R E V I E W S
NATURE REVIEWS | GENETICS
VOLUME 6 | FEBRUARY 2005 | 109
cost per true positive, we discuss in more detail the
rationale for using large sample sizes in light of the
smallest allelic risks that are feasible to detect,the choice
of SNPs to be genotyped,study-design efficiencies and
certain aspects ofthe statistical analyses ofsuch data.We
are not advocating an abandonment of linkage studies
of common disease9–12.We still cannot say whether the
LINKAGE ANALYSISapproach has ‘failed’in a general sense,
because almost all published studies have used small
sample sizes13(fewer than 500 AFFECTED SIB-PAIRS),so this
alone cannot be used as a justification for carrying out
genome-wide association studies.Genome-wide link-
age analysis will remain an essential approach until
technology is available that allows the association analy-
sis of both rare and common variants at a practicalcost
and high throughput.
Furthermore, as described previously14, we view
genome-wide association studies not as a new approach
in itself,but as a more cost-efficient way to survey com-
mon genetic variation compared with the gene-by-gene
functional-candidate approach.The latter approach
has been successful but,as only small numbers ofgenes
have been studied so far and,as we discuss,sample sizes
might have been too small,few true positives have been
identified, despite numerous studies and enormous
effort.By exploiting the non-random association ofalle-
les at nearby loci (LINKAGE DISEQUILIBRIUM(LD)),which is an
The development ofcommon disease results from com-
plex interactions between numerous environmental fac-
tors and alleles ofmany genes.Identifying the alleles that
affect the risk of developing disease will help in under-
standing disease aetiology and sub-classification.Over
the past 30 years,genetic studies ofmultifactorial human
diseases have identified ~50 genes and their allelic vari-
ants that can be considered irrefutable or true positives1,2.
However,there are probably hundreds of susceptibility
loci that increase the risk for each common disease.The
key question is how to harness the marked recent
improvements in our knowledge ofthe genome sequence
and its variation in populations,together with advances in
genotyping technologies,to accelerate susceptibility-locus
discovery at the lowest cost.
In an accompanying review in this journal,Hirsch-
horn and Daly3put a case forward for the genome-wide
association approach,“in which a dense set of SNPs
across the genome is genotyped to survey the most
common genetic variation for a role in disease or to
identify the heritable quantitative traits that are risk fac-
tors for disease”.They recommend caution in applying
the latest high-throughput methods for genotyping4–8,
as the cost of failure is potentially huge for studies that
are designed and executed with low statistical power and
inadequate quality control. Here, in the context of
genome-wide association studies and ofminimizing the
William Y.S.Wang*‡,Bryan J.Barratt*§,David G.Clayton* and John A.Todd*
Abstract | To fully understand the allelic variation that underlies common diseases, complete
genome sequencing for many individuals with and without disease is required. This is still not
technically feasible. However, recently it has become possible to carry out partial surveys of the
genome by genotyping large numbers of common SNPs in genome-wide association studies.
Here, we outline the main factors — including models of the allelic architecture of common
diseases, sample size, map density and sample-collection biases — that need to be taken into
account in order to optimize the cost efficiency of identifying genuine disease-susceptibility loci.
*Juvenile Diabetes Research
Diabetes and Inflammation
Institute for Medical
Cambridge CB2 2XY,UK.
‡Basic and Clinical
School ofMedical Sciences
and Institute for Biomedical
§Research and Development
Cheshire SK10 4TG,UK.
Correspondence to J.A.T.
© 2005 Nature Publishing Group
Mapping genes by typing genetic
markers in families to identify
chromosome regions that are
associated with disease or trait
values within pedigrees more
often than are expected by
chance.Such linked regions are
more likely to contain a causal
AFFECTED SIB-PAIR (ASP)
Linkage studies that are based on
the collection of a large number
of families,consisting of affected
siblings,and their parents if
available.In linkage analyses,the
studies rely on the principle that
ASPs share half their
The non-random association of
alleles of different linked
polymorphisms in a population.
MINOR ALLELE FREQUENCY
(MAF).The frequency of the less
common allele of a polymorphic
locus.It has a value that lies
between 0 and 0.5,and can
vary between populations.
110 | FEBRUARY 2005 | VOLUME 6
R E V I E W S
power ofgenetic association studies,and therefore their
likelihood ofsuccess and the cost per true-positive result.
Here,we first discuss the impact that these two factors
are likely to have on the feasibility ofgenome-wide asso-
ciation studies,and then provide an overview of what is
known so far about the allelic spectra of commondis-
eases.It should be noted that other factors also affect
statistical power — for example,confounding factors,
such as population structure and geography,misclassifi-
cation errors and selection biases — and some of these
factors are discussed in a later section.
Implications for association studies. FIGURE 1shows that
if susceptibility alleles have MINOR ALLELE FREQUENCIES
(MAFs) of less than 0.1 and their effect sizes are less
than an ODDS RATIO of 1.3, then unrealistically large
samplesizes of more than 10,000 cases and 10,000 con-
trols (or 10,000 families) would be required to achieve
convincing statistical support for a disease association.
We cannot estimate with any accuracy what proportion
of disease-susceptibility alleles will lie outside this range
(that is, those with odds ratios of 1.3 or above and
MAFs of >0.1) and therefore be feasible for detection in
genome-wide association studies,and this limitation is
discussed below.However,we suggest that studies aimed
at detecting such alleles — requiring the analysis of
thousands of samples,rather than hundreds of samples
— will provide an overall lower cost per true-positive
result compared with current candidate-gene and
A study of 6,000 cases and 6,000 controls (or 6,000
families with 2 parents and an affected offspring) would
provide,under ideal conditions,approximately 0%,3%,
43% and 94% power to detect disease susceptibility
variants with an odds ratio of 1.3 and MAFs of 0.01,
0.02,0.05 and 0.1,in corresponding order,at a signifi-
cance level of P<10–6(FIG.1).Significance thresholds in
the order of P<10–6have been proposed for genome-
wide association studies,owing to the need to allow for
the very small prior probability that any given locus or
region is truly associated with disease3,14,22–24,103,104.There
is a steep decline in power for odds ratios of 1.2 or less
(for example,34% for an MAF of0.1) (FIG.1).Conversely,
for an odds ratio of 2,even for an MAF of 0.005 there is
76% power.However,we suspect that such high odds
ratios will be rare in common diseases (see below).
Undoubtedly,even the best-designed studies,aimed
at a minimum MAF of10% and an odds ratio of1.3,will
have less power than expected owing to many factors,
including genotype and phenotype misclassification and
confounding factors,so that even larger sample sizes
might be required.It is noted,however,that in a study of
12,000 cases and controls,for example,genotyping can
be performed in stages with little loss ofpower.This pro-
vides significant savings in genotyping costs,as most of
the genotyping is performed in the first stage in a frac-
tion (about 20–30%) ofthe total number ofsamples (see
REFS 3,25for more detail about such methods).
In the following sections we discuss theoretical
models of the allelic spectra of common diseases and
estimate their likely distributions.
important and widespread feature ofthe genome5,15–18,it
is now possible to survey in an association study a sig-
nificant proportion of the common variation of a large
number of genes that occur in regions of high LD.Cost
efficiency can be gained,as it is not necessary to geno-
type SNPs that are in strong LD with other SNPs;this
can be done by choosing a subset set of SNPs (known as
tag SNPs(see Online links box)) that capture most of
the allelic variation in a region19.The rationale and limi-
tations of this strategy will be discussed, bearing in
mind the inadequacy of tag SNPs in detecting rare sus-
ceptibility variants and,by definition,their lack of cost-
saving advantage in regions of low LD,which might
constitute about 20% of the human genome.As well
as discussing these more practical issues,we first dis-
cuss theoretical considerations concerning two as yet
unknown parameters that determine the potential sta-
tistical power of an association study — the frequency
of susceptibility alleles among the population and the
size oftheir effects on disease phenotypes.
