Vol 449 | 18 October 2007 | doi:10.1038/nature06258

ARTICLES
A second generation human haplotype
map of over 3.1 million SNPs
The International HapMap Consortium*
We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs)
genotyped in 270 individuals from four geographically diverse populations and includes 25–35% of common SNP variation in
the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r2 of
between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide
genotyping products captures common Phase II SNPs with an average maximum r2 of up to 0.8 in African and up to 0.95 in
non-African populations, and that potential gains in power in association studies can be obtained through imputation. These
data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10–30% of pairs of individuals within
a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of allcommon variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination
rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased
differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength orefficacy of natural selection between populations.
Advances made possible by the Phase I haplotype map
The International HapMap Project was launched in 2002 with theaim of providing a public resource to accelerate medical genetic
research. The objective was to genotype at least one common SNP
every 5 kilobases (kb) across the euchromatic portion of the genome
in 270 individuals from four geographically diverse populations1,2: 30
mother–father–adult child trios from the Yoruba in Ibadan, Nigeria(abbreviated YRI); 30 trios of northern and western European ancestry living in Utah from the Centre d’Etude du Polymorphisme
Humain (CEPH) collection (CEU); 45 unrelated Han Chinese individuals in Beijing, China (CHB); and 45 unrelated Japanese individuals in Tokyo, Japan (JPT). The YRI samples and the CEU samples
each form an analysis panel; the CHB and JPT samples together form
an analysis panel. Approximately 1.3 million SNPs were genotyped inPhase I of the project, and a description of this resource was published in 2005 (ref. 3).
The initial HapMap Project data had a central role in the development of methods for the design and analysis of genome-wide association studies. These advances, alongside the release of commercial
platforms for performing economically viable genome-wide genotyping, have led to a new phase in human medical genetics. Already,large-scale studies have identified novel loci involved in multiple
complex diseases4,5. In addition, the HapMap data have led to novel
insights into the distribution and causes of recombination hotspots3,6, the prevalence of structural variation7,8 and the identity ofgenes that have experienced recent adaptive evolution3,9. Because the
HapMap cell lines are publicly available, many groups have been able
to integrate their own experimental data with the genome-wide SNP
data to gain new insight into copy-number variation10, the relationship between classical human leukocyte antigen (HLA) types and
SNP variation11, and heritable influences on gene expression12–14.
The ability to combine genome-wide data on such diverse aspectsof genetic variation with molecular phenotypes collected in the same
samples provides a powerful framework to study the connection of
DNA sequence to function.

In Phase II of the HapMap Project, a further 2.1 million SNPs
were successfully genotyped on the same individuals. The resulting
HapMap has an SNP density of approximately one per kilobase
and is estimated to contain approximately 25–35% of all the 9–10
million common SNPs (minor allele frequency (MAF) $ 0.05) in theassembled human genome (that is, excluding gaps in the reference
sequence alignment; see Supplementary Text 1), although this number shows extensive local variation. This paper describes the Phase II
resource, its implications for genome-wide association studies andadditional insights into the fine-scale structure of linkage disequilibrium, recombination and natural selection.
Construction of the Phase II HapMap
Most of the additional genotype data for the Phase II HapMap wereobtained using the Perlegen amplicon-based platform15. Briefly, thisplatform uses custom oligonucleotide arrays to type SNPs in DNA
segmentally amplified via long-range polymerase chain reaction(PCR). Genotyping was attempted at 4,373,926 distinct SNPs, whichcorresponds, with exceptions (see Methods), to nearly all SNPs in
dbSNP release 122 for which an assay could be designed. Additional
submissions were included from the Affymetrix GeneChip Mapping
Array 500K set, the Illumina HumanHap100 and HumanHap300
SNP assays, a set of ,11,000 non-synonymous SNPs genotyped by
Affymetrix (ParAllele) and a set of ,4,500 SNPs within the extended
major histocompatibility complex (MHC)11. Genotype submissions
were subjected to the same quality control (QC) filters as described
previously (see Methods) and mapped to NCBI build 35 (Universityof California at Santa Cruz (UCSC) hg17) of the human genome. The
re-mapping of SNPs from Phase I of the project identified 21,177
SNPs that had an ambiguous position or some other feature indicative of low reliability; these are not included in the filtered Phase II
data release. All genotype data are available from the HapMap Data
Coordination Center (http://www.hapmap.org) and dbSNP (http://
www.ncbi.nlm.nih.gov/SNP); analyses described in this paper referto release 21a. Three data sets are available: ‘redundant unfiltered’*Lists of participants and affiliations appear at the end of the paper.

851©2007 Nature Publishing GroupARTICLES

NATURE | Vol 449 | 18 October 2007

contains all genotype submissions, ‘redundant filtered’ contains all
submissions that pass QC, and ‘non-redundant filtered’ contains asingle QC1 submission for each SNP in each analysis panel.
The QC filters remove SNPs showing gross errors. However, it isalso important to understand the magnitude and structure of more
subtle genotyping errors among SNPs that pass QC. We therefore
carried out a series of analyses to assess the influence of the long-range
PCR amplicon structure on genotyping error, the concordance rates
between genotype calls from different genotyping platforms andbetween those platforms and re-sequencing assays, as well as the rates
of false monomorphism and mis-mapping of SNPs (see Supplementary Text 2, Supplementary Figs 1–3 and Supplementary Tables 1–4).
We estimate that the average per genotype accuracy is at least 99.5%.
However, there are higher rates of missing data and genotype discrepancies at non-reference alleles, with some clustering of errors resulting from the amplicon design and a few incorrectly mapped SNPs.
Table 1 shows the numbers of SNPs attempted and converted to
QC1 SNPs in each analysis panel (Supplementary Table 5 shows abreakdown by each major submission). Haplotypes and missing data
were estimated for each analysis panel separately using both trio
information and statistical methods based on the coalescent model(see Methods). To enable cross-population comparisons, a consensus data set was created consisting of 3,107,620 SNPs that were
QC1 in all analysis panels and polymorphic in at least one analysis
panel. The equivalent figure from Phase I was 931,340 SNPs. Unlessstated otherwise, all analyses have been carried out on the consensus
data set. An additional set of haplotypes was created for those SNPs in
the consensus where a putative ancestral state could be assigned bycomparison of the human alleles to the orthologous position in the
chimpanzee and rhesus macaque genomes.
The variation in SNP density within the Phase II HapMap is shownin Fig. 1. On average there are 1.14 genotyped polymorphic SNPs perkilobase (average spacing is 875 base pairs (bp)) and 98.6% of theassembled genome is within 5 kb of the nearest polymorphic SNP.
Still, there is heterogeneity in genotyped SNP density at both broad(Fig. 1a) and fine (Fig. 1b) scales. Furthermore, there are systematicchanges in genotyped SNP density around genomic features including genes (Fig. 1c).
The Phase II HapMap differs from the Phase I HapMap not onlyin SNP spacing, but also in minor allele frequency distribution andpatterns of linkage disequilibrium (Supplementary Fig. 4). Because
the criteria for choosing additional SNPs did not include consideration of SNP spacing or preferential selection for high MAF, the SNPs
added in Phase II are, on average, more clustered and have lower
MAF than the Phase I SNPs. Because MAF predictably influences the
distribution of linkage disequilibrium statistics, the average r2 at agiven physical distance is typically lower in Phase II than in Phase I;
conversely, the jD9j statistic is typically higher (data not shown). One
notable consequence is that the Phase II HapMap includes a better
representation of rare variation than the Phase I HapMap.
The increased resolution provided by Phase II of the project is
illustrated in Fig. 2. Broadly, an additional SNP added to a region
shows one of three patterns. First, it may be very similar in distribution
to SNPs present in Phase I. Second, it may provide detailed resolutionof haplotype structure (for example, a group of chromosomes withidentical local haplotypes in Phase I can be shown in Phase II to carry

Table 1 | Summary of Phase II HapMap data (release 21)
Phase

SNP categories

Analysis panel
YRI

CEU

CHB1JPT

I

Assays submitted
Passed QC
Did not pass QC.20% missing.1 duplicate inconsistent.1 mendelian error,0.001 Hardy–Weinberg P -value
Other failures

1,304,199
1,177,312 (90%)
126,887 (10%)
82,463 (65%)
6,049 (5%)
18,916 (15%)
10,265 (8%)
19,345 (15%)

1,344,616
1,217,902 (91%)
126,714 (9%)
95,684 (76%)
5,126 (4%)
11,310 (9%)
8,922 (7%)
13,858 (11%)

1,306,125
1,187,800 (91%)
118,325 (9%)
78,323 (66%)
9,242 (8%)
N/A
13,722 (12%)
20,674 (17%)

II

Assays submitted
Passed QC
Did not pass QC.20% missing.1 duplicate inconsistent.1 mendelian error,0.001 Hardy–Weinberg P -value
Other failures

5,044,989
3,150,433 (62%)
1,894,556 (38%)
1,419,000 (75%)
0 (0%)
172,339 (9%)
96,231 (5%)
334,511 (18%)

5,044,996
3,204,709 (64%)
1,840,287 (36%)
1,398,166 (76%)
0 (0%)
127,923 (7%)
82,268 (4%)
337,906 (18%)

