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 Re: النوبة والنوبا (Re: خالد حاكم)
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هذة دراسة قامت بها جامعة مريلاند الامريكية بالتعاون مع معهد الامراض المتواطنة وقد شاركت انا واستاذى بروفيسور منتصر الطيب ابراهيم وسف فى هذة الدراسة. وتجد اسم واستاذى يحملان الرقم 9
The Genetic Structure and History of
Africans and African Americans
Sarah A. Tishkoff,1,2* Floyd A. Reed,1†‡ Françoise R. Friedlaender,3‡ Christopher Ehret,4
Alessia Ranciaro,1,2,5§ Alain Froment,6§ Jibril B. Hirbo,1,2 Agnes A. Awomoyi,1and#8741;
Jean-Marie Bodo,7 Ogobara Doumbo,8 Muntaser Ibrahim,9 Abdalla T. Juma,9 Maritha J. Kotze,10
Godfrey Lema,11 Jason H. Moore,12 Holly Mortensen,1¶ Thomas B. Nyambo,11 Sabah A. Omar,13
Kweli Powell,1# Gideon S. Pretorius,14 Michael W. Smith,15 Mahamadou A. Thera,8
Charles Wambebe,16 James L. Weber,17 Scott M. Williams18
Africa is the source of all modern humans, but characterization of genetic variation and of
relationships among populations across the continent has been enigmatic. We studied 121 African
populations, four African American populations, and 60 non-African populations for patterns of
variation at 1327 nuclear microsatellite and insertion/deletion markers. We identified 14 ancestral
population clusters in Africa that correlate with self-described ethnicity and shared cultural and/or
linguistic properties. We observed high levels of mixed ancestry in most populations, reflecting
historical migration events across the continent. Our data also provide evidence for shared ancestry
among geographically diverse hunter-gatherer populations (Khoesan speakers and Pygmies). The
ancestry of African Americans is predominantly from Niger-Kordofanian (~71%), European
(~13%), and other African (~8%) populations, although admixture levels varied considerably
among individuals. This study helps tease apart the complex evolutionary history of Africans and
African Americans, aiding both anthropological and genetic epidemiologic studies.
Modern humans originated in Africa
~200,000 years ago and then spread
across the rest of the globe within the
past ~100,000 years (1). Thus, modern humans
have existed continuously in Africa longer than
in any other geographic region and have maintained
relatively large effective population sizes,
resulting in high levels of within-population genetic
diversity (1, 2). Africa contains more than
2000 distinct ethnolinguistic groups representing
nearly one-third of the world’s languages (3).
Except for a few isolates that show no clear relationship
with other languages, these languages
have been classified into four major macrofamilies:
Niger-Kordofanian (spoken across a
broad region of Africa), Afroasiatic (spoken predominantly
in Saharan, northeastern, and eastern
Africa), Nilo-Saharan (spoken predominantly in
Sudanic, Saharan, and eastern Africa), and
Khoesan (languages containing click-consonants,
spoken by San in southern Africa and by Hadza
and Sandawe in eastern Africa) (fig. S1) (4).
Despite the importance of African population
genetics, the pattern of genome-wide nuclear genetic
diversity across geographically and ethnically
diverse African populations is largely
uncharacterized (1, 2, 5). Because of considerable
environmental diversity, African populations
show a range of linguistic, cultural, and phenotypic
variation (1, 2, 4). Characterizing the pattern
of genetic variation among ethnically diverse
African populations is critical for reconstructing
human evolutionary history, clarifying the population
history of Africans and African Americans,
and determining the proper design and interpretation
of genetic disease association studies (1, 6),
because substructure can cause spurious results
(7). Furthermore, variants associated with disease
could be geographically restricted as a result of
new mutations, genetic drift, or region-specific
selection pressures (1). Thus, our in-depth characterization
of genetic structure in Africa benefits
research of biomedical relevance in both African
and African-diaspora populations.
We genotyped a panel of 1327 polymorphic
markers, consisting of 848 microsatellites, 476
indels (insertions/deletions), and three SNPs
(single-nucleotide polymorphisms), in 2432
Africans from 113 geographically diverse populations
(fig. S1), 98 African Americans, and 21
Yemenites (table S1). To incorporate preexisting
African data and to place African genetic variability
into a worldwide context, we integrated
these data with data from the panel of markers
genotyped in 952 worldwide individuals from the
CEPH-HGDP (Centre d’and#201;tude du Polymorphisme
Humain–Human Genome Diversity Panel)
(8–10) in 432 individuals of Indian descent (11)
and in 10 Native Australians (tables S1 and S2).
African variation in a worldwide context.
African and African American populations, with
the exception of the Dogon of Mali, show the
highest levels of within-population genetic diversity
(q = 4Nem, where q is the level of genetic diversity
based on variance of microsatellite allele length, Ne
is the effective population size, and m is the
microsatellite mutation rate) (figs. S2 and S3). In
addition, genetic diversity declines with distance
from Africa (fig. S2, A to C), consistent with
proposed serial founder effects resulting from the
migration of modern humans out of Africa and
across the globe (9, 11–13).Within Africa, genetic
diversity estimated from expected heterozygosity
significantly correlates with estimates from microsatellite
variance (fig. S4) (4) and varies by linguistic,
geographic, and subsistence classifications
(fig. S5). Three hunter-gatherer populations (Baka
Pygmies, Bakola Pygmies, and San) were among
the five populations with the highest levels of
genetic diversity based on variance estimates (fig.
S2A) (4). In addition, more private alleles exist in
Africa than in other regions (fig. S6A). Consistent
with bidirectional gene flow (14), African and
Middle Eastern populations shared the greatest
number of alleles absent from all other populations
(fig. S6B). Within Africa, the most private alleles
were in southern Africa, reflecting those in southern
African Khoesan (SAK) San and !Xun/Khwe
populations (fig. S6C) (12). Eastern and Saharan
Africans shared the most alleles absent from other
African populations examined (fig. S6D).
