Download Gene Flow and Natural Selection in Oceanic

Gene Flow and Natural Selection in Oceanic Human Populations Inferred from
Genome-Wide SNP Typing
Ryosuke Kimura,* Jun Ohashi, Yasuhiro Matsumura,à Minato Nakazawa,§ Tsukasa Inaoka,k
Ryutaro Ohtsuka,{ Motoki Osawa,* and Katsushi Tokunaga *Department of Forensic Medicine, Tokai University School of Medicine, Kanagawa, Japan; Department of Human Genetics,
Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; àDivision of Health Informatics and Education, National
Institute of Health and Nutrition, Tokyo, Japan; §Socio-Environmental Health Sciences, Graduate School of Medicine, Gunma
University, Gunma, Japan; kDepartment of Environmental Sociology, Faculty of Agriculture, Saga University, Saga, Japan; and
{National Institute for Environmental Studies, Ibaraki, Japan
It is suggested that the major prehistoric human colonizations of Oceania occurred twice, namely, about 50,000 and
4,000 years ago. The first settlers are considered as ancestors of indigenous people in New Guinea and Australia. The
second settlers are Austronesian-speaking people who dispersed by voyaging in the Pacific Ocean. In this study, we
performed genome-wide single-nucleotide polymorphism (SNP) typing on an indigenous Melanesian (Papuan)
population, Gidra, and a Polynesian population, Tongans, by using the Affymetrix 500K assay. The SNP data were
analyzed together with the data of the HapMap samples provided by Affymetrix. In agreement with previous studies, our
phylogenetic analysis indicated that indigenous Melanesians are genetically closer to Asians than to Africans and
European Americans. Population structure analyses revealed that the Tongan population is genetically originated from
Asians at 70% and indigenous Melanesians at 30%, which thus supports the so-called Slow train model. We also applied
the SNP data to genome-wide scans for positive selection by examining haplotypic variation and identified many
candidates of locally selected genes. Providing a clue to understand human adaptation to environments, our approach
based on evolutionary genetics must contribute to revealing unknown gene functions as well as functional differences
between alleles. Conversely, this approach can also shed some light onto the invisible phenotypic differences between
populations.
Introduction
The peopling of Oceania has intrigued anthropologists
because it is one of the most mysterious adventures in human history. The first colonization of New Guinea and Australia by modern humans is thought to have occurred by
about 50 thousand years ago (KYA) when these lands
formed a continent called Sahul (White and O’Connell
1979; Roberts et al. 1990). These first settlers are considered as ancestors of indigenous Melanesians (Papuans) and
Australians, who are anthropologically classified into the
Australoid. The second major migration to Oceania was
made about 4 KYA by Austronesian-speaking people
(Bellwood 1989, 1991). They voyaged to Polynesia during
1-3 KYA. As for the origin of Polynesians, essentially 2 opposite models have been proposed. One is called the ‘‘Express
train’’ model, which supposes that Austronesian-speaking
people rapidly dispersed to Polynesia with negligible admixture with indigenous Melanesians (Diamond 1988). The other
is the ‘‘Entangled bank’’ model hypothesizing that Polynesians were derived from Melanesian populations affected
by social and trade networks with people from Southeast Asia
(Terrell 1988). Between these polar opposites, there are models such as the ‘‘Slow train’’ that suggests significant genetic
contributions from both original Austronesian-speaking people and indigenous Melanesians.
To elucidate the origin of these populations, genetic
evidence is most direct and informative. It has been well
known that indigenous Melanesians and Australians, who
have the phenotypes similar to Africans in visible traits such
as skin color and hair shape, are genetically closer to Asians
Key words: adaptive evolution, gene flow, human genome, SNP,
Oceania.
E-mail: [email protected].
Mol. Biol. Evol. 25(8):1750–1761. 2008
doi:10.1093/molbev/msn128
Advance Access publication June 3, 2008
Ó The Author 2008. Published by Oxford University Press on behalf of
the Society for Molecular Biology and Evolution. All rights reserved.
For permissions, please e-mail: [email protected]
than to Africans and Europeans (Nei and Roychoudhury
1993; Zhivotovsky et al. 2004). This fact indicates that
the first settlers of Oceania have shared a common ancestry
with Asians after the divergence from Europeans. The genetic origin of Polynesians is still controversial although
a number of studies have focused on this point. The analyses of mitochondrial DNA (mtDNA) have supported an
Asian origin of Polynesians without admixture with indigenous Melanesians as expected in the Express train model
(Lum et al. 1994, 1998; Melton et al. 1995; Redd et al.
1995), whereas the analyses of the Y chromosome have revealed that indigenous Melanesians predominantly contributed to the genetic components of Polynesians as described
in the Entangled bank model (Kayser et al. 2000; Su et al.
2000; Capelli et al. 2001). However, both mtDNA and the
Y chromosome are haploids that transmit without recombination. Classical studies analyzing autosomal markers have
shown different results depending on the marker used (Hill
et al. 1985; Serjeantson 1985; O’Shaughnessy et al. 1990;
Cavalli-Sforza et al. 1994; Martinson 1996; Serjeantson
and Gao 1996). Therefore, analyses using a large number
of autosomal loci are required for further elucidation.
The first settlers of Oceania, that is, indigenous Melanesians and Australians, must have been exposed to various selective pressures due to environmental differences
during the long migration from Africa and due to the
uniqueness of environments in Oceania after the settlement.