5,043,775
3,244,897 (64%)
1,798,878 (36%)
1,403,543 (78%)
6,617 (0%)
N/A
108,880 (6%)
340,370 (19%)

Overall

Assays submitted
Passed QC
Did not pass QC.20% missing.1 duplicate inconsistent.1 mendelian error,0.001 Hardy–Weinberg P -value
Other failuresNon-redundant (unique) SNPs
Monomorphic
Polymorphic

6,349,188
4,327,745 (68%)
2,021,443 (32%)
1,501,463 (74%)
6,049 (0%)
191,255 (9%)
106,496 (5%)
353,856 (18%)
3,796,934
861,299 (23%)
2,935,635 (77%)

6,389,612
4,422,611 (69%)
1,967,001 (31%)
1,493,850 (76%)
5,126 (0%)
139,233 (7%)
91,190 (5%)
351,764 (18%)
3,868,157
1,246,183 (32%)
2,621,974 (68%)

6,349,900
4,432,697 (70%)
1,917,203 (30%)
1,481,866 (77%)
15,859 (1%)
N/A
122,602 (6%)
361,044 (19%)
3,890,416
1,410,152 (36%)
2,480,264 (64%)

SNP categories

All analysis panels

Unique QC-passed SNPs
Passed in one analysis panel
Passed in two analysis panels
Passed in three analysis panels (QC13)
QC13 and monomorphic acrossthree analysis panels
QC13 and polymorphic in at least one analysis panel
QC13 and polymorphic in all three analysis panels
QC13 and MAF $ 0.05 in at least
one of three analysis panels

4,000,107
88,140 (2%)
268,534 (7%)
3,643,433 (91%)
535,813
3,107,620
2,006,352
2,819,322

852©2007 Nature Publishing GroupARTICLES

NATURE | Vol 449 | 18 October 2007b
>2.5
2.0–2.5
1.5–2.0
1.25–1.5
1.0–1.25
0.75–1.0
0.5–0.75
0.1–0.5
<0.1

X
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1

10,00010,02010,04010,06010,08010,100

c

50

0

150
100
Position (Mb)

200

250

2.2
2.1
2.0
1.9
1.8
1.7

1.1
1.0
0.9
0.8
–100 –80 –60 –40 –20 0 20 40 60 80 100
Position (kb)dbSNP polymorphic SNP
density (per kb)

Position NCBI build 35 (kb)
HapMap polymorphic
SNP density (per kb)

ChromosomeaFigure 1 | SNP density in the Phase II HapMap. a, SNP density across the
genome. Colours indicate the number of polymorphic SNPs per kb in theconsensus data set. Gaps in the assembly are shown as white. b, Example ofthe fine-scale structure of SNP density for a 100-kb region on chromosome
17 showing Perlegen amplicons (black bars), polymorphic Phase I SNPs in
the consensus data set (red triangles) and polymorphic Phase II SNPs in theconsensus data set (blue triangles). Note the relatively even spacing of Phase

I SNPs. c, The distribution of polymorphic SNPs in the consensus Phase II
HapMap data (blue line and left-hand axis) around coding regions. Also
shown is the density of SNPs in dbSNP release 125 around genes (red line
and right-hand axis). Values were calculated separately 59 from the coding
start site (the left dotted line) and 39 from the coding end site (right dottedline) and were joined at the median midpoint position of the coding unit
(central dotted line).

multiple related haplotypes). Third, the novel SNP (or group of added
SNPs) may reveal previously missed recombinant haplotypes. The
extent to which each type of event occurs varies among populations
and chromosomal regions. The greatest gains in resolution, in terms ofidentifying new recombinant haplotypes and haplotype groupings,
occur in YRI. Consequently, the Phase II HapMap provides increased
resolution in the estimated fine-scale genetic map and improved
power to detect and localize recombination hotspots (Fig. 2b).

In this section, we quantify the gain in resolution and outline how
the HapMap data can be used to improve the power of association
studies.
Improved coverage of common variation. We previously predictedthat the vast majority of common SNPs would be correlated to Phase
II HapMap SNPs by extrapolation from the ten HapMap ENCODE
regions3. Using the actual Phase II marker spacing and frequency
distributions (Table 2), we repeated the simulations and estimatethat Phase II HapMap marker sets capture the overwhelming majority of all common variants at high r2. For common variants(MAF $ 0.05) the mean maximum r2 of any SNP to a typed one is0.90 in YRI, 0.96 in CEU and 0.95 in CHB1JPT. The impact of the

The use of the Phase II HapMap in association studies
The increased SNP density of the Phase II HapMap has already been
extensively exploited in genome-wide studies of disease association.
ab

OR51V1

HBB

HBD

HBG1 HBG2

HBE

cM Mb–1

80
60
40
20
0
5160000

5180000

5200000

5220000

5240000

Position (NCBI build 35)

Figure 2 | Haplotype structure and recombination rate estimates from thePhase II HapMap. a, Haplotypes from YRI in a 100 kb region around theb-globin (HBB) gene. SNPs typed in Phase I are shown in dark blue.
Additional SNPs in the Phase II HapMap are shown in light blue. Only SNPs
for which the derived allele can be unambiguously identified by parsimony(by comparison with an outgroup sequence) are shown (89% of SNPs in theregion); the derived allele is shown in colour. b, Recombination rates (lines)
and the location of hotspots (horizontal blue bars) estimated for the same
region from the Phase I (dark blue) and Phase II HapMap (light blue) data.
Also shown are the location of genes within the region (grey bars) and thelocation of the experimentally verified recombination hotspot57,58 at the 59
end of the HBB gene (black bar).
853©2007 Nature Publishing GroupARTICLES

NATURE | Vol 449 | 18 October 2007

Table 2 | Estimated coverage of the Phase II HapMap in the ten HapMap ENCODE regions
Panel

MAF binPhase I HapMap3

Phase II HapMap
Pairwise linkage disequilibrium

Additional 2-SNP tests

r2 $ 0.8 (%)

Mean maximum r2

r2 $ 0.8 (%)

Mean maximum r2

r2 $ 0.8 (%)

Mean maximum r2

YRI

$0.05
,0.05
0.05–0.10
0.10–0.25
0.25–0.50

45

0.67

82
61
81
90
87

0.90
0.76
0.89
0.94
0.93

87
62
81
90
92

0.93
0.78
0.89
0.95
0.96

CEU

$0.05
,0.05
0.05–0.10
0.10–0.25
0.25–0.50

74

0.85

93
70
87
94
95

0.96
0.79
0.92
0.96
0.97

95
72
88
95
97

0.97
0.81
0.93
0.97
0.98

CHB1JPT

$0.05
,0.05
0.05–0.10
0.10–0.25
0.25–0.50

72

0.83

92
65
81
90
94

0.95
0.74
0.89
0.94
0.96

95
65
82
90
97

0.97
0.74
0.89
0.95
0.98

2-SNP tests, linkage disequilibrium to haplotypes formed from two nearby SNPs.

Table 3 | Number of tag SNPs required to capture common (MAF $ 0.05)
Phase II SNPs
Threshold

YRI

CEU

CHB1JPT

r2 $ 0.5
r2 $ 0.8
r2 5 1.0

627,458
1,093,422
1,616,739

290,969
552,853
1,024,665

277,831
520,111
1,078,959

increased density of the Phase II HapMap is most notable in YRI (inthe Phase I HapMap the mean maximum r2 was 0.67). Similar results
are found if a threshold of r2 $ 0.8 is used to determine whether an
SNP is captured (Table 2). As expected, very common SNPs with
MAF . 0.25 are captured extremely well (mean maximum r2 of 0.93
in YRI to 0.97 in CEU), whereas rarer SNPs with MAF , 0.05 are lesswell covered (mean maximum r2 of 0.74 in CHB1JPT to 0.76 in
YRI). The latter figure is probably an overestimate because it is basedon lower frequency SNPs discovered via re-sequencing 48 HapMap
individuals, and does not include a much larger number of very rare
SNPs. We also assessed the increase in coverage provided by using
two-SNP haplotypes as proxies for SNPs that are poorly captured bysingle SNPs16 (Table 2). These two-SNP haplotypes lead to a modest
increase in mean maximum r2 of 0.01 to 0.03 across all allele frequencies. However, in some regions, particularly where marker density islow, gains from multi-marker and imputation approaches in practical situations can be substantial (see below).
Currently, the Phase II HapMap provides the most complete available resource for selecting tag SNPs genome-wide. Using a simple
pairwise tagging approach, we find that 1.09 million SNPs are
required to capture all common Phase II SNPs with r2 $ 0.8 in
YRI, with slightly more than 500,000 required in CEU and
CHB1JPT (Table 3). These numbers are approximately twice thoserequired to capture SNPs in the Phase I HapMap (which has onethird as many SNPs). The number of SNPs required to achieve perfect tagging (r2 5 1.0) in each analysis panel is almost double that
required to achieve the r2 $ 0.8 threshold. It becomes increasingly

