Vol 449 | 18 October 2007 | doi:10.1038/nature06250

LETTERS
Genome-wide detection and characterization of
positive selection in human populations
Pardis C. Sabeti1*, Patrick Varilly1*, Ben Fry1, Jason Lohmueller1, Elizabeth Hostetter1, Chris Cotsapas1,2,
Xiaohui Xie1, Elizabeth H. Byrne1, Steven A. McCarroll1,2, Rachelle Gaudet3, Stephen F. Schaffner1, Eric S. Lander1,4,5,6& The International HapMap Consortium{

With the advent of dense maps of human genetic variation, it
is now possible to detect positive natural selection across thehuman genome. Here we report an analysis of over 3 million
polymorphisms from the International HapMap Project Phase 2(HapMap2)1. We used ‘long-range haplotype’ methods, whichwere developed to identify alleles segregating in a population thathave undergone recent selection2, and we also developed new
methods that are based on cross-population comparisons to discover alleles that have swept to near-fixation within a population.
The analysis reveals more than 300 strong candidate regions.
Focusing on the strongest 22 regions, we develop a heuristic
for scrutinizing these regions to identify candidate targets
of selection. In a complementary analysis, we identify 26 nonsynonymous, coding, single nucleotide polymorphisms showing
regional evidence of positive selection. Examination of thesecandidates highlights three cases in which two genes in a common
biological process have apparently undergone positive selectionin the same population: LARGE and DMD, both related to
infection by the Lassa virus3, in West Africa; SLC24A5 and
SLC45A2, both involved in skin pigmentation4,5, in Europe; andEDAR and EDA2R, both involved in development of hair follicles6,
in Asia.
An increasing amount of information about genetic variation,
together with new analytical methods, is making it possible to explore
the recent evolutionary history of the human population. The firstphase of the International Haplotype Map, including ,1 million
single nucleotide polymorphisms (SNPs)7, allowed preliminary
examination of natural selection in humans. Now, with the publication of the Phase 2 map (HapMap2)1 in a companion paper, over
3 million SNPs have been genotyped in 420 chromosomes from threecontinents (120 European (CEU), 120 African (YRI) and 180 Asianfrom Japan and China (JPT 1 CHB)).
In our analysis of HapMap2, we first implemented two widely used
tests that detect recent positive selection by finding common alleles
carried on unusually long haplotypes2. The two, the Long-Range
Haplotype (LRH)8 and the integrated Haplotype Score (iHS)9 tests,
rely on the principle that, under positive selection, an allele may rise
to high frequency rapidly enough that long-range association withnearby polymorphisms—the long-range haplotype8—will not havetime to be eliminated by recombination. These tests control for local
variation in recombination rates by comparing long haplotypes toother alleles at the same locus. As a result, they lose power as selected
alleles approach fixation (100% frequency), because there are thenfew alternative alleles in the population (Supplementary Fig. 2 andSupplementary Tables 1–2).
We next developed, evaluated and applied a new test, Cross
Population Extended Haplotype Homozogysity (XP-EHH), to detectselective sweeps in which the selected allele has approached or
achieved fixation in one population but remains polymorphic in
the human population as a whole (Methods, and Supplementary
Fig. 2 and Supplementary Tables 3–6). Related methods have recently
also been described10–12.
Our analysis of recent positive selection, using the three methods,
reveals more than 300 candidate regions1(Supplementary Fig. 3 andSupplementary Table 7), 22 of which are above a threshold such thatno similar events were found in 10 Gb of simulated neutrally evolving
sequence (Methods). We focused on these 22 strongest signals(Table 1), which include two well-established cases, SLC24A5 and
LCT2,5,13, and 20 other regions with signals of similar strength.
The challenge is to sift through genetic variation in the candidate
regions to identify the variants that were the targets of selection. Ourcandidate regions are large (mean length, 815 kb; maximum length,
3.5 Mb) and often contain multiple genes (median, 4; maximum, 15).
A typical region harbours ,400–4,000 common SNPs (minor allele
frequency .5%), of which roughly three-quarters are represented incurrent SNP databases and half were genotyped as part of HapMap2(Supplementary Table 8).
We developed three criteria to help highlight potential targets of
selection (Supplementary Fig. 1): (1) selected alleles detectable by ourtests are likely to be derived (newly arisen), because long-haplotype
tests have little power to detect selection on standing (pre-existing)variation14; we therefore focused on derived alleles, as identified bycomparison to primate outgroups; (2) selected alleles are likely to behighly differentiated between populations, because recent selection isprobably a local environmental adaptation2; we thus looked foralleles common in only the population(s) under selection; (3)selected alleles must have biological effects. On the basis of current
knowledge, we therefore focused on non-synonymous coding SNPs
and SNPs in evolutionarily conserved sequences. These criteria areintended as heuristics, not absolute requirements. Some targets of
selection may not satisfy them, and some will not be in current SNP
databases. Nonetheless, with ,50% of common SNPs in these populations genotyped in HapMap2, a search for causal variants is timely.
We applied the criteria to the regions containing SLC24A5 and
LCT, each of which already has a strong candidate gene, mutationand trait. At SLC24A5, the 600 kb region contains 914 genotyped