The proportion of genetic variation among all
studied African populations was 1.71%(table S3). In
comparison, Native American and Oceanic populations
showed the greatest proportion of genetic
variation among populations (8.36% and 4.59%, respectively),
most likely due to genetic drift (9, 15, 16).
Distinct patterns of the distribution of variation
amongAfrican populations classified by geography,
language, and subsistence were also observed (4).
RESEARCHARTICLE
1Department of Biology, University of Maryland, College Park,
MD 20742, USA. 2Departments of Genetics and Biology,
University of Pennsylvania, Philadelphia, PA 19104, USA.
3Independent researcher, Sharon, CT 06069, USA. 4Department
of History, University of California, Los Angeles, CA 90095, USA.
5Dipartimento di Biologia ed Evoluzione, Università di Ferrara,
44100 Ferrara, Italy. 6UMR 208, IRD-MNHN, Musée de
l’Homme, 75116 Paris, France. 7Ministère de la Recherche
Scientifique et de l’Innovation, BP 1457, Yaoundé, Cameroon.
8Malaria Research and Training Center, University of Bamako,
Bamako, Mali. 9Department of Molecular Biology, Institute of
Endemic Diseases, University of Khartoum, 15-13 Khartoum,
Sudan. 10Department of Pathology, Faculty of Health Sciences,
University of Stellenbosch, Tygerberg 7505, South Africa.
11Department of Biochemistry, Muhimbili University of Health
and Allied Sciences, Dar es Salaam, Tanzania. 12Departments of
Genetics and Community and Family Medicine, Dartmouth
Medical School, Lebanon, NH 03756, USA. 13Kenya Medical
Research Institute, Center for Biotechnology Research and
Development, 54840-00200 Nairobi, Kenya. 14Division of Human
Genetics, Faculty of Health Sciences, University of
Stellenbosch, Tygerberg 7505, South Africa. 15Laboratory of
Genomic Diversity, National Cancer Institute, Frederick, MD
21702, USA. 16International Biomedical Research in Africa,
Abuja, Nigeria. 17Marshfield Clinic Research Foundation,
Marshfield, WI 54449, USA. 18Department of Molecular Physiology
and Biophysics, Center for Human Genetics Research,
Vanderbilt University, Nashville, TN 37232, USA.
*To whom correspondence should be addressed. E-mail:
[email protected]
†Present address: Department of Evolutionary Genetics,
Max Planck Institute for Evolutionary Biology, 24306 Pland#246;n,
Germany.
‡These authors contributed equally to this work.
§These authors contributed equally to this work.
||Present address: Department of Internal Medicine, Ohio
State University, Columbus, OH 43210, USA.
¶Present address: Office of Research and Development,
National Center for Computational Toxicology, U.S. Environmental
Protection Agency, Research Triangle Park, NC 27711,
USA.
#Present address: College of Education, University of
Maryland, College Park, MD 20742, USA.
www.sciencemag.org SCIENCE VOL 324 22 MAY 2009 1035
Fig. 1. Neighbor-joining tree from pairwise D2
genetic distances between populations (65). African
population branches are color-coded according to
language family classification. Population clusters
by major geographic region are noted; bootstrap
values above 700 out of 1000 are indicated by
thicker lines and bootstrap number.
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RESEARCH ARTICLE
Phylogenetic trees constructed from genetic
distances between populations generally showed
clustering by major geographic region, both on a
global scale and within Africa (Fig. 1 and figs.
S7 and S8). Within Africa, the two SAK
populations cluster together and are the most
distant from other populations, consistent with
mitochondrial DNA (mtDNA), Y chromosome,
and autosomal chromosome diversity studies, indicating
that SAK populations have the most
diverged genetic lineages (12, 17–21). The Pygmy
populations cluster near the SAK populations in
the tree constructed fromD2 genetic distances (Fig.
1), whereas the Hadza and Sandawe cluster near
the SAK populations in the tree constructed from
RST genetic distances (fig. S8) (4). Note that population
clustering in the tree may reflect common
ancestry and/or admixture. African populations
with high levels of non-African admixture [e.g., the
Cape Mixed Ancestry (CMA) population, commonly
referred to as “Cape Coloured” in South
Africa] cluster in positions that are intermediate
between Africans and non-Africans, whereas the
AfricanAmerican populations, which are relatively
less admixed with non-Africans, cluster more
closely with West Africans. Additionally, populations
with high levels of genetic drift (i.e., the
Americas, Oceania, and Pygmy, Hadza, and SAK
hunter-gatherers) have longer branch lengths.
Geographic distances (great circle routes)
and genetic distances (dm)2 between population
pairs were significantly correlated, consistent
with an isolation-by-distance model (figs. S9 to
S11 and table S4) (13). A heterogeneous pattern
of correlations across global regions was observed,
consistent with a previous study (16);
the strongest correlations were in Europe and
the Middle East (Spearman’s r = 0.88 and 0.83,
respectively; P and#8804; 0.0001 for both), followed by
Africa (Spearman’s r = 0.40; P < 0.0001).
Correlations were not significant for central Asia
or India.Within Africa, the strongest correlations
between genetic and geographic distances were
in Saharan Africa and central Africa (Spearman’s
r = 0.76 and 0.55, respectively; P < 0.0001 for
both) (fig. S11 and table S4). The smallest correlation
was observed in eastern Africa (r = 0.19;
P < 0.0001).
Genetic structure on a global level. Global
patterns of genetic structure and individual ancestry
were inferred by principal components analysis
(PCA) (22) (Fig. 2A) and a Bayesian modelbased
clustering approach with STRUCTURE
(23) (Figs. 3 and 4 and figs. S12 to S14).
Worldwide, 72 significant principal components
(PCs) were identified by PCA (P < 0.05) (22).
PC1 (accounting for 19.5% of the extracted variation)
distinguishes Africans from non-Africans.
The CMA and African American individuals
cluster between Africans and non-Africans,
reflecting both African and non-African ancestry.
