Criar uma Loja Virtual Grátis

DOI: 10.1126/science.1188021
, 710 (2010); 328 Science
et al. Richard E. Green
A Draft Sequence of the Neandertal Genome
This copy is for your personal, non-commercial use only.
clicking here. colleagues, clients, or customers by
, you can order high-quality copies for your If you wish to distribute this article to others
here. following the guidelines
can be obtained by Permission to republish or repurpose articles or portions of articles
): January 10, 2014 (this information is current as of
The following resources related to this article are available online at
version of this article at:
including high-resolution figures, can be found in the online Updated information and services,
can be found at: Supporting Online Material
found at:
can be related to this article A list of selected additional articles on the Science Web sites
, 29 of which can be accessed free: cites 81 articles This article
8 article(s) on the ISI Web of Science cited by This article has been
100 articles hosted by HighWire Press; see: cited by This article has been
subject collections: This article appears in the following
registered trademark of AAAS.
is a Science 2010 by the American Association for the Advancement of Science; all rights reserved. The title
Copyright American Association for the Advancement of Science, 1200 New York Avenue NW, Washington, DC 20005.
(print ISSN 0036-8075; online ISSN 1095-9203) is published weekly, except the last week in December, by the Science
on January 10, 2014 Downloaded from
A Draft Sequence of the
Neandertal Genome
Richard E. Green, 1 *†‡ Johannes Krause, 1 †§ Adrian W. Briggs, 1 †§ Tomislav Maricic, 1 †§
Udo Stenzel, 1 †§ Martin Kircher, 1 †§ Nick Patterson, 2 †§ Heng Li, 2 † Weiwei Zhai, 3 †||
Markus Hsi-Yang Fritz, 4 † Nancy F. Hansen, 5 † Eric Y. Durand, 3 † Anna-Sapfo Malaspinas, 3 †
Jeffrey D. Jensen, 6 † Tomas Marques-Bonet, 7,13 † Can Alkan, 7 † Kay Prüfer, 1 † Matthias Meyer, 1 †
Hernán A. Burbano, 1 † Jeffrey M. Good, 1,8 † Rigo Schultz, 1 Ayinuer Aximu-Petri, 1 Anne Butthof, 1
Barbara Höber, 1 Barbara Höffner, 1 Madlen Siegemund, 1 Antje Weihmann, 1 Chad Nusbaum, 2
Eric S. Lander, 2 Carsten Russ, 2 Nathaniel Novod, 2 Jason Affourtit, 9 Michael Egholm, 9
Christine Verna, 21 Pavao Rudan, 10 Dejana Brajkovic, 11 Željko Kucan, 10 Ivan Gušic, 10
Vladimir B. Doronichev, 12 Liubov V. Golovanova, 12 Carles Lalueza-Fox, 13 Marco de la Rasilla, 14
Javier Fortea, 14 ¶ Antonio Rosas, 15 Ralf W. Schmitz, 16,17 Philip L. F. Johnson, 18 † Evan E. Eichler, 7 †
Daniel Falush, 19 † Ewan Birney, 4 † James C. Mullikin, 5 † Montgomery Slatkin, 3 † Rasmus Nielsen, 3 †
Janet Kelso, 1 † Michael Lachmann, 1 † David Reich, 2,20 *† Svante Pääbo 1 *†
Neandertals, the closest evolutionary relatives of present-day humans, lived in large parts of Europe
and western Asia before disappearing 30,000 years ago. We present a draft sequence of the Neandertal
genome composed of more than 4 billion nucleotides from three individuals. Comparisons of the
Neandertal genome to the genomes of five present-day humans from different parts of the world
identify a number of genomic regions that may have been affected by positive selection in ancestral
modern humans, including genes involved in metabolism and in cognitive and skeletal development.
We show that Neandertals shared more genetic variants with present-day humans in Eurasia than with
present-day humans in sub-Saharan Africa, suggesting that gene flow from Neandertals into the
ancestors of non-Africans occurred before the divergence of Eurasian groups from each other.
he morphological features typical of Nean-
dertals first appear in the European fossil
record about 400,000 years ago (1–3).
Progressively more distinctive Neandertal forms
subsequently evolved until Neandertals disap-
peared from the fossil record about 30,000 years
ago (4). During the later part of their history,
Neandertals lived in Europe and Western Asia
as far east as Southern Siberia (5) and as far
south as the Middle East. During that time, Nean-
dertals presumably came into contact with ana-
at least 80,000 years ago (6, 7) and subsequently
in Europe and Asia.
Neandertals are the sister group of all present-
day humans. Thus, comparisons of the human
genome to the genomes of Neandertals and
apes allow features that set fully anatomically
modern humans apart from other hominin forms
to be identified. In particular, a Neandertal ge-
nome sequence provides a catalog of changes
that have become fixed or have risen to high
frequency in modern humans during the last
few hundred thousand years and should be
informative for identifying genes affected by
positive selection since humans diverged from
Substantial controversy surrounds the question
of whether Neandertals interbred with anatomi-
cally modern humans. Morphological features
of present-day humans and early anatomically
modern human fossils have been interpreted as
evidence both for (8, 9) and against (10, 11) ge-
netic exchange between Neandertals and the pre-
sumed ancestors of present-day Europeans.
Similarly, analysis of DNA sequence data from
present-day humans has been interpreted as evi-
dence both for (12, 13) and against (14) a genetic
contribution by Neandertals to present-day hu-
mans. The only part of the genome that has been
examined from multiple Neandertals, the mito-
chondrial DNA (mtDNA) genome, consistently
falls outside the variation found in present-day
humans and thus provides no evidence for inter-
breeding (15–19). However, this observation
does not preclude some amount of interbreeding
(14, 19) or the possibility that Neandertals con-
tributed other parts of their genomes to present-
day humans (16). In contrast, the nuclear genome
is composed of tens of thousands of recombin-
ing, and hence independently evolving, DNA seg-
ments that provide an opportunity to obtain a
clearer picture of the relationship between Nean-
dertals and present-day humans.
A challenge in detecting signals of gene flow
between Neandertals and modern human ances-
tors is that the two groups share common ances-
tors within the last 500,000 years, which is no
deeper than the nuclear DNA sequence variation
within present-day humans. Thus, even if no gene
flow occurred, in many segments of the genome,
Neandertals are expected to be more closely re-
lated to some present-day humans than they are to
each other (20). However, if Neandertals are, on
average across many independent regions of the
genome, more closely related to present-day hu-
mans in certain parts of the world than in others,
this would strongly suggest that Neandertals ex-
changed parts of their genome with the ances-
tors of these groups.
Several features of DNA extracted from Late
Pleistocene remains make its study challenging.
The DNA is invariably degraded to a small aver-
age size of less than 200 base pairs (bp) (21, 22),
almost always contain only small amounts of en-
dogenous DNA but large amounts of DNA from
microbial organisms that colonized the specimens
after death. Over the past 20 years, methods for
largely based on the polymerase chain reaction
(PCR) (27). In the case of the nuclear genome of
Neandertals, four short gene sequences have been
determined by PCR: fragments of the MC1R gene
involved in skin pigmentation (28), a segment of
the FOXP2 gene involved in speech and language
of ancient DNA can be multiplexed (32), it does
not allow the retrieval of a large proportion of the
genome of an organism.
The development of high-throughput DNA se-
quencing technologies (33, 34) allows large-scale,
genome-wide sequencing of random pieces of
DNA extracted from ancient specimens (35–37)
and has recently made it feasible to sequence ge-
1 DepartmentofEvolutionaryGenetics,Max-PlanckInstitutefor
EvolutionaryAnthropology,D-04103Leipzig,Germany. 2 Broad
Institute of MIT and Harvard, Cambridge, MA 02142, USA.
3 Department of Integrative Biology, University of California,
Berkeley, CA 94720, USA.
4 European Molecular Biology
Laboratory–European Bioinformatics Institute, Wellcome Trust
Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.
5 Genome Technology Branch, National Human Genome Re-
search Institute, National Institutes of Health, Bethesda, MD
20892,USA. 6 PrograminBioinformaticsandIntegrativeBiology,
University of Massachusetts Medical School, Worcester, MA
01655, USA.
7 Howard Hughes Medical Institute, Department
of Genome Sciences, University of Washington, Seattle, WA
98195, USA.
8 Division of Biological Sciences, University of
Montana, Missoula, MT 59812, USA.
9 454 Life Sciences,
10 CroatianAcademyofSciencesand
Arts, Zrinski trg 11, HR-10000 Zagreb, Croatia.
11 Croatian
Academy of Sciences and Arts, Institute for Quaternary
Croatia. 12 ANOLaboratoryofPrehistory,St.Petersburg,Russia.
13 Institute of Evolutionary Biology (UPF-CSIC), Dr. Aiguader
88, 08003 Barcelona, Spain.
14 Área de Prehistoria Departa-
mento de Historia Universidad de Oviedo, Oviedo, Spain.
15 DepartamentodePaleobiología,MuseoNacionaldeCiencias
Naturales, CSIC, Madrid, Spain.
16 Der Landschaftverband
Rheinlund–Landesmuseum Bonn, Bachstrasse 5-9, D-53115
Bonn,Germany. 17 AbteilungfürVor-undFrühgeschichtliche
Archäologie, Universität Bonn, Germany.
18 Department of
Biology,EmoryUniversity,Atlanta,GA30322,USA. 19 Department
of Microbiology, University College Cork, Cork, Ireland.
20 Depart-
ment of Genetics, Harvard Medical School, Boston, MA 02115,
21 Department of Human Evolution, Max-Planck Institute
for Evolutionary Anthropology, D-04103 Leipzig, Germany.
*To whom correspondence should be addressed. E-mail: (R.E.G.);
edu (D.R.); (S.P.)
†Members of the Neandertal Genome Analysis Consortium.
‡Present address: Department of Biomolecular Engineer-
ing, University of California, Santa Cruz, CA 95064, USA.
§These authors contributed equally to this work.
||Present address: Beijing Institute of Genomics, Chinese
Academy of Sciences Beijing 100029, P.R. China.
7 MAY 2010 VOL 328 SCIENCE 710
nomes from late Pleistocene species (38). How-
ever, because a large proportion of the DNA
present in most fossils is of microbial origin,
comparison to genome sequences of closely
related organisms is necessary to identify the
DNA molecules that derive from the organism
under study (39). In the case of Neandertals, the
finished human genome sequence and the chim-
panzee genome offer the opportunity to identify
Neandertal DNA sequences (39, 40).
