Vol 437|27 October 2005|doi:10.1038/nature04226

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A haplotype map of the human genome
The International HapMap Consortium*
Inherited genetic variation has a critical but as yet largely uncharacterized role in human disease. Here we report apublic database of common variation in the human genome: more than one million single nucleotide polymorphisms(SNPs) for which accurate and complete genotypes have been obtained in 269 DNA samples from four populations,
including ten 500-kilobase regions in which essentially all information about common DNA variation has been extracted.
These data document the generality of recombination hotspots, a block-like structure of linkage disequilibrium and low
haplotype diversity, leading to substantial correlations of SNPs with many of their neighbours. We show how the
HapMap resource can guide the design and analysis of genetic association studies, shed light on structural variation andrecombination, and identify loci that may have been subject to natural selection during human evolution.

Despite the ever-accelerating pace of biomedical research, the root
causes of common human diseases remain largely unknown, preventative measures are generally inadequate, and available treatments
are seldom curative. Family history is one of the strongest risk factors
for nearly all diseases—including cardiovascular disease, cancer,
diabetes, autoimmunity, psychiatric illnesses and many others—
providing the tantalizing but elusive clue that inherited genetic
variation has an important role in the pathogenesis of disease.
Identifying the causal genes and variants would represent an important step in the path towards improved prevention, diagnosis andtreatment of disease.
More than a thousand genes for rare, highly heritable ‘mendelian’disorders have been identified, in which variation in a single gene isboth necessary and sufficient to cause disease. Common disorders, incontrast, have proven much more challenging to study, as they
are thought to be due to the combined effect of many different
susceptibility DNA variants interacting with environmental factors.
Studies of common diseases have fallen into two broad categories:
family-based linkage studies across the entire genome, and population-based association studies of individual candidate genes.
Although there have been notable successes, progress has been slow
due to the inherent limitations of the methods; linkage analysis has
low power except when a single locus explains a substantial fractionof disease, and association studies of one or a few candidate genes
examine only a small fraction of the ‘universe’ of sequence variationin each patient.
A comprehensive search for genetic influences on disease would
involve examining all genetic differences in a large number of affectedindividuals and controls. It may eventually become possible toaccomplish this by complete genome resequencing. In the meantime,
it is increasingly practical to systematically test common genetic
variants for their role in disease; such variants explain much of thegenetic diversity in our species, a consequence of the historicallysmall size and shared ancestry of the human population.
Recent experience bears out the hypothesis that common variants
have an important role in disease, with a partial list of validated
examples including HLA (autoimmunity and infection)1, APOE4(Alzheimer’s disease, lipids)2, Factor VLeiden (deep vein thrombosis)3,
PPARG (encoding PPARg; type 2 diabetes)4,5, KCNJ11 (type 2

diabetes)6, PTPN22 (rheumatoid arthritis and type 1 diabetes)7,8,
insulin (type 1 diabetes)9, CTLA4 (autoimmune thyroid disease, type1 diabetes)10, NOD2 (inflammatory bowel disease)11,12, complement
factor H (age-related macular degeneration)13–15 and RET (Hirschsprung disease)16,17, among many others.
Systematic studies of common genetic variants are facilitated by
the fact that individuals who carry a particular SNP allele at one site
often predictably carry specific alleles at other nearby variant sites.
This correlation is known as linkage disequilibrium (LD); a particular combination of alleles along a chromosome is termed a haplotype.
LD exists because of the shared ancestry of contemporary chromosomes. When a new causal variant arises through mutation—whether
a single nucleotide change, insertion/deletion, or structural alteration—it is initially tethered to a unique chromosome on which it
occurred, marked by a distinct combination of genetic variants.
Recombination and mutation subsequently act to erode this association, but do so slowly (each occurring at an average rate of about
1028 per base pair (bp) per generation) as compared to the numberof generations (typically 104 to 105) since the mutational event.
The correlations between causal mutations and the haplotypes on
which they arose have long served as a tool for human genetic
research: first finding association to a haplotype, and then subsequently identifying the causal mutation(s) that it carries. This was
pioneered in studies of the HLA region, extended to identify causal
genes for mendelian diseases (for example, cystic fibrosis18 anddiastrophic dysplasia19), and most recently for complex disorders
such as age-related macular degeneration13–15.
Early information documented the existence of LD in the humangenome20,21; however, these studies were limited (for technicalreasons) to a small number of regions with incomplete data, andgeneral patterns were challenging to discern. With the sequencing of
the human genome and development of high-throughput genomic
methods, it became clear that the human genome generally
displays more LD22 than under simple population genetic models23,
and that LD is more varied across regions, and more segmentally
structured24–30, than had previously been supposed. These observations indicated that LD-based methods would generally havegreat value (because nearby SNPs were typically correlated withmany of their neighbours), and also that LD relationships would*Lists of participants and affiliations appear at the end of the paper.

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Table 1 | Genotyping centres
Centre

RIKEN
Wellcome Trust Sanger Institute
McGill University and Génome Québec Innovation Centre
Chinese HapMap Consortium*
Illumina
Broad Institute of Harvard and MIT
Baylor College of Medicine with ParAllele BioScience
University of California, San Francisco, with Washington University in St Louis
Perlegen Sciences

Chromosomes

Technology

5, 11, 14, 15, 16, 17, 19
1, 6, 10, 13, 20
2, 4p
3, 8p, 21
8q, 9, 18q, 22, X
4q, 7q, 18p, Y, mtDNA
127p
5 Mb (ENCODE) on 2, 4, 7,
8, 9, 12, 18 in CEU

Third Wave Invader
Illumina BeadArray
Illumina BeadArray
Sequenom MassExtend, Illumina BeadArray
Illumina BeadArray
Sequenom MassExtend, Illumina BeadArray
ParAllele MIP
PerkinElmer AcycloPrime-FP
High-density oligonucleotide array* The Chinese HapMap Consortium consists of the Beijing Genomics Institute, the Chinese National Human Genome Center at Beijing, the University of Hong Kong, the Hong Kong Universityof Science and Technology, the Chinese University of Hong Kong, and the Chinese National Human Genome Center at Shanghai.

need to be empirically determined across the genome by studying
polymorphisms at high density in population samples.
The International HapMap Project was launched in October 2002
to create a public, genome-wide database of common humansequence variation, providing information needed as a guide to
genetic studies of clinical phenotypes31. The project had become
practical by the confluence of the following: (1) the availability of
the human genome sequence; (2) databases of common SNPs
(subsequently enriched by this project) from which genotyping
assays could be designed; (3) insights into human LD; (4) development of inexpensive, accurate technologies for high-throughput SNP
genotyping; (5) web-based tools for storing and sharing data; and(6) frameworks to address associated ethical and cultural issues32.
The project follows the data release principles of an international
community resource project (http://www.wellcome.ac.uk/
doc_WTD003208.html), sharing information rapidly and without
restriction on its use.
The HapMap data were generated with the primary aim of guiding
the design and analysis of medical genetic studies. In addition, theadvent of genome-wide variation resources such as the HapMap
opens a new era in population genetics, offering an unprecedented
opportunity to investigate the evolutionary forces that have shaped
variation in natural populations.
The Phase I HapMap
Phase I of the HapMap Project set as a goal genotyping at least one
common SNP every 5 kilobases (kb) across the genome in each of 269
DNA samples. For the sake of practicality, and motivated by the allele
frequency distribution of variants in the human genome, a minor
allele frequency (MAF) of 0.05 or greater was targeted for study. (Forsimplicity, in this paper we will use the term ‘common’ to mean a
SNP with MAF $ 0.05.) The project has a Phase II, which is
attempting genotyping of an additional 4.6 million SNPs in each of
the HapMap samples.
To compare the genome-wide resource to a more complete
database of common variation—one in which all common SNPs
and many rarer ones have been discovered and tested—a representative collection of ten regions, each 500 kb in length, was selected fromthe ENCODE (Encyclopedia of DNA Elements) Project33. Each
500-kb region was sequenced in 48 individuals, and all SNPs inthese regions (discovered or in dbSNP) were genotyped in thecomplete set of 269 DNA samples.
The specific samples examined are: (1) 90 individuals (30 parent–
offspring trios) from the Yoruba in Ibadan, Nigeria (abbreviation
YRI); (2) 90 individuals (30 trios) in Utah, USA, from the Centred’Etude du Polymorphisme Humain collection (abbreviation CEU);(3) 45 Han Chinese in Beijing, China (abbreviation CHB); (4) 44
Japanese in Tokyo, Japan (abbreviation JPT).
Because none of the samples was collected to be representative of alarger population such as ‘Yoruba’, ‘Northern and Western European’,‘Han Chinese’, or ‘Japanese’ (let alone of all populations from ‘Africa’,‘Europe’, or ‘Asia’), we recommend using a specific local identifier
1300(for example, ‘Yoruba in Ibadan, Nigeria’) to describe the samples
initially. Because the CHB and JPT allele frequencies are generally
very similar, some analyses below combine these data sets. When
doing so, we refer to three ‘analysis panels’ (YRI, CEU, CHBþJPT) to
avoid confusing this analytical approach with the concept of a
‘population’.
Important details about the design of the HapMap Project arepresented in the Methods, including: (1) organization of the project;(2) selection of DNA samples for study; (3) increasing the number
and annotation of SNPs in the public SNP map (dbSNP) from
2.6 million to 9.2 million (Fig. 1); (4) targeted sequencing of the ten
ENCODE regions, including evaluations of false-positive and falsenegative rates; (5) genotyping for the genome-wide map; (6) intense
efforts that monitored and established the high quality of the data;and (7) data coordination and distribution through the project Data
Coordination Center (DCC) (http://www.hapmap.org).
Description of the data. The Phase I HapMap contains 1,007,329
SNPs that passed a set of quality control (QC) filters (see Methods) ineach of the three analysis panels, and are polymorphic across the 269
samples. SNP genotyping was distributed across centres by chromosomal region, with several technologies employed (Table 1). Eachcentre followed the same standard rules for SNP selection, quality
control and data release; all SNPs were genotyped in the full set of 269
samples. Some centres genotyped more SNPs than required by therules.
Extensive, blinded quality assessment (QA) exercises documented
that these data are highly accurate (99.7%) and complete (99.3%, see

Figure 1 | Number of SNPs in dbSNP over time. The cumulative number of
non-redundant SNPs (each mapped to a single location in the genome) isshown as a solid line, as well as the number of SNPs validated by genotyping(dotted line) and double-hit status (dashed line). Years are divided intoquarters (Q1–Q4).

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also Supplementary Table 1). All genotyping centres produced highquality data (accuracy more than 99% in the blind QA exercises,
Supplementary Tables 2 and 3), and missing data were not biased
against heterozygotes. The Supplementary Information contains thefull details of these efforts.
Although SNP selection was generally agnostic to functionalannotation, 11,500 non-synonymous cSNPs (SNPs in coding regionsof genes where the different SNP alleles code for different amino acidsin the protein) were successfully typed in Phase I. (An effort wasmade to prioritize cSNPs in Phase I in choosing SNPs for each 5-kb
region; all known non-synonymous cSNPs were attempted as part ofPhase II.)
Across the ten ENCODE regions (Table 2), the density of SNPs wasapproximately tenfold higher as compared to the genome-wide map:
17,944 SNPs across the 5 megabases (Mb) (one per 279 bp).
More than 1.3 million SNP genotyping assays were attempted(Table 3) to generate the Phase I data on more than 1 million SNPs.
The 0.3 million SNPs not part of the Phase I data set include 73,652 thatpassed QC filters but were monomorphic in all 269 samples. The
remaining SNPs failed the QC filters in one or more analysis panels
mostly because of inadequate completeness, non-mendelian inheritance, deviations from Hardy–Weinberg equilibrium, discrepant
genotypes among duplicates, and data transmission discrepancies.
SNPs on the Phase I map are evenly spaced, except on Y and
mtDNA. The Phase I data include a successful, common SNP
every 5 kb across most of the genome in each analysis panel(Supplementary Fig. 1): only 3.3% of inter-SNP distances are longer
than 10 kb, spanning 11.9% of the genome (Fig. 2; see also Supplementary Fig. 2). One exception is the X chromosome (Supplementary Fig. 1), where a much higher proportion of attempted SNPs wererare or monomorphic, and thus the density of common SNPs is lower.
Two intentional exceptions to the regular spacing of SNPs on thephysical map were the mitochondrial chromosome (mtDNA), which
does not undergo recombination, and the non-recombining portionof chromosome Y. On the basis of the 168 successful, polymorphic
SNPs, each HapMap sample fell into one of 15 (of the 18 known)
mtDNA haplogroups34 (Table 4). A total of 84 SNPs that characterize
the unique branches of the reference Y genealogical tree35–37 were
genotyped on the HapMap samples. These SNPs assigned each Y
chromosome to 8 (of the 18 major) Y haplogroups previously
described (Table 4).
Highly accurate phasing of long-range chromosomal haplotypes.
Despite having collected data in diploid individuals, the inclusion ofparent–offspring trios and the use of computational methods made it
possible to determine long-range phased haplotypes of extremely
high quality for each individual. These computational algorithms
take advantage of the observation that because of LD, relatively few ofthe large number of possible haplotypes consistent with the genotype
data actually occur in population samples.
The project compared a variety of algorithms for phasing haplotypes from unrelated individuals and trios38, and applied the algorithm that proved most accurate (an updated version of PHASE39)