expensive to improve the coverage afforded by tags from the Phase I
and, now, the Phase II HapMap, because additional tag SNPs areunlikely to capture large groups of additional SNPs.
Phase II HapMap and genome-wide association studies. Although
the efficient choice of tag SNPs is one use of the Phase II HapMap, formost disease studies the tag SNPs genotyped will be primarily determined by the choice of a commercial platform for the experiment17,18.
Using Phase II data, we estimated the coverage of several available
products on which genome-wide association studies are alreadyunderway (Table 4). Similar to earlier estimates17,18, these products
typically perform well in CEU and CHB1JPT, and some also perform well in YRI. For example, arrays of approximately 500,000 SNPs
capture 68–88% (depending on selection method) of all HapMap
Phase II variation with r2 $ 0.8 in CEU. SNPs that are not included in
the Phase II HapMap will be covered more poorly because mostgenotyping products were designed using HapMap data.
HapMap data have several additional roles in the analysis of disease-association studies using fixed marker sets. For example, thehigh-quality haplotype information within the Phase II HapMap
can be used to aid the phasing of genotype data from new samples
because additional haplotypes are likely to be locally very similar to atleast one haplotype in the Phase II data. By a similar argument,
missing genotypes can potentially be inferred through comparison
to the Phase II haplotypes. Genotypes may be missing either becauseof genotyping failure or because the SNP was not assayed within
the experiment. Therefore, the HapMap haplotypes provide a wayof in silico genotyping Phase II SNPs that were not included in theexperiment.
Although there is no clear consensus yet about the role of SNP
imputation in the analysis of genome-wide association studies, highimputation accuracy can be achieved using model-based methods19–23 and can lead to an increase in power23,24. To illustrate thepossibilities, in the 500-kb HapMap ENCODE region on 8q24.11(Supplementary Fig. 5) we evaluated imputation of Phase II SNPs
from the Affymetrix GeneChip 500K array. To do this, we used a

Table 4 | Estimated coverage of commercially available fixed marker arrays
Platform*

Affymetrix GeneChip 500K
Affymetrix SNP Array 6.0
Illumina HumanHap300
Illumina HumanHap550
Illumina HumanHap650Y
Perlegen 600K

YRI

CEU

CHB1JPT

r2 $ 0.8 (%)

Mean maximum r2

r2 $ 0.8 (%)

Mean maximum r2

r2 $ 0.8 (%)

Mean maximum r2

46
66
33
55
66
47

0.66
0.80
0.56
0.73
0.80
0.68

68
82
77
88
89
92

0.81
0.90
0.86
0.92
0.93
0.94

67
81
63
83
84
84

0.80
0.89
0.78
0.89
0.90
0.90

* Assuming all SNPs on the product are informative and pass QC; in practice these numbers are overestimates.

854©2007 Nature Publishing GroupARTICLES

NATURE | Vol 449 | 18 October 2007

leave-one-out procedure to assess the accuracy of genotype prediction in the YRI. For SNPs with MAF $ 0.2, the average maximum r2
to a typed SNP in the region is 0.59 compared to an average genotype
prediction r2 of 0.86. Furthermore, whereas 44% of such SNPs in theregion have no single-marker proxy with r2 $ 0.5, fewer than 6% of
the SNPs have a genotype imputation accuracy of r2 , 0.5, establishing that accurate imputation can be achieved even in the population
where linkage disequilibrium is the weakest.
New insights into linkage disequilibrium structure
The paradigm underlying association studies is that linkage disequilibrium can be used to capture associations between markers andnearby untyped SNPs. However, the Phase II HapMap has revealed
several properties of linkage disequilibrium that illustrate the fullcomplexity of empirical patterns of genetic variation. Two striking
features are the long-range similarity among haplotypes, and SNPs
that show almost no linkage disequilibrium with any other SNP.
The extent of recent common ancestry and segmental sharing. A
simplified view of linkage disequilibrium is that genetic variation isorganized in relatively short stretches of strong linkage disequilibrium (haplotype blocks), each containing only a few common haplotypes and separated by recombination hotspots across which little
association remains25. Although this view has heuristic value, if chromosomes share a recent common ancestor then similarity between
chromosomes can extend over considerable genetic distance and span
multiple recombination hotspots26. The extent of such recent ancestry
in the four populations surveyed here has not been characterizedaNA06994 NA12892 (CEU)PIBD1 = 0.06
12 segments, 152.1 Mb

NA12006 NA12155 (CEU)PIBD1 = 0.01
1 segment, 7.6 Mb

Chromosome

NA19130 NA19192 (YRI)PIBD1 = 0.48
52 segments, 1,330.8 Mb

Physical position

b
70

*

Total length (Mb)

60
50

previously. Therefore we identified stretches of identity between pairsof chromosomes, both within and across individuals, reflecting autozygosity and identity-by-descent (IBD) (Fig. 3a). After first checking
for stratification within each analysis panel (see Supplementary Text 3;
none was found for YRI, CEU and JPT, and only small stratification
was found for CHB), we calculated genome-wide probabilities ofsharing 0, 1 or 2 chromosomes identical by descent for each pair ofindividuals (see Supplementary Text 4). In addition to identifying a
few close relationships (as reported in HapMap Phase I3), we estimate
that, on average, any two individuals from the same population shareapproximately 0.5% of their genome through recent IBD (Table 5).
Using a hidden Markov model approach27 (see Supplementary Text5), we searched for such shared segments over 1-megabase (Mb) longand containing at least 50 SNPs, after first pruning the list of SNPs toremove local linkage disequilibrium. We find that 10–30% of pairs
in each analysis panel share regions of extended identity resulting
from sharing a common ancestor within 10–100 generations. These
regions typically span hundreds of SNPs and can extend over tens of
megabases (Table 5).
Similarly, extended stretches of homozygosity are indicative ofrecent inbreeding within populations28,29. Although short runs ofhomozygosity are commonplace, covering up to one-third of the
genome and showing population differences reflective of ancient
linkage disequilibrium patterns (Table 5 and Fig. 3b), very longhomozygous runs exist that are clearly distinct from this process.
Including two JPT individuals who have unusually high levels of
homozygosity (NA18987 and NA18992) and one CEU individual(NA12874), we identified 79 homozygous regions over 3 Mb in 51
individuals, with many segments extending over 10 Mb (Supplementary Tables 7 and 8). Segments intersecting with suspected deletions
were first removed from the analysis (Supplementary Text 6).
In studies of rare mendelian diseases, the extended haplotype sharing surrounding recent mutations, usually with a frequency of muchless than 1%, has been exploited to great advantage through homozygosity mapping30,31 and haplotype sharing32 methods. In studies ofcommon disease, extended haplotype sharing among patients potentially offers a route for identifying rare variants (MAF in the range of
1–5%) of high penetrance33,34, which tend to be poorly captured
through single-marker association with genome-wide arrays. To
illustrate the idea, we identified SNPs where only two copies of theminor allele are present (referred to as ‘2-SNPs’), which have minor
allele frequencies of 1–2%. We find that these are enriched approximately sevenfold (Table 5) among regions of IBD identified by thehidden Markov model approach. Notably, identification of IBD regions can be performed with the same genome-wide SNP data being

40

Table 5 | Relatedness, extended segmental sharing and homozygosity

30
20
10
0

1 2 3 4 5 6 7 8 9 10111213141516171819202122 X

Chromosome

Figure 3 | The extent of recent co-ancestry among HapMap individuals.
a, Three pairs of individuals with varying levels of identity-by-descent (IBD)
sharing illustrate the continuum between very close and very distantrelatedness and its relation to segmental sharing. The three pairs are: highsharing (NA19130 and NA19192 from YRI; previously identified as seconddegree relatives3), moderate sharing (NA06994 and NA12892 from CEU)
and low sharing (NA12006 and NA12155 from CEU). Along eachchromosome, the probability of sharing at least one chromosome IBD isplotted, based on the HMM method described in Supplementary Text 5. Red
sections indicate regions called as segments: in general, the proportion of the
genome in segments is similar to each pair’s estimated global relatedness.
b, The extent of homozygosity on each chromosome for each individual ineach analysis panel. Excludes segments ,106 kb and chromosome X inmales. Asterisk, NA12874, length 5 107 Mb. YRI, green; CEU, orange; CHB,
blue; JPT, magenta.

Property

YRI

CEU

CHB

JPT

Number of pairs included
Mean identity by state (IBS) (%)
Mean identity by descent (IBD) (%)
Number of pairs with .1% IBD (%)
Number of pairs with one or moresegment (%)
Total number of segments
Total distance spanned (Mb)
Mean segment length (Mb)
Maximum segment length (Mb)
Maximum segment length (Mb)(including close relatives)
Total number of 2-SNPs
Number of 2-SNPs in segments
2-SNP fold increase
Number of homozygous segments(3103)*
SNPs in homozygous segments (3105)
Total length of homozygous segments
(Mb)

1,767
81.9
0.04
8.8
195
(11.0)
250
1,416
5.7
51.7
141.4

1,708
83.7
0.34
20.4
350
(20.5)
427
2,336
5.5
56.2
128.5

990
85.0
0.36
21.1
135
(13.6)
146
704
4.8
15.0
N/A

861
85.1
0.42
29.7
216
(25.1)
273
1,301
4.8
25.3
N/A

6,219
109
6.7
0.9

9,220
162
7.3
2.2

8,174
116
7.6
2.6

8,750
132
7.0
2.6

1.6
160

4.2
410

5.3
510

5.4
520

2-SNP, SNPs where only two copies of the minor allele are present.
* Homozygous segments .106 kb.

