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The genome-wide structure of the Jewish people

July 2010
Nature 466(7303):238-42

July 2010
466(7303):238-42

DOI:10.1038/nature09103

Source
PubMed

Authors:

Bayazit Yunusbayev

Institute of Genomics, University of Tartu

Mait Metspalu

University of Tartu

Ene Metspalu

University of Tartu

Show all 21 authorsHide

Contemporary Jews comprise an aggregate of ethno-religious communities whose worldwide members identify with each other through various shared religious, historical and cultural traditions. Historical evidence suggests common origins in the Middle East, followed by migrations leading to the establishment of communities of Jews in Europe, Africa and Asia, in what is termed the Jewish Diaspora. This complex demographic history imposes special challenges in attempting to address the genetic structure of the Jewish people. Although many genetic studies have shed light on Jewish origins and on diseases prevalent among Jewish communities, including studies focusing on uniparentally and biparentally inherited markers, genome-wide patterns of variation across the vast geographic span of Jewish Diaspora communities and their respective neighbours have yet to be addressed. Here we use high-density bead arrays to genotype individuals from 14 Jewish Diaspora communities and compare these patterns of genome-wide diversity with those from 69 Old World non-Jewish populations, of which 25 have not previously been reported. These samples were carefully chosen to provide comprehensive comparisons between Jewish and non-Jewish populations in the Diaspora, as well as with non-Jewish populations from the Middle East and north Africa. Principal component and structure-like analyses identify previously unrecognized genetic substructure within the Middle East. Most Jewish samples form a remarkably tight subcluster that overlies Druze and Cypriot samples but not samples from other Levantine populations or paired Diaspora host populations. In contrast, Ethiopian Jews (Beta Israel) and Indian Jews (Bene Israel and Cochini) cluster with neighbouring autochthonous populations in Ethiopia and western India, respectively, despite a clear paternal link between the Bene Israel and the Levant. These results cast light on the variegated genetic architecture of the Middle East, and trace the origins of most Jewish Diaspora communities to the Levant.

PCA of high-density array data.a, Scatter plot of Old World individuals, showing the first two principal components. Each ring corresponds to one individual and the colour indicates the region of origin (for the full figure see Supplementary ). b–d, A series of magnifications showing samples from Europe and the Middle East (b), Ethiopia (c) and south Asia (d). Each letter code (Supplementary Table 1) corresponds to one individual, and the colour indicates the geographic region of origin. In b, a polygon surrounding all of the individual samples belonging to a group designation highlights several population groups.

…

PCA of west Eurasian high-density array data.Plot of kernel densities (Supplementary Note 2) for each population sample (n > 10) was estimated on the basis of PC1 and PC2 coordinates in Supplementary . Individuals from these samples were plotted by using PC1 and PC2 coordinates and were overlaid with the plot of kernel density.

…

Population structure inferred by ADMIXTURE analysis.Each individual is represented by a vertical (100%) stacked column of genetic components proportions shown in colour for K = 8. The Jewish communities are labelled in colour and bold. T and B further specify Sephardi Jews from Turkey and Bulgaria, respectively. Populations introduced for the first time in this study and analysed together with the Human Genome Diversity Panel18 data are marked with an asterisk.

…

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LETTERS

The genome-wide structure of the Jewish people

Doron M. Behar

1,2

*, Bayazit Yunusbayev

2,3

*, Mait Metspalu

*, Ene Metspalu

, Saharon Rosset

,Ju

¨ri Parik

Siiri Rootsi

, Gyaneshwer Chaubey

, Ildus Kutuev

2,3

, Guennady Yudkovsky

1,5

, Elza K. Khusnutdinova

Oleg Balanovsky

, Ornella Semino

, Luisa Pereira

8,9

, David Comas

, David Gurwitz

, Batsheva Bonne-Tamir

Tudor Parfitt

, Michael F. Hammer

, Karl Skorecki

1,5

& Richard Villems

Contemporary Jews comprise an aggregate of ethno-religious

communities whose worldwide members identify with each other

through various shared religious, historical and cultural tradi-

tions

1,2

. Historical evidence suggests common origins in the Middle

East, followed by migrations leading to the establishment of com-

munities of Jews in Europe, Africa and Asia, in what is termed the

Jewish Diaspora

3–5

. This complex demographic history imposes

special challenges in attempting to address the genetic structure

of the Jewish people

. Although many genetic studies have shed

light on Jewish origins and on diseases prevalent among Jewish

communities, including studies focusing on uniparentally and

biparentally inherited markers

7–16

, genome-wide patterns of

variation across the vast geographic span of Jewish Diaspora com-

munities and their respective neighbours have yet to be addressed.