PC2 (5.01%) distinguishes Oceanians, East
Asians, and Native Americans from others. PC3
(3.5%) distinguishes the Hadza hunter-gatherers
from others. The remaining PCs each extract less
than 3% of the variation, and the 22nd to 72nd
PCs extract less than 1% combined, with some
minor PCs corresponding to regional and/or
ethnically defined populations, consistent with
STRUCTURE results below.
STRUCTURE analysis revealed 14 ancestral
population clusters (K = 14) on a global level
(Figs. 3 and 4) (4). Middle Eastern and Oceanic
populations exhibit low levels of East African
ancestry up to K = 8, consistent with possible
gene flow into these regions and with studies
suggesting early migration of modern humans
into southern Asia and Oceania (16, 24). The
Hadza, and to a lesser extent the Pygmy, SAK,
and Sandawe hunter-gatherers, are distinguished
at K = 5. The 11th cluster (K = 11) distinguishes
Mbuti Pygmy and SAK individuals, indicating
common ancestry of these geographically distant
hunter-gatherers.A number of Africans (predominantly
CMA, Fulani, and eastern Afroasiatic
speakers) exhibit low to moderate levels of
European–Middle Eastern ancestry, consistent with
possible gene flow from those regions. We found
more African substructure on a global level (nine
clusters) than previously observed (9–12, 20). A
phylogenetic tree of genetic distances from inferred
ancestral clusters (fig. S14) indicates that within
Africa, the Pygmy and SAK associated ancestral
clusters (AACs) form a clade, as do the Hadza and
Sandawe AACs and the Nilo-Saharan and Chadic
AACs, reflecting their ancient common ancestries.
Genetic structure within Africa. PCA of
genetic variation within Africa indicated the
presence of 43 significant PCs (P < 0.05 with a
Tracy-Widom distribution). PC1 (10.8% of the
extracted variation) distinguishes eastern and
Saharan Africa from western, central, and southern
Africa (Fig. 2B). The second PC (6.1%)
distinguishes the Hadza; the third PC (4.9%)
distinguishes Pygmy and SAK individuals from
other Africans. The fourth PC (3.7%) is associated
with the Mozabites, some Dogon, and the
CMA individuals, who show ancestry from the
European–Middle Eastern cluster. The fifth PC
(3.1%) is associated with SAK speakers. The
10th PC was of particular interest (2.2%) because
it associates with the SAK, Sandawe, and
some Dogon individuals, suggesting shared
ancestry.
We incorporated geographic data into a Bayesian
clustering analysis, assuming no admixture
(TESS software) (25) and distinguished six clusters
within continental Africa (Fig. 5A). The most
geographically widespread cluster (orange)
extends from far Western Africa (the Mandinka)
through central Africa to the Bantu speakers of
South Africa (the Venda and Xhosa) and corresponds
to the distribution of the Niger-Kordofanian
language family, possibly reflecting the spread of
Bantu-speaking populations from near the Nigerian/
Cameroon highlands across eastern and southern
Africa within the past 5000 to 3000 years (26, 27).
Another inferred cluster includes the Pygmy and
SAK populations (green), with a noncontiguous
geographic distribution in central and southeastern
Africa, consistent with the STRUCTURE (Fig. 3)
and phylogenetic analyses (Fig. 1). Another geographically
contiguous cluster extends across northern
Africa (blue) into Mali (the Dogon), Ethiopia,
and northern Kenya. With the exception of the
Fig. 2. Principal components
analysis (22)
created on the basis of
individual genotypes.
(A) Global data set and
(B) African data set.
www.sciencemag.org SCIENCE VOL 324 22 MAY 2009 1037
RESEARCH ARTICLE
Dogon, these populations speak an Afroasiatic
language. Chadic-speaking and Nilo-Saharan–
speaking populations from Nigeria, Cameroon,
and central Chad, as well as several Nilo-
Saharan–speaking populations from southern Sudan,
constitute another cluster (red). Nilo-Saharan
and Cu####ic speakers from the Sudan, Kenya, and
Tanzania, as well as some of the Bantu speakers
from Kenya, Tanzania, and Rwanda (Hutu/Tutsi),
Fig. 3. STRUCTURE analysis of the global data set with 1327 markers
genotyped in 3945 individuals. Each vertical line represents an individual.
Individuals were grouped by self-identified ethnic group (at bottom)
and ethnic groups are clustered by major geographic region (at
top). Colors represent the inferred ancestry from K ancestral populations.
STRUCTURE results for K = 2 to 14 (left) are shown with the number of
similar runs (F) for the primary mode of 25 STRUCTURE runs at each K
value (right).
1038 22 MAY 2009 VOL 324 SCIENCE www.sciencemag.org
RESEARCH ARTICLE
constitute another cluster (purple), reflecting linguistic
evidence for gene flow among these
populations over the past ~5000 years (28, 29).
Finally, the Hadza are the sole constituents of a
sixth cluster (yellow), consistent with their distinctive
genetic structure identified by PCA and
STRUCTURE.
STRUCTURE analysis of the Africa data set
indicated 14 ancestral clusters (Fig. 5, B and C,
and figs. S15 to S18). Analyses of subregions
within Africa indicated additional substructure
(figs. S19 to S29). At low K values, the Africawide
STRUCTURE results (fig. S15) recapitulated
the PCA and worldwide STRUCTURE
results. However, as K increased, additional population
clusterswere distinguished (4): theMbugu
[who speak a mixed Bantu and Cu####ic language
(30), shown in dark purple]; Cu####ic-speaking
individuals of southern Ethiopian origin (light
purple); Nilotic Nilo-Saharan–speaking individuals
(red); central Sudanic Nilo-Saharan–speaking
individuals (tan); and Chadic-speaking and
Baggara individuals (maroon). At K = 14, subtle
substructure between East African Bantu speakers
(light orange) andWest CentralAfrican Bantu
speakers (medium orange), and individuals from
Nigeria and farther west, who speak various non-
Bantu Niger-Kordofanian languages (dark orange),
was also apparent (Fig. 5, B and C). Bantu
speakers of South Africa (Xhosa, Venda) showed
substantial levels of the SAK and western
African Bantu AACs and low levels of the East
African Bantu AAC (the latter is also present in
Bantu speakers from Democratic Republic of
Congo and Rwanda). Our results indicate distinct
East African Bantu migration into southern
Africa and are consistent with linguistic and
archeological evidence of East African Bantu
migration froman area west of Lake Victoria (28)
and the incorporation of Khoekhoe ancestry into
several of the Southeast Bantu populations
~1500 to 1000 years ago (31).
