ARTICLE
Denisova Admixture and the First Modern Human
Dispersals into Southeast Asia and Oceania
David Reich,1,2,* Nick Patterson,2 Martin Kircher,3 Frederick Delfin,3 Madhusudan R. Nandineni,3,4Irina Pugach,3 Albert Min-Shan Ko,3 Ying-Chin Ko,5 Timothy A. Jinam,6 Maude E. Phipps,7
Naruya Saitou,6 Andreas Wollstein,8,9 Manfred Kayser,9 Svante Pääbo,3 and Mark Stoneking3,*
It has recently been shown that ancestors of New Guineans and Bougainville Islanders have inherited a proportion of their ancestry from
Denisovans, an archaic hominin group from Siberia. However, only a sparse sampling of populations from Southeast Asia and Oceania
were analyzed. Here, we quantify Denisova admixture in 33 additional populations from Asia and Oceania. Aboriginal Australians, Near
Oceanians, Polynesians, Fijians, east Indonesians, and Mamanwa (a ‘‘Negrito’’ group from the Philippines) have all inherited genetic
material from Denisovans, but mainland East Asians, western Indonesians, Jehai (a Negrito group from Malaysia), and Onge (a Negrito
group from the Andaman Islands) have not. These results indicate that Denisova gene flow occurred into the common ancestors of New
Guineans, Australians, and Mamanwa but not into the ancestors of the Jehai and Onge and suggest that relatives of present-day East
Asians were not in Southeast Asia when the Denisova gene flow occurred. Our finding that descendants of the earliest inhabitants ofSoutheast Asia do not all harbor Denisova admixture is inconsistent with a history in which the Denisova interbreeding occurred inmainland Asia and then spread over Southeast Asia, leading to all its earliest modern human inhabitants. Instead, the data can bemost parsimoniously explained if the Denisova gene flow occurred in Southeast Asia itself. Thus, archaic Denisovans must have lived
over an extraordinarily broad geographic and ecological range, from Siberia to tropical Asia.

Introduction
The history of the earliest arrival of modern humans inSoutheast Asia and Oceania from Africa remains controversial. Archaeological evidence has been interpreted tosupport either a single wave of settlement1 or, alternatively,
multiple waves of settlement, the first leading to the initial
peopling of Southeast Asia and Oceania via a southern routeand subsequent dispersals leading to the peopling of all of
East Asia.2 Mitochondrial DNA studies have been interpreted as supporting a single wave of migration via asouthern route,3–5 although other interpretations are
possible,6,7 and single-locus studies are unlikely to resolve
this issue.8 The largest genetic study of the region to date,based on 73 populations genotyped at 55,000 SNPs,
concluded that the data were consistent with a single
wave of settlement of Asia that moved from south to northand gave rise to all of the present-day inhabitants of theregion.9 However, another study of genome-wide SNP
data argued for two waves of settlement10 as did an analysis
of diversity in the bacterium Helicobacter pylori.11
The recent finding that Near Oceanians (New Guineans
and Bougainville Islanders) have received 4%–6% of their
genetic material from archaic Denisovans12 in principle
provides a powerful tool for understanding the earliest
human migrations to the region and thus for resolving
the question of the number of waves of settlement. The

Denisova genetic material in Southeast Asians should be
easily recognizable because it is very divergent from modern
human DNA. Thus, the presence or absence of Denisova
genetic material in particular populations should provide
an informative probe for the migration history of Southeast
Asia and Oceania, in addition to being interesting in its own
right. However, the populations previously analyzed forsignatures of Denisova admixture12 comprise a very thinsampling of Southeast Asia and Oceania. In particular, nogroups from island Southeast Asia or Australia weresurveyed. Here, we report an analysis of genome-wide
data from an additional 33 populations from south Asia,
Southeast Asia, and Oceania; analyze the data for signaturesof Denisova admixture; and use the results to infer thehistory of human migration(s) to this part of the world.

Material and Methods
SNP Array Data
We analyzed data for modern humans genotyped on Affymetrix
6.0 SNP arrays. We began by assembling previously publisheddata for YRI (Yoruba in Ibadan, Nigeria) West Africans, CHB(Han Chinese in Beijing, China) Han Chinese and CEU (Utah residents with Northern and Western European ancestry from the
CEPH collection) European Americans from HapMap 3;13 Onge
Andaman ‘‘Negritos’’;14 and New Guinea highlanders, Fijians,
one Bornean population, and Polynesians from seven islands.10

1

Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; 2Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA;
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig D-04103, Germany; 4Laboratory of DNA Fingerprinting, Centre for DNA Fingerprinting and Diagnostics, Nampally, Hyderabad 500 001, India; 5Center of Excellence for Environmental Medicine,
Kaohsiung Medical University, Kaohsiung City 807, Taiwan; 6Division of Population Genetics, National Institute of Genetics, Yata 1111, Mishima, Shizuoka 411-8540, Japan; 7School of Medicine and Health Sciences, Monash University (Sunway Campus), Selangor 46150, Malaysia; 8Cologne Center
for Genomics, University of Cologne, Cologne D-50931, Germany; 9Department of Forensic Molecular Biology, Erasmus MC University Medical Center
Rotterdam, 3000 CA Rotterdam, The Netherlands*Correspondence: reich@genetics.med.harvard.edu (D.R.), stoneking@eva.mpg.de (M.S.)
DOI 10.1016/j.ajhg.2011.09.005. Ó2011 by The American Society of Human Genetics. All rights reserved.

3

516 The American Journal of Human Genetics 89, 516–528, October 7, 2011

We also assembled data including two aboriginal Australian populations: one from the Northern Territories15 and one from a humandiversity cell line panel in the European Collection of Cell
Cultures. The data also include nine Indonesian populations:four from the Nusa Tenggaras, two from the Moluccas, one from
Borneo, and two from Sumatra. Finally, the data include three
Malaysian populations (Temuan and Jehai [a Negrito group]
both from the Malay peninsula, and Bidayuh from Sarawak on
the island of Borneo), two Philippine populations (Manobo anda Negrito group, the Mamanwa), six aboriginal Taiwanese populations, one Dravidian population from southern India, and San
Bushmen from southern Africa from the Centre d’Étude duPolymorphisme Humain (CEPH)-Human Genome Diversity
Panel.16 All volunteers provided informed consent for research
into population history and the approval of appropriate local
ethical review boards was obtained. This project was approvedby the ethical review boards of the University of Leipzig Medical
Faculty and Harvard Medical School. The genotype data that weanalyzed for this study are available from the authors on request.



n 
P
ziOutgroup  ziArchaic ziEast Asian  zix

pD ðXÞ ¼ n i¼1

P i
zOutgroup  ziArchaic ziEast Asian  ziNew Guinea
i¼1¼

f4 ðOutgroup; Archaic; East Asian; XÞ
f4 ðOutgroup; Archaic; East Asian; New GuineaÞ
(Equation 1)


n 
P
ziOutgroup  ziAustralia zix  ziNew GuineapN ðXÞ ¼ 1  n i¼1

P i
zOutgroup  ziAustralia ziEast Asia  ziNew Guinea
i¼1

¼1

f4 ðOutgroup; Australia; X; New GuineaÞ
f4 ðOutgroup; Australia; East Asia; New GuineaÞ
(Equation 2)


n 
P
ziOutgroup  ziNew Guinea zix  ziAustraliapA ðXÞ ¼ 1  n i¼1

P i
zOutgroup  ziNew Guinea ziEast Asia  ziAustralia
i¼1

Merging Genotyping Data with Chimpanzee,
Denisova, and Neandertal¼1

We merged the SNP array data from modern humans with genome
sequence data from chimpanzee (CGSC 2.1/PanTro217), Denisova,12 and Neandertal.18 We eliminated A/Tand C/G SNPs to minimize strand misidentification. After removing SNPs with low genotyping completeness, we had data for 353,143 autosomal SNPs.