Here we use high-density bead arrays to genotype individuals from

14 Jewish Diaspora communities and compare these patterns of

genome-wide diversity with those from 69 Old World non-Jewish

populations, of which 25 have not previously been reported.

These samples were carefully chosen to provide comprehensive

comparisons between Jewish and non-Jewish populations in the

Diaspora, as well as with non-Jewish populations from the Middle

East and north Africa. Principal component and structure-like

analyses identify previously unrecognized genetic substructure

within the Middle East. Most Jewish samples form a remarkably

tight subcluster that overlies Druze and Cypriot samples but not

samples from other Levantine populations or paired Diaspora host

populations. In contrast, Ethiopian Jews (Beta Israel) and Indian

Jews (Bene Israel and Cochini) cluster with neighbouring auto-

chthonous populations in Ethiopia and western India, respec-

tively, despite a clear paternal link between the Bene Israel and

the Levant. These results cast light on the variegated genetic archi-

tecture of the Middle East, and trace the origins of most Jewish

Diaspora communities to the Levant.

Recently, the capacity to obtain whole-genome genotypes with the

use of array technology has provided a robust tool forelucidating fine-

scale population structure and aspects of demographic history

17–23

This approach, initially used to account for population stratification

in genome-wide association studies, identified genome-wide patterns

of variation that distinguished between Ashkenazi Jews and non-

Jews of European descent

7,11,12,14–16

. Similarly, a large-scale survey of

autosomal microsatellites found that samples from four Jewish

communities clustered close to each other and intermediate between

non-Jewish Middle Eastern and European populations

Illumina 610K and 660K bead arrays were used to genotype 121

samples from 14 Jewish communities. The results were compared

with 1,166 individuals from 69 non-Jewish populations (Supplemen-

tary Note 1 and Supplementary Table 1), with particular attention to

neighbouring or ‘host’ populations in corresponding geographic

regions. These results were also integrated with analyses of genotype

data from about 8,000 Y chromosomes and 14,000 mitochondrial

DNA (mtDNA) samples (Supplementary Note 6 and Supplemen-

tary Tables 4 and 5). Several questions were then addressed: What

are the locations of the various Jewish communities in a global genetic

variation context? What are the features of the Middle Eastern (Sup-

plementary Fig. 1) population genetic substructure? What are the

genetic distances between contemporary Jewish communities, their

Diaspora neighbours and Middle Easternpopulations? Can the genetic

origin of Jews be pinpointed within the Middle East?

The EIGENSOFT package

was used to identify the principal

components (PCs) of autosomal variation in our Old World sample

set (Fig. 1 and Supplementary Fig. 2a). This analysis places the

studied samples along two well-established geographic axes of global

genetic variation

18,19,22

: PC1 (sub-Saharan Africa versus the rest of the

Old World) and PC2 (east versus west Eurasia). Focusing on the

Middle Eastern populations in the PC1–PC2 plot (Fig. 1b) reveals

more geographically refined groupings. Populations of the Caucasus,

flanked by Cypriots, form an almost uninterrupted rim that separates

the bulk of Europeans from Middle Eastern populations. Bedouins,

Jordanians, Palestinians and Saudi Arabians are located in close

proximity to each other, which is consistent with a common origin

in the Arabian Peninsula

, whereas the Egyptian, Moroccan,

Mozabite Berber, and Yemenite samples are located closer to sub-

Saharan populations (Fig. 1a and Supplementary Fig. 2a).

Most Jewish samples, other than those from Ethiopia and India,

overlie non-Jewish samplesfrom the Levant (Fig. 1b). The tight cluster

comprising the Ashkenazi, Caucasus (Azerbaijani and Georgian),

Middle Eastern (Iranian and Iraqi), north African (Moroccan) and

Sephardi (Bulgarian and Turkish) Jewish communities, as well as

Samaritans, strongly overlaps Israeli Druze and is centrally located

on the principal component analysis (PCA) plot when compared with

Middle Eastern, European Mediterranean, Anatolian and Caucasus

non-Jewish populations (Fig. 1). This Jewish cluster consists of

*These authors contributed equally to this work.

Molecular Medicine Laboratory, Rambam Health Care Campus, Haifa 31096, Israel.