1
Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02139, USA. 2Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114,
USA. 3Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts 02138, USA. 4Department of Biology, MIT, Cambridge, Massachusetts 02139,
USA. 5Whitehead Institute for Biomedical Research, Cambridge, Massachusetts 02142, USA. 6Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
02115, USA.
*These authors contributed equally to this work.
{Lists of participants and affiliations appear at the end of the paper.

913©2007 Nature Publishing GroupLETTERS

NATURE | Vol 449 | 18 October 2007

Table 1 | The twenty-two strongest candidates for natural selection
RegionChr:position(MB, HG17)

Selected population

Long Haplotype Test

Size (Mb)

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

chr1:166
chr2:72.6
chr2:108.7
chr2:136.1
chr2:177.9
chr4:33.9
chr4:42
chr4:159
chr10:3
chr10:22.7
chr10:55.7
chr12:78.3
chr15:46.4
chr15:61.8
chr16:64.3
chr16:74.3
chr17:53.3
chr17:56.4
chr19:43.5
chr22:32.5
chr23:35.1
chr23:63.5

CHB 1 JPTCHB 1 JPTCHB 1 JPT
CEU
CEU,CHB 1 JPT
CEU,YRI, CHB 1 JPT
CHB 1 JPTCHB 1 JPT
CEU
CEU, CHB 1 JPT
CHB1JPTYRI
CEU
CHB 1 JPTCHB 1 JPTCHB 1 JPT, YRI
CHB 1 JPT
CEU
YRI
YRI
YRI
YRI
Total SNPs

LRH, iHS
XP-EHH
LRH, iHS, XP-EHH
LRH, iHS, XP-EHH
LRH, iHS, XP-EHH
LRH, iHS
LRH, iHS, XP-EHH
LRH, iHS, XP-EHH
LRH, iHS, XP-EHH
XP-EHH
LRH, iHS, XP-EHH
LRH, iHS
XP-EHH
XP-EHH
XP-EHH
LRH, iHS
XP-EHH
XP-EHH
LRH, iHS, XP-EHH
LRH
LRH, iHS
LRH, iHS

0.4
0.8
1.0
2.4
1.2
1.7
0.3
0.3
0.3
0.3
0.4
0.8
0.6
0.2
0.4
0.6
0.2
0.4
0.3
0.4
0.6
3.5
16.74

Subset of Subset of SNPs
Total SNPs with Subset ofthat fulfil
SNPs thatLong Haplotype SNPs that
Signalfulfil criteria fulfil criteria criteria 1, 2
and 3
1 and 2
1

92
732
972
1,213
1,388
413
249
233
179
254
735
151
867
252
484
55
143
290
83
318
50
13
9,166