High levels of heterogeneous ancestry (i.e.,
multiple cluster assignments) were observed in
nearly all African individuals, with the exception
of western and central African Niger-Kordofanian
speakers (medium orange), who are relatively
homogeneous at large K values (Fig. 5C and fig.
Fig. 4. Expanded view
of STRUCTURE results at
K = 14. Populations from
the CEPH diversity panel
are identified by asterisks.
Languages spoken
by populations are classified
as Niger-Kordofanian
(NK), Nilo-Saharan (NS),
Afroasiatic (AA), Khoesan
(KS), or Indo-European (IE).
www.sciencemag.org SCIENCE VOL 324 22 MAY 2009 1039
RESEARCH ARTICLE
Fig. 5. Geographic and genetic structure of populations within Africa. (A) Geographic
discontinuities among African populations using TESS, assuming a model of no population
admixture (25). Circles indicate location of populations included in the study. (B) Inferred
proportions of ancestral clusters from STRUCTURE analysis at K = 14 for individuals grouped
by geographic region and language classification. Classifications of languages spoken by selfidentified
ethnic affiliation in the Africans are as in Fig. 1. (C) Inferred proportion of ancestral
clusters in individuals from STRUCTURE analysis at K = 14.
1040 22 MAY 2009 VOL 324 SCIENCE www.sciencemag.org
RESEARCH ARTICLE
S15). Considerable Niger-Kordofanian ancestry
(shades of orange) was observed in nearly all populations,
reflecting the recent spread of Bantu speakers
across equatorial, eastern, and southern Africa (27)
and subsequent admixture with local populations
(28). Many Nilo-Saharan–speaking populations in
East Africa, such as the Maasai, show multiple
cluster assignments from the Nilo-Saharan (red) and
Cu####ic (dark purple) AACs, in accord with
linguistic evidence of repeated Nilotic assimilation
of Cu####es over the past 3000 years (32) and with
the high frequency of a shared East African–specific
mutation associated with lactose tolerance (33).
Our data support the hypothesis that the Sahel
has been a corridor for bidirectional migration
between eastern and western Africa (34–36). The
highest proportion of the Nilo-Saharan AAC was
observed in the southern and central Sudanese
populations (Nuer, Dinka, Shilluk, and Nyimang),
with decreasing frequency from northern Kenya
(e.g., Pokot) to northern Tanzania (Datog, Maasai)
(Fig. 5, B and C, and fig. S15). Additionally, all
Nilo-Saharan–speaking populations from Kenya,
Tanzania, southern Sudan, and Chad clustered
with west central Afroasiatic Chadic–speaking
populations in the global analysis at K and#8804; 11 (Fig.
3), which is consistent with linguistic and
archeological data suggesting bidirectional migration
ofNilo-Saharans from source populations
in Sudan within the past ~10,500 to 3000 years
(4, 29). The proposed migration of proto-Chadic
Afroasiatic speakers ~7000 years ago from the
central Sahara into the Lake Chad Basin may
have resulted in a Nilo-Saharan to Afroasiatic
language shift among Chadic speakers (37).
However, our data suggest that this shift was
not accompanied by large amounts of Afroasiatic
gene flow. Other populations of interest, including
the Fulani (Nigeria and Cameroon), the
Baggara Arabs (Cameroon), the Koma (Nigeria),
and Beja (Sudan), are discussed in (4).
Genetic structure in East Africa. East Africa,
the hypothesized origin of the migration of
modern humans out of Africa, has a remarkable
degree of ethnic and linguistic diversity, as
reflected by the greatest level of regional substructure
in Africa (figs. S15, S16, and S19 to
S21). The diversity among populations from this
region reflects the proposed long-term presence
of click-speaking Hadza and Sandawe huntergatherers
and successive waves of immigration
of Cu####ic, Nilotic, and Bantu populations
within the past 5000 years (4, 29, 32, 38, 39).
Within eastern Africa, including southern and central
Sudan, clustering is primarily associated with
language families, including Niger-Kordofanian,
Afroasiatic, Nilo-Saharan, and two click-speaking
hunter-gatherer groups: the Sandawe and Hadza
(figs. S19 to S21). However, individuals from the
Afroasiatic Cu####ic Iraqw and Gorowa (Fiome)
and the Nilo-Saharan Datog, who are in close
geographic proximity, also cluster. Additionally,
several hunter-gatherer populations were distinct,
including the Okiek, Akie, and Yaaku and El
Molo. Of particular interest is the common ancestry
of the Akie (who have remnants of a
Cu####ic language) and the Eastern Cu####ic El
Molo and Yaaku at K = 9, consistent with
linguistic data suggesting that these populations
originated from southern Ethiopia and migrated
into Kenya and Tanzania within the past ~4000
years (4, 29, 32, 39).
Origins of hunter-gatherer populations in
Africa. Our analyses demonstrate potential shared
ancestry of a number of populations who practice
(or until recently practiced) a traditional hunting
and gathering lifestyle. For example, we observed
a Hadza AAC (yellow) at K = 5 and K = 3
in the global and African STRUCTURE analyses,
respectively (Fig. 3 and fig. S15), which is at
moderate levels (0.18 to 0.32) in the SAK and
Pygmy populations and at low levels (0.03 to
0.04) in the Sandawe and neighboring Burunge
with whom the Sandawe have admixed (tables
S8 and S9). The SAK and Pygmies continue to
cluster at higher K values (Fig. 3 and fig. S15)
and in the TESS (Fig. 5A) and phylogenetic (Fig.