Allelic spectra of common diseases
The allelic spectrum or architecture ofa disease refers to
the number of disease variants that exist,their allele fre-
quencies and the risks that they confer9,20,21.Numerous
sources, from both theoretical models and practical
experiments, have provided insights into the allelic
architecture of common diseases,demonstrating the
multiplicity of loci that are involved and their range of
effects. Regardless of the exact shape of the spectra,
which will differ between diseases,the allele frequencies
ofvariants that predispose to disease and the strength of
their phenotypic effects indicate the potential statistical
Frequency of disease-susceptibility allele
0.4 0.5 0.60.7
Sample size required (number of individuals)
Figure 1 | Effects of allele frequency on sample-size requirements. The numbers of cases
and controls that are required in an association study to detect disease variants with allelic odds
ratios of 1.2 (red), 1.3 (blue), 1.5 (yellow) and 2 (black) are shown. Numbers shown are for a
statistical power of 80% at a significance level of P <10–6, assuming a multiplicative model for the
effects of alleles and perfect correlative linkage disequlibrium between alleles of test markers and
© 2005 Nature Publishing Group
NATURE REVIEWS | GENETICS
VOLUME 6 | FEBRUARY 2005 | 111
R E V I E W S
Arguments used to support these two hypotheses
have largely been based on population-genetic theories
and will therefore be influenced by the underlying
assumptions of these theories20,31.Empirical evidence
indicates that both high- and low-frequency alleles con-
tribute to common diseases2,32–38. For example, in a
review ofmapped QUANTITATIVE TRAIT LOCI(QTL),approxi-
mately 50% of the candidate causal variants had MAFs
exceeding 0.05,whereas the other halfhad lower MAFs9.
We suggest that it is preferable to avoid this polariza-
tion ofrare versus common disease-susceptibility alleles,
and instead consider the divergence of the allelic spec-
trum of disease variants from that of all variants (with
or without phenotypic effects) in the human genome
(FIG. 3). The most neutral hypothesis would be that
the allelic spectrum ofdisease variants is the same as the
general spectrum of all genetic variants17,39,40.Under this
neutral model,although most susceptibility variants are
rare (with MAFs of less than 0.01),SNPs with MAFs of
greater than 0.01 would account for more than 90%
of genetic differences between individuals and should
contribute significantly to phenotypes17,41.Compared
with the overall allelic spectrum, the CDCV model
could be considered as a shift towards common vari-
ants and the heterogeneity model a shift towards rare
variants40(FIG. 3). Protein-coding regions of the
genome have polymorphisms with lower MAFs than
the genome in general and,therefore,disease variants
that cause non-synonymous changes42,43might con-
tribute to a rare shift.Different evolutionary forces can
result in different spectral shifts;for example, PURIFYING
SELECTIONmight result in a rare shift31.By contrast,dis-
eases that are mediated by immune responses,such as
autoimmune disorders,might be caused by alleles that
have been under POSITIVE SELECTIONto provide resistance
to infectious diseases and have therefore reached higher
population frequencies36.Similarly,metabolic diseases
such as type 2 diabetes(see Online links box),in which
alleles are selected for adaptive responses to starvation or
energy balance,might affect susceptibility in the modern
environment — the thrifty gene hypothesis44.The allelic
spectrum will therefore vary between different com-
mon diseases and is likely to consist of a complex mix-
ture of allele frequencies26,32,approximating the curved
L-shaped distributions that are shown in FIG.3(note that
the curves would be U-shaped if allele frequencies
between 0 and 1.0 were represented,instead of 0 to 0.5
when considering only minor alleles).
For the genome as a whole,it has been predicted that
of the expected 10 to 15 million SNPs with MAFs of
greater than 0.01 (REFS 41,45),approximately half have
MAFs of greater than 0.1,and the other half have MAFs
that are between 0.01 and 0.1 Given that the number of
disease variants conferring mild to moderate risks
might be large (as explained in the next section),then
unless shifts in allelic spectra are severe — which seems
unlikely,given the multiplicity of genetic and environ-
mental effects in common disease — there are likely
to be hundreds of common and rare variants con-
tributing to the familial clustering of each common
Allele frequencies for susceptibility loci.Two polarized
views have dominated much of the literature on the
allelic frequencies of common diseases9,21.The common
disease/common variant (CDCV) hypothesis proposes,
as its name suggests,that common diseases are a result
of common variants20.Under this model,disease sus-
ceptibility is suggested to result from the joint action of
several common variants,and unrelated affected indi-
viduals share a significant proportion of disease alleles.
The extreme alternative to CDCV is the classical dis-
ease heterogeneity hypothesis (or multiple rare-variant
hypothesis),in which disease susceptibility is due to dis-
tinct genetic variants in different individuals and disease-
susceptibility alleles have low population frequencies26
(MAFs ofless than 0.01).
The allelic spectra of most common diseases proba-
bly fall between these two extremes.The classical hetero-
geneity model,with multiple rare variants contributing
additively and independently (in a biological sense),
leads to correlations between traits in related subjects
falling off linearly with the distance of the relationship
between them27(FIG.2).This is the result oflinear reduc-
tions in sharing ofdisease alleles with the increasing dis-
tance of relationships.By contrast,if a common disease
is largely due to the interdependent interactions of sev-
eral loci with common alleles,the decline in risk with the
degree of relatedness will be more rapid than a linear
decline.Investigations ofwhether this correlation applies
to different common diseases and traits have yielded
different results,providing support for genetic additivity
in some cancers28and in stature29,and non-additivity in
type 1 diabetes30(see Online links box).
A measurement of association
that is commonly used in
case-control studies.It is defined
as the odds of exposure to the
susceptible genetic variant
in cases compared with that in
controls.If the odds ratio is
significantly greater than one,
then the genetic variant is
associated with the disease.
QUANTITATIVE TRAIT LOCI
Genetic loci that contribute to
variations in quantitative,that is
Evolutionary selective forces that
reduce the frequency of specific
polymorphisms that have
The effect of evolutionary
selective forces that favour
certain variants and tend to
increase their allele frequencies.
1 0.8 0.6
Risk of disease
Risk in population
Figure 2 | Models of the risks conferred by disease-associated variants. The risk of disease
as a function of genetic relatedness to affected individuals is shown. Two hypothetical common
diseases are considered (blue and black lines), which have the same monozygotic risk (the risk of a
monozygotic twin of a disease case also being affected by the disease; where genetic relatedness
is 1) and the same underlying risk in the population (red dashed line). For the disease represented
by the blue line, the risk of disease falls linearly with decreased genetic relatedness, consistent with
disease heterogeneity, owing to the reduction in the number of shared rare alleles — the disease
heterogeneity model. For the disease indicated by the black line, the fall in risk as a function of
genetic relatedness is more rapid, as can occur when multiple, common, interacting alleles
contribute to disease — an example of the common disease/common variant (CDCV) model.
