Genetic Variation and Population Structure in Native Americans

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DOI: 10.1371/journal.pgen.0030185 · Source: PubMed
We examined genetic diversity and population structure in the American landmass using 678 autosomal microsatellite markers genotyped in 422 individuals representing 24 Native American populations sampled from North, Central, and South America. These data were analyzed jointly with similar data available in 54 other indigenous populations worldwide, including an additional five Native American groups. The Native American populations have lower genetic diversity and greater differentiation than populations from other continental regions. We observe gradients both of decreasing genetic diversity as a function of geographic distance from the Bering Strait and of decreasing genetic similarity to Siberians--signals of the southward dispersal of human populations from the northwestern tip of the Americas. We also observe evidence of: (1) a higher level of diversity and lower level of population structure in western South America compared to eastern South America, (2) a relative lack of differentiation between Mesoamerican and Andean populations, (3) a scenario in which coastal routes were easier for migrating peoples to traverse in comparison with inland routes, and (4) a partial agreement on a local scale between genetic similarity and the linguistic classification of populations. These findings offer new insights into the process of population dispersal and differentiation during the peopling of the Americas.
Genetic Variation and Population Structure
in Native Americans
Sijia Wang
, Cecil M. Lewis Jr.
, Mattias Jakobsson
, Sohini Ramachandran
, Nicolas Ray
, Gabriel Bedoya
Winston Rojas
, Maria V. Parra
, Julio A. Molina
, Carla Gallo
, Guido Mazzotti
, Giovanni Poletti
, Kim Hill
Ana M. Hurtado
, Damian Labuda
, William Klitz
, Ramiro Barrantes
, Maria Ca
tira Bortolini
Francisco M. Salzano
, Maria Luiza Petzl-Erler
, Luiza T. Tsuneto
, Elena Llop
, Francisco Rothhammer
Laurent Excoffier
, Marcus W. Feldman
, Noah A. Rosenberg
, Andre
s Ruiz-Linares
1 The Galton Laboratory, Department of Biology, University College London, London, United Kingdom, 2 Department of Human Genetics, University of Michigan, Ann Arbor,
Michigan, United States of America, 3 Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, Michigan, United States of America, 4 Department
of Biological Sciences, Stanford University, Stanford, California, United States of America, 5 Computational and Molecular Population Genetics Lab, University of Bern, Bern,
Switzerland, 6 Laboratorio de Gene
tica Molecular, Universidad de Antioquia, Medellı
n, Colombia, 7 Center for Neurobehavioral Genetics, University of California Los Angeles,
Los Angeles, United States of America, 8 Laboratorios de Investigacio
n y Desarrollo, Facultad de Ciencias y Filosofı
a, Universidad Peruana Cayetano Heredia, Lima, Peru
9 Facultad de Medicina, Universidad Peruana Cayetano Heredia, Lima, Peru
, 10 Department of Anthropology, University of New Mexico, Albuquerque, New Mexico, United
States of America, 11 De
partement de Pe
diatrie, CHU Sainte-Justine, Universite
de Montre
al, Montre
al, Quebec, Canada, 12 School of Public Health, University of California
Berkeley, Berkeley, California, United States of America, 13 Public Health Institute, Oakland, California, United States of America, 14 Escuela de Biologı
a, Universidad de Costa
Rica, San Jose
, Costa Rica, 15 Departamento de Gene
tica, Instituto de Biocie
ncias, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil,
16 Departamento de Gene
tica, Universidade Federal do Parana
, Curitiba, Parana
, Brazil, 17 Programa de Gene
tica Humana, Instituto de Ciencias Biome
dicas, Facultad de
Medicina, Universidad de Chile, Santiago, Chile, 18 Instituto de Alta Investigacio
n, Universidad de Tarapaca
, Arica, Chile
We examined genetic diversity and population structure in the American landmass using 678 autosomal microsatellite
markers genotyped in 422 individuals representing 24 Native American populations sampled from North, Central, and
South America. These data were analyzed jointly with similar data available in 54 other indigenous populations
worldwide, including an additional five Native American groups. The Native American populations have lower genetic
diversity and greater differentiation than populations from other continental regions. We observe gradients both of
decreasing genetic diversity as a function of geographic distance from the Bering Strait and of decreasing genetic
similarity to Siberians—signals of the southward dispersal of human populations from the northwestern tip of the
Americas. We also observe evidence of: (1) a higher level of diversity and lower level of population structure in western
South America compared to eastern South America, (2) a relative lack of differentiation between Mesoamerican and
Andean populations, (3) a scenario in which coastal routes were easier for migrating peoples to traverse in comparison
with inland routes, and (4) a partial agreement on a local scale between genetic similarity and the linguistic
classification of populations. These findings offer new insights into the process of population dispersal and
differentiation during the peopling of the Americas.
Citation: Wang S, Lewis CM Jr, Jakobsson M, Ramachandran S, Ray N, et al (2007) Genetic variation and population structure in Native Americans. PLoS Genet 3(11): e185.
Patterns of genetic diversity and population structure in
human populations constitute an important foundation for
many areas of research in human genetics. Most noticeably,
they provide an invaluable source of data for inferences
about human evolutionary history [1–3]. In addition, the
distribution of genetic variation informs the design and
interpretation of studies that search for genes that confer an
increased susceptibility to disease [4–6].
Recent genomic studies have produced detailed genome-
wide descriptions of genetic diversity and population struc-
ture for a wide variety of human populations, both at the global
level [7–19] and for individual geographic regions, including
East Asia [20], Europe [21,22], and India [23]. Here we report
the first such analysis of indigenous populations from the
American landmass, using 678 microsatellites genotyped in
530 individuals from 29 Native American populations. The
study is designed to investigate several questions about genetic
variation in Native Americans: what records of the original
colonization from Siberia are retained in Native American
genetic variation? What geographic routes were taken in the
Americas by migrating peoples? What is the genetic structure
of Native American populations? To what extent does genetic
differentiation among populations parallel the differentiation
of Native American languages? In addressing these questions,
our analyses identify several surprising features of genetic
variation and population history in the Americas.
Editor: Chris Tyler-Smith, The Wellcome Trust Sanger Institute, United Kingdom
Received March 21, 2007; Accepted September 10, 2007; Published November
23, 2007
Copyright: Ó 2007 Wang et al. This is an open-access article distributed under the
terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author
and source are credited.
Abbreviations: CEPH, Centre d’Etude du Polymorphisme Humain; HGDP, Human
Genome Diversity Project
* To whom correspondence should be addressed. E-mail:
[ These authors contributed equally to this work.
PLoS Genetics | November 2007 | Volume 3 | Issue 11 | e1852049
We collected genome-wide microsatellite genotype data for
751 autosomal markers in 422 individuals from 24 Native
American populations spanning ten countries and seven
linguistic ‘‘stocks’’ (Tables S1 and S2). We also collected data
on 14 individuals from a Siberian population, Tundra Nentsi.
To enable comparisons with data previously reported in the
worldwide collection of populations represented by the
Human Genome Diversity Project–Centre d’Etude du Poly-
morphisme Humain (HGDP–CEPH) cell line panel [7,11,13],
data analysis was restricted to 678 loci typed across all
populations (see Methods). The combined dataset contains
genotypes for 1,484 individuals from 78 populations, includ-
ing 29 Native American groups and two Siberian groups
(Figure 1).
Genetic Diversity
We compared levels of genetic diversity across geographic
regions worldwide (Table 1). A serial founding African-origin
model of human evolution [10,11]—in which each successive
human migration involved only a subset of the genetic
variation available at its source location, and in which the
Bering Strait formed the only entry point to the American
landmass—predicts reduced genetic diversity in Native
Americans compared to other populations, as well as a
north-to-south decline in genetic diversity among Native
American populations. Indeed, Native Americans were found
to have lower genetic diversity, as measured by heterozygos-
ity, than was seen in populations from other continents
(Table 1). Additionally, applying a sample size-corrected
measure of the number of distinct alleles in a population
[24,25], Native Americans had fewer distinct alleles per locus
compared to populations in other geographic regions (Figure
2A). Among Native American populations, the highest
heterozygosities were observed in the more northerly
populations, and the lowest va lues were seen in South
American populations (Table 2). The lowest heterozygosities
of any populations worldwide occurred in isolated Amazo-
nian and eastern South American populations, such as Surui
and Ache. More generally, heterozygosity was reduced in
eastern populations from South America compared to
western populations (Table 1, p ¼ 0.02, Wilcoxon rank sum
test). Eastern South American populations also had fewer
distinct alleles per locus than populations elsewhere in the
Americas (Figure 2B).
Assuming a single source for a collection of populations,
the serial founding model predicts a linear decline of genetic
diversity with geographic distance from the source location
[11,26]. Such a pattern is observed at the worldwide level, as a
linear reduction of heterozygosity is seen with increasing
distance from Africa, where distance to Native American
populations is measured via a waypoint near the Bering Strait
(Figure 3A). To investigate the source location for Native
Americans, we considered only the Native American data and
allowed the source to vary, measuring the correlation of
heterozygosity with distance from putative points of origin.
Consistent with the founding from across the Bering Strait,
the correlation of heterozygosity with geographic distance
from a hypothesized source location had the most strongly
negative values (r ¼0.436) when the source for Native
Americans was placed in the northernmost part of the
American landmass (Figure 3B). The smallest value for the
correlation coefficient was seen at 55.68N 98.88W, in central
Canada, but as a result of relatively sparse sampling in North
America, all correlations in the quartile with the smallest
values, plotted in the darkest shade in Figure 3B, were within
a narrow range (0.436 to 0.424).
One way to examine the support for particular coloniza-
tion routes within the American landmass is to determine if a
closer relationship between heterozygosity and geography is
observed when ‘‘ effective’’ geographic distances are com-
puted along these routes, rather than along shortest-distance
paths. Using PATHMATRIX [27] to take the precise locations
of continental boundaries into consideration in effective
geographic distance calculations (see Methods)—rather than
using a waypoint approach [11] to measure distance—does
not substantially alter the correlation of heterozygosity with
distance from the Bering Strait (r ¼0.430, 1:1 coastal/inland
cost ratio in Figure 4A). However, when coastlines are treated
as preferred routes of migration in comparison with inland
routes, the percent of variance in heterozygosity explained by
effective distance increases to 34% (r ¼0.585 for a coastal/
inland cost ratio of 1:10 in Figure 4A). In contrast, all
scenarios tested that had coastal/inland cost ratios greater
than 1 explain a smaller proportion of the variance in
heterozygosity than do the scenarios with coastal/inland cost
ratio of 1 or less.