They were isolated until recent time and developed their
own lifestyle, which might have thus resulted in new selective pressures. Especially, the slow growth, short stature,
and lightweight characteristics of New Guineans are generally assumed to reflect an adaptation to the low energy
and nutrient densities of diets in which tubers and root crops
predominate (Norgan 1995). In contrast, a distinctive characteristic of Polynesians is their large body size. From this
phenotype, there has been implied the presence of a ‘‘thrifty
Gene Flow and Natural Selection in Oceania 1751
genotype’’ that is associated with saved energy expenditure
and efficient fat storage (Neel 1962; Bindon and Baker
1997). An alternative explanation for Polynesian’s large
body size is based on the ‘‘Bergman’s rule,’’ a principle that
correlates body mass with environmental temperature
(Houghton 1990; Bindon and Baker 1997). However, the
validity of the thrifty genotype and Bergman’s rule hypotheses in Polynesians is still open to debate.
Recent advances in DNA technologies have now enabled us to perform genome-wide single-nucleotide polymorphism (SNP) typing. The Affymetrix GeneChip
Human 500K arrays used in this study are commercially
provided DNA chips that can genotype about 500,000
SNPs for each individual. The preponderant number of typing data would assure us of accurate estimation of the admixture rate between Asian and Melanesian lineages in
Polynesians. Moreover, genome-wide SNP data are applicable to genome-wide scans for genetic regions under positive selection. Several researchers have recently developed
methods to identify signatures of positive selection from
SNP data based on hitchhiking events and selective sweeps
and have conducted genome-wide scans using SNP databases from the HapMap project and Perlegen Sciences
(Kim and Stephan 2002; Sabeti et al. 2002; Nielsen et al.
2005; Voight et al. 2006; Wang et al. 2006; Kimura et al.
2007; Tang et al. 2007; Williamson et al. 2007). The strategy
based on evolutionary genetics has provided cues to reveal
genotype–phenotype association (Fujimoto et al. 2008;
Kayser, Liu, et al. 2008).
The present study investigates the peopling of Oceania
with a special focus on the admixture rate between Asian
and indigenous Melanesian lineages in Polynesians. We
also performed genome-wide scans for positive selection
on Oceanic populations. For these purposes, we subjected
an indigenous Melanesian (Papuan) population, Gidra, and
a Polynesian population, Tongans, for genome-wide SNP
typing with the Affymetrix GeneChip Human 500K array
set.
from Beijing (CHB) and Japanese from Tokyo (JPT) (The
International HapMap Consortium 2005). Here, East
Asians (EAS) denote JPT and CHB together. We selected
only unrelated individuals (parents in trios) from YRI and
CEU and also removed a JPT individual that showed high
inbreeding. In total, 209 HapMap individuals (60 YRI;
60 CEU; 45 CHB; and 44 JPT) were thus used for our
analyses.
Genotyping and Data Quality Control
SNP genotyping was performed with the Affymetrix
GeneChip Human 500K array set. In brief, genomic DNA
(250 ng) was digested with a restriction enzyme (NspI or
StyI) and ligated to adaptors that recognize the cohesive
4-bp overhangs. These fragments were amplified with polymerase chain reaction using a generic primer that recognizes the adaptor sequence. The amplified DNA was
then fragmented, labeled, and hybridized to a microarray
chip. The chip was scanned with Affimetrix GeneChip
Scanner 3000. The genotypes were determined with GeneChip Genotyping Analysis Software based on the Dynamic Model algorithm, in which a strict confidence
threshold of P 5 0.26 was selected. Only the autosomal
SNPs (490,031 SNPs) were analyzed in this study. The
SNPs were filtered with a criterion of missing rate
,0.25 in every population (supplementary table S1, Supplementary Material online). According to our typing, the
missing rates for GDP and TGN were slightly high probably due to DNA quality. We excluded SNPs with
P , 0.01 in chi-square test for the Hardy–Weinberg equilibrium, which accounted for 0.024–0.033 of the polymorphic loci (2.4–3.3 times higher than expected), because it
was highly possible for these SNPs to be mistyped or to
be located on copy number variations. We also removed
those SNPs that were monomorphic in all the populations.
Finally, 393,971 autosomal SNPs remained. Because all the
SNPs covered 2.7 Gbp of the genome, the average SNP interval was 6.8 kbp/SNP.
Materials and Methods
Samples
Individuals from 2 Oceanic populations, Gidra in Papua New Guinea (GDP samples, n 5 24) and Tongans from
Nukualofa, Kingdom of Tonga (TGN samples, n 5 24),
were subjected to our study. The Gidra are Papuan-speaking
people that inhabit the lowlands of Western Province,
Papua, New Guinea. This population has been reported
to have a small size and to be isolated (Ohtsuka 1986). Tongans are Austronesian language–speaking people in Polynesia. Informed consent for participation was obtained from
all the subjects in their own language. This study was approved by the Research Ethics Committee at The University
of Tokyo. In addition to these populations, we referred to
genotyping data of the HapMap samples that were publicly
provided by Affymetrix (http://www.affymetrix.com). The
HapMap samples included 30 trios (90 individuals) each
from Yoruba from Ibadan in Nigeria (YRI) and US residents with ancestry from northern and western Europe
(CEU) and 45 unrelated individuals each from Han Chinese
FST between Populations and Phylogenetic Tree among
Individuals
For each SNP, we calculated FST between pairs of
populations. The genetic distance between each pair of individuals was calculated simply from the average nucleotide difference of 2 chromosomes drawn at random from
different individuals. For locus l, the nucleotide difference
between individuals x and y is defined as hxy,l 5 (d11 þ d12
þ d21 þ d22)/4, where indicator dab is 1 when chromosome
a in individual x is different from chromosome b in individual y and zero when otherwise. For biallelic loci, hxy,l can
only take 3 values: 0 (e.g., AA:AA), 1/2 (e.g., AA:AB or
AB:AB), and 1 (e.g., AA:BB). The average nucleotide difference between 2 individuals x and y (Hxy) can be obtained