Table 2 | ENCODE project regions and genotyping
Genomic
interval (NCBI)(base
numbers)†Genedensity Conservation(%)‡
score (%)§

Pedigree-based
recombinationrate(cM Mb21)kPopulationbased
recombinationrate(cM Mb21){GþCcontent#

dbSNPqSequence**

Total

Successfully
genotyped
SNPs††

Available SNPs

Region
name

Chromosome
band

ENr112

2p16.3

51,633,239–
52,133,238

0

3.8

0.8

0.9

0.35

1,570

1,762

3,332

2,275

ENr131

2q37.1

234,778,639–
235,278,638

4.6

1.3

2.2

2.5

0.43

1,736

1,259

2,995

1,910

ENr113

4q26

0

3.9

0.6

0.9

0.35

1,444

2,053

3,497

2,201

ENm0107p15.2

118,705,475–
119,205,474
26,699,793–
27,199,792

5.0

22.0

0.9

0.9

0.44

1,220

1,795

3,015

1,271

5.5

4.4

0.4

0.5

0.38

1,394

1,917

3,311

1,807

2.9

11.2

0.4

0.9

0.39

1,320

1,664

2,984

1,966

3.2

11.4

0.6

1.1

0.41

1,430

1,508

2,938

1,758

5.9

8.3

2.7

2.6

0.52

1,444

1,523

2,967

1,324

3.1

1.7

0.3

0.8

0.36

1,877

1,379

3,256

1,792

0.9

7.4

1.2

0.9

0.37

1,330

1,459

2,789

1,640

–

–

–

–

–

14,765

16,31931,084

17,944ENm013* 7q21.13
ENm014* 7q31.33
ENr321

8q24.11

ENr232

9q34.11

ENr123

12q12

ENr21318q12.1

Total–

89,395,718–
89,895,717
126,135,436–
126,632,577
118,769,628–
119,269,627
127,061,347–
127,561,346
38,626,477–
39,126,476
23,717,221–
24,217,220–

Sequencing
centre/genotyping
centre(s)‡‡Broad/
McGillGQIC
Broad/
McGillGQIC
Broad/
Broad
Baylor/
UCSF-WU,
Broad
Broad/
Broad
Broad/
Broad
Baylor/Illumina
Baylor/Illumina
Baylor /Baylor
Baylor/Illumina–

McGill-GQIC, McGill University and Génome Québec Innovation Centre.
* These regions were truncated to 500 kb for resequencing.
†Sequence build 34 coordinates.
‡ Gene density is defined as the percentage of bases covered either by Ensembl genes or human mRNA best BLAT alignments in the UCSC Genome Browser database.
§Non-exonic conservation with mouse sequence was measured by taking 125 base non-overlapping sub-windows inside the 500,000 base windows. Sub-windows with less than 75% of theirbases in a mouse alignment were discarded. Of the remaining sub-windows, those with at least 80% base identity were used to calculate the conservation score. The mouse alignments inregions corresponding to the following were discarded: Ensembl genes, all GenBank mRNA Blastz alignments, FGeneshþþ gene predictions, Twinscan gene predictions, spliced ESTalignments, and repeats.
kThe pedigree-based sex-averaged recombination map is from deCODE Genetics48.
{ Recombination rate based on estimates from LDhat46.
#G þ C content calculated from the sequence of the stated coordinates from sequence build 34.
qSNPs in dbSNP build 121 at the time the ENCODE resequencing began and SNPs added to dbSNP in builds 122–125 independent of the resequencing.
**New SNPs discovered through the resequencing reported here (not found by other means in builds 122–125).
††SNPs successfully genotyped in all analysis panels (YRI, CEU, CHB þ JPT).
‡‡Perlegen genotyped a subset of SNPs in the CEU samples.

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Table 3 | HapMap Phase I genotyping success measures
Analysis panel
SNP categories

YRI

CEU

CHB þ JPT

Assays submitted
Passed QC filters

1,273,716
1,123,296 (88%)

1,302,849
1,157,650 (89%)

1,273,703
1,134,726 (89%)

Did not pass QC filters*. 20% missing data
. 1 duplicate inconsistent. 1 mendelian error, 0.001 Hardy–Weinberg P-value
Other failures†

150,420 (12%)
98,116 (65%)
7,575 (5%)
22,815 (15%)
12,052 (8%)
23,478 (16%)

145,199 (11%)
107,626 (74%)
6,254 (4%)
13,600 (9%)
9,721 (7%)
17,692 (12%)

138,977 (11%)
93,710 (67%)
10,725 (8%)
0 (0%)
16,176 (12%)
23,722 (17%)

Non-redundant (unique) SNPs
Monomorphic
Polymorphic

1,076,392
156,290 (15%)
920,102 (85%)

1,104,980
234,482 (21%)
870,498 (79%)

1,087,305
268,325 (25%)
818,980 (75%)

All analysis panels

Unique QC-passed SNPs
Passed in one analysis panel
Passed in two analysis panels
Passed in three analysis panels

1,156,772
52,204 (5%)
97,231 (8%)
1,007,337 (87%)

Monomorphic across three analysis panels
Polymorphic in all three analysis panels

75,997682,397

MAF $ 0.05 in at least one of three analysis panels

877,351* Out of 95 samples in CEU, YRI; 94 samples in CHB þ JPT.
†‘Other failures’ includes SNPs with discrepancies during the data transmission process. Some SNPs failed in more than one way, so these percentages add up to more than 100%.

separately to each analysis panel. (Phased haplotypes are available fordownload at the Project website.) We estimate that ‘switch’ errors—
where a segment of the maternal haplotype is incorrectly joined to thepaternal—occur extraordinarily rarely in the trio samples (every 8 Mb
in CEU; 3.6 Mb in YRI). The switch rate is higher in the CHBþJPT
samples (one per 0.34 Mb) due to the lack of information fromparent–offspring trios, but even for the unrelated individuals, statistical reconstruction of haplotypes is remarkably accurate.
Estimating properties of SNP discovery and dbSNP. Extensive
sequencing and genotyping in the ENCODE regions characterized

the false-positive and false-negative rates for dbSNP, as well as
polymerase chain reaction (PCR)-based resequencing (seeMethods). These data reveal two important conclusions: first, that
PCR-based sequencing of diploid samples may be biased against veryrare variants (that is, those seen only as a single heterozygote), andsecond, that the vast majority of common variants are either
represented in dbSNP, or show tight correlation to other SNPs thatare in dbSNP (Fig. 3).
Allele frequency distributions within population samples. The
underlying allele frequency distributions for these samples are best

Figure 2 | Distribution of inter-SNP distances. The distributions are shown for each analysis panel for the HapMappable genome (defined in the Methods),
for all common SNPs (with MAF $ 0.05).
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estimated from the ENCODE data, where deep sequencing reduces
bias due to SNP ascertainment. Consistent with previous studies,most SNPs observed in the ENCODE regions are rare: 46% had
MAF , 0.05, and 9% were seen in only a single individual (Fig. 4).
Although most varying sites in the population are rare, mostheterozygous sites within any individual are due to common SNPs.
Specifically, in the ENCODE data, 90% of heterozygous sites in eachindividual were due to common variants (Fig. 4). With ever-deeper
sequencing of DNA samples the number of rare variants will rise
linearly, but the vast majority of heterozygous sites in each person
will be explained by a limited set of common SNPs now contained (orcaptured through LD) in existing databases (Fig. 3).
Consistent with previous descriptions, the CEU, CHB and JPT
samples show fewer low frequency alleles when compared to the YRI
samples (Fig. 5), a pattern thought to be due to bottlenecks in thehistory of the non-YRI populations.
In contrast to the ENCODE data, the distribution of allele
frequencies for the genome-wide data is flat (Fig. 5), with much
more similarity in the distributions observed in the three analysis
panels. These patterns are well explained by the inherent andintentional bias in the rules used for SNP selection: we prioritizedusing validated SNPs in order to focus resources on common (rather
than rare or false positive) candidate SNPs from the public databases.
For a fuller discussion of ascertainment issues, including a shift infrequencies over time and an excess of high-frequency derived alleles
due to inclusion of chimpanzee data in determination of double-hit
status, see the Supplementary Information (Supplementary Fig. 3).
SNP allele frequencies across population samples. Of the 1.007
million SNPs successfully genotyped and polymorphic across thethree analysis panels, only a subset were polymorphic in any given
panel: 85% in YRI, 79% in CEU, and 75% in CHBþJPT. The joint
distribution of frequencies across populations is presented in Fig. 6(for the ENCODE data) and Supplementary Fig. 4 (for the genomewide map). We note the similarity of allele frequencies in the CHB
and JPT samples, which motivates analysing them jointly as a single
analysis panel in the remainder of this report.

A simple measure of population differentiation is Wright’s F ST,
which measures the fraction of total genetic variation due tobetween-population differences40. Across the autosomes, F ST estimated from the full set of Phase I data is 0.12, with CEU andCHBþJPT showing the lowest level of differentiation (F ST ¼ 0.07),
and YRI and CHBþJPT the highest (F ST ¼ 0.12). These values areslightly higher than previous reports41, but differences in the types ofvariants (SNPs versus microsatellites) and the samples studied makecomparisons difficult.
As expected, we observed very few fixed differences (that is, cases
in which alternate alleles are seen exclusively in different analysis
panels). Across the 1 million SNPs genotyped, only 11 have fixed
differences between CEU and YRI, 21 between CEU and CHBþJPT,and 5 between YRI and CHBþJPT, for the autosomes.
The extent of differentiation is similar across the autosomes, buthigher on the X chromosome (F ST ¼ 0.21). Interestingly, 123 SNPs
on the X chromosome were completely differentiated between
YRI and CHBþJPT, but only two between CEU and YRI and one
between CEU and CHBþJPT. This seems to be largely due to a single
region near the centromere, possibly indicating a history of natural

Table 4 | mtDNA and Y chromosome haplogroups
DNA sample*
MtDNA haplogroup

L1
L2
L3
A
B
C
D
M/E
H
V
J
T
K
U
W

YRI (60)

CEU (60)

CHB (45)

JPT (44)

0.22
0.35
0.43
–
–
–
–
–
–
–
–
–
–
–
–

–
–
–
–
–
–
–
–
0.45
0.07
0.08
0.12
0.03
0.23
0.02

–
–
–
0.13
0.33
0.09
0.22
0.22
–
–
–
–
–
–
–

–
–
–
0.04
0.30
0.07
0.34
0.25
–
–
–
–
–
–
–

DNA sample*
Y chromosome haplogroup

YRI (30)

CEU (30)

CHB (22)

JPT (22)E1
E3a
F, H, K
I
R1
C
D
NO

0.07
0.93
–
–
–
–
–
–

–
–
0.03
0.27
0.70
–
–
–

–
–
0.23
–
–
0.09
–
0.68

–
–
0.14
–
–
0.09
0.45
0.32

* Number of chromosomes sampled is given in parentheses.

Figure 3 | Allele frequency and completeness of dbSNP for the ENCODE
regions. a–c, The fraction of SNPs in dbSNP, or with a proxy in dbSNP, areshown as a function of minor allele frequency for each analysis panel (a, YRI;
b, CEU; c, CHBþJPT). Singletons refer to heterozygotes observed in a single
individual, and are broken out from other SNPs with MAF , 0.05. Because
all ENCODE SNPs have been deposited in dbSNP, for this figure we define a
SNP as ‘in dbSNP’ if it would be in dbSNP build 125 independent of the
HapMap ENCODE resequencing project. All remaining SNPs (not in
dbSNP) were discovered only by ENCODE resequencing; they are
categorized by their correlation (r 2) to those in dbSNP. Note that the
number of SNPs in each frequency bin differs among analysis panels,
because not all SNPs are polymorphic in all analysis panels.

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selection at this locus (see below; M. L. Freedman et al., personalcommunication).
Haplotype sharing across populations. We next examined theextent to which haplotypes are shared across populations. We useda hidden Markov model in which each haplotype is modelled in turnas an imperfect mosaic of other haplotypes (see Supplementary
Information)42. In essence, the method infers probabilisticallywhich other haplotype in the sample is the closest relative (nearest
neighbour) at each position along the chromosome.
Unsurprisingly, the nearest neighbour most often is from the same
analysis panel, but about 10% of haplotypes were found most closely
to match a haplotype in another panel (Supplementary Fig. 5). All
individuals have at least some segments over which the nearest
neighbour is in a different analysis panel. These results indicatethat although analysis panels are characterized both by different
haplotype frequencies and, to some extent, different combinations ofalleles, both common and rare haplotypes are often shared acrosspopulations.