The preferred routes in the optimal scenario of a 1:10
coastal/inland cost ratio include a path to the Ache, Guarani,
and Kaingang populations that travels around northern
South America (Figure 4B). With these three populations
excluded, the role of coastlines is almost unchanged (Figure
S1), and a 1:10 ratio continues to explain the largest fraction
of variation in heterozygosity (r ¼0.595). Applying a reduced
cost only to the Pacific coast, a preference is still seen for
ratios slightly less than 1 compared to ratios greater than 1,
and the scenario producing the closest fit is a 1:2 ratio (Figure
S2). A stronger preference for a Pacific coastal route was
observed excluding from the computations the Chipewyan,
Cree, and Ojibwa populations, three groups that follow an
Arctic route in Figure 4B, or excluding Ache, Guarani, and
Kaingang in addition to Chipewyan, Cree, and Ojibwa (Figure
S3). We did not find a closer fit of heterozygosity and effective
PLoS Genetics | November 2007 | Volume 3 | Issue 11 | e1852050
Genetic Variation in Native Americans
Author Summary
Studies of genetic variation have the potential to provide
information about the initial peopling of the Americas and the
more recent history of Native American populations. To investigate
genetic diversity and population relationships in the Americas, we
analyzed genetic variation at 678 genome-wide markers genotyped
in 29 Native American populations. Comparing Native Americans to
Siberian populations, both genetic diversity and similarity to
Siberians decrease with geographic distance from the Bering Strait.
The widespread distribution of a particular allele private to the
Americas supports a view that much of Native American genetic
ancestry may derive from a single wave of migration. The pattern of
genetic diversity across populations suggests that coastal routes
might have been important during ancient migrations of Native
American populations. These and other observations from our study
will be useful alongside archaeological, geological, and linguistic
data for piecing together a more detailed description of the
settlement history of the Americas.
distance assuming a reduced cost for travel along major
rivers, and indeed we observed that a higher cost for riverine
routes was preferred (Figure S3).
Intercontinental Population Structure
To investigate population structure at the worldwide level,
we used unsupervised model-based clustering as imple-
mented in the STRUCTURE program [28,29]. Using STRUC-
TURE, we applied a mixture model that allows for allele
frequency correlation across a set of K genetic clusters, with
respect to which individual membership coefficients are
estimated (see Methods).
As has been observed previously [7,9,13,16,23], cluster
analysis with worldwide populations identifies a major
genetic cluster corresponding to Native Americans (Figure
5), indicating an excess similarity of individual genomes
within the Americas compared to genomes in other regions.
Inclusion of the Native American data collected here did not
substantially alter the clusters identified in previous analyses.
When the genotypes were analyzed using a model with five
clusters, the clusters corresponded to Sub-Saharan Africa,
Eurasia west of the Himalayas, Asia east of the Himalayas,
Oceania, and the Americas. For a model with six clusters, the
sixth cluster corresponded mainly to the isolated Ache and
Surui populations from South America. Almost no genetic
membership from the cluster containing Africans and a
relatively small amount of membership from the cluster
containing Europeans were detected in the Native Americans,
indicating that with relatively few exceptions, the samples
examined here represent populations that have experienced
little recent European and African admixture.
To search for signals of similarity to Siberians in the Native
American populations, we used a supervised cluster analysis
[28,29] in which Native Americans were distributed over five
clusters (Figure 6). Four of these clusters were forced to
correspond to Africans, Europeans, East Asians excluding
Figure 1. Populations Included in This Study
The world map shows the 78 populations investigated in the combined dataset, with the locations of the 29 populations studied in the Americas shown
in detail in the larger map. The 25 newly examined populations, including the Siberian Tundra Nentsi, are marked in red, and the previously genotyped
HGDP-CEPH populations are marked in yellow.
PLoS Genetics | November 2007 | Volume 3 | Issue 11 | e1852051
Genetic Variation in Native Americans
Siberians, and Siberians (Tundra Nentsi and Yakut), and the
fifth cluster was not associated with any particular group a
priori. Most Native American individuals were seen to have
majority membership in this fifth cluster, and considering
their estimated membership in the remaining clusters, Native
Americans were genetically most similar to Siberians. A
noticeable north-to-south gradient of decreasing similarity to
Siberians was observed, as can be seen in the declining
membership in the red cluster from left to right in Figure 6.
Genetic similarity to Siberia is greatest for the Chipewyan
population from northern Canada and for the more south-
erly Cree and Ojibwa populations. Detectable Siberian
similarity is visible to a greater extent in Mesoamerican and
Andean populations than in the populations from eastern
South America.
Intracontinental Population Structure
The level of populati on structure observed among Native
Americans, as determined using F
[30], was 0.081, exceed-
ing that of other geographic region s (Table 1). Comparing
regions within the Americas, the highest F
value was
observed i n eastern South A merica, with intermediate
values o ccurring in western South America and Central
America and with the smallest value occurring in North
America (Table 1). These results are compatible with the
lower overall level of Native A merican genetic variation,
particularly in eastern South Amer ica, as the mathemati cal
connection between heterozygosity and F
predicts that
low heterozygosities will tend to produce higher F
Applying unsupervised model-based clustering [28,29] to
Table 1. Heterozygosity and F
(3100) for Various Geographic Regions
Number of
(Average across
Worldwide 78 0.740 (0.073) 0.685 (0.076) 7.1 (6.8, 7.4)
Africa 7 0.774 (0.072) 0.754 (0.073) 3.0 (2.8, 3.2)
Europe 8 0.732 (0.075) 0.728 (0.076) 0.8 (0.7, 0.9)
Middle East 4 0.740 (0.073) 0.733 (0.072) 1.4 (1.2, 1.5)
Central/South Asia 9 0.738 (0.076) 0.730 (0.076) 1.3 (1.2, 1.5)
East Asia 19 0.714 (0.098) 0.704 (0.097) 1.4 (1.2, 1.5)
Oceania 2 0.690 (0.120) 0.668 (0.122) 6.4 (5.7, 7.1)
America 29 0.676 (0.111) 0.623 (0.104) 8.1 (7.9, 8.3)
North America 3 0.697 (0.099) 0.684 (0.099) 3.4 (3.0, 3.7)
Central America 8 0.669 (0.119) 0.638 (0.115) 5.5 (5.2, 5.8)
Western South America 10 0.672 (0.114) 0.635 (0.109) 5.7 (5.5, 6.0)
Eastern South America 8 0.639 (0.132) 0.571 (0.116) 14.7 (14.2, 15.3)
Heterozygosities are listed with standard deviations across loci. Pooled heterozygosity is significantly lower in Native Americans than in any of the other major geographic regions (p ,
0.0001 for each comparison, Wilcoxon signed rank test). For convenience, values of 100 times F
are shown in the table rather than F
values, and they are listed with 95% bootstrap
confidence intervals across loci. The populations included in the various subdivisions of the Americas are listed in Table 2.
Figure 2. The Mean and Standard Error Across 678 Loci of the Number of Distinct Alleles as a Function of the Number of Sampled Chromosomes
(A) Geographic regions worldwide. (B) Subregions within the Americas. For a given locus, region, and sample size g, the number of distinct alleles
averaged over all possible subsamples of g chromosomes from the given region is computed according to the rarefaction method [24,25]. For each
sample size g, loci were considered only if their sample sizes were at least g in each geographic region. Error bars denote the standard error of the mean
across loci.
PLoS Genetics | November 2007 | Volume 3 | Issue 11 | e1852052
Genetic Variation in Native Americans
the Native Americans, considerable population substructure
is detectable (Figure 7). For a model with two clusters, one
cluster corresponds largely to the northernmost populations,
while the other corresponds to populations from eastern
South America; the remaining populations are partitioned
between these two clusters, with greater membership of the
more northerly populations in the ‘‘ northern’’ cluster. As the
number of clusters is increased, the least genetically variable
groups form distinctive clusters (for example, the Ache,
Karitiana, and Surui populations). However, variation exists
across replicates in the nature of the partitioning, and to
illustrate the range of solutions observed, Figure 7 summa-
rizes each clustering solution that was seen in at least 12% of
replicate analyses for each K from two to nine. These
summaries indicate that the main clustering solutions with
a given K ‘‘ refine’’ the partitions observed with K 1 clusters,
in the sense that each of the K clusters is either identical to,
or is a subset of, one of the K 1 clusters. A likely explanation
for the multimodality is the presence of several population
subgroups that are roughly equally likely to form individual
clusters. For small K, not enough slots are available, and only
when K is sufficiently large is each of these groups able to
occupy its own cluster.
For K ¼ 7, a relatively stable clustering solution is observed,
appearing in 44 of 100 replicates (compared to seven of 100
for the next most frequently observed solution). This
clustering solution has distinctive clusters for three of the
smallest and least genetically variable groups in the sample—
Karitiana and Surui from Brazil, and Ache from Paraguay.
Two separate samples from the Amazonian Ticuna group of
Colombia form the basis for a cluster, as does the Pima group
from Mexico. The remaining two clusters include one
centered on the North American groups and one centered
on the Chibchan–Paezan language stock from Central and
South America. The cluster containing Chibchan–Paezan
populations—the only cluster at K ¼ 7 that corresponds well
to a major language stock—separates into two subclusters
when K is increased to nine. Despite the large geographic
distance between Mesoamerica and the Andes, Mesoamerican
populations (Mixtec, Zapotec, Mixe, and Maya from Mexico
and Kaqchikel from Guatemala) and Andean populations
(Inga from Colombia, Quechua from Peru, and Aymara and
Huilliche from Chile) have similar estimated membership
across clusters when K ¼ 7, and together with five additional
populations (Zenu, Wayuu, and Piapoco from Colombia, and
Kaingang and Guarani from Brazil), they comprise a single
cluster when K ¼ 9.
Genes and Languages
We compared the classification of the populations into
linguistic ‘‘ stocks’’ [34,35] (Table S2) with their genetic
relationships as inferred on a neighbor-joining tree con-
structed from Nei genetic distances [36] between pairs of
populations (Figure 8). As the use of a single-family grouping
Table 2. Heterozygosity for Newly Sampled Populations and for the Five Previously Sampled Native American Populations (Pima,
Maya, Piapoco, Karitiana, and Surui)
Population Geographic
Deviation across Loci
Tundra Nentsi East Asia/Siberia 14 0.719 0.103
Chipewyan North America 29 0.668 0.115
Cree North America 18 0.695 0.115
Ojibwa North America 20 0.689 0.115
Pima Central America 25 0.605 0.159
Mixtec Central America 20 0.646 0.141
Zapotec Central America 19 0.668 0.138
Mixe Central America 20 0.642 0.136
Maya Central America 25 0.675 0.121
Kaqchikel Central America 12 0.662 0.138
Cabecar Central America 20 0.622 0.146
Guaymi Central America 18 0.583 0.174
Kogi Western South America 17 0.560 0.175
Arhuaco Western South America 17 0.619 0.145
Waunana Western South America 20 0.610 0.156
Embera Western South America 11 0.616 0.155
Zenu Western South America 18 0.639 0.142
Inga Western South America 17 0.640 0.140
Quechua Western South America 20 0.671 0.123
Aymara Western South America 18 0.661 0.132
Huilliche Western South America 20 0.670 0.119
Kaingang Eastern South America 7 0.623 0.178
Guarani Eastern South America 10 0.644 0.144
Wayuu Western South America 17 0.670 0.125
Piapoco Eastern South America 13 0.611 0.160
Ticuna (Tarapaca) Eastern South America 18 0.577 0.171
Ticuna (Arara) Eastern South America 17 0.585 0.163
Karitiana Eastern South America 24 0.558 0.175
Surui Eastern South America 21 0.492 0.196
Ache Eastern South America 19 0.482 0.205
PLoS Genetics | November 2007 | Volume 3 | Issue 11 | e1852053
Genetic Variation in Native Americans
(Amerind) of all languages not belonging to the Na–Dene or
Eskimo–Aleutian families is controversial [37], we focused our
analysis on the taxonomically lower level of linguistic stocks.