Estonian Biocentre and Department of Evolutionary Biology, University of Tartu, Tartu 51010,

Estonia.

Institute of Biochemistry and Genetics, Ufa Research Center, Russian Academy of Sciences, Ufa 450054, Russia.

Department of Statistics and Operations Research, School

of Mathematical Sciences, Tel Aviv University, Tel Aviv 69978, Israel.

Rappaport Faculty of Medicine and Research Institute, Technion

–

Israel Institute of Technology, Haifa 31096,

Israel.

Research Centre for Medical Genetics, Russian Academy of Medical Sciences, Moscow 115478, Russia.

Dipartimento di Genetica e Microbiologia, Universita

`di Pavia, Pavia

27100, Italy.

Instituto de Patologia e Imunologia Molecular da Universidade do Porto (IPATIMUP), Porto 4200-465, Portugal.

Faculdade de Medicina, Universidade do Porto, Porto

4200-319, Portugal.

Institute of Evolutionary Biology (CSIC-UPF), CEXS-UPF-PRBB and CIBER de Epidemiologı

a y Salud Pu

´blica, Barcelona 08003, Spain.

Department of Human

Molecular Genetics and Biochemistry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.

Department of the Languages and Cultures of the Near and Middle

East, Faculty of Languages and Cultures, School of Oriental and African Studies (SOAS) , University of London, London WC1H 0XG, UK.

ARL Division of Biotechnology, University of

Arizona, Tucson, Arizona 85721, USA.

doi:10.1038/nature09103

samples from most Jewish communities studied here, which together

cover more than 90% of the current world Jewish population

; this is

consistent with an ancestral Levantine contribution to much of con-

temporary Jewry. A compact cluster of Yemenite Jews, which is also

located within an assemblage of Levantine samples, overlaps primarily

with Bedouins but also with Saudi individuals (Fig. 1b). In contrast,

Ethiopian and Indian Jews are located close to those from neighbour-

ing host populations (Fig. 1c,d). Ethiopian Jews clustered with

Semitic-speaking rather than Cushitic-speaking Ethiopians. See Sup-

plementary Note 2 for a discussion of the assignment of samples repre-

senting the Belmonte and Uzbek (Bukharan) Jewish communities.