by averaging hxy,l over L analyzed loci. Suppose aX,l and bX,l
are the frequency of the allele A and B, respectively, at locus
l in the population X, E(Hxy) 5 R2aX,lbX,l/L ([DX) when
individuals x and y are randomly extracted from the same
1752 Kimura et al.
FIG. 1.—Analyses of population phylogeny, structure, and LD. (A and B) Neighbor-Joining trees were constructed with (A) and without (B) TGN
individuals. (C) The proportion of membership for each individual in a STRUCTURE analysis. The number of groups (k) was assumed to be 2–5.
(D and E) LD coefficients and physical distance. To calculate the LD coefficients, 48 chromosomes were used in each population.
population X. On the other hand, E(Hxy) 5 R(1 aX,laY,l bX,lbY,l)/L ([DXY) when individuals x and y are derived
from different populations X and Y. Therefore, under a large
number of loci analyzed, every Hxy value becomes nearly
equal to DX or DXY. From a distant matrix obtained, we constructed phylogenetic trees of individuals using the NeighborJoining method (Saitou and Nei 1987) with Molecular Evolutionary Genetics Analysis version 3.1 (Kumar et al. 2004).
The length of the outer branch for an individual in the phylogenetic trees (fig. 1A and B) is nearly equal to DX/2, whereas
the length of the inner branch between 2 populations is nearly
equal to the Nei’s (1973) minimum genetic distance, Dm 5
DXY (DX þ DY)/2. We also performed multidimensional
scaling (MDS) analyses using the distance matrix for individuals to observe the homogeneity of the populations (Kruskal
and Wish 1978).
Population Structure Analysis
A cluster analysis for population structure was performed using the STRUCTURE version 2.0 software program (Pritchard et al. 2000). Because a number of SNPs
were typed and neighboring SNPs were in strong linkage
disequilibrium (LD) with each other, 1/100 of the ‘‘typed
SNPs’’ (3,940 SNPs) were selected for a set, and 4 different
sets were used for this analysis. In all runs of the STRUCTURE algorithm, we used 10,000 Markov chain Monte
Carlo replications after a burn-in of length 10,000, with
a model of correlated allele frequencies. The number of
groups from k 5 2 to k 5 5 were tested in a population
model with admixture. We averaged the proportions of
memberships over the 4 different sets that showed almost
the same results.
Estimation of Haplotypes and Missing Genotypes
The estimation of the haplotypes and missing genotypes was performed with fastPHASE version 1.2
(Scheet and Stephens 2006). We used 5 random starts of
the expectation-maximization algorithm with population label information. An allele frequency spectrum for each population was drawn after estimating the missing genotypes.
The LD coefficients, D# and r2, for each population were
also calculated using 48 chromosomes when the physical
distance between 2 SNPs was less than 250 kb. Although
haplotype estimation may be inaccurate, especially for rare
haplotypes in the presence of low LD, the accuracy in the
frequency of major haplotypes would be retained to some
extent. Therefore, the inaccuracy in haplotype estimation is
thought to have only a slight effect on the following analyses for scanning positive selection.
Gene Flow and Natural Selection in Oceania 1753
Modified Long-Range Haplotype Test
The long-range haplotype (LRH) test (Sabeti et al.
2002) was modified and performed as described below.
The extended haplotype homozygosity (EHH) statistic is
defined as the probability that any 2 chromosomes of a particular core allele have the same extended haplotype. The
unbiased estimate of this statistic is calculated as:
m
P
eAi
2
i51
EHHA 5 ;
cA
2
where cA denotes the number of chromosomes with a particular allele A, eAi denotes the number of chromosomes
with ith extended haplotype, and m denotes the number
of extended haplotypes. To detect an unusual increase in
EHH, we can compare the target allele and the other allele
at the core SNP. In this study, every SNP with a minor allele
frequency 10% was subjected to EHH computation. The
EHH value for the target allele (EHHT) was calculated in
the range from the core SNP to the position just before
EHHT drops below 0.4, where we do not need to use the
physical (bp) or genetic (cM) distance to decide the range
for calculation (supplementary fig. S1, Supplementary Material online). In comparison to the integrated EHH (iHH)
reported previously (Voight et al. 2006) in which EHH are
integrated until EHH reaches 0.05, our definition of EHH
calculation hardly changes the detection power but dramatically reduces the computational time (data not shown). The
EHH value for the other reference allele (EHHR) was calculated also in the same range. Here, EHHR/EHHT, which
is reciprocal to the relative EHH defined generally, was
used as a statistic because EHHR is zero when all the extended haplotypes for the reference allele are unique. The
EHHR/EHHT values for both teromeric and centromeric
sides of the target SNP were averaged. We thereafter further
calculated the average of the relative EHH (AREHH) for
the major allele (50% , frequency 90%) over continuous 15 loci. Because the number of loci for windows should
be large to some extent to stabilize the AREHH value, we
picked 15 SNPs windows for generating the AREHH values. A definition of the window by fixed physical size (such
as 200 kbp) can generate windows with a small number of
SNPs because of low SNP density in the DNA chip, which
are prone to yield low AREHH values by chance. Although
the physical size of the windows can be very large depending on the SNP density in our definition (supplementary fig.