In the neighbor-joining tree (Figure 8), a reasonably well-
supported cluster (86%) includes all non-Andean South
American populations, together with the Andean-speaking
Inga population from southern Colombia. Within this South
American cluster, strong support exists for separate cluster-
ing of Chibchan–Paezan (97%) and Equatorial–Tucanoan
(96%) speakers (except for the inclusion of the Equatorial–
Tucanoan Wayuu population with its Ch ibchan–Paezan
geographic neighbors, and the inclusion of Kaingang, the
single Ge–Pano–Carib population, with its Equatorial–Tu-
canoan geographic neighbors). Within the Chibchan–Paezan
and Equatorial–Tucanoan subclusters several subgroups have
stron g support, including Ember a and Waunana (96%),
Arhuaco and Kogi (100%), Cabecar and Guaymi (100%),
and the two Ticuna groups (100%). When the tree-based
clustering is repeated with alternate genetic distance meas-
ures, despite the high Mantel correlation coefficients [38]
between distance matrices (0.98, 0.98, and 0.99 for compar-
isons of the Nei and Reynolds matrices, the Nei and chord
matrices, and the Reynolds and chord matrices, respectively),
higher-level groupings tend to differ slightly or to have
reduced bootstrap support (Figures S4 and S5). However,
local groupings such as Cabecar and Guaymi, Arhuaco and
Kogi, Aymara and Quechua, and Ticuna (Arara) and Ticuna
(Tarapaca) continue to be supported (100%). This observa-
Figure 3. Heterozygosity in Relation to Geography
(A) Relationship between heterozygosity and geographic distance from
East Africa. Populations in Sub-Saharan Africa and Oceania are marked
with gray triangles and squares, respectively, and the remaining non-
American populations from Europe, Asia, and northern Africa are marked
with gray pentagons. Within the Americas, populations are color-coded
and symbol-coded by language stock (see Figure 8). Denoting
heterozygosity by H and geographic distance in thousands of kilometers
by D, the regression line for the graph is H ¼ 0.7679 0.00658D, with
correlation coefficient 0.862. (B) The fit of a linear decline of
heterozygosity with increasing distance from a putative source,
considering Native American populations only. The color of a point
indicates a correlation coefficient r between expected heterozygosity
and geographic distance from the point, with darker colors denoting
more strongly negative correlations. Across the Americas, the correlation
ranges from 0.436 to 0.575, and color bins are set to equalize the
number of points drawn in the four colors. From darkest to lightest, the
four colors represent points with correlations in (0.436, 0.424), (0.424,
0.316), (0.316, 0.494), and (0.494, 0.575), respectively.
Figure 4. Heterozygosity and Least-Cost Paths in a Coastal Migration
(A) R
(square of the correlation) between heterozygosity H and effective
geographic distance (least-cost distance), assuming differential perme-
ability of coastal regions compared to inland regions. Correlations
significant at the 0.05 level are indicated by closed symbols, and those
that are not significant are indicated by open symbols. (B) Least-cost
routes for the scenario with 1:10 coastal/inland cost ratio.
PLoS Genetics | November 2007 | Volume 3 | Issue 11 | e1852054
Genetic Variation in Native Americans
tion of strongly supported genetic relationships for geo-
graphically proximate linguistically similar groups coupled
with smaller support at the scale of major linguistic groupings
is also seen in Native American mitochondrial data [39].
To more quantitatively test the correspondence of genetic
and linguistic variation in the Americas, we computed the
Mantel correlation of genetic and linguistic distances (Table
3). Nei’s D
distance [36] was used for the genetic computa-
tions, and linguistic distances were measured along a discrete
scale (see Methods). Considering all of the Native American
populations and treating all linguistic stocks as equidistant
(Table S3), the Mantel correlation of Nei genetic distance
with linguistic distance is small (r ¼ 0.04). The correlation is
also small when usin g between-stock linguistic distance
measures (Tables S4–S11) that make use of shared etymolo-
gies identified by Greenberg [34]. For two ways of computing
linguistic distance, using the Dice and Jaccard indices (see
Methods), respectively, the correlations are r ¼0.01 and r ¼
0.02. When the effects of geography are controlled, or when
stocks are excluded from the computation individually, the
partial correlations of linguistic and genetic distance [40]
remain low.
A potential explanation for the low correlation coeffi-
cients—suggested by the apparent genetic and linguistic
correspondence in the neighbor-joining tree for closely
related groups—is that sizeable correlation between genetic
and linguistic distance may exist only below a certain level of
linguistic distance. Considering genetic and linguistic differ-
entiation only for pairs of populations within linguistic
stocks, the correlation of genetic distance and linguistic
distance increases (r ¼ 0.53). The partial correlation of genetic
distance and linguistic distance remains fairly high when the
effect of geographic distance is controlled (r ¼ 0.40), although
11% of random matrix permutations produce higher values
(Table 3).
By excluding language stocks from the computation
individually, it is possible to investigate the extent to which
individual linguistic stocks are responsible for the within-
stock correlation of genetic and linguistic distance. When the
Equatorial–Tucanoan stock is excluded, the correlation
increases to 0.68, and the partial correlation controlling for
geographic distance increases to 0.66. Excluding the Andean
stock, however, both the correlation and the partial correla-
tion decrease (to 0.46 and 0.26, respectively). Excluding any of
the three other stocks for which more than one population is
represented (Northern Amerind, Central Amerind, Chib-
chan–Paezan) does not lead to a sizeable change in either the
correlation coefficient (0.54, 0.51, 0.55) or the partial
correlation coefficient (0.40, 0.39, 0.40).
Native American Private Alleles
Considering alleles found only in one major geographic
region worldwide, Native Americans have the fewest private
alleles (Figure 9A). Private alleles, which lie at the extreme
ends of the allele size range more often than expected by
chance (p , 0.023), usually have low frequencies in the
geographic region where they are found (13%). Within the
Americas, counting alleles private to one of four subregions,
northern populations have the most and eastern South
Figure 5. Unsupervised Analysis of Worldwide Population Structure
The number of clusters in a given plot is indicated by the value of K. Individuals are represented as thin vertical lines partitioned into segments
corresponding to their membership in genetic clusters indicated by the colors.
Figure 6. Supervised Population Structure Analysis, Using Five Clusters, Four of Which Were Forced to Correspond to Africans, Europeans, East Asians
Excluding Siberians, and Siberians
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Genetic Variation in Native Americans
American populations have the fewest private alleles, with
western South American populations having slightly more
than Central American populations (Figure 9B).
Despite this general lack of high-frequency private alleles,
especially in Native Americans, we observed that the only
common (.13%) regionally private variant in the worldwide
dataset was a Native American private allele. This allele,
corresponding to a length of 275 base pairs at locus D9S1120,
was found at a frequency of 36 .4% in the full Native
American sample, and was absent from the other 49 world
populations. Allele 275 is the smallest variant observed at the
locus and it is present in each of the 29 Native American
Figure 8. Neighbor-Joining Tree of Native American Populations
Each language stock is given a color, and if all populations subtended by an edge belong to the same language stock, the clade is given the color that
corresponds to that stock. Branch lengths are scaled according to genetic distance, but for ease of visualization, a different scale is used on the left and
right sides of the middle tick mark at the bottom of the figure. The tree was rooted along the branch connecting the Siberian populations and the
Native American populations, and for convenience, the forced bootstrap score of 100% for this rooting is indicated twice.
Figure 7. Unsupervised Analysis of Native American Population Structure
The colored plots at the left show the estimated population structure of Native Americans, obtained using STRUCTURE. The number of clusters in a
given plot is indicated by the value of K on the right side of the figure. Next to the K ¼ 7 plot, the population names and the major language stocks of
the populations are also displayed. The left-to-right order of the individuals is the same in all plots. The diagram on the right summarizes the outcomes
of 100 replicate STRUCTURE runs for each of several values of K. Each row represents a value of K, and within each row, each box represents a clustering
solution that appeared at least 12 times in 100 replicates (see Methods). The number of appearances of a solution is listed above the box, and the boxes
are arrayed from left to right in decreasing order of the frequencies of the solutions to which they correspond. The DISTRUCT plot shown on the left
corresponds to the leftmost box on the right side of the figure. An approximate description of the clusters is located inside the box, with each row in
the box representing a different cluster. The numbers 1, 2, and 3 are used to refer to the green cluster in the K ¼ 2 DISTRUCT plot, the blue cluster in the
K ¼ 2 DISTRUCT plot, and the yellow cluster in the K ¼ 9 DISTRUCT plot, respectively. The following population abbreviations are also used: A, Ache; Arh,
Arhuaco; Cab, Cabecar; Chip, Chipewyan; E, Embera; G, Guaymi; K, Karitiana; Kog, Kogi; P, Pima; S, Surui; T, Ticuna (both Ticuna groups combined); W,
Waunana. Clusters are indicated using set notation; for example fAg represents a cluster containing Ache only, and 2\fA,Sg represents a cluster that
corresponds to cluster 2 (the blue cluster for K ¼ 2), excluding Ache and Surui. An asterisk indicates approximately 50% membership of a population in a
cluster. A line is drawn from a box representing a solution with K clusters to a box representing a solution with Kþ1 clusters if the solution with Kþ1
clusters refines the solution with K clusters—that is, if all of the clusters in the solution with Kþ1 clusters subdivide the clusters in the solution with K
clusters. In case of ties for the highest-frequency solution (K ¼ 4 and K ¼ 5), boxes are oriented in order to avoid the crossing of lines between them.
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Genetic Variation in Native Americans
populations—at frequencies ranging from 11.1% in Ticuna
(Tarapaca) to 97.1% in Surui (Figure 10). This allele has now
been observed in every Native American population in which
the locus has been investigated [41,42], and it has only been
seen elsewhere in two populations at the far eastern edge of
Siberia [42].