Properties of LD in the human genome
Traditionally, descriptions of LD have focused on measures calculated between pairs of SNPs, averaged as a function of physicaldistance. Examples of such analyses for the HapMap data arepresented in Supplementary Fig. 6. After adjusting for known
confounders such as sample size, allele frequency distribution,marker density, and length of sampled regions, these data are highly
similar to previously published surveys43.
Because LD varies markedly on scales of 1–100 kb, and is often
discontinuous rather than declining smoothly with distance,
averages obscure important aspects of LD structure. A fuller exploration of the fine-scale structure of LD offers both insight into thecauses of LD and understanding of its application to disease research.
LD patterns are simple in the absence of recombination. The mostnatural path to understanding LD structure is first to consider thesimplest case in which there is no recombination (or gene conversion), and then to add recombination to the model. (For simplicity
we ignore genotyping error and recurrent mutation in this discussion, both of which seem to be rare in these data.)
In the absence of recombination, diversity arises solely throughmutation. Because each SNP arose on a particular branch of thegenealogical tree relating the chromosomes in the current populations, multiple haplotypes are observed. SNPs that arose on thesame branch of the genealogy are perfectly correlated in the sample,
whereas SNPs that occurred on different branches have imperfectcorrelations, or no correlation at all.
We illustrate these concepts using empirical genotype data from 36
adjacent SNPs in an ENCODE region (ENr131.2q37), selected
because no obligate recombination events were detectable among
them in CEU (Fig. 7). (We note that the lack of obligate recombination events in a small sample does not guarantee that no recombinants have occurred, but it provides a good approximation for
illustration.)
In principle, 36 such SNPs could give rise to 236 different haplotypes. Even with no recombination, gene conversion or recurrent
mutation, up to 37 different haplotypes could be formed. Despite thisgreat potential diversity, only seven haplotypes are observed (fiveseen more than once) among the 120 parental CEU chromosomesstudied, reflecting shared ancestry since their most recent common
ancestor among apparently unrelated individuals.
In such a setting, it is easy to interpret the two most common
pairwise measures of LD: D 0 and r 2. (See the Supplementary
Information for fuller definitions of these measures.) D 0 is defined
to be 1 in the absence of obligate recombination, declining only due
to recombination or recurrent mutation27. In contrast, r 2 is simply

Figure 5 | Allele frequency distributions for autosomal SNPs. For eachanalysis panel we plotted (bars) the MAF distribution of all the Phase I SNPs
with a frequency greater than zero. The solid line shows the MAF

distribution for the ENCODE SNPs, and the dashed line shows the MAF
distribution expected for the standard neutral population model withconstant population size and random mating without ascertainment bias.

Figure 4 | Minor allele frequency distribution of SNPs in the ENCODE data,
and their contribution to heterozygosity. This figure shows thepolymorphic SNPs from the HapMap ENCODE regions according to minor
allele frequency (blue), with the lowest minor allele frequency bin (,0.05)
separated into singletons (SNPs heterozygous in one individual only, shownin grey) and SNPs with more than one heterozygous individual. For thisanalysis, MAF is averaged across the analysis panels. The sum of thecontribution of each MAF bin to the overall heterozygosity of the ENCODE
regions is also shown (orange).

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the squared correlation coefficient between the two SNPs. Thus, r 2 is
1 when two SNPs arose on the same branch of the genealogy andremain undisrupted by recombination, but has a value less than 1
when SNPs arose on different branches, or if an initially strong
correlation has been disrupted by crossing over.
In this region, D 0 ¼ 1 for all marker pairs, as there is no evidence ofhistorical recombination. In contrast, and despite great simplicity of
haplotype structure, r 2 values display a complex pattern, varying
from 0.0003 to 1.0, with no relationship to physical distance. This
makes sense, however, because without recombination, correlations
among SNPs depend on the historical order in which they arose, notthe physical order of SNPs on the chromosome.
Most importantly, the seeming complexity of r 2 values can be
deconvolved in a simple manner: only seven different SNP configurations exist in this region, with all but two chromosomes matching
five common haplotypes, which can be distinguished from eachother by typing a specific set of four SNPs. That is, only a small
minority of sites need be examined to capture fully the informationin this region.
Variation in local recombination rates is a major determinant of
LD. Recombination in the ancestors of the current population has
typically disrupted the simple picture presented above. In the humangenome, as in yeast44, mouse45 and other genomes, recombination
rates typically vary dramatically on a fine scale, with hotspots ofrecombination explaining much crossing over in each region28. The
generality of this model has recently been demonstrated throughcomputational methods that allow estimation of recombination rates(including hotspots and coldspots) from genotype data46,47.

The availability of nearly complete information about common
DNA variation in the ENCODE regions allowed a more precise
estimation of recombination rates across large regions than in any
previous study. We estimated recombination rates and identified
recombination hotspots in the ENCODE data, using methods
previously described46 (see Supplementary Information for details).
Hotspots are short regions (typically spanning about 2 kb) overwhich recombination rates rise dramatically over local backgroundrates.
Whereas the average recombination rate over 500 kb across thehuman genome is about 0.5 cM48, the estimated recombination rate
across the 500-kb ENCODE regions varied nearly tenfold, from aminimum of 0.19 cM (ENm013.7q21.13) to a maximum of 1.25 cM(ENr232.9q34.11). Even this tenfold variation obscures much more
dramatic variation over a finer scale: 88 hotspots of recombination
were identified (Fig. 8; see also Supplementary Fig. 7)—that is, one
per 57 kb—with hotspots detected in each of the ten regions (from 4
in 12q12 to 14 in 2q37.1). Across the 5 Mb, we estimate that about
80% of all recombination has taken place in about 15% of thesequence (Fig. 9, see also refs 46, 49).
A block-like structure of human LD. With most human recombination occurring in recombination hotspots, the breakdown of LD
is often discontinuous. A ‘block-like’ structure of LD is visuallyapparent in Fig. 8 and Supplementary Fig. 7: segments of consistently
high D 0 that break down where high recombination rates, recombination hotspots and obligate recombination events50 all cluster.
When haplotype blocks are more formally defined in the
ENCODE data (using a method based on a composite of local D 0

Figure 6 | Comparison of allele frequencies in the ENCODE data for all pairsof analysis panels and between the CHB and JPT sample sets. For eachpolymorphic SNP we identified the minor allele across all panels (a–d) andthen calculated the frequency of this allele in each analysis panel/sample set.
The colour in each bin represents the number of SNPs that display eachgiven set of allele frequencies. The purple regions show that very few SNPs
are common in one panel but rare in another. The red regions show that
there are many SNPs that have similar low frequencies in each pair ofanalysis panels/sample sets.

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Figure 7 | Genealogical relationships among haplotypes and r 2 values in aregion without obligate recombination events. The region of chromosome
2 (234,876,004–234,884,481 bp; NCBI build 34) within ENr131.2q37
contains 36 SNPs, with zero obligate recombination events in the CEU
samples. The left part of the plot shows the seven different haplotypes
observed over this region (alleles are indicated only at SNPs), with theirrespective counts in the data. Underneath each of these haplotypes is abinary representation of the same data, with coloured circles at SNP
positions where a haplotype has the less common allele at that site. Groupsof SNPs all captured by a single tag SNP (with r 2 $ 0.8) using a pairwise
tagging algorithm53,54 have the same colour. Seven tag SNPs corresponding
to the seven different colours capture all the SNPs in this region. On the rightthese SNPs are mapped to the genealogical tree relating the seven haplotypes
for the data in this region.

values30, or another based on the four gamete test51), most of thesequence falls into long segments of strong LD that contain many
SNPs and yet display limited haplotype diversity (Table 5).
Specifically, addressing concerns that blocks might be an artefactof low marker density52, in these nearly complete data most of thesequence falls into blocks of four or more SNPs (67% in YRI to 87%
in CEU) and the average sizes of such blocks are similar to initial
estimates30. Although the average block spans many SNPs (30–70),
the average number of common haplotypes in each block ranged
only from 4.0 (CHB þ JPT) to 5.6 (YRI), with nearly all haplotypes
in each block matching one of these few common haplotypes. These
results confirm the generality of inferences drawn from diseasemapping studies27 and genomic surveys with smaller sample sizes29
and less complete data30.
Long-range haplotypes and local patterns of recombination.
Although haplotypes often break at recombination hotspots (andblock boundaries), this tendency is not invariant. We identified all

unique haplotypes with frequency more than 0.05 across the 269
individuals in the phased data, and compared them to the fine-scale
recombination map. Figure 10 shows a region of chromosome 19
over which many such haplotypes break at identified recombination
hotspots, but others continue. Thus, the tendency towards colocalization of recombination sites does not imply that all haplotypes
break at each recombination site.
Some regions display remarkably extended haplotype structure
based on a lack of recombination (Supplementary Fig. 8a, b). Most
striking, if unsurprising, are centromeric regions, which lack recombination: haplotypes defined by more than 100 SNPs span several
megabases across the centromeres. The X chromosome has multiple
regions with very extensive haplotypes, whereas other chromosomes
typically have a few such domains.
Most global measures of LD become more consistent whenmeasured in genetic rather than physical distance. For example,
when plotted against physical distance, the extent of pairwise LD

Table 5 | Haplotype blocks in ENCODE regions, according to two methods
YRI

CEU

CHB þ JPT

Method based on a composite of local D’ values
Average number of SNPs per block
Average length per block (kb)Fraction of genome spanned by blocks (%)
Average number of haplotypes (MAF $ 0.05) per block
Fraction of chromosomes due to haplotypes with MAF $ 0.05 (%)

30.3
7.3
67
5.57
94

70.1
16.3
87
4.66
93

54.4
13.2
81
4.01
95

Method based on the four gamete test51
Average number of SNPs per block
Average length per block (kb)Fraction of genome spanned by blocks (%)
Average number of haplotypes (MAF $ 0.05) per block
Fraction of chromosomes due to haplotypes with MAF $ 0.05 (%)

19.9
4.8
86
5.12
91

24.3
5.9
84
3.63
95

24.3
5.9
84
3.63
95

Parameter
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varies by chromosome; when plotted against average recombination
rate on each chromosome (estimated from pedigree-based geneticmaps) these differences largely disappear (Supplementary Fig. 6).
Similarly, the distribution of haplotype length across chromosomes
is less variable when measured in genetic rather than physicaldistance. For example, the median length of haplotypes is 54.4 kb
on chromosome 1 compared to 34.8 kb on chromosome 21. Whenmeasured in genetic distance, however, haplotype length is much
more similar: 0.104 cM on chromosome 1 compared to 0.111 cM on
chromosome 21 (Supplementary Fig. 9).
The exception is again the X chromosome, which has more
extensive haplotype structure after accounting for recombination
rate (median haplotype length ¼ 0.135 cM). Multiple factors could

explain different patterns on the X chromosome: lower SNP density,
smaller sample size, restriction of recombination to females andlower effective population size.

Figure 8 | Comparison of linkage disequilibrium and recombination for two
ENCODE regions. For each region (ENr131.2q37.1 and ENm014.7q31.33),
D 0 plots for the YRI, CEU and CHBþJPT analysis panels are shown: white,
D 0 , 1 and LOD , 2; blue, D 0 ¼ 1 and LOD , 2; pink, D 0 , 1 and
LOD $ 2; red, D 0 ¼ 1 and LOD $ 2. Below each of these plots is shown theintervals where distinct obligate recombination events must have occurred(blue and green indicate adjacent intervals). Stacked intervals represent
regions where there are multiple recombination events in the sample history.
The bottom plot shows estimated recombination rates, with hotspots shown
as red triangles46.

A view of LD focused on the putative causal SNP
Although genealogy and recombination provide insight into why
nearby SNPs are often correlated, it is the redundancies among SNPs
that are of central importance for the design and analysis ofassociation studies. A truly comprehensive genetic association
study must consider all putative causal alleles and test each for its
potential role in disease. If a causal variant is not directly tested in the
disease sample, its effect can nonetheless be indirectly tested if it iscorrelated with a SNP or haplotype that has been directly tested.

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Figure 9 | The distribution of recombination events over the ENCODE
regions. Proportion of sequence containing a given fraction of allrecombination for the ten ENCODE regions (coloured lines) and combined(black line). For each line, SNP intervals are placed in decreasing order of
estimated recombination rate46, combined across analysis panels, and thecumulative recombination fraction is plotted against the cumulative
proportion of sequence. If recombination rates were constant, each line
would lie exactly along the diagonal, and so lines further to the right reveal
the fraction of regions where recombination is more strongly locallyconcentrated.

The typical SNP is highly correlated with many of its neighbours.
The ENCODE data reveal that SNPs are typically perfectly correlated
to several nearby SNPs, and partially correlated to many others.
We use the term proxy to mean a SNP that shows a strong

Figure 10 | The relationship among recombination rates, haplotype lengths
and gene locations. Recombination rates in cM Mb21 (blue). Nonredundant haplotypes with frequency of at least 5% in the combined sample(bars) and genes (black segments) are shown in an example gene-dense
1308

correlation with one or more others. When two variants are perfectly
correlated, testing one is exactly equivalent to testing the other; werefer to such collections of SNPs (with pairwise r 2 ¼ 1.0 in the
HapMap samples) as ‘perfect proxy sets’.
Considering only common SNPs (the target of study for the
HapMap Project) in CEU in the ENCODE data, one in five SNPs
has 20 or more perfect proxies, and three in five have five or more.
In contrast, one in five has no perfect proxies. As expected, perfectproxy sets are smaller in YRI, with twice as many SNPs (two in five)having no perfect proxy, and a quarter as many (5%) having 20 ormore (Figs 11 and 12). These patterns are largely consistent acrossthe range of frequencies studied by the project, with a trend
towards fewer proxies at MAF , 0.10 (Fig. 11). Put another way,
the average common SNP in ENCODE is perfectly redundant withthree other SNPs in the YRI samples, and nine to ten other SNPs in
the other sample sets (Fig. 13).
Of course, to be detected through LD in an association study,
correlation need not be complete between the genotyped SNP and
the causal variant. For example, under a multiplicative disease modeland a single-locus x 2 test, the sample size required to detectassociation to an allele scales as 1/r 2. That is, if the causal SNP has
an r 2 ¼ 0.5 to one tested in the disease study, full power can be
maintained if the sample size is doubled.
The number of SNPs showing such substantial but incomplete
correlation is much larger. For example, using a looser threshold for
declaring correlation (r 2 $ 0.5), the average number of proxies
found for a common SNP in CHBþJPT is 43, and the average in
YRI is 16 (Fig. 12). These partial correlations can be exploited
through haplotype analysis to increase power to detect putative
causal alleles, as discussed below.
Evaluating performance of the Phase I map. To estimate theproportion of all common SNPs captured by the Phase I map, we

region of chromosome 19 (19q13). Haplotypes are coloured by the numberof detectable recombination events they span, with red indicating many
events and blue few.