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collected in large-scale association studies, making haplotypesharing approaches an attractive and complementary analysis to
standard SNP association tests, with the potential to identify rarevariants associated with complex disease.
The distribution and causes of untaggable SNPs. Despite the SNP
density of the Phase II HapMap, there are high-frequency SNPs
for which no tag can be identified. Among high-frequency SNPs
(MAF $ 0.2), we marked as untaggable SNPs to which no other
SNP within 100 kb has an r2 value of at least 0.2. In Phase II, approximately 0.5–1.0% of all high-frequency SNPs are untaggable and the
proportion in YRI is approximately twice as high as in the otherpanels. Similar proportions are observed across the ten HapMap
ENCODE regions.
To identify factors influencing the location of untaggable SNPs
we considered their distribution relative to segmental duplications,
repeat sequence, CpG dinucleotide density, regions of low SNP density, unusual allele frequency distribution, linkage disequilibrium
patterns and recombination hotspots. We find no evidence for anenrichment of untaggable SNPs in segmental duplications or repeat
sequence, as would be expected from mis-mapping of SNPs (2% and
35% of common SNPs lie in segmental duplications and repeat
sequence, respectively, compared to 1.8% and 29%, respectively, of
untaggable SNPs). Untaggable SNPs are slightly enriched in CpG
islands (0.37% of common SNPs are in CpG islands compared to
1.4% of untaggable SNPs) and have slightly reduced MAF (Fig. 4).
Most notably, untaggable SNPs are strongly enriched in regions of
low linkage disequilibrium, particularly in recombination hotspots.
To test whether these untaggable SNPs are themselves responsible forthe identification of recombination hotspots, we eliminated themfrom 100 randomly chosen recombination hotspots and reassessed
the evidence for a local peak in recombination. In all cases we still findevidence for a considerable increase in local recombination rate.
Over 50% of all untaggable SNPs lie within 1 kb of the centre of adetected recombination hotspot and over 90% are within 5 kb.
Because only 3–4% of all SNPs lie within 1 kb from the centre of adetected recombination hotspot (16% are within 5 kb), this constitutes a marked enrichment and implies that at least 10% of all SNPsab

SNP density

Allele frequencycLinkage disequilibrium

Max. r 2

1.2

0.10

1.1

0.4

HotspotsecM Mb–1

0.30
0.20
0.10

10–100
5–10
1–5
0–1
0–1
1–5
5–10
10–1000
10–100
5–10
1–5
0–1
0–1
1–5
5–10
10–100

10–100
5–10
1–5
0–1
0–1
1–5
5–10
10–100
Position (kb)

d

0.6

0.2
0

1.0

Hotspots per kb

0.8

0.20

1.3
MAF

SNPs per kb

1.4

Position (kb)Position (kb)

Recombination rate
30
20
10
0

10–100
5–10
1–5
0–1
0–1
1–5
5–10
10–100

10–100
5–10
1–5
0–1
0–1
1–5
5–10
10–1000Position (kb)Position (kb)Figure 4 | Properties of untaggable SNPs. a–e, Properties of the genomic
regions surrounding untaggable SNPs in terms of: a, the density ofpolymorphic SNPs within the consensus data set; b, mean minor allele
frequency of polymorphic SNPs; c, maximum r2 of SNPs to any others in the
Phase II data; d, the density of estimated recombination hotspots (definedfrom hotspot centres); and e, the estimated mean recombination rate. YRI,
green; CEU, orange; CHB1JPT, purple.

within 1 kb of hotspots are untaggable. The implication for association mapping is that when a region of interest contains a known
hotspot it may be prudent to perform additional sequencing within
the hotspot. Many of the variants identified in this manner will be
untaggable SNPs that should be genotyped directly in association
studies. From a biological perspective, the proximity of untaggable
SNPs to the centre of hotspots suggests that they may lie within geneconversion tracts associated with the repair of double-strand breaks.
Double-strand breaks are thought to resolve as crossover events only
5–25% of the time35. Consequently, SNPs lying near the centre of a
hotspot are liable to be included within gene conversion tracts andwill experience much higher effective recombination rates than predicted from crossover rates alone.
The distribution of recombination
In the Phase II HapMap we identified 32,996 recombination hotspots3,6,36 (an increase of over 50% from Phase I) of which 68%
localized to a region of #5 kb. The median map distance induced
by a hotspot is 0.043 cM (or one crossover per 2,300 meioses) andthe hottest identified, on chromosome 20, is 1.2 cM (one crossoverper 80 meioses). Hotspots account for approximately 60% of recombination in the human genome and about 6% of sequence(Supplementary Fig. 6). We do not find marked differences among
chromosomes in the concentration of recombination in hotspots,
which implies that obligate differences in recombination among
chromosomes of different size result from differences in hotspot
density and intensity6.
The increased number of well-defined hotspots allows us to understand better the influence of genomic features on the distribution ofrecombination. Previous work identified specific DNA motifs that
influence hotspot location6,37 as well as additional influences of local
sequence context including the location of genes6 and base composition38. The Phase II HapMap provides the resolution to separate these
influences. Figure 5a shows the distribution of recombination, hotspot motifs and base composition around genes. Within the transcribed region of genes there is a marked decrease in the estimatedrecombination rate. However, 59 of the transcription start site is apeak in recombination rate with a corresponding local increase in thedensity of hotspot motifs. This region also shows a marked increasein G1C content, reflecting the presence of CpG islands in promoter
regions. There is also an asymmetry in recombination rate acrossgenes, with recombination rates 39 of transcribed regions being elevated (as are motif density and G1C content) compared to regions 59
of genes. Studies in yeast have previously suggested an association
between promoter regions and recombination hotspots39. Our results
suggest a significant, although weak, relationship between promotersand recombination in humans. Nevertheless, the vast majority of
hotspots in the human genome are not in gene promoters. The association may reflect a general association between regions of accessible
chromatin and crossover activity.
Systematic differences in recombination rate by gene class.
Previous work has demonstrated differences in the magnitude oflinkage disequilibrium, as measured at a megabase scale, among
genes associated with different functions3,40. Using the fine-scale genetic map estimated from the Phase II HapMap data we can quantify
local increases in recombination rate associated with genes of different function using the Panther gene ontology annotation41. Average
recombination rates vary more than sixfold among such geneclasses (Fig. 5b), with defence and immunity genes showing the highest rates (1.9 cM Mb21) and chaperones showing the lowest rates(0.3 cM Mb21). Gene functions associated with cell surfaces andexternal functions tend to show higher recombination rates (immunity, cell adhesion, extracellular matrix, ion channels, signalling)whereas those with lower recombination rates are typically internal
to cells (chaperones, ligase, isomerase, synthase). Controlling for systematic differences between gene classes in base composition andgene clustering, the differences between groups remain significant.

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NATURE | Vol 449 | 18 October 2007

We also find that the density of hotspot-associated DNA motifs varies
systematically among gene classes and that variation in motif density
explains over 50% of the variance in recombination rate among genefunctions (Supplementary Fig. 7).
These results pose interesting evolutionary questions. Because
recombination involves DNA damage through double-strand breaks,
hotspots may be selected against in some highly conserved parts ofthe genome. In regions exposed to recurrent selection (for example,
from changes in environment or pathogen pressure) it is plausiblethat recombination may be selected for. However, because the finescale structure of recombination seems to evolve rapidly42,43 it will be
important to learn whether patterns of recombination rate heterogeneity among molecular functions are conserved between species.
Natural selection
The Phase I HapMap data have been used to identify genomic regionsthat show evidence for the influence of adaptive evolution3,9, primarily through extended haplotype structure indicative of recent positive selection. Using two established approaches9,44, we identified
approximately 200 regions with evidence of recent positive selection
from the Phase II HapMap (Supplementary Table 9). These regions
include many established cases of selection, such as the genes HBB
and LCT, the HLA region, and an inversion on chromosome 17.
Many other regions have been previously identified in HapMap
Phase I including LARGE, SYT1 and SULT1C2 (previously called
SULT1C1). A detailed description of the findings from the Phase II
HapMap is published elsewhere45.
The Phase II HapMap also provides new insights into the forces
acting on SNPs in coding regions. Effort was made to genotype as
many known or putative non-synonymous SNPs as possible. Of the
56,789 non-synonymous SNPs identified in dbSNP release 125,
attempts were made to genotype 36,777, which resulted in 17,427
that are QC1 in all three analysis panels and polymorphic. We
selected only those SNPs for which ancestral allele information wasavailable (approximately 90%). For comparison, we used patternsof variation at synonymous SNPs. As previously reported46,47, nonsynonymous SNPs show an increase in frequency of rare variants anda slight decrease of common variants compared to synonymous
SNPs, compatible with widespread purifying selection against nonsynonymous mutations (Fig. 6a). In contrast, we find no excess ofhigh-frequency derived non-synonymous mutations, as might be
expected if positive selection were widespread.
Natural selection also influences the extent to which allele frequencies differ between populations, not only through local selective pressures that drive alleles to different frequencies48,49, but also throughlocal variation in the strength of purifying selection. We compared the
distribution of population differentiation (as measured by FST, theproportion of total variation in allele frequency that is due to differences between populations) at non-synonymous SNPs and synonymous SNPs matched for allele frequency (Fig. 6b). We find a systematic
bias for non-synonymous SNPs to show stronger differentiation thansynonymous SNPs. Among SNPs showing high levels of differentiation there is a strong tendency for the derived allele to be at higher
frequency in non-YRI populations. Among SNPs with FST . 0.5
between CEU and YRI, in 79% and 75% of non-synonymous andsynonymous variants, respectively, the derived allele is more common
in CEU. Although this difference between non-synonymous andsynonymous SNPs is not significant, among the eight exonic SNPs
with FST . 0.95, all are non-synonymous. We see no such bias
towards increased MAF in CEU at high-differentiation SNPs, indicating that SNP ascertainment is unlikely to explain the difference.
Rather, this effect can largely be explained by more genetic drift in
the non-African populations, as confirmed by simulations (datanot shown). In addition, reduced selection against deleterious mutations and local adaptation within non-African populations will bothact to increase the frequency of derived variants in non-African
populations.
To assess the evidence for widespread local adaptation influencing
non-synonymous mutations we considered the distribution of
integrated extended haplotype homozygosity (iEHH) statistics9,44(Fig. 6c). We find no evidence for systematic differences between
non-synonymous and synonymous SNPs, suggesting that local
adaptation does not explain their higher differentiation. Although
hitch-hiking effects will tend to obscure differences between selectedab
G+C content