Because of the likely submergence of key archaeological
sites along the Pacific coast, the relative absence of a written
record, and the comparatively recent time scale of the initial
colonization, population-genetic approaches provide a par-
Table 3. Correlation of Genetic and Linguistic Distances
Population Comparison Distance Measure
between Stocks
Excluded Stock Correlation of Genetic and
Linguistic Distance
Partial Correlation of
Genetic and Linguistic
Distance Controlling for
Geographic Distance
All pairs Equal 0.04 0.18 0.01 0.43
All pairs excluding one stock Equal Northern Amerind 0.06 0.16 0.04 0.28
Equal Central Amerind 0.06 0.13 0.05 0.21
Equal Chibchan–Paezan 0.01 0.41 0.02 0.61
Equal Andean 0.05 0.19 0.05 0.77
Equal Equatorial–Tucanoan 0.19 0.0003 0.16 0.01
All pairs Dice 0.01 0.56 0.08 0.79
All pairs excluding one stock Dice Northern Amerind 0.10 0.83 0.15 0.95
Dice Central Amerind 0.02 0.58 0.04 0.64
Dice Chibchan–Paezan 0.06 0.75 0.14 0.93
Dice Andean 0.15 0.02 0.04 0.69
Dice Equatorial–Tucanoan 0.04 0.38 0.06 0.65
All pairs Jaccard 0.02 0.56 0.08 0.83
All pairs excluding one stock Jaccard Northern Amerind 0.09 0.84 0.15 0.95
Jaccard Central Amerind 0.03 0.60 0.05 0.69
Jaccard Chibchan–Paezan 0.07 0.77 0.15 0.94
Jaccard Andean 0.16 0.01 0.04 0.70
Jaccard Equatorial–Tucanoan 0.05 0.36 0.05 0.65
All pairs within stocks 0.53 0.11 0.40 0.11
All pairs within stocks excluding one stock Northern Amerind 0.54 0.11 0.40 0.11
Central Amerind 0.51 0.13 0.39 0.13
Chibchan–Paezan 0.55 0.16 0.40 0.15
Andean 0.46 0.33 0.26 0.33
Equatorial–Tucanoan 0.68 0.01 0.66 0.01
Figure 9. The Mean and Standard Error Across 678 Loci of the Number of Private Alleles as a Function of the Number of Sampled Chromosomes
For a given locus, region, and sample size g, the number of private alleles in the region—averaging over all possible subsamples that contain g
chromosomes each from the five regions—is computed according to an extension of the rarefaction method [25]. For each sample size g, loci were
considered only if their sample sizes were at least g in each geographic region. Error bars denote the standard error of the mean across loci.
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Genetic Variation in Native Americans
ticularly important source of data for the study of Native
American population history [43–52]. In this article, building
upon recent investigations that have increased the size of
Native American genetic datasets beyond classical marker, Y-
chromosomal, mitochondrial, and single-gene studies
[7,11,13,16,41,53–65], we have examined genome-wide pat-
terns of variation in a dataset that—in terms of total
genotypes—re presents the largest continent-wide Native
American population-genetic study performed to date. Our
results have implications for a variety of topics in the
demographic history of Native Americans, including (1) the
process by which the American landmass was originally
populated, (2) the routes taken by the founders during and
subsequent to the migration, and (3) the extent to which
genes and languages have traveled together during the
diversification of Native American populations. We discuss
these issues in sequence.
Genetic Signatures of the Colonization from Siberia
The lower level of genetic diversity observed in the
Americas compared to other continental regions is compat-
ible with a reduction in population size associated with a
geographically discrete founding, representing one of the
most recent in a series of major bottlenecks during human
expansions outward from Africa [11]. Gradients of genetic
diversity (Figure 3) and decreasing similarity to Siberians
(Figure 6) also point to extant Native Americans as the
descendants of a colonization process initiated from the
northwestern part of the American landmass. An alternative
possibility that could produce a genetic diversity gradient—
namely, a north-to-south gradient of recent admixture from
high-diversity European populations—can be eliminated as a
possible explanation given that (1) European admixture is not
strongly correlated with distance from the Bering Strait (r ¼
0.135), (2) inclusion of a European admixture covariate in
the regression of heterozygosity on distance from the Bering
Strait is not supported (p ¼ 0.37) and only slightly increases
the fit of the regression model (R
¼ 0.208 compared to R
0.182), and (3) the regression of heterozygosity on distance
from the Bering Strait does not change substantially when the
most highly admixed populations are excluded from the
analysis (Table S12). The genetic diversity and population
structure gradients—which are generally compatible with
principal component maps of allele frequencies at small
numbers of classical markers [1,66] and with some analyses of
mitochondrial, X-chromosomal, and Y-chromosomal data
[67,68]—are more clearly visible in our study of a larger
number of loci.
Although gradients of genetic diversity and Siberian
similarity constitute major features of the pattern of Native
American variation when considering all of the loci together,
one important aspect of Native American variation—the
distribution of a private allele at locus D9S1120—deviates
from the genome-wide pattern and does not show a north-to-
south frequency gradient. The geographic distribution of this
allele is similar to the distributions of certain mitochondrial
and Y-chromosomal variants that are also ubiquitous in the
Americas, but that are absent elsewhere or that are found
outside the Americas only in extreme northeast Siberia [69–
74]. Such distributions are most easily explained by the
spatial diffusion of initially rare variants during the colo-
nization of the continent, rather than by continent-wide
natural selection or by an origin considerably later than the
colonization [42,75,76]. The restricted distribution in Asia of
D9S1120 allele 275 and similar Y-chromosomal and mito-
chondrial variants suggests one of several explanations [42]:
the ancestral population that migrated to the Americas may
have already acquired a degree of genetic differentiation
from other Asian populations [77], descendants of the
original Native American founders are no longer present
elsewhere in Asia, or these descendants have not yet been
genotyped at loci t hat carry apparently private Native
American variants.
The genomic continent-wide patterns observed here can be
explained most parsimoniously by a single main colonization
event, as proposed by some interpretations of archaeological,
mitochondrial, and Y-chromosomal data [67,74,77–83]. In this
view, at each step in the migration, a subset of the population
splitting off from a parental group moves deeper into the
Americas, taking with it a subset of the genetic variation
present in the parental population. This scenario would be
expected to produce a set of low-diversity populations with
distinctive patterns of variation at the far terminus of the
migration, such as those we and others [84] observe in the
Ache and Surui populations. It can also explain the gradient
of Siberian similarity, and the continent-wide distribution of
D9S1120 allele 275. Alternatively, similar patterns could
result from gene flow across the Bering Strait in the last
Figure 10. Allele Frequency Distribution at Tetranucleotide Locus D9S1120
For each population the sizes of the colored bars are proportional to allele frequencies in the population, with alleles color-coded as in the legend.
Alleles are ordered from bottom to top by increase in size, with the smallest allele, a Native American private allele of size 275, shown in red, and the
largest allele, 315, shown in dark blue.
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Genetic Variation in Native Americans
few thousand years, together with continual interactions
between neighbors on both sides of the Bering Strait [47]. It is
also possible to envision a series of prehistoric migrations,
possibly from the same source population, with the more
recent descendants gradually diffusing into pre-existing
Native American populations.
Routes of Population Dispersal
Largely on the basis of archaeological data, a classical
model for the colonization of the Americas posits that
humans entered the region towards the end of the Wisconsin
glaciation (;11,000 y ago) via a mid-continental ice-free
corridor between the Cordilleran and Laurentide glaciers
[78,79]. According to this model, migration southwards would
have followed a pattern with a front of advance at
approximately the same latitude across North America.
It is interesting to consider the patterns of genetic
structure observed here within the context of the emphasis
placed recently on the Pacific coast as an alternative to the
inland ice-free corridor route of population dispersal in the
Americas [79,85–87]. The late timing of the rapid inland
colonization model has been put into some doubt by the
discovery of early archaeological sites that predate by
thousands of years the most recent deglaciation of North
America [88]. In addition, recent geological evidence in-
dicates that ice-free areas west of the Cordilleran ice sheet
may have existed as early as ;14,000 y ago [79], suggesting the
possibility of an early coastal migration. Within South
America, the coastal colonization model suggests an early
southward migration along the western side of the Andes and
is consistent with an interpretation that modern speakers of
Andean languages may represent descendants of the first
occupiers of the region [1]. Recent computer simulations also
suggest that a coastal colonization model may more easily
explain observed patterns of classical marker and mitochon-
drial DNA diversity [89].
Several observations from our data are compatible with the
proposal of a coastal colonization route. The stronger
correlation of genetic diversity with geographic distance
when higher coastal mobility is taken into account (Figure 4)
supports a possible role for population dispersals along the
coast (note, however, that the difference in the tree structure
induced by the optimal route in Figure 4 and the tree in
Figure 8 suggests that alternative routes might be preferred if
more aspects of the genetic data were incorporated into the
coast al analys is). C onsist ent with observations of recent
migration paths of certain Amazonian populations [43], we
did not find support for migrations along major rivers.
Finally, the relative genetic similarity of Andean populations
to populations from Mesoamerica (Figure 7) is also compat-
ible with an early Pacific coastal colonization. Under this
view, the east-to-west difference in genetic diversity in South
America, a pattern also observed with mitochondrial and Y-
chromosomal markers [90–92] (including the extremely low
diversity in the Ache [93,94] and Surui [94] populations),
could reflect an initial colonization of western South America
followed by subsampling of western populations to form the
eastern populations.
An alternative interpretation of the Mesoamerican and
Andean similarity is that this pattern is recent in origin. In
this case, the reduced diversity and increased population
structure in eastern South America may reflect a deep
divergence between western and eastern populations, so that
their different levels of differentiation could result from
different levels of gene flow and genetic drift in western and
eastern South America. The genetic similarity among Andean
populations, and their relative similarity to the populations
sampled from Mesoamerica, would perhaps then reflect
recent gene flow along the coast.
Similar to results seen in some mitochondrial studies [95–
97], Central American and South American populations from
the Chibchan–Paezan language stock had slightly reduced
heterozygosity compared to neighboring populations. Inter-
estingly, the Cabecar and Guaymi populations from lower
Central America (Costa Rica and Panama) were robustly
placed at the tips of a northwest South American Chibchan–
Paezan cluster in the tree of Figure 8. One explanation of this
observation is that these populations may be of South
American origin, as the ancestral group for the cluster could
have been a South American population, most of whose
descendants remain in South America. Alternatively, the large
cluster containing the Chibc han–Paezan and Equatorial–
Tucanoan populations could be the result of a colonization
of South America separate from the colonization by the
Andean populations—with the founder population possibly
speaking a language from which modern Chibchan–Paezan
languages have descended [98]. In this view, Guaymi and
Cabecar are the only sampled Central American populations
descended from the ancestors of this second migration.
Genetic and Linguistic Differentiation
At a qualitative level, the topology of the tree of Figure 8
shows some correspondence between genetic distance and
linguistic stock assignment. High bootstrap values are seen
for population clusters corresponding mainly to speakers of
Chibchan–Paezan and Equatorial–Tucanoan languages and,
to a lesser extent, Central Amerind languages. Although the
high bootstrap values support previous qualitative compar-
isons that have suggested a considerable degree of relation-
ship between genetic and linguistic distances [1], quantitative
analyses based on matrix correlation coefficients for genetic
and linguistic distances have been somewhat more equivocal
[39,99–101]. Indeed, the correlation of genetic and linguistic
similarity considering all populations in our dataset is quite
small (Table 3). Considering only pairs of populations from
within major language stocks, however, the correlation
increases. Although several populations that do not group
in the neighbor-joining tree with their linguistic neighbors
appear most genetically similar to their geographic neigh-
bors, the correlation remains moderate when geographic
distance is controlled. The within-stock correlations are in
most cases not unusually high when applying permutation
tests, but are perhaps suggestive that at the local scale,
dissimilarities in languages either play a partial role in
producing genetic barriers or otherwise co-occur with factors
that impede gene flow. The lack of a more general correlation
may be due to such factors as deviations from a tree-like
history for genetic evolution or for linguistic evolution, or to
uncertainties in the linguistic classification [39].