S1, Supplementary Material online), its effect is conservative in statistical testing. The windows across the genome
were decided without overlap in each population. In the previous genome-wide scans based on EHH-related tests, values of the original statistic (such as unbiased iHS) for each
bin of the allele frequency were standardized according to
their empirical distribution (Voight et al. 2006; Sabeti et al.
2007; Tang et al. 2007). However, the standardization consumes time and, in addition, conceals the distribution of
values of the original statistic, which disturbs fair comparison in the distribution between real data and simulation
results. This can be a problem if the demographic model
assumed in the simulation is not adequately imitated to
the real situation. In our method, there is no necessity
for standardizing the original statistic.
Comparison of Haplotype Homozygosity between
Populations
To detect local selective sweeps, the haplotype variation was compared between the test and reference populations. In addition, the haplotype homozygosity (H) and
homozygosity for the test population’s most frequent haplotype (HM) and their interpopulation ratio (RH and RM, respectively) were herein calculated for statistical purposes
(Kimura et al. 2007). To determine the blocks for the calculation of these statistics, we used 2 ways: at least 2 SNPs
with HM 0.9 (method 1) or HM 0.5 (method 2) in the
test population. Thereafter, HM and H were calculated not
only in the test population (HMT and HT) but also in the
reference populations (HMR and HR) using the blocks defined with the test population. Because the haplotypes were
estimated in this study, we used the expected haplotype homozygosities, that is, HM 5 p12 and H 5 Rpi2, where pi is
the frequency of the ith frequent haplotype in the test population. The RM and RH between 2 populations were defined
as HMR/HMT and HR/HT, respectively. Because hitchhiking
events cause the rapid increase in the frequency of the haplotype in which advantageous mutation was generated, low
RM and low RH values can indicate the high differentiation
and low diversity of haplotypes, respectively, thus being
signatures of strong selection in the test population.
Computer Simulations for the Modified LRH Test
To elucidate the behavior of AREHH, computer simulations were performed. Because the SNPs typed in the
DNA chips were chosen according to the allele frequency
in populations analyzed in large-scale projects, not in local
populations analyzed in this study, it is not easy to reflect
such a process in a general coalescent simulation. An important point is that DNA chips are expected to contain
SNPs with decreased heterozygosity under selective
sweeps in our studied populations but not to include SNPs
specific to them. To control such bias, the simulations were
divided into 2 phases: a neutral ancestral phase and a selection phase (supplementary fig. S2, Supplementary Material
online). The neutral ancestral phase was operated with a coalescent simulation for choosing typed SNPs and creating
a founder state of the selection phase. The selection phase
was carried out with forward-time simulation. Another
strong point of this strategy is that we can extract the results
at any point of generations in the forward-time simulation.
However, because of the computational load, the forwardtime simulation restricts the population size and the number
of sites. Therefore, we assumed a small population size,
N 5 1,000, and instead a high recombination rate, r 5 107
(per base pairs per generation), so that 4Nr 5 4 104.
In the forward-time simulation of the selection phase,
we simulated 81 loci (including the selected locus under
positive selection at the center) with constant 6-kb intervals
1754 Kimura et al.
without assuming new mutation. Here, ith locus of jth chromosome of the founder generation was denoted by (i, j), not
by allelic state, and thus the identical-by-descent state at
each generation could be obtained. We examined the
strength of selection at s 5 0.15 or 0.075 (2Ns 5 300 or
150) for codominant selection conditions or at s 5 0 for
neutral conditions, where s and s/2 are the selection coefficients for homozygotes and heterozygotes, respectively.
In addition to the constant population model (N 5 1,000),
a population decline (N 5 500) model in the selection
phase was tested (supplementary fig. S2, Supplementary
Material online). Under the selection condition, we assumed that an advantageous mutation generated in a single
chromosome increases by positive selection. The simulation results were extracted at those generations where the
advantageous allele frequencies become 15%, 25%,
35%, 45%, 55%, 65%, 75%, and 85% (supplementary
fig. S2, Supplementary Material online), which are near
to 100 and 200 generations in the case of s 5 0.15 and
0.075, respectively. Under neutral conditions, the simulation results were extracted at 100 or 200 generations.
For each parameter setting, the simulation runs were replicated 500 times.
The coalescent simulation of the neutral ancestral
phase was operated with cosi program (Schaffner et al.
2005), which is a modification of Hudson’s ms program.
In the simulation, we assumed a 500-kbp region, a constant
population size of N 5 1,000, and a mutation rate of
l 5 1.5 107 (per base pairs per generation) in which
we have 4Nl 5 6 104 and sampled all the chromosomes in the population (2n 5 2N 5 2,000). To choose
typed SNPs, we set 2-kb windows with 4-kb intervals between adjoining windows (total 80 windows). Thereafter,
the SNPs with the highest minor allele frequency in every
window were chosen. These SNPs were relocated to have
constant 6-kbp intervals, which were used as the founder
state of the selection phase. The coalescent simulation
was then repeated to create 500 founder states.