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evaluated redundancy among SNPs on the genome-wide map, andperformed simulations based on the more complete ENCODE data.
The two methods give highly similar answers, and indicate that Phase
I should provide excellent power for CEU, CHB and JPT, andsubstantial power for YRI. Phase II, moreover, will provide nearlycomplete power for all three analysis panels.
Redundancies among SNPs in Phase I HapMap. Redundancy offers
one measure that Phase I has sampled densely in comparison to theunderlying scale of correlation. Specifically, 50% (YRI) to 75%(CHBþJPT, CEU) of all SNPs on the Phase I map are highly
correlated (r 2 $ 0.8) to one or more others on the map (Fig. 13;
see also Supplementary Fig. 10). Over 90% of all SNPs on the map
have highly statistically significant correlation to one or moreneighbours. These partial correlations can be combined to form
haplotypes that are even better proxies for a SNP of interest.
Modelling Phase I HapMap from complete ENCODE data. A
second approach to evaluating the completeness of the Phase I data
involves thinning the more complete ENCODE data to match Phase I
for allele frequency and SNP density. Simulated Phase I HapMaps
were used to evaluate coverage in relation to the full set of common
SNPs (Table 6), and provided nearly identical estimates to thoseabove: 45% (YRI) to 74% (CHBþJPT, CEU) of all common SNPs are
predicted to have a proxy with r 2 $ 0.8 to a SNP included in the
Phase I HapMap (Supplementary Fig. 11).

Statistical power in association studies may be more closely
approximated by the average (maximal) correlation value between
a SNP and its best proxy on the map, rather than by the proportion
exceeding an arbitrary (and stringent) threshold. The average values
for maximal r 2 to a nearby SNP range from 0.67 (YRI) to 0.85 (CEU
and CHBþJPT).
Modelling Phase II HapMap from complete ENCODE data. A
similar procedure was used to generate simulated Phase II HapMaps
from ENCODE data (Table 6). Phase II is predicted to capture themajority of common variation in YRI: 81% of all common SNPs
should have a near perfect proxy (r 2 $ 0.8) to a SNP on the map,with the mean maximal r 2 value of 0.90. Unsurprisingly, the CEU,
CHB and JPT samples, already well served by Phase I, are nearly
perfectly captured: 94% of all common sites have a proxy on the mapwith r 2 $ 0.8, with an average maximal r 2 value of 0.97.
These analyses indicate that the Phase I and Phase II HapMap
resources should provide excellent coverage for common variation inthese population samples.

Figure 11 | The number of proxy SNPs (r 2 $ 0.8) as a function of MAF in
the ENCODE data.

Figure 12 | The number of proxies per SNP in the ENCODE data as afunction of the threshold for correlation (r 2).

Selection of tag SNPs for association studies
A major impetus for developing the HapMap was to guide the design
and prioritization of SNP genotyping assays for disease association
studies. We refer to the set of SNPs genotyped in a disease study astags. A given set of tags can be analysed for association with a© 2005 Nature Publishing Group

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Table 6 | Coverage of simulated Phase I and Phase II HapMap to capture
all common SNPs in the ten ENCODE regions
Analysis panel

Figure 13 | Relationship in the Phase I HapMap between the threshold for
declaring correlation between proxies and the proportion of all SNPs
captured.

Per cent
maximum r 2 $ 0.8

Meanmaximum r 2

Phase I HapMap
YRI
CEUCHBþJPT

45
74
72

0.67
0.85
0.83

Phase II HapMap
YRI
CEUCHBþJPT

81
94
94

0.90
0.97
0.97

Simulated Phase I HapMaps were generated from the phased ENCODE data (release 16c1)
by randomly picking SNPs that appear in dbSNP build 121 (excluding ‘non-rs’ SNPs in release16a) for every 5-kb bin until a common SNP was picked (allowing up to three attempts perbin). The Phase II HapMap was simulated by picking SNPs at random to achieve an overall
density of 1 SNP per 1 kb. These numbers are averages over 20 independent iterations for all
ENCODE regions in all three analysis panels.

phenotype using a variety of statistical methods which we term tests,
based either on the genotypes of single SNPs or combinations ofmultiple SNPs.
The shared goal of all tag selection methods is to exploit redundancy among SNPs, maximizing efficiency in the laboratory while
minimizing loss of information24,27. This literature is extensive andvaried, despite its youth. Some methods require that a single SNP
serve as a proxy for other, untyped variants, whereas other methods
allow combinations of alleles (haplotypes) to serve as proxies; somemake explicit use of LD blocks whereas others are agnostic to suchdescriptions. Although it is not practical to implement all suchmethods at the project website, the HapMap genotypes are freely
available and investigators can apply their method of choice to thedata. To assist users, both a single-marker tagging method and a more
efficient multimarker method have been implemented at http://
www.hapmap.org.
Tagging using a simple pairwise method. To illustrate generalprinciples of tagging, we first applied a simple and widely used
pairwise algorithm53,54: SNPs are selected for genotyping until allcommon SNPs are highly correlated (r 2 $ 0.8) to one or moremembers of the tag set.
Starting from the substantially complete ENCODE data, thedensity of common SNPs can be reduced by 75–90% with essentiallyno loss of information (Fig. 14). That is, the genotyping burden canbe reduced from one common SNP every 500 bp to one SNP every
2 kb (YRI) to 5 kb (CEU and CHBþJPT). Because LD often extends
for long distances, studies of short gene segments tend to underestimate the redundancy across the genome43.

Although tags selected based on LD offer the greatest improvements in efficiency and information capture, even randomly chosen
subsets of SNPs offer considerable efficiencies (Fig. 14).
The data also reveal a rule of diminishing returns: a small set of
highly informative tags captures a large fraction of all variation, withadditional tags each capturing only one or a few proxies. For
example, in CHBþJPT the most informative 1% of all SNPs (oneper 50 kb) is able to proxy (at r 2 $ 0.8) for 40% of all common SNPs,
whereas a substantial proportion of SNPs have no proxies at all.
These observations are encouraging with respect to genome-wide
association studies. A set of SNPs typed every 5–10 kb across the
genome (within the range of current technology) can capture nearly
all common variation in the genome in the CEU and CHBþJPT
samples, with more SNPs required in the YRI samples.
Tagging from the genome-wide map. Whereas analysis of
the complete ENCODE data set reveals the maximal efficiency
likely to be possible with this tag selection strategy, analysis of thePhase I map illuminates the extent to which the current resource canbe used for near-term studies. Specifically, using the same pairwise
tagging approach above, 260,000 (CHBþJPT) to 474,000 (YRI)
SNPs are required to capture all common SNPs in the Phase I dataset (Table 7). That is, being incomplete and thus less redundant,
the Phase I data are much less compressible by tag SNP selection
than are the ENCODE data. Nevertheless, even at this level a half
to a third of all SNPs can be selected as proxies for the remainder(and, by inference, the bulk of other common SNPs in the
genome).
Increasing the efficiency of tag SNPs. Although the pairwise method

Figure 14 | Tag SNP information capture. The proportion of common SNPs
captured with r 2 $ 0.8 as a function of the average tag SNP spacing is shownfor the phased ENCODE data, plotted (left to right) for tag SNPs prioritizedby Tagger (multimarker and pairwise) and for tag SNPs picked at random.
Results were averaged over all the ENCODE regions.

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is simple, complete and straightforward, efficiency can be improved
with a number of simple changes. First, relaxing the threshold on r 2
for tag SNP selection substantially reduces the number of tag SNPs
selected, with only a modest decrease in the correlations among SNPs
(Table 7). For example, reducing the r 2 threshold from 0.8 to 0.5
decreases the number of tag SNPs selected from the HapMap by 39%
in CHBþJPT (260,000 to 159,000) and 32% in YRI (474,000 to
325,000). The average r 2 value between tags and other (unselected)
SNPs falls much less dramatically than the number of tags selected,
increasing efficiency. Whether such a loss of power is justified by thedisproportionate reduction in work is a choice each investigator willneed to make.
A second enhancement exploits multimarker haplotypes. Many
investigators have discussed using multiple SNPs (in haplotypes andregression models) to serve as proxies for untyped sites55–58, which
may reduce the number of tags required and increase the power ofanalyses performed. Figure 14 illustrates the point with one suchmethod55, showing that a multimarker method allows greater coverage for a fixed set of markers (or, alternatively, fewer markers toachieve the same coverage). Although a full consideration of thisissue is beyond the scope of this paper, the availability of these andother data should allow the comparison and application of suchmethods.
A third approach to increasing efficiency is to prioritize tags basedon the number of other SNPs captured. Whereas 260,000 SNPs are
required to provide r 2 $ 0.8 for all SNPs in the Phase I HapMap(CHBþJPT), the best 10,000 such SNPs (4%) capture 22% of allcommon variable sites with r 2 $ 0.8 (Table 8). Such prioritization
can be applied using different weights for SNPs based on genomic
annotation (for example, non-synonymous coding SNPs, SNPs inconserved non-coding sequence, and candidate genes of biologicalinterest).
Tag transferability across populations. The most complete set oftags would be those based on all 269 samples; however, many studies
may be performed in individuals more closely related to one
particular HapMap population, and efficiency may be gained by
selecting tags only from that population sample. (Selecting tags in a
HapMap population sample that is known to be more distantly
related than is another, for example, using CEU to pick tags for astudy of Japanese, seems inefficient.)
An important question is how tags selected in one or more analysis
panels will transfer to disease studies performed in these or otherpopulations. Our data do not address this question directly, although
the known similarity of allele and haplotype frequencies acrosspopulations within continents41 is encouraging. More data are clearlyneeded, however.
Tag selection based on initial genotyping. Whereas the discussions
above assume de novo selection of SNPs, many investigators will
have already performed initial studies, and wish to design followon experiments. The HapMap data can be used to highlight SNPs
that might potentially explain a positive association signal, or
those that were poorly captured (and thus still need to be tested)
after a negative scan. In cases where multiple SNPs are both
associated with the trait and with each other, the HapMap data
can be queried to identify whether samples from any other analysis

panel show a breakdown of LD in that region, and thus thepossibility of narrowing the span over which the causal variant
may reside.
Applications to the analysis of association data
Beyond guiding selection of tag SNPs, HapMap data can informthe subsequent analysis and interpretation in disease association
studies.
Analysis of an existing genotype data set. The HapMap can be used
to inform association testing, regardless of how tags were selected.
Specifically, as long as the SNPs genotyped in a disease study havealso been typed in the HapMap samples, it is possible to identify
which SNPs are well captured by the genotyped SNPs (either singly,
or in haplotype combinations), and which are not55.
This is of particular importance for genome-wide association
studies performed using array-based, standardized genotyping
reagents, which do not allow investigators to choose their own setsof tag SNPs. The Affymetrix 120K SNP array data included in Phase Iof the HapMap provides a simple example: in CEU 48% of HapMap
SNPs have substantial pairwise correlation (r 2 $ 0.5) to one or moreof the 120K SNPs on the array. An additional 13%, however, are notcorrelated to a single SNP, but are to a specific haplotype of two
members of the 120K panel. By identifying such haplotype predictors
in the HapMap, and testing them (in addition to the single SNPs) in adisease study, it is likely that power will be increased (I. Pe’er et al.,manuscript in preparation).
Evaluating statistical significance and interpreting results. An
important challenge in genome-wide association testing is to develop
statistical procedures that minimize false positives without greatly
sacrificing true positives. The challenge is amplified by the correlated
nature of polymorphism data, which makes simple frequentist
approaches that assume independence (such as Bonferonni correction) highly conservative. To illustrate this point, we used the
ENCODE data to estimate the ‘effective number of independent
tests’ (the statistical burden of testing all common (MAF $ 0.05)
variation) across large genomic regions. Specifically, we re-sampledfrom the phased ENCODE chromosomes to create mock casecontrol panels in which all common SNPs were observed, but there
was not a causal allele. The resulting x 2 distribution for association
indicates that complete testing of common variation in each 500-kb
region is equivalent to performing about 150 independent statisticaltests (in CEU and CHBþJPT) and about 350 tests (in YRI). Although
it will probably be desirable to perform such empirical estimates ofsignificance within each disease study, these results illustrate
how Bonferonni correction overestimates the statistical penalty of
performing many correlated tests.
Study of less common alleles. We have focused primarily on the
hypothesis that a single, common causal allele exists, and needs to betested for association to disease. Of course, in many cases the causal
allele(s) will be less common, and might be missed by such an
approach.
It is possible to perform additional haplotype tests, beyond those

Table 8 | Proportion of common SNPs in Phase I captured by sets of tag
SNPs
Tag SNP set size

Table 7 | Number of selected tag SNPs to capture all observed common
SNPs in the Phase I HapMap
r 2 threshold*2

r $ 0.5
r 2 $ 0.8
r 2 ¼ 1.0

YRI

CEU

CHB þ JPT

324,865
474,409604,886

178,501
293,835
447,579

159,029
259,779
434,476

Tag SNPs were picked to capture common SNPs in HapMap release 16c1 using the software
program Haploview.
* Pairwise tagging at different r 2 thresholds.