39
250
265
282
399
161
94
67
63
93
221
91
233
73
137
35
41
98
30
188
35
3
2,898

30
0
7
24
79
33
65
34
16
0
5
25
5
40
2
28
0
26
0
35
25
1
480

2
0
1
3
9
0
6
1
1
0
2
0
1
6
0
3
0
3
0
3
0
0
41

Genes at or near SNPs that
fulfil all three criteria

BLZF1, SLC19A2EDAR
RAB3GAP1, R3HDM1, LCT
PDE11A
SLC30A9

PCDH15
SLC24A5
HERC1
CHST5, ADAT1, KARS
BCAS3
LARGE

Twenty-two regions were identified at a high threshold for significance (Methods), based on the LRH, iHS and/or XP-EHH test. Within these regions, we examined SNPs with the best evidence ofbeing the target of selection on the basis of having a long haplotype signal, and by fulfilling three criteria: (1) being a high-frequency derived allele; (2) being differentiated between populations andcommon only in the selected population; and (3) being identified as functional by current annotation. Several candidate polymorphisms arise from the analysis including well-known LCT and SLC24A5(ref. 2), as well as intriguing new candidates.

common in Europe and rare in Asia and Africa. Among these five
SNPs, there is only one implicated as functional by current knowledge; it has the strongest signal of positive selection and encodes the
A111T polymorphism associated with pigment differences in

SNPs. Applying filters progressively (Table 1 and Fig. 1a–d), we
found that 867 SNPs are associated with the long-haplotype signal,of which 233 are high-frequency derived alleles, of which 12 arehighly differentiated between populations, and of which only 5 area

XP-EHH and iHS–log(P value)

SLC24A5cdPopulation
Populationdifferentiation (FST) differentiation (FST)

Derived-allele
frequency

b

DUT

FBN1

SULT1C3SLC5A7 SULT1C2

CEP152

e

9
867

6

RANBP2
EDAR

972

6
3

3
0

f

1.0
0.8 233
0.6
0.4
0.2
0

0
1.0
0.8

265

0.6
0.4
0.2
0

1.0
39
0.8
0.6
0.4
0.2
0

g 1.0

1.0
5
0.8
0.6
0.4
0.2
0
60.8

h 1.0

0.8
0.6
0.4
0.2

SLC24A5
Alanine threonine
61.1

61.4

61.7

62

39

7
0.8
0.6
0.4
0.2
0
129.3

Position on chromosome 15 (cM)

Figure 1 | Localizing SLC24A5 and EDAR signals of selection.
a–d, SLC24A5. a, Strong evidence for positive selection in CEU samples at a
chromosome 15 locus: XP-EHH between CEU and JPT 1 CHB (blue), CEU
and YRI (red), and YRI and JPT 1 CHB (grey). SNPs are classified as having
low probability (bordered diamonds) and high probability (filled diamonds)
potential for function. SNPs were filtered to identify likely targets of
selection on the basis of the frequency of derived alleles (b), differences
between populations (c) and differences between populations for highfrequency derived alleles (less than 20% in non-selected populations)(d). The number of SNPs that passed each filter is given in the top left cornerin red. The threonine to alanine candidate polymorphism in SLC24A5 is the

EDAR
Valine129.7

alanine

130.1

130.5

Position on chromosome 2 (cM)

clear outlier. e–h, EDAR. e, Similar evidence for positive selection in
JPT 1 CHB at a chromosome 2 locus: XP-EHH between CEU and
JPT 1 CHB (blue), between YRI and JPT 1 CHB (red), and between CEU
and YRI (grey); iHS in JPT 1 CHB (green). A valine to alanine
polymorphism in EDAR passes all filters: the frequency of derived alleles(f), differences between populations (g) and differences between
populations for high-frequency derived alleles (less than 20% in nonselected populations) (h). Three other functional changes, a DRE change in
SULT1C2 and two SNPs associated with RANBP2 expression (Methods),
have also become common in the selected population.