Removal of Outlier Samples
We carried out principal components analysis by using
EIGENSOFT.19 We removed samples that were visual outliers relative to others from the same population on eigenvectors thatwere statistically significant by using a Tracy-Widom statistic (p <0.05),19 resulting in the removal of three YRI, two CHB, five Polynesians, one New Guinea highlander, two Jehai, and three Mamanwa.

f4 ðOutgroup; New Guinea; X; AustraliaÞ
f4 ðOutgroup; New Guinea; East Asia; AustraliaÞ
(Equation 3)

The right side of each equation shows that these statistics can also
be expressed as ratios of f4 statistics,14 which provide unbiased
estimates of admixture proportions even in the absence of populations that are closely related to the analyzed populations(Appendix A). For the ancestry estimates reported in Table 1, we
use Outgroup ¼ YRI (West Africans), Archaic ¼ Denisova, andEast Asian ¼ CHB (Han Chinese). Table S2 and Table S3 demonstrate that consistent values are obtained when we replace thesechoices with a variety of distantly related populations. Further
details are provided in Appendix A.

Block Jackknife Standard Error and Statistical Testing
Sequencing Data
We prepared DNA sequencing libraries with 300 bp insert sizes froma Papua New Guinea highlander (SH10) and Mamanwa Negrito(ID36) individual by using a previously described protocol.12 The
two libraries were sequenced on an Illumina Genome Analyzer
IIx instrument with 2 3 101 þ 7 cycles according to the manufacturer’s instructions for multiplex sequencing (FC-104-400x v4
sequencing chemistry and PE-203-4001 cluster generation kit v4).
Bases and quality scores were generated with the Ibis base caller,20
and the reads were aligned with the Burrows-Wheeler Aligner(BWA) software 21 to the human (NCBI 36/hg18) and chimpanzee(CGSC 2.1/pantro2) genomes with default parameters. The resulting BAM files were filtered as follows: (1) a mapping quality of atleast 30 was required; (2) we removed duplicated reads with thesame outer coordinates; and (3) we removed reads with sequence
entropy < 1.0, calculated by summing p$log2(p) for each of thefour nucleotides. The sequencing data are publicly available fromthe European Nucleotide Archive (Project ID ERP000121), andsummary statistics are provided in Table S1, available online.

Estimating Denisova pD(X), Near Oceanian pN(X)and Australian pA(X) ancestry

We define the frequency of one of the alleles at a SNP i as zix . We
can then compute three statistics for a given population X thatare informative about admixture:

We used a block jackknife22,23 to compute standard errors, dropping each nonoverlapping five cM stretch of the genome in turnand studying the variance of each statistic of interest to obtainan approximately normally distributed standard error.12,18 To
test whether pD(X), pN(X), pA(X), and pD(X)  pN(X) are statisticallyconsistent with zero for any tested population X, we computed
the statistics along with a standard error from the block jackknife,and then used a two-sided Z test that computes the number ofstandard errors from zero. To implement the 4 Population Test14
for whether an unrooted phylogenetic tree ([A,B],[C,D]) relating
four populations is consistent with the data, we computed thestatistic f4(A,B;C,D) and assessed the number of standard errors
from zero.

Results
Quantifying Denisova Admixture from Genome-wide
SNP Data
To investigate which modern humans have inherited
genetic material from Denisovans, we assembled SNP
data from 33 populations from mainland East Asia, island
Southeast Asia, New Guinea, Fiji, Polynesia, Australia, andIndia, and genotyped all of them on Affymetrix 6.0 arrays.
After removing samples that were outliers with respect to

The American Journal of Human Genetics 89, 516–528, October 7, 2011 517

Table 1.

Estimates of Denisovan and Near Oceanian Ancestry from SNP Data

Population InformationpD(X): Denisovan Ancestry
as % of New Guinea

Broad Grouping

Estimated
AncestrypN(X): Near Oceanian
ancestry

p value forDifferenceStandard
Error in the
Estimate

Estimated
Z Score Ancestry

Standard
Error in the
Estimate

Z Score pN(X)  pD(X)Detailed

Code NNew Guinea

HighlanderSH

24 100%

0%

n/a

100%

0%

n/a

n/a

Australianall

10 103%

6%

17.1

n/a

n/a

n/a

n/a

Northern Territories AU1

8

103%

6%

16.6

n/a

n/a

n/a

n/a

Cell Cultures

AU2

2

103%

7%

14.1

n/a

n/a

n/a

n/a

Fiji

Fiji

FI

25 56%

3%

17.7

58%

1%

94.6

0.38

Nusa Tenggarasall

10 40%

3%

12.8

38%

1%

54.7

0.34

Philippines

Moluccas

Polynesia

Alor

AL

2

51%

6%

8.3

49%

1%

35.6

0.69

Flores

FL

1

40%

8%

5.0

37%

2%

19.8

0.68

Roti

RO

4

27%

4%

6.4

27%

1%

29.4

0.85

Timor

TI

3

50%

5%

9.8

45%

1%

41.7

0.29

27 28%

3%

8.2

6%

1%

10.6

3.4 3 1010allMamanwa (N)MA

11 49%

5%

9.2

11%

1%

11.4

1.5 3 1012

ManoboMN

16 13%

3%

4.2

4%

1%

5.7

0.0018all

10 35%

4%

10.1

34%

1%

46.0

0.59

Hiri

HI

7

35%

4%

9.0

32%

1%

38.4

0.36

Ternate

TE

3

36%

5%

7.2

38%

1%

33.7

0.67

all

PO

19 20%

4%

5.1

27%

1%

34.8

0.052

Cook

2

16%

6%

2.5

24%

1%

17.3

0.21

Futuna

4

28%

5%

5.3

29%

1%

26.9

0.87

Niue

1

27%

8%

3.3

30%

2%

16.3

0.72

Samoa

5

13%

5%

2.6

24%

1%

23.3

0.024

Tokelau

2

22%

6%

3.5

31%

1%

23.8

0.14

Tonga

2

17%

7%

2.5

31%

1%

22.5

0.027

Tuvalu

3

21%

6%

3.6

28%

1%

22.8

0.28

AndamaneseOnge (N)AN

10 10%

6%

1.6

3%

1%

1.8

0.27

Taiwanall

TA

Malaysia

Sumatra

12 4%

3%

1.2

1%

1%

1.5

0.35

Puyuma

2

4%

6%

0.6

2%

1%

1.8

0.79

Rukai

2

0%

6%

0.0

2%

1%

1.6

0.74

Paiwan

2

5%

6%

0.8

3%

1%

2.2

0.67

Atayal

2

5%

5%

0.9

0%

1%

0.3

0.34

Bunun

2

12%

6%

2.1

2%

1%

1.6

0.01

Pingpu

2

7%

6%

1.2

1%

1%

1.1

0.30

all

18 5%

3%

1.4

0%

1%

0.2

0.16

Jehai (N)