0.60

Recombination rate Motif density(cM Mb–1)(motifs per kb)

Mean recombination rate within category (cM Mb–1)

0.15

0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9 2.1
Defence/immunity protein (269)
Cell adhesion molecule (268)
Extracellular matrix (262)
Ion channel (264)Signalling molecule (627)Protease (394)
Receptor (1,158)
Transporter (474)
Select calcium-binding protein (190)
Cell junction protein (77)
Hydrolase (508)
Cytoskeletal protein (547)Miscellaneous function (591)Transfer/carrier protein (230)
Transcription factor (1,322)

0.55
0.50
0.45

0.13
0.11
0.09

Oxidoreductase (461)
Select regulatory molecule (821)Transferase (614)Membrane traffic protein (249)
Lyase (112)
Phosphatase (197)
Kinase (513)
Nucleic acid binding (1,567)
Synthase and synthetase (170)
Isomerase (107)
Ligase (305)
Chaperone (128)

0.07
1.7
1.5
1.3
1.1

Significance level (P)
200

150

100

50
0
50
Position (kb)

100

150

Figure 5 | Recombination rates around genes. a, The recombination rate,density of recombination-hotspot-associated motifs (all motifs with up to
1 bp different from the consensus CCTCCCTNNCCAC) and G1C content
around genes. The blue line indicates the mean. For the recombination rate,
grey lines indicate the quartiles of the distribution. Values were calculated
separately 59 from the transcription start site (the first dotted line) and 39
from the transcription end site (third dotted line) and were joined at themedian midpoint position of the transcription unit (central dotted line).
Note the sharp drop in recombination rate within the transcription unit, the200

0.0001 0.001 0.01

0.1

1

0.1

0.01 0.001 0.0001

local increase around the transcription start site and the broad decrease awayfrom the 39 end of genes. These patterns only partly reflect the distribution of
G1C content and the hotspot-associated motif, suggesting that additional
factors influence recombination rates around genes. b, Recombination rates
within genes of different molecular function41. The chart shows the increaseor decrease for each category compared to the genome average. P values were
estimated by permutation of category; numbers of genes are shown inparentheses.
857©2007 Nature Publishing GroupARTICLES

NATURE | Vol 449 | 18 October 2007

and neutral SNPs, these results are consistent with a scenario in which
the higher differentiation of non-synonymous SNPs is primarily driven by a reduction in the strength or efficacy of purifying selection in
non-African populations.
Discussion and prospects
The International HapMap Project has been instrumental in making
well-powered, large-scale, genome-wide association studies a reality.
It is now clear that the HapMap can be a useful resource for the design
and analysis of disease association studies in populations across theworld50–53. Furthermore, the decreasing costs and increasing SNP
density of standard genotyping panels mean that the focus of attention in disease association studies is shifting from candidate geneapproaches towards genome-wide analyses. Alongside developments
in technology, new statistical methodologies aimed at improving
aspects of analysis, such as genotype calling21,54, the identificationof and correction for population stratification and relatedness55,56,
and imputation of untyped variants21–23, are increasing the accuracy
and reliability of genome-wide association studies.
Within this context, it is important to consider the future of the
HapMap Project. Currently, additional samples from the populations used to develop the initial HapMap, as well as samples fromseven additional populations (Luhya in Webuye, Kenya; Maasai in
Kinyawa, Kenya; Tuscans in Italy; Gujarati Indian in Houston, Texas,
USA; Denver (Colorado) metropolitan Chinese community; peopleof Mexican origin in Los Angeles, California, USA; and people with
African ancestry in the southwestern United States; http://ccr.coriell.
org/Sections/Collections/NHGRI/?SsId511) will be sequenced and
YRI

CEU

CHB+JPT

0.20

0.20

0.20

0.10

0.10

0.10

0

0

0
0 0.2 0.4 0.6 0.8 1.0

0 0.2 0.4 0.6 0.8 1.0

DAF

DAF

DAF

Proportion of SNPs
with FST > 0.5

b

c
**

0.08

*
0.04

0
0 0.2 0.4 0.6 0.8 1.0

Average DAF across panels

1.0
0.8
0.6

METHODS SUMMARY
Of approximately 6.9 million SNPs in dbSNP release 122 approximately 4.7
million were selected for genotyping by Perlegen. 2.5 million SNPs were excluded
because no assay could be designed and a further 350,000 were excluded for otherreasons (see Methods). Perlegen performed genotyping using custom highdensity oligonucleotide arrays as previously described15. Additional genotype
submissions are described in the text. QC filters were applied as previously
described3. Where multiple submissions met the QC criteria the submission withthe lowest missing data rate was chosen for inclusion in the non-redundantfiltered data set. Haplotypes were estimated from genotype data as described
previously3. Ancestral states at SNPs were inferred by parsimony by comparison
to orthologous bases in the chimpanzee (panTro2) and rhesus macaque(rheMac2) assemblies. Recombination rates and the location of recombination
hotspots were estimated as described previously3. Additional details can befound in the Methods section and the Supplementary Information. The data
described in this paper are in release 21 of the International HapMap Project.
Full Methods and any associated references are available in the online version of
the paper at www.nature.com/nature.
Received 12 April; accepted 18 September 2007.
1.

0 0.2 0.4 0.6 0.8 1.0

Proportion of top 5% iEHH
non-synonymous SNPs

Proportionagenotyped extensively to extend the HapMap, providing information
on rarer variants and helping to enable genome-wide association
studies in additional populations. There are also ongoing efforts bymany groups to characterize additional forms of genetic variation,such as structural variation, and molecular phenotypes in the
HapMap samples. Finally, in the future, whole-genome sequencing
will provide a natural convergence of technologies to type both SNP
and structural variation. Nevertheless, until that point, and evenafter, the HapMap Project data will provide an invaluable resource
for understanding the structure of human genetic variation and its
link to phenotype.

**

0.4
0.2

*

0
0 0.2 0.4 0.6 0.8 1.0

DAF

Figure 6 | Properties of non-synonymous and synonymous SNPs. a, The
derived allele frequency (DAF) spectrum in each analysis panel for all SNPs
(black), synonymous SNPs (green) and non-synonymous SNPs (red). Notethe excess of rare variants for coding sequence SNPs but no excess of highfrequency derived variants. b, Enrichment of non-synonymous SNPs among
genic SNPs showing high differentiation. For each of ten classes of derived
allele frequency (averaged across analysis panels) the fraction of nonsynonymous (red) and synonymous (green) variants in that class that show
FST . 0.5 is shown. Note the strong enrichment of non-synonymous SNPs
among SNPs of moderate to high derived-allele frequency (asterisk,
P , 0.05; double asterisk, P , 0.01). c, Lack of enrichment of nonsynonymous SNPs among those showing long-range haplotype structure.
The integrated extended haplotype homozygosity (iEHH) statistic9 was
calculated for non-synonymous and synonymous SNPs in each analysis
panel (YRI, green; CEU, orange; CHB1JPT, purple). For each of ten derived
allele frequency classes, the proportion of non-synonymous SNPs among
those showing the 5% most extreme statistics (within the allele frequency
class) is shown (points). Also shown is the proportion of non-synonymous
SNPs among SNPs in the coding sequence for each frequency class (dottedlines). Differences between synonymous and non-synonymous SNPs aretested for using a contingency table test.