10,00020,00050,000100,000
250,000

Common SNPs captured (%)
YRI

CEU

CHB þ JPT

12.3
19.1
32.7
47.2
70.1

20.4
30.9
50.4
68.5
94.1

21.9
33.2
53.6
72.2
98.5

As in Table 7, tag SNPs were picked to capture common SNPs in HapMap release 16c1 using
Haploview, selecting SNPs in order of the fraction of sites captured. Common SNPs were
captured by fixed-size sets of pairwise tags at r 2 $ 0.8.

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that capture known polymorphisms, in the hope of capturing lesscommon or unrepresented alleles56. Such haplotype analysis has along history and proven value in mendelian genetics; the causal
mutation is generally rare and unexamined during initial genotyping, but is frequently recognized by its presence on a long, unique
haplotype of common alleles18,19,59–62.
Admixture mapping. Although not designed specifically to enable
admixture mapping63, the HapMap has helped lay the groundworkfor this approach. Admixture mapping requires a map of SNPs thatare highly differentiated in frequency across population groups. By
typing many SNPs in samples from multiple geographical regions,the data have helped to identify such SNPs for the design of genomewide admixture mapping panels64,65 and can be further used to
identify candidate SNPs with large allele frequency differences forfollow-up of positive admixture scan results66.
Loss of heterozygosity in tumours. Loss of heterozygosity (LOH) intumour tissue can be a powerful indicator of the location of tumour
suppressor genes, and genome-wide, fine-scale LOH analysis has
been empowered by genome-wide SNP arrays67. Germline DNA is
not always available from the same subjects, however, and even if
available, typing of germline DNA doubles project costs. In lower
density scans for LOH (with markers far apart relative to the scale ofLD), long runs of homozygosity in tumours are nearly always
indicative of LOH. However, at higher densities runs of homozygosity can be due to haplotype homozygosity in the inherited
germline DNA, rather than LOH.

The HapMap data can help minimize this difficulty; previous
probabilities for homozygosity based on known frequencies of
haplotypes in the HapMap data can be used to distinguish homozygosity due to haplotype sharing rather than LOH68.

Figure 15 | Length of LD spans. We fitted a simple model for the decay oflinkage disequilibrium103 to windows of 1 million bases distributed
throughout the genome. The results of model fitting are summarized for theCHBþJPT analysis panel, by plotting the fitted r 2 value for SNPs separated
by 30 kb. The overall pattern of variation was very similar in the otheranalysis panels84 (see Supplementary Information).

1312

Identifying structural variants in HapMap data
Structural variations—segments where DNA is deleted, duplicated,or rearranged—are common69,70 and have an important role indiseases71–73. The HapMap can provide some insight into structuralvariation because, in many cases, structural variants reveal
themselves through signatures in SNP genotype data. In particular,
polymorphic deletions are important to discover, because loss ofgenetic material is of obvious functional relevance, and resultsin aberrant patterns of SNP genotypes. These include apparent
non-mendelian inheritance of SNP alleles, null genotypes anddeviations from Hardy–Weinberg equilibrium. However, such
SNPs are routinely discarded as technical failures of genotyping.
Thus, we scanned the unfiltered Phase I HapMap data using anapproach developed and validated to identify polymorphic deletions from clusters of SNPs with aberrant genotype patterns(calibrated across the multiple centres and genotyping platforms74). In total, 541 candidate deletion polymorphisms wereidentified, of which 150 were common enough to be observed as
homozygotes.
The properties of these candidate deletions, including experimental validation of 90 candidates, are described in ref. 74. Validated© 2005 Nature Publishing GroupARTICLES

NATURE|Vol 437|27 October 2005

polymorphisms include 10 that remove coding exons of genes, suchthat in many cases individuals are homozygous null for the encodedtranscript. Analysis of confirmed deletions often shows strong LD
with nearby SNPs, indicating that LD-based approaches can be usefulfor detecting disease associations due to structural (as well as SNP)
variants.
Polymorphic inversions may also be reflected in the HapMap
data as long regions where multiple SNPs are perfectly correlated:because recombination between an inverted and non-inverted copy
is lethal, the inverted and non-inverted copies of the region evolve
independently. A striking example corresponds to the known inversion polymorphism on chromosome 17, present in 20% of the CEU
chromosomes, that has been associated with fertility and total
recombination rate in females among Icelanders75. Long LD may
also arise, however, due to a low recombination rate or certain formsof natural selection, as discussed below.
Insights into recombination and natural selection
In addition to its intended function as a resource for disease studies,
the HapMap data provide clues about the biology of recombinationand history of natural selection.
A genome-wide map of recombination rates at a fine scale. On thebasis of the HapMap data, we created a fine-scale genetic map
spanning the human genome (Supplementary Fig. 12), including
21,617 identified recombination hotspots (one per 122 kb).
Both the number and intensity of hotspots contribute to overallvariation in recombination rate. For example, we selected 25 regionsof 5 Mb as having the highest (.2.75 cM Mb21 ) and lowest(,0.5 cM Mb21) rates of recombination in the deCODE (pedigreebased) genetic map48. We detected recombination hotspots in all
regions, even the lowest. But in the high cM Mb21 regions hotspots
are more closely spaced (one per 84 kb) and have a higher average

Table 9 | High-differentiation non-synonymous SNPs
Chromosome

Position(base number)

Gene*

SNP

1
1
1
2
2
2
2
2
2
2
2
3
3
4
4
4
8
8
10
10
11
15
15
16
16
17
17
18
19
19
22
X

54,772,383
156,000,000
244,000,000
3,184,917
73,563,622
73,589,553
73,591,645
73,592,163
73,629,222
73,629,311
109,000,000
182,000,000
185,000,000
41,844,599
46,567,077
101,000,000
10,517,787
146,000,000
50,402,145
71,002,210
46,701,579
46,213,776
61,724,262
30,996,126
46,815,699
26,322,430
26,399,303
66,022,323
5,782,891
47,723,209
18,164,095
65,608,007

THEA
FY
Q8NGY8_human†
COLEC11
ALMS1
ALMS1
ALMS1
ALMS1
ALMS1
ALMS1
EDAR
FXR1
MCF2L2
SLC30A9ENSG00000172895.1
ADH1B
RP1L1
SLC39A4ERCC6NEUROG3
F2
SLC24A5
HERC1
ZNF646
ABCC11RNF135
ENSG00000184253.2
RTTN
FUT6
CEACAM1
GNB1L
EDA2R

rs1702003
rs12075rs7555046
rs7567833
rs3813227
rs6546837
rs6724782
rs6546839
rs2056486
rs10193972
rs3827760
rs11499
rs7639705
rs1047626
rs5825rs1229984
rs6601495
rs1871534
rs4253047
rs4536103
rs5896rs1426654
rs7162473
rs749670
rs17822931
rs7225888
rs6505228
rs3911730
rs364637
rs8110904
rs2073770
rs1385699* Where no standard gene abbreviation exists, the ENSEMBL gene ID has been given.
†It is unclear from current annotations whether this is a pseudogene.

intensity (0.124 cM) as compared to the low cM Mb21 regions (oneevery 208 kb, and 0.051 cM, respectively).
Estimates of recombination rates and identified hotspots arerobust to the specific markers and samples studied. Specifically, we
compared these results to a similar analysis76 of the data of ref. 77(with about 1.6 million SNPs genotyped in 71 individuals). We findnearly complete correlation in rate estimates at a coarse scale (5 Mb)
between these two surveys (r 2 ¼ 0.99) and to the pedigree map(r 2 ¼ 0.95). Very substantial correlation is found at finer scales:r 2 ¼ 0.8 at 50 kb and r 2 ¼ 0.59 at 5 kb. Moreover, of the 21,617
hotspots identified using the HapMap data, 78% (16,923) were also
identified using the data of ref. 77.
The ability to detect events depends on marker density, with thelarger number of SNPs studied by ref. 77 increasing power to detect
hotspots, and presumably precision of rate estimates. There are,however, substantial genomic regions where the HapMap data have ahigher SNP density. For example, more hotspots are detected on
chromosomes 9 and 19 from the HapMap data. We expect that Phase
II of HapMap will provide a genome-wide recombination map of
substantially greater precision than either ref. 77, or Phase I, at finescales.
Little is yet known about the molecular determinants of recombination hotspots. In an analysis of the data of ref. 77, another study(ref. 76) found significant evidence for an excess of the THE1A/B
retrotransposon-like elements within recombination hotspots, andmore strikingly for a sixfold increase of a particular motif(CCTCCCT) within copies of the element in hotspots, compared
to copies of the element outside hotspots. In analysing the HapMap
data, we confirmed these findings (Supplementary Fig. 13). Furthermore, THE1B elements with the motif are particularly enrichedwithin 1.5 kb of the centre of the hotspots compared to flanking
sequence (P , 10216).
Correlations of LD with genomic features. Variation in recombination rate is important, in large part, because of its impact on LD. We
thus examined genome-wide LD for correlation to recombination
rates, sequence composition and gene features.
We confirmed previous observations that LD is generally low neartelomeres, elevated near centromeres, and correlated with chromosome length (Fig. 15; see also Supplementary Figs 8b and 14)48,78–80.
These patterns are due to recombination rate variation as discussed
above. We also confirmed previously described relationships between
LD and GþC content78,81,82, sequence polymorphism83 and repeat
composition78,82.
We observe, for the first time, that LD tracks with both the densityand functional classification of genes. We examined quartiles of the
genome based on extent of LD, and looked for correlations to genedensity. Surprisingly, we find that both the top and bottom quartilesof the genome have greater gene density as compared to the middle
quartiles (6.7 as compared to 6.1 genes per Mb), as well as percentage
of bases in codons (1.24% as compared to 1.08%). We have no
explanation for this observation.
Although the majority of gene classes are equally divided between
these two extreme quartiles of the genome, some classes of genes
show a marked skew in their distribution64,84,85. Genes involved in
immune responses and neurophysiological processes are more often
located in regions of low LD, whereas genes involved in DNA andRNA metabolism, response to DNA damage and the cell cycle are
preferentially located in regions of strong linkage disequilibrium. It is
intriguing to speculate that the extent of LD (and sequence diversity)
might track with gene function due to natural selection, withincreased diversity being favoured in genes involved in interface
with the environment such as the immune response86, and disadvantageous for core cell biological processes such as DNA repair andpackaging87,88.
Natural selection. The preceding observation highlights the hypothesis that signatures of natural selection are present in the HapMap
data. The availability of genome-wide variation data makes it© 2005 Nature Publishing Group1313

ARTICLES

NATURE|Vol 437|27 October 2005

Figure 16 | The distribution of the long range haplotype (LRH92) test
statistic for natural selection. In the YRI analysis panel, diversity
around the HBB gene is highlighted by the red point. In the CEU

analysis panel, diversity within the LCT gene region is similarly
highlighted.

possible to scan the genome for such signatures to discover genes thatwere subject to selection during human evolution89; the HapMap
data also provide a genome-wide empirical distribution against
which previous claims of selection can be evaluated (rather thanrelying solely on theoretical computer simulations).
Natural selection influences patterns of genetic variation in various ways, such as through the removal of deleterious mutations, thefixation of advantageous variants, and the maintenance of multiple
alleles through balancing selection. Each form of selection may haveoccurred uniformly across the world (and thus be represented in all
human populations) or have been geographically localized (and thus
differ among populations).
Nearly all methods for recognizing natural selection rely on thecollection of complete sequence data. The HapMap Project’s focus oncommon variation—and the process of SNP selection that achieved a
preponderance of high-frequency alleles (Fig. 5)—thus prevents theirstraightforward application. Adjusting for the effect of SNP choice iscomplex, moreover, because SNP choice varied over time as dbSNP
evolved, and was implemented locally at each centre.
For these reasons, we focus here on two types of analysis. First, we
examined the distributions of signatures of selection across the
genome. Although the absolute value of these measures is difficult
to interpret (owing to SNP ascertainment), the most extreme cases ina genome-wide distribution are important candidates to evaluate forselection. Second, we compared across functional categories, because
SNP choice was largely agnostic to such features, and thus systematicdifferences may be a sign of selection.
The outcomes of these analyses confirm a number of previous
hypotheses about selection and identify new loci as candidates forselection.
Evidence for selective sweeps in particular genomic regions. First
we consider population differentiation, generally accepted as a clue
to past selection in one of the populations. The HapMap data reveal
926 SNPs with allele frequencies that differ across the analysis panels