The results of the selection phase that denoted by (i, j)
were connected to the results of the neutral ancestral phase
one by one, and the denotations were replaced by allelic
state. We calculated EHHR/EHHT values for major alleles
with the frequency 90% as described above. In a few
cases that the EHHT value did not decay below 0.4 at
the end SNP (1st or 81st), the EHHR/EHHT value was calculated at the end SNP. To compute AREHH, the selected
locus was excluded, and 15 SNPs around the selected locus
were used. Although our simulation models may lack rigorousness to imitate the actual demographic history of populations, they are useful to estimate roughly the behavior of
the statistic.
To determine the null distribution of the EHH statistic
across the genome under neutrality, we also performed a
genome-size neutral simulation as previously reported
(Kimura et al. 2007). In brief, a neutral coalescent simulation
using cosi program was performed for African, European,
and East Asian populations with a flexible recombination
rate and a fitting demographic model proposed previously
(Schaffner et al. 2005). To correct the ascertainment bias of
the selected SNPs on the Affymetrix 500K chips, we extracted the typed SNPs from the simulation data using a re-
jection method based on the allele frequency spectrum of
the simulation and real data.
Computer Simulations for RM and RH Test
In the same manner as that described in a previous
study (Kimura et al. 2007), we simulated the detection
powers of RM and RH to see the effect of the SNP density,
sample size, and the initial number of the advantageous alleles. We therefore designed 2 constant-size populations
(N 5 1,000) that diverged for 200 generations and assumed
s 5 0.15 (2Ns 5 300) for a model of complete selective
sweeps. The frequency of the selected allele was set at a single chromosome or 20% when positive selection began to
take effect. In addition, we examined a model of partial selective sweeps in which the advantageous mutation reaches
an 80% frequency under the positive selection of s 5 0.085
(2Ns 5 170) for 200 generations.
Results and Discussion
Genetic Differentiation and Admixture between
Populations
FST values exhibited a genetic differentiation between
GDP and another non-African population which was relatively high in comparison to that between any other nonAfrican pairs (supplementary fig. S3, Supplementary
Material online). A Neighbor-Joining tree among individuals demonstrated the GDP individuals to have a small diversity within the population (fig. 1A). Taken together,
these results are consistent with the fact that this population
has been isolated and also possessed a small population size
(Ohtsuka 1986). A close relationship between TGN and
EAS was inferred from the MDS analyses (supplementary
fig. S4, Supplementary Material online) as well as low FST
values, whereas TGN individuals lay between EAS and
GDP individuals in the phylogenetic tree (fig. 1A). Because
the population admixture can distort the shape of the phylogenetic tree, we reconstructed another tree removing the
TGN individuals. In the reconstructed tree (fig. 1B), the
branch length changed but the topology was still retained.
This means that indigenous Melanesians have a stronger genetic affinity with Asians than with Africans and European
Americans as previously reported (Nei and Roychoudhury
1993; Zhivotovsky et al. 2004).
The results of the STRUCTURE analyses clearly suggested Tongans to originate from an admixture population
between Asians and indigenous Melanesians (fig. 1C).
When the number of groups assumed (k) was 4 in the
STRUCTURE analyses, then individuals in YRI, CEU,
EAS, and GDP were assigned to 4 respective groups, which
are thought to correspond to classical human races, that is,
Negroid, Caucasoid, Mongoloid, and Australoid. These
analyses suggested that the Tongan population is genetically derived from Mongoloid at 70.1%, from Australoid
at 27.7%, and from the others at 2.2%, which are proportions that are similar to those estimated in some of the previous small-scale studies (Serjeantson 1985; Martinson
1996). Most recent studies analyzing a large number of
Gene Flow and Natural Selection in Oceania 1755
autosomal microsatellites have also showed almost same
genetic contributions of Asians and indigenous Melanesians to Polynesians (Friedlaender et al. 2008; Kayser,
Lao, et al. 2008). Only a few individuals showed a small
genetic contribution from Europeans, thus indicating relatively recent immigration. On the other hand, because the
proportion of genetic contribution from Asians and Melanesians in Tongan individuals was homogeneous, it is suggested that the admixture occurred long ago and people
have only randomly mated after that. This is also inferred
from a tight cluster of TGN individuals in the MDS analysis
for the 3 populations (TGN, GDP, and EAS) (supplementary fig. S4, Supplementary Material online). Our results
support the Slow train model, obviously ruling out the Express train and Entangled bank models. In addition, the proportions observed in this study were compatible with the
sex-biased contribution inferred from previous mtDNA
and Y-chromosome data, that is, a nearly 100% Asian origin for maternal lineage and 35% Asian and 65% indigenous Melanesian origins for paternal lineage (Kayser et al.
2006).
Linkage Disequilibrium
The allele frequency spectra after the estimation of the
haplotype phase and missing genotype with the fastPHASE
algorithm are shown in supplementary figure S5 (Supplementary Material online). We calculated LD coefficients,
D# and r2, in each population using 48 chromosomes
(fig. 1D and E). Both of the coefficients were high in
GDP and TGN, low in YRI, and intermediate in EAS
and CEU, which is thought to reflect their past population
sizes. As for TGN, the high LD coefficients can also be attributed partly to the population admixture.