JE

8

7%

5%

1.4

1%

1%

0.8

0.21

Temuan

TM

10 3%

4%

0.8

1%

1%

0.9

0.32

17 4%

3%

1.4

0%

1%

0.3

0.17

All
Besemah

BE

8

5%

3%

1.5

1%

1%

0.9

0.20

Semende

SM

9

3%

4%

0.9

0%

1%

0.3

0.31

518 The American Journal of Human Genetics 89, 516–528, October 7, 2011

Table 1. ContinuedpN(X): Near Oceanian
ancestry

Population InformationpD(X): Denisovan Ancestry
as % of New Guinea

Broad Grouping DetailedStandard
Standard
Estimated Error in the
Estimated Error in theAncestry
Estimate
Z Score Ancestry
Estimate
Z Score pN(X)  pD(X)BorneoCode Nall

India

p value forDifference

49 1%

2%

0.6

1%

1%

1.3

0.79

Bidayuh

BI

10 6%

4%

1.7

1%

1%

1.4

0.80

Barito River

BO

23 0%

3%

0.2

1%

1%

1.7

0.18

Land Dayak

DY

16 0%

3%

0.1

0%

1%

0.2

0.94

DravidianSI

12 7%

5%

1.5

n/a

n/a

n/a

n/a

We provide each population’s estimated ancestry, the standard error in the estimate, and the Z score for deviation from zero (Z). Negrito populations are marked
with (N). The New Guinea highlanders by definition have 100% Denisovan and 100% Near Oceanian ancestry because they are used as a reference population forcomputations. Results are not provided for Australians and Dravidians for whom the phylogenetic relationships do not allow the estimate (n/a). The last column
reports the two-sided p value for a difference based on a block jackknife and a Z test.

a population as a fraction of that in New Guineans. Our
main analyses in Figure 1 and Table 1 compute pD(X) asa ratio of two f4 statistics,14 each of which measures thecorrelation in allele frequency differences between thetwo populations used as outgroups (Yoruba and Denisova)
and two East or Southeast Asian populations (Han and X ¼
tested population). If Han and X descend from a single
ancestral population without any subsequent admixture

their own populations (reflecting admixture in the last few
generations or genotyping error), we had data from 243
individuals (Table 1). We restricted the analysis to autosomal SNPs with high genotyping completeness and withdata from the Denisova genome, leaving 353,143 SNPs.
To quantify the proportion of Denisova genes in each
population X, we computed a statistic pD(X), whichmeasures the proportion of Denisova genetic material in

DENISOVA

OR
HE

MO DR
XI

AL
AN
AU
BE
BG
BI
BO
CA
DA
DR
DY
FI
FL
HA
HE
HI
JA
JE
LA
MA
MI

UY

JA

HA

TU
TJ
NA

SE

YI

MI

TA

DA
SI

MA

LA
CA

AN
JE

TM

MN
BI
HI

BE

DY

BO

TE
FL

RO

MN
MO
NA
NG
OR
PO
RO
SE
SH
SI
SM
TA
TE
TI
TJ
TM
TU
UY
XI
YI

M
Manobob
MongolaNaxi
New Guinea
OroqenPolynesia
Roti
She
S. Highlands
Southern India
SemendeTaiwanTernateTimor
Tujia
Temuan
Tu
Uygur
Xibo
Yi

BG

AL

SM

Al
Alor
Andaman (Onge)
Australian
BesemahBougainville
BidayuhBorneoCambodia
Dai
Daur
Dayak
Fijij
FloresHan
Hezhen
Hiri
Japan
Jehai
Lahu
Mamanwa
Miao

SH

NG

FI

PO

TI
AU1

AU2

Figure 1. Denisovan Genetic Material as a Fraction of that in New Guineans
Populations are only shown as having Denisova ancestry if the estimates are more than two standard errors from zero (we combine estimates for populations in this study with analogous estimates from CEPH- Human Genome Diversity Panel populations reported previously12). No population has an estimate of Denisova ancestry that is significantly more than that in New Guineans, and hence we at
most plot 100%. The sampling location of the AU2 population is unknown and hence the position of this population is not precise.