1) analyses, consistent with an exclusively shared
Y chromosome lineage (B2b4) (40). Additionally,
we observed clustering of the SAK, Sandawe,
and Hadza in the RST phylogenetic tree (fig. S8)
and of the SAK, Sandawe, and Mbuti Pygmies at
low K values in the secondary modes of Africa
STRUCTURE analyses (fig. S16), consistent
with observed low frequency of the Khoesanspecific
mitochondrial hap####pe (L0d) in the
Sandawe (18, 19), the presence of Khoesanrelated
rock art near the Sandawe homeland (41),
and similarities between the Sandawe and SAK
languages (42). These results suggest the possibility
that the SAK, Hadza, Sandawe, and Pygmy
populations are remnants of a historically more
widespread proto–Khoesan-Pygmy population
of hunter-gatherers. Analyses of mtDNA and Y
chromosome lineages in the Khoesan-speaking
populations suggest that divergence may be
>35,000 years ago (4, 17–19). The shared
ancestry, identified here, of Khoesan-speaking
populations with the Pygmies of central Africa
suggests the possibility that Pygmies, who lost
their indigenous language, may have originally
spoken a Khoesan-related language, consistent
with shared music styles between the SAK and
Pygmies (4, 43).
Shared ancestry of western and eastern
Pygmies, who do not become differentiated until
larger K values in STRUCTURE analyses (Fig. 3
and fig. S15), was also supported by the
phylogenetic trees (Fig. 1 and figs. S7 and S8),
consistent with mtDNA and autosomal studies
indicating that the western and eastern Pygmies
diverged >18,000 years ago (44–47). Western
Pygmy populations usually clustered (Fig. 3 and
fig. S15), consistent with a proposed recent
common ancestry within the past ~3000 years
(48). However, subtle substructure within the
western Pygmies was apparent in the analysis
of central Africa (fig. S24), probably due to
recent geographic isolation and genetic drift.
Asymmetric Bantu gene flow into Pygmy populations
was also observed, with Bantu ancestry
ranging from 0.13 in Mbuti to 0.54 in the
Bedzan (table S8), consistent with prior studies
(40, 44, 49, 50).
The Hadza, with a census size of ~1000,
were genetically distinct on a global level with
STRUCTURE, PCA, and TESS (Figs. 2 to 5),
consistent with linguistic data indicating that the
Hadza language is divergent from or unrelated to
otherKhoesan languages (42, 51, 52). TheHadza,
who havemaintained a traditional hunter-gatherer
lifestyle, show low levels of asymmetric gene
flow from neighboring populations, whereas the
Sandawe, with a census size of >30,000 (39),
show evidence of bidirectional gene flow with
neighboring populations, from whom they may
have adopted mixed farming technologies (Figs.
3 to 5 and fig. S15). In fact, we observed high
levels of the Sandawe AAC in northern Tanzania
and low levels in northern Kenya and southern
Ethiopia (Fig. 3 and fig. S15) (K = 8 to 13), consistent
with linguistic and genetic data suggesting
that Khoesan populations may once have extended
from Somalia through eastern Africa and
into southern Africa (28, 38, 53–55). Although
the Hadza and Sandawe show evidence of common
ancestry (Fig. 1 and figs. S7, S8, S14, S18,
and S21), we observe no evidence of recent gene
flow between them despite their geographic
proximity, consistent with mtDNA and Y chromosome
studies indicating divergence >15,000
years ago (19). The origins of other African
hunter-gatherer populations (Dorobo, Okiek,
Yaaku, Akie, El Molo, and Wata) are discussed
in (4).
Origins of human migration within and
out of Africa. The geographic origin for the
expansion of modern humans was inferred, as in
(13), from the correlation between genetic diversity
and geographic position of populations (r) (figs.
S30 and S31). Both the point of origin of human
migration and waypoint for the out-of-Africa
migration were optimized to fit a linear relationship
between genetic diversity and geographic distance
(4). This analysis indicates that modern human
migration originated in southwestern Africa, at
12.5°E and 17.5°S, near the coastal border of
Namibia and Angola, corresponding to the current
San homeland, with the waypoint in northeast
Africa at 37.5°E, 22.5°N near the midpoint of the
Red Sea (figs. S2C, S30, and S31). However, the
geographic distribution of genetic diversity in
modern populations may not reflect the distribution
of those populations in the past, although our
waypoint analysis is consistent with other studies
suggesting a northeast African origin of migration
of modern humans out of Africa (1, 56).
Correlation between genetic and linguistic
diversity in Africa. Genetic clustering of populations
was generally consistent with language
classification, with some exceptions (Fig. 1 and
fig. S32). For example, the click-speaking Hadza
and Sandawe, classified as Khoesan, were separated
from the SAK populations in the D2 and
(dm)2 phylogenetic trees (Fig. 1 and fig. S7).
www.sciencemag.org SCIENCE VOL 324 22 MAY 2009 1041
RESEARCH ARTICLE
However, this observation is consistent with
linguistic studies indicating that these Khoesan
languages are highly divergent (42, 51) andmay
reflect gene flow between the Hadza and
Sandawe with neighboring populations in East
Africa subsequent to divergence from the SAK.
Additionally, the Afroasiatic Chadic–speaking
populations from northern Cameroon cluster
close to the Nilo-Saharan–speaking populations
from Chad, rather than with East African Afroasiatic
speakers (Fig. 1), consistent with a language
replacement among the Chadic populations.
Other divergences between genetic and linguistic
classifications include the Pygmies, who
lost their indigenous language and adopted the
neighboring Niger-Kordofanian language (27),
and the Fulani, who speak aWest African Niger-
Kordofanian language but cluster near the Chadicand
Central Sudanic–speaking populations in the
phylogenies (Fig. 1 and figs. S7 and S8), consistent
with Y chromosome studies (34). Additionally,
the Nilo-Saharan–speaking Luo of Kenya
show predominantly Niger-Kordofanian ancestry
in the STRUCTURE analyses (orange) (Figs. 3
and 4, Fig. 5, B and C, and fig. S15) and cluster
together with eastern African Niger-Kordofanian–
speaking populations in the phylogenetic trees
(Fig. 1 and figs. S7 and S8).