A special challenge in analyzing DNA se-
quences from the Neandertal nuclear genome
is that most DNA fragments in a Neandertal are
expected to be identical to present-day humans
(41). Thus, contamination of the experiments
with DNA from present-day humans may be
mistaken for endogenous DNA. We first applied
high-throughput sequencing to Neandertal speci-
mens from Vindija Cave in Croatia (40, 42), a
site from which cave bear remains yielded some
of the first nuclear DNA sequences from the late
Pleistocene in 1999 (43). Close to one million bp
of nuclear DNA sequences from one bone were
directly determined by high-throughput sequenc-
ing on the 454 platform (40), whereas DNA frag-
ments from another extract from the same bone
were cloned in a plasmid vector and used to
sequence ~65,000 bp (42). These experiments,
while demonstrating the feasibility of generating
a Neandertal genome sequence, were preliminary
in that they involved the transfer of DNA extracts
prepared in a clean-room environment to conven-
tional laboratories for processing and sequencing,
creating an opportunity for contamination by
present-day human DNA. Further analysis of
the larger of these data sets (40) showed that it
was contaminated with modern human DNA (44)
to an extent of 11 to 40% (41). We employed a
number of technical improvements, including the
attachment of tagged sequence adaptors in the
clean-room environment (23), to minimize the risk
of contamination and determine about 4 billion
bp from the Neandertal genome.
Paleontological samples. We analyzed a
total of 21 Neandertal bones from Vindija Cave
in Croatia that are of little morphological value.
From below the surface of each of these bones,
we removed 50 to 100 mg of bone powder using
a sterile dentistry drill in our Leipzig clean-room
facility. All samples were screened for the pres-
ence of Neandertal mtDNA by PCR, and three
bones were selected for further analysis (Fig. 1A)
[Supporting Online Material (SOM) Text 2]. The
first of these bones, Vi33.16 (previously Vi-80)
was discovered in stratigraphic layer G3 by Malez
and co-workers in 1980 and has been directly
dated by carbon-14 accelerator mass spectrometry
to 38,310 T 2,130 years before the present (B.P.)
(uncalibrated) (19). It has been previously used for
genome sequencing (40, 42) and for the deter-
mination of a complete mtDNA sequence (45).
The second bone, Vi33.25, comes from layer I,
which is deeper and thus older than layer G. A
complete mtDNA sequence has been determined
from this bone (15). It does not contain enough
collagen to allow a direct date. The third bone,
Vi33.26, comes from layer G (sublayer unknown)
and has not been previously used for large-scale
DNA sequencing. It was directly dated to 44,450 T
550 years B.P. (OxA-V-2291-18, uncalibrated).
Sequencing library construction. A total of
nine DNA extracts were prepared from the three
bones (table S4) using procedures to minimize
laboratory contamination that we have devel-
oped over the past two decades (22, 41). Samples
of each extract were used to construct Roche/454
sequencing libraries that carry the project-specific
tag sequence 5′-TGAC-3′ in their 3′-ends. Each
library was amplified with the primers used in the
454 sequencing emulsion PCR process. To esti-
mate the percentage of endogenous Neandertal
DNA in the extracts, we carried out sequencing
runs using the 454 Life Sciences GS FLX plat-
form and mapped the reads against the human,
chimpanzee, rhesus, and mouse genomes as well
as all nucleotide sequences in GenBank. DNA
sequences with a significantly better match to the
primate genomes than to any of the other sources
of sequences were further analyzed. Mitochon-
drial DNA contamination from modern humans
was estimated by primer extension capture (46)
using six biotinylated primers that target inform-
ative differences between human and Neandertal
mtDNA (45), followed by sequencing on the GS
FLX platform. Extracts that contained more than
1.5% hominin DNA relative to other DNA were
used to construct further libraries. These were sim-
ilarly analyzed to assess the percentage of hominin
DNA and, if found suitable, were used for pro-
duction sequencing on the 454 Life Sciences GS
FLX/Titanium and Illumina GAII platforms.
Enrichment of Neandertal DNA. Depend-
ing on the extract, between 95 and 99% of the
DNA sequenced in the libraries was derived from
nonprimate organisms, which are presumably
derived from microbes that colonized the bone
after the death of the Neandertals. To improve the
ratio of Neandertal to microbial DNA, we iden-
tified restriction enzymes that preferentially cut
bacterialDNA sequences in the librariesand treated
the libraries with these to increase the relative
proportion of Neandertal DNA in the libraries
(SOM Text 1). Such enzymes, which have recog-
nition sites rich in the dinucleotide CpG, allowed
a 4- to 6-fold increase in the proportion of Nean-
dertal DNA in the libraries sequenced. This is
expected to bias the sequencing against GC-rich
regions of the genome and is therefore not suit-
able for arriving at a complete Neandertal genome
sequence. However, for producing an overview of
the genome at about one-fold coverage, it drasti-
cally increases the efficiency of data production
without unduly biasing coverage, especially in
view of the fact that GC-rich sequences are over-
represented in ancient DNA sequencing libraries
(23, 45) so that the restriction enzyme treatment
may help to counteract this bias.
Sequencing platforms and alignments. In
the initial phase of the project, we optimized
production sequencing on the 454 Life Sciences
GS FLX platform from the bones Vi33.16 and
Vi33.26 (0.5 Gb and 0.8 Gb of Neandertal se-
quence, respectively). In the third phase, we
carried out production sequencing on the Illumina/
Solexa GAII platform from the bones Vi33.16,
respectively) (table S4). Each molecule was se-
quenced from both ends (SOM Text 2), and bases
were called with the machine learning algorithm
Ibis (48). All reads were required to carry correct
were not used (40, 42) were not included in this
study. Except when explicitly stated, the analyses
below are based on the largest data sets, generated
on the Illumina platform. In total, we generated 5.3
Gb of Neandertal DNA sequence from about 400
mg of bone powder. Thus, methods for extracting
and sequencing DNA from ancient bones are now
efficient enough to allow genome-wide DNA
sequence coverage with relatively minor damage
to well-preserved paleontological specimens.
causes C to T transitions in the DNA sequences,
El Sidron
Neander Valley
~ 40,000
> 38,000
Vi33-16 Vi33-25 Vi33-26
Fig. 1. Samples and sites from which DNA was retrieved. (A) The three bones from Vindija from which
Neandertal DNA was sequenced. (B) Map showing the four archaeological sites from which bones were
used and their approximate dates (years B.P.). SCIENCE VOL 328 7 MAY 2010 711
at the first position ~40% of cytosine residues can
appear as thymine residues. The frequency of C
to T misincorporations progressively diminishes
further into the molecules. At the 3′-ends, comple-
enzymatic fill-in procedure in which blunt DNA
ends are created before adaptor ligation (23). We
implemented an alignment approach that takes
these nucleotide misincorporation patterns into
account (SOM Text 3) and aligned the Neandertal
sequences to either the reference human genome
(UCSC hg18), the reference chimpanzee genome
(panTro2), or the inferred human-chimpanzee
common ancestral sequence (SOM Text 3).
To estimate the error rate in the Neandertal
DNA sequences determined, we compared reads
that map tothe mitochondrialgenomes,which we
assembled to 35-, 29- and 72-fold coverage for
4). Although C to T and G to A substitutions,
most 0.3% (fig. S4). Because we sequence each
DNA fragment from both sides, and most frag-
ments more than once (49), the latter error rate is
substantially lower than the error rate of the
Illumina platform itself (48, 50).
Number of Neandertal individuals. To assess
whether the three bones come from different
individuals, we first used their mtDNAs. We have
previously determined the complete mtDNA
sequences from the bones Vi33.16 and Vi33.25
(15, 45), and these differ at 10 positions. There-
fore, Vi33.16 and Vi33.25 come from different
Neandertal individuals. For the bone Vi33.26, we
assembled the mtDNA sequence (SOM Text 4)
and found it to be indistinguishable from Vi33.16,
suggesting that it could come from the same in-
from the three bones (SOM Text 4) by asking
whether the frequency of nucleotide differences
between pairs of bones was significantly higher
We find that the within-bone differences are
significantly fewer than the between-bone differ-
ences for all three comparisons (P ≤ 0.001 in all
cases). Thus, all three bones derive from different
individuals, although Vi33.16 and Vi33.26 may
stem from maternally related individuals.
Estimates of human DNA contamination.
We used three approaches that target mtDNA, Y
chromosomal DNA, and nuclear DNA, respec-
tively, to gauge the ratio of present-day human
relative to Neandertal DNA in the data produced.
To analyze the extent of mtDNA contamination,
we used the complete mtDNA from each bone to
identify positions differing from at least 99% of
a worldwide panel of 311 contemporary human
mtDNAs, ignoring positions where a substitu-
tion in the sequences from the Neandertal library
could be due to cytosine deamination (45). For
each sequencing library, the DNA fragments that
cover these positions were then classified ac-
cording to whether they appear to be of Neandertal
or modern human origin (SOM Text 5 and table
S15). For each bone, the level of mtDNA contam-
ination is estimated to be below 0.5% (Table 1).
Because prior to this study no fixed differ-
ences between Neandertal and present-day
humans in the nuclear genome were known, we
used two alternative strategies to estimate levels
of nuclear contamination. In the first strategy, we
determined the sex of the bones. For bones de-
modern human male DNA contamination by
fragments (SOM Text 6). For this purpose, we
ing parts of the human reference Y chromosome
that are located in contiguous DNAsegmentsofat
least 500 nucleotides, carry no repetitive elements,
and contain no 30-nucleotide oligomer elsewhere
in the genome with fewer than three mismatches.
Between 482 and 611 such fragments would be
expected for a male Neandertal bone. However,
only 0 to 4 fragments are observed (Table 1). We
conclude that the three bones are all from female
Neandertals and that previous suggestions that
Vi33.16 was a male (40, 42) were due to mismap-
Y chromosome. We estimate the extent of DNA
contamination from modern human males in the
combined data to be about 0.60%, with an upper
95% bound of 1.53%.
In the second strategy, we take advantage of
a high frequency of a derived allele (i.e., not seen
in chimpanzee) while Neandertals carry a high
chimpanzee) provide information about the ex-
tent of contamination. To implement this idea, we
identified sites where five present-day humans
that we sequenced (see below) all differ from the
chimpanzee genome by a transversion. We further
restricted the analysis to sites covered by two
fragments in one Neandertal and one fragment in
another Neandertal and where at least one an-
cestral allele was seen in both individuals. The
provides an estimate of contamination in combi-
nation with heterozygosity at this class of sites
(Table 1). Using these data (SOM Text 7), we de-
rive a maximum likelihood estimate of contami-
mtDNA contamination produce estimates of less
these data represent bona fide Neandertal DNA
Average DNA divergence between Neandertals
and humans. To estimate the DNA sequence
divergence per base pair between the genomes
of Neandertals and the reference human genome
sequence, we generated three-way alignments
between the Neandertal, human, and chimpan-
zee genomes, filtering out genomic regions that
may be duplicated in either humans or chimpan-
sequence of the common ancestor of humans and
chimpanzees as a reference (51) to avoid potential
biases (39). We then counted the number of sub-
stitutions specific to the Neandertal, the human,
number of substitutions unique to the Neandertal
resulting from deamination of cytosine residues in
the Neandertal DNA, we restricted the divergence
estimates to transversions. We then observed four
to six times as many on the Neandertal as on
the human lineage, probably due to sequencing
errors in the low-coverage Neandertal DNA se-
quences. The numbers of transversions on the
the Neandertal-human ancestor to the chimpan-
zee, were used to estimate the average divergence
between DNA sequences in Neandertals and
present-day humans, as a fraction of the lineage
Table 1. Estimates of human DNA contamination in the DNA sequences produced. Numbers in bold indicate summary contamination estimates over all
Vindija data.