To glean further details of Levantine genetic structure, we repeated

PCA on a restricted set of samples from west Eurasia (Fig. 2, Sup-

plementary Fig. 3 and Supplementary Note 2) and by inspect-

ing lower-ranked PCs in the Old World context (Supplementary

Fig. 2b, c; PC1 versus PC3 and PC4). These analyses reveal three

−0.02 00.02

0.04 0.06

−0.08 −0.06 −0.04 −0.02 0.00 0.02

Eigenvector 2

Eigenvector 1

0.0

ector 2

FB Orc

Orc

Fr Fr

Orc

Tus

Orc

Fr Fr

Tus

Orc

FB Fr

Tus

Orc

Tus

Orc

Tus

Orc

Tus

Orc

FB Orc

Chu

Bel

BelBel

Bel

Hng

HngHng

Hng

Lit

Lit Lit

Spa

Rmn Rmn

Rmn

RmnRmn

Rmn Rmn

Rmn

Europeans

Levantine

non-Jewish populations

Bedouins

Palestinians

Saudis

Jordanians

Spa

SpSp

Druze

Cypriots

Samaritans

Hng

Rmn

ngngngng

Armenians

Georgians

Turks

Iranians

Lezgins

Adygei

Ady

Ady Ady

Ady

Pal

Pal Pal

Pal

Pal Pal

Pal

PalPal Pal

Pal

alP

Pal Pal

GoGo

Go Go

Arm

Tur

TurTu r

Tur

TurTur

Irn

Yem

Lzg

LzgLzg

Lzg Lzg

Lzg

Drz Drz

Drz

Drz Drz

Drz

LebLeb

Leb

Cyr

CyrCyr

Cyr

Cyr Cyr

Cyr

Jor

Jor Jor

Jor

Syr

Sdi

Sdi Sdi

Sdi

InJ

InJInJ

InJ

IqJ IqJ

IqJIqJ

IqJ

IqJ IqJ

IqJ

SJ SJ SbJ AJ

AJAJ

AJ AJ

AJ UJ

MJ MJ

AzJ

InJ, Iranian Jews

IqJ, Iraqi Jews

SJ, Sephardi Jews

AJ, Ashkenazi Jews

UJ, Uzbekistani Jews

MJ, Moroccan Jews

AzJ, Azerbaijani Jews

GJ, Georgian Jews

YJ, Yemenite Jews

Leb, Lebanese

Syr, Syrians

Yem, Yemenites

Jor

Mor

EtO

EtT

EtO

EtA

EtTEtT

EtT

EtA

EJ, Ethiopian Jews

Jor, Jordanians

Mor, Moroccans

EtT, Tigray Ethiopians

EtO, Oromo Ethiopians

EtA, Amhara Ethiopians

Blo

Sin

Ptn

Bur

Sin

Ptn

Bur

Sin

Ptn

Bur Bur

Blo

SinSin

Ptn Bur

Bur

Sin

Ptn

Bur

Sin

Ptn

Bur

Sin Sin

Ptn Ptn

Bur

Sin

Ptn

Bur Bur

Sin

Bur

Ptn

Bur

Sin

Ptn

Bur

Ind Ind Ind

Ind

IcJ

IcJIbJ

IbJ IbJ

IbJ

Ptn, Pathan

Blo, Balochi

Bur, Burusho

Sin, Sindhi

Ind, India (southern)

IcJ, Cochini Jews

IbJ, Mumbai Jews

Sub-Saharan Africa

North and east Africa

Middle East

Europe

Jewish communities

South Asia

East Asia

SbJ, Belmonte Jew

Figure 1

PCA of high-density array data. a, Scatter plot of Old World

individuals, showing the first two principal components. Each ring

corresponds to one individual and the colour indicates the region of origin

(for the full figure see Supplementary Fig. 2). b–d, A series of magnifications

showing samples from Europe and the Middle East (b), Ethiopia (c) and

south Asia (d). Each letter code (Supplementary Table 1) corresponds to one

individual, and the colour indicates the geographic region of origin. In b,a

polygon surrounding all of the individual samples belonging to a group

designation highlights several population groups.

LETTERS NATURE

distinct Near Eastern Jewish subclusters: the first group is located

between Middle Eastern and European populations and consists of

Ashkenazi, Moroccan and Sephardi Jews. The second group, com-

prising the Middle Eastern and Caucasus Jewish communities, is

positioned within the large conglomerate of non-Jewish populations

of the region. The third group contains only a tight cluster of

Yemenite Jews.

After elucidation of these groupings by PCA, we turned to

structure-like analysis

with the algorithm ADMIXTURE

to assign

individuals proportionally to hypothetical ancestral populations

(Supplementary Note 3). Initially, all Jewish samples were analysed

jointly with 25 novel reference populations (Supplementary Note 1)

in combination with the Human Genome Diversity Panel

samples

representing Africa, the Middle East, Europe, and central, south and

east Asia (Fig. 3 and Supplementary Fig. 4). This analysis significantly

refines and reinforces the previously proposed partitioning of Old

World population samples into continental groupings

18,19

(Sup-

plementary Fig. 4 and Supplementary Note 4). We note that mem-

bership of a sample in a component that is predominant in, but not

restricted to, a specific geographic region is not sufficient to infer its

genetic origins. Membership in several genetic components can imply

either a shared genetic ancestry or a recent admixture of sampled

individuals

18,28

. An illustrative example at K58 (Fig. 3 and Sup-

plementary Note 3) is the pattern of membership of Ashkenazi,

Caucasus (Azerbaijani and Georgian), Middle Eastern (Iranian and

Iraqi), north African (Moroccan), Sephardi (Bulgarian and Turkish)

and Yemenite Jewish communities in the light-green and light-

blue genetic components, which is similar to that observed for

Middle Eastern non-Jewish populations, suggesting a shared regional

origin of these Jewish communities. This inference is consistent with

historical records describing the dispersion of the people of ancient

Israel throughout the Old World

1–4

. Our conclusion favouring

common ancestry over recent admixture is further supported by the

fact that our sample contains individuals that are known not to be

admixed in the most recent one or two generations. It is also evident

that among the Ashkenazi, Moroccan and Sephardi Jewish com-

munities the dark-blue component dominating European populations

is more substantial than the corresponding proportion of this com-

ponent amongthe Middle Eastern Jewish communities (Fig. 3).For the

Indian and Ethiopian Jewish communities the dark-green and light-

brown genetic components are consistent with corresponding mem-

bership of theirrespective host populations (Fig. 3).ADMIXTURE was

also run on the west Eurasian subset of the Old World sample, which

highlights differentiation between the Middle East and Europe (Sup-

plementary Fig. 4b). Here, comparison between the ADMIXTURE-

derived component patterns for Sephardi and Ashkenazi Jews shows

that the former have only slightly greater similarity to the pattern

observed for Middle Eastern populations than do the latter.