914©2007 Nature Publishing GroupLETTERS

NATURE | Vol 449 | 18 October 2007

humans and thought to be the target of positive selection5. Ourcriteria thus uniquely identify the expected allele.
At the LCT locus, we found similar degrees of filtration. Within
the 2.4 Mb selective sweep, 24 polymorphisms fulfil the first two
criteria (Table 1, and Supplementary Fig. 4), with the polymorphism
thought to confer adult persistence of lactase among them. However,this SNP was only identified as functional after extensive study of the
LCT gene15. Thus LCT shows both the utility and the limits of theheuristics.
Given the encouraging results for SLC24A5 and LCT, we performed a similar analysis on all 22 candidate regions (Table 1).
Filtering the 9,166 SNPs associated with the long-haplotype signal,
we found that 480 satisfied the first two criteria. We identified 41 outof the 480 SNPs (0.2% of all SNPs genotyped in the regions) as
possibly functional on the basis of a newly compiled database ofpolymorphisms in known coding elements, evolutionarily conserved
elements and regulatory elements (Methods; B.F., unpublished),
together containing , 5.5% of all known SNPs.
Eight of the forty-one SNPs encode non-synonymous changes(Table 1 and Supplementary Table 9). Apart from the well-known
case of SLC24A5, they are found in EDAR, PCDH15, ADAT1, KARS,HERC1, SLC30A9 and BLFZ1. The remaining 33 potentially functional SNPs lie within conserved transcription factor motifs, introns,
UTRs and other non-coding regions.
To identify additional candidates, we reversed the process bytaking non-synonymous coding SNPs with highly differentiatedhigh-frequency derived alleles; these SNPs comprise a tiny fractionof all SNPs and have a higher a priori probability of being targets of
selection. Of the 15,816 non-synonymous SNPs in HapMap2, 281(Supplementary Table 10) have both a high derived-allele frequency(frequency .50%) and clear differentiation between populations(FST is in the top 0.5 percentile). We examined these 281 SNPs toidentify those embedded within long-range haplotypes16, andidentified 26 putative cases of positive selection. These include theeight non-synonymous SNPs identified in the genome-wide analysis
above.
Interestingly, analysis of the top regions and the non-synonymous
SNPs together revealed three cases of two genes in the same pathway
both having strong evidence of selection in a single population.
In the European sample, there is strong evidence for two genes
already shown to be associated with skin pigment differences among
humans. The first is SLC24A5, described above. We further examined
the global distribution (Fig. 2) and the predicted effect on protein
activity of the SLC24A5 A111T polymorphism (Supplementary Fig.
5, 6). The second, SLC45A2, has an important role in pigmentation inzebrafish, mouse and horse4. An L374F substitution in SLC45A2 is
at 100% frequency in the European sample, but absent in the Asianand African samples. A recent association study has shown that thePhe-encoding allele is correlated with fair skin and non-black hair in
Europeans4. Together, the data support SLC45A2 as a target of
positive selection in Europe10,17.
In the African sample (Yoruba in Ibadan, Nigeria), there is evidence of selection for two genes with well-documented biological links
to the Lassa fever virus. The strongest signal in the genome, on thebasis of the LRH test, resides within a 400 kb region that lies entirely
within the gene LARGE. The LARGE protein is a glycosylase that
post-translationally modifies a-dystroglycan, the cellular receptor
for Lassa fever virus (as well as other arenaviruses), and the modification has been shown to be critical for virus binding3. The virus
name is derived from Lassa, Nigeria, where the disease is endemic,
with 21% of the population showing signs of exposure18. We also
noted that the DMD locus is on our larger candidate list of regions,with the signal of selection again in the Yoruba sample. DMD encodes
a cytosolic adaptor protein that binds to a-dystroglycan and is criticalfor its function. We hypothesize that Lassa fever created selective
pressure at LARGE and DMD12. This hypothesis can be tested bycorrelating the geographical distribution of the selected haplotype

with endemicity of the Lassa virus, studying infection of genotyped
cells in vitro, and searching for an association between the selected
haplotype and clinical outcomes in infected patients.
In the Asian samples, we found evidence of selection for nonsynonymous polymorphisms in two genes in the ectodysplasin(EDA) pathway, which is involved in development of hair, teeth
and exocrine glands6. The genes are EDAR and EDA2R, which encode
the key receptors for the ligands EDA A1 and EDA A2, respectively.
Notably, the EDA signalling pathway has been shown to be underpositive selection for loss of scales in multiple distinct populations offreshwater stickleback fish19. A mutation encoding a V370A substitution in EDAR is near fixation in Asia and absent in Europe andAfrica (Fig. 1e–h). An R57K substitution in EDA2R has derived-allele
frequencies of 100% in Asia, 70% in Europe and 0% in Africa.
The EDAR polymorphism is notable because it is highly differentiated between the Asian and other continental populations (the 3rd
most differentiated among 15,816 non-synonymous SNPs), and also
within Asian populations (in the top 1% of SNPs differentiatedbetween the Japanese and Chinese HapMap samples). Genotypingof the EDAR polymorphism in the CEPH (Centre d’Etudie duPolymorphisme Humain) global diversity panel20 shows that it is athigh but varying frequency throughout Asia and the Americas (forexample, 100% in Pima Indians and in parts of China, and 73% inJapan) (Fig. 2, and Supplementary Fig. 7). Studying populations likethe Japanese, in which the allele is still segregating, may provide clues
to its biological significance.
EDAR has a central role in generation of the primary hair follicle
pattern, and mutations in EDAR cause hypohidrotic ectodermalaSLC24A5
EuropePakistan