Y chromosomal
diversity (1/2)
plus contamination*
Nuclear ML
Human Neandertal Percent 95% C.I. Observed Expected Percent 95% C.I. Percent Upper 95% C.I.
(95% C.I.)
Vi33.16 56 20,456 0.27 0.21–0.35 4 255 1.57 0.43–3.97 1.4 2.2 n/a
Vi33.25 7 1,691 0.41 0.17–0.85 0 201 0.0 0.00–1.82 1.0 1.7 n/a
Vi33.26 10 4,810 0.21 0.10–0.38 0 210 0.0 0.00–1.74 1.1 1.9 n/a
All data 73 26,957 0.27 0.21–0.34 4 666 0.60 0.16–1.53 1.2 1.6 0.7 (0.6–0.8)
*Assuming similar extents of contamination in the three bones and that individual heterozygosity and population nucleotide diversity is the same for this class of sites.
7 MAY 2010 VOL 328 SCIENCE 712
ancestor of Neandertals, humans, and chimpan-
zees. For autosomes, this was 12.7% for each of
the three bones analyzed. For the X chromosome,
it was 11.9 to 12.4% (table S26). Assuming an
average DNA divergence of 6.5 million years be-
tween the human and chimpanzee genomes (52),
this results in a point estimate for the average di-
vergence of Neandertal and modern human auto-
somal DNA sequences of 825,000 years. We
caution that this is only a rough estimate because
humans and chimpanzees.
Additional Neandertal individuals. To put the
divergence of the Neandertal genome sequences
from Vindija Cave into perspective with regard
to other Neandertals, we generated a much smaller
amount of DNA sequence data from three Ne-
andertal bones from three additional sites (SOM
Text 8) that cover much of the geographical range
of late Neandertals (Fig. 1B): El Sidron in Asturias,
Spain, dated to ~49,000 years B.P. (53); Feldhofer
Cave in the Neander Valley, Germany, from which
we sequenced the type specimen found in 1856
Cave in the Caucasus, Russia, dated to 60,000 to
70,000 years B.P. (55). DNA divergences esti-
mated for each of these specimens to the human
reference genome (table S26) show that none of
them differ significantly from the Vindija individ-
uals, although these estimates are relatively uncer-
tain due to the limited amount of DNA sequence
data. It is noteworthy that the Mezmaiskaya spec-
imen, which is 20,000 to 30,000 years older than
the other Neandertals analyzed and comes from
the easternmost location, does not differ in diver-
gence from the other individuals. Thus, within the
resolution of our current data, Neandertals from
across a great part of their range in western Eurasia
are equally related to present-day humans.
Five present-day human genomes. To put the
divergence of the Neandertal genomes into per-
spective with regard to present-day humans, we
Africa, one Yoruba from West Africa, one Papua
New Guinean, one Han Chinese, and one French
from Western Europe to 4- to 6-fold coverage on
the Illumina GAII platform (SOM Text 9). These
sequences were aligned to the chimpanzee and
human reference genomes and analyzed using a
similar approach to that used for the Neandertal
data. Autosomal DNA sequences of these indi-
viduals diverged 8.2 to 10.3% back along the
lineage leading to the human reference genome,
considerably less than the 12.7% seen in Nean-
dertals (SOM Text 10). We note that the diver-
gence estimate for the Yoruba individual to the
human genome sequence is ~14% greater than
previous estimates for an African American in-
dividual (56) and similarly greater than the
heterozygosity measured in another Yoruba in-
dividual (33). This may be due to differences in
the alignment and filtering procedures between
this and previous studies (SOM Text 9 and 10).
Nevertheless, the divergence of the Neandertal
genome to the human reference genome is greater
than for any of the present-day human genomes
Distributions of DNA divergences to humans.
To explore the variation of DNA sequence
divergence across the genome, we analyzed the
to the reference human genome in 100 kilobase
windows for which at least 50 informative trans-
versions were observed. The majority of the Ne-
andertal divergences overlap with those of the
humans (Fig. 3), reflecting the fact that Nean-
dertals fall inside the variation of present-day hu-
mans. However, the overall divergence is greater
for the three Neandertal genomes. For example,
their modes are around divergences of ~11%,
whereas for the San the mode is ~9% and for the
other present-day humans ~8%. For the Nean-
20%, whereas this is the case for 2.5% to 3.7% of
windows in the current humans.
Papuan individuals, 9.8%, 7.8%, and 5.9% of
windows, respectively, show between 0% and
2% divergence to the human reference genome,
2.2 to 2.5% of windows show 0% to 2% diver-
gence to the reference genome.
A catalog of features unique to the human
genome. The Neandertal genome sequences al-
low us to identify features unique to present-day
humans relative to other, now extinct, hominins.
Of special interest are features that may have
functional consequences. We thus identified, from
whole genome alignments, sites where the human
genome reference sequence does not match chim-
panzee, orangutan, and rhesus macaque. These
are likely to have changed on the human lineage
since the common ancestor with chimpanzee.
Where Neandertal fragments overlapped, we
constructed consensus sequences and joined them
into “minicontigs,” which were used to determine
the Neandertal state at the positions that changed
n H
n C =449,619 n H =30,413 n N =129,103
Neandertal base
aligned base
n C =478,270 n H =32,347 n N =204,845
Neandertal base
aligned base
n C =451,459 n H =30,548 n N =111,215
Neandertal base
aligned base
n N
n C
Fig. 2. Nucleotide substitutions inferred to have occurred on the evolutionary lineages leading to the
Neandertals, the human, and the chimpanzee genomes. In red are substitutions on the Neandertal lineage,
in yellow the human lineage, and in pink the combined lineage from the common ancestor of these to the
shown. The excess of C to T and G to A substitutions are due to deamination of cytosine residues in the
Neandertal DNA.
0 10 20 30 40 50
divergence to hg18 in 100kb bins
(% of lineage to human/chimpanzee common ancestor)
fraction of bins
Fig. 3. Divergence of Neandertal and human ge-
nomes. Distributions of divergence from the human
genome reference sequence among segments of
present-day humans. SCIENCE VOL 328 7 MAY 2010 713
Table 2. Aminoacidchangesthatarefixedinpresent-dayhumansbutancestral
in Neandertals. The table is sorted by Grantham scores (GS). Based on the
classification proposed by Li et al. in (87), 5 amino acid substitutions are radical
(>150), 7 moderately radical (101 to 150), 33 moderately conservative (51 to
showing multiple substitutions have bold SwissProt identifiers.(TableS15shows
ID Pos AA GS Description/function
RPTN 785 */R – Multifunctional epidermal matrix protein
GREB1 1164 R/C 180 Response gene in estrogen receptor–regulated pathway
OR1K1 267 R/C 180 Olfactory receptor, family 1, subfamily K, member 1
SPAG17 431 Y/D 160 Involved in structural integrity of sperm central apparatus axoneme
NLRX1 330 Y/D 160 Modulator of innate immune response
NSUN3 78 S/F 155 Protein with potential SAM-dependent methyl-transferase activity
RGS16 197 D/A 126 Retinally abundant regulator of G-protein signaling
BOD1L 2684 G/R 125 Biorientation of chromosomes in cell division 1-like
CF170 505 S/C 112 Uncharacterized protein: C6orf170
STEA1 336 C/S 112 Metalloreductase, six transmembrane epithelial antigen of prostate 1
F16A2 630 R/S 110 Uncharacterized protein: family with sequence similarity 160, member A2
LTK 569 R/S 110 Leukocyte receptor tyrosine kinase
BEND2 261 V/G 109 Uncharacterized protein: BEN domain-containing protein 2
O52W1 51 P/L 98 Olfactory receptor, family 52, subfamily W, member 1
CAN15 427 L/P 98 Small optic lobes homolog, linked to visual system development
SCAP 140 I/T 89 Escort protein required for cholesterol as well as lipid homeostasis
TTF1 474 I/T 89 RNA polymerase I termination factor
OR5K4 175 H/D 81 Olfactory receptor, family 5, subfamily K, member 4
SCML1 202 T/M 81 Putative polycomb group (PcG) protein
TTL10 394 K/T 78 Probable tubulin polyglutamylase, forming polyglutamate side chains on tubulin
AFF3 516 S/P 74 Putative transcription activator, function in lymphoid development/oncogenesis
EYA2 131 S/P 74 Tyrosine phosphatase, dephosphorylating “Tyr-142” of histone H2AX
NOP14 493 T/R 71 Involved in nucleolar processing of pre-18S ribosomal RNA
PRDM10 1129 N/T 65 PR domain containing 10, may be involved in transcriptional regulation
BTLA 197 N/T 65 B and T lymphocyte attenuator
O2AT4 224 V/A 64 Olfactory receptor, family 2, subfamily AT, member 4
CAN15 356 V/A 64 Small optic lobes homolog, linked to visual system development
ACCN4 160 V/A 64 Amiloride-sensitive cation channel 4, expressed in pituitary gland
PUR8 429 V/A 64 Adenylsuccinate lyase (purine synthesis)
MCHR2 324 A/V 64 Receptor for melanin-concentrating hormone, coupled to G proteins
AHR 381 V/A 64 Aromatic hydrocarbon receptor, a ligand-activated transcriptional activator
FAAH1 476 A/G 60 Fatty acid amide hydrolase
SPAG17 1415 T/A 58 Involved in structural integrity of sperm central apparatus axoneme
ZF106 697 A/T 58 Zinc finger protein 106 homolog / SH3-domain binding protein 3
CAD16 342 T/A 58 Calcium-dependent, membrane-associated glycoprotein (cellular recognition)
K1C16 306 T/A 58 Keratin, type I cytoskeletal 16 (expressed in esophagus, tongue, hair follicles)
LIMS2 360 T/A 58 Focal adhesion protein, modulates cell spreading and migration
ZN502 184 T/A 58 Zinc finger protein 502, may be involved in transcriptional regulation
MEPE 391 A/T 58 Matrix extracellular phosphoglycoprotein, putative role in mineralization
FSTL4 791 T/A 58 Follistatin-related protein 4 precursor
SNTG1 241 T/S 58 Syntrophin, gamma 1; binding/organizing subcellular localization of proteins
RPTN 735 K/E 56 Multifunctional epidermal matrix protein
BCL9L 543 S/G 56 Nuclear cofactor of beta-catenin signaling, role in tumorigenesis
SSH2 1033 S/G 56 Protein phosphatase regulating actin filament dynamics
PEG3 1521 S/G 56 Apoptosis induction in cooperation with SIAH1A
DJC28 290 K/Q 53 DnaJ (Hsp40) homolog, may have role in protein folding or as a chaperone
CLTR2 50 F/V 50 Receptor for cysteinyl leukotrienes, role in endocrine and cardiovascular systems
KIF15 827 N/S 46 Putative kinesin-like motor enzyme involved in mitotic spindle assembly
SPOC1 355 Q/R 43 Uncharacterized protein: SPOC domain containing 1
TTF1 229 R/Q 43 RNA polymerase I termination factor
F166A 134 T/P 38 Uncharacterized protein: family with sequence similarity 166, member A
CL066 426 V/L 32 Uncharacterized protein: chromosome 12 open reading frame 66
PCD16 763 E/Q 29 Calcium-dependent cell-adhesion protein, fibroblasts expression
TRPM5 1088 I/V 29 Voltage-modulated cation channel (VCAM), central role in taste transduction
S36A4 330 H/R 29 Solute carrier family 36 (proton/amino acid symporter)
GP132 328 E/Q 29 High-affinity G-protein couple receptor for lysophosphatidylcholine (LPC)
ZFY26 237 H/R 29 Zinc finger FYVE domain-containing, associated with spastic paraplegia-15
continued on next page
7 MAY 2010 VOL 328 SCIENCE 714
on the human lineage. To minimize alignment
errors and substitutions, we disregarded all sub-
stitutions and insertions or deletions (indels) with-
in 5 nucleotides of the ends of minicontigs or
within 5 nucleotides of indels.