Table 10 | Candidate loci in which selection occurred
Chromosome

Position (base number)at centre

Genes in region

Population

Haplotype
frequency

Empirical
P-value

2
5
7
7
12
15
16
17

137,224,699
22,296,347
79,904,387
73,747,934
109,892,896
78,558,508
75,661,011
3,945,580

CEU
YRI
YRI
CEU
CEU
YRI
YRI
YRI

0.65
0.25
0.24
0.76
0.36
0.32
0.46
0.70

1.25 £ 1029
5.77 £ 1028
2.72 £ 1026
3.37 £ 1026
7.95 £ 1029
6.92 £ 1027
5.01 £ 1027
9.26 £ 1027

18
22
X
X
X
X

24,502,756
32,459,471
20,171,291
64,323,320
42,763,073
34,399,948

LCT
CDH12, PMCHL1
CD36
PMS2L5, WBSCR16
CUTL2
ARNT2
DesertITGAE, GSG2, HSA277841,
CAMKK1, P2RX1
DesertLARGE
DesertHEPH
MAOB
Desert

CEU
YRI
YRI
YRI
CEU
CEU

0.57
0.36
0.33
0.55
0.53
0.57

2.23 £ 1027
7.82 £ 1029
5.02 £ 1029
3.02 £ 1028
4.21 £ 1029
8.85 £ 1028

1314

© 2005 Nature Publishing GroupARTICLES

NATURE|Vol 437|27 October 2005

in a manner as extreme as the well-accepted example of selection at
the Duffy (FY) locus (Supplementary Fig. 8c). Of these 926 SNPs, 32
are non-synonymous coding SNPs and many others occur in transcribed regions, making them strong candidates for functional
polymorphisms that have experienced geographically restricted
selection pressures (see Table 9 and Supplementary Information
for details). In particular, the ALMS1 gene on chromosome 2 has
six amino acid polymorphisms that show very strong populationdifferentiation.
Another signature of an allele having risen to fixation throughselection is that all other diversity in the region is eliminated (known
as a selective sweep). We identified extreme outliers in the joint
distribution of heterozygosity (as assessed from shotgun sequencing
SNP discovery projects) and either population differentiation or
skewing of allele frequency towards rare alleles in each analysis
panel (Supplementary Fig. 15). We identified 19 such genomic
regions (13 on autosomes, 6 on the X chromosome) as candidates
for future study (Supplementary Table 4); these include candidates
for population-specific sweeps and sweeps in the ancestral
population. Encouragingly, this analysis includes among its topscoring results the LCT gene, which influences the ability to digest
dairy products90 and has been shown to be subject to past natural
selection91.
Long haplotypes as candidates for natural selection. Selective
sweeps that fail to fix in the population, as well as balancing selection,lead to haplotypes that are relatively high in frequency and long induration. In the HLA region (which is widely believed to have been
influenced by balancing selection) multiple haplotypes of 500 SNPs
that extend more than 1 cM in length are observed with a frequency
in the HapMap samples of more than 1%. We identified other suchoccurrences of long haplotypes across the genome (Supplementary
Fig. 8 and Supplementary Tables 5 and 6).
An approach to long haplotypes designed specifically to identify
regions having undergone partial selective sweeps is the long range
haplotype (LRH) test91,92, which compares the length of each haplotype to that of others at the locus, matched across the genome basedon frequency. Previously identified outliers to the genome-wide
distribution for the LRH test (Fig. 16) that have been identified as
candidates for selection include the LCT gene in the CEU sample(empirical P-value ¼ 1.3 £ 1029), which was an outlier for theheterozygosity/allele frequency test above, and the HBB gene
(empirical P-value ¼ 1.39 £ 1025) in the YRI sample. However,most of the strongest signals in the LRH test (Table 10) were notpreviously hypothesized as undergoing selection.
These four tests overlap only partially in the hypotheses they
address—heterozygosity, for example, is sensitive to older sweeps,
whereas the haplotype tests are most powerful for partial sweeps—
but encouragingly some candidate regions are found by more than
one test. In particular, six regions are identified both by long
haplotypes and by low heterozygosity, and three regions (LCT on
chromosome 2, and two regions on the X chromosome at 20 and
65 Mb) are identified by three different tests.
Confirming purifying selection at conserved non-coding
elements. Finally, we used the HapMap data to test an important
hypothesis from comparative genomics. Genomic sequencing has
shown that about 5% of the human sequence is highly conserved
across species, yet less than half of this sequence spans known
functional elements such as exons45. It is widely assumed thatconserved non-genic sequences lack diversity because of selective
constraint (that is, purifying selection), but such regions may simply
be coldspots for mutation, and thus be of little value as candidates forfunctional study.
Analysis of allele frequencies helps to resolve this uncertainty.
Functional constraint, but not a low mutation rate, results in a
downward skew in allele frequencies for conserved sequences ascompared to neutral sequences93,94. We find that conserved nongenic sequences display a greater skew towards rare alleles than dointergenic regions, as predicted under purifying selection. This skew
is less extreme than that observed for exons (Supplementary Fig. 16),reflecting either stronger purifying selection or the prioritization ofcoding SNPs for genotyping by the HapMap centres regardless ofvalidation status. This novel evidence for ongoing constraint showsthat conserved non-genic sequences are not mutational coldspots,
and thus remain of high interest for functional study.
Conclusions
The International HapMap Project set out to create a resource thatwould accelerate the identification of genetic factors that influence
medical traits. Analyses reported here confirm the generalityof hotspots of recombination, long segments of strong LD,
and limited haplotype diversity. Most important is the extensive
redundancy among nearby SNPs, providing (1) the potential toextract extensive information about genomic variation withoutcomplete resequencing, and (2) efficiencies through selection oftag SNPs and optimized association analyses. Beyond the biomedical context, these data have made it possible to identify deletion
variants in the genome, explore the nature of fine-scale recombination and identify regions that may have been subject to natural
selection.
The HapMap Project (along with a previous genome-wide assessment of LD77) is a natural extension of the Human Genome Project.
Where the reference sequence constructed by the Human Genome
Project is informative about the vast majority of bases that areinvariant across individuals, the HapMap focuses on DNA sequence
differences among individuals. Our understanding of SNP variationand LD around common variants in the sampled populations isreasonably complete; the current picture is unlikely to change withadditional data. In other aspects—such as the fine details of local
correlation among SNPs, rarer alleles, structural variants, and interpopulation differences—these resources are only a first step on thepath towards a complete characterization of genetic variation of thehuman population. Planned extensions of the Phase I map include
Phase II of HapMap, with genotyping of another 4.6 million SNPs
attempted in the HapMap samples, and detailed genotyping of the
HapMap ENCODE regions in additional members of each HapMap
population sampled, as well as in samples from additional populations. These results should guide understanding of the robustness
and transferability of LD inferences and tag SNPs selected from thecurrent set of HapMap samples.
An important application of the HapMap data is to help makepossible comprehensive, genome-wide association studies. There are
now laboratory tools that make it practical to undertake such studies,and initial results are encouraging13. Given the low prior probabilityof causality for each SNP in the genome, however, rigorous standardsof statistical significance will be needed to avoid a flood offalse-positive results. Multiple replications in large samples provide
the most straightforward path to identifying robust and broadly
relevant associations. Given the potential for confusion if associations of uncertain validity are widely reported (and a persistenttendency towards genetic determinism in public discourse), we urge
conservatism and restraint in the public dissemination and interpretation of such studies, especially if non-medical phenotypes are
explored. It is time to create mechanisms by which all results ofassociation studies, positive and negative, are reported and discussed
without bias.
The success of the HapMap will be measured in terms of thegenetic discoveries enabled, and improved knowledge of diseaseaetiology. Specifically, identifying which genes and pathways arecausal in humans has the potential to provide a new and solid
foundation for biomedical research. This is equally true whether thevariants that lead to the discovery of those genes are themselves rare
or common, or of large or small effect. The impact on diagnostics andtargeted prevention, however, will depend on how predictive eachgiven allele may be. Where genetic mechanisms underlie treatment© 2005 Nature Publishing Group1315

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NATURE|Vol 437|27 October 2005

responses, both more efficient trials and individualized preventive
and treatment strategies may become practical95.
Success identifying alleles conferring susceptibility or resistance tocommon diseases will also provide a deeper understanding of thearchitecture of disease: how many genes are involved in each case,whether and how alleles interact with one another96 and withenvironmental exposures to shape clinical phenotypes. In this regard,
it will be important to invest heavily in the discovery and characterization of relevant lifestyle factors, environmental exposures, detailed
characterization of clinical phenotypes, and the ability to obtain suchinformation in longitudinal studies of adequate size. Where environmental and behavioural factors vary across studies, replication will be
hard to come by (as will clinical utility) unless we can learn to capture
these variables with the same precision and completeness as genotypic variation. Technological innovation and international collaboration in these realms will probably be required (as they have beenin the Human Genome Project and the HapMap) to advance theshared goal of understanding, and ultimately preventing, common
human diseases.
METHODS
The project was undertaken by investigators from Japan, the United Kingdom,
Canada, China, Nigeria and the United States, and from multiple disciplines:
sample collection, sequencing and genotyping, bioinformatics, population
genetics, statistics, and the ethical, legal, and social implications of genetic
research. The Supplementary Information contains information about project
participants and organization.
Choice of DNA samples. Any choice of DNA samples represents a compromise:a single population offers simplicity, but cannot be representative, whereas gridsampling is representative of the current worldwide population, but is neither
practical nor captures historical genetic diversity. The project chose to include
DNA samples based on well-known patterns of allele frequencies acrosspopulations41, reflecting historical genetic diversity31,32.
For practical reasons, the project focused on SNPs present at a minor allele
frequency (MAF) $ 0.05 in each analysis panel, and thus studied a sufficientnumber of individuals to provide good power for this frequency range31.
Cell lines and DNA are available at the Coriell Institute for Medical Research(http://locus.umdnj.edu/nigms/products/hapmap.html).
Community engagement was employed to explain the project, and to learn how
the project was viewed, in the communities where samples were collected31,32.
Papers describing the community engagement processes are being prepared.
One JPT sample was replaced for technical reasons, but not in time forinclusion in this report. We surveyed cryptic relatedness among the study
participants, and identified a small number of pairs with unexpectedly high allele
sharing (Supplementary Information). As the total level of sharing is not great, andas a subset of analyses performed without these individuals were unchanged, we
include these individuals in the data and analyses presented here.
Genome-wide SNP discovery. The project required a dense map of SNPs, ideallycontaining information about validation and frequency of each candidate SNP.
When the project started, the public SNP database (dbSNP) contained
2.6 million candidate SNPs, few of which were annotated with the required
information.
To generate more SNPs and obtain validation information, shotgun sequencing of DNA from whole-genome libraries and flow-sorted chromosomes was
performed31, augmented by analysis of sequence traces produced by Applied
Biosystems97,98, and information on 1.6 million SNPs genotyped by Perlegen
Sciences77, including 425,000 not in dbSNP when released (Supplementary
Table 7). The HapMap Project contributed about 6 million new SNPs to dbSNP.
At the time of writing (October 2005) dbSNP (http://www.ncbi.nlm.nih.gov/
projects/SNP/) contains 9.2 million candidate human SNPs, of which 3.6 million
have been validated by both alleles having been seen two or more times during
discovery (‘double-hit’ SNPs), and 2.4 million have genotype data (Fig. 1).
Comprehensive study of common variation across 5 Mb of DNA. To study
patterns of genetic variation as comprehensively as possible, we selected ten 500kb regions from the ENCODE Project33. These ten regions were chosen inaggregate to approximate the genome-wide average for GþC content, recombination rate, percentage of sequence conserved relative to mouse sequence, andgene density (Table 2).
In each such region additional sequencing and genotyping were performed toobtain a much more complete inventory of common variation. Specifically,bidirectional PCR-based sequencing was performed across each 500-kb region in
1316