In a genomic study of a relatively large number of Native
American populations, our work provides support to a
variety of hypotheses about fundamental aspects of Native
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Genetic Variation in Native Americans
American demographic history. In particular, we find genetic
evidence that supports a single main colonization event from
Siberia, a coastal colonization route, and a divergence process
that may have been facilitated at the local scale partly by
differences between languages. As genomic data proliferate,
more formal genetic tests of these hypotheses, together with
accumulating evidence from fields such as archae ology
[78,79,102] , geology [103], and linguistics [104–106], will
surely result in a more detailed picture of the settlement by
and differentiation of indigenous human populations in the
American landmass.
Samples. A total of 436 individuals from 24 Native American
populations and one Siberian population were included in this study,
in addition to data on 1,048 individuals from 53 worldwide
populations represented in the HGDP–CEPH human genome
diversity cell line panel [107,108]. Alternate names for the Native
American populations, together with sample sizes and approximate
geographic coordinates, are given in Table S1.
Populations from the HGDP–CEPH panel were classified into
geographic regions as in Rosenberg et al. (2002) [7], and the Tundra
Nentsi population from Siberia was classified as East Asian. In
analyses subdivided by geographic region within the Americas, we
grouped the populations as North American (Chipewyan, Cree,
Ojibwa), Central American (Cabecar, Guaymi, Kaqchikel, Maya, Mixe,
Mixtec, Pima, Zapotec), western South American (Arhuaco, Aymara,
Embera, Huilliche, Inga, Kogi, Quechua, Waunana, Wayuu, Zenu),
and eastern South American (Ache, Guarani, Kaingang, Karitiana,
Piapoco, Surui, Ticuna [Arara], and Ticuna [Tarapaca]). The
populations from Mexico, which except Pima were from the southern
part of Mexico, were considered as part of the Central American
group. Populations were placed linguistically using the classification
of Ruhlen [35]. Although dis agreement exists about lin guistic
classifications in the Americas, there is greater agreement at the
level of lingui stic stocks and at lowe r levels in the linguistic
classification hierarchy, on which we focus.
Markers. Each of the newly sampled individuals was genotyped by
the Mammalian Genotyping Service for 751 microsatellites spread
across all 22 autosomes. The microsatellite markers were drawn from
Marshfield Screening Sets 16 and 54 (http://research.marshfieldclinic.
org/genetics/). Considering all individuals, we checked each pair of
markers to determine if genotypes at one member of the marker pair
were identical to those at the other member of the pair, up to a
constant of translation. This procedure identified one pair of
duplicated markers—MFD600 and MFD601—and MFD600 was
discarded from the analysis.
Combined dataset with the HGDP–CEPH diversity panel. Among
the 750 remaining microsatellites that were genotyped in the new
samples, 693 had previously been genotyped in the HGDP–CEPH
diversity panel [7,11,13]. For some of these loci, there was a change in
primer length or position between the two studies, or a systematic
change occurred in the algorithm by which allele size was determined
from raw genotyping products—or both. In cases where the primers
changed, allele sizes from the new dataset were adjusted by the
appropriate length in order to align its list of allele sizes with the
earlier list for the HGDP–CEPH dataset.
To identify systematic changes between datasets, for each locus the
allele sizes of one dataset were translated by a constant and the G test
statistic of independence between allele frequencies and dataset
(older HGDP–CEPH dataset versus newly genotyped dataset) was then
computed [23]. Considering all possible constants for translation of
allele sizes, the one that minimized the G statistic was determined. In
implementing the G test, two groups of comparisons were performed.
In the first group of comparisons, the constant of translation was
determined by comparing 80 Jewish individuals genotyped simulta-
neously with the Native Americans to all 255 individuals from Europe
and the Middle East in the HGDP–CEPH H1048 dataset [109],
excluding Mozabites. The second group of comparisons involved 346
Native American individuals from Central and South America in this
newer dataset (all 336 sampled Central and South Americans
excluding Ache, and ten additional individuals who were later
excluded) and 63 Native American individuals from the Maya, Pima,
and Piapoco populations in the older H1048 dataset (the Piapoco
population is described as ‘‘ Colombian’’ in previous analyses of these
data). The constants expected based on the two G tests—labeled S
the comparison of the Jewish populations to European and Middle
Eastern populations and S
for the Native American comparison—
were then compared with the constant of translation expected from
consideration of three additional sources of information available for
the two datasets: the genotypes of a Mammalian Genotyping Service
size standard (S
Genotyping Service indicating the nature of the change in primers
), and the locations of the primers themselves in the human
genome sequence (S
Among the 693 markers, 687 had the same optimal constant of
translation (that is, the constant that minimizes the G statistic) in the
two different sets of population comparisons (S
¼ S
). The remaining
six markers with different optimal constants of translation in the two
G tests were compared with the value expected from the locations of
the old and new primers in the human genome (S
). In all six cases,
the optimal constant for the comparison of the Jewish and European/
Middle Eastern datasets agreed with the value based on the primer
locations (S
¼ S
). As real population differences between datasets
are more likely in Native Americans due to the larger overall level of
genetic differentiation in t he Americas, we used the constant
obtained based on the Jewish and European/Middle Eastern compar-
ison (S
) for allele size calibration.
Of the remaining 687 markers, 638 had an optimal constant of
translation that agreed with the value expected based on the code
letter provided by the Mammalian Genotyping Service (S
¼ S
¼ S
Thus, there were 49 markers for which the code letter was either
uninformative or produced a constant of translation that disagreed
with S
and S
. For 35 of these markers, the constant of translation
based on the size standard (S
) agreed with S
and S
. For eight of the
remaining 14 markers, the constant of translation based on the
primer sequences (S
) agreed with S
and S
. The six markers with
disagreements (AAT263P, ATT070, D15S128, D6S1021, D7S817, and
TTTAT002Z), having S
, were then discarded. For the remaining
687 markers that were not discarded, 685 had G , 48 in both G tests,
while the other two markers (D14S587 and D15S822) had G . 91 in
the Jewish versus European/Middle Eastern comparison. These two
extreme outliers, which also had the highest G values for the Native
American comparison, were then excluded (Figure S6).
To further eliminate loci with extreme genotyping errors, we
performed Hardy-Weinberg tests [110] within individual populations
for the 685 remaining markers. This analysis, performed using
PowerMarker [111], used only the 44 populations in which all 685
markers were polymorphic. We calculated the fraction of populations
with a significant p-value (,0.05) for the Hardy-Weinberg test (Figure
S7). Two markers (GAAA1C11 and GATA88F08P) were extreme
outliers, with more than 43% of populations producing p , 0.05. For
the remaining markers, the proportion of tests significant at p , 0.05
varied from 0 to 35% without any clear outliers, and with most
markers having less than 10% of tests significant at p , 0.05.
Excluding the two Hardy-Weinberg outliers, 683 markers remained.
Five addit ional markers (AGAT120, AGAT142P, D14S592, GA-
TA135G01, and TTTA033) were excluded due to missing data: for
each of these markers there was at least one population in which all
genotypes were missing. Thus, 678 loci remained for the combined
analysis with the HGDP–CEPH panel.
Final dataset. After the elimination of problematic markers, ten
individuals who had potentially been mislabeled were discarded.
Seven of these were admixed individuals from Guatemala who,
through a clerical error, had been incorporated in the data cleaning
phase of the study as members of the Kaqchikel population. The
other three were individuals who, on the basis of elevated allele
sharing, were inferred to be siblings, but who were classified as
belonging to two different populations (Wayuu and Zenu). The final
dataset, combining the HGDP–CEPH data and the new data,
contained 1484 individuals and 678 markers, with a missing data
rate of 4.0%. Each marker had some data present in all populations,
with a minimum 88.7% genotypes per marker and 50.1% genotypes
per individual. Of the 1,484 individuals, 1,419 had a missing data rate
of less than 10%.
Detection of relatives. Identification of pairs of close relatives was
performed using identity-by-state allele sharing combined with
likelihood inference as implemented in Relpair [112,113]. A critical
value of 100 was used in the likelihood analysis, and the genotyping
error rate was set at 0.008. In each population, Relpair was applied
using count estimates of allele frequencies in that population.
Identification of recommended panels with no first-degree relatives
and with no first- or second-degree relatives followed the procedure
of Rosenberg [109], except that when an arbitrary decision was
required about which individual in a relative pair should be excluded,
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Genetic Variation in Native Americans
the individual with more missing data was discarded. Beginning from
the 436 newly sampled individuals (termed panel N436), this analysis
produced a panel of 379 individuals with no first-degree relatives and
a panel of 354 individuals with no first- or second-degree relatives.
These panels are termed N379 and N354. Details on the properties of
these panels can be found in Tables S13–S26, and plots of allele
sharing are shown in Figures S8–S13.
Geographic computations. Geographic coordinates for the newly
sampled populations are specified in Table S1, and coordinates for
the other populations were taken from Rosenberg et al. [13]. For the
production of Figure 3B, distances between populations were
computed using great circle routes [13], with obligatory waypoints
as specified by Ramachandran et al. [11]. Routes to South America
required an additional waypoint in Panama at 8.9678N 79.5338W. The
computation of Figure 3B excluded the waypoint used by Ramachan-
dran et al. [11] at Prince Rupert, and did not use the Panama
wayp oint when the origin was placed on a Caribbean island.
Geographic distances from East Africa (Figure 3A) were computed
using an origin at Addis Ababa [11].
Compared to the waypoint-based geographic distances, effective
distances incorporate more detailed information on the effects of
landscape components. They are computed as least-cost paths on the
basis of a spatial cost map that incorporates these landscape
components. For example, a coastal/inland ratio of 1:10 means that
it is ten times more costly to go through land than through coastline.
The effective distance between two points is computed as the sum of
costs (so-called ‘‘ least-cost distance’’ ) along t he least-cos t path
connecting the points. Because the relative costs of landscape
components are somewhat arbitrary, several combinations were
tested. We used PATHMATRIX [27] to compute least-cost distances
based on a ‘‘ uniform’’ cost over the continent (that is, when the
boundaries of continental landmasses are the only spatial constraint,
so that the coastal/inland cost ratio is 1:1), as well as using the
following coastal/inland relative cost combinations: 1:2, 1:5, 1:10, 1:20,
1:30, 1:40, 1:50, 1:100, 1:200, 1:300, 1:400, and 1:500. Inverse cost
combinations were also tested (2:1, 5:1, 10:1, 20:1, 30:1, 40:1, 50:1,
100:1, 200:1, 300:1, 400:1, 500:1). We also considered scenarios where
the cost differed only for the Pacific coast instead of for all coasts, and
where it differed not along coasts, but along major rivers.
Least-cost paths were computed on a Lambert azimuthal equal-
area projection of the American landmass (central meridian 808W,
reference latitude 108N) divided into a grid of 100 km
square cells.
For each cost scheme, we computed a Pearson correlation between
heterozygosity and effective distance from the Bering Strait, as
specified by the Anadyr waypoint [11] at 648N 1778E, and we obtained
its significance by using the t-distribution transformation [114].
Heterozygosity and F
. For each population, expected hetero-
zygosity was computed for each locus using an unbiased estimator
[115], and the average across loci was taken as the population
estimate. Heterozygosity was calculated for pooled collections of
populations, and average heterozygosity across populations was
obtained within individual geographic regions. Computations of
were performed using Equation 5.3 of Weir [30], with confidence
intervals obtained using 1,000 bootstrap resamples across loci.