Among 10,535,445 substitutions and 479,863
indels inferred to have occurred on the human
lineage, we have information in the Neandertal
genome for 3,202,190 and 69,029, i.e., 30% and
14%, respectively. The final catalog thus repre-
sents those sequenced positions where we have
high confidence in their Neandertal state (SOM
Text 11). As expected, the vast majority of those
substitutions and indels (87.9% and 87.3%,
respectively) occurred before the Neandertal
divergence from modern humans.
Features that occur in all present-day humans
(i.e., have been fixed), although they were absent
or variable in Neandertals, are of special interest.
We found 78 nucleotide substitutions that change
humans are fixed for a derived state and where
Neandertals carry the ancestral (chimpanzee-like)
state (Table 2 and table S28). Thus, relatively few
amino acid changes have become fixed in the last
few hundred thousand years of human evolution;
an observation consistent with a complementary
study (57). We found only five genes with more
than one fixed substitution changing the primary
structure of the encoded proteins. One of these is
axoneme, a structure responsible for the beating of
the sperm flagellum (58). The second is PCD16,
which encodes fibroblast cadherin-1, a calcium-
dependent cell-cell adhesion molecule that may be
a transcription termination factor that regulates
ribosomal gene transcription (60). The fourth is
CAN15, which encodes a protein of unknown
expressed in the epidermis and at high levels in
and the filiform papilli of the tongue.
One ofthe substitutionsin RPTN creates a stop
rather than 892 amino acids (SOM Text 11). We
Neandertals and chimpanzees has been lost in
some present-day humans. TRPM1 encodes mela-
statin, an ion channel important for maintaining
genes that either carry multiple fixed substitutions
changing amino acids or in which a start or stop
codon has been lost or gained. This suggests that
have changed on the hominin lineage.
We also identified a number of potential reg-
ulatory substitutions that are fixed in present-day
humans but not Neandertals. Specifically, we find
42 substitutions and three indels in 5′-untranslated
regions, and 190 substitutions and 33 indels in 3′-
untranslated regions that have become fixed in
humans since they diverged from Neandertals. Of
special interest are microRNAs (miRNAs), small
RNAs that regulate gene expression by mRNA
cleavage or repression of translation. We found
one miRNAwhere humans carry a fixed substitu-
tion at a position that was ancestral in Neandertals
(hsa-mir-1304) and one case of a fixed single nu-
cleotide insertion where Neandertal is ancestral
(AC109351.3). While the latter insertion is in a
bulge in the inferred secondary structure of the
targets, the substitution in mir-1304 occurs in the
seed region, suggesting that it is likely to have al-
tered target specificity in modern humans relative
to Neandertals and other apes (fig. S16).
Human accelerated regions (HARs) are de-
fined as regions of the genome that are conserved
throughout vertebrate evolution but that changed
their common ancestor.Weexamined2613HARs
(SOM Text 11) and obtained reliable Neandertal
sequence for 3259 human-specific changes in
HARs. The Neandertals carry the derived state at
91.4% of these, significantly more than for other
human-specific substitutions and indels (87.9%).
Thus, changes in the HARs tend to predate the
split between Neandertals and modern humans.
However, we also identified 51 positions in 45
HARs where Neandertals carry the ancestral
version whereas all known present-day humans
carry the derived version. These represent recent
changes that may be particularly interesting to
explore functionally.
Neandertalsegmentalduplications. We ana-
lyzed Neandertal segmental duplications by mea-
suring excess read-depth to identify and predict
the copynumberofduplicatedsequences,defined
of 94 Mb of segmental duplications were pre-
dicted in the Neandertal genome (table S33),
which is in close agreement with what has been
found in present-day humans (62) (fig. S18). We
identified 111 potentially Neandertal-specific seg-
mental duplications (average size 22,321 bp and
total length 1862 kb) that did not overlap with
direct experimental validation is not possible, we
note that 81% (90/111) of these regions also
showed excess sequence diversity (>3 SD beyond
the mean) consistent with their being bona fide
show some evidence of increased copy number
in humans, although they have not been pre-
viously classified as duplications (fig. S22). We
ID Pos AA GS Description/function
CALD1 671 I/V 29 Actin- and myosin-binding protein, regulation of smooth muscle contraction
CDCA2 606 I/V 29 Regulator of chromosome structure during mitosis
GPAA1 275 E/Q 29 Glycosylphosphatidylinositol anchor attachment protein
ARSF 200 I/V 29 Arylsulfatase F precursor, relevant for composition of bone and cartilage matrix
OR4D9 303 R/K 26 Olfactory receptor, family 4, subfamily D, member 9
EMIL2 155 R/K 26 Elastin microfibril interface-located protein (smooth muscle anchoring)
PHLP 216 K/R 26 Putative modulator of heterotrimeric G proteins
TKTL1 317 R/K 26 Transketolase-related protein
MIIP 280 H/Q 24 Inhibits glioma cells invasion, down-regulates adhesion and motility genes
SPTA1 265 N/D 23 Constituent of cytoskeletal network of the erythrocyte plasma membrane
PCD16 777 D/N 23 Calcium-dependent cell-adhesion protein, fibroblasts expression
CS028 326 L/F 22 Uncharacterized protein: chromosome 19 open reading frame 28
PIGZ 425 L/F 22 Mannosyltransferase for glycosylphosphatidylinositol-anchor biosynthesis
DISP1 1079 V/M 21 Segment-polarity gene required for normal Hedgehog (Hh) signaling
RNAS7 44 M/V 21 Protein with RNase activity for broad-spectrum of pathogenic microorganisms
KR241 205 V/M 21 Keratin-associated protein, formation of a rigid and resistant hair shaft
SPLC3 108 I/M 10 Short palate, lung, and nasal epithelium carcinoma-associated protein
NCOA6 823 I/M 10 Hormone-dependent coactivation of several receptors
WWC2 479 M/I 10 Uncharacterized protein: WW, C2, and coiled-coil domain containing 2
ASCC1 301 E/D 0 Enhancer of NF-kappa-B, SRF, and AP1 transactivation
PROM2 458 D/E 0 Plasma membrane protrusion in epithelial and nonepithelial cells SCIENCE VOL 328 7 MAY 2010 715
duplications with no evidence of duplication
among humans or any other primate (fig. S23),
and none contained known genes.
A comparison to any single present-day
human genome reveals that 89% of the detected
duplications are shared with Neandertals. This is
lower than the proportion seen between present-
day humans (around 95%) but higher than what
is observed when the Neandertals are compared
with the chimpanzee (67%) (fig. S19).
a pool of three individuals and represents an aver-
created two resampled sets from three human
genomes (SOM Text 12) at a comparable level
of mixture and coverage (table S34 and figs. S24
and S25). The analysis of both resampled sets
show a nonsignificant trend toward more dupli-
cated sequences among Neandertals than among
present-day humans (88,869 kb, N = 1129 re-
gions for present-day humans versus 94,419 kb,
N = 1194 for the Neandertals) (fig. S25).
We also estimated the copy number for
three previously analyzed human genomes (SOM
Text 12). Copy number was correlated between
the twogroups(r 2 =0.91)(fig.S29),withonly43
genes (15 nonredundant genes >10 kb) showing a
S36). Of these genes, 67% (29/43) are increased in
Neandertals compared with present-day humans,
and most of these are genes of unknown function.
One of the most extreme examples is the gene
PRR20 (NM_198441), for which we predicted 68
copies in Neandertals, 16 in humans, and 58 in the
chimpanzee. It encodes a hypothetical proline-rich
dicted highercopy numberinhumansasopposed
to Neandertals included NBPF14 (DUF1220),
and TBC1D3 (NM_001123391).
humans. Neandertals fall within the variation of
present-day humans for many regions of the
genome; that is, Neandertals often share derived
single-nucleotide polymorphism (SNP) alleles
that takes advantage of this fact by looking for
genomic regions where present-day humans share
from Neandertals, and Neandertals therefore lack
derived alleles found in present-day humans
(except in rare cases of parallel substitutions)
(Fig. 4A). Gene flow between Neandertals and
modern humans after their initial population sep-
aration might obscure some cases of positive se-
lection by causing Neandertals and present-day
humans to share derived alleles, but it will not
cause false-positive signals.
We identified SNPs as positions that vary
among the five present-day human genomes of
ancestral state (SOM Text 13). We ignored SNPs
at CpG sites since these evolve rapidly and may
thus be affected by parallel mutations. We iden-
tified 5,615,438 such SNPs, at about 10% of
which Neandertals carry the derived allele. As
expected, SNPs with higher frequencies of the
derived allele in present-day humans were more
likely to show the derived allele in Neandertals
Region width (cM)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
43.0 43.5 43.8
43.1 43.2 43.3 43.4 43.6 43.7
chromosome 2 position (Mb)
ln(O(N D ,s,e) / E(N D ,s,e))
SNPs (N D )
Neandertals Neandertals French
PNG Yoruba San French
PNG Yoruba San
Fig. 4. Selectivesweepscreen.(A)Schematicillustrationof
the rationale for the selective sweep screen. For many
is old enough to include Neandertals (left). Thus, for SNPs
allele (blue). However, in genomic regions where an
advantageous mutation arises (right, red star) and sweeps
to high frequency or fixation in present-day humans,
Neandertals will be devoid of derived alleles. (B) Candidate
regions of selective sweeps. All 4235 regions of at least
25 kb where S (see SOM Text 13) falls below two standard
deviations of the mean are plotted by their S and genetic
width. Regions on the autosomes are shown in orange and
those on the X chromosome in blue. The top 5% by S are
shadowed in light blue. (C) The top candidate region from
THADA. The red line shows the log-ratio of the number of
observed Neandertal-derived alleles versus the number of
expected Neandertal-derived alleles, within a 100 kilobase window. The blue dots above the panel indicate all SNP positions, and the green dots indicate SNPs
where the Neandertal carries the derived allele.