Scans for Selective Sweeps with an LRH Test
To scan for partial selective sweeps in the genome, we
employed a modified LRH test. Figure 2 represents the
manner that the pattern of EHHR/EHHT for SNPs around
the selected locus becomes bipolar as the frequency of
the advantageous allele increases. This indicates that
a hitchhiking allele, which generally has a higher frequency
than the selected allele, showed a low EHHR/EHHT value
and the other allele showed a high EHHR/EHHT value.
When the frequency of the advantageous allele is still
low, the distribution of the EHHR/EHHT values is similar
to the neutral case, suggesting difficulty of detecting positive selection in such a case. However, after the selected
allele becomes the major allele (over 50%), the major allele
of neighboring SNPs also showed a very low EHHR/EHHT
value. Therefore, we can detect such a signature of strong
positive selection even without typing the locus under selection using the EHHR/EHHT values for the major allele of
neighboring SNPs. In this study, we calculated AREHH,
that is, the average of the EHHR/EHHT values for alleles
having a 50–90% frequency over 15 continuous SNPs. Before we applied this method to real data, its performance
was examined with a computer simulation. Figure 3 exhib-
its the results of simulations for estimating the power of our
method, which was affected by the frequency of the selected allele (fig. 3A). Under neutrality, the first percentile
of the value of AREHH was 0.819. When the threshold was
set at this value, then the advantageous mutation (2Ns
5 300) that reached frequencies of 55%, 65%, 75%, and
85% was detected at a probability of 72.8%, 87.2%,
92.8%, and 86.2%, respectively. Because the number of
sampled chromosomes hardly altered the detection power
(fig. 3B), we thought that the 24 individuals sampled in this
study were therefore adequate. The strength of selection
(2Ns 5 300 or 150) had a substantial effect on the detection
power (fig. 3C), which may reflect the opportunity for recombination events that depend on the time needed to reach
the examined frequency. In addition, a population decline
caused a decrease in the detection power and an increase in
the false positive rate at a certain threshold (fig. 3C), thus
suggesting the limits of an approach based on a comparison
between alleles.
The real distributions of AREHH across genomic windows were considerably different among the populations
(fig. 3D). To determine the null distribution of this statistic
under the neutrality, we also carried out a genome-size coalescent simulation for East Asian, European, and African
populations according to a validated demographic model
reported previously (Schaffner et al. 2005) (fig. 3D). The
results of the simulation demonstrated the demographic history of populations to affect the AREHH values, which can
partly explain the interpopulation difference in the distribution. However, the real and null distributions were substantially deviated in CEU and EAS, whereas those matched
well in YRI. This deviation may tell us how often humans
have been exposed to selective pressures in the out-of-Africa
duration. Alternatively, because the deviation is too large, it
may be attributed to population substructures and more
strong bottlenecks that were not considered in the simulation
model used.
To identify candidates, we set the threshold at the second percentile of the empirical distribution (AREHH below
0.484 in TGN, 0.452 in GDP, 0.512 in EAS, 0.579 in CEU,
and 0.842 in YRI) although these thresholds may hold only
a low power to detect positive selection especially in GDP.
Nonetheless, our modified method could detect the selected
genes that have been previously reported such as LCT in
CEU and ALDH1A2 in EAS (Bersaglieri et al. 2004; Oota
et al. 2004). The AREHH values are plotted on their chromosomal positions in supplementary figures S6 and S7
(Supplementary Material online). We also exhibit the rank
of AREHH values in supplementary data S1–S5 (Supplementary Material online).
Scans for Population-Specific Selective Sweeps
The approach based on interallelic comparison in EHH
is not applicable to scanning for loci fixed already, and it
has only a low power when the population has experienced
severe bottlenecks as described above. Although the composite likelihood test that is based on the allele frequency
spectrum can detect complete selective sweeps (Kim
and Stephan 2002; Nielsen et al. 2005; Williamson et al.
1756 Kimura et al.
FIG. 2.—Change of the pattern of EHHR/EHHT values around the selected loci. The results of simulations (2Ns 5 300) under the various
frequencies of the allele under selection (p) are shown. EHHR/EHHT values of SNPs within 200 kb around the selected loci were fractionated (y axis)
and counted for each bin of the allele frequency (x axis). The data from 500 replications of simulation are put together. The vertical line represents P.
2007), similarly to the aforementioned approach, this test
captures loci under positive selection even if it has operated
in the common ancestral population. However, we are now
most interested in the selective sweeps occurring locally in
Oceanic populations. To detect population-specific selective sweeps, therefore, we calculated the interpopulation ratio of haplotype homozygosity, RH, and the interpopulation
ratio of homozygosity for the test population’s most frequent haplotype, RM (Kimura et al. 2007). The RH value
can be an indicator of nucleotide diversity and past recombination events, whereas the RM value can be an indicator of
genetic differentiation like FST. Previous reports (Sabeti
et al. 2007; Tang et al. 2007) have proposed similar approaches based on comparison between populations, which
require calculation of the interpopulation ratio of EHH values for every allele. In the RH and RM test, we can avoid
redundant tests for neighboring SNPs in strong LD with
each other. In addition, the RM value measuring haplotypic
Gene Flow and Natural Selection in Oceania 1757
FIG. 3.—The results of computer simulations for estimating the power of statistics. (A–D) Cumulative distributions of AREHH. Various values of
the selected allele frequency (P 5 55%, 65%, 75%, or 85%) (A), the chromosome sample size (2n 5 120 or 48) (B), the strength of selection
(2Ns 5 300 or 150) and the population size (constant or decline) (C) were simulated to examine the detection power. The 15 SNPs window for the
AREHH calculation spanned but excluded the selected locus. In the panels (B) and (C), the cases of P 5 75% are shown for the selection condition. In
the default setting, the population size (N) is 1,000, the number of chromosomes sampled (2n) is 120, and the strength of selection (2Ns) is 300 in the
selection (Sel) condition or 0 in the neutral (Neu) condition . Under the population decline models, N is 500 in the selection phase. The number of
generations after the population decline (G) is dependent on the condition of the simulations. Cumulative distributions for the real data and the data
from the genome-size neutral simulation using a demographic model are also shown (D). (E–H) Estimation of the detection powers of RM and RH.