48 individuals (16 YRI, 16 CEU, 8 CHB, 8 JPT). Although the intent was for thesesame DNA samples to be included in Phase I, eight Yoruba and one Han Chinese
sample used in sequencing were not among the 269 samples genotyped. (Thenine samples are available from Coriell.)
All variants found by sequencing, and any others in dbSNP (build 121) notfound by sequencing, were genotyped in all 269 HapMap samples. If the firstattempt at genotyping was unsuccessful, a second platform was tried for each SNP.
False-positive and false-negative rates in PCR-based SNP discovery. The
false-positive rate of SNP discovery by PCR-based resequencing was estimated
at 7–11% (for the two sequencing centres), based on genotyping of eachcandidate SNP in the same samples used for discovery.
The false-negative rate of SNP discovery by PCR resequencing was estimated
at 6%, using as the denominator a set of SNPs previously in dbSNP andconfirmed by genotyping in the specific individuals sequenced. The falsenegative rate was considerably higher, however, for singletons (SNPs seen only
as a single heterozygote): 15% of singletons covered by high-quality sequence
data were not detected by the trace analysis, and another 25% were missed due toa failure to obtain a high-quality sequence over the relevant base in the one
heterozygous individual (D. J. Richter et al., personal communication).
False-positive and false-negative rates in dbSNP. The false positive rate(candidate SNPs that cannot be confirmed as variable sites) estimated for
dbSNP was 17%. This represents an upper bound, because dbSNP entries thatare monomorphic in the 269 HapMap samples could be rare variants, or
polymorphic in other samples. We note that as the catalogue of dbSNP gets
deeper, the rate at which candidate SNPs are monomorphic in any given sample
is observed to rise (Supplementary Table 8). This is expected because the numberof rare SNPs and false positives scales with depth of sequencing, whereas thenumber of true common variants will plateau.
SNP genotyping for the genome-wide map. Genotyping assays were designed
from dbSNP, with priority given to SNPs validated by previous genotyping dataor both alleles having been seen more than once in discovery. Data from the
Chimpanzee Genome Sequencing Project99 were used in SNP validation if they
confirmed the ancestral status of a human allele seen only once in discovery(Supplementary Information). Non-synonymous coding SNPs were also prioritized for genotyping. Two whole-genome, array-based genotyping reagents wereused efficiently to increase SNP density: 40,000 SNPs from Illumina, and 120,000
SNPs from Affymetrix100.
To monitor progress, the genome was partitioned into 5-kb bins, withgenotyping continuing through iterative rounds until a set of predetermined‘stopping rules’ was satisfied in each analysis panel. (1) Minor allele frequency: ineach analysis panel a common SNP (MAF $ 0.05) was obtained in each 5-kb
bin. (2) Spacing: the distance between adjacent SNPs was 2–8 kb, with at least
9 SNPs across 50 kb. (3) ‘HapMappable’ genome: with available technologies it ischallenging to study centromeres, telomeres, gaps in genome sequence, andsegmental duplications. The project identified such regions101 (Supplementary
Table 9), spanning 4.4% of the finished human genome sequence, in which only asingle attempt to develop a genotyping assay was required. (4) Three strikes,
you’re out: if the above rules were not satisfied after three attempts to develop an
assay in a given 5-kb region, or if all available SNPs in dbSNP had been tried,
genotyping was considered complete for Phase I. Two attempts were consideredsufficient if one attempt was of a SNP previously shown to have MAF $ 0.05 in
the appropriate population sample in a previous genome-wide survey77. (5)Quality control: ongoing and standardized quality control (QC) filters and threerounds of quality assessment (QA) were used to ensure and document the highquality of the genotype data.
QC filters were systematically performed, with each SNP tested for completeness (.80%), consistency across five duplicate genotypes (#1 discrepancy),
mendelian inheritance in 60 trios (#1 discrepancy in each of YRI and CEU), andHardy–Weinberg equilibrium (P . 0.001102). SNPs in the Phase I data set passed
all the QC filters in all the analysis panels and were polymorphic in the HapMap
samples. Failing SNPs were released (with a special flag), as they can help toidentify polymorphisms under primers, insertions/deletions, paralogous lociand natural selection.
Three QA exercises were carried out. First, a calibration exercise to ‘benchmark’ each platform and laboratory protocol. Second, a mid-project evaluationof each genotyping centre. Third, a blind analysis of a random sample of thecomplete Phase I data set. A number of SNPs were genotyped more than once
during the project, or by other investigators, providing additional information
about data quality. See the Supplementary Information for full information
about the QA exercises.
An exhaustive approach was taken to mtDNA. Alignment of more than 1,000
publicly available mtDNA sequences of African (n ¼ 87), European (n ¼ 928)
and Asian (n ¼ 238) geographical origin34 was used to identify 210 common© 2005 Nature Publishing GroupARTICLES

NATURE|Vol 437|27 October 2005

variants (MAF $ 0.05 in at least one continental region) that were attempted in
the samples.
Data release. Data deposited at the Data Coordination Center and released at
http://www.hapmap.org, a Japanese mirror site http://hapmap.jst.go.jp/ anddbSNP include ascertainment status of each SNP at the time of selection,primer sequences, protocols for genotyping, genotypes for each sample, allele
frequencies, and, for SNPs that failed QC filters, a code indicating the mode(s) of
failure.
Initially, because of concern that third parties might seek patents on HapMap
data, users were required to agree to a web-based ‘click-wrap license’, assentingthat they would not prevent others from using the data (http://www.hapmap.org/cgi-perl/registration). In December 2004 this license was dropped, and alldata were released without restriction into the public domain.
Received 11 August; accepted 12 September 2005.
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Supplementary Information is linked to the online version of the paper at
www.nature.com/nature.
Acknowledgements We thank many people who contributed to this project:
J. Beck, C. Beiswanger, D. Coppock, A. Leach, J. Mintzer and L. Toji (Coriell
Institute for Medical Research) for transforming the Yoruba, Japanese and Han
Chinese samples, distributing the DNA and cell lines, storing the samples foruse in future research, and producing the community newsletters and reports;
J. Greenberg and R. Anderson (NIH National Institute of General Medical
Sciences) for providing funding and support for cell line transformation andstorage in the NIGMS Human Genetic Cell Repository at the Coriell Institute;
T. Dibling, T. Ishikura, S. Kanazawa, S. Mizusawa and S. Saito (SNP ResearchCenter, RIKEN) for help with genotyping; C. Hind and A. Moghadam fortechnical support in genotyping and all members of the subcloning andsequencing teams at the Wellcome Trust Sanger Institute; X. Ke (Wellcome
Trust Centre for Human Genetics at the University of Oxford) for help with data
analysis; Oxford E-Science Centre for provision of high-performance computing
resources; H. Chen, W. Chen, L. Deng, Y. Dong, C. Fu, L. Gao, H. Geng, J. Geng,
M. He, H. Li, H. Li, S. Li, X. Li, B. Liu, Z. Liu, F. Lu, F. Lu, G. Lu, C. Luo, X. Wang,
Z. Wang, C. Ye and X. Yu (Beijing Genomics Institute) for help with genotyping
and sample collection; X. Feng, Y. Li, J. Ren and X. Zhou (Beijing Normal
University) for help with sample collection; J. Fan, W. Gu, W. Guan, S. Hu,
H. Jiang, R. Lei, Y. Lin, Z. Niu, B. Wang, L. Yang, W. Yang, Y. Wang, Z. Wang,
S. Xu, W. Yan, H. Yang, W. Yuan, C. Zhang, J. Zhang, K. Zhang and G. Zhao
(Chinese National Human Genome Center at Shanghai) for help withgenotyping; P. Fong, C. Lai, C. Lau, T. Leung, L. Luk and W. Tong (University ofHong Kong, Genome Research Centre) for help with genotyping; C. Pang
(Chinese University of Hong Kong) for help with genotyping; K. Ding, B. Qiang,
J. Zhang, X. Zhang and K. Zhou (Chinese National Human Genome Center at
Beijing) for help with genotyping; Q. Fu, S. Ghose, X. Lu, D. Nelson, A. Perez,
S. Poole, R. Vega and H. Yonath (Baylor College of Medicine); C. Bruckner,
T. Brundage, S. Chow, O. Iartchouk, M. Jain, M. Moorhead and K. Tran
(ParAllele Bioscience Inc.); N. Addleman, J. Atilano, T. Chan, C. Chu, C. Ha,
T. Nguyen, M. Minton and A. Phong (UCSF) for help with genotyping, and
D. Lind (UCSF) for help with quality control and experimental design;
R. Donaldson and S. Duan (Washington University) for help with genotyping,and J. Rice and N. Saccone (Washington University) for help with experimental
design; J. Wigginton (University of Michigan) for help with implementing andtesting QA/QC software; A. Clark, B. Keats, R. Myers, D. Nickerson and
A. Williamson for providing advice to NIH; J. Melone, M. Weiss and
E. DeHaut-Combs (NHGRI) for help with project management; M. Gray fororganizing phone calls and meetings; D. Leja for help with figures; the Yoruba
people of Ibadan, Nigeria, the people of Tokyo, Japan, and the community at
Beijing Normal University, who participated in public consultations andcommunity engagements; the people in these communities who were generous
in donating their blood samples; and the people in the Utah CEPH community
who allowed the samples they donated earlier to be used for the Project. We
also thank A. Clark, E. Lander, C. Langley and R. Lifton for comments on earlier
drafts of the manuscript. This work was supported by the Japanese Ministry of
Education, Culture, Sports, Science, and Technology, the Wellcome Trust,Nuffield Trust, Wolfson Foundation, UK EPSRC, Genome Canada, GénomeQuébec, the Chinese Academy of Sciences, the Ministry of Science andTechnology of the People’s Republic of China, the National Natural Science
Foundation of China, the Hong Kong Innovation and Technology Commission,
the University Grants Committee of Hong Kong, the SNP Consortium, the US
National Institutes of Health (FIC, NCI, NCRR, NEI, NHGRI, NIA, NIAAA, NIAID,NIAMS, NIBIB, NIDA, NIDCD, NIDCR, NIDDK, NIEHS, NIGMS, NIMH, NINDS,
NLM, OD), the W.M. Keck Foundation, and the Delores Dore Eccles
Foundation.
Author Contributions David Altshuler, Lisa D. Brooks, Aravinda Chakravarti,
Francis S. Collins, Mark J. Daly and Peter Donnelly are members of the writing
group responsible for this manuscript.
Author Information Reprints and permissions information is available at
npg.nature.com/reprintsandpermissions. The authors declare competing
financial interests: details accompany the paper at www.nature.com/nature.
Correspondence and requests for materials should be addressed to D.A.
(altshuler@molbio.mgh.harvard.edu) or P.D. (donnelly@stats.ox.ac.uk).