The American Journal of Human Genetics 89, 516–528, October 7, 2011 519

from Denisova, then the allele frequency differences
between Han and X must have arisen solely since theirseparation from their common ancestor, and the two
frequency differences should be uncorrelated; thus, the f4
statistic has an expected value of zero. However, if population X inherited some of its ancestry from an archaic
population related to Denisovans, then the allele
frequency differences between Han and X will be correlated, the higher the admixture from the archaic population, the higher the correlation. Because the f4 statistic in
the numerator uses X as the test population, and the f4
statistic in the denominator uses New Guinea as the test
population, the ratio pD(X) estimates a quantity proportional to the percentage of Denisova ancestry qX; that is,
the Denisova admixture fraction in X divided by that in
New Guinea, qX/qNew Guinea (Appendix A).
We computed pD(X) for a range of non-African populations and found that for mainland East Asians, western
Negritos (Jehai and Onge), or western Indonesians, pD(X)is within two standard errors of zero when a standard error
is computed from a block jackknife (Table 1 and Figure 1).
Thus, there is no significant evidence of Denisova genetic
material in these populations. However, there is strong
evidence of Denisovan genetic material in Australians(1.03 5 0.06 times the New Guinean proportion; one standard error), Fijians (0.56 5 0.03), Nusa Tenggaras islandersof southeastern Indonesia (0.40 5 0.03), Moluccas
islanders of eastern Indonesia (0.35 5 0.04), Polynesians(0.020 5 0.04), Philippine Mamanwa, who are classified
as a ‘‘Negrito’’ group (0.49 5 0.05), and Philippine Manobo(0.13 5 0.03) (Table 1 and Figure 1). The New Guineans
and Australians are estimated to have indistinguishable
proportions of Denisovan ancestry (within the statisticalerror), suggesting Denisova gene flow into the common
ancestors of Australians and New Guineans prior to theirentry into Sahul (Pleistocene New Guinea and Australia),that is, at least 44,000 years ago.24,25 These results are
consistent with the Common Origin model of presentday New Guineans and Australians.26,27 We further confirmed the consistency of the Common Origin modelwith our data by testing for a correlation in the allele
frequency difference of two populations used as outgroups(Yoruba and Han) and the two tested populations (NewGuinean and Australian).The f4 statistic that measures
their correlation is only jZj ¼ 0.8 standard errors fromzero, as expected if New Guineans and Australians descendfrom a common ancestral population after they split from
East Asians, without any evidence of a closer relationshipof one group or the other to East Asians. Two alternative
histories, in which either New Guineans or Australians
have a common origin with East Asians, are inconsistent
with the data (both jZj > 52).
To assess the robustness of these estimates of Denisova
admixture proportion, we recomputed pD(X) for diverse
choices of A (YRI, San, and chimpanzee), B (Denisova,
Neandertal, and chimpanzee), C (CHB and Borneo) andX (17 different populations). For any population X, weobtain consistent estimates of the archaic mixture proportion, regardless of the choice of A, B, and C. Thus, themethod is robust to the choice of comparison populations,suggesting that the underlying model of population relationships (Appendix A) provides a reasonable fit to thedata and that our pD(X) ancestry estimates are reliable.
For our main estimates of admixture proportion, we reportresults for A ¼ YRI, B ¼ Denisova and C ¼ CHB because
Table S2 shows that the standard errors are smallest (inpart because of larger sample sizes).
To test whether our estimates of pD(X) are robust to ascertainment bias—the complex ways that SNPs were chosen
for inclusion on genotyping arrays originally designed
for medical genetics studies—we also estimated Denisova
admixture by using sequencing data. For this purpose, we
generated new shotgun sequencing data from a Philippine
Mamanwa individual (~13) and a New Guinea highlander(~33, from a different New Guinean group than the one
sampled in the Human Genome Diversity Panel16). We
merged these with data from Neandertal, Denisova, chimpanzee, and 12 present-day humans analyzed as part of the
Neandertal and Denisova genome sequencing studies.12,18
We then computed the same pD(X) statistics for the sequencing as for the genotyping data, replacing YRI witha Yoruba (HGDP00927), CHB with a Han (HGDP00778),
and New Guinea with a Papuan sample (Papuan2;
HGDP00551). Both the full sequence data and the SNP
data produce consistent estimates of pD(X) (Table 2), suggesting that ascertainment bias is not influencing thepD(X) estimates from genome-wide SNP data.
Near Oceanian Ancestry Explains Denisovan Genes
Outside of Australia and the Philippines
A parsimonious explanation for the Denisova genetic material that we detect in the non-Australian populations is thewell-documented admixture that has occurred in many
Southeast Asian and Oceanian groups between (1) Near
Oceanian populations related to New Guineans and (2)populations from island Southeast Asia related to mainland
East Asians, who are the primary populations of Taiwan
and Indonesia today.28–31 Thus, many groups might have
Denisova admixture as an indirect consequence of their
history of Near Oceanian admixture. For those populations
whose Denisova ancestry is explained in this way, their fraction of Denisovan ancestry is predicted to be exactly
proportional to their fraction of Near Oceanian ancestry.
To test this hypothesis, we designed a second statistic,pN(X), to estimate the fraction of a population’s Near Oceanian ancestry, defined here as the proportion of its ancestry
inherited from a population that is more closely related to
New Guineans than to Australians (Appendix A). A virtueof pN(X) is that it provides an unbiased estimate of a population’s Near Oceanian ancestry proportion even without
access to close relatives of the ancestral populations(Appendix A), whereas previous estimators10,30 dependon the accuracy of the surrogate contemporary populations used to approximate the ancestral populations. We

520 The American Journal of Human Genetics 89, 516–528, October 7, 2011

Table 2.

Denisovan Admixture pD(X) Estimated from Sequencing versus Genotyping Data
Sequencing Data

Genotyping Data

SampleHGDP ID for
Sequence Data

Estimated
AncestryStandard Errorin the Estimate

Z ScoreEstimated
AncestryStandard Errorin the Estimate

Z ScorePapuanHGDP00542

105%

9%

11.8

100%

n/a

n/a

104%

9%

11.7

100%

n/a

n/a

83%

10%

8.3

82%

5%

15.9

28%

10%

2.9

49%

5%

9.2

New Guinea Highlander
Bougainville

HGDP00491

Mamanwa
Cambodian

HGDP00711

19%

9%

2.0

3%

3%

0.8

Karitiana

HGDP00998

9%

12%

0.7

4%

6%

0.7

Mongolian

HGDP01224

6%

12%

0.5

3%

3%

1.1

For the sequencing data, we present the ratio f4(Yoruba, Denisova; Han, X)/f4(Yoruba, Denisova; Han, Papuan2), estimating the proportion of Denisova ancestry ina population X as a fraction of that in the Papuan2 sample (for the first line, the Papuan sample in the numerator is Papuan1 HGDP000551). For the genotyping
data, we present the ratio f4(YRI, Denisova; CHB, X)/f4(YRI, Denisova; CHB, Papuan). No standard errors are given for the genotyping-based estimates in the firsttwo rows because the Papuans and New Guineans are the reference populations, and so by definition those fractions are 100%.

compared pD(X) and pN(X) for all relevant populations(Table 1, Figure 2, and Figure S1) and found that, allowing
for sampling error, they occur in a one-to-one ratio for the
populations from the Nusa Tenggaras, Moluccas, Polynesia,
and Fiji. Common ancestry with Near Oceania thus canaccount for the Denisova genetic material in these groups.
A striking exception is observed in the two Philippine
populations, neither of which conforms to this relationship: pD(Mamanwa) ¼ 0.49 5 0.05 versus pN(Mamanwa) ¼
0.11 5 0.01 (p ¼ 1.5 3 1012 for the difference) andpD(Manobo) ¼ 0.13 5 0.03 versus pN(Manobo) ¼ 0.04 5
0.01 (p ¼ 0.0018) (Figure 2). An alternative hypothesisthat could account for the Denisovan genetic material in
the Philippines is common ancestry with Australians.32,33
We thus computed a third statistic, pA(X), that estimates
the relative proportion of Australian ancestry (Appendix
A). However, Australian ancestry cannot explain thesepatterns either: pD(Mamanwa) ¼ 0.49 5 0.05 versuspA(Mamanwa) ¼ 0.13 5 0.01 and pD(Manobo) ¼ 0.13 5
0.03 versus pA(Manobo) ¼ 0.05 5 0.01. The estimates ofpN(X) and pA(X) are consistent for a variety of outgroups(Appendix A and Table S3). Thus, the Denisova genetic
material in Mamanwa, as well as the smaller proportionin their Manobo neighbors, cannot be due to common
ancestry with Near Oceanians or Australians after thetwo groups diverged from one another. In the following
section, we focus on the Mamanwa because they havea higher proportion of Denisova genetic material and allow
us to study the pattern at a higher resolution.
Modeling Denisova Admixture and Population
History
To test whether the patterns observed in the Philippine
populations might reflect a history of Denisova gene flow
into a population that was ancestral to New Guineans,
Australians, and Mamanwa, followed by separation of
the Mamanwa first and then divergence of the New Guineans from Australians, we fit f statistics summarizing theallele frequency correlations among all possible sets of
populations to admixture graphs.14 Admixture graphs areformal models of population relationships with the important feature that simply by specifying a topology of population relationships, admixture proportions, and genetic
drift values on each lineage, they produce precise predictions of the values that will be observed at f4, f3, and f2
statistics (Appendix B). These predictions can then becompared to the empirically observed values (with standard

Figure 2. Denisovan and Near Oceanian Ancestry Are Proportional Except in the Philippines
We plot pD(X), the estimated percentage of Denisova ancestry asa fraction of that seen in New Guineans, against the estimatedpercentage of Near Oceanian ancestry pN(X) by using the values
from Table 1 (horizontal and vertical bars specify 51 standard
errors). The Mamanwa deviate significantly from the pD(X) ¼
pN(X) line, indicating that their Denisova genetic material does
not owe its origin to gene flow from a population related to Near
Oceanians. A weaker deviation is seen in the Manobo, who live
near the Mamanwa on the island of Mindanao.