7 MAY 2010 VOL 328 SCIENCE 716
(fig. S31A). We took advantage of this fact to
calculate (fig. S31C) the expected number of
the human genome. The observed numbers of de-
numbers to identify regions where the Neandertal
carriesfewer derivedallelesthanexpectedrelative
method is that it has more power to detect older
selective sweeps where allele frequency spectra in
present-day humans have recovered to the point
that appreciable derived allele frequencies are ob-
served, whereas it has relatively low power to
detect recent selective sweeps where the derived
alleles are at low frequencies in present-day
humans. It is therefore particularly suited to detect
positive selection that occurred early during the
with, or shortly after, their population divergence
from Neandertals (Fig. 4A).
We identified a total of 212 regions contain-
ing putative selective sweeps (Fig. 4B and SOM
Text 13). The region with the strongest statistical
signal contained a stretch of 293 consecutive
SNP positions in the first half of the gene AUTS2
where only ancestral alleles are observed in the
Neandertals (fig. S34).
We ranked the 212 regions with respect to
theirgenetic width in centimorgans (Fig. 4B, and
a selective sweep will be larger the fewergenera-
tions it took for the sweep to reach fixation, as
fewer recombination events will then have oc-
curred during the sweep. Thus, the more intense
the affected region is expected to be. Table 3 lists
the 20 widest regions and the genes encoded in
genes. These may thus contain structural or reg-
ulatory genomic features under positive selection
during early human history. The remaining 15
regions contain between one and 12 genes. The
widest region is located on chromosome 2 and
contains the gene THADA, where a region of 336
kb is depleted of derived alleles in Neandertals.
SNPs in the vicinity of THADA have been asso-
ciated with type II diabetes, and THADA expres-
sion differs between individuals with diabetes
early modern humans. The largest deficit of
consecutive human SNP positions (Fig. 4C). In
of ~700 bp that is conserved from mouse to pri-
mates, whereas the human reference genome as
well as the four humans for which data are avail-
is polymorphic in humans, as it is in dbSNP.
Mutations in several genes in Table 3 have
been associated with diseases affecting cognitive
capacities. DYRK1A, which lies in the Down syn-
drome critical region, is thought to underlie some
three copies of chromsome 21 (64). Mutations in
NRG3 have been associated with schizophrenia, a
conditionthathasbeensuggestedto affecthuman-
specific cognitive traits (65, 66). Mutations in
CADPS2 have been implicated in autism (67), as
have mutations in AUTS2 (68). Autism is a de-
velopmental disorder of brain function in which
social interactions, communication, activity, and
interest patterns are affected, as well as cognitive
aspects crucial for human sociality and culture
(69). It may thus be that multiple genes involved
in cognitive development were positively selected
during the early history of modern humans.
It is the only gene in the genome known to cause
cleidocranial dysplasia, which is characterized by
delayed closure of cranial sutures, hypoplastic
or aplastic clavicles, a bell-shaped rib cage, and
dental abnormalities (70). Some of these features
affect morphological traits for which modern
humans differ from Neandertals as well as other
earlier hominins. For example, the cranial malfor-
mations seen in cleidocranial dysplasia include
frontal bossing, i.e., a protruding frontal bone. A
between modern humans and Neandertals as well
as other archaic hominins. The clavicle, which is
affected in cleidocranial dysplasia, differs in mor-
(71) and is associated with a different architecture
of the shoulder joint. Finally, a bell-shaped rib
cage is typical of Neandertals and other archaic
evolutionary change in RUNX2 was of impor-
tance in the origin of modern humans and that
the upper body and cranium.
Population divergence of Neandertals and
modern humans. A long-standing question is
when the ancestral populations of Neandertals and
modern humans diverged. Population divergence,
defined as the time point when two populations
last exchanged genes, is more recent than the
DNA sequence divergence because the latter is
the average time to the common ancestors of
DNA sequences within the ancestral population.
The divergence time of two populations can be
Table 3. Top 20 candidate selective sweep regions.
Region (hg18) S Width (cM) Gene(s)
chr2:43265008-43601389 -6.04 0.5726 ZFP36L2;THADA
chr11:95533088-95867597 -4.78 0.5538 JRKL;CCDC82;MAML2
chr10:62343313-62655667 -6.1 0.5167 RHOBTB1
chr21:37580123-37789088 -4.5 0.4977 DYRK1A
chr10:83336607-83714543 -6.13 0.4654 NRG3
chr14:100248177-100417724 -4.84 0.4533 MIR337;MIR665;DLK1;RTL1;MIR431;MIR493;MEG3;MIR770
chr3:157244328-157597592 -6 0.425 KCNAB1
chr11:30601000-30992792 -5.29 0.3951
chr2:176635412-176978762 -5.86 0.3481 HOXD11;HOXD8;EVX2;MTX2;HOXD1;HOXD10;HOXD13;
chr11:71572763-71914957 -5.28 0.3402 CLPB;FOLR1;PHOX2A;FOLR2;INPPL1
chr7:41537742-41838097 -6.62 0.3129 INHBA
chr10:60015775-60262822 -4.66 0.3129 BICC1
chr6:45440283-45705503 -4.74 0.3112 RUNX2;SUPT3H
chr1:149553200-149878507 -5.69 0.3047 SELENBP1;POGZ;MIR554;RFX5;SNX27;CGN;TUFT1;PI4KB;
chr7:121763417-122282663 -6.35 0.2855 RNF148;RNF133;CADPS2
chr7:93597127-93823574 -5.49 0.2769
chr16:62369107-62675247 -5.18 0.2728
chr14:48931401-49095338 -4.53 0.2582
chr6:90762790-90903925 -4.43 0.2502 BACH2
chr10:9650088-9786954 -4.56 0.2475 SCIENCE VOL 328 7 MAY 2010 717
inferred from the frequency with which derived
alleles of SNPs discovered in one population are
likely it is that derived alleles discovered in one
population are due to novel mutations in that
population. We compared transversion SNPs
identified in a Yoruba individual (33) to other
humans and used the chimpanzee and orangutan
genomes to identify the ancestral alleles. We
found that the proportion of derived alleles is
30.6% in the Yoruba, 29.8% in the Han Chinese,
29.7% in the French, 29.3% in the Papuan,
26.3% in the San, and 18.0% in Neandertals. We
used four models of Yoruba demographic history
to translate derived allele fractions to population
divergence (SOM Text 14). All provided similar
estimates. Assuming that human-chimpanzee
average DNA sequence divergence was 5.6 to
8.3 million years ago, this suggests that Nean-
dertals and present-day human populations
separated between 270,000 and 440,000 years
ago (SOM Text 14), a date that is compatible
with some interpretations of the paleontological
and archaeological record (2, 72).
Neandertals are closer to non-Africans than
to Africans. TotestwhetherNeandertalsaremore
closely related to some present-day humans than
to others, we identified SNPs by comparing one
randomly chosen sequence from each of two
present-day humans and asking if the Neandertals
match the alleles of the two individuals equally
ern humans ceased before differentiation between
present-day human populations began, this is ex-
pected to be the case no matter which present-day
humans are compared. The prediction of this null
hypothesis of no gene flow holds regardless of
population expansions, bottlenecks, or substruc-
ture that might have occurred in modern human
when single chromosomes are analyzed in the
twopresent-daypopulations,differencesin demo-
graphic histories in the two populations will not
affect the results even if they may profoundly
model of later gene flow between Neandertals
and modern humans, we expect Neandertals to
match alleles in individuals from some parts of
the world more often than the others.
We restricted this analysis to biallelic SNPs
where two present-day humans carry different
alleles and where the Neandertals carried the
derivedallele, i.e.,notmatching chimpanzee.We
measured the difference in the percent matching
by a statistic D(H 1 , H 2 , Neandertal, chimpanzee)
(SOM Text 15) that does not differ significantly
from zero when the derived alleles in the Ne-
andertal match alleles in the two humans equally
often. If D is positive, Neandertal alleles match
alleles in the second human (H 2 ) more often,
while if D is negative, Neandertal alleles match
alleles in the first human (H 1 ) more often. We per-
formed this test using eight present-day humans:
two European Americans (CEU), two East Asians
(ASN), and four West Africans (YRI), for whom
sequences have been generated with Sanger
along with the Neandertal reads to the chim-
panzee genome. We find that the Neandertals
are equally close to Europeans and East Asians:
0.46% (<1.2 SD from 0% or P = 0.25). How-
ever, the Neandertals are significantly closer to
non-Africans than to Africans: D(YRI, CEU, Ne-
ASN, Neandertal, chimpanzee) = 4.81 T 0.39%
(both >11 SD from 0% or P << 10 −12 ) (table S51).
The greater genetic proximity of Neandertals
to Europeans and Asians than to Africans is seen
no matter how we subdivide the data: (i) by
individual pairs of humans (Table 4), (ii) by
chromosome, (iii) by substitutions that are tran-
versus all other sites, (v) by Neandertal sequences
shorter or longer than 50 bp, and (vi) by 454 or
Illumina data. It is also seen when we restrict the
analysis to A/T and C/G substitutions, showing
that our observations are unlikely to be due to
biased allele calling or biased gene conversion
(SOM Text 15).
A potential artifact that might explain these
observations is contamination of the Neander-
tal sequences with non-African DNA. However,
the magnitude of contamination necessary to
are both over 10% and thus inconsistent with our
estimates of contamination in the Neandertal data,
which are all below 1% (Table 1). In addition to
the low estimates of contamination, there are two
reasons that contamination cannot explain our
bones Vi33.16, Vi33.25, and Vi33.26 separately,
we obtain consistent values of the D statistics,
which is unlikely to arise under the hypothesis of
contamination because each specimen was indi-
vidually handled and was thus unlikely to have
(SOM Text 15). Second, if European contami-
nation explains the skews, the ratio D(H 1 , H 2 ,
Neandertal, chimpanzee)/D(H 1 , H 2 , European,
chimpanzee) should provide a direct estimate of
the contamination proportion a,because the ratio
measures how close the Neandertal data are to
what would be expected from entirely European
contamination.However,whenweestimatea for
all three population pairs, we obtain statistically
inconsistent results: a = 13.9 T 1.1% for H 1 -H 2 =
CEU-YRI, a = 18.9 T 1.9% for ASN-YRI, and
a = –3.9 T 5.1% for CEU-ASN. This indicates
that the skews cannot be explained by a unifying
hypothesis of European contamination.
to a more diverse set of modern humans, we
repeated the analysis above using the genome
sequences of the French, Han, Papuan, Yoruba,
and San individuals that we generated (SOM
(Papuan-French-Han) or within Africa (Yoruba-
San) shows significant skews in D (|Z| < 2 SD).