Cumulative distributions of RM (E) and RH (F) in method 1 (block definition: HM 0.9) and those (G and H) in method 2 (block definition: HM 0.5)
are shown. Different initial frequencies (IFs) of the selected allele at the start of the selection phase (single chromosome or 20%) and different final
frequencies of the selected mutation (fixed or 80%) were tested using methods 1 and 2, respectively. Different values of the SNP density (2 or 6 kb/
SNP) and chromosome sample size (2n 5 120 or 48) were also simulated.
1758 Kimura et al.
Table 1
The Number of Blocks Detected with RM and RH Test
Test Population
Method 1 (HM 0.9)
TGN
GDP
Method 2 (HM 0.5)
TGN
GDP
Method 2 (HM 0.5)
GDP
Total Blocks
30,822
37,010
48,037
42,510
42,510
versus EAS
RM , 0.05
0
472
RM , 0.1
286
5,181
RM , 0.01
1,146
RH , 0.3
7
900
RH , 0.5
2,075
8,640
RH , 0.25
1,663
differentiation enables us to capture the differentiation of
untyped polymorphisms more powerfully than the FST
value for each SNP (Kimura et al. 2007).
The block definition of HM 0.9, which we call
method 1 here, is appropriate to detect complete selective
sweeps in which advantageous alleles have reached (near)
fixation. When we performed a simulation assuming 2 diverged populations with a constant size and 6 kb of SNP
intervals that is the similar density as mounted on the Affymetrix 500K chips, thresholds of RM , 0.05 and RH
, 0.3 realized approximately 80% power (fig. 3E and
F). The block definition of HM 0.5, or method 2, is potentialized to detect the alleles under selection that have
reached a frequency of over approximately 70%. Figure
3G and H shows the detection power for the cases in which
a single advantageous mutation increased to 80% frequency, which is lower than the power for a complete selective sweep. For a selected allele with 80% frequency, the
thresholds of RM , 0.1 and RH , 0.5 had approximately
an 80% power. As previously reported (Kimura et al. 2007),
the distributions of RM and RH shift depend on the demographic history of the populations. Especially, it should be
noted that decline of the test population’s size results in
downshift of the distributions in both cases of selection
and neutrality. Therefore, if we test a population that has
experienced a decline in size, then our simulations assuming a model with constant-size populations are thought to
give conservative estimation of the detection power.
Our first interest is to elucidate whether there are any
mutations that were generated after the divergence from
Asians and then reached fixation in Polynesians. For this
purpose, we applied method 1 (HM 0.9) to the test for
TGN using EAS as the reference population (TGN vs.
EAS). As a result, we did not observe any block satisfying
the thresholds of RM , 0.05 and RH , 0.3 (table 1). Taking into account the powerfulness of these thresholds (fig.
3E and F), the result suggests that there was no (or few, if
any) mutation newly generated and fixed in Polynesians.
Because the dispersal of Austronesian-speaking people is
thought to be dated at 6 KYA at the most, then the divergence time would be too short for a new mutation on autosomes to reach fixation in Tongans. Although the near
fixation of an Austronesian-specific type of mtDNA has
previously been observed in Polynesians (Redd et al.
1995), this would be due to the small population size of
mtDNA that is one-fourth of the autosomal population size.
As for Polynesian-specific complete selective sweeps, it remains an alternative possibility that old-standing alleles
versus CEU
RM , 0.05
73
852
RM , 0.1
3,141
6,693
RM , 0.01
1,712
RH , 0.3
291
1,692
RH , 0.5
7,294
11,761
RH , 0.25
2,494
versus YRI
RM , 0.05
952
2,516
RM , 0.1
8,592
11,362
RM , 0.01
4,342
RH , 0.3
2,551
5,316
RH , 0.5
17,121
19,582
RH , 0.25
7,246
All Filters
0
81
54
1,738
202
originated from Asians and/or from indigenous Melanesians have been fixed by positive selection in Polynesians.
A looser threshold of the statistics may detect such loci, yet
only a low power can be expected if the frequency of the
selected allele was relatively high at the time when the selective pressure started to operate (fig. 3E and F). When we
chose the threshold of RM , 0.25 in TGN versus EAS,
most of such blocks showed high RM values in TGN versus
GDP instead (supplementary data S6, Supplementary Material online). This indicates that when a haplotype with
a low frequency in Asians reached either complete or near
fixation in Polynesians, then the same haplotype from indigenous Melanesians may have also contributed to the fixation in most cases. Although these loci may be potential
candidates for complete selective sweeps on standing alleles, careful interpretation is needed because most of these
may be false positives generated by genetic drift. When we
applied method 2 (HM 0.5) to TGN using the threshold
of RM , 0.1 and RH , 0.5, numerous blocks were detected as candidates for loci where a single mutation gained
a high frequency but did not reach fixation (table 1). As the
reference population (EAS, CEU, or YRI) becomes genetically closer to TGN, the number of blocks with RM , 0.1
and RH , 0.5 becomes smaller (table 1). Giving an attention to the overlap of the results of TGN versus EAS, TGN
versus CEU, and TGN versus YRI, we could then further
narrow the candidates down to 54 regions (0.11% of
the total blocks) (supplementary data S7, Supplementary
Material online).