Both language and geography explained a
significant proportion of the genetic variance, but
differences exist between and within the language
families (table S5 and fig. S33, A to C) (4).
For example, among the Niger-Kordofanian
speakers, with or without the Pygmies, more of
the genetic variation is explained by linguistic
variation (r2 = 0.16 versus 0.11, respectively; P <
0.0001 for both) than by geographic variation
(r2 = 0.02 for both; P < 0.0001 for both), consistent
with recent long-range Bantu migration
events. The reverse was true for Nilo-Saharan
speakers (r2 = 0.06 for linguistic distance versus
0.21 for geographic distance; P < 0.0001 for
both), possibly due to admixture among Nilo-
Saharan–, Cu####ic-, and Bantu-speaking populations
in eastern Africa, which might reduce the
variation explained by language. The Afroasiatic
family had the highest r2 for both linguistic and
geographic distances (0.20 and 0.34, respectively).
However, when subfamilies were analyzed
independently, the Chadic-speaking populations
showed a strong association between geography
and genetic variation (0.39), but not between
linguistic and genetic variation (0.0012), as expected
on the basis of a possible language replacement,
whereas the Cu####ic-speaking populations
were significant for both (0.29 and 0.27, respectively)
(4).
Genetic ancestry of African Americans and
CMA populations. In contrast to prior studies
of African Americans (57–61), we inferred African
American ancestry with the use of genomewide
nuclear markers from a large and diverse set
of African populations. African American populations
from Chicago, Baltimore, Pittsburgh, and
North Carolina showed substantial ancestry from
Fig. 6. Analyses of
Cape Mixed Ancestry
(CMA)andAfricanAmerican
populations. Frequencies
of inferred
ancestral clusters are
shown for K=14with
the global data set
for individuals (top
row) and proportion of
AACs in self-identified
populations (bottom
row). The proportions
of AACs in the CMA
and African American
populations are highlighted
in the center
bottom row; proportions
of AACs in individuals,
sorted by
Niger-Kordofanian, European,
SAK, and/or
Indian ancestry, are
shown to the left and
right, bottom row.
1042 22 MAY 2009 VOL 324 SCIENCE -20
RESEARCH ARTICLE
the African Niger-Kordofanian AAC, most common
in western Africa (means 0.69 to 0.74), and
from the European–Middle Eastern AAC (means
0.11 to 0.15) (Fig. 6 and tables S6 and S8), consistent
with prior genetic studies and the history of
the slave trade (4, 57–62). European and African
ancestry levels varied considerably among individuals
(Fig. 6). We also detected low levels of
ancestry fromthe Fulani AAC(means 0.0 to 0.03,
individual range 0.00 to 0.14), Cu####ic AAC
(means 0.02, individual range 0.00 to 0.10),
Sandawe AAC (means 0.01 to 0.03, individual
range 0.0 to 0.12), East Asian AAC (means 0.01
to 0.02, individual range 0.0 to 0.08), and Indian
AAC (means 0.04 to 0.06, individual range 0.01
to 0.17) (table S6) (4). We observed very low
levels of Native American ancestry, although
other U.S. regions may reveal Native American
ancestry (57).
Supervised STRUCTURE analysis (fig. S34)
(4) was used to infer African American ancestry
from global training populations, including both
Bantu (Lemande) and non-Bantu (Mandinka)
Niger-Kordofanian–speaking populations (fig.
S34 and table S7). These results were generally
consistent with the unsupervised STRUCTURE
analysis (table S6) and demonstrate that most
African Americans have high proportions of both
Bantu (~0.45mean) and non-Bantu (~0.22mean)
Niger-Kordofanian ancestry, concordant with
diasporas originating as far west as Senegambia
and as far south as Angola and South Africa (62).
Thus, most African Americans are likely to have
mixed ancestry from different regions of western
Africa. This observation, together with the subtle
substructure observed among Niger-Kordofanian
speakers, will make it a challenge to trace the
ancestry of African Americans to specific ethnic
groups in Africa, unless considerably more
markers are used.
The CMA population shows the highest
levels of intercontinental admixture of any global
population, with nearly equal high levels of SAK
ancestry (mean 0.25, individual range 0.01 to
0.48), Niger-Kordofanian ancestry (mean 0.19,
individual range 0.01 to 0.71), Indian ancestry
(mean 0.20, individual range 0.0 to 0.69), and
European ancestry (mean 0.19, individual range
0.0 to 0.86) (Fig. 6 and tables S6 and S8). The
CMA population also has low levels of East
Asian ancestry (mean 0.08, individual range 0.0
to 0.21) and Cu####ic ancestry (mean 0.03, individual
range 0.0 to 0.40). These results are
consistent with the supervised STRUCTURE
analyses (fig. S34 and table S7) and with the
history of the CMA population (4, 63).
The genetic, linguistic, and geographic landscape
of Africa. The differentiation observed
among African populations is likely due to ethnicity,
language, and geography, as well as technological,
ecological, and climatic shifts (including
periods of glaciation and warming) that contributed
to population size fluctuations, fragmentations,
and dispersals in Africa (1, 4, 34, 64). We
observed significant associations between genetic
and geographic distance in all regions of
Africa, although their strengths varied. We also
observed significant associations between genetic
and linguistic diversity, reflecting the concomitant
spread of languages, genes, and often
culture [e.g., the spread of farming during the
Bantu expansion (28)]. Of interest for future
anthropological studies are the cases in which
populations have maintained their culture in the
face of extensive genetic introgression (e.g.,
Maasai and Pygmies) and populations that have
maintained both cultural and genetic distinction
(e.g., Hadza).
Given the extensive amount of ethnic diversity
in Africa, additional sampling—particularly
from underrepresented regions such as North and
Central Africa—is important. Because of the
extensive levels of substructure in Africa, ethnically
and geographically diverse African populations
need to be included in resequencing,
genome-wide association, and pharmacogenetic
studies to identify population- or region-specific
functional variants associated with disease or
drug response (1). The high levels of mixed ancestry
from genetically divergent ancestral population
clusters in African populations could also
be useful for mapping by admixture disequilibrium.