However, all comparisons of non-Africans and
Africans show that the Neandertal is closer to the
(Table 4). Thus, analyses of present-day humans
consistently show that Neandertals share signifi-
with Africans, whereas they share equal amounts
Direction of gene flow. A parsimonious ex-
planation for these observations is that Nean-
dertals exchanged genes with the ancestors of
non-Africans. To determine the direction of gene
flow consistent with the data, we took advantage
of the fact that non-Africans are more distantly
related to San than to Yoruba (73–75) (Table 4).
This is reflected in the fact that D(P, San, Q,
chimpanzee) is 1.47 to 1.68 times greater than
non-Africans (SOM Text 15). Under the hypoth-
esis of modern human to Neandertal gene flow,
D(P, San, Neandertal, chimpanzee) should be
greater than D(P, Yoruba, Neandertal, chimpan-
zee) by the same amount, because the deviation
of the D statistics is due to Neandertals inheriting
a proportion of ancestry from a non-African-like
population Q. Empirically, however, the ratio is
significantly smaller (1.00 to 1.03, P << 0.0002)
flow detected was from Neandertals into modern
Segments of Neandertal ancestry in non-
African genomes. If Neandertal-to-modern hu-
find DNA segments with an unusually low diver-
gence to Neandertal in present-day humans. Fur-
thermore, we expect that such segments will tend
to have an unusually high divergence to other
present-day humans because they come from
with low divergence to Neandertals are expected
to arise due to other effects, for example, a low
mutation rate in a genomic segment since the
split from the chimpanzee lineage. However, this
will cause present-day humans to tend to have
lowdivergencefrom each other insuch segments,
i.e., the opposite effect from gene flow. The qual-
us to detect a signal of gene flow. To search for
segments with relatively few differences between
individual, both alleles would have to be derived
from Neandertals to produce a strong signal. To
obtain haploid human sequences, we took advan-
tage of the fact that the human genome reference
sequence is composed of a tiling path of bacterial
artificial chromosomes (BACs), which each rep-
resent single human haplotypes over scales of
50 to 150 kb, and we focused on BACs from
RPCI11, the individual that contributed about
two-thirds of the reference sequence and that has
been previously shown to be of about 50% Euro-
pean and 50% African ancestry (SOM Text 16)
7 MAY 2010 VOL 328 SCIENCE 718
day human divergence and found that in the ex-
treme tail of low-divergence BACs there was a
greater proportion of European segments than Af-
rican segments, consistent with the notion that
somegenomic segments (SOM Text 16) were ex-
changed between Neandertals and non-Africans.
To determine whether these segments are
unusual in their divergence to other present-day
humans, we examined the divergence of each
segment to the genome of Craig Venter (77). We
find that present-day African segments with the
lowest divergence to Neandertals have a diver-
gence to Venter that is 35% of the genome-wide
average and that their divergence to Venter in-
creases monotonically with divergence to Nean-
dertals, as would be expected if these segments
were similar in Neandertals and present-day
humans due to, for example, a low mutation
rate in these segments (Fig. 5A). In contrast, the
Neandertals have a divergence to Venter that is
140% of the genome-wide average, which drops
behavior is significant at P < 10 −9 and is unex-
pected in the absence of gene flow from Nean-
dertals into the ancestors of non-Africans. The
reason for this is that other causes for a low di-
vergence to Neandertals, such as low mutation
or gene flow into Neandertals, would produce
monotonic behaviors. Among the segments with
low divergence to Neandertals and high diver-
ancestry (Fig. 5B), suggesting that segments of
likely Neandertal ancestry in present-day humans
can be identified with relatively high confidence.
Non-Africans haplotypes match Neandertals
unexpectedly often. An alternative approach to
detect gene flow from Neandertals into modern
humans is to focus on patterns of variation in
Table 4. Neandertals are more closely related to present-day non-
Africans than to Africans. For each pair of modern humans H 1 and H 2
that we examined, we reported D (H 1 , H 2 , Neandertal, Chimpanzee): the
difference in the percentage matching of Neandertal to two humans at
sites where Neandertal does not match chimpanzee, with T1 standard
error. Values that deviate significantly from 0% after correcting for 38
hypotheses tested are highlighted in bold (|Z| > 2.8 SD). Neandertal is
skewed toward matching non-Africans more than Africans for all pairwise
comparisons. Comparisons within Africans or within non-Africans are all
consistent with 0%.
Population comparison H 1 H 2
% Neandertal matching to H 2 –
% Neandertal matching to H 1
(T1 standard error)
ABI3730 sequencing (~750 bp reads) used to discover H 1 -H 2 differences
African to African NA18517 (Yoruba) NA18507 (Yoruba) -0.1 T 0.6
NA18517 (Yoruba) NA19240 (Yoruba) 1.5 T 0.7
NA18517 (Yoruba) NA19129 (Yoruba) -0.1 T 0.7
NA18507 (Yoruba) NA19240 (Yoruba) -0.5 T 0.6
NA18507 (Yoruba) NA19129 (Yoruba) 0.0 T 0.5
NA19240 (Yoruba) NA19129 (Yoruba) -0.6 T 0.7
African to Non-African NA18517 (Yoruba) NA12878 (European) 4.1 ± 0.8
NA18517 (Yoruba) NA12156 (European) 5.1 ± 0.7
NA18517 (Yoruba) NA18956 (Japanese) 2.9 ± 0.8
NA18517 (Yoruba) NA18555 (Chinese) 3.9 ± 0.7
NA18507 (Yoruba) NA12878 (European) 4.2 ± 0.6
NA18507 (Yoruba) NA12156 (European) 5.5 ± 0.6
NA18507 (Yoruba) NA18956 (Japanese) 5.0 ± 0.7
NA18507 (Yoruba) NA18555 (Chinese) 5.8 ± 0.6
NA19240 (Yoruba) NA12878 (European) 3.5 ± 0.7
NA19240 (Yoruba) NA12156 (European) 3.1 ± 0.7
NA19240 (Yoruba) NA18956 (Japanese) 2.7 ± 0.7
NA19240 (Yoruba) NA18555 (Chinese) 5.4 ± 0.9
NA19129 (Yoruba) NA12878 (European) 3.9 ± 0.7
NA19129 (Yoruba) NA12156 (European) 4.9 ± 0.7
NA19129 (Yoruba) NA18956 (Japanese) 5.1 ± 0.8
NA19129 (Yoruba) NA18555 (Chinese) 4.7 ± 0.8
Non-African to Non-African NA12878 (European) NA12156 (European) -0.5 T 0.8
NA12878 (European) NA18956 (Japanese) 0.4 T 0.8
NA12878 (European) NA18555 (Chinese) 0.3 T 0.8
NA12156 (European) NA18956 (Japanese) -0.3 T 0.8
NA12156 (European) NA18555 (Chinese) 1.3 T 0.7
NA18956 (Japanese) NA18555 (Chinese) 2.5 T 0.9
Illumina GAII sequencing (~76 bp reads) used to discover H 1 -H 2 differences
African - African HGDP01029 (San) HGDP01029 (Yoruba) -0.1 T 0.4
African to Non-African HGDP01029 (San) HGDP00521 (French) 4.2 ± 0.4
HGDP01029 (San) HGDP00542 (Papuan) 3.9 ± 0.5
HGDP01029 (San) HGDP00778 (Han) 5.0 ± 0.5
HGDP01029 (Yoruba) HGDP00521 (French) 4.5 ± 0.4
HGDP01029 (Yoruba) HGDP00542 (Papuan) 4.4 ± 0.6
HGDP01029 (Yoruba) HGDP00778 (Han) 5.3 ± 0.5
Non-African to Non-African HGDP00521 (French) HGDP00542 (Papuan) 0.1 T 0.5
HGDP00521 (French) HGDP00778 (Han) 1.0 T 0.6
HGDP00542 (Papuan) HGDP00778 (Han) 0.7 T 0.6 SCIENCE VOL 328 7 MAY 2010 719
the Neandertal genome—in order to identify re-
gions that are the strongest candidates for being
derived from Neandertals. If these candidate re-
gions match the Neandertals at a higher rate than
is expected by chance, this provides additional
evidence for gene flow from Neandertals into
modern humans.
We thus identified regions in which there is
considerably more diversity outside Africa than
inside Africa, as might be expected in regions that
have experienced gene flow from Neandertals to
non-Africans. We used 1,263,750 Perlegen Class
A SNPs, identified in individuals of diverse
ancestry (78), and found 13 candidate regions of
Neandertal ancestry (SOM Text 17). A prediction
of Neandertal-to-modern human gene flow is that
from Neandertals will tend to match Neandertal
more often than their frequency in the present-day
human population. To test this prediction, we
identified 166 “tag SNPs” that separate 12 of the
haplotype clades in non-Africans (OOA) from the
cosmopolitan haplotype clades shared between
Africans and non-Africans (COS) and for which
we had data from the Neandertals. Overall, the
Neandertals match the deep clade unique to non-
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6
hsRef-Venter divergence normalized by human-
chimp. divergence and scaled by the average
hsRef-Neandertal divergence normalized by
human-chimp. divergence and scaled by the average
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6
hsRef-Venter divergence normalized by human-
chimp. divergence and scaled by the average
hsRef-Neandertal divergence normalized by
human-chimp. divergence and scaled by the average
Fig. 5. Segments of Neandertal ancestry in the human reference genome.
We examined 2825 segments in the human reference genome that are of
African ancestry and 2797 that are of European ancestry. (A) European
segments, with few differences from the Neandertals, tend to have many
differences from other present-day humans, whereas African segments do
not, as expected if the former are derived from Neandertals. (B) Scatter plot
of the segments in (A) with respect to their divergence to the Neandertals
and to Venter. In the top left quandrant, 94% of segments are of European
ancestry, suggesting that many of them are due to gene flow from
Table 5. Non-AfricanhaplotypesmatchNeandertalatanunexpectedrate.We
identified 13 candidate gene flow regions by using 48 CEU+ASN to represent
We identified tag SNPs for each region that separate an out-of-Africa specific
clade (OOA) from a cosmopolitan clade (COS) and then assessed the rate at
based on their ancestral and derived status in Neandertal and whether they
match the OOA-specific clade or not. Thus, the categories are AN (Ancestral
Nonmatch), DN (Derived Nonmatch), DM (Derived Match), and AM (Ancestral
Match). We do not list the sites where matching is ambiguous.