The American Journal of Human Genetics 89, 516–528, October 7, 2011 521

98.7% 1.3%
93%

7%
49%

76%

24%
73%

YorubaChineseJehai (N)

51%

27%

Onge (N) Mamanwa (N) New Guinea

Australianerrors from a block jackknife) to assess the fit to the data.14
The best-fitting admixture graph for seven populations(Neandertal, Denisova, Yoruba, Han Chinese, Mamanwa,
Australians, and New Guineans) specifies Denisova geneflow into a population ancestral to New Guineans, Australians, and Mamanwa, followed by the splitting of the ancestors of the Mamanwa and much more recent admixture
between them and populations related to East Eurasians(Figure 3 and Figure S2). For this model, the admixture graphpredicts the values of 91 allele frequency correlation statistics (f statistics) relating the seven analyzed populations,and only one f statistic has an observed value more than
three standard errors from the prediction (Appendix B).
Encouraged by the fit of the admixture graph to the datafrom the seven populations, we extended the model toinclude two additional populations—Andaman Islanders(Onge) and Negrito groups from Malaysia (Jehai)—bothof which have been hypothesized to descend from thesame migration that gave rise to Australians and New
Guineans4,5 (Figure 3 and Figure S3). This analysis provides
overwhelming support for common ancestry for the Onge
and Jehai: an admixture graph specifying such a history is
an excellent fit to the joint data in the sense that only oneof the 246 possible f statistics is more than three standard
errors from expectation (Appendix B). The analysis alsosuggests that after their separation from the Onge, the Jehai received substantial admixture (about three-quartersof their genome) from populations related to mainland
East Asians (Appendix B). In contrast, a model in which
the Onge have no recent East Asian admixture is a good
fit to the data, providing further evidence that the Onge
have been unadmixed (at least with non-South Asians8)
since their initial arrival in the region.14
A striking finding that emerges from the admixture
graph model fitting is the evidence of an episode of additional gene flow into Australian and New Guinean ancestors—after their ancestors separated from those of the Ma-

Denisova

Neandertal

Figure 3. A Model of Population Separation and Admixture that Fits the Data
The admixture graph suggests Denisovarelated gene flow into a common ancestral
population of Mamanwa, New Guineans,and Australians, followed by admixture of
New Guinean and Australian ancestors
with another population that did notexperience Denisova gene flow. We cannot
distinguish the order of population divergence of the ancestors of Chinese, Onge/
Jehai, and Mamanwa/New Guineans/
Australians, and hence show a trifurcation.
Admixture proportion estimates (red) arepotentially affected by ascertainment bias
and hence should be viewed with caution.
In addition, although admixture graphs
are precise about the topology of population relationships, they are not informative regarding timing. Thus, the lengthsof lineages should not be interpreted interms of population split times and admixture events.

manwa—from a modern human population that did nothave Denisova genetic material. A model in which thisadmixture accounts for half of the genetic material inAustralians and New Guineans is an excellent fit to thedata (Figure 3, Figures S2 and S3, and Appendix B). Admixture graphs that do not model a second admixture event
are much poorer fits, producing 11 f statistics at jZj > 3
standard errors from expectation (Appendix B). Our
analysis further suggests that the modern humans who
admixed with the ancestors of Australians and New Guineans were closer to Andamanese and Malaysian Negritos
than to mainland East Asians (Figure 3), although thisis a weaker signal (1 f statistic with jZj > 3 versus 3) (Figure S3). This suggests that populations with Denisova
admixture could have been in proximity to the ancestorsof the Onge and Jehai during the earliest settlement of
the region but provides no evidence for ancestors of present-day East Asians in the region at that time (Appendix B).
Thus, these findings suggest that the present-day East
Asian and Indonesian populations are primarily descended
from more recent migrations to the region.

Discussion
This study has shown that Southeast Asia was settled bymodern humans in multiple waves: One wave contributed
the ancestors of present-day Onge, Jehai, Mamanwa, New
Guineans, and Australians (some of whom admixed with
Denisovans), and a second wave contributed much of
the ancestry of present-day East Asians and Indonesians.
This scenario of human dispersals is broadly consistent
with the archaeologically-motivated hypothesis of an earlysouthern route migration leading to the colonization of
Sahul and East Asia2 but also further clarifies this scenario.
In particular, our data provide no evidence for multiple
dispersals of modern humans out of Africa, as all nonAfricans have statistically indistinguishable amounts of

522 The American Journal of Human Genetics 89, 516–528, October 7, 2011

Neandertal genetic material.12,18 Instead, our data areconsistent with a single dispersal out of Africa (as proposedin some versions of the early southern route hypothesis1)
from which there were multiple dispersals to South andEast Asia.
This study is also important in providing a clue about thegeographic location of the Denisova gene flow. Given thehigh mobility of human populations, it is difficult to use
genetic data from present-day populations to infer the location of past demographic events with high confidence.
Nevertheless, the fact that Denisova genetic material ispresent in eastern Southeast Asians and Oceanians (Mamanwa, Australians, and New Guineans), but not in thewest (Onge and Jehai) or northwest (the Eurasian continent) suggests that interbreeding might have occurred inSoutheast Asia itself. Further evidence for a Southeast Asian
location comes from our evidence of ancient gene flow from
relatives of the Onge and Jehai into the common ancestorsof Australians and New Guineans after the initial Denisova
gene flow (Figure 3); this suggests that ancestors of both ofthese groups (but not of East Asians) were present in theregion at the time. Although some of the observed patterns
could alternatively be explained by a history in which there
was initially some Denisova genetic material throughout
Southeast Asia—which was subsequently displaced bymajor migrations of people related to present-day East
Asians—such a history cannot parsimoniously explain theabsence of Denisova genetic material in the Onge and Jehai.
Our evidence of a Southeast Asian location for the Denisovan admixture thus suggests that Denisovans were spread
across a wider ecological and geographic region—from thedeciduous forests of Siberia to the tropics—than any otherhominin with the exception of modern humans.
Finally, this study is methodologically important inshowing that there is much to learn about the relationships among modern humans by analyzing patterns ofgenetic material contributed by archaic humans. Because
the archaic genetic material is highly divergent, it is easily
detected in a modern human even if it contributes only asmall proportion of the ancestry; this makes it possible touse archaic genetic material to study subtle and ancientgene flow much as a medical imaging dye injected into apatient allows the tracing of blood vessels. A priority for
future research should be to obtain direct estimates forthe dates of the Denisova and Neandertal gene flow, as
these will provide a better understanding of the interactions among Denisovans, Neandertals, and the ancestorsof various present-day human populations.