Private alleles. To assess whether private alleles lie more often at
the ends of the allele size range, for a given allele frequency cutoff, c,
all private alleles with frequency at least c in their region of
occurrence were obtained. Under the null hypothesis that all alleles
are equally likely to be private, the number of private alleles expected
to be at one of the two ends of the allele size range was obtained as
the sum over the private alleles of 2/k
, where k
denotes the number
of distinct alleles worldwide at the locus that produced private allele i.
A difference from the value expected was evaluated using a chi-
square goodness-of-fit test with one degree of freedom. Considering
this test for all possible cutoffs c below 0.06 (above which only seven
private alleles were observed), the most conservative p-value was
0.0228, although most values of c produced considerably more
stringent p -values (Figure S14). In depicting allele frequencies at
tetranucleotide locus D9S1120 (Figure 10), five of 2,914 observations
not differing from the remaining alleles by a multiple of four are
grouped with the nearest allele sizes (in one case where the allele was
halfway between steps, it was assigned to the larger allele).
Population structure analysis. Analysis of population structure was
performed using STRUCTURE [28,29]. Replicate runs of STRUCTURE
used a burn-in period of 20,000 iterations followed by 10,000
iterations from which estimates were obtained. All runs were based
on the admixture model, in which each individual is assumed to have
ancestry in multiple genetic clusters, using the F model of correlation
in allele frequencies across clusters. Graphs of STRUCTURE results
were produced using DISTRUCT [116].
Worldwide population structure. Using the full worldwide data, ten
replicate unsupervised STRUCTURE runs were performed for each
value of the number of clusters K from one to 20. For each pair of
runs with a given K, the symmetric similarity coefficient [117] (SSC)
was computed as a measure of the similarity of the outcomes of the
two population structure estimates. Using the Greedy algorithm of
CLUMPP [117], distinct modes among the ten runs with a given K
were then identified by finding sets of runs so that each pair in a set
had SSC 0.9. The average was then taken of the estimated cluster
membership coefficients for all runs with the same clustering mode.
Of the ten runs, the number of runs that exhibited the mode shown
was ten for K ¼ 2 and K ¼ 4, nine for K ¼ 3 (with the tenth run
grouping Africans and East Asians rather than Europeans and other
Asians), five for K ¼ 5 (with the remaining runs subdividing various
combinations among Karitiana, Surui, and Ache, rather t han
separating the two populations from Oceania), and six for K ¼ 6
(with the remaining runs subdividing the Native Americans into three
clusters rather than separating the two populations from Oceania).
Supervised clustering. Using STRUCTURE, individuals from Europe,
Sub-Saharan Africa, East Asia (excluding Siberia), and Siberia were
forced into separate clusters, and supervised analysis of the Native
American data was performed with K ¼ 5 clusters. Ten replicates were
performed, each of which yielded the same clustering mode, and the
average membership coefficients across these replicates are displayed
in Figure 6.
Native Americans. Using the Native Americans only, 100 replicate
unsupervised STRUCTURE runs were performed for each value of K
from one to 15 clusters. The settings for the runs were the same as in
the worldwide analysis, and modes were identified in a similar
manner. For K 9, average membership coefficients for the most
frequently observed mode at each K are displayed in Figure 7. For
each value of K, the figure presents the average membership
estimates across all replicates that produced the most frequently
occurring solution. Because of the high level of multimodality for K
3, no single mode provides a complete re presentation of the
STRUCTURE results with a given K. Using CLUMPP [117], we
identified all modes appearing at least 12 times in 100 replicates,
using the SSC 0.9 criterion. Computations of SSC were based on the
best alignment of the 100 replicate analyses obtained using the
LargeKGreedy algorithm of CLUMPP with 1,000 (2 K 11) or 200
(12 K 15) random input sequences.
For 2 K 9, using the criterion SSC 0.9, the relation ‘‘ in the
same mode’’ had the property of being transitive, so that if runs
) were in the same mode and runs (R
) were in the same
mode, then runs (R
) were also in the same mode. For K 10, with
the criterion SSC 0.9, ‘‘ in the same mode’’ was not always transitive.
While other cutoffs c could sometimes be identified so that ‘‘ in the
same mode’’ was transitive when the criterion SSC . c was applied,
for K 10 there was no clear plateau in the cumulative probability
distribution of SSC values across pairs of runs (Figure S15). Such
plateaus, which are observed for 2 K 9, represent a large gap
between SSC values for pairs of runs truly in the same mode (high
SSC) and pairs of runs not in the same mode (lower SSC). The fact that
for K 10 the probability is high that a randomly chosen pair of runs
has SSC , 0.9 is also indicative of considerable multimodality across
Considering the modes with successive numbers of clusters, we
identified all sets of modes with Kþ1 clusters that ‘‘ refined’’ modes
with K clusters. A mode with Kþ1 clusters is a refinement of a mode
with K clusters if the mode with Kþ1 clusters consists of K1 of the
clusters in the K-cluster mode together with two clusters obtained by
splitting the Kth cluster into two subgroups. More generally, a mode
with K . L clusters refines a mode with L clusters if each cluster in
the K-cluster mode is either the same as or a subdivision of a cluster
in the L-cluster mode. As an example, in Figure 7, the mode depicted
for K ¼ 7 is a refinement of all modes depicted for smaller values of K.
For the Native American data, we performed a separate analysis
using TESS [118,119], a genetic clustering program that estimates a
preferred value of the number of clusters K less than or equal to a
prespecified maximum value K
. If the estimated K equals K
then the choice of K
is insufficiently large. Using the TESS
admixture model with burn-in period of length 10,000 followed by
20,000 iterations from which estimates were obtained, we performed
200 runs of TESS with K
¼ 10, 20 each for ten values of a spatial
autocorrelation parameter W at intervals of 0.2 from 0.2 to 2. Of these
200 replicates, 183 supported an inference of K ¼ 6, 7, 8, or 9, and
only one supported an inference of K ¼ 10. This suggests that the
most important components of population structure are apparent
with K , 10.
Population tree. An unrooted neighbor-joining [120] population
PLoS Genetics | November 2007 | Volume 3 | Issue 11 | e1852062
Genetic Variation in Native Americans
tree was constr ucted for the Native American and S iberian
populations based on the D
distance of Nei et al. [36], which was
found to perform comparatively well in estimation of population
trees from microsatellite allele frequency data [121]. To visualize the
tree, the root was placed between the Siberian and Native American
populations. Confidence values were obtained from 1000 bootstrap
resamples across loci. The computation of bootstrap distances was
performed using PowerMarker [111], and the consensus tree was
obtained and plotted using MEGA3 [122]. For comparison, trees
based on Reynolds [123] and chord distances [124] were obtained
analogously. Genetic distance matrices based on the Nei, Reynolds,
and chord distances are shown in Tables S27–S29.
Correlations of genes and languages. We used a discretized scale to
measure linguistic distance [125,126]. Two populations from different
language stocks or ‘‘ groups’’ (Table S2) were scored as having distance
4, and within stocks, two populations had distance of 1, 2, or 3
depending on the level at which their languages diverged (Table S3).
For some computations, we devised discretized measures of linguistic
distance between stocks on the basis of shared and unshared
etymologies tabulated in Table C.1 of Greenberg [34] (Tables S4
and S5). Using these etymologies, we computed the Dice (simple
matching) and Jaccard indices of dissimilarity between stocks [127]
(Tables S6 and S7), which we then converted into discretized
between-stock distances (Tables S8 and S9).
For comparison with linguistic distances, D
genetic distances were
used (Table S27), and the Mantel correlation coefficients [38] between
pairs of distance matrices (among genetic, geographic, and linguistic)
were obtained, with significance assessed using 10,000 permutations
of rows and columns. Waypoint-based distances (Table S30) were
used for the geographic computations. For computations within
linguistic stocks, the correlation and significance level were com-
puted as in tests involving the full matrix, except that all entries
between language stocks were omitted from the evaluation of the
correlation coefficient. Partial correlations of genetic and linguistic
distance controlling for geographic distance were also obtained [128],
with geographic distance calculated using the waypoint approach as
Analysis excluding relatives. As the inclusion of relatives has the
potential to influence various types of population-genetic analysis, we
compared some of our results based on the full collection of 1,484
individuals to results based on 1,306 individuals—the H952 set from
the HGDP–CEPH diversity panel [109] together with the N354 set
from the newly genotyped individuals. The inclusion of relatives does
not lead to a bias in allele frequency estimates (that is, E[
] still
equals p
), but it does inflate Var[
]. The estimator H
of hetero-
zygosity is ½n=ðn 1Þð1
Þ, where n is sample size, the sum
proceeds over alleles, and
is the estimated frequency of allele i.
Expanding the expression for the expectation E[H
], it can be
observed that the coefficient for the Var[
] term is negative. Thus,
inclusion of relatives is expected to reduce the estimate of
heterozygosity through an increase in Var[
The population heterozygosities based on the full and reduced
datasets are plotted in Figure S16. The correlation coefficient
between population heterozygosities based on the reduced and full
datasets was 0.997; as expected, however, heterozygosity was system-
atically higher in the reduced set (mean difference of 0.0033 across
populations; p, 0.001, Wilcoxon signed rank test). Given the greater
proportion of individuals excluded when relatives were removed
from N436 (18.8%) compared to H1048 (9.2%), the difference in
heterozygosities between full and reduced datasets is greater in the 25
newly sampled populations (mean difference of 0.0052; p , 0.001)
compared to the 53 HGDP–CEPH populations (mean difference of
0.0024; p , 0.001).
Despite the detectable effect of the removal of relatives on
heterozygosity, the systematic nature of this small effect was such that
very little difference was observed on the relationship of hetero-
zygosity with distance from the Bering Strait (Figure S17). A number
of other analyses, including the analyses of linguistic correlations and
numbers of private and distinct alleles, also produced nearly identical
inferences when relatives were excluded (Figures S18–S20 and Tables
S31–S33), two exceptions being a noticeable decrease in population
differentiation (Table S31) and a shift in the position of several
populations in the neighbor-joining tree (Figure S19). Via the
connection between heterozygosity and differentiation [11,31–33],
the decrease in differentiation is a consequence of the increase in
heterozygosity upon exclusion of relatives. In the case of the tree,
despite a Mantel correlation of 0.99 between genetic dista nce
matrices including and excluding relatives (Tables S27 and S33), the
Cree, Huilliche, Maya, Ojibwa, Wayuu and Zenu populations shifted
positions slightly, and the Kaqchikel population moved nearer to its
geographic neighbors. Although the population groupings were
generally quite similar, several bootstrap values decreased, magnify-
ing the effect of the slight decrease in population differentiation.
Supporting Information
Dataset S1. Gzipped File with the Genotypes for 1,484 Individuals at
678 Loci
Found at doi:10.1371/journal.pgen.0030185.sd001 (2.1 MB GZ).
Figure S1. Heterozygosity and Least-Cost Paths in a Coastal Migration
Scenario, with Ache, Guarani, and Kaingang Excluded from the
The figure design follows that of Figure 4, with part B based on the
scenario with 1:10 coastal/inland cost ratio.