© 2005 Nature Publishing Group
112 | FEBRUARY 2005 | VOLUME 6
R E V I E W S
the existence ofcommon variants are likely to yield a sig-
nificant number ofpositive results unless there have been
extreme shifts in the allelic spectra.
Risks associated with disease-susceptibility variants.The
second main question concerning allelic architecture is
the distribution of genetic risks conferred by individual
variants.Although it is not possible to predict an accu-
rate distribution of allelic effects for any given common
disease, several lines of evidence point to potential
underlying distributions.For example,such evidence
has come from using mutagenesis,selection and link-
age approaches in studies of QTLs in Drosophila
melanogaster,crops and livestock and studies of rodent
models of human disease. These studies have indi-
cated that the distribution of phenotypic-effect sizes
of genetic variants is consistent with the existence offew
genetic loci with large effects and numerous loci with
small effects9,46–54.The resulting curved,L-shaped distri-
butions have been modelled by using either exponential
or γ-distributions (see the graph in FIG.4,which has a
different shape and origin from the curves in FIG.3).
These results are consistent with current evolutionary
theories in which,by factoring GENETIC DRIFTand muta-
tional effects into classical models of adaptation55,the
expected distribution of QTL effects is exponential56.
The potential for a large number of variants with small
individual contributions to human phenotypes is fur-
ther supported by recent findings that allelic variation
frequently affects gene expression and exon splicing57–60
— which is likely to have smaller effects than polymor-
phisms that affect the coding sequence — and that loci
with alleles that affect the regulation of gene expression
can be detected by linkage analyses61,62.
Most irrefutable disease-susceptibility variants that
have been identified so far — mainly from functional-
candidate association studies — have allelic odds ratios
that are in the order of 1.1–1.5 (REFS 1,2)and contribute
little to familial recurrence risks11,22,63. For example,
assuming a multiplicative model for the effects of alleles
and interactions between loci,a disease-susceptibility
allele with a frequency of 0.1 that confers a 1.5-fold
increase in risk would be responsible for a SIBLING RELATIVE
RECURRENCE RISK(λs) of less than 1.02,which if the over-
all λs was 5,would equate to a 1.2% contribution.It is
not unreasonable to expect that QTLs would con-
tribute effects of similar sizes to a quantitative trait.
We do not know, however, if this is a representative
range of effect sizes in common diseases, as only a
small fraction of the genome has been evaluated in
well-designed association studies (see,for example,the
T1DBase database in the Online links box for genes
studied in type 1 diabetes).Nevertheless,we believe
that it would be unwise to undertake genome-wide
association studies that do not have sufficient power to
detect disease and quantitative-trait effects of this
SNP choice in genome-wide association studies
To target the variation that occupies the range of
MAFs of >0.1 and odds ratios of >1.3 in a statistically
As an example,using the hypothetical spectra in FIG.3,
consider a complex disease in which there are 20 disease-
susceptibility variants contributing to that disease under
the neutral model,in which MAFs of these variants are
greater than 0.1 and their odds ratios are high enough for
them to be identified in genome-wide association studies.
In this case,a rare shift might result in ~10 variants with
MAFs of greater than 0.1,and a common shift might
result in ~40 variants.The implication for genome-wide
association analyses is that experiments that are based on
Changes in allele frequencies in a
population from one generation
to another as the result of chance
events in mating,meiosis and
number of offspring.
The risk of developing disease in
a sibling ofan affected individual
relative to that of an individual
in the general population.
Commonly used as an
indication of the heritability of a
A set of alleles that is present on
a single chromosome.
0 0.1 0.20.3 0.40.5
Population frequency of minor allele
Number of polymorphisms
Figure 3 | Possible allelic spectra of human diseases. Three possible distributions of disease-
associated variants with different population frequencies are shown. The orange line shows an
allelic spectrum that is similar to that of the genome as a whole (that is, similar to that for all known
variants, whether disease-associated or not). The red line shows a ‘rare shift’, with more rare
disease-susceptibility variants and fewer common ones, leading to greater disease heterogeneity.
The brown line shows a ‘common shift’, in which disease alleles tend to have high population
frequencies. Modified, with permission, from REF.40 (2004) Elsevier Science.
Size of phenotypic effect
Number of genetic variants
Figure 4 | Putative distribution of phenotypic-effect
sizes among disease-susceptibility variants. A probable
distribution of genetic variants that is based on an exponential
distribution is the existence of a small number of variants with
large effects and a large number of variants with small effects.
© 2005 Nature Publishing Group
NATURE REVIEWS | GENETICS
VOLUME 6 | FEBRUARY 2005 | 113
R E V I E W S
powerful way,we need to know all the common vari-
ants in the population that the cases and controls are
taken from. Although there has been a rapid recent
increase in our knowledge of human genome varia-
tion17— mostly in the form of SNPs — as many as
30% of common variants might remain undetected.
This can be corrected by further genome resequencing
for a larger set of unrelated individuals (discussed in a
later section). Nevertheless, even without this rese-
quencing,because we know that many SNPs have alleles
that show strong LD with other nearby SNP alleles (the
average range over which SNPs show LD is 60–200 kbin
general populations18,64),it might be possible to carry
out reasonably comprehensive genome-wide associa-
tion studies that are based on known variants.We can
predict that in regions of the genome with strong LD,a
selection of evenly spaced SNPs,or those chosen on the
basis of their LD with other SNPs (tag SNPs),can pro-
vide adequate ‘coverage’of the region in an association
study.In the following sections we define these terms
and describe the benefits that LD provides for genome-
wide association studies in the absence of both a com-
plete SNP map and a technology that can type all
known SNPs in an affordable way.
Tag SNPs.The degree of LDbetween alleles at two loci
can be described in terms of the metric r2(BOX 1).r2is
informative in association analyses because it is inversely
proportional to the sample size that is required for
detecting disease association given a fixed genetic risk65,66.
For example,consider a genotyped marker SNPthat is
near to a susceptibility locus that is also a SNP, but
which is not itself typed in an association study.If the r2
between these two loci is 0.5,then the effective sample
size for this marker,which determines the statistical
power of the association study (FIG.1),is halved (that is,
the actual sample size would need to be doubled for the
same statistical power).This leads to a large reduction
in power from more than 90% to less than 40% for a
study of6,000 cases and 6,000 controls for an MAF of0.1
to obtain a Pvalue in the order of 10–6.By contrast,an
r2of 1 indicates perfect LD, and there is no loss of
power when using a marker tag SNP instead of directly
genotyping the disease causal varient.
Therefore,the general consensus is that an r2of 0.8
or greater is sufficient for tag SNP mapping to obtain a
good coverage ofuntyped SNPs,allowing genotyping of
a lower number of marker SNPs with relatively small
losses in power.So,if we knew the identity of all the
common SNPs in a region and there was LD between
them,then by iterative pair-wise comparisons,the opti-
mal set of tags could be chosen. Here, ‘optimal’ is
defined as the smallest number of SNPs that needs to be
genotyped to cover the other SNPs at an r2of 0.8 or
greater19,45,67–69.If the LD between SNPs is strong,this
could result in the need to carry out up to 70–80% less
genotyping.However,if LD in a region is low,almost
every SNP might have to be genotyped to ensure com-
prehensive coverage of the region. For example, the
interferonβ-1 fibroblast (IFNB1) gene is only 1 kb in
length but requires 17 tag SNPs to cover the 19 common
Box 1 | Applications of linkage-disequilibrium metrics
Several metrics have been devised to measure linkage disequilibrium (LD)96.The two
most commonly used ofthese are D′and r2.Both are related to the basic unit ofLD,D.