Appendix A: Statistics Used for Estimating
Admixture Proportions
pD(X) Statistic Used for Estimating Denisova
Admixture Proportion
We first discuss the pD(X) statistic that we use for estimating the Denisova admixture proportion in any popula-tion X. Define the frequency of allele i in a sample from
population Y as ziY . Then pD(X) is defined as in Equation 1.
The rightmost part of Equation 1 shows that pD(X) can
also be expressed as a ratio of f4 statistics, which we introduced previously14 to measure the correlation in allele
frequency differences between pairs of populations. We
previously reported simulations showing that the expectedvalues of f4 statistics are in practice robust to ascertainment
bias (how the polymorphisms are chosen for inclusion inan analysis), making them useful for learning about
history with SNP array data.14
The expected values of f4 statistics can be understoodvisually by following the arrows through the phylogenetic
trees with admixture relating sets of samples, assumingthat these are accurate models for the relationships among
the populations.14 Figure 4 illustrates how the ratio of f4
statistics computed in Equation 1 estimates an admixture
proportion. Both the numerator and denominator can beviewed as a correlation of two allele frequency differences:ziA  ziB is the correlation in the allele frequency difference between an Outgroup ‘‘A’’ that did not experience
admixture and an Archaic group ‘‘B’’ hypothesized to berelated to the admixing group (e.g., A ¼ {chimpanzee,
Yoruba, or San} and B ¼ {Denisova or Neandertal}). This
follows the blue arrows in Figure 4.
ziC  ziX is the correlation in the allele frequency difference between a modern non-African population ‘‘C’’ anda test population ‘‘X’’ (e.g., C ¼ {Chinese or Bornean}).
This follows the red arrows in Figure 4.
If populations C and X are sister groups that descend froma homogeneous non-African ancestral population, then theallele frequency differences are expected to have arisen
entirely since the split from that common ancestral population, and thus the correlation to A and B is expected to be
zero (no overlap of the arrows). In contrast, if population
X has inherited some proportion qX of its lineages from anarchaic population, then the expected value of the productof the frequency differences is proportional to qX times
the overlap of the paths of A and B and C and X in Figure 4,
which corresponds to genetic drift a þ b. While we do
not know the value of a þ b, when we take the ratio of
the numerator and denominator to compute the pD(X)statistic, this unknown quantity cancels, and we obtain
qX/qNew Guinea, the proportion of archaic ancestry in a population as a fraction of that in New Guineans (Figure 4).
Two issues merit further discussion. First, Figure 4 is anoversimplification in that it does not show two archaic
gene-flow events (corresponding to Denisovans and Neandertals). However, we have previously reported that thedata are consistent with the same amount of Neandertal
gene flow into the ancestors of East Asians (C, such as
CHB) and populations with Denisovan ancestry (X).12,18
As a result, the same genetic drift terms are added to thenumerator and denominator, which then cancel in theratio pD(X) so that they do not affect results. Second,pD(X) is expected to provide an unbiased estimate of the
admixture proportion even if the genetic drift on various

The American Journal of Human Genetics 89, 516–528, October 7, 2011 523

Figure 4. Computation of the Estimateof Denisovan Ancestry pD(X)The black lines show the model for how
populations are related that is the basis
for the pD(X) ancestry estimate. Population
X arose from an admixture of a proportion(1  qX) of ancestry from an ancestral nonAfrican population C0 and (qX) fromarchaic population B0 (C and B are their
unmixed descendants). The expectedvalue of f4(A,B;C,X) is proportional to thecorrelation in the allele frequency differences A  B and C  X, and can be computed as the overlap in the drift paths
separating A  B (blue arrows) and C  X(red arrows). These paths only overlap
over the branches a and b, in proportion
to the percentage qX of the lineages of population X that are of archaic ancestry andso the expected value is qX(a þ b). When
we compute the ratio pD(X), (a þ b) cancels
from both the numerator and denominator, and we obtain qX/qNew Guinea, thefraction of archaic ancestry in a population
X divided by that in New Guinea. This
provides unbiased estimates of the mixture
proportion even if populations C and B have experienced a large amount of genetic drift since splitting from their ancestors, that is, evenif we do not have good surrogates for the ancestral populations. This robustness arises because the genetic drift on the branches B/B0
and C/C0 does not contribute to the expectations.

lineages has been large. This contrasts with previous
methods for estimating admixture, which have required
accurate proxies for the ancestral populations.10
pN(X) and pA(X) Statistics for Estimating Near
Oceanian and Denisova Admixture
We next discuss the statistics that we use for estimating the
New Guinean pN(X) or Australian pA(X) mixture proportionin any East Eurasian or island Southeast Asian population
X, which are defined in Equations 2 and 3, respectively.
Figure 5 shows the admixture graph corresponding to
the computation of pN(X). Both the numerator and thedenominator are of the form f4(A,Australia; X,New
Guinea). The first term measures the correlation in allele
frequency differences between (A  Australia) and (X 
New Guinea). If X and New Guinea descended from acommon ancestral population since the split from Australians, then they are perfect sister groups, and the expectedvalue of f4 is zero (the sample is consistent with 100%
Near Oceanian ancestry). On the other hand, if X has
a proportion (1  qX) of non-Near Oceanian ancestry,then the two terms will have a nonzero correlation, which
as shown in Figure 5 is proportional to the genetic drift
shared between the two population comparisons and has
an expected value of (1  qX)[(1  pX)b þ g] (the proportionsof ancestry flowing along various genetic drift paths times
the genetic drift on each of these lineages, indicated by
the overlap of the red and blue arrows). When we takeone minus the ratio pN(X) ¼ 1  f4(A,Australia; X,New
Guinea)/f4(A,Australia; CHB,New Guinea), the complicatedterm on the right side of this expectation cancels, and we
obtain E[pN(X)] ¼ qX. As with Figure 4, we do not show theindependent Neandertal admixture because the effect of
this term is to cancel from the numerator and denominator.
In Table S3 we report the pN(X) estimates for diverse
choices of outgroup populations A (Yoruba, San, and chimpanzee) and E (China and Borneo). The estimates are consistent whatever the choice of A and E, suggesting that ourinferences are robust. (We do not report pN(X) estimates inTable S3 for the Australians because this population is notexpected to conform to the population relationships
shown in Figure 5; indeed, the pN(X) estimates for Australians, when we do compute them, are significantly greaterthan 1.) Further evidence for the usefulness of the pN(X)estimates comes from the fact that it is consistent withthe pD(X) estimate for nearly all the populations in Table
1 (except for the Philippine populations, in which the Denisova ancestry does not appear to be explainable by Near
Oceanian gene flow as described in the main text).
We also computed a statistic pA(X) that is identical topN(X) except for the transpositions of the positions of Australia and New Guinea in the statistics (Equations 2 and 3).
Once again, we obtain consistent inferences of pA(X) in
Table S3 regardless of the choice of outgroup populations.
Because New Guinea and Australia are sister groups, descending from a common ancestral population, the justifications for the two statistics are very similar.
The only problem we found with the estimation of pN(X)procedure is that when X is any non-African population
known to have West Eurasian ancestry (e.g., Europeans orSouth Asians), we often obtained negative pN(X) statistics.
Two hypotheses could be consistent with this observation:(1) In unpublished data, we have attempted to write downa model of population separation and mixture analogous