Start of candidate
region in Build 36
End of candidate
region in Build 36
ratio of
gene tree
frequency of
tag in OOA
Neandertal does
(N)ot match
1 168,110,000 168,220,000 110,000 2.9 6.3% 5 10 1 0 OOA
1 223,760,000 223,910,000 150,000 2.8 6.3% 1 4 0 0 OOA
4 171,180,000 171,280,000 100,000 1.9 5.2% 1 2 0 0 OOA
5 28,950,000 29,070,000 120,000 3.8 3.1% 16 16 6 0 OOA
6 66,160,000 66,260,000 100,000 5.7 28.1% 6 6 0 0 OOA
9 32,940,000 33,040,000 100,000 2.8 4.2% 7 14 0 0 OOA
10 4,820,000 4,920,000 100,000 2.6 9.4% 9 5 0 0 OOA
10 38,000,000 38,160,000 160,000 3.5 8.3% 5 9 2 0 OOA
10 69,630,000 69,740,000 110,000 4.2 19.8% 2 2 0 1 OOA
15 45,250,000 45,350,000 100,000 2.5 1.1% 5 6 1 0 OOA
17 35,500,000 35,600,000 100,000 2.9 (no tags) – – – – –
20 20,030,000 20,140,000 110,000 5.1 64.6% 0 0 10 5 COS
22 30,690,000 30,820,000 130,000 3.5 4.2% 0 2 5 2 COS
Relative tag SNP frequencies in actual data 34% 46% 15% 5%
Relative tag SNP simulated under a demographic model without introgression 34% 5% 33% 27%
Relative tag SNP simulated under a demographic model with introgression 23% 31% 37% 9%
*To qualitatively assess the regions in terms of which clade the Neandertal matches, we asked whether the proportion matching the OOA-specific clade (AM and DM) is much more than 50%. If
so, we classify it as an OOA region, and otherwise a COS region. One region is unclassified because no tag SNPs were found. We also compared to simulations with and without gene flow (SOM
Text 17), which show that the rate of DM and DN tag SNPs where Neandertal is derived are most informative for distinguishing gene flow from no gene flow.
7 MAY 2010 VOL 328 SCIENCE 720
12 regions where tag SNPs occur show an excess
of OOA over COS sites. Given that the OOA
in non-Africans (average of 13%, and all less than
30%) (Table 5), the fact that the candidate regions
suggests that they largely derive from Neandertals.
explained by contamination, even if all Neandertal
data were composed of present-day non-African
DNA (P= 0.0025) (SOM Text 17).
This analysis shows that some old haplotypes
most likely owe their presence in present-day non-
not all old haplotypes in non-Africans may have
such an origin. For example, it has been suggested
that the H2 haplotype on chromosome 17 and the
D haplotype of the microcephalin gene were
contributed by Neandertals to present-day non-
Africans (12, 79, 80). This is not supported by the
current data because the Neandertals analyzed do
not carry these haplotypes.
The extent of Neandertal ancestry. To es-
timate the proportion of Neandertal ancestry, we
compare the similarity of non-Africans to Nean-
dertals with the similarity of two Neandertals, N1
and N2, to each other. Under the assumption that
there was no gene flow from Neandertals to the
ancestors of modern Africans, the proportion of
S(N2,AFR,N1,Chimpanzee), where the S statistic
is an unnormalized version of the D statistic
(SOM Text 18, Eq. S18.4). Using Neandertals
from Vindija, as well as Mezmaiskaya, we esti-
mate f to be between 1.3% and 2.7% (SOM Text
a population genetic model to the D statistics in
Table 4 and SOM Text 15 as well as to other
summary statistics of the data. Assuming that
gene flow from Neandertals occurred between
50,000 and 80,000 years ago, this method
estimates f to be between 1 and 4%, consistent
with the above estimate (SOM Text 19). We note
that a previous study found a pattern of genetic
variation in present-day humans that was
hypothesized to be due to gene flow from
Neandertals or other archaic hominins into
modern humans (81). The authors of this study
estimated the fraction of non-African genomes
affected by “archaic” gene flow to be 14%,
almost an order of magnitude greater than our
estimates, suggesting that their observations may
not be entirely explained by gene flow from
Implications for modern human origins.
all present-day humans trace all their ancestry back
to a small African population that expanded and
replaced archaic forms of humans without admix-
ture. Our analysis of the Neandertal genome may
not be compatible with this view because Nean-
dertals are on average closer to individuals in
Eurasia than to individuals in Africa. Furthermore,
individuals in Eurasia today carry regions in their
genome that are closely related to those in Ne-
andertals and distant from other present-day hu-
mans. The data suggest that between 1 and 4% of
the genomes of people in Eurasia are derived from
Neandertals. Thus, while the Neandertal genome
presents a challenge to the simplest version of an
continuesto support the view thatthe vastmajority
of genetic variants that exist at appreciable fre-
quencies outside Africa came from Africa with
the spread of anatomically modern humans.
A striking observation is that Neandertals are
as closely related to a Chinese and Papuan in-
dividual as to a French individual, even though
morphologically recognizable Neandertals exist
only in the fossil record of Europe and western
Asia. Thus, the gene flow between Neandertals
and modern humans that we detect most likely
occurred before the divergence of Europeans,
by mixing of early modern humans ancestral to
Middle East before their expansion into Eurasia.
Such a scenario is compatible with the archaeo-
logicalrecord,which shows that modern humans
ago whereas the Neandertals existed in the same
ago (82).
signal compatible with gene flow from Neander-
tals into ancestors of present-day humans outside
gene flow from Neandertals into modern humans
but no reciprocal gene flow from modern humans
into Neandertals. Although gene flow between
different populations need not be bidirectional, it
small number of breeding events along the wave
front of expansion into new territory can result in
substantial introduction of genes into the coloniz-
ing population as introduced alleles can “surf” to
high frequency as the population expands. As a
consequence, detectable gene flow is predicted to
the colonizing population, even if gene flow also
occurred in the other direction (83). Another
prediction of such a surfing model is that even a
very small number of events of interbreeding can
result in appreciable allele frequencies of Nean-
dertal alleles in the present-day populations.Thus,
the actual amount of interbreeding between
Neandertals and modern humans may have been
of the genome of present-day non-Africans.
forgreater gene flow from Neandertals to present-
day Europeans than to present-day people in
eastern Asia given that the morphology of some
hominin fossils in Europe has been interpreted as
evidence for gene flow from Neandertals into
early modern humans late in Neandertal history
[e.g., (84)] (Fig. 6). It is possible that later mi-
grations into Europe, for example in connection
with the spread of agriculture, have obscured
the traces of such gene flow. This possibility
sequences from preagricultural early modern
humans in Europe (85). It is also possible that if
the expansion of modern humans occurred dif-
ferently in Europe than in the Middle East, for
example by already large populations interacting
with Neandertals, then there may be little or no
trace of any gene flow in present-day Europeans
even if interbreeding occurred. Thus, the con-
tingencies of demographic history may cause
some events of past interbreeding to leave traces
in present-day populations, whereas other events
that left little or no traces in the present-day gene
pool is of little or no consequence from a genetic
perspective, although it may be of interest from a
historical perspective.
Although gene flow from Neandertals into
modern humans when they first left sub-Saharan
Africa seems to be the most parsimonious model
Chinese PNG Yoruba San
Homo erectus
Fig. 6. Four possible scenarios of genetic mixture
involving Neandertals. Scenario 1 represents gene
flow into Neandertal from other archaic hominins,
here collectively referred to as Homo erectus. This
genome with unexpectedly high divergence from
present-day humans. Scenario 2 represents gene
flow between late Neandertals and early modern
humans in Europe and/or western Asia. We see no
evidence of this because Neandertals are equally
distantly related to all non-Africans. However, such
gene flow may have taken place without leaving
traces in the present-day gene pool. Scenario 3
represents gene flow between Neandertals and the
ancestors of all non-Africans. This is the most par-
we detect gene flow only from Neandertals into
modern humans, gene flow in the reverse direction
may also have occurred. Scenario 4 represents old
substructure in Africa that persisted from the origin
of Neandertals until the ancestors of non-Africans
left Africa. This scenario is also compatible with the
current data. SCIENCE VOL 328 7 MAY 2010 721
compatible with the current data, other scenarios
rule out a scenario in which the ancestral pop-
ulation of present-day non-Africans was more
closely related to Neandertals than the ancestral
population of present-day Africans due to ancient
substructure within Africa (Fig. 6). If after the
divergence of Neandertals there was incomplete
genetic homogenization between what were to
become the ancestors of non-Africans and Afri-
cans, present-day non-Africans would be more
closely related to Neandertals than are Africans.
In fact, old population substructure in Africa has
been suggested based on genetic (81) as well as
paleontological data (86).
from an extinct late Pleistocene hominin can be
genome shows that they are likely to have had
a role in the genetic ancestry of present-day
humans outside of Africa, although this role was
relatively minor given that only a few percent of
are derived from Neandertals. Our results also
point to a number of genomic regions and genes
as candidates for positive selection early in mod-
ern human history, for example, those involved in
cognitive abilities and cranial morphology. We
expect that further analyses of the Neandertal ge-
nome as well as the genomes of other archaic
hominins will generate additional hypotheses
and provide further insights into the origins and
early history of present-day humans.
References and Notes
1. J. L. Bischoff et al., High-Resolution U-Series Dates from
the Sima de los Huesos Hominids Yields 600+/−66 kyrs:
Implications for the Evolution of the Early Neanderthal
Lineage (Elsevier, Amsterdam, PAYS-BAS, 2007), vol. 34.
2. J. J. Hublin, Proc. Natl. Acad. Sci. U.S.A. 106, 16022
3. C. B. Stringer, J. Hublin, J. Hum. Evol. 37, 873 (1999).
4. C. Finlayson et al., Nature 443, 850 (2006).
5. J. Krause et al., Nature 449, 902 (2007).
6. R. Grün et al., J. Hum. Evol. 49, 316 (2005).
7. N. Mercier, H. Valladas, in Late Quaternary Chronology and
Palaeoclimate of the Eastern Mediterranean, Radiocarbon,
O. Bar-Yosef, R. Kra, Eds. (1994), pp. 13–20.
8. E. Trinkaus et al., Proc. Natl. Acad. Sci. U.S.A. 100,
11231 (2003).
9. J. Zilhão, E. Trinkaus, in Trabalhos de Arqueologia (Instituto
Português de Arqueologia, Lisbon, 2002), vol. 22.
10. S. E. Bailey, T. D. Weaver, J. J. Hublin, J. Hum. Evol. 57,
11 (2009).
11. G. Bräuer, H. Broeg, C. Stringer, in Neanderthals Revisited:
New Approaches and Perspectives. (2006), pp. 269–279.
12. P. D. Evans, N. Mekel-Bobrov, E. J. Vallender, R. R.
Hudson, B. T. Lahn, Proc. Natl. Acad. Sci. U.S.A. 103,
18178 (2006).
13. J. D. Wall, M. F. Hammer, Curr. Opin. Genet. Dev. 16,
606 (2006).