Algeria

Derived allele (A)Ancestral allele (G)

b
EDAR

China

Japan

Pima Indians
Cambodia

Derived allele (C)Ancestral allele (T)Figure 2 | Global distribution of SLC24A5 A111T and EDAR V370A.
Worldwide allele-frequency distributions for candidate polymorphisms
with the strongest evidence for selection20. a, SLC24A5 A111T is common in
Europe, Northern Africa and Pakistan, but rare or absent elsewhere. b, EDAR
V370A is common in Asia and the Americas, but absent in Europe andAfrica.
915©2007 Nature Publishing GroupLETTERS

NATURE | Vol 449 | 18 October 2007

dysplasia (HED) in humans and mice, characterized by defects in thedevelopment of hair, teeth and exocrine glands6. The V370A polymorphism, proposed to be the target of selection, lies within EDAR’s
highly conserved death domain (Supplementary Fig. 8), the locationof the majority of EDAR polymorphisms causing HED21. Our structural modelling predicts that the polymorphism lies within the binding site of the domain (Fig. 3).
Our analysis only scratches the surface of the recent selective
history of the human genome. The results indicate that individual
candidates may coalesce into pathways that reveal traits under selection, analogous to the alleles of multiple genes (for example, HBB,G6PD and DARC) that arose and spread in Africa and other tropical
populations as a result of the partial protection they confer against
malaria2,12. Such endeavours will be enhanced by continuing
development of analytical methods to localize signals in candidate
regions, generation of expanded data sets, advances in comparative
genomics to define coding and regulatory regions, and biologicalfollow-up of promising candidates. True understanding of the roleof adaptive evolution will require collaboration across multiple disciplines, including molecular and structural biology, medical andpopulation genetics, and history and anthropology.

METHODS SUMMARY
Genotyping data. Phase 2 of the International Haplotype Map (HapMap2)(www.hapmap.org) contains 3.1 million SNPs genotyped in 420 chromosomes
in 3 continental populations (120 European (CEU), 120 African (YRI) and 180
Asian (JPT1CHB))1. We further genotyped our top HapMap2 functional candidates in the HGDR-CEPH Human Genome Diversity Cell Line Panel20.
LRH, iHS and XP-EHH tests. The Long-Range Haplotype (LRH), integrated
Haplotype Score (iHS) and Cross Population EHH (XP-EHH) tests detect alleles
that have risen to high frequency rapidly enough that long-range association
with nearby polymorphisms—the long-range haplotype—has not been erodedby recombination; haplotype length is measured by the EHH8,9. The first two
tests detect partial selective sweeps, whereas XP-EHH detects selected alleles thathave risen to near fixation in one but not all populations. To evaluate the tests, we
simulated genomic data for each HapMap population in a range of demographic
scenarios—under neutral evolution and twenty scenarios of positive selection—
developing the program Sweep (www.broad.mit.edu/mpg/sweep) for analysis.
For our top candidates by the three tests, we tested for haplotype-specific recombination rates and copy-number polymorphisms, possible confounders.
Localization. We calculated FST and derived-allele frequency for all SNPs within
the top candidate regions. We developed a database for those regions to annotate
all potentially functional DNA changes (B.F., unpublished), including nonsynonymous variants, variants disrupting predicted functional motifs, variants
within regions of conservation in mammals and variants previously associatedwith human phenotypic differences, as well as synonymous, intronic anduntranslated region variants.
Structural model. We generated a homology model of the EDAR death domain(DD) from available DD structures using Modeller 9v1 (ref. 22). The distribution of conserved residues, built using ConSurf23 with an EDAR sequencealignment from 22 species, shows a bias to the protein core in helices H1, H2and H5, supporting our model.
Full Methods and any associated references are available in the online version of
the paper at www.nature.com/nature.