Dmeasures the deviation of HAPLOTYPEfrequencies from the equilibrium state97.LD
occurs when Dis significantly greater than zero.Consider two linked SNPs with alleles
(A,a) and (B,b),resulting in four possible haplotypes:AB,Ab,aBand ab.Dcan be
calculated as in equation 1,where f(X) represents the frequency ofthe Xallele or
D′is the absolute ratio ofDcompared with its maximum value,Dmax,when D≥0,or
compared with its minimal value,Dmin,when D<0 (REF.98).D′= 1 denotes complete LD,
and historical recombination results in the decay ofD′towards zero.
r2is the statistical coefficient ofdetermination — a measurement ofcorrelation between a
pair ofvariables99(see equation 2).
r2is ofparticular importance in genetic mapping as it is inversely related to the required
sample size for association mapping,given a fixed genetic effect65,66.For example,ifonly
one ofa pair ofSNPs was genotyped and r2between the SNPs was found to be 0.5,then
to provide the same statistical power for the ungenotyped SNP compared with the case
where r2= 1,the sample size would need to be doubled.When r2= 1,knowing the
genotypes ofalleles ofone SNP is directly predictive ofthe genotypes ofanother SNP.
The alternative notation R2is used when individual variables are predicted using the
multiple regression ofa constellation ofother variables67,70.
Relationship between D′and r2
D′and r2can be written in terms ofeach other and allele frequencies.Without losing
generality,the four alleles can be chosen such that D≥0 and f(A) ≥f(B).So D′and Dmax
have the relations in equations 3 and 4.
Equation 5 shows the relationship between D′,r2and allele frequencies.As f(A) ≥f(B),
r2has the upper bound of (D′)2,and reaches it only when f(A) = f(B).The implication
of this is that D′,a commonly used measure of historical recombination,provides
information on the physical extent of useful LD (in terms of association mapping and
statistical power) by providing the upper limit of r2.Dense LD maps that are based on
high-frequency SNPs (MAF >0.1) can reveal regions of historical recombination.
Knowing the level of D′decay in these maps directly provides the maximum potential
level of useful LD in association mapping (based on r2) for high-frequency SNPs,even
if a significant proportion of common SNPs remains undiscovered.For example,if a
recombination point resulted in a D′of 0.7 for SNPs on either side of it,the maximum
possible r2for these SNPs would be 0.49,and sample sizes would need to be more than
doubled to maintain the same statistical power for association mapping.It should be
noted that both D′and r2suffer from sampling biases given a small number of
individuals and for rare variants15,68,100.Confidence intervals for D′have been used by
D = f(AB) – f(A) f(B)
Dmax = f(a) f(B)
r2 = (D′)2 × f(A) f(b)
f (A) f (a) f (B) f (b)
© 2005 Nature Publishing Group
114 | FEBRUARY 2005 | VOLUME 6
R E V I E W S
having an r2of at least 0.8 with a SNP that can be
genotyped robustly), it is necessary to obtain near-
complete genome sequences from a sufficiently large
number of unrelated individuals.
Towards a complete SNP map.The HapMap,including
the 2005 version with the Perlegen SNPs added,origi-
nates from non-contiguous resequencing of5–60 differ-
ent chromosomes.Previously,we simulated the sampling
ofcommon SNPs (with MAFs of≥0.1) from 73 existing
near-complete SNP maps of specific genomic regions68
(compiled by the University of Washington and Fred
Hutchinson Cancer Research Center Variation Discovery
Resource database (see Online links box)). This was
done by near-contiguous resequencing,using a PCR-
based method, for DNA samples from 47 unrelated
individuals45.Simulated incomplete SNP maps of1 SNP
per 2.5 kb,5 kb and 10 kb ascertained approximately
75%,50% and 40% of the total underlying SNP varia-
tion,in corresponding order (median values)68.However,
there were large variations in the SNP densities that were
required to uncover all genetic variants for different
genes,and for 7 of the 73 genes there was less than 50%
SNP coverage for SNP maps of 1 SNP per 2.5 kb. To
obtain 80% SNP coverage,sampling at densities ofmore
than 1 SNP per kb was required in 10–20% of genomic
regions.Similar conclusions have been reached by other
We estimate that the coverage provided by the
HapMap will reach at least 1 SNP per 5 kb by the end of
2005,and that about 300,000 tag SNPs will need to be
genotyped to cover 50% ofthe genome at an r2of0.8 or
greater.However,even with an average density of 1 SNP
every 2.5 kb,~25% of SNPs will still not be captured
adequately by LD and tag SNPs68.To tag most variants
with MAFs of >0.1, as many as 500,000 more SNPs
might be required to cover in a comprehensive fashion
the remaining 25% of the genome that shows lower LD.
Nevertheless,as few as 75,000 tag SNPs might well cover
25% of the genome (in regions of high LD), a vast
increase in efficiency over a candidate gene-by-gene
approach.Further cost-efficiency and power could be
gained by choosing not only the regions of highest LD,
but also those regions that contain the highest density
Whereas the initial selection of tags is relatively
straightforward (although there are now many meth-
ods, they will all probably give similar results), the
assessment of the statistical association of tags in a
disease-association study requires further research.It
might be that for some methods of tag selection and
analysis,statistical power can also be saved by grouping
tags into the LD blocks that are defined by D′patterns
across the genome.
In BOX 2, we estimate that the number of human
chromosomes,if resequenced in a contiguous fashion,
would provide a complete map for common variants is
in the order of 60.With current sequencing technology,
this is a massive task,but emphasis could be placed on
resequencing regions of lower LD, as detected by D′
(perhaps 20% ofthe genome).
SNPs present70.Furthermore,the well-studied common
SNP FokI T>C (rs10735810) in the vitamin D receptor
gene is not in strong LD with any other SNP in the
flanking LD blocks and needs to genotyped directly
in any disease-association study: it is an obligatory
member of the tag set71.
Breaks in LD occur on average about once every
200 kb in the genome,although there is wide variabil-
ity in the lengths of regions of high LD15,18.LD blocks
can be located using the other commonly used LD
metric,D′,which is closely related to r2and provides
information about the recombination breakpoints of
chromosomes (BOX 1).This is one of the main outputs
of the International HapMap Project(see Online links
box),in which 270 DNA samples (mostly from unre-
lated individuals) are being genotyped for several mil-
lion SNPs across the genome. The SNP map has
recently been supplemented by the release of data by
Perlegen Sciences, Inc. (see Online links box) for
almost 1.5 million SNPs,which will be included in the
Regions of low LD might well be regions of intense
homologous recombination and GENE CONVERSION,caus-
ing the scrambling of the association of alleles between
loci — and so the reduction in LD — at a faster rate
than regions with less recombination18,72–74.The con-
tinuing HapMap project and several recent additional
studies indicate that approximately 70–80% of the
genome has regions of high LD15,16,18,75,which has sig-
nificant implications for genome-wide association
studies3,17. These patches of stronger LD that have
high D′ values between SNPs — and the mapping
redundancy that this results in — counterbalance,to a
certain extent,the current incompleteness of the SNP
map (see below) and the fact that the new high-
throughput technologies convert only about 50% of
SNPs into robust assays76.