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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: allmembers of the genotyping laboratory and the sample, primer, bioinformatics,
data quality and IT groups at Perlegen Sciences for technical and infrastructural
support; J. Beck, C. Beiswanger, D. Coppock, A. Leach, J. Mintzer and L. Toji fortransforming the Yoruba, Japanese and Han Chinese samples, distributing the
DNA and cell lines, storing the samples for use in future research, and producing
the community newsletters and reports; J. Greenberg and R. Anderson forproviding funding and support for cell line transformation and storage in the
NIGMS Human Genetic Cell Repository at the Coriell Institute; T. Dibling,
T. Ishikura, S. Kanazawa, S. Mizusawa and S. Saito for help with genotyping; C. Hind
and A. Moghadam for technical support in genotyping and all members of the
subcloning and sequencing teams at the Wellcome Trust Sanger Institute; X. Ke forhelp with data analysis; Oxford E-Science Centre for provision of high-performancecomputing 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 for help with genotyping and sample collection; X. Feng,
Y. Li, J. Ren and X. Zhou 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 for help
with genotyping; P. Fong, C. Lai, C. Lau, T. Leung, L. Luk and W. Tong for help withgenotyping; C. Pang for help with genotyping; K. Ding, B. Qiang, J. Zhang, X. Zhang
and K. Zhou for help with genotyping; Q. Fu, S. Ghose, X. Lu, D. Nelson, A. Perez,
S. Poole, R. Vega and H. Yonath for help with genotyping; C. Bruckner, T. Brundage,
S. Chow, O. Iartchouk, M. Jain, M. Moorhead and K. Tran for help with genotyping;
N. Addleman, J. Atilano, T. Chan, C. Chu, C. Ha, T. Nguyen, M. Minton and A. Phongfor help with genotyping, and D. Lind for help with quality control and experimental
design; R. Donaldson and S. Duan for help with genotyping, and J. Rice and
N. Saccone for help with experimental design; J. Wigginton for help with
implementing and testing QA/QC software; A. Clark, B. Keats, R. Myers,
D. Nickerson and A. Williamson for providing advice to NIH; C. Juenger, C. Bennet,
C. Bird, J. Melone, P. Nailer, M. Weiss, J. Witonsky and E. DeHaut-Combs for help
with project management; M. Gray for organizing 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 and community engagements; the people in these communities who
donated their blood samples; and the people in the Utah CEPH community who
allowed the samples they donated earlier to be used for the Project. This work was
supported by the Japanese Ministry of Education, Culture, Sports, Science andTechnology, the Wellcome Trust, Nuffield Trust, Wolfson Foundation, UK EPSRC,
Genome Canada, Génome Québec, the Chinese Academy of Sciences, the Ministryof Science and Technology 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. All SNPs genotyped within the HapMap Project are available
from dbSNP (http://www.ncbi.nlm.nih.gov/SNP); all genotype information isavailable from dbSNP and the HapMap website (http://www.hapmap.org).
Author Information Reprints and permissions information is available atwww.nature.com/reprints. The authors declare competing financial interests:
details accompany the full-text HTML version of the paper at www.nature.com/
nature. Correspondence and requests for materials should be addressed to G.M.
(mcvean@stats.ox.ac.uk) or M.D. (mjdaly@chgr.mgh.harvard.edu).

The International HapMap Consortium (Participants are arranged by institution andthen alphabetically within institutions except for Principal Investigators and Project
Leaders, as indicated.)
Genotyping centres: Perlegen Sciences Kelly A. Frazer (Principal Investigator)1,
Dennis G. Ballinger2, David R. Cox2, David A. Hinds2, Laura L. Stuve2; Baylor College ofMedicine and ParAllele BioScience Richard A. Gibbs (Principal Investigator)3, John W.
Belmont3, Andrew Boudreau4, Paul Hardenbol5, Suzanne M. Leal3, Shiran Pasternak6,
David A. Wheeler3, Thomas D. Willis4, Fuli Yu7; Beijing Genomics Institute Huanming
Yang (Principal Investigator)8, Changqing Zeng (Principal Investigator)8, Yang Gao8,
Haoran Hu8, Weitao Hu8, Chaohua Li8, Wei Lin8, Siqi Liu8, Hao Pan8, Xiaoli Tang8, Jian
Wang8, Wei Wang8, Jun Yu8, Bo Zhang8, Qingrun Zhang8, Hongbin Zhao8, Hui Zhao8,
Jun Zhou8; Broad Institute of Harvard and Massachusetts Institute of Technology859©2007 Nature Publishing GroupARTICLES

NATURE | Vol 449 | 18 October 2007

Stacey B. Gabriel (Project Leader)7, Rachel Barry7, Brendan Blumenstiel7, Amy
Camargo7, Matthew Defelice7, Maura Faggart7, Mary Goyette7, Supriya Gupta7, Jamie
Moore7, Huy Nguyen7, Robert C. Onofrio7, Melissa Parkin7, Jessica Roy7, Erich Stahl7,
Ellen Winchester7, Liuda Ziaugra7, David Altshuler (Principal Investigator)7,9; Chinese
National Human Genome Center at Beijing Yan Shen (Principal Investigator)10,
Zhijian Yao10; Chinese National Human Genome Center at Shanghai Wei Huang(Principal Investigator)11, Xun Chu11, Yungang He11, Li Jin12, Yangfan Liu11, Yayun Shen11,
Weiwei Sun11, Haifeng Wang11, Yi Wang11, Ying Wang11, Xiaoyan Xiong11, Liang Xu11;
Chinese University of Hong Kong Mary M. Y. Waye (Principal Investigator)13, Stephen
K. W. Tsui13; Hong Kong University of Science and Technology Hong Xue (Principal
Investigator)14, J. Tze-Fei Wong14; Illumina Luana M. Galver (Project Leader)15,
Jian-Bing Fan15, Kevin Gunderson15, Sarah S. Murray1, Arnold R. Oliphant16, Mark S.
Chee (Principal Investigator)17; McGill University and Génome Québec Innovation
Centre Alexandre Montpetit (Project Leader)18, Fanny Chagnon18, Vincent Ferretti18,
Martin Leboeuf18, Jean-François Olivier4, Michael S. Phillips18, Stéphanie Roumy15,
Clémentine Sallée19, Andrei Verner18, Thomas J. Hudson (Principal Investigator)20;
University of California at San Francisco and Washington University Pui-Yan Kwok
(Principal Investigator)21, Dongmei Cai21, Daniel C. Koboldt22, Raymond D. Miller22,
Ludmila Pawlikowska21, Patricia Taillon-Miller22, Ming Xiao21; University of Hong
Kong Lap-Chee Tsui (Principal Investigator)23, William Mak23, You Qiang Song23, PaulK. H. Tam23; University of Tokyo and RIKEN Yusuke Nakamura (Principal
Investigator)24,25, Takahisa Kawaguchi25, Takuya Kitamoto25, Takashi Morizono25,
Atsushi Nagashima25, Yozo Ohnishi25, Akihiro Sekine25, Toshihiro Tanaka25,
Tatsuhiko Tsunoda25; Wellcome Trust Sanger Institute Panos Deloukas (Project
Leader)26, Christine P. Bird26, Marcos Delgado26, Emmanouil T. Dermitzakis26, Rhian
Gwilliam26, Sarah Hunt26, Jonathan Morrison27, Don Powell26, Barbara E. Stranger26,
Pamela Whittaker26, David R. Bentley (Principal Investigator)28
Analysis groups: Broad Institute Mark J. Daly (Project Leader)7,9, Paul I. W. de
Bakker7,9, Jeff Barrett7,9, Yves R. Chretien7, Julian Maller7,9, Steve McCarroll7,9, Nick
Patterson7, Itsik Pe’er29, Alkes Price7, Shaun Purcell9, Daniel J. Richter7, Pardis Sabeti7,
Richa Saxena7,9, Stephen F. Schaffner7, Pak C. Sham23, Patrick Varilly7, David Altshuler(Principal Investigator)7,9; Cold Spring Harbor Laboratory Lincoln D. Stein (Principal
Investigator)6, Lalitha Krishnan6, Albert Vernon Smith6, Marcela K. Tello-Ruiz6,
Gudmundur A. Thorisson30; Johns Hopkins University School of Medicine Aravinda
Chakravarti (Principal Investigator)31, Peter E. Chen31, David J. Cutler31, Carl S.
Kashuk31, Shin Lin31; University of Michigan Gonçalo R. Abecasis (Principal
Investigator)32, Weihua Guan32, Yun Li32, Heather M. Munro33, Zhaohui Steve Qin32,
Daryl J. Thomas34; University of Oxford Gilean McVean (Project Leader)35, Adam
Auton35, Leonardo Bottolo35, Niall Cardin35, Susana Eyheramendy35, Colin Freeman35,
Jonathan Marchini35, Simon Myers35, Chris Spencer7, Matthew Stephens36, Peter
Donnelly (Principal Investigator)35; University of Oxford, Wellcome Trust Centre for
Human Genetics Lon R. Cardon (Principal Investigator)37, Geraldine Clarke38, David
M. Evans38, Andrew P. Morris38, Bruce S. Weir39; RIKEN Tatsuhiko Tsunoda (Principal
Investigator)25, Todd A. Johnson25; US National Institutes of Health James C.
Mullikin40; US National Institutes of Health National Center for Biotechnology
Information Stephen T. Sherry41, Michael Feolo41, Andrew Skol42
Community engagement/public consultation and sample collection groups: Beijing
Normal University and Beijing Genomics Institute Houcan Zhang43, Changqing
Zeng8, Hui Zhao8; Health Sciences University of Hokkaido, Eubios Ethics Institute,and Shinshu University Ichiro Matsuda (Principal Investigator)44, Yoshimitsu
Fukushima45, Darryl R. Macer46, Eiko Suda47; Howard University and University of
Ibadan Charles N. Rotimi (Principal Investigator)48, Clement A. Adebamowo49, Ike
Ajayi49, Toyin Aniagwu49, Patricia A. Marshall50, Chibuzor Nkwodimmah49,
Charmaine D. M. Royal48; University of Utah Mark F. Leppert (Principal
Investigator)51, Missy Dixon51, Andy Peiffer51
Ethical, legal and social issues: Chinese Academy of Social Sciences Renzong Qiu52;
Genetic Interest Group Alastair Kent53; Kyoto University Kazuto Kato54; Nagasaki
University Norio Niikawa55; University of Ibadan School of Medicine Isaac F.
Adewole49; University of Montréal Bartha M. Knoppers19; University of Oklahoma
Morris W. Foster56; Vanderbilt University Ellen Wright Clayton57; Wellcome Trust
Jessica Watkin58
SNP discovery: Baylor College of Medicine Richard A. Gibbs (Principal Investigator)3,John W. Belmont3, Donna Muzny3, Lynne Nazareth3, Erica Sodergren3, George M.
Weinstock3, David A. Wheeler3, Imtaz Yakub3; Broad Institute of Harvard andMassachusetts Institute of Technology Stacey B. Gabriel (Project Leader)7, Robert C.
Onofrio7, Daniel J. Richter7, Liuda Ziaugra7, Bruce W. Birren7, Mark J. Daly7,9, David
Altshuler (Principal Investigator)7,9; Washington University Richard K. Wilson(Principal Investigator)59, Lucinda L. Fulton59; Wellcome Trust Sanger Institute JaneRogers (Principal Investigator)26, John Burton26, Nigel P. Carter26, Christopher M.
Clee26, Mark Griffiths26, Matthew C. Jones26, Kirsten McLay26, Robert W. Plumb26,
Mark T. Ross26, Sarah K. Sims26, David L. Willey26
Scientific management: Chinese Academy of Sciences Zhu Chen60, Hua Han60, Le
Kang60; Genome Canada Martin Godbout61, John C. Wallenburg62; Génome Québec
Paul L’Archevêque63, Guy Bellemare63; Japanese Ministry of Education, Culture,
Sports, Science and Technology Koji Saeki64; Ministry of Science and Technology of
the People’s Republic of China Hongguang Wang65, Daochang An65, Hongbo Fu65,