Future large-scale resequencing and genotyping
of Africans will be informative for
reconstructing human evolutionary history, for
understanding human adaptations, and for identifying
genetic risk factors (and potential treatments)
for disease in Africa.
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66. We thank the thousands of people who donated DNA
samples used in this study. We thank D. Bygott,
S. J. Deo, D. Guracha, J. Hanby, D. Kariuki, P. Lufungulo,
A. Mabulla, A. A. Mohamed, W. Ntandu, L. A. Nyindodo,
C. Plowe, and A. Tibwitta for assisting with sample
collection; K. Panchapakesan and L. Pfeiffer for
assistance in sample preparation; S. Dobrin for assistance
with genotyping; J. Giles, J. Bartlett, N. Kodaman, and
J. Jarvis for assistance with analyses; and N. Rosenberg,
J. Pritchard, A. Brooks, J. S. Friedlaender, J. Jarvis,
C. Lambert, B. Payseur, N. Patterson, and J. Plotkin for
-23 SCIENCE VOL 324 22 MAY 2009 1043
RESEARCH ARTICLE
helpful suggestions and discussions. Conducted in part
using the ACCRE computing facility at Vanderbilt
University, Nashville, TN. Supported by L. S. B. Leakey
and Wenner Gren Foundation grants, NSF grants BCS-
0196183, BSC-0552486, and BCS-0827436, NIH grants
R01GM076637 and 1R01GM083606-01, and David and
Lucile Packard and Burroughs Wellcome Foundation
Career Awards (S.A.T.); NIH grant F32HG03801 (F.A.R.);
and NIH grant R01 HL65234 (S.M.W. and J.H.M.).
Genotyping was supported by the NHLBI Mammalian
Genotyping Service. The content of this publication does
not necessarily reflect the views or policies of the
Department of Health and Human Services, nor does
mention of trade names, commercial products, or
organizations imply endorsement by the U.S. government.
The project included in this manuscript has been funded in
part with federal funds from the National Cancer Institute
under contract N01-CO-12400. Original genotype data
are available at -15
genetics/genotypingData_Statistics/humanDiversityPanel.
asp. Data used for analyses in the current manuscript are
available at -24
files.html and at -16
supplementary-data.
Supporting Online Material
-25
Materials and Methods
SOM Text
Figs. S1 to S35
Tables S1 to S9
References
13 February 2009; accepted 17 April 2009
Published online 30 April 2009;
10.1126/science.1172257
Include this information when citing this paper.
REPORTS
Dispersion of the Excitations of
Fractional Quantum Hall States
Igor V. Kukushkin,1,2 Jurgen H. Smet,1* Vito W. Scarola,3,4
Vladimir Umansky,5 Klaus von Klitzing1
The rich correlation physics in two-dimensional (2D) electron systems is governed by the dispersion
of its excitations. In the fractional quantum Hall regime, excitations involve fractionally charged
quasi particles, which exhibit dispersion minima at large momenta referred to as rotons. These
rotons are difficult to access with conventional techniques because of the lack of penetration depth
or sample volume. Our method overcomes the limitations of conventional methods and traces the
dispersion of excitations across momentum space for buried systems involving small material
volume. We used surface acoustic waves, launched across the 2D system, to allow incident radiation
to trigger these excitations at large momenta. Optics probed their resonant absorption. Our
technique unveils the full dispersion of such excitations of several prominent correlated ground
states of the 2D electron system, which has so far been inaccessible for experimentation.
In two-dimensional electron systems (2DESs)
exposed to a strong perpendicular magnetic
field B, interaction effects give rise to a
remarkable set of quantum fluids.When all electrons
reside in the lowest electronic Landau level,
the kinetic energy is quenched and the Coulomb
interaction then dominates. The strong repulsive
interaction gives rise to the incompressible fractional
quantum Hall fluids at rational fillings Vp of
the lowest Landau level of the form vp = p/[2p T
1], p = 1, 2, 3,… (1). The appearance of these
fluids may also be understood as a result of
Landau quantization of a Fermi sea, which forms
at filling factor vpand#8594;and#8734; = 1/2 and is composed of
quasi particles referred to as composite fermions
(2–4). At this filling, these composite fermions
experience a vanishing effective magnetic field
Beff. When moving away from half filling, the
composite fermions are sent into circular cyclotron
orbits that they execute with frequency wc,CFand#186;
|Beff|. Landau quantization of these composite
fermion orbits and the successive depopulation
of the associated Landau levels give rise to the
incompressible fractional quantum Hall fluids.
The lowest energy-neutral excitation of these
fluids involves a negatively charged quasi particle
with a fractional charge of e/(2p T 1), where
e is the charge on the electron (5–7), and a
positively charged quasi hole that is left behind.
This excitation requires an energy that, in the
weakly interacting picture, corresponds to the
energy gap separating adjacent composite fermion
Landau levels (4, 8). According to theory,
these neutral excitations at fractional filling vp
possess an energy dispersion with p minima at
large wave vectors q on the order of the inverse
of the magnetic length lB ¼ pffieffiffi=ffiffihffiffiBffiffiffi, where h
is Planck’s constant, or about 108 m–1 for typical
densities of gallium arsenide–based 2DESs
(1, 9–14).
The minima are referred to as magneto-roton
minima and are analogous to the roton minimum
in the excitation dispersion that was introduced
by Landau (15) to account for the anomalous
heat capacity observed in superfluid He-II (16).
The magneto-roton minima govern the low-
1Max Planck Institute for Solid State Research, D-70569
Stuttgart, Germany. 2Institute of Solid State Physics, Russian
Academy of Science, Chernogolovka 142432, Russia. 3Department
of Chemistry and Pitzer Center for Theoretical Chemistry,
University of California at Berkeley, Berkeley, CA 94720, USA.
4Theoretische Physik, Eidgenand#246;ssische Technische Hochschule
Zürich, 8093 Zürich, Switzerland. 5Department of Condensed
Matter Physics, Weizmann Institute of Science, Rehovot 76100,
Israel.