Implications of an incomplete SNP map. The calcula-
tions of sample sizes needed for adequate statistical
power that are shown in FIG.1 represent an ideal situ-
ation.In a genome-wide association study there will
be significant losses in power and gaps in the map of
all the SNPs that are covered if some SNPs are in
weak LD (r2<0.5) with the marker SNPs that can be
genotyped.Even within an LD block with a high level
of D′ between markers,there will often be SNPs that
have low r2values with other SNPs within the block.
Despite the intimate relationship between them (BOX 1),
r2cannot be estimated based on D′ and MAFs alone.
For r2values to be high, the alleles of two SNPs
should not only have similar frequencies, but also
need to be correlated and to occur on the same ances-
tral haplotype.Haplotypes for a chromosome region
can be visualized as a tree,with the different branches
representing ancestral haplotypes, differing from
each other owing to recombination,gene conversion
and mutation.Minor alleles with similar frequencies
that are found on different haplotypes have low r2
values77.So,for a complete SNP map with contiguous
coverage (with every SNP or other polymorphism
A non-reciprocal recombination
process that results in an
alteration of the sequence of a
gene to that of its homologue
© 2005 Nature Publishing Group
NATURE REVIEWS | GENETICS
VOLUME 6 | FEBRUARY 2005 | 115
R E V I E W S
models describing extreme forms of epistasis are possi-
ble in which a genetic variant has no overall effect on its
own,despite having strong effects within certain sub-
groups of the population that are defined by variants in
other genes83,84. However, such extreme scenarios
require interactions that are even stronger than those
predicted by Bateson — with one gene reversing the
effect of another — and this is unlikely to be a wide-
spread phenomenon.In less extreme situations,taking
into account possible statistical interactions with other
genes could increase the power to detect a novel causal
variant85.However,the need to protect against false
positives that are due to SUBGROUP ANALYSES means that
the power gain might be relatively modest in the pres-
ence of interactions and,of course,must be set against
the inevitable loss of power when interactions are
very small or absent. The prior probability that any
given locus or region is truly associated with disease
becomes even lower. This remains an area of some
controversy,as does the closely related question of the
relevance of gene–environment interactions to the dis-
covery of genetic or environmental causes of disease86.
In our view,consistent with that of Hirschhorn and
Daly3, the presence of epistasis places even greater
pressure on the collection of well-defined,large sam-
ples, and on the necessity of replication due to the
consequent increase in the subgroup analysis problem
that occurs in the analysis of higher-order interactions.
Loss of cost efficiency by other means
In addition to the considerations described above,there
are several other ways that the idealized estimated
requirements for obtaining adequate statistical power
that are shown in FIG.1might be reduced in a genome-
wide association study. Some of these are discussed
Epistasis and subgroup analyses.Epistasis,which refers
to gene–gene interactions,was originally a mechanistic
and deterministic idea — William Bateson described
it as one gene cancelling out the effect of another80.In
seeking to generalize this to quantitative traits, the
concept was extended to be synonymous with the statis-
tical definition of ‘interaction’, which simply means
non-additivity of effects that are measured on some
specified scale.Although epidemiologists once believed
that testing for statistical interaction would be informa-
tive for biological mechanisms,this approach has not
been productive.There are two reasons for this;first,
the power to detect interactions is often low;and sec-
ond,even if detected,interpretation is difficult as many
biologicalexplanations could be possible81,82.
Nevertheless,statistical interaction might be rele-
vant to our ability to detect phenotype–genotype asso-
ciations.As a possible explanation of the small effect
sizes reported so far for common-disease-susceptibility
loci,some authors have suggested that mathematical
In genome-wide association
analyses,or any other
association study,there is a very
low prior probability that any
given locus or region is
associated with disease.If the
samples or data are divided into
analysis of epistatic interactions
between loci — a departure
from statistical independence in
the joint distributions of
genotypes between the loci —
then the prior probability of a
true positive is even lower.
Box 2 | Genome resequencing for full coverage in genome-wide association studies
The optimal number of individuals that should be
initially resequenced to provide contiguous and
reliable linkage disequilibrium (LD) data for tag SNP
selection is unclear.There is an obvious trade off
between the laboratory effort required for
resequencing and the reliability of the data:some
fragments of the genome are difficult to amplify by
PCR,especially G+C-rich 5′regions of genes and
common repeat-rich regions,and automated
detection of SNPs is still not possible using currently
available di-deoxy-sequencing chemistries.
We undertook resampling of real data to examine the reliability of the allelic R2method of Chapman and
colleagues67,in which the correlation for tag selection is based on multiple comparisons (R2),rather than pairwise
comparisons (r2) (see BOX 1).For each of five regions used in previous LD simulations68,a central contiguous
genomic region containing 28 SNPs was chosen.In each trial,a smaller number or subset of individuals was
randomly sampled from the complete set,and this provided information for tag SNP selection.Set sizes that were
considered comprised 16,20,23,32,48 and 96 individuals,and 1,000 trials were carried out for each set size in each
genomic region.For the selection of tag SNPs,an allelic R2cut-off of 0.8 was used67.The performance of the tag
SNPs that were selected from the test sets was then evaluated on the basis of their ability to provide information
about the whole set of SNPs.Our aim was to select tag SNPs with R2≥0.8 for all remaining SNPs;the average
percentage of SNPs over the five genomic regions that failed to achieve R2of 0.6 and 0.7 in the population are shown
in the table.
These results indicate that tag SNP selection requires resequencing for only a relatively small number of
individuals:32 individuals,or 64 chromosomes,might be sufficient to provide R2≥0.8 for more than 98% of cases,
and this is consistent with previous studies for different tag SNP methods45,68,101.For training sets of 32 or more
individuals,variation between the five genes in the level of adequate tagging was minor.However,SNPs with
MAFs of <0.1 were in general more difficult to tag than common SNPs,and variations between genes can be
problematic when 23 or fewer individuals are used.It is also unclear how many individuals would be required in
non-European populations,although greater haplotype diversity in African populations indicates that larger
sample sizes would be required15,75.
No. of individuals
in training set
R2<0.6 (%)R2<0.7 (%)
© 2005 Nature Publishing Group
116 | FEBRUARY 2005 | VOLUME 6
R E V I E W S
aim is that controls should be drawn from a population
that is sufficiently similar to the study base to allow the
relevant allele and haplotype frequencies to be reliably
Selection bias occurs when the control frequencies
differ systematically from those in the study base.We
should discriminate between selection bias that is due to
causal effects, such as effects on personality leading
to ‘volunteer bias’,which might affect a limited number
of loci,and more general effects that are due to differ-
ences in population substructure between controls and
the study base. These more general biases can be
addressed,as described earlier,by the use ofAIMs and
by GENOMIC CONTROL90,93.
A powerful protection against being misled by selec-
tion bias is the use ofmultiple groups.A design in which
several disease groups are compared against one or two
control groups allows the consistency of findings to be
examined,and this strengthens the inference.An example
ofthis is Doll and Hill’s94classical study ofthe association
between smoking and lung cancer,which used two differ-
ent comparison groups — comprising patients suffering
from two different diseases;Doll95cited the similarity of
smoking rates in the control groups as strengtheningthe
evidence for a causal effect on lung cancer.
Despite the caveats outlined above, it seems that
genome-wide association studies of the role of com-
mon variants in complex disease will be carried out in
the near future.Initial studies will define more accu-
rately the principal factors,which have been summa-
rized above,that can reduce the power of such studies.