Qing Li65, Zhen Wang65; The Human Genetic Resource Administration of China
Renwu Wang66; The SNP Consortium Arthur L. Holden15; US National Institutes of
Health Lisa D. Brooks67, Jean E. McEwen67, Mark S. Guyer67, Vivian Ota Wang67,68,
Jane L. Peterson67, Michael Shi69, Jack Spiegel70, Lawrence M. Sung71, Lynn F.
Zacharia67, Francis S. Collins72; Wellcome Trust Karen Kennedy61, Ruth Jamieson58,
John Stewart58
1
The Scripps Research Institute, 10550 North Torrey Pines Road MEM275, La Jolla,
California 92037, USA. 2Perlegen Sciences, Inc., 2021 Stierlin Court, Mountain View,
California 94043, USA. 3Baylor College of Medicine, Human Genome Sequencing
Center, Department of Molecular and Human Genetics, 1 Baylor Plaza, Houston, Texas
77030, USA. 4Affymetrix, Inc., 3420 Central Expressway, Santa Clara, California 95051,
USA. 5Pacific Biosciences, 1505 Adams Drive, Menlo Park, California 94025, USA. 6Cold
Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA.
7
The Broad Institute of Harvard and Massachusetts Institute of Technology, 1 Kendall
Square, Cambridge, Massachusetts 02139, USA. 8Beijing Genomics Institute, Chinese
Academy of Sciences, Beijing 100300, China. 9Massachusetts General Hospital andHarvard Medical School, Simches Research Center, 185 Cambridge Street, Boston,
Massachusetts 02114, USA. 10Chinese National Human Genome Center at Beijing, 3-707
N. Yongchang Road, Beijing Economic-Technological Development Area, Beijing 100176,
China. 11Chinese National Human Genome Center at Shanghai, 250 Bi Bo Road, Shanghai
201203, China. 12Fudan University and CAS-MPG Partner Institute for Computational
Biology, School of Life Sciences, SIBS, CAS, Shanghai 201203, China. 13The Chinese
University of Hong Kong, Department of Biochemistry, The Croucher Laboratory for
Human Genetics, 6/F Mong Man Wai Building, Shatin, Hong Kong. 14Hong Kong
University of Science and Technology, Department of Biochemistry and Applied
Genomics Center, Clear Water Bay, Knowloon, Hong Kong. 15Illumina, 9885 Towne
Centre Drive, San Diego, California 92121, USA. 16Complete Genomics, Inc., 658 North
Pastoria Avenue, Sunnyvale, California 94085, USA. 17Prognosys Biosciences, Inc., 4215
Sorrento Valley Boulevard, Suite 105, San Diego, California 92121, USA. 18McGill
University and Génome Québec Innovation Centre, 740 Dr. Penfield Avenue, Montréal,Québec H3A 1A4, Canada. 19University of Montréal, The Public Law Research Centre(CRDP), PO Box 6128, Downtown Station, Montréal, Québec H3C 3J7, Canada.
20
Ontario Institute for Cancer Research, MaRS Centre, South Tower, 101 College Street,
Suite 500, Toronto, Ontario M5G 1L7, Canada. 21University of California, San Francisco,
Cardiovascular Research Institute, 513 Parnassus Avenue, Box 0793, San Francisco,California 94143, USA. 22Washington University School of Medicine, Department of
Genetics, 660 South Euclid Avenue, Box 8232, St Louis, Missouri 63110, USA.
23
University of Hong Kong, Genome Research Centre, 6/F, Laboratory Block, 21 Sassoon
Road, Pokfulam, Hong Kong. 24University of Tokyo, Institute of Medical Science, 4-6-1
Sirokanedai, Minato-ku, Tokyo 108-8639, Japan. 25RIKEN SNP Research Center, 1-7-22
Suehiro-cho, Tsurumi-ku Yokohama, Kanagawa 230-0045, Japan. 26Wellcome Trust
Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA,
UK. 27University of Cambridge, Department of Oncology, Cambridge CB1 8RN, UK.
28
Solexa Ltd, Chesterford Research Park, Little Chesterford, Nr Saffron Walden, Essex
CB10 1XL, UK. 29Columbia University, 500 West 120th Street, New York, New York
10027, USA. 30University of Leicester, Department of Genetics, Leicester LE1 7RH, UK.
31
Johns Hopkins University School of Medicine, McKusick-Nathans Institute of Genetic
Medicine, Broadway Research Building, Suite 579, 733 North Broadway, Baltimore,
Maryland 21205, USA. 32University of Michigan, Center for Statistical Genetics,
Department of Biostatistics, 1420 Washington Heights, Ann Arbor, Michigan 48109,
USA. 33International Epidemiology Institute, 1455 Research Boulevard, Suite 550,
Rockville, Maryland 20850, USA. 34Center for Biomolecular Science and Engineering,Engineering 2, Suite 501, Mail Stop CBSE/ITI, UC Santa Cruz, Santa Cruz, California
95064, USA. 35University of Oxford, Department of Statistics, 1 South Parks Road,
Oxford OX1 3TG, UK. 36University of Chicago, Department of Statistics, 5734 South
University Avenue, Eckhart Hall, Room 126, Chicago, Illinois 60637, USA. 37Fred
Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, Washington
98109, USA. 38University of Oxford/Wellcome Trust Centre for Human Genetics,
Roosevelt Drive, Oxford OX3 7BN, UK. 39University of Washington Department of
Biostatistics, Box 357232, Seattle, Washington 98195, USA. 40US National Institutes of
Health, National Human Genome Research Institute, 50 South Drive, Bethesda,
Maryland 20892, USA. 41US National Institutes of Health, National Library of Medicine,National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, Maryland
20894, USA. 42University of Chicago, Department of Medicine, Section of Genetic
Medicine, 5801 South Ellis, Chicago, Illinois 60637, USA. 43Beijing Normal University, 19
Xinjiekouwai Street, Beijing 100875, China. 44Health Sciences University of Hokkaido,
Ishikari Tobetsu Machi 1757, Hokkaido 061-0293, Japan. 45Shinshu University School of
Medicine, Department of Medical Genetics, Matsumoto 390-8621, Japan. 46United
Nations Educational, Scientific and Cultural Organization (UNESCO Bangkok), 920
Sukhumwit Road, Prakanong, Bangkok 10110, Thailand. 47University of Tsukuba, Eubios
Ethics Institute, PO Box 125, Tsukuba Science City 305-8691, Japan. 48Howard
University, National Human Genome Center, 2216 6th Street, NW, Washington DC
20059, USA. 49University of Ibadan College of Medicine, Ibadan, Oyo State, Nigeria.
50
Case Western Reserve University School of Medicine, Department of Bioethics, 10900
Euclid Avenue, Cleveland, Ohio 44106, USA. 51University of Utah, Eccles Institute of
Human Genetics, Department of Human Genetics, 15 North 2030 East, Salt Lake City,
Utah 84112, USA. 52Chinese Academy of Social Sciences, Institute of Philosophy/Center
for Applied Ethics, 2121, Building 9, Caoqiao Xinyuan 3 Qu, Beijing 100067, China.
53
Genetic Interest Group, 4D Leroy House, 436 Essex Road, London N130P, UK. 54Kyoto
University, Institute for Research in Humanities and Graduate School of Biostudies,
Ushinomiya-cho, Sakyo-ku, Kyoto 606-8501, Japan. 55Nagasaki University Graduate