Found at doi:10.1371/journal.pgen.0030185.sg001 (635 KB PDF).
Figure S2. Heterozygosity and Least-Cost Paths in a Pacific Coastal
Migration Scenario
The figure design follows that of Figure 4, with part B based on the
scenario with 1:2 Pacific coastal/inland cost ratio.
Found at doi:10.1371/journal.pgen.0030185.sg002 (644 KB PDF).
Figure S3. Heterozygosity and Least-Cost Paths in Five Migration
The figure design follows that of Figure 4A.
Found at doi:10.1371/journal.pgen.0030185.sg003 (1.1 MB PDF).
Figure S4. Neighbor-Joining Tree of Native American Populations
Based on Reynolds Genetic Distance
The figure design follows that of Figure 8.
Found at doi:10.1371/journal.pgen.0030185.sg004 (481 KB PDF).
Figure S5. Neighbor-Joining Tree of Native American Populations
Based on Chord Genetic Distance
The figure design follows that of Figure 8.
Found at doi:10.1371/journal.pgen.0030185.sg005 (462 KB PDF).
Figure S6. G Test Statistics for Agreement of Calibrated Allele Sizes in
Two Population Comparisons
Found at doi:10.1371/journal.pgen.0030185.sg006 (1.3 MB PDF).
Figure S7. Frequency Distribution Across Markers of the Fraction of
Populations Whose p-Values for a Test of Hardy-Weinberg Equili-
brium Were Below 0.05
Found at doi:10.1371/journal.pgen.0030185.sg007 (538 KB PDF).
Figure S8. Allele Sharing for Pairs of Individuals from Different
The four points farthest to the left all involve pairs in which one
individual is Kogi 2463 and the other is from the Arhuaco population.
Found at doi:10.1371/journal.pgen.0030185.sg008 (412 KB PDF).
Figure S9. Allele Sharing for Within-Population Pairs of Individuals
from Each of Five Populations: Ache, Arhuaco, Aymara, Cabecar, and
Parent/offspring (PO), full sib (FS), and second-degree (2nd) relative
pairs are indicated on the graphs.
Found at doi:10.1371/journal.pgen.0030185.sg009 (1.0 MB PDF).
Figure S10. Allele Sharing for Within-Population Pairs of Individuals
from Each of Five Populations: Cree, Embera, Guarani, Guaymi, and
Parent/offspring (PO), full sib (FS), and second-degree (2nd) relative
pairs are indicated on the graphs.
Found at doi:10.1371/journal.pgen.0030185.sg010 (959 KB PDF).
Figure S11. Allele Sharing for Within-Population Pairs of Individuals
from Each of Five Populations: Inga, Kaingang, Kaqchikel, Kogi, and
Parent/offspring (PO), full sib (FS), and second-degree (2nd) relative
pairs are indicated on the graphs.
Found at doi:10.1371/journal.pgen.0030185.sg011 (969 KB PDF).
Figure S12. Allele Sharing for Within-Population Pairs of Individuals
PLoS Genetics | November 2007 | Volume 3 | Issue 11 | e1852063
Genetic Variation in Native Americans
from Each of Five Populations: Mixtec, Ojibwa, Quechua, Ticuna
(Arara), and Ticuna (Tarapaca)
Parent/offspring (PO), full sib (FS), and second-degree (2nd) relative
pairs are indicated on the graphs.
Found at doi:10.1371/journal.pgen.0030185.sg012 (1.0 MB PDF).
Figure S13. Allele Sharing for Within-Population Pairs of Individuals
from Each of Five Populations: Tundra Nentsi, Waunana, Wayuu,
Zapotec, and Zenu
Parent/offspring (PO), full sib (FS), and second-degree (2nd) relative
pairs are indicated on the graphs.
Found at doi:10.1371/journal.pgen.0030185.sg013 (1.0 MB PDF).
Figure S14. p-Value for the Goodness-of-Fit Test of the Hypothesis
that Private Allele Sizes Match the Expectation, as a Function of the
Minimum Frequency of Private Alleles Considered
Found at doi:10.1371/journal.pgen.0030185.sg014 (213 KB PDF).
Figure S15. Cumulative Probability Distribution of the 4,950 Pairwise
Symmetric Similarity Coefficients among 100 Runs of STRUCTURE
with Given Values of K
The cutoff SSC ¼ 0.9 is marked by a vertical line.
Found at doi:10.1371/journal.pgen.0030185.sg015 (173 KB PDF).
Figure S16. Heterozygosity of Individual Populations in the Reduced
Dataset of 1,306 Individuals versus Heterozygosity in the Full Dataset
of 1,484 Individuals
The 25 newly examined populations are marked in red, and the
previously genotyped populations are marked in yellow.
Found at doi:10.1371/journal.pgen.0030185.sg016 (173 KB PDF).
Figure S17. Heterozygosity in Relation to Geography for the Reduced
Dataset of 1,306 Individuals
The figure design follows that of Figure 3. Denoting heterozygosity by
H and geographic distance in thousands of kilometers by D, the
regression line for the graph is H ¼ 0.7668 0.00624D, with
correlation coefficient 0.867. Across the Americas, the correlation
between heterozygosity and distance from the Bering Strait ranges
from 0.457 to 0.573, and color bins are set to equalize the number of
points drawn in the four colors. From darkest to lightest, the four
colors represent points with correlations in (0.457, 0.444), (0.444,
0.328), (0.328, 0.498), and (0.498, 0.573), respectively. The most
strongly negative correlation occurs at 58.21178N 95.28W. Of 1,246
points plotted, 44 change colors compared to Figure 3.
Found at doi:10.1371/journal.pgen.0030185.sg017 (74 KB PDF).
Figure S18. The Mean and Standard Error Across 678 Loci of the
Number of Distinct Alleles as a Function of the Number of Sampled
Chromosomes, for the Reduced Dataset of 1,306 Individuals
The figure design follows that of Figure 2, with the results based on
1,306 individuals superimposed using thin lines on the results
obtained with all 1,484 individuals.
Found at doi:10.1371/journal.pgen.0030185.sg018 (530 KB PDF).
Figure S19. Neighbor-Joining Tree of Native American Populations
Based on Nei Genetic Distance, Using the Reduced Dataset of 1,306
The figure design follows that of Figure 8.
Found at doi:10.1371/journal.pgen.0030185.sg019 (472 KB PDF).
Figure S20. The Mean and Standard Error Across 678 Loci of the
Number of Private Alleles as a Function of the Number of Sampled
Chromosomes, for the Reduced Dataset of 1,306 Individuals
The figure design follows that of Figure 9, with the results based on
1,306 individuals superimposed using thin lines on the results
obtained with all 1,484 individuals.
Found at doi:10.1371/journal.pgen.0030185.sg020 (514 KB PDF).
Table S1. Native American Populations Included in This Study, and
the Coordinates Used for Their Sampling Locations
Found at doi:10.1371/journal.pgen.0030185.st001 (18 KB PDF).
Table S2. Languages of the Native American Populations Included in
This Study, Classified According to Greenberg [34] and Ruhlen [35]
Found at doi:10.1371/journal.pgen.0030185.st002 (16 KB PDF).
Table S3. Discretized Matrix of Linguistic Distances for Pairs of
Populations, with All Stocks Treated as Equidistant
Found at doi:10.1371/journal.pgen.0030185.st003 (42 KB PDF).
Table S4. Number of Etymologies Shared between Language Stocks,
Among the 281 Etymologies Examined by Greenberg [34]
Found at doi:10.1371/journal.pgen.0030185.st004 (14 KB PDF).
Table S5. Number of Etymologies Unshared between Language
Stocks, Among the 281 Etymologies Examined by Greenberg [34]
Found at doi:10.1371/journal.pgen.0030185.st005 (14 KB PDF).
Table S6. Matrix of Linguistic Distances between Stocks, Based on the
Dice Index
Found at doi:10.1371/journal.pgen.0030185.st006 (15 KB PDF).
Table S7. Matrix of Linguistic Distances between Stocks, Based on the
Jaccard Index
Found at doi:10.1371/journal.pgen.0030185.st007 (15 KB PDF).
Table S8. Discretized Matrix of Linguistic Distances between Stocks,
Based on the Dice Index
Found at doi:10.1371/journal.pgen.0030185.st008 (14 KB PDF).
Table S9. Discretized Matrix of Linguistic Distances between Stocks,
Based on the Jaccard Index
Found at doi:10.1371/journal.pgen.0030185.st009 (14 KB PDF).
Table S10. Discretized Matrix of Linguistic Distances for Pairs of
Populations, with Stock Distances Based on the Dice Index
Found at doi:10.1371/journal.pgen.0030185.st010 (45 KB PDF).
Table S11. Discretized Matrix of Linguistic Distances for Pairs of
Populations, with Stock Distances Based on the Jaccard Index
Found at doi:10.1371/journal.pgen.0030185.st011 (46 KB PDF).
Table S12. Correlation of Heterozygosity with Distance from the
Bering Strait When Excluding Populations with the Highest Level of
European Admixture
Found at doi:10.1371/journal.pgen.0030185.st012 (12 KB PDF).
Table S13. Inferred Relative Pairs within Populations (Part I)
Found at doi:10.1371/journal.pgen.0030185.st013 (14 KB PDF).
Table S14. Inferred Relative Pairs within Populations (Part II)
Found at doi:10.1371/journal.pgen.0030185.st014 (14 KB PDF).
Table S15. Inferred Relative Pairs within Populations (Part III)
Found at doi:10.1371/journal.pgen.0030185.st015 (14 KB PDF).
Table S16. Inferred Relative Pairs within Populations (Part IV)
Found at doi:10.1371/journal.pgen.0030185.st016 (14 KB PDF).
Table S17. Inferred Relative Pairs within Populations (Part V)
Found at doi:10.1371/journal.pgen.0030185.st017 (14 KB PDF).
Table S18. Number of Inferred Relative Pairs within Populations
Found at doi:10.1371/journal.pgen.0030185.st018 (13 KB PDF).
Table S19. The 38 Inferred Parent/Offspring Pairs in N436
Found at doi:10.1371/journal.pgen.0030185.st019 (15 KB PDF).
Table S20. The 47 Inferred Full Sib Pairs in N436
Found at doi:10.1371/journal.pgen.0030185.st020 (15 KB PDF).
Table S21. The 31 Inferred Within-Population Second-Degree
Relative Pairs in N436
Found at doi:10.1371/journal.pgen.0030185.st021 (15 KB PDF).
Table S22. The Five Inferred Parent/Parent/Offspring Trios in N436
Found at doi:10.1371/journal.pgen.0030185.st022 (12 KB PDF).
Table S23. Numbers of Individuals Excluded from N436 in N379 and
Found at doi:10.1371/journal.pgen.0030185.st023 (14 KB PDF).
Table S24. The 57 Individuals Included in N436 but Not in N379
Found at doi:10.1371/journal.pgen.0030185.st024 (18 KB PDF).
