 31, 32, 33 including faulty study

design, inaccurate phenotyping, bias introduced during ascertainment and analyses, andconfounding variables.34 Issues specific to genetic association studies are briefly

discussed below.

When disease-related alleles have only a small effect on the phenotype, the statisticalpower of association studies will be low, because such associations can be difficult toreproduce. Furthermore, gene effects are context-dependent and can be modified by thepresence of other genetic or environmental factors, which can vary within study

populations.35

Spurious associations result from the presence of genetically differentstrata in a study sample (population stratification), each strata has varying frequencies of disease and different allele frequencies at the marker locus. Another important cause of 

irreproducible findings could be variation in linkage disequilibrium (Box 1).36 The geneticmarker used might be distinct from the polymorphism that affects disease but could be in

linkage disequilibrium with the polymorphism (i.e. they are inherited together in a unit).Notably, the degree of linkage disequilibrium between the polymorphism and the markercan also vary among study populations. Genetic heterogeneity, wherein the diseasephenotype results from multiple uncommon variations or variants with extremely lowfrequency37 (as posited by the 'common disease–rare variants' hypothesis),16, 38, 39 couldalso decrease the chances of replicating association study findings.

The results of several association studies for CHD have been validated by subsequentstudies or in independent samples (Table 3). An example is the external validation of the

role of  ALOX5AP variants in several vascular disease phenotypes.19, 20 In another study,Ozaki et al . used two independent sample sets to validate that a functional SNP in the 5'-

untranslated regions of PSMA6 (proteasome subunit, alpha type 6) conferred anincreased risk for myocardial infarction.40 Connelly et al . identified the transcription factorGATA2 (GATA-binding protein 2), which regulates several endothelial-specific genes, as anovel susceptibility gene for CHD in two independent case–control samples.41 Anotherexample of association study 'replication' is the study by Shiffman et al .42 Theinvestigators genotyped 11,053 putative functional SNPs in 6,891 genes and used athree-step process to reduce the number of hypotheses tested, thus identifying variantsin four genes associated with myocardial infarction (PALLD, palladin, cytoskeletal

associated protein; ROS1, v-ros UR2 sarcoma virus oncogene homolog 1 (avian);

TAS2R50, taste receptor, type 2, member 50; and OR13G1, olfactory receptor, family13, subfamily G, member 1). Further investigation will be needed to assess the utility of these polymorphisms in assessing CHD risk or in identifying new targets for drug

therapy.

Table 3 Examples of single-nucleotide polymorphism or haplotype associationstudies of coronary heart diseasea.

Gene Gene nameSNPs or

haplotypeSNP class

Supportingstudy(ies)

 ALOX5AP 

Arachidonate 5-

lipoxgenase-activatingprotein Haplotype A NA Helgadottir et al .

20

 APOE  Apolipoprotein E 4 allele NonSyn Song et al .93 (a meta-

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Gene Gene nameSNPs or

haplotypeSNP class

Supportingstudy(ies)

rs4986978 Intron analysis)

CX3CR1Chemokine (C-X3-C

motif) receptor 1rs3732378

NonSyn

(T280M)

Moatti et al .,94

McDermott et al .95

GJA4 Gap junction protein 1 rs1764391

NonSyn

(P319S)

Yamada et al .,96

Hirashiki et al .97

MMP3 Matrix metallopeptidase 3 rs3025058 Regulatory

Terashima et al .,98

Rauramaa et al .,99

Yamada et al .,96

Hirashiki et al .97

SERPINE 

1

Serpin peptidase inhibitor,clade E member 1

rs1799889 RegulatoryMargaglione et al .,100

Yamada et al .96

PDE4DPhosphodiesterase 4D,cAMP-specific

haplotypes NA

Gretarsdottir et al .,101

Zee et al .,102 Brophy et al .103

PSMA6

Proteasome subunit type

6 rs1048990 5' UTR Ozaki et al .