In these studies, large sample sizes should be used,
biases taken into account, multiple-testing issues
addressed and replication studies carried out,there-
fore optimizing experimental design,statistical power
and cost efficiency.Close evaluation of the yields of
true susceptibility loci in relation to the cost of such
rigorously designed studies will determine whether
the genome-wide analyses of common SNPs is a
worthwhile approach in the continuing dissection of
the genetic basis of common disease.
Population substructure and other sources of error.
Several problems in study design can lead to both false-
positives and false-negatives in association analyses3.In
the past decade,case-control studies and family-based
association studies have emerged as the two main
strategies in association analyses of common diseases3.
Although cases and controls are generally more power-
ful and logistically easier to collect,they can suffer from
hidden POPULATION STRATIFICATIONand ADMIXTURE87–89;how-
ever,the magnitude of such effects remains unclear3.
Differences in DNA quality and less-than-optimal
genotyping can also lead to increased false-positive
rates in both family-based and population-based stud-
ies.With the large sample sizes that we now believe to
be necessary, such influences become more pro-
nounced.Close assessment of technical difficulties —
taking into account population substructure and
admixture effects using ANCESTRY-INFORMATIVE MARKERS
(AIMs)90–92,replication studies in different populations,
and the use of both case-control and family-based
studies (with P-values of less than 10–6obtained in at
least one dataset) — will be necessary to establish
irrefutable and accurately quantified evidence of
genetic association.It remains to be quantified empiri-
cally,by genotyping large numbers of SNPs,how signif-
icant the effects of population substructure are.This
will vary both for different populations and depending
on how closely cases and controls in any particular
country can be matched according to geographical sub-
regions.AIMs for a wide variety of populations and
ancestral groups should be identified,given the possi-
bility of important effects owing to substructure even
in geographically matched groups.
Selection bias. Selection bias in case-control studies
arises when the case and control groups are not truly
comparable.Ideally,the controls would be drawn from
the same population as the cases — known as the ‘study
base’ — and would be subject to the same selection
biases.In practice,it is only possible to approach this
ideal in case-control studies that are nested in COHORT
STUDIESand these are rarely,if ever,sufficiently large for
the purposes discussed in this review.A more realistic
The presence of several
population subgroups that show
such subgroups differ both in
allele frequency and in disease
prevalence,this can lead to
erroneous results in association
The mixture of two or more
genetically distinct populations.
Genetic markers that have
different frequencies between
populations and can be used to
readily estimate the ancestral
origins of a person or
Observational studies in which
defined groups of people (the
cohorts) are followed over time
and outcomes are compared in
subsets of the cohort who were
exposed to different levels of
factors of interest.These studies
can either be performed
prospectively or retrospectively
from historical records.
A statistical genetics approach
that provides an adjustment of
the chi-squared threshold for
statistical significance in a
genetic association study to
help allow for population
1. Ioannidis, J. P., Trikalinos, T. A., Ntzani, E. E. & Contopoulos-
Ioannidis, D. G. Genetic associations in large versus small
studies: an empirical assessment. Lancet 361, 567–571
Lohmueller, K. E., Pearce, C. L., Pike, M., Lander, E. S. &
Hirschhorn, J. N. Meta-analysis of genetic association
studies supports a contribution of common variants to
susceptibility to common disease. Nature Genet. 33,
Hirschhorn, J. N. & Daly, M. J. Genome-wide association
studies for common diseases and complex traits. Nature
Rev. Genet. 6, 95–108 (2005).
A review of the issues that are involved in the design
of large-scale association mapping, including
marker selection and sources of false-positive and
Livak, K. J., Marmaro, J. & Todd, J. A. Towards fully
automated genome-wide polymorphism screening. Nature
Genet. 9, 341–342 (1995).
Patil, N. et al. Blocks of limited haplotype diversity revealed
by high-resolution scanning of human chromosome 21.
Science 294, 1719–1723 (2001).
6. Syvanen, A. C. Accessing genetic variation: genotyping
single nucleotide polymorphisms. Nature Rev. Genet. 2,
Miller, R. D., Duan, S., Lovins, E. G., Kloss, E. F. &
Kwok, P. Y. Efficient high-throughput resequencing of
genomic DNA. Genome Res. 13, 717–720 (2003).
Hardenbol, P. et al. Multiplexed genotyping with
sequence-tagged molecular inversion probes. Nature
Biotechnol. 21, 673–678 (2003).
Blangero, J. Localization and identification of human
quantitative trait loci: King Harvest has surely come.
Curr. Opin. Genet. Dev. 14, 233–240 (2004).
10. Terwilliger, J. D. & Weiss, K. M. Confounding,
ascertainment bias, and the blind quest for a
genetic ‘fountain of youth’. Ann. Med. 35, 532–544
11. Wang, W. Y., Cordell, H. J. & Todd, J. A. Association
mapping of complex diseases in linked regions:
estimation of genetic effects and feasibility of testing rare
variants. Genet. Epidemiol. 24, 36–43 (2003).
12. Stefansson, H., Steinthorsdottir, V., Thorgeirsson, T. E.,
Gulcher, J. R. & Stefansson, K. Neuregulin 1 and
schizophrenia. Ann. Med. 36, 62–71 (2004).
13. Altmuller, J., Palmer, L. J., Fischer, G., Scherb, H. & Wjst, M.
Genomewide scans of complex human diseases: true linkage
is hard to find. Am. J. Hum. Genet. 69, 936–950 (2001).
This is an analyses of 101 linkage studies. It
demonstrates the difficulties in achieving significant
linkage, and argues for a need for larger sample sizes.
14. Neale, B. M. & Sham, P. C. The future of association studies:
gene-based analysis and replication. Am. J. Hum. Genet.
75, 353–362 (2004).
A review of the design of association-mapping
strategies. It argues for changing the focus from
SNPs to genomic regions, and outlines strategies to
15. Gabriel, S. B. et al. The structure of haplotype blocks in
the human genome. Science 296, 2225–2229 (2002).
16. Dawson, E. et al. A first-generation linkage disequilibrium map
of human chromosome 22. Nature418, 544–548 (2002).
17. International HapMap Consortium. The International
HapMap Project. Nature 426, 789–796 (2003).
This paper outlines the International HapMap Project,
which is currently in progress, and will provide SNP
maps, LD information and tag SNPs throughout the
genome for different human populations.
© 2005 Nature Publishing Group
NATURE REVIEWS | GENETICS
VOLUME 6 | FEBRUARY 2005 | 117
R E V I E W S
18. McVean, G. A. et al. The fine-scale structure of
recombination rate variation in the human genome.
Science 304, 581–584 (2004).
19. Johnson, G. C. et al. Haplotype tagging for the
identification of common disease genes. Nature Genet.
29, 233–237 (2001).
The authors introduce the concept of tag SNPs
based on LD to minimize laboratory effort for SNP
genotyping in association analyses.
20. Reich, D. E. & Lander, E. S. On the allelic spectrum
of human disease. Trends Genet. 17, 502–510
21. Pritchard, J. K. & Cox, N. J. The allelic architecture of
human disease genes: common disease–common
variant…or not? Hum. Mol. Genet. 11, 2417–2423
22. Risch, N. & Merikangas, K. The future of genetic studies
of complex human diseases. Science 273, 1516–1517
This paper showed in explicit terms the greater
power of whole-genome association studies over
affected sib-pair linkage for the mapping of
23. Dahlman, I. et al. Parameters for reliable results in genetic
association studies in common disease. Nature Genet.
30, 149–150 (2002).