524 The American Journal of Human Genetics 89, 516–528, October 7, 2011

Figure 5. Computation of the Estimateof Near Oceanian Ancestry pN(X)The test population X is assumed to havearisen from a mixture of a proportion(1  qX) of ancestry from ancestral East
Asians E0 and (qX) of ancestral Near Oceanians N0 . The Near Oceanians are, in turn,assumed to have received a proportion pXof their ancestry from the Denisovans(E and New Guinea are assumed to be
unmixed descendants of these two). The
expected value of f4(A,Australia; X, New
Guinea) can be computed from the correlation in the allele frequency differences A 
Australia (blue arrows) and X  New Guinea(red arrows). These paths only overlap
along the proportion (1  qX) of theancestry of population X that takes theEast Asian path, where the expected shareddrift is (1  pX)bþg as shown in the figure.
Thus, the expected value of the f4 statistic
is (1  qX)(1  pX)bþg. Because qX ¼
0 for the denominator of pN(X) (no Near
Oceanian ancestry), the ratio of f4 statistics
has an expected value of (1  qX) and E[pN(X)] ¼ qX.

to that in Figure 3 that jointly fits the genetic data comparing eastern and western Eurasian populations andhave so far not succeeded in developing a model that passes
goodness-of-fit tests. This suggests that the population
relationships between eastern and western Eurasians might
be more complex than we have been able to model to date,
and therefore we cannot use them in the pN(X) computation. (2) An alternative possibility is that the negative
pN(X) statistics reflect an artifact of ascertainment bias on
SNP arrays. Ascertainment bias is likely to be particularlycomplex with regard to the joint information from Europeans and East Asians because these populations wereheavily used in choices of SNPs for medical genetics arrays.
Thus, it might be difficult to make inferences using populations from both regions together with data from conventional SNP arrays developed for medical genetic studies.
Whatever the explanation, we have some reason tobelieve that estimates of Near Oceanian admixture byusing data from populations with West Eurasians might
be unreliable. Thus, we have excluded West Eurasians
from the estimates reported in Table 1.

Appendix B: Admixture Graphs
Overview of Admixture Graphs
A key finding from this study is that there is Denisova
genetic material in the Mamanwa, a Negrito group fromthe Philippines, which cannot be explained by a history ofrecent gene flow from relatives of New Guineans (Near Oceanians) or Australians. To further understand this history,
we use the admixture graph methodology that we initiallydeveloped for a study of Indian genetic variation14 to test
whether various hypotheses about population relationships
are consistent with the data. Specifically, we tested the

hypothesis of a single episode of Denisovan gene flow into
the ancestors of New Guineans, Australians, and Mamanwa,
prior to the separation of New Guineans and Australians.
Admixture graphs refer to generalizations of phylogenetic trees that incorporate the possibility of gene flow.
Like phylogenetic trees, admixture graphs describe thetopology of population relationships without specifying
the timing of events (such as population splits or geneflow events), or the details of population size changes on
different lineages. While this can be a disadvantage inthat fitting admixture graphs to data does not allow inferences of these important details, it is also an advantage inthat one can fit genetic data to an admixture graph without
having to specify a demographic history. This allows for
inferences that are more robust to uncertainties about
important parameters of history. Once the topology of the
population relationships is inferred, one can in principle
use other methods to make inferences about the timing of
events and population size changes. This makes the
problem of learning about history simpler than if one had
to simultaneously infer topology, timing, and demography.
An admixture graph makes precise predictions about thepatterns of correlation in allele frequency differences
across all subsets of two, three, and four populations in
an analysis, as measured for example by the f2, f3, and f4
statistics of Reich et al.14 Given n populations, there aren(n  1)/2 f2 statistics, n(n  1)(n  2)/6 f3 statistics, andn(n1)(n2)(n3)/24 f4 statistics. To fit an admixture
graph to data, one first proposes a topology, then identifies
the set of admixture proportions and genetic drift values
on each lineage (variation in allele frequency corresponding to random sampling of alleles from generation to
generation in a population of finite size) that are the best
match to the data under that model. The admixture graphtopology, admixture proportions, and genetic drift values

The American Journal of Human Genetics 89, 516–528, October 7, 2011 525

on each lineage together generate expected values for the
f2, f3 and f4 statistics14 that can be compared to theobserved values—which have empirical standard errors
from a block jackknife—to assess the adequacy of thebest fit under the proposed topology. As we showed previously,14 the topology relating populations in an admixture
graph can be accurately inferred even if the polymorphisms used in an analysis are affected by substantial ascertainment bias. The software that we have developed forfitting admixture graphs carries out a hill-climb to findthe genetic drift values and admixture proportions thatminimize the discrepancy between the observed and expected f2, f3, and f4 statistics for a given topology relating
a set of populations.
A complication in fitting admixture graphs to data isthat we do not know how many effectively independent
f statistics there are, out of the [n(n  1)/2][1 þ (n  2)/
3 þ (n  3)/12] that are computed. These statistics arehighly correlated, and in fact can be related algebraicallyto each other; for example, all the f3 and f4 statistics area linear combinations of the f2 statistics. Although we
believe that it is possible to construct a reasonable score
for how well the model fits the data by studying the covariance matrix of the f statistics—and indeed a score of thistype is the basis for our hill-climbing software—we have
not yet found a formal way to assess how many independent hypotheses are being tested, and thus we do not atpresent have a goodness-of-fit test. Instead, we simply
compute all possible f statistics and search for extreme
outliers (e.g., Z scores of 3 or more from expectation). A
large number of Z scores greater than 3 are not likely tobe observed if the admixture graph topology is an accurate
description of a set of population relationships.
Denisova Gene Flow into Mamanwa/New Guinean/
Australian Ancestors
We initially fit an admixture graph to the data from
Mamanwa, New Guineans, Australians, Denisova, Neandertal, West Africans (YRI), and Han Chinese (CHB), basingsome of the proposed population relationships on previous work that hypothesized a model of an out-of-Africa
migration of modern humans, Neandertal gene flow into
the ancestors of all non-Africans, and sister group status
for Neandertals and Denisovans.12 A complication infitting an admixture graph to these data is that becauseof the low coverage of the Neandertal and Denisova
genomes, we could not accurately infer the diploid genotype at each SNP. Thus, we sampled a single read from
Neandertal and Denisova to represent each site and (incorrectly) assumed that these individuals were homozygous
for the observed allele at each analyzed SNP. This meansthat the estimates of genetic drift on the Neandertal andDenisova branches are not reliable (the genetic drift values
are overestimated). However, these sources of error do not
introduce a correlation in allele frequencies across populations and hence are not expected to generate a false inference about the population relationships.