14. M. Currat, L. Excoffier, PLoS Biol. 2, e421 (2004).
15. A. W. Briggs et al., Science 325, 318 (2009).
16. M. Krings et al., Cell 90, 19 (1997).
17. L. Orlando et al., Curr. Biol. 16, R400 (2006).
18. I. V. Ovchinnikov et al., Nature 404, 490 (2000).
19. D. Serre et al., PLoS Biol. 2, E57 (2004).
20. S. Pääbo, Trends Cell Biol. 9, M13 (1999).
21. S. Pääbo, Proc. Natl. Acad. Sci. U.S.A. 86, 1939 (1989).
22. S. Pääbo et al., Annu. Rev. Genet. 38, 645 (2004).
23. A. W. Briggs et al., Proc. Natl. Acad. Sci. U.S.A. 104,
14616 (2007).
24. P. Brotherton et al., Nucleic Acids Res. 35, 5717 (2007).
25. M. Hofreiter, V. Jaenicke, D. Serre, A. von Haeseler,
S. Pääbo, Nucleic Acids Res. 29, 4793 (2001).
26. M. Höss, P. Jaruga, T. H. Zastawny, M. Dizdaroglu,
S. Pääbo, Nucleic Acids Res. 24, 1304 (1996).
27. R. K. Saiki et al., Science 230, 1350 (1985).
28. C. Lalueza-Fox et al., Science 318, 1453 (2007).
29. J. Krause et al., Curr. Biol. 17, 1908 (2007).
30. C. Lalueza-Fox et al., BMC Evol. Biol. 8, 342 (2008).
31. C. Lalueza-Fox, E. Gigli, M. de la Rasilla, J. Fortea,
A. Rosas, Biol. Lett. 5, 809 (2009).
32. J. Krause et al., Nature 439, 724 (2006).
33. D. R. Bentley et al., Nature 456, 53 (2008).
34. M. Margulies et al., Nature 437, 376 (2005).
35. H. N. Poinar et al., Science 311, 392 (2006).
36. M. Rasmussen et al., Nature 463, 757 (2010).
37. M. Stiller et al., Proc. Natl. Acad. Sci. U.S.A. 103, 13578
38. W. Miller et al., Nature 456, 387 (2008).
39. K. Prüfer et al., Genome Biol. 11, R47 (2010).
40. R. E. Green et al., Nature 444, 330 (2006).
41. R. E. Green et al., EMBO J. 28, 2494 (2009).
42. J. P. Noonan et al., Science 314, 1113 (2006).
43. A. D. Greenwood, C. Capelli, G. Possnert, S. Pääbo,
Mol. Biol. Evol. 16, 1466 (1999).
44. J. D. Wall, S. K. Kim, PLoS Genet. 3, e175 (2007).
45. R. E. Green et al., Cell 134, 416 (2008).
46. A. W. Briggs et al., J. Vis. Exp. 2009, 1573 (2009).
47. T. Maricic, S. Pääbo, Biotechniques 46, 51, 54 (2009).
48. M. Kircher, U. Stenzel, J. Kelso, Genome Biol. 10, R83
49. A. W. Briggs et al., Nucleic Acids Res. 38, e87 (2010).
50. J. C. Dohm, C. Lottaz, T. Borodina, H. Himmelbauer,
Nucleic Acids Res. 36, e105 (2008).
51. B. Paten et al., Genome Res. 18, 1829 (2008).
52. M. Goodman, Am. J. Hum. Genet. 64, 31 (1999).
53. T. de Torres et al., Archaeometry published online
29 October 2009; 10.1111/j.1475-4754.2009.00491.x.
54. R. W. Schmitz et al., Proc. Natl. Acad. Sci. U.S.A. 99,
13342 (2002).
55. A. R. Skinner et al., Appl. Radiat. Isot. 62, 219 (2005).
56. N. Patterson, D. J. Richter, S. Gnerre, E. S. Lander,
D. Reich, Nature 441, 1103 (2006).
57. H. A. Burbano et al., Science 328, 723 (2010).
58. Z. Zhang et al., Mol. Cell. Proteomics 4, 914 (2005).
59. N. Matsuyoshi, S. Imamura, Biochem. Biophys. Res. Commun.
235, 355 (1997).
60. P. Richard, J. L. Manley, Genes Dev. 23, 1247 (2009).
61. M. Huber et al., J. Invest. Dermatol. 124, 998 (2005).
62. C. Alkan et al., Nat. Genet. 41, 1061 (2009).
63. H. Parikh, V. Lyssenko, L. C. Groop, BMC Med. Genomics
2, 72 (2009).
64. B. Hämmerle, C. Elizalde, J. Galceran, W. Becker,
F. J. Tejedor, J. Neural Transm. Suppl. 2003, 129 (2003).
65. T. J. Crow, Eur. Neuropsychopharmacol. 5 (suppl), 59
66. P. Khaitovich et al., Genome Biol. 9, R124 (2008).
67. T. Sadakata et al., J. Clin. Invest. 117, 931 (2007).
68. R. Sultana et al., Genomics 80, 129 (2002).
69. M. Tomasello, M. Carpenter, J. Call, T. Behne, H. Moll,
Behav. Brain Sci. 28, 675, discussion 691 (2005).
70. S. Mundlos et al., Cell 89, 773 (1997).
71. J. L. Voisin, J. Hum. Evol. 55, 438 (2008).
72. T. D. Weaver, C. C. Roseman, C. B. Stringer, Proc. Natl. Acad.
Sci. U.S.A. 105, 4645 (2008).
73. D. M. Behar et al; Genographic Consortium, Am. J. Hum.
Genet. 82, 1130 (2008).
74. J. X. Sun, J. C. Mullikin, N. Patterson, D. E. Reich,
Mol. Biol. Evol. 26, 1017 (2009).
75. E. T. Wood et al., Eur. J. Hum. Genet. 13, 867 (2005).
76. D. Reich et al., PLoS Genet. 5, e1000360 (2009).
77. S. Levy et al., PLoS Biol. 5, e254 (2007).
78. D. A. Hinds et al., Science 307, 1072 (2005).
79. J. Hardy et al., Biochem. Soc. Trans. 33, 582 (2005).
80. H. Stefansson et al., Nat. Genet. 37, 129 (2005).
81. J. D. Wall, K. E. Lohmueller, V. Plagnol, Mol. Biol. Evol.
26, 1823 (2009).
82. O. Bar-Yosef, in Neandertals and Modern Humans in
Western Asia, T. Akazawa, K. Aoki, O. Bar-Yosef, Eds.
(Plenum, New York, 1999), pp. 39–56.
83. M. Currat, M. Ruedi, R. J. Petit, L. Excoffier, Evolution 62,
1908 (2008).
84. J. Zilhão et al., PLoS ONE 5, e8880 (2010).
85. J. Krause et al., Curr. Biol. 20, 231 (2010).
86. P. Gunz et al., Proc. Natl. Acad. Sci. U.S.A. 106, 6094
87. W. H. Li, C. I. Wu, C. C. Luo, Mol. Biol. Evol. 2, 150 (1985).
88. We thank E. Buglione, A. Burke, Y.-J. Chen, J. Salem,
P. Schaffer, E. Szekeres, and C. Turcotte at 454 Life
Sciences Corp. for production sequencing on the 454
platform; S. Fisher, J. Wilkinson, J. Blye, R. Hegarty,
A. Allen, S. K. Young, and J. L. Chang for nine Illumina
sequencing runs performed at the Broad Institute;
J. Rothberg and E. Rubin for input leading up to this
project; O. Bar-Yosef, L. Excoffier, M. Gralle, J.-J. Hublin,
D. Lieberman, M. Stoneking, and L. Vigilant for
constructive criticism; I. Janković for assistance with the
Vindija collection; S. Ptak, M. Siebauer, and J. Visagie for
help with data analysis, M. Richards and S. Talamo for
carbon dating; J. Dabney for editorial assistance; the
Genome Center at Washington University for prepublication
use of the orangutan genome assembly; and K. Finstermeier
for expert graphical design. Neandertal bone extract
sequence data have been deposited at European
Bioinformatics Institute under STUDY accession ERP000119,
alias Neandertal Genome project. HGDP sequence data have
been deposited at EBI under STUDY accession ERP000121,
alias Human Genome Diversity Project. We are grateful to
the Max Planck Society, and particularly the Presidential
Innovation Fund, for making this project possible. C.L.-F.
was supported by a grant from the Ministerio de Ciencia e
Innovación; E.Y.D. and M.S. were supported in part
by grant GM40282; A.-S.M. was supported by a
Janggen-Pöhn fellowship; N.F.H. and J.C.M. were supported
in part by the Intramural Research Program of the National
Human Genome Research Institute, National Institutes of
Health; and D.R. by a Burroughs Wellcome Career
Development Award in the Biomedical Sciences. Author
contributions: S.P. conceived and coordinated the project;
D.R. coordinated population genetic analyses; R.E.G.
and J.Ke. coordinated bioinformatic aspects; R.E.G., J.Kr.,
A.W.B., M.E., and S.P. developed the initial project
strategies; J.Kr. and T.M. collected and analyzed fossil
samples; J.Kr., T.M., A.W.B., and M.M. developed the DNA
extraction and library preparation protocols and performed
laboratory work prior to sequencing; K.P. designed the
restriction enzyme enrichment method; A.A.-P., A.B., B.Hb.,
B.Hff., M.Sg., R.S., A.W., J.A., M.E., and M.K. performed and
coordinated DNA sequencing on the 454 and Illumina
platforms; J.A. and M.E. organized and coordinated
sequence production on the 454 platform; C.N., E.S.L.,
C.R., and N.N. organized and performed nine sequencing
runs on the Illumina platform at the Broad Institute;
M.K. and J.Ke. compiled the catalog of human-specific
genomic features; U.S., M.K., N.H., J.M., J.Ke., K.P., and
R.E.G. developed and implemented the primary sequence
alignment and analysis methodologies; R.E.G., U.S., J.Kr.,
A.W.B., H.B., P.L.F.J. and M.L. developed and implemented
the wet lab and bioinformatic assays for human DNA
contamination; C.A., T.M.-B., and E.E.E. performed structural
variation analyses; H.L., J.M., and D.R. designed and
implemented analyses of population divergences; R.E.G.,
N.P., W.Z., J.M., H.L., M.H.-Y.F., E.Y.D., A.S.-M., P.L.F.J., J.J.,
J.G., M.L., D.F., M.S., E.B., R.N., S.P., and D.R. developed
and implemented population genetics comparisons; R.E.G.,
M.L., J.G., D.F., J.D.J., D.R., and S.P. designed and
implemented the screen for selective sweeps; P.R., D.B.,
Z.K., I.G., C.V., V.B.D., L.V.G., C.L.-F., M.R., J.F., A.R., and
R.S. provided samples, analyses, and paleontological
expertise; D.R. and S.P. edited the manuscript.
Supporting Online Material
Materials and Methods
SOM Text
Figs. S1 to S51
Tables S1 to S58
8 February 2010; accepted 2 April 2010
7 MAY 2010 VOL 328 SCIENCE 722