24. Freimer, N. & Sabatti, C. The use of pedigree, sib-pair
and association studies of common diseases for genetic
mapping and epidemiology. Nature Genet. 36,
A clear and unbiased review of the main current
genetic mapping strategies that discusses
analyses using extended pedigrees, affected
sib-pairs and association.
25. Lowe, C. E. et al. Cost-effective analysis of candidate
genes using htSNPs: a staged approach. Genes Immun.
5, 301–305 (2004).
26. Smith, D. J. & Lusis, A. J. The allelic structure of
common disease. Hum. Mol. Genet. 11, 2455–2461
27. Fisher, R. A. Correlation between relatives on the
supposition of Mendelian inheritance. Trans. R. Soc.
Edinb. 52, 399–433 (1918).
28. Risch, N. The genetic epidemiology of cancer:
interpreting family and twin studies and their
implications for molecular genetic approaches.
Cancer Epidemiol. Biomarkers Prev. 10, 733–741
29. Hirschhorn, J. N. et al. Genomewide linkage analysis of
stature in multiple populations reveals several regions
with evidence of linkage to adult height. Am. J. Hum.
Genet. 69, 106–116 (2001).
30. Rich, S. S. Mapping genes in diabetes. Genetic
epidemiological perspective. Diabetes 39, 1315–1319
31. Pritchard, J. K. Are rare variants responsible for
susceptibility to complex diseases? Am. J. Hum. Genet.
69, 124–137 (2001).
32. Todd, J. A. Human genetics. Tackling common disease.
Nature 411, 537–539 (2001).
33. Cohen, J. C. et al. Multiple rare alleles contribute to low
plasma levels of HDL cholesterol. Science 305, 869–872
34. Corder, E. H. et al. Gene dose of apolipoprotein E
type 4 allele and the risk of Alzheimer’s disease
in late onset families. Science 261, 921–923
35. Bell, G. I., Horita, S. & Karam, J. H. A polymorphic locus
near the human insulin gene is associated with insulin-
dependent Diabetes mellitus. Diabetes 33, 176–183
36. Ueda, H. et al. Association of the T-cell regulatory gene
CTLA4 with susceptibility to autoimmune disease. Nature
423, 506–511 (2003).
37. Hugot, J. P. et al. Association of NOD2 leucine-rich
repeat variants with susceptibility to Crohn’s disease.
Nature 411, 599–603. (2001).
38. Ogura, Y. et al. A frameshift mutation in NOD2 associated
with susceptibility to Crohn’s disease. Nature 411,
39. Long, A. D. & Langley, C. H. The power of association
studies to detect the contribution of candidate genetic
loci to variation in complex traits. Genome Res. 9,
40. Wang, W. Y. & Pike, N. The allelic spectra of
common diseases may resemble the allelic spectrum
of the full genome. Med. Hypotheses 63, 748–751
41. Kruglyak, L. & Nickerson, D. A. Variation is the spice of
life. Nature Genet. 27, 234–236 (2001).
Using a neutral coalescence model, this article
estimates the frequency distribution of SNPs in the
42. Botstein, D. & Risch, N. Discovering genotypes
underlying human phenotypes: past successes for
Mendelian disease, future approaches for complex
disease. Nature Genet. 33, 228–237 (2003).
43. Clark, A. G. Finding genes underlying risk of complex
disease by linkage disequilibrium mapping. Curr. Opin.
Genet. Dev. 13, 296–302 (2003).
44. Neel, J. V. Diabetes mellitus: a ‘thrifty’ genotype rendered
detrimental by ‘progress’? Am. J. Hum. Genet. 14,
45. Carlson, C. S. et al. Additional SNPs and linkage-
disequilibrium analyses are necessary for whole-genome
association studies in humans. Nature Genet. 33,
46. Nezer, C. et al. Haplotype sharing refines the location of
an imprinted quantitative trait locus with major effect on
muscle mass to a 250-kb chromosome segment
containing the porcine IGF2 gene. Genetics 165,
47. Vyse, T. J. & Todd, J. A. Genetic analysis of autoimmune
disease. Cell 85, 311–318 (1996).
48. Robertson, A. in Population Biology and Evolution
(ed. Lewontin, R. C.) 265–280 (Syracuse Univ. Press,
New York, 1967).
49. Paterson, A. H. et al. Mendelian factors underlying
quantitative traits in tomato: comparison across species,
generations, and environments. Genetics 127, 181–197
50. Mackay, T. F., Lyman, R. F. & Jackson, M. S. Effects of
P element insertions on quantitative traits in Drosophila
melanogaster. Genetics 130, 315–332 (1992).
51. Hayes, B. & Goddard, M. E. The distribution of the effects
of genes affecting quantitative traits in livestock. Genet.
Sel. Evol. 33, 209–229 (2001).
52. Barton, N. H. & Keightley, P. D. Understanding
quantitative genetic variation. Nature Rev. Genet. 3,
53. Wright, A., Charlesworth, B., Rudan, I., Carothers, A. &
Campbell, H. A polygenic basis for late-onset disease.
Trends Genet. 19, 97–106 (2003).
54. Risch, N., Ghosh, S. & Todd, J. A. Statistical evaluation of
multiple-locus linkage data in experimental species and
its relevance to human studies: application to nonobese
diabetic (NOD) mouse and human insulin-dependent
Diabetes mellitus (IDDM). Am. J. Hum. Genet. 53,
55. Fisher, R. A. The Genetical Theory of Natural Selection
(Oxford Univ. Press, Oxford, 1930).
56. Orr, H. A. The population genetics of adaptation: the
distribution of factors fixed during adaptive evolution.
Evolution 52, 935–949 (1998).
57. Pagani, F. & Baralle, F. E. Genomic variants in exons and
introns: identifying the splicing spoilers. Nature Rev.
Genet. 5, 389–396 (2004).
58. Hoogendoorn, B. et al. Functional analysis of human
promoter polymorphisms. Hum. Mol. Genet. 12,
59. Lo, H. S. et al. Allelic variation in gene expression is
common in the human genome. Genome Res. 13,
60. Mira, M. T. et al. Susceptibility to leprosy is associated
with PARK2 and PACRG. Nature 427, 636–640 (2004).
61. Morley, M. et al. Genetic analysis of genome-wide
variation in human gene expression. Nature 430,
62. Kleinjan, D. A. & van Heyningen, V. Long-range control
of gene expression: emerging mechanisms and
disruption in disease. Am. J. Hum. Genet. 76, 8–32
63. Rybicki, B. A. & Elston, R. C. The relationship between
the sibling recurrence-risk ratio and genotype relative
risk. Am. J. Hum. Genet. 66, 593–604 (2000).
64. Jorde, L. B. Linkage disequilibrium and the search for
complex disease genes. Genome Res. 10, 1435–1444
65. Sham, P. C., Cherny, S. S., Purcell, S. & Hewitt, J. K.
Power of linkage versus association analysis of
quantitative traits, by use of variance-components
models, for sibship data. Am. J. Hum. Genet. 66,
66. Pritchard, J. K. & Przeworski, M. Linkage disequilibrium
in humans: models and data. Am. J. Hum. Genet. 69,
67. Chapman, J. M., Cooper, J. D., Todd, J. A. & Clayton, D. G.
Detecting disease associations due to linkage
disequilibrium using haplotype tags: a class of tests and
the determinants of statistical power. Hum. Hered. 56,
This paper examines analyses of tag SNPs and
suggests that it might be best to discard haplotype
information and consider only the main effects of
tag SNPs to avoid losing power owing to increased
degrees of freedom.