Figure S2 shows an admixture graph that proposes that theMamanwa, New Guineans, and Australians descend froma common ancestral population; the Mamanwa split first
and the New Guinean and Australian ancestors split later.
This is an excellent fit to the data in the sense that only
one of 91 f statistics is more than three standard errors
from zero (jZj ¼ 3.4). An interesting feature of this admixture
graph is that it specifies an additional admixture event, after
the Mamanwa lineage separated, into the ancestors ofAustralians and New Guineans that contributed about halfof their ancestry and involved a population without Denisova admixture. A model that does not include such asecondary admixture event is strongly rejected (see below).
The estimated proportion of Neandertal ancestry in all
non-Africans from the admixture graph fitting in Figure 3,
at 1.3%, is at the low end of the 1%–4% previously estimated from sequencing data.18 Similarly, we infer a proportion of Denisova ancestry in New Guineans of 3.5% ¼
6.6% 3 53%, which is lower than the 4%–6% previously
estimated based on sequencing data but not significantly
so when one takes into account the standard errors quoted
in that study.12 These low numbers could reflect statisticaluncertainty from the previously reported analyses ofsequencing data or in the admixture graph estimates (thelatter possibility is especially important to consider
because we do not at present understand how to compute
standard errors on the admixture estimates derived fromadmixture graphs). Another possible explanation for the
low estimates of mixture proportions is ascertainment
bias affecting the way SNPs were selected, which can affectestimates of mixture proportions and branch lengths(while having much less impact on the inference of
topology). Further support for the hypothesis that ascertainment bias might be contributing to our lower estimatesof mixture proportions comes from the fact that in unpublished work we have found that the polymorphisms mostenriched for signals of archaic admixture are those inwhich the derived allele is present in the archaic population, absent in West Africans, and present at low minor
allele frequency in the studied population. In our admixture graph fitting, we filtered out this class of SNPs, as
the f statistics used in the admixture graph have denominators that require frequency estimates from a polymorphic reference population, and we used YRI as our reference. Thus, when we refitted the same admixture graphwith CHB instead of YRI as the reference population, we
obtained the same topology but the Neandertal mixture
proportion increased to 1.9%. We have chosen to use YRI
as the reference population in all of our reported admixture graphs because they are a better outgroup for themodern populations whose history we are studying thanthe CHB (populations related to the Chinese were directly
involved in admixture events in Southeast Asia).
Adding Onge and Jehai
The Andamanese Negrito group (Onge) and Malaysian
Negrito group (Jehai) have been proposed to share ancient

526 The American Journal of Human Genetics 89, 516–528, October 7, 2011

common ancestry with Philippine Negritos (e.g., Mamanwa). The fact that neither the Onge nor the Jehai
have evidence of Denisova genetic material, however,suggests that any common ancestry must date to before
the Denisova gene flow into the ancestors of the Mamanwa, New Guineans, and Australians. To explore therelationship between the Onge and Jehai and the otherpopulations, we added them into the admixture graph.
The only family of admixture graphs that we could identify
as fitting the data have the Onge as a deep lineage ofmodern humans, with the Jehai deriving ancestry fromthe same lineage but also harboring a substantial additional
contribution of East Asian related admixture (Figure S3).
A striking feature of the family of admixture graphs shownin Figure S3 is that both the Jehai and Mamanwa are inferredto have up to about three-quarters of their ancestry due torecent East Eurasian admixture, which is not too surprising
given that these populations have been living side by sidewith populations of East Eurasian ancestry for thousandsof years. Moreover, both Y-chromosome and mtDNA analyses strongly suggest recent East Asian admixture in the
Mamanwa.32,34 In contrast, the genome-wide SNP data for
the Onge are consistent with having no non-Negrito admixture within the limits of our resolution, perhaps reflecting
their greater geographic isolation.
We next sought to resolve how the lineage including
Onge and Jehai ancestors, the mainland East Asian (e.g.,Chinese), and the eastern group (including Mamanwa,
Australian and New Guinean ancestors) are related. Three
relationships are all consistent with the data. Specifically,
for all three of the admixture graphs shown in Figure S3,
only one of the 246 possible f statistics has a score of
jZj > 3. Thus, we cannot discern the order of splitting ofthese three lineages and represent the relationships asa trifurcation in Figure 3. The actual estimates of mixture
proportions are similar for all three figures as well.

Second, we considered the possibility that the secondarygene-flow event into the ancestors of Australians and
New Guineans came from relatives of Chinese (CHB)
rather than western Negritos such as the Onge. However,when we fit this alternative history to the data, we find
three f statistics (rather than one) with scores of jZj > 3,
a substantially worse fit. We conclude that the modern
human population with which the ancestors of Australians
and New Guineans interbred was likely to have been moreclosely related to western Negritos than to mainland East
Asians.

Perturbing the Best-Fitting Admixture Graph to Assess
the Robustness of Our Inferences
To assess the robustness of the admixture graphs, we perturbed Figure S3 (in practice, we perturbed Figure 3A, but
given the fact that the graphs are statistically indistinguishable we expected that results would be similar for
all three). First, we considered the possibility that after
the initial Denisova gene flow into the ancestors of Mamanwa, New Guineans, and Australians, the New Guineanand Australian ancestors did not experience an additional
gene-flow event with a population without Denisovan
admixture. However, when we try to fit this simpler modelto the data, we find that instead of one f statistic that is
jZj > 3 standard errors from expectation, there are now
11, and all but one of them involve the Mamanwa, suggesting that this population is poorly fit by such a model. Thus,an additional admixture event in the ancestry of New
Guineans and Australians (resulting in a decrease in their
proportion of Denisova ancestry) results in a major
improvement in the fit.

Web Resources

Supplemental Data
Supplemental Data include three figures and three tables and canbe found with this article online at http://www.cell.com/AJHG/.

Acknowledgments
We thank the volunteers who donated DNA samples. We acknowledge F.A. Almeda Jr., J.P. Erazo, D. Gil, the late J. Kuhl, E.S. Larase, I.
Motinola, G. Patagan, W. Sinco, A. Sofro, U. Tadmor, and R. Trent
for assistance with sample collections. We thank M. Meyer for
preparing DNA libraries for high-throughput sequencing; A. Barikand P. Nürenberg for assistance with genotyping; and O. Bar-Yosef,
K. Bryc, R.E. Green, J.-J. Hublin, J. Kelso, D. Lieberman, B. Pakendorf, M. Slatkin, and B. Viola for comments on the manuscript.
T.A. Jinam was supported by a grant from the SOKENDAI Graduate
Student Overseas Travel Fund. This work was supported by the
Max Planck Society and by a National Science Foundation
HOMINID grant (1032255).

Received: August 11, 2011
Revised: September 8, 2011
Accepted: September 8, 2011
Published online: September 22, 2011

The URLs for data presented herein are as follows:
Burrows-Wheeler Aligner, http://bio-bwa.sourceforge.net/index.
shtml
CEPH-Human Genome Diversity Cell Line Panel, http://www.
cephb.fr/en/hgdp/diversity.php
EIGENSOFT, http://genepath.med.harvard.edu/~reich/Software.htm
European Collection of Cell Cultures, http://www.hpacultures.
org.uk/pages/Ethnic_DNA_Panel.pdf
European Nucleotide Archive (Project ID ERP000121), http://
www.ebi.ac.uk/ena/
Ibis, http://bioinf.eva.mpg.de/Ibis/
SAMtools, http://samtools.sourceforge.net/

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