Genetic relationships between our population samples were then

explored with the measure of allele sharing distances (ASDs)

.Table1

provides genetic distances between each Jewish community and its

corresponding host population, all Jewish communities, west

Eurasian Jewish communities, their respective Jewish group inferred

from the PCA, and non-Jewish Levantine populations. The Ashkenazi,

Sephardi, Moroccan, Iranian, Iraqi, Azerbaijani and Uzbekistani

Jewish communities have the lowest ASD values when compared with

their PCA-based inferred Jewish sub-cluster (Fig. 3 and Supplemen-

tary Figs 2c and 3). In all except the Sephardi Jewish community, this

ASD difference is statistically significant (P,0.01, bootstrap t-test).

ASD values between Ashkenazi, Sephardi and Caucasus Jewish popu-

lations and their respective hosts are lower than those between each

Jewish population and non-Jewish populations from the Levant. This

might be the result of a bias inherent in our calculations as a result of

the genetically more diverse non-Jewish populations of the Levant.

The Ethiopian and Indian Jewish communities show the lowest ASD

values when compared with their host population (Supplementary

Tables 2 and 3 and Supplementary Note 5).

Although uniparental markers

8,9

(Supplementary Note 6) are limited

in their capacity to uncover genetic substructure within the Middle

East, they do provide important insights into sex-specific processes that

are not unambiguously evident from the autosomal data alone. For

example, Y-chromosome data point to a unique paternal genetic link

between the Bene Israel community and the Levant, whereas the

absence of sub-Saharan African maternal lineages in Yemenite and

Moroccan Jews (in contrast to their hosts) suggests limited maternal

gene flow.

–0.10 –0.05 0 0.05

–0.05

0.05

envector 2, ei

envalue = 2.8

Eigenvector 1, eigenvalue = 6.1

Adygei

Lezgins

Armenians

Georgians

Chuvashi

Fr. Basque

Sardinians

Spaniards

French

Russians

Romanians

Hungarians

Lithuanians

Orcadians

Iranians

Saudis

Bedouins

Syrians

Jordanians

Druze

Turk s

Cypriots

Ashkenazi Jews

Iraqi Jews

Yemenite Jews

Sephardi Jews

Moroccan Jews o

Azerbaijani Jews (o)

Georgian Jews (g)

L, Lebanese

S, Samaritans

T, Tuscans

Iranian Jews (J)

B, Belorussians

SbJ

U, Uzb. Jews

SbJ, Sephardi Belmonte

Palestinians

Figure 2

PCA of west Eurasian high-density array data. Plot of kernel

densities (Supplementary Note 2) for each population sample (n.10) was

estimated on the basis of PC1 and PC2 coordinates in Supplementary Fig. 3.

Individuals from these samples were plotted by using PC1 and PC2

coordinates and were overlaid with the plot of kernel density.

Africa Middle East Europe Central, south and east Asia

Biaka P ygmies

Mbuti Pygmies

San

Bantu

Yoruba

Mandenkas

*Ethiopian J ews

*Ethiopians

Mozabi tes

*Moroccans

*Moroccan Je ws

*Egyptians

*Saudis

*Yemenese

*Yemenite Je ws

Bedouins

Palestini ans

*Syrians

*Jordanians

Druze

*Lebanese

*Samari tans

*Turks

*Iraqi Jews

*Iranian Jews

*Iranians

*Armenians

*Georgians

*Georgian Je ws

*Azerbaijani Je ws

Adyge i

*Lezgins

*Sephardi J ews

*Ashkenazi Jews

Orcadi ans

French Basque

French

*Spaniards

Tuscans

Sardinians

*Cypriots

*Romanians

*Hungarians

*Lithuanians

*Belorussians

Russians

*Chuvashs

*Uzbeks

Uygurs

Hazara

Burusho

Pathan

Brahui

Balochi

Sindhi

Makrani

*South Indians

Yakuts

Cambodians

Dai

Lahu

Miaozu

She

Han

Tuji a

Naxi

Yizu

Xibo

Oroqe n

Mongo ls

Daur

Hezhen

Japanese

*Uzbekistani Jews

*Mumbai Jews

*Cochini Jews

Figure 3

Population structure inferred by ADMIXTURE analysis. Each

individual is represented by a vertical (100%) stacked column of genetic

components proportions shown in colour for K58. The Jewish

communities are labelled in colour and bold. T and B further specify

Sephardi Jews from Turkey and Bulgaria, respectively. Populations

introduced for the first time in this study and analysed together with the

Human Genome Diversity Panel

data are marked with an asterisk.