860©2007 Nature Publishing GroupARTICLES

NATURE | Vol 449 | 18 October 2007

School of Biomedical Sciences, Department of Human Genetics, Sakamoto 1-12-4,
Nagasaki 852-8523, Japan. 56University of Oklahoma, Department of Anthropology, 455
West Lindsey Street, Norman, Oklahoma 73019, USA. 57Vanderbilt University, Center
for Genetics and Health Policy, 507 Light Hall, Nashville, Tennessee 37232, USA.
58
Wellcome Trust, 215 Euston Road, London NW1 2BE, UK. 59Washington University
School of Medicine, Genome Sequencing Center, Box 8501, 4444 Forest Park Avenue, St
Louis, Missouri 63108, USA. 60Chinese Academy of Sciences, 52 Sanlihe Road, Beijing
100864, China. 61Genome Canada, 150 Metcalfe Street, Suite 2100, Ottawa, Ontario K2P
1P1, Canada. 62McGill University, Office of Technology Transfer, 3550 University Street,Montréal, Québec H3A 2A7, Canada. 63Génome Québec, 630, boulevard
René-Lévesque Ouest, Montréal, Québec H3B 1S6, Canada. 64Ministry of Education,
Culture, Sports, Science, and Technology, 3-2-2 Kasumigaseki, Chiyodaku, Tokyo

100-8959, Japan. 65Ministry of Science and Technology of the People’s Republic ofChina, 15 B. Fuxing Road, Beijing 100862, China. 66The Human Genetic Resource
Administration of China, b7, Zaojunmiao, Haidian District, Beijing 100081, China. 67US
National Institutes of Health, National Human Genome Research Institute, 5635 Fishers
Lane, Bethesda, Maryland 20892, USA. 68US National Institutes of Health, Office ofBehavioral and Social Science Research, 31 Center Drive, Bethesda, Maryland 20892,
USA. 69Novartis Pharmaceuticals Corporation, Biomarker Development, One Health
Plaza, East Hanover, New Jersey 07936, USA. 70US National Institutes of Health, Officeof Technology Transfer, 6011 Executive Boulevard, Rockville, Maryland 20852, USA.
71
University of Maryland School of Law, 500 West Baltimore Street, Baltimore, Maryland
21201, USA. 72US National Institutes of Health, National Human Genome ResearchInstitute, 31 Center Drive, Bethesda, Maryland 20892, USA.

861
©2007 Nature Publishing Groupdoi:10.1038/nature06258

METHODS
SNP selection and genotyping. All SNPs in dbSNP release 122 were considered
for genotyping by Perlegen. Among these the following were excluded: SNPs forwhich no assay could be designed (primarily through location in repeat-richregions; approximately 2.5 million); SNPs shown previously in samples from
related populations15 to be most probably in perfect association (r2 5 1) with a
Phase I SNP (approximately 122,000); all but one of SNPs shown previously15 tobe most probably in perfect association (r2 5 1) with each other but not with a
Phase I SNP (approximately 62,000); and SNPs shown previously15 to have
MAF , 0.05 (approximately 119,000). In addition, a few SNPs were excluded
for efficiency (for example, if an amplicon contained a single SNP).
Approximately 30,000 SNPs that had been typed in Phase I were deliberately
retyped in Phase II to allow detailed comparisons of data quality, and an additional 15,000 SNPs that showed discrepancies between multiple genotyping
attempts in Phase I were re-typed in Phase II. A further 2,000 SNPs identified
by the Mammalian Gene Collection were also typed.
Perlegen performed genotyping using custom high-density oligonucleotide
arrays as previously described15. Initially, a pilot phase was carried out on chromosome 2p to optimize experimental workflow and data handling. Detailsof amplicons used in the experiment and PCR primers can be found at
http://genome.perlegen.com/pcr/ and also on the HapMap website. The arrays
were tiled with sets of 25-bp probes for each SNP, with either 40 or 24 probes per
SNP. These consisted of four sets of features, corresponding to forward andreverse strand tilings of sequences complementary to each of the two SNP alleles.
Within a feature set, the position of the SNP within the oligonucleotide varied
from position 11 to position 15. Mismatch probes were used to measure background, and by comparison with the perfect match probes, to detect the presenceor absence of a specific PCR product. The 40-feature and 24-feature tilings bothprovided 10 perfect-match features for each SNP allele and differed only in thenumber of mismatch probes.
Genotypes were scored by clustering intensity measurements as previously
described15. In addition, quality scores similar to Phred scores were computedfor each genotype call, based on a combination of experimental metrics correlated to data quality. Assays with overall call rates less than 80% or with poor
average quality scores were flagged as failed. About 38% of the tiled assays failed
these basic criteria, and the remainder were processed using the more rigorous
HapMap Project data quality control filters. For analysis of the whole genome,probes for 4,373,926 distinct SNPs were tiled onto 32 chip designs, with 32 SNPs
tiled in replicate onto each chip design for quality control (QC). Perlegen did not
type the samples by plates as had been done for the Phase I genotyping, insteadtyping large numbers of SNPs one sample at a time. Consequently, blank wells oneach plate were not included as a component of QC for this genotyping. In the
Phase I HapMap a single JPT sample had been excluded because of technicalproblems. Perlegen typed a replacement sample (from the original JPT collection) for all new SNPs. This sample was not specifically genotyped on the Phase I
SNPs, although a substantial fraction of these was typed in Phase II.
Additional genotype submissions came from the Affymetrix GeneChip
Human Mapping 500K array called with the BRLMM algorithm. In release21a additional genotype submissions were incorporated from the MHC haplotype consortium11, the Illumina HumanHap300 BeadChip, the Illumina
Human-1 Genotyping BeadChip and the 10K non-synonymous SNP set fromAffymetrix (ParAllele).
Details of primer design, DNA amplification, DNA labelling and hybridization and signal detection for the Perlegen platform can be found in Supplementary Text 7.

QC analyses. Genotype submissions were assessed for mendelian errors (where
possible), missing data rates and Hardy–Weinberg proportions. QC filters wereapplied as previously described3; to achieve QC1 status a SNP had to have fewer
than two mendelian errors, less than 20% missing data and P . 0.001 for Hardy–
Weinberg analysis. The consensus data set consists only of SNPs for which QC1
submissions were available from all analysis panels. Where multiple submissions
met the QC criteria the submission with the lowest missing data rate was chosen
for inclusion in the non-redundant filtered data set. Comparison of the Phase II
HapMap with the Affymetrix 500K genotypes has shown approximately 20 SNPs
where the reported minor allele is discrepant (referred to as ‘allele-flipping’).
Over the entire data set, we expect that 500–2,000 SNPs have this problem andthe vast majority will occur in SNPs from Phase I of the project. The Data
Coordination Center (DCC) is working to resolve as many of these as possible.
Analyses of data quality. See Supplementary Text 2.
Analyses of population stratification, relatedness and homozygosity. See
Supplementary Texts 3–6.
Analysis of recombination rate and gene ontology. We used the Panther
Database41 to obtain details of the gene molecular function and biological process. Genes are grouped into 28 top-level molecular function groups and 30 toplevel biological process groups, with each gene allowed to exist in more than one
group. We identified 14,979 non-overlapping autosomal genes from the Panther
RefSeq Annotation for which we could obtain recombination rates. Of these,
9,735 had at least one assigned molecular function and 9,432 had at least one
assigned biological process. Genes without a molecular function or biologicalprocess were removed from the corresponding analysis. To control for gene size,
we estimated the mean recombination rate over a 20-kb region centred on the
mid-point of each gene transcription region.
Genes were grouped based on molecular function and biological process. A
mean recombination rate was calculated for each group. The significance ofthe result from each group was calculated via a permutation test involving 105
random groupings of genes. No correction was made for multiple testing. To
account for the effect of G1C content on recombination, we performed alinear regression between the G1C content and recombination rate of all
genes in each sample. Using the estimated regression parameters, the proportion of recombination explained by G1C content was subtracted from each
gene.
Identification of non-synonymous SNPs and tests for natural selection. Using
annotations from dbSNP release 125 we identified 17,427 polymorphic nonsynonymous SNPs in release 21 and 15,976 polymorphic synonymous SNPs. Of
these, 15,583 non-synonymous and 14,324 synonymous SNPs were autosomal
and could have ancestral allele status unambiguously assigned by parsimony
through comparison to the chimpanzee and macaque genomes. We used thephased haplotypes for analysis in which missing data had been imputed. FST was
calculated using the method of Weir and Cockerham59.
To detect recent partial selective sweeps we used the long-range haplotype(LRH) test44,49 and the integrated haplotype score (iHS) test9. On simulated
data45, we found that the tests have similar power to detect recent selection
but the iHS test has slightly lower power at low haplotype frequency and the
LRH test has slightly lower power at high frequency. This can be seen in applications to HapMap Phase I data3,9, where the iHS test misses the well-known casesof HBB and CD36 and the LRH test misses the SULT1C2 region. Although bothtests are based on the concept of EHH44, we observed that the false positives
produced by the two tests tend not to overlap and thus that signals detected byboth tests have a very low false-positive rate.

©2007 Nature Publishing Group