40

TNFSF4Tumor necrosis factor(ligand) superfamily,

member 4

rs3850641 Intron Wang et al .104

LTA4H  Leukotriene A4 hydrolase Haplotype K NA Helgadottir et al .105

USF1Upstream transcriptionfactor 1

Haplotypeacross USF1

NA Komulainen et al .106

GATA2 GATA binding protein 2rs2713604rs3803

Intron 5' UTR Connelly et al .41

aReplication implies in independent samples. Abbreviations: NA, not applicable; NonSyn,nonsynonymous; rs, reference SNP accession number; UTR, untranslated region.

NEW APPROACHES FOR IDENTIFYING GENETIC DETERMINANTS OF CHD

ACCURATE PHENOTYPING

Although considerable overlap exists among various CHD phenotypes (Table 1), theunderlying pathophysiology could vary considerably. Multiple risk factors and theirinteractions influence plaque stability and inflammation, platelet function, and thecoagulation cascade. Different combinations of these risk factors can, therefore,predispose individuals to the development of different phenotypes of CHD.43 

Heterogeneity in the mechanisms underlying these phenotypes could explain why in

some cases there is no overlap between linked regions in related disease states—linkedregions that seem specific for myocardial infarction are not also specific for angiographic

coronary artery disease.18 At the outset of any genetic study, it is important to accuratelydefine the phenotype of cases and controls. Given the multiple possible presentations of CHD, careful characterization of the phenotype is especially important. Rigorous, uniformcriteria for cardiovascular events such as myocardial infarction or sudden cardiac deathshould be specified at the onset of the study. To avoid ascertainment bias, imagingstudies that provide information about both atherosclerotic burden and plaque activityshould be performed in community-based cohorts rather than in patients referred forsuch studies. To help diminish the likelihood of bias and reduce population stratification,

it is preferable that cases and control individuals are drawn from the same geographicregion and matched for age, sex and race. Furthermore, concomitant with efforts to

determine accurate phenotypes, further improvement is needed in the assessment andmeasurement of environmental factors relevant to CHD such as cigarette smoking,physical activity and dietary intake.

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LINKAGE STUDIES

As shown by Helgadottir et al .19 and by Wang et al .,21 linkage analyses for complexdiseases have the potential to identify new candidate genes that previously would haveremained unsuspected on the basis of  a priori knowledge of disease mechanisms. The

limitations associated with linkage studies of complex diseases include low statisticalpower and the inability to specify precise limits on the location of the causal gene ormutation. The statistical power of linkage studies could be improved by using larger

sample sizes and pedigrees, and disease susceptibility loci can be defined more preciselyby using a large number of markers across the genome. For example, John et al . used

11,245 SNPs in a genomic scan of families with rheumatoid arthritis and found that highSNP density localized disease susceptibility loci more precisely than the conventional 10-

cM microsatellite scan that used approximately 400 microsatellite markers.44

Linkage analysis in pedigrees is an unbiased approach for identifying genomic loci forquantitative disease phenotypes.45, 46 Quantitative traits have a simpler geneticarchitecture than the disease phenotype and could, therefore, be easier to map.

Quantitative traits related to atherosclerotic vascular disease and CHD that can bemeasured accurately, without bias in large population genetic studies include thefollowing: carotid intima-medial thickness, presence and quantity of coronary arterycalcium, coronary artery disease on angiography, coronary atherosclerotic burden on

intravascular ultrasonography, carotid or femoral artery plaque burden andcharacteristics on MRI, ankle–brachial index, and aortic pulse-wave velocity.

ADMIXTURE MAPPING

A new alternative to conventional linkage analysis is admixture mapping.47, 48, 49 This

technique can be applied in a population formed by relatively recent (e.g. 5 generations)admixture of two or more ancestral populations (e.g. African Americans who have WestAfrican and white European ancestry). For African Americans with a particular disease,genomic regions that have an unusually high proportion of ancestry from eitherEuropeans or Africans could harbor disease susceptibility variants. An example of the useof admixture mapping relevant to CHD is a study by Zhu et al .39 In this investigation, a

genomic admixture scan in 737 African Americans with hypertension and 573 controlsusing 269 microsatellite markers was performed. Evidence for association on

chromosomes 6q24 and 21q21 was found. Although confirmatory studies are needed,these results suggest that admixture mapping could be useful in identifying genome

regions that influence complex disease susceptibility.50

ASSOCIATION STUDIES

Developments include attempts to improve the replicability of association studies,

candidate-gene resequencing studies, and genome-wide association studies. To obtainrobust results from association studies the use of large samples (i.e. thousands of casesand controls, instead of hundreds) and stringent thresholds for statistical significancehave been proposed.51 Biologically plausible associations and risk alleles with functionaleffects are more likely to be 'true' associations, hence, should have replicable findings.