NATURE LETTERS

Our PCA, ADMIXTURE and ASD analyses, which are based on

genome-wide data from a large sample of Jewish communities, their

non-Jewish host populations, and novel samples from the Middle

East, are concordant in revealing a close relationship between most

contemporary Jews and non-Jewish populations from theLevant. The

most parsimonious explanation for these observations is a common

genetic origin, which is consistent with an historical formulation of

the Jewish people as descending from ancient Hebrew and Israelite

residents of the Levant. This inference underscores the significant

genetic continuity that exists among most Jewish communities and

contemporary non-Jewish Levantine populations, despite their long-

term residence in diverse regions remote from the Levant and isola-

tion from one another. This study further uncovers genetic structure

that partitions most Jewish samples into Ashkenazi–north African–

Sephardi, Caucasus–Middle Eastern, and Yemenite subclusters

(Fig. 2). There are several mutually compatible explanations for the

observed pattern: a splintering of Jewish populations in the early

Diaspora period, an underappreciated level of contact betweenmem-

bers of each of these subclusters, and low levels of admixture with

Diaspora host populations. Equally interesting are the inferences that

can be gleaned from more distant Diaspora communities, such as the

Ethiopian and Indian Jewish communities. Strong similarities to their

neighbouring host populations may have resulted from one or more

of the following: large-scale introgression, asymmetrical sex-biased

gene flow, or religious and cultural diffusion during the process of

becoming one of the many and varied Jewish communities.

METHODS SUMMARY

Blood or buccal samples were collected with informed consent from unrelated

volunteers who self-identified as members of one of the Jewish communities or

non-Jewish populations studied here (Supplementary Note 1). The term ‘Old

World’ refers to populations of the Eastern Hemisphere, specifically Europe,

Asia and Africa. Whenever the term Jewish is not part of the population

designation, this refers to a non-Jewish population. DNA samples chosen for

the biparental analysis were genotyped on Illumina 610K or 660K bead arrays

and showed a genotyping success rate of more than 97%. Data management and

quality control were aided by PLINK 1.05 (ref. 30). For comparison, the relevant

populations from the Illumina 650K-based data set of the Human Genome

Diversity Panel, excluding relatives

, were included in our analysis. After iden-

tification of the intersection of genotypes from the various Bead-Arrays, quality

control (QC) and linkage disequilibrium (LD) pruning, a total of 226,839 auto-

somal single nucleotide polymorphisms (SNPs) remained for further analysis.

PCA of autosomal variation using the smartpca of the EIGENSOFT package

was performed (Supplementary Note 2). Samples were modelled as comprising a

mixture of major genetic components using the structure-like ADMIXTURE

program

, and the inferred genetic membership of each individual from this

analysis was studied (Supplementary Notes 3 and 4). ASD

between groups

was assessed, and a bootstrap procedure to determine the significance of differ-

ences in ASD between pairs of populations was adapted (Supplementary Note 5).

Our uniparental data was merged with previously reported data sets for

Y-chromosome and mtDNA analysis (Supplementary Note 6). A matrix of

Y-chromosome and mtDNA haplogroup frequencies was constructed, and

PCA was performed in the R environment (using the function princomp).

Full Methods and any associated references are available in the online version of

the paper at www.nature.com/nature.

Received 9 December 2009; accepted 21 April 2010.

Published online 9 June 2010.