T403M

Received 8 August; accepted 13 September 2007.
1.

R375H

2.
G382S

V370A

3.

L377F
N

4.

I418T

5.
Potential
binding region

6.
R420Q

7.
T413P

8.
9.
10.

C

Figure 3 | Structural model of the EDAR death domain. Ribbon
representation of a homology model of the EDAR death domain (DD),based on the alignment of the EDAR DD amino acid sequence (EDAR
residues 356–431), with multiple known DD structures. The helices arelabelled H1 to H6. Residues in blue (the H1–H2 and H5–H6 loops, residues
370–376 and 419–425, respectively) correspond to the homologous
residues in Tube that interact with Pelle in the Tube-DD–Pelle-DDstructure24. These EDAR-DD residues therefore form a potential region ofinteraction with a DD-containing EDAR-interacting protein, such as
EDARADD. The V370A polymorphic residue (red) is located prominently
within this potential binding region in the H1–H2 loop. Seven of thethirteen known mis-sense mutations in EDAR that lead to hypohidrotic
ectodermal dysplasia (HED) in humans are located in the EDAR-DD: theonly four mutations in EDAR that lead to the dominant transmission of
HED (green) and three recessive mutations (yellow)21. Four of thesemutations, R375H, L377F, R420Q and I418T are located in the vicinity ofthe predicted interaction interface.

11.
12.
13.
14.
15.

16.
17.

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Supplementary Information is linked to the online version of the paper at
www.nature.com/nature.

(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

Acknowledgements P.C.S. is funded by a Burroughs Wellcome Career Award in
the Biomedical Sciences and has been funded by the Damon Runyon Cancer
Fellowship and the L’Oreal for Women in Science Award. We thank A. Schier,
B. Voight, R. Roberts, M. Kreiger, A. Abzhanov, D. Degusta, M. Burnette,
E. Lieberman, M. Daly, D. Altshuler, D. Reich, D. Lieberman and I. Woods for helpful
discussions on our analysis and results. We also thank L. Ziaugra, D. Tabbaa and
T. Rachupka for experimental assistance. This work was funded in part by grants
from the National Human Genome Research Institute (to E.S.L.) and from theBroad Institute of MIT and Harvard.

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

Author Contributions P.C.S., P.V., B.F. and E.S.L. initiated the project. P.V., B.F. and
P.C.S. developed key software. P.C.S., P.V., B.F., S.F.S., J.L., E.H., C.C., X.X., E.B.,
S.A.McC. and R.G. performed analysis. P.C.S., E.B. and E.H. performed experiments.
P.C.S., E.S.L., P.V. and S.F.S. wrote the manuscript.

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

Author Information Reprints and permissions information is available atwww.nature.com/reprints. Correspondence and requests for materials should be
addressed to P.C.S. (pardis@broad.mit.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 Technology
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

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, 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, 3420 Central Expressway, Santa Clara, California 95051, USA.
5
Pacific 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, 658 North
Pastoria Avenue, Sunnyvale, California 94085, USA. 17Prognosys Biosciences, 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

917©2007 Nature Publishing GroupLETTERS

NATURE | Vol 449 | 18 October 2007

(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 S. Euclid Avenue, Box 8232, St Louis, Missouri 63110, USA. 23University ofHong 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, Minatoku, 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, 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. 31Johns
Hopkins University School of Medicine, McKusick-Nathans Institute of Genetic
Medicine, Broadway Research Building, Suite 579, 733 N. 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 S.
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, Districtof Columbia 20059, USA. 49University of Ibadan College of Medicine, Ibadan, Oyo State,
Nigeria. 50Case 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 ofPhilosophy/Center for Applied Ethics, 2121, Building 9, Caoqiao Xinyuan 3 Qu, Beijing
100067, China. 53Genetic 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 School of Biomedical Sciences, Department of Human Genetics,
Sakamoto 1-12-4, Nagasaki 852-8523, Japan. 56University of Oklahoma, Department of
Anthropology, 455 W. Lindsey Street, Norman, Oklahoma 73019, USA. 57Vanderbilt
University, Center for Genetics and Health Policy, 507 Light Hall, Nashville, Tennessee37232, USA. 58Wellcome 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 SanliheRoad, 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 Republicof China, 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 W. Baltimore Street, Baltimore, Maryland
21201, USA. 72US National Institutes of Health, National Human Genome ResearchInstitute, 31 Center Drive, Bethesda, Maryland 20892, USA.