PLoS Genetics | November 2007 | Volume 3 | Issue 11 | e1852064
Genetic Variation in Native Americans
Table S25. The 82 Individuals Included in N436 but Not in N354
Found at doi:10.1371/journal.pgen.0030185.st025 (18 KB PDF).
Table S26. The Missing Data Rate in N436, N379, and N354
Found at doi:10.1371/journal.pgen.0030185.st026 (13 KB PDF).
Table S27. Nei Genetic Distance Matrix for Native American
Found at doi:10.1371/journal.pgen.0030185.st027 (21 KB PDF).
Table S28. Reynolds Genetic Distance Matrix for Native American
Found at doi:10.1371/journal.pgen.0030185.st028 (21 KB PDF).
Table S29. Chord Genetic Distance Matrix for Native American
Found at doi:10.1371/journal.pgen.0030185.st029 (22 KB PDF).
Table S30. Waypoint Geographic Distance Matrix for Native
American Populations, in Kilometers
Found at doi:10.1371/journal.pgen.0030185.st030 (20 KB PDF).
Table S31. Heterozygosity and F
(3100) for Various Geographic
Regions, Based on a Reduced Dataset of 1,306 Individuals
Found at doi:10.1371/journal.pgen.0030185.st031 (17 KB PDF).
Table S32. Correlation of Genetic and Linguistic Distances, Based on
a Reduced Set of 1,306 Individuals
Found at doi:10.1371/journal.pgen.0030185.st032 (12 KB PDF).
Table S33. Nei Genetic Distance Matrix for Native American
Populations, Based on a Reduced Set of 1,306 Individuals
Found at doi:10.1371/journal.pgen.0030185.st033 (21 KB PDF).
Text S1. Readme Text Accompanying the File with the Genotypes for
1,484 Individuals at 678 Loci
Found at doi:10.1371/journal.pgen.0030185.sd002 (1 KB TXT).
This paper is dedicated to the memory of Guido Mazzotti who passed
away while this work was being carried out. We thank Phabiola
Herrera, Roman Michalski, David E. Ruiz R., and Cheryl Winkler for
assistance with various aspects of this work. We also thank Howard
Cann, Rolando Gonzalez-Jose, Jeff Long, Merritt Ruhlen, Kari
Schroeder, and three anonymous reviewers for discussions and
comments on the manuscript.
The genotypes analyzed in this study are available in Dataset S1
(described in Text S1).
Author contributions. ARL, MWF, and NAR designed the study.
FMS, MLPE, LTT, EL, and FR contributed DNA samples, which were
prepared for genotyping by SW. NAR, MWF, LE, and ARL supervised
the data analysis. SW, CML, MJ, SR, NR, ARL, and NAR analyzed the
data, with the following primary contributions: data cleaning, SW;
relatives, CML, SW, NAR; geographic computations, SR, NR;
linguistic computations, CML; population structure, SW, MJ; pop-
ulation trees, CML, ARL; private alleles, MJ, NAR; population-genetic
summary statistics, MJ, SR, CML, SW, NAR. NAR and ARL jointly
supervised this study, wrote the text of the paper, and incorporated
contributions from all coauthors.
Funding. Support for this work was provided by C anadian
Institutes of Health Research grant MOP-67150 (DL), Fondecyt
Proyecto 1050595 (FR), Swiss National Foundation grant 3100A0–
112072 (LE), National Institutes of Health grants R01NS043538 (ARL)
and GM28016 (MWF), by a postdoctoral fellowship from the Center
for Genetics in Health and Medicine at the University of Michigan
(MJ), by a UK Overseas Research Studentship, a K. C. Wong
Studentship (SW), and by a postdoctoral fellowship from NIH
training grant T32 HG00040 (CML). Grant support was also provided
by Colciencias (GB), Fundac¸a
o de Amparo a
Pesquisa do Estado do
Rio Grande do Sul (MCB and FMS), the Fundac¸a
o Arauca
ria (MLPE),
Universidad de Antioquia (GB), the Brazilian Institutos do Mileˆ nio
and Apoio a Nu
cleos de Exceleˆ ncia Programs, Conselho Nacional de
Desenvolvimento Cientı´fico e Tecnolo´ gico (MCB, MLPE and FMS),
the Burroughs Wellcome Fund (NAR), and the Alfred P. Sloan
Foundation (NAR). Part of this work was completed during visits of
SW to the lab of NAR, supported by a Charlotte and Yule Bogue
Research Fellowship from University College London and by a grant
from the Horace H. Rackham School of Graduate Studies at the
University of Michigan. Genotyping at the Mammalian Genotyping
Service was supported by the National Heart, Lung, and Blood
Competing interests. The authors have declared that no competing
interests exist.
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    • "Briefly, all Immunochip SNPs in LD were removed, using an r 2 threshold >0.1 prior to analysis (performed in PLINK (Purcell et al. 2007)) and the remaining SNPs were used to estimate ancestry. Using an admixture model in STRUCTURE, (Falush et al. 2003; Pritchard et al. 2000) model probability was maximized using K = 3; the inferred ancestral groups matched the expected distribution of Amerindian, African, and European ancestry (Wang et al. 2007Wang et al. , 2008 ). To validate this assumption, we obtained genotype data for the European CEU and African YRI populations from Phase 2 of HapMap (International Hapmap Consortium 2005) the Spanish Iberian (IBS) population from the 1000 Genomes Project (Abecasis et al. 2012), and the H952 subset of the Karitania, Surui, and Colombian populations in the Human Genome Diversity Project (Cavalli-Sforza 2005). "
    [Show abstract] [Hide abstract] ABSTRACT: Gastric cancer incidence varies considerably among populations, even those with comparable rates of Helicobacter pylori infection. To test the hypothesis that genetic variation plays a role in gastric disease, we assessed the relationship between genotypes and gastric histopathology in a Colombian study population, using a genotyping array of immune-related single nucleotide polymorphisms (SNPs). Two synonymous SNPs (rs6061243 and rs6587239) were associated with progression of premalignant gastric lesions in a dominant-effects model after correction for multiple comparisons (p = 2.63E−07 and p = 7.97E−07, respectively); effect sizes were β = −0.863 and β = −0.815, respectively, where β is an estimate of effect on histopathology scores, which ranged from 1 (normal) to 5 (dysplasia). In our replication cohort, a second Colombian population, both SNPs were associated with histopathology when additively modeled (β = −0.256, 95 % CI = −0.47, −0.039; and β = −0.239, 95 % CI = −0.45, −0.024), and rs6587239 was significantly associated in a dominant-effects model (β = −0.330, 95 % CI = −0.66, 0.00). Because promoter methylation of GATA5 has previously been associated with gastric cancer, we also tested for the association of methylation status with more advanced histopathology scores in our samples and found a significant relationship (p = 0.001). A multivariate regression model revealed that the effects of both the promoter methylation and the exonic SNPs in GATA5 were independent. A SNP-by-methylation interaction term was also significant. This interaction between GATA5 variants and GATA5 promoter methylation indicates that the association of either factor with gastric disease progression is modified by the other.
    Article · May 2016
    • "A greater correlation (r 2 5 0.98) was obtained if the Tehuelche group was not considered. Moreover, by excluding Tehuelche, the average 9RA frequency increased to 0.29, which is closer to what has been previously published for Native American populations (Wang et al., 2007). The analysis of locus D9S1120 contributes to a better understanding of the genetic background of the Argentinean population. "
    [Show abstract] [Hide abstract] ABSTRACT: Objective: Genetic data have complemented archaeological and linguistic investigations for understanding the peopling of the Americas. Aiming to investigate the Native South American genetic background in Argentina, seven Amerindian and one urban population were selected. The analysis focused on locus D9S1120 due to its potential anthropological information about Native American origins. Methods: The sample set included 603 individuals belonging to nine isolated Argentinean aboriginal communities from seven tribes (N = 296), 100 individuals living in Buenos Aires city, and three potentially parental population references samples (N = 207). We computed allele and genotype frequency distributions, genetic distances, and pairwise differences among and within them. Admixture proportion was determined by means of typing 13 autosomal short tandem repeats plus D9S1120 in all populations, and comparing the data with those from three parental groups including Native American, European and Sub Saharan West African. Results: The Native American-specific allele 9RA was found at an average frequency of 0.26 in aboriginal groups. Statistically significant differences were observed among the Native American groups when compared with the Buenos Aires urban population using analysis of molecular variance (AMOVA) (Fst = 0.05669; P < 0.0001). Admixture analysis denoted different results between the cohorts of Amerindian samples displaying the specific 9RA allele, compared with those lacking it. A linear correlation was established between positive 9RA and Native American ancestry. Conclusions: Autosomal-based genetic admixture showed that the studied communities have considerable European and Native America contributions. Our results concerning D9S1120 further contribute to a better understanding of the admixture process between Sub Saharan African, Native American, and European individuals that shaped the genetic background of Argentinean extant population. Am. J. Hum. Biol., 2015. © 2015 Wiley Periodicals, Inc.
    Full-text · Article · Jun 2015
    • "The methodologies for delimiting cryptic species used in this study should prove widely effective for population genetic data sets that include geographically widespread taxa. In this study, our population genetic approach combined methods for distinguishing withinspecies geographic structure (Rosenberg et al. 2002; Garnier et al. 2004; Wang et al. 2007; Ledoux et al. 2010; Mokhtar-Jama€ ı et al. 2011) with fairly new applications of hierarchical clustering within structure to delimit species, applications that have been successful in delimiting species in turtles (Shaffer & Thomson 2007), insects (Bernasconi et al. 2010; Ross et al. 2010), plants (Hausdorf & Hennig 2010), lichens (Leavitt et al. 2011) and other corals (Pinzon & LaJeunesse 2011; Ladner & Palumbi 2012; Davies et al. 2015). We applied Mallet's (1995) 'genotypic cluster' criterion, in which species represent distinct genetic units, in combination with the principle of Good & Wake (1992), which requires that allopatric populations of a given species demonstrate genetic cohesiveness across space. "
    [Show abstract] [Hide abstract] ABSTRACT: Mounting evidence of cryptic species in a wide range of taxa highlights the need for careful analyses of population genetic datasets to unravel within-species diversity from potential interspecies relationships. Here we use microsatellite loci and hierarchical clustering analysis to investigate cryptic diversity in sympatric and allopatric (separated by 450 km) populations of the widespread coral Seriatopora hystrix on the Great Barrier Reef. Structure analyses delimited unique genetic clusters that were confirmed by phylogenetic and extensive population-level analyses. Each of four sympatric yet distinct genetic clusters detected within S. hystrix demonstrated greater genetic cohesion across regional scales than between genetic clusters within regions (<10km). Moreover, the magnitude of genetic differentiation between different clusters (>0.620 G"ST ) was similar to the difference between S. hystrix clusters and the congener S. caliendrum (mean G"ST 0.720). Multiple lines of evidence, including differences in habitat specificity, mitochondrial identity, Symbiodinium associations and morphology, corroborate the nuclear genetic evidence that these distinct clusters constitute different species. Hierarchical clustering analysis combined with more traditional population genetic methods provide a powerful approach for delimiting species and should be regularly applied to ensure that ecological and evolutionary patterns interpreted for single species are not confounded by the presence of cryptic species. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
    Full-text · Article · May 2015
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