The replication of SNP disease associations in independent samples is crucial forvalidating results, and by genotyping 'neutral' markers throughout the genome,

potentially confounding population substructures can be excluded.52, 53 Alternatively,replication can be shown within a single study by dividing the study subjects into a 'test'

group and a 'validation' group,54 with both groups independently powered to detect anassociation. Association mapping could also be more successful with population isolatesin which genetic stratification is minimal, as shown by the success of the deCODE project

in Iceland.55

Candidate gene resequencing studies involve sequencing an entire candidate gene incases and controls and identifying the sequence variants that clearly differ in frequency

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between the two groups. These studies are labor intensive and expensive but can identifyrare variants that influence complex diseases or traits. This approach was used toidentify rare variants of MC4R (the melanocortin 4 receptor gene) that were associatedwith severe early-onset obesity.56 Cohen et al . also successfully used this approach toidentify rare nonsynonymous SNPs that influence plasma levels of HDL cholesterol 16 andLDL cholesterol57 in the general population. Extension of this approach to a genomic scale('genome resequencing') with a large number of cases and controls would be the most

comprehensive means of identifying genetic variants underlying complex diseases.Although, genome resequencing in large case–control studies is not feasible at present, itcould soon become standard, as the costs for sequencing continue to drop.

In the interim, a genome-wide association approach, in which variants are tested forassociation with a trait or disease of interest, has become possible with data from the

HapMap project and high-throughput SNP-typing platforms.58 In 2002, the HapMapproject was undertaken to catalogue patterns of genetic variation as a means of identifying common genomic variants contributing to the cause of prevalent diseases.58, 59 

The human genome seems to be organized into a series of haplotype blocks,60, 61, 62 eachhaplotype block shows low diversity and SNPs within a haplotype block show high linkagedisequilibrium. One strategy to reduce genotyping effort in association mapping of 

complex diseases uses tag SNPs,63

which correlate with much of the common variation ina genomic region, and, therefore, could serve as a marker of this common variation.

Several studies have already reported convincing statistical evidence that links geneticpolymorphisms with CHD risk factors as well as with CHD phenotypes: polymorphisms in

INSIG264 (encoding insulin induced gene 2), and FTO65(fat mass and obesity associated)have been associated with obesity; polymorphisms in IFIH166 (interferon-induced helicaseC domain-containing protein 1) and IL2RA67 (interleukin-2 receptor alpha chain) havebeen associated with type 1 diabetes mellitus; polymorphisms in TCF7L268, 69 

(transcription factor 7-like 2), SLC30A869, 70, 71, 72 (zinc transporter 8), a locus nearCDKN2A (cyclin-dependent kinase inhibitor 2A) and CDKN2B69, 71, 72 (cyclin-dependentkinase inhibitor 2B), IGF2BP269, 71, 72 (insulin-like growth factor 2 mRNA binding protein2), and in CDKAL169, 71, 72, 73 (CDK5 regulatory subunit associated protein 1-like 1) have

been associated with type 2 diabetes mellitus; and a polymorphism in a locus nearCDKN2A and CDKN2B on chromosome 9p21 has been associated with CHD in several

genome-wide association studies.74, 75, 76

Several different approaches fall under the rubric of genome-wide association studies(Figure 2). In broad terms, these approaches can be classified as 'map-based' (usinguniformly spaced SNPs or tag SNPs) or 'gene-based' (using putative functional SNPs).77 

In the map-based approach, SNPs to be genotyped could be evenly spaced or tag SNPscould be used, with the presumption that linkage disequilibrium between such a tag SNPand the causal SNP would allow the detection of the causal SNP. The evenly spaced SNPscollection could provide sparse (e.g. 100,000 SNPs) or dense (e.g. 500,000–1,000,000

SNPs) coverage of the genome. The genotyping burden can be reduced markedly,however, by using tag SNPs across the genome. A collection of 250,000 tag SNPs, for

example, would cover approximately 85% of the genome.78 In the gene-based approach,putative functional SNPs throughout the genome are genotyped, including

nonsynonymous SNPs, regulatory SNPs and SNPs in splice sites.