68. Wang, W. Y. & Todd, J. A. The usefulness of different
density SNP maps for disease association studies of
common variants. Hum. Mol. Genet. 12, 3145–3149
Based on sampling simulations of published,
near-complete SNP maps, this study assesses the
usefulness of different density SNP maps for LD
69. Ke, X. et al. The impact of SNP density on fine-scale
patterns of linkage disequilibrium. Hum. Mol. Genet. 13,
70. Clayton, D., Chapman, J. & Cooper, J. Use of unphased
multilocus genotype data in indirect association studies.
Genet. Epidemiol. 27, 415–428 (2004).
71. Nejentsev, S. et al. Comparative high-resolution
analysis of linkage disequilibrium and tag single
nucleotide polymorphisms between populations in the
vitamin D receptor gene. Hum. Mol. Genet. 13,
72. Jeffreys, A. J., Kauppi, L. & Neumann, R. Intensely
punctate meiotic recombination in the class II region of
the major histocompatibility complex. Nature Genet. 29,
73. Twells, R. C. et al. Haplotype structure, LD blocks, and
uneven recombination within the LRP5 gene. Genome
Res. 13, 845–855 (2003).
74. Jeffreys, A. J. & May, C. A. Intense and highly localized
gene conversion activity in human meiotic crossover hot
spots. Nature Genet. 36, 151–156 (2004).
75. Wall, J. D. & Pritchard, J. K. Haplotype blocks and
linkage disequilibrium in the human genome. Nature Rev.
Genet. 4, 587–597 (2003).
76. Pask, R. et al. Investigating the utility of combining Φ29
whole genome amplification and highly multiplexed single
nucleotide polymorphism BeadArray genotyping. BMC
Biotechnol. 4, 15 (2004).
77. Cordell, H. J. & Clayton, D. G. Genetic association
studies. Lancet (in the press).
78. Carlson, C. S. et al. Selecting a maximally informative set
of single-nucleotide polymorphisms for association
analyses using linkage disequilibrium. Am. J. Hum.
Genet. 74, 106–120 (2004).
79. Ke, X. et al. Efficiency and consistency of haplotype
tagging of dense SNP maps in multiple samples. Hum.
Mol. Genet. 13, 2557–2565 (2004).
80. Bateson, W. Mendel’s Principles of Heredity (Cambridge
Univ. Press, Cambridge, 1909).
81. Thompson, W. D. Effect modification and the limits of
biological inference from epidemiologic data. J. Clin.
Epidemiol. 44, 221–232 (1991).
82. Cordell, H. J. Epistasis: what it means, what it doesn’t
mean, and statistical methods to detect it in humans.
Hum. Mol. Genet. 11, 2463–2468 (2002).
83. Culverhouse, R., Suarez, B. K., Lin, J. & Reich, T.
A perspective on epistasis: limits of models displaying
no main effect. Am. J. Hum. Genet. 70, 461–471
84. Thornton-Wells, T. A., Moore, J. H. & Haines, J. L.
Genetics, statistics and human disease: analytical
retooling for complexity. Trends. Genet. 20, 640–647
85. Hoh, J. & Ott, J. Mathematical multi-locus approaches to
localizing complex human trait genes. Nature Rev. Genet.
4, 701–709 (2003).
86. Clayton, D. & McKeigue, P. M. Epidemiological methods
for studying genes and environmental factors in complex
diseases. Lancet 358, 1356–1360 (2001).
87. Pato, C. N., Macciardi, F., Pato, M. T., Verga, M. &
Kennedy, J. L. Review of the putative association of
dopamine D2 receptor and alcoholism: a meta-analysis.
Am. J. Med. Genet. 48, 78–82 (1993).
88. Freedman, M. L. et al. Assessing the impact of
population stratification on genetic association studies.
Nature Genet. 36, 388–393 (2004).
89. Marchini, J., Cardon, L. R., Phillips, M. S. & Donnelly, P.
The effects of human population structure on large
genetic association studies. Nature Genet. 36, 512–517
© 2005 Nature Publishing Group
118 | FEBRUARY 2005 | VOLUME 6 Download full-text
R E V I E W S
90. Pritchard, J. K. & Rosenberg, N. A. Use of unlinked
genetic markers to detect population stratification in
association studies. Am. J. Hum. Genet. 65, 220–228
91. Hoggart, C. J. et al. Control of confounding of genetic
associations in stratified populations. Am. J. Hum.
Genet. 72, 1492–1504 (2003).
92. Marchini, J., Cardon, L. R., Phillips, M. S. & Donnelly, P.
Reply to ‘Genomic control to the extreme’. Nature Genet.
36, 1131 (2004).
93. Devlin, B. & Roeder, K. Genomic control for association
studies. Biometrics 55, 997–1004 (1999).
94. Doll, R. & Hill, A. B. The mortality of doctors in relation to
their smoking habits. BMJ 228, 1451–1455 (1954).
95. Doll, R. Retrospective and Prospective Studies
(ed. Witts, L. J.) (Oxford Univ. Press, London, 1959).
96. Devlin, B. & Risch, N. A comparison of linkage
disequilibrium measures for fine-scale mapping.
Genomics 29, 311–322 (1995).
97. Lewontin, R. C. & Kojima, K. The evolutionary dynamics
of complex polymorphisms. Evolution 14, 458–472
98. Lewontin, R. C. The interaction of selection and linkage.
I. General considerations; heterotic models. Genetics 49,
99. Hill, W. G. & Robertson, A. The effects of inbreeding at
loci with heterozygote advantage. Genetics 60, 615–628
100. Weiss, K. M. & Clark, A. G. Linkage disequilibrium and the
mapping of complex human traits. Trends Genet. 18, 19–24
101. Thompson, D., Stram, D., Goldgar, D. & Witte, J. S.
Haplotype tagging single nucleotide polymorphisms
and association studies. Hum. Hered. 56, 48–55 (2003).
102. Wall, J. D. & Pritchard, J. K. Assessing the performance of
the haplotype block model of linkage disequilibrium. Am. J.
Hum. Genet. 73, 502–515 (2003).
A review on haplotype blocks and LD in the human
103. Thomas, D. C. & Clayton, D. G. Betting odds and
genetic associations. J. Natl Cancer Inst. 96, 421–423
104. Wacholder, S. et al. Assessing the probability that a positive
report is false: an approach for molecular epidemiology
studies. J. Natl Cancer Inst. 96, 434–442 (2004).
W.Y.S.W. received scholarships from the University of Cambridge,
the University of Sydney and Gonville and Caius College,
Cambridge, UK. This work was financed by the Wellcome Trust
and the Juvenile Diabetes Research Foundation International.
Competing interests statement
The authors declare no competing financial interests.
The following terms in this article are linked online to:
Type 1 diabetes | type 2 diabetes
David Clayton’s tag SNP web site: http://www-gene.cimr.cam.
International HapMap Project: http://www.hapmap.org
NCBI Single Nucleotide Polymorphism database web site:
Perlegen Sciences, Inc.: http://www.els.net
TD1Base — a genetics and bioinformatics resource for
type 1 diabetes researchers: http://www.t1dbase.org/
University of Washington and Fred Hutchinson Cancer
Research Center Variation Discovery Resource database:
Access to this interactive links box is free online.
© 2005 Nature Publishing Group