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Table 1

Genetic distances (ASD) between Jewish, Levantine and Diaspora host populations

Jewish community Host population Hosts Levant*All Jews West Eurasian Jews{Jewish cluster{

Ashkenazi Europe10.236 0.239I0.240 0.236 0.235

Sephardi Spain 0.236 0.238 0.239 0.236 0.235

Moroccan Morocco 0.246 0.239 0.240 0.237 0.236

Georgian Georgia 0.234 0.238 0.239 0.236 0.236

Azerbaijani Lezgin 0.238 0.240 0.241 0.238 0.237

Iranian Iran 0.239 0.239 0.240 0.237 0.236

Iraqi Syria, Iran 0.238 0.238 0.239 0.236 0.236

Uzbekistani Uzbekistan 0.243 0.238 0.239 0.236 0.235

Bene Israel India (Mumbai) 0.240 0.245 0.245 0.243 0.241

Cochini India (Kerala) 0.238 0.247 0.247 0.245 0.241

Ethiopian Ethiopia"0.245 0.253 0.255 0.254

Yemenite Yemen 0.243 0.238 0.240 0.237

*Levant populations included Bedouin, Cypriots, Druze, Jordanians, Lebanese, Palestinians, Samaritans and Syrians.

{All Jewish populations excluding Ethiopian and Indian Jews.

{Jewish communities in the same cluster as obtained from the PCA analysis (Supplementary Fig. 3) are indicated by bold, italic or underlined type under the heading Jewish community.

1Russians, Romanians, Hungarians, Belorussians, French and Lithuanians.

ISignificance throughout the table: italic entries are significantly bigger than ASD from hosts (that is, further away), bold entries are significantly smaller than ASD from hosts; see Supplementary

Table 3 for details.

"Amhara, Oromo and Tigray.

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Supplementary Information is linked to the online version of the paper at

www.nature.com/nature.

Acknowledgements We thank the individuals who provided DNA samples for this

study, including the National Laboratoryfor the Genetics of Israeli Populations; Mari

Nelis, Georgi Hudjashov and Viljo Soo for conducting the autosomal genotyping;

Lauri Anton for computational help. R.V. and D.M.B. thank the European

Commission, Directorate-General for Researchfor FP7 Ecogene grant 205419. R.V.

thanks the European Union, Regional Development Fund through a Centre of

Excellence in Genomics grant and the Swedish Collegium for Advanced Studies for

support during the initial stage of this study. E.M. and Si.R. thank the Estonian

Science Foundation for grants 7858 and 7445, respectively. K.S. thanks the Arthur

and Rosalinde Gilbert Foundation fund of the American Technion Society. Sa.R.

thanks the European Union for Marie Curie International Reintegration grant

CT-2007-208019,and the Israeli Science Foundation for grant 1227/09. IPATIMUP

is an Associate Laboratory of the Portuguese Ministry of Science, Technology and

Higher Education and is partlysupported by Fundac¸a

˜o para a Cie

ˆncia ea Tecnologia,

the Portuguese Foundation for Science and Technology.

Author Contributions D.M.B. and R.V. conceived and designed the study. B.B.T.,

D.C., D.G., D.M.B., E.K.K., G.C., I.K., L.P., M.F.H., O.B., O.S., T.P. and R.V. provided

DNA samples to this study. E.M., J.P. and G.Y. screened and prepared the samples

for the autosomal genotyping. D.M.B., E.M., G.C., M.F.H. and Si.R. generated and

summarized the databasefor the uniparental analysis. B.Y., M.M. and Sa.R. designed

and applied the modelling methodologyand statistical analysis. T.P. provided expert

input regarding the relevant historical aspects. B.Y., D.M.B., K.S., M.F.H., M.M., R.V.

and Sa.R. wrote the paper. B.Y., D.M.B. and M.M. contributed equally to the paper.

All authors discussed the results and commented on the manuscript.

Author Information The array data described in this paper are deposited in the

Gene Expression Omnibus under accession number GSE21478. Reprints and

permissions information is available at www.nature.com/reprints. The authors

declare no competing financial interests. Readers are welcome to comment on the

online version of this article at www.nature.com/nature. Correspondence and

requests for materials should be addressed to D.M.B. (behardm@usernet.com),

K.S. (skorecki@tx.technion.ac.il) or R.V. (rvillems@ebc.ee).

NATURE LETTERS

METHODS

Sample collection. All samples reported here were derived from a buccal swab or

blood cells collected with informed consent in accordance with protocols

approved by the National Human Subjects Review Committee in Israel and

Institutional Review boards of the participating research centres. Participants

were recruited during scheduled archaeogenetics lectures addressing the general

public, genealogical societies, heritage centres and the scientific community.

Each volunteer reported ancestry by providing information on the origin of all

four grandparents. Samples were also obtained from the National Laboratory for

the Genetics of Israeli Populations (http://www.tau.ac.il/medicine/NLGIP/).