918
©2007 Nature Publishing Groupdoi:10.1038/nature06250

METHODS
Genotyping data. The chromosomes examined in HapMap 2 were phased by the
consortium using PHASE25.
The HGDR-CEPH Human Genome Diversity Cell Line Panel20 consists of
1,051 individuals from 51 populations across the world. We obtained DNA forthe panel from the Foundation Jean Dausset (CEPH) and genotyped our top
functional candidates for selection in the panel.
LRH, iHS, and XP-EHH tests. The Long-Range Haplotype (LRH) and theintegrated Haplotype Score (iHS) tests have been previously described8,9 andour methods are given in Supplementary Methods.
EHH between two SNPs, A and B, is defined as the probability that two
randomly chosen chromosomes are homozygous at all SNPs between A and B,
inclusive8; it is usually calculated using a sample of chromosomes from a single
population. Explicitly, if the N chromosomes in a sample form G homozygous
groups, with each group i having ni elements, EHH is defined as
 
G
P
ni
i~1
EHH~ 

N2


2
The XP-EHH test detects selective sweeps in which the selected allele has risen
to high frequency or fixation in one population, but remains polymorphic in thehuman population as a whole; for this purpose it is more powerful than either
iHS or LRH (Supplementary Fig. 2 and Supplementary Tables 3–6). XP-EHH
uses cross-population comparison of haplotype lengths to control for local variation in recombination rates. Such cross-population comparison is complicatedby the fact that haplotype lengths also depend on population history, such as
bottlenecks and expansions26. The XP-EHH test normalizes for genome-wide
differences in haplotype length between populations.
We define the XP-EHH test with respect to two populations, A and B, a given
core SNP and a given direction (centromere distal or proximal). EHH is calculated for all SNPs in population A between the core SNP and X, and the value
integrated with respect to genetic distance, with the result defined as IA. IB is
defined analogously for population B. The statistic ln(IA/IB) is then calculated; anunusually positive value suggests selection in population A, a negative value
selection in B. For identifying outliers, the log-ratio is normalized to have zeromean and unit variance. Details are given in Supplementary Methods.
We developed a computer program, Sweep, to implement these tests (LRH,iHS and XP-EHH) for positive selection, (Supplementary Methods; www.broad.
mit.edu/mpg/sweep). In identifying the 22 strongest candidate regions, we considered regions with signals in at least two of five tests (LRH, iHS and XP-EHH in
the three pairwise comparisons among the three populations), as well as thosethat had the strongest signal for each individual test. With this threshold wefound no events in 10 Gb of simulated neutrally evolving sequence. For the top
candidates by the three tests, we have taken additional steps to rule out the effects
of recombination rate variation and copy number polymorphisms (Supplementary Methods).
Simulations and power calculations. We simulated the evolution of 1 MB sections of 120 chromosomes from each of the three continental HapMap populations, using a previously validated demographic model27, under neutrality andunder twenty scenarios of positive selection. We studied the effects of demography by further simulating recent bottlenecks with a range of intensity. Detailsof simulations and power calculations are given in Supplementary Methods.
Functional annotation. We developed an annotation database for our candidate
regions to identify all DNA changes with potential functional consequence (B.F.,
unpublished). We first examined candidates most likely to be functional, including non-synonymous mutations, variants that disrupt predicted functional
motifs (transcription factor motifs in conserved regions up to 10-kb 59 of known