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Figure 2 Strategies for genome-wide association studies using SNPs.

These approaches can be classified as (A) 'map-based' (using uniformly spaced SNPs ortag SNPs) or (B) 'gene-based' (using putative functional SNPs).77 Under the 'map-based'approach, a subset of SNPs is selected for genotyping—these can be evenly spaced SNPsor tag SNPs. SNPs in strong linkage disequilibrium are likely to be inherited together, so

one can use a subset of 'tag' SNPs as proxies for the entire set. For the 'gene-based'approach, putatively functional SNPs located in regulatory regions and non-synonymous

SNPs are selected to be genotyped. Abbreviation: SNP, single-nucleotide polymorphism.

The development of appropriate statistical techniques to analyze the massive amount of genetic data gained from genome-wide association studies is crucial for the identificationof genes for complex diseases. As hundreds of thousands of potential statistical testsmight be computed in such studies, a major challenge is correction for the multiple

testing that must be performed.79 One approach is a multistage design that reduces thenumber of genotyped SNPs in each stage, achieving stepwise genome-wide

significance.28, 80 The Bonferroni correction is considered to be overly conservative, 81 andother approaches have been proposed, including estimation of the false discovery rate.82,

83

A CLINICAL PERSPECTIVE

We feel it is necessary to highlight three important considerations relevant to clinical

practice. First, as CHD clusters in families, obtaining detailed information on a patient'sfamily history is important. Family history has been described as a "... free, well-proven,personalized genomic tool that captures many of the genes and environmental

interactions and can serve as the cornerstone for individualized disease prevention."84 

Second, although genetic testing is not part of current CHD risk stratification algorithms,it is likely that multilocus genotyping to assess CHD risk will become part of clinicalpractice in the future. Not unexpectedly, entrepreneurial zeal has overtaken carefulscientific validation, and several companies now market gene-based tests for assessing

cardiovascular risk to patients directly via the internet.85 Third, the immediate promise of identifying genetic determinants of CHD is greater in the therapeutic arena than in

refining risk prediction. This potential includes use of genetic tests to allow individualizedtreatment (pharmacogenetics) and to facilitate discovery of new molecular pathways of 

CHD and drug targets. Of interest, the results of a drug trial that used the findings of agenetic study that identified a novel therapeutic target for CHD have already beenpublished.22

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CONCLUSIONS

We have attempted to summarize the current state of knowledge about the genetic basisof CHD and the new approaches that might lead to further successes in elucidating thebasis of this disease. Increased knowledge of the genetic architecture of CHD will

improve risk prediction and facilitate the development of new therapies for patients withCHD. Although considerable challenges exist, advances such as high-throughput SNP

genotyping platforms and newer statistical and phenotyping methods show promise foraccelerating progress in this field. Well-designed studies are needed to define clinically

relevant phenotypes, identify genes and define environmental contributions to CHD.Given that CHD is a clinically heterogeneous chronic disease with multiple genetic andenvironmental contributions, identification of causal genes for this disease requires avigorous multidisciplinary approach that includes physician investigators and laboratoryscientists, and epidemiologists and statisticians with expertise in genetics. The task ischallenging, but the goals justify the effort and the expense.

KEY POINTS

•

Understanding the genetic basis of coronary heart disease (CHD) is a priority as itis projected to become the leading cause of mortality worldwide

• Challenges in identifying clinically relevant genetic determinants of complex

diseases such as CHD include phenotypic and genetic heterogeneity, gene–gene

and gene–environment interactions, and the fact that the etiologic spectrum

includes both common genetic variants with small effects as well as rare genetic

variants with large effects

• Linkage and association mapping are two conventional approaches in identifying

genetic determinants of CHD

• Advances such as cataloging of human genetic variation, the development of 

high-throughput single-nucleotide polymorphisms genotyping platforms and

genome-wide association studies will increase the likelihood of success in the

search for genetic determinants of CHD al

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