Comparative data sets for the uniparental and biparental analysis were

assembled from the literature as summarized in Supplementary Note 1 and

Supplementary Tables 1 and 4 and 5.

Genotyping autosomal markers. Illumina 610K or 660K bead arrays were used

for genotyping with standard protocols, and Bead Studio software was used to

assign genotypes. PLINK 1.05 (ref. 30) was used to perform data management

and QC operations. Samples and SNPs with success rates of less than 97% were

excluded. A total of 475 novel samples were analysed, 121 of which were from 14

Jewish communities representing most of the known geographic range of Jews

during the past 100 years. The other 354 samples were chosen from 27 non-

Jewish populations to enable paired analysis with the Jewish sample set. For

comparison, relevant populations were further included (Supplementary

Table 1) from the Illumina 650K-based data set of the Human Genome

Diversity Panel after excluding relatives as in ref. 18. Because background LD

can distort both PCA

and structure-like analysis

results, one member of any

pair of SNPs in strong LD (r

.0.4) in windows of 200 SNPs (sliding the window

by 25 SNPs at a time) was removed using indep-pairwise in PLINK. After iden-

tifying the intersection of genotypes from the two types of bead array (Illumina

610K and 660K), QC and LD pruning, a total of 226,839 autosomal SNPs were

chosen for all autosomal analyses.

Principal component analysis. PC analysis was performed with the smartpca

program of the EIGENSOFT package

. To express the relative importance of the

top two eigenvectors in the resulting PC plot, two axes were scaled by a factor

equal to the square root of the corresponding eigenvalue (Supplementary Note

2). Our analysis was repeated for the entire set of populations and for the subset

of west Eurasian populations (Supplementary Table 1). The R environment was

used to perform PCA (using the function princomp) and plot the results for all

analyses of uniparental data.

Structure-like analysis. The recently introduced structure-like approach was

applied as assembled in the program ADMIXTURE

(Supplementary Notes 3

and 4). ADMIXTURE was run on our global and west Eurasian data sets 100

times in parallel at K52toK510 (using random seeds). Convergence between

independent runs at the same Kwas monitored by comparing the resulting log-

likelihood scores (LLs). The minimal variation in LLs (less than 1 LL unit) within

a fraction (10%) of runs with the highest LLs was assumed to be a reasonable

proxy for inferring convergence

. In the global data set, convergence was

observed in the case of all explored Kvalues (K52toK510). Results from

runs at all values of Kare shown rather than restricting the reader to one chosenK

(Supplementary Note 3). To focus on population structure in the relevant

regions of the Middle East and Europe we performed analyses on a data set

restricted to west Eurasian samples. In this analysis, convergence was reached

at K52toK55; K57 and K58. Only K54 was highlighted in Supplemen-

tary Fig. 5 because components appearing at higher values of Kwere predomi-

nantly restricted to a single population and were therefore less informative for

our purposes. Judging from the distribution of LLs of the converged Kvalues, the

maximum-likelihood solutions with LLs very close to the highest LLs were also

the most frequent solutions (except for K56 of the global data set). One run

from the top LLs fraction of each converged K(from global and west Eurasian

data set) was plotted with Excel (Supplementary Fig. 4a, b).

Allele sharing distances. ASD was used for measuring genetic distances between

populations. ASD is less sensitive to small sample size than the Fixation Index

) and other measures

, and more appropriate for our goal of measuring

genetic distances between groups regardless of their internal diversity. Standard

errors of ASD values were calculated with a bootstrap approach, accounting for

variance resulting from both sample selection and site selection. ASDs between

individual Jewish populations and population groups representing a geographic

region or ethnic group were calculated. In each case, the population under

consideration was removed from all groupings with which it was compared.

To test significance of differences in pairs of ASD values in each row in

Table 1, a bootstrap approach was used (Supplementary Note 5 and Supplemen-

tary Tables 2 and 3).

Genotyping uniparental markers. Our data from the Y chromosome and

mtDNA were combined with previously published data sets from populations

of interest (Supplementary Note 6). Markers were chosen to match the phylo-

genetic level of resolution achieved in previously reported data sets. A total of

8,210 samples were assembled for Y-chromosome analysis (Supplementary

Table 4). Genotypes for these sites were determined by using multiple tech-

niques, such as allele-specific PCR, TaqMan, Kaspar and direct sequencing. A

total of 13,919 samples were assembled for mtDNA analysis (Supplementary

Table 5).

doi:10.1038/nature09103

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