*To whom correspondence should be addressed. E-mail:
-14
Fig. 1. Experimental arrangement for the detection of resonant microwave absorption at large wave
vectors. (Left) Sample geometry consisting of a 0.1-mm-wide and 1-mm-long mesa. At its ends, the
mesa widens and hosts two interdigital transducers with period pSAW. High-frequency radiation drives
the left transducer. The transducer launches SAWs across the sample. In the active-device region,
light from a 780-nm laserdiode triggers a luminescence signal. This region of the sample is also
irradiated with a quasi-monochromatic microwave by using a second high-frequency generator.
Electrodes 1 and 4, which belong to transducers on opposite sides of the mesa, serve as a dipole
antenna. (Right) Schematic of the cryostat configuration and the high-frequency chip carrier.
1044 22 MAY 2009 VOL 324 SCIENCE -26 Muntaser Ibrahim,9 Abdalla T. Juma,9
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Date |
 النوبة والنوبا | cantona_1 | 07-26-14, 07:43 PM |
| Re: النوبة والنوبا | سيف اليزل سعد عمر | 07-26-14, 08:00 PM |
| Re: النوبة والنوبا | ترهاقا | 07-26-14, 08:11 PM |
| Re: النوبة والنوبا | فقيرى جاويش طه | 07-26-14, 08:48 PM |
| Re: النوبة والنوبا | cantona_1 | 07-26-14, 08:59 PM |
| Re: النوبة والنوبا | cantona_1 | 07-26-14, 09:02 PM |
| Re: النوبة والنوبا | أحمد الطيب بدرالدين | 07-26-14, 09:13 PM |
| Re: النوبة والنوبا | Bashasha | 07-26-14, 09:21 PM |
| Re: النوبة والنوبا | أحمد الطيب بدرالدين | 07-26-14, 09:37 PM |
| Re: النوبة والنوبا | فقيرى جاويش طه | 07-26-14, 09:40 PM |
| Re: النوبة والنوبا | عبد الله شم | 07-26-14, 09:52 PM |
| Re: النوبة والنوبا | Bashasha | 07-27-14, 01:54 AM |
| Re: النوبة والنوبا | خالد حاكم | 07-27-14, 02:39 AM |
| Re: النوبة والنوبا | خالد حاكم | 07-27-14, 03:05 AM |
| Re: النوبة والنوبا | cantona_1 | 07-27-14, 03:14 PM |
| Re: النوبة والنوبا | محمد على طه الملك | 07-27-14, 02:44 AM |
| Re: النوبة والنوبا | ترهاقا | 07-27-14, 03:02 AM |
| Re: النوبة والنوبا | خالد حاكم | 07-27-14, 03:28 AM |
| Re: النوبة والنوبا | Ahmed Alim | 07-27-14, 05:32 AM |
| Re: النوبة والنوبا | Ahmed Alim | 07-27-14, 05:34 AM |
| Re: النوبة والنوبا | Ahmed Alim | 07-27-14, 05:40 AM |
| Re: النوبة والنوبا | Ahmed Alim | 07-27-14, 05:43 AM |
 Re: النوبة والنوبا | Albino Akoon Ibrahim Akoon | 07-27-14, 12:08 PM |
| Re: النوبة والنوبا | Bashasha | 07-27-14, 11:30 AM |
| Re: النوبة والنوبا | Albino Akoon Ibrahim Akoon | 07-27-14, 12:33 PM |
| Re: النوبة والنوبا | بريمة محمد | 07-27-14, 01:01 PM |
| Re: النوبة والنوبا | cantona_1 | 07-27-14, 04:36 PM |
| Re: النوبة والنوبا | cantona_1 | 07-27-14, 04:30 PM |
| Re: النوبة والنوبا | Elmosley | 07-28-14, 00:14 AM |
| Re: النوبة والنوبا | محمد داؤد محمد | 07-27-14, 04:12 PM |
| Re: النوبة والنوبا | Albino Akoon Ibrahim Akoon | 07-27-14, 06:10 PM |
 Re: النوبة والنوبا | Albino Akoon Ibrahim Akoon | 07-27-14, 06:31 PM |
| Re: النوبة والنوبا | Bashasha | 07-27-14, 06:56 PM |
| Re: النوبة والنوبا | Ahmed Alim | 07-27-14, 08:50 PM |
| Re: النوبة والنوبا | جمال ود القوز | 07-27-14, 09:15 PM |
| Re: النوبة والنوبا | Bashasha | 07-27-14, 10:53 PM |
| Re: النوبة والنوبا | Bashasha | 07-27-14, 10:56 PM |
| Re: النوبة والنوبا | Bashasha | 07-27-14, 11:11 PM |
| Re: النوبة والنوبا | cantona_1 | 07-28-14, 00:05 AM |
| Re: النوبة والنوبا | Bashasha | 07-28-14, 01:17 AM |
| Re: النوبة والنوبا | cantona_1 | 07-28-14, 02:04 AM |
| Re: النوبة والنوبا | احمد حامد صالح | 07-28-14, 02:00 AM |
| Re: النوبة والنوبا | احمد حامد صالح | 07-28-14, 02:12 AM |
| Re: النوبة والنوبا | Bashasha | 07-28-14, 02:24 AM |
| Re: النوبة والنوبا | محمد على طه الملك | 07-28-14, 02:51 AM |
| Re: النوبة والنوبا | Abdalla Teia | 07-29-14, 09:57 PM |
| Re: النوبة والنوبا | السر جميل | 07-28-14, 05:16 AM |
| Re: النوبة والنوبا | احمد حامد صالح | 07-29-14, 06:29 PM |
| Re: النوبة والنوبا | Albino Akoon Ibrahim Akoon | 07-31-14, 03:19 PM |
| Re: النوبة والنوبا | Nasr | 07-31-14, 05:45 PM |
| Re: النوبة والنوبا | خالد حاكم | 07-31-14, 08:44 PM |
| Re: النوبة والنوبا | Abdalla Teia | 07-31-14, 10:26 PM |
| Re: النوبة والنوبا | cantona_1 | 08-01-14, 02:44 AM |
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