genes and miRNA binding-site motifs in conserved 39 untranslated regions ofknown genes), and variations reported to be associated with human phenotypic
differences. For the last category, we identified variations associated with aclinical state (for example, malaria resistance) by a review of the publishedliterature and those associated with changes to gene expression in lymphoblastoid cell lines from the HapMap individuals. The annotation included insertion/
deletion mutations of all sizes. We also examined candidates with lower probability of being functional, including synonymous, intronic and untranslated
variations and those that occur within regions of conservation in mammalian
species. These methods are described in greater detail in Supplementary
Methods.
Structural model of EDAR’s death domain. We generated a homology model
for EDAR’s death domain (DD) using six solved DD structures: p75 NGFR-DD,
RAIDD-DD, Pelle-DD, FADD-DD, Fas-DD and IRAK4-DD24,28–32. We aligned
the corresponding protein sequences using SALIGN33. We then added the aminoacid sequence of EDAR’s DD (residues 356–431) to this structural alignment
using Modeller 9v1 (ref. 22). The resulting alignment was used as the input toModeller 9v1 to build ten EDAR-DD structure models, and the best model was
selected based on the Objective Function Score. Owing to the high DOPE scoresin the H1–H2 loop we performed a loop refinement using Modeller9v1, significantly reducing the energy of this region. We further evaluated the model by
examining the distribution of conserved residues using ConSurf23 with an alignment of EDAR-DD sequences from 22 species. We observed a bias of conserved
residues to the protein core in H1, H2 and H5, which supports our EDAR-DD
model. To identify potential binding regions of EDAR-DD, we used LSQMAN34
to superimpose the model to the Tube-DD–Pelle-DD complex structure24. The
H1–H2 and H5–H6 loops of the EDAR-DD correspond to Tube residues interacting with Pelle, and H2–H3 and H4–H5 loops to Pelle residues interacting with
Tube. We focused our analysis on the residues corresponding to the interacting
region in Tube because our EDAR-DD model is most similar to Tube. Figures
were generated with PyMOL35.
Other analysis. Description of methods for calculating FST, derived-allele frequency, alignment of the SLC24 amino acids, species alignments, conservationgraphs, and estimation of the fraction of SNPs genotyped in HapMap2 andidentified in dbSNP, are given in Supplementary Methods.
25. Stephens, M., Smith, N. J. & Donnelly, P. A new statistical method for haplotype
reconstruction from population data. Am. J. Hum. Genet. 68, 978–989 (2001).
26. Crawford, D. C. et al. Evidence for substantial fine-scale variation inrecombination rates across the human genome. Nature Genet. 36, 700–706(2004).
27. Schaffner, S. F. et al. Calibrating a coalescent simulation of human genome
sequence variation. Genome Res. 15, 1576–1583 (2005).
28. Berglund, H. et al. The three-dimensional solution structure and dynamic
properties of the human FADD death domain. J. Mol. Biol. 302, 171–188 (2000).
29. Huang, B., Eberstadt, M., Olejniczak, E. T., Meadows, R. P. & Fesik, S. W. NMR
structure and mutagenesis of the Fas (APO-1/CD95) death domain. Nature 384,
638–641 (1996).
30. Lasker, M. V., Gajjar, M. M. & Nair, S. K. Cutting edge: molecular structure of the
IL-1R-associated kinase-4 death domain and its implications for TLR signaling.
J. Immunol. 175, 4175–4179 (2005).
31. Liepinsh, E., Ilag, L. L., Otting, G. & Ibanez, C. F. NMR structure of the death domainof the p75 neurotrophin receptor. EMBO J. 16, 4999–5005 (1997).
32. Park, H. H. & Wu, H. Crystal structure of RAIDD death domain implicatespotential mechanism of PIDDosome assembly. J. Mol. Biol. 357, 358–364 (2006).
33. Marti-Renom, M. A., Madhusudhan, M. S. & Sali, A. Alignment of protein
sequences by their profiles. Protein Sci. 13, 1071–1087 (2004).
34. Kleywegt, G. J. Use of non-crystallographic symmetry in protein structurerefinement. Acta Crystallogr. D 52, 842–857 (1996).
35. DeLano, W. L. MacPyMOL: A PyMOL-based Molecular Graphics Application for
MacOS X. (DeLano Scientific LLC, Palo Alto, California, USA, 2007).

©2007 Nature Publishing Group