© 2005 Nature Publishing GroupARTICLES

NATURE|Vol 437|27 October 2005

The International HapMap Consortium (Participants are arranged by institution, listed alphabetically, and then alphabetically within
institutions except for Principal Investigators and Project Leaders, as indicated.)
Genotyping centres: Baylor College of Medicine and ParAllele BioScience Richard A. Gibbs (Principal Investigator)1, John W. Belmont1,
Andrew Boudreau2, Suzanne M. Leal1, Paul Hardenbol2, Shiran Pasternak1, David A. Wheeler1, Thomas D. Willis2, Fuli Yu1; Beijing Genomics
Institute Huanming Yang (Principal Investigator)3, Changqing Zeng (Principal Investigator)3, Yang Gao3, Haoran Hu3, Weitao Hu3, Chaohua
Li3, Wei Lin3, Siqi Liu3, Hao Pan3, Xiaoli Tang3, Jian Wang3, Wei Wang3, Jun Yu3, Bo Zhang3, Qingrun Zhang3, Hongbin Zhao3, Hui Zhao3, Jun
Zhou3; Broad Institute of Harvard and Massachusetts Institute of Technology Stacey B. Gabriel (Project Leader)4, Rachel Barry4, Brendan
Blumenstiel4, Amy Camargo4, Matthew Defelice4, Maura Faggart4, Mary Goyette4, Supriya Gupta4, Jamie Moore4, Huy Nguyen4, Robert C.
Onofrio4, Melissa Parkin4, Jessica Roy4, Erich Stahl4, Ellen Winchester4, Liuda Ziaugra4, David Altshuler (Principal Investigator)4,5; Chinese
National Human Genome Center at Beijing Yan Shen (Principal Investigator)6, Zhijian Yao6; Chinese National Human Genome Center at
Shanghai Wei Huang (Principal Investigator)7, Xun Chu7, Yungang He7, Li Jin7, Yangfan Liu7, Yayun Shen7, Weiwei Sun7, Haifeng Wang7, Yi
Wang7, Ying Wang7, Ying Wang7, Xiaoyan Xiong7, Liang Xu7; Chinese University of Hong Kong Mary M. Y. Waye (Principal Investigator)8,
Stephen K. W. Tsui8; Hong Kong University of Science and Technology Hong Xue (Principal Investigator)9, J. Tze-Fei Wong9; Illumina
Launa M. Galver (Project Leader)10, Jian-Bing Fan10, Sarah S. Murray10, Arnold R. Oliphant11, Mark S. Chee (Principal Investigator)12; McGill
University and Génome Québec Innovation Centre Alexandre Montpetit (Project Leader)13, Fanny Chagnon13, Vincent Ferretti13, Martin
Leboeuf13, Jean-François Olivier13, Michael S. Phillips13, Stéphanie Roumy13, Clémentine Sallée14, Andrei Verner13, Thomas J. Hudson (Principal
Investigator)13; Perlegen Sciences Kelly A. Frazer (Principal Investigator)15, Dennis G. Ballinger15, David R. Cox15, David A. Hinds15, Laura L.
Stuve15; University of California at San Francisco and Washington University Pui-Yan Kwok (Principal Investigator)16, Dongmei Cai16,
Daniel C. Koboldt17, Raymond D. Miller17, Ludmila Pawlikowska16, Patricia Taillon-Miller17, Ming Xiao16; University of Hong Kong Lap-Chee
Tsui (Principal Investigator)18, William Mak18, Pak C. Sham18, You Qiang Song18, Paul K. H. Tam18; University of Tokyo and RIKEN Yusuke
Nakamura (Principal Investigator)19,20, Takahisa Kawaguchi20, Takuya Kitamoto20, Takashi Morizono20, Atsushi Nagashima20, Yozo
Ohnishi20, Akihiro Sekine20, Toshihiro Tanaka20, Tatsuhiko Tsunoda20; Wellcome Trust Sanger Institute Panos Deloukas (Project Leader)21,
Christine P. Bird21, Marcos Delgado21, Emmanouil T. Dermitzakis21, Rhian Gwilliam21, Sarah Hunt21, Jonathan Morrison21, Don Powell21, Barbara
E. Stranger21, Pamela Whittaker21, David R. Bentley (Principal Investigator)22
Analysis groups: Broad Institute of Harvard and Massachusetts Institute of Technology Mark J. Daly (Project Leader)4,5, Paul I. W. de
Bakker4,5, Jeff Barrett4,5, Ben Fry4, Julian Maller4,5, Steve McCarroll4,5, Nick Patterson4, Itsik Pe’er4,5, Shaun Purcell5, Daniel J. Richter4, Pardis
Sabeti4, Richa Saxena4,5, Stephen F. Schaffner4, Patrick Varilly4, David Altshuler (Principal Investigator)4,5; Cold Spring Harbor Laboratory
Lincoln D. Stein (Principal Investigator)23, Lalitha Krishnan23, Albert Vernon Smith23, Gudmundur A. Thorisson23; Johns Hopkins University
School of Medicine Aravinda Chakravarti (Principal Investigator)24, Peter E. Chen24, David J. Cutler24, Carl S. Kashuk24, Shin Lin24; Universityof Michigan Gonçalo R. Abecasis (Principal Investigator)25, Weihua Guan25, Heather M. Munro25, Zhaohui Steve Qin25, Daryl J. Thomas26;
University of Oxford Gilean McVean (Project Leader)27, Leonardo Bottolo27, Susana Eyheramendy27, Colin Freeman27, Jonathan Marchini27,
Simon Myers27, Chris Spencer27, Matthew Stephens28, Peter Donnelly (Principal Investigator)27; University of Oxford, Wellcome TrustCentre for Human Genetics Lon R. Cardon (Principal Investigator)29, Geraldine Clarke29, David M. Evans29, Andrew P. Morris29, Bruce S.
Weir30; RIKEN Tatsuhiko Tsunoda20; US National Institutes of Health James C. Mullikin31, Stephen T. Sherry32, Michael Feolo32
Community engagement/public consultation and sample collection groups: Beijing Normal University and Beijing Genomics InstituteHoucan Zhang33, Changqing Zeng3, Hui Zhao3; Health Sciences University of Hokkaido, Eubios Ethics Institute, and Shinshu University
Ichiro Matsuda (Principal Investigator)34, Yoshimitsu Fukushima35, Darryl R. Macer36, Eiko Suda37; Howard University and University of
Ibadan Charles N. Rotimi (Principal Investigator)38, Clement A. Adebamowo39, Ike Ajayi39, Toyin Aniagwu39, Patricia A. Marshall40, Chibuzor
Nkwodimmah39, Charmaine D. M. Royal38; University of Utah Mark F. Leppert (Principal Investigator)41, Missy Dixon41, Andy Peiffer41
Ethical, legal and social issues: Chinese Academy of Social Sciences Renzong Qiu42; Genetic Interest Group Alastair Kent43; Kyoto
University Kazuto Kato44; Nagasaki University Norio Niikawa45; University of Ibadan School of Medicine Isaac F. Adewole39; University ofMontréal Bartha M. Knoppers14; University of Oklahoma Morris W. Foster46; Vanderbilt University Ellen Wright Clayton47; Wellcome Trust
Jessica Watkin48
SNP discovery: Baylor College of Medicine Richard A. Gibbs (Principal Investigator)1, John W. Belmont1, Donna Muzny1, Lynne Nazareth1,
Erica Sodergren1, George M. Weinstock1, David A. Wheeler1, Imtiaz Yakub1; Broad Institute of Harvard and Massachusetts Institute ofTechnology Stacey B. Gabriel (Project Leader)4, Robert C. Onofrio4, Daniel J. Richter4, Liuda Ziaugra4, Bruce W. Birren4, Mark J. Daly4,5, David
Altshuler (Principal Investigator)4,5; Washington University Richard K. Wilson (Principal Investigator)49, Lucinda L. Fulton49; Wellcome
Trust Sanger Institute Jane Rogers (Principal Investigator)21, John Burton21, Nigel P. Carter21, Christopher M. Clee21, Mark Griffiths21,
Matthew C. Jones21, Kirsten McLay21, Robert W. Plumb21, Mark T. Ross21, Sarah K. Sims21, David L. Willey21
Scientific management: Chinese Academy of Sciences Zhu Chen50, Hua Han50, Le Kang50; Genome Canada Martin Godbout51, John C.
Wallenburg52; Génome Québec Paul L’ Archevêque53, Guy Bellemare53; Japanese Ministry of Education, Culture, Sports, Science andTechnology Koji Saeki54; Ministry of Science and Technology of the People’s Republic of China Hongguang Wang55, Daochang An55,
Hongbo Fu55, Qing Li55, Zhen Wang55; The Human Genetic Resource Administration of China Renwu Wang56; The SNP Consortium
Arthur L. Holden57; US National Institutes of Health Lisa D. Brooks58, Jean E. McEwen58, Christianne R. Bird58, Mark S. Guyer58, Patrick J.
Nailer58, Vivian Ota Wang58, Jane L. Peterson58, Michael Shi59, Jack Spiegel60, Lawrence M. Sung61, Jonathan Witonsky62, Lynn F. Zacharia58,
Francis S. Collins63; Wellcome Trust Karen Kennedy48, Ruth Jamieson48 & John Stewart48
Affiliations for participants: 1Baylor College of Medicine, Human Genome Sequencing Center, Department of Molecular and Human Genetics, 1 Baylor Plaza, Houston, Texas
77030, USA. 2ParAllele Bioscience, Inc., 7300 Shoreline Court, South San Francisco, California 94080, USA. 3Beijing Genomics Institute, Chinese Academy of Sciences, Beijing
100300, China. 4The Broad Institute of Harvard and Massachusetts Institute of Technology, 1 Kendall Square, Cambridge, Massachusetts 02139, USA. 5Massachusetts General
Hospital and Harvard Medical School, Simches Research Center, 185 Cambridge Street, Boston, Massachusetts 02114, USA. 6Chinese National Human Genome Center at Beijing,© 2005 Nature Publishing Group1319

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3-707 N. Yongchang Road, Beijing Economic-Technological Development Area, 100176, China. 7Chinese National Human Genome Center at Shanghai, 250 Bi Bo Road, Shanghai
201203, China. 8The Chinese University of Hong Kong, Department of Biochemistry, The Croucher Laboratory for Human Genomics, 6/F Mong Man Wai Building, Shatin, HongKong. 9Hong Kong University of Science and Technology, Department of Biochemistry, Clear Water Bay, Kowloon, Hong Kong. 10Illumina, 9885 Towne Centre Drive, San Diego,
California 92121, USA. 1115518 Markar Road, Poway, California 92064, USA. 12Prognosys Biosciences, Inc., 4215 Sorrento Valley Boulevard, Suite 105, San Diego, California 92121,
USA. 13McGill University and Génome Québec Innovation Centre, 740 Drive Penfield Avnue, Montréal, Québec H3A 1A4, Canada. 14University of Montréal, The Public Law
Research Centre (CRDP), P.O. Box 6128, Downtown Station, Montréal, Québec H3C 3J7, Canada. 15Perlegen Sciences, Inc., 2021 Stierlin Court, Mountain View, California 94043,
USA. 16University of California, San Francisco, Cardiovascular Research Institute, 513 Parnassus Avenue, Box 0793, San Francisco, California 94143, USA. 17Washington University
School of Medicine, Department of Genetics, 660 S. Euclid Avenue, Box 8232, St Louis, Missouri 63110, USA. 18University of Hong Kong, Genome Research Centre, 6/F,
Laboratory Block, 21 Sassoon Road, Pokfulam, Hong Kong. 19University of Tokyo, Institute of Medical Science, 4-6-1 Sirokanedai, Minato-ku, Tokyo 108-8639, Japan. 20RIKEN
SNP Research Center, 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama, Kanagawa 230-0045, Japan. 21Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus,
Hinxton, Cambridge CB10 1SA, UK. 22Solexa Ltd, Chesterford Research Park, Little Chesterford, Nr Saffron Walden, Essex CB10 1XL, UK. 23Cold Spring Harbor Laboratory, 1
Bungtown Road, Cold Spring Harbor, New York 11724, USA. 24Johns Hopkins University School of Medicine, McKusick-Nathans Institute of Genetic Medicine, Broadway
Research Building, Suite 579, 733 N. Broadway, Baltimore, Maryland 21205, USA. 25University of Michigan, Center for Statistical Genetics, Department of Biostatistics, 1420
Washington Heights, Ann Arbor, Michigan 48109, USA. 26Center for Biomolecular Science and Engineering, Engineering 2, Suite 501, Mail Stop CBSE/ITI, UC Santa Cruz, Santa
Cruz, California 95064, USA. 27University of Oxford, Department of Statistics, 1 South Parks Road, Oxford OX1 3TG, UK. 28University of Washington, Department of Statistics,
Box 354322, Seattle, Washington 98195, USA. 29University of Oxford/Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK. 30North Carolina
State University, Bioinformatics Research Center, Campus Box 7566, Raleigh, North Carolina 27695, USA. 31US National Institutes of Health, National Human Genome ResearchInstitute, 50 South Drive, Bethesda, Maryland 20892, USA. 32US National Institutes of Health, National Library of Medicine, National Center for Biotechnology Information, 8600
Rockville Pike, Bethesda, Maryland 20894, USA. 33Beijing Normal University, 19 Xinjiekouwai Street, Beijing 100875, China. 34Health Sciences University of Hokkaido, Ishikari
Tobetsu Machi 1757, Hokkaido 061-0293, Japan. 35Shinshu University School of Medicine, Department of Medical Genetics, Matsumoto 390-8621, Japan. 36United Nations
Educational, Scientific and Cultural Organization (UNESCO Bangkok), 920 Sukhumwit Road, Prakanong, Bangkok 10110, Thailand. 37University of Tsukuba, Eubios Ethics Institute,
P.O. Box 125, Tsukuba Science City 305-8691, Japan. 38Howard University, National Human Genome Center, 2216 6th Street, NW, Washington DC 20059, USA. 39University of
Ibadan College of Medicine, Ibadan, Oyo State, Nigeria. 40Case Western Reserve University School of Medicine, Department of Bioethics, 10900 Euclid Avenue, Cleveland, Ohio
44106, USA. 41University of Utah, Eccles Institute of Human Genetics, Department of Human Genetics, 15 North 2030 East, Salt Lake City, Utah 84112, USA. 42Chinese Academyof Social Sciences, Center for Applied Ethics, 2121, Building 9, Caoqiao Xinyuan 3 Qu, Beijing 100054, China. 43Genetic Interest Group, 4D Leroy House, 436 Essex Road, London
N1 3QP, UK. 44Kyoto University, Institute for Research in Humanities and Graduate School of Biostudies, Ushinomiya-cho, Sakyo-ku, Kyoto 606-8501, Japan. 45Nagasaki
University Graduate School of Biomedical Sciences, Department of Human Genetics, Sakamoto 1-12-4, Nagasaki 852-8523, Japan. 46University of Oklahoma, Department of
Anthropology, 455 W. Lindsey Street, Norman, Oklahoma 73019, USA. 47Vanderbilt University, Center for Genetics and Health Policy, 507 Light Hall, Nashville, Tennessee37232-0165, USA. 48Wellcome Trust, 215 Euston Road, London NW1 2BE, UK. 49Washington University School of Medicine, Genome Sequencing Center, Box 8501, 4444 Forest
Park Avenue, St Louis, Missouri 63108, USA. 50Chinese Academy of Sciences, 52 Sanlihe Road, Beijing 100864, China. 51Genome Canada, 150 Metcalfe Street, Suite 2100,
Ottawa, Ontario K2P 1P1, Canada. 52McGill University, Office of Technology Transfer, 3550 University Street, Montréal, Québec H3A 2A7, Canada. 53Génome Québec, 630,
boulevard René-Lévesque Ouest, Montréal, Québec H3B 1S6, Canada. 54Ministry of Education, Culture, Sports, Science, and Technology, 3-2-2 Kasumigaseki, Chiyodaku, Tokyo
100-8959, Japan. 55Ministry of Science and Technology of the People’s Republic of China, 15 B. Fuxing Road, Beijing 100862, China. 56The Human Genetic Resource
Administration of China, b7, Zaojunmiao, Haidian District, Beijing 100081, China. 57The SNP Consortium, 3 Parkway North, Deerfield, Illinois 60015, USA. 58US National Institutesof Health, National Human Genome Research Institute, 5635 Fishers Lane, Suite 4076, Bethesda, Maryland 20892, USA. 59Novartis Pharmaceuticals Corporation, Biomarker
Development, One Health Plaza, East Hanover, New Jersey 07936, USA. 60US National Institutes of Health, Office of Technology Transfer, 6011 Executive Boulevard, Rockville,
Maryland 20852, USA. 61University of Maryland School of Law, 500 W. Baltimore Street, Baltimore, Maryland 21201, USA. 62Frost & Sullivan, 2400 Geng Road, Suite 201, Palo
Alto, California 94303, USA. 63US National Institutes of Health, National Human Genome Research Institute, 31 Center Drive, Bethesda, Maryland 20892, USA.

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