In a scan for complete selective sweeps on GDP using
method 1, blocks showing very low RM and RH values were
abundant even when EAS was used as the reference population (table 1). This downshift in the distribution is
thought to reflect the small population size as well as the
long divergence time from the other populations (Kimura
et al. 2007). The number of the blocks that satisfy RM
, 0.05 and RH , 0.3 against the 3 reference populations
(EAS, CEU, and YRI) was 81 regions across the genome
(0.22% of the total blocks) (table 1 and supplementary data
S8 [Supplementary Material online]). In method 2, the
blocks with RM , 0.1 and RH , 0.5 were so many that
we could not effectively reduce the candidates (1,738 regions) (table 1). Because these thresholds are thought to
be too conservative for testing a small-size population
(Kimura et al. 2007), we would be able to use more strict
thresholds while keeping a high detection power. Therefore, we used RM , 0.01 and RH , 0.25, which correspond to approximately 50% power in our simulation
Gene Flow and Natural Selection in Oceania 1759
assuming constant-size populations (fig. 3G and H). These
conditions could reduce the candidates down to 202 regions
(0.48% of the total blocks) (table 1 and supplementary data
S9 [Supplementary Material online]).
Candidate Regions under Selective Sweeps
The methods used to scan for selective sweeps in this
study have their own characteristics. The test using
AREHH is potentialized to detect selective sweeps where
the selected allele has gained a greater than 50% frequency,
but it has not yet reached fixation. This test can detect selective sweeps occurring in the common ancestry of different populations as well as in a local population. In the
method 1 of the RM and RH test (HM 0.9), the thresholds
of RM , 0.05 and RH , 0.3 detect only loci fixed or nearly
fixed by population-specific positive selection. If a looser
threshold such as RM , 0.25 is used in the same test,
we may identify positive selections that have acted on
old-standing alleles, but only a low detection power and
high false positive rate can be expected. Method 2 of the
RM and RH test (HM 0.5) is applicable to a scan for those
regions where the locally selected allele reached over approximately 70% frequency including fixation. The chromosomal positions of the candidate regions detected by
the respective methods are exhibited in supplementary figure S8 (Supplementary Material online). In some regions,
the signatures detected by different methods overlapped.
Such regions are considered to have a higher possibility
to be true positives. Other regions show the signature
unique to one method, which may be attributed to the
uniqueness of the characteristics of the methods or to type
I and type II errors.
Our scans suggested no private mutation to exist on the
Tongan autosomes that had reached fixation. However,
there remain alternative possibilities that old-standing alleles have reached fixation by local selective pressures
and that newly generated advantageous mutations have
gained a high frequency but have not yet reached fixation.
The block showing the lowest RM value (0.076) in the test
of TGN versus EAS using method 1 was located at
92788024–92838919 on chromosome 12 (supplementary
data S6, Supplementary Material online), which is at 41-kb
distance from the CRADD gene (fig. 4). It is worth noting
that an approximately 500-kb deletion around this gene
in mouse has been reported to cause a ‘‘high growth’’ mutant that shows a proportional increase in tissue and organ
size without obesity (Horvat and Medrano 1998). Another
candidate for the selected region in which an old-standing
allele reached fixation was VLDLR (supplementary data S6,
Supplementary Material online), which is involved in triglyceride and fatty acid metabolism (Tacken et al. 2001).
In addition, overlapping signatures in both methods
1 and 2 (supplementary data S6 and S7, Supplementary Material online) were observed in the gene region of EXT2,
which is a causal gene of the type II form of multiple exostoses, and it plays a crucial role in bone formation (Stickens and Evans 1997). These genes can be candidates that
are associated with the large fat, muscle, and bone masses
of Polynesians. A recent paper examining the interpopulation differentiation of the type II diabetes–associated genes
FIG. 4.—A candidate of selective sweeps in Tongans. (A) RH values
(TGN vs. EAS of the method 1) on chromosome 12. (B) CRADD region
and frequencies of the Tongan’s major allele in all the populations. The
region indicated by left right arrow shows a signature of selective sweep
in Tongans.
has suggested that a susceptible allele of PPARGC1A may
play a role in the large difference in the prevalence of the
disease between Polynesians and neighboring populations
(Myles et al. 2007). However, our scans did not identify any
signature of positive selection on the gene region of
PPARGC1A.
One of the strongest signatures of selective sweeps in
GDP was located at the region including the LHX4 and
ACBD6 genes on chromosome 1 (supplementary data
S8, Supplementary Material online). LHX4 encodes a transcriptional regulator involved in the control of the development of the pituitary gland, and mutations in this gene are
associated with syndromic short stature and pituitary defects (Machinis et al. 2001; Castro-Feijoo et al. 2005).
ACBD6 is a binding protein of acyl-coenzyme A that has
a role in fatty acid metabolism (Entrez Gene). The gene region of IGF1R, which is the receptor for insulin-like growth
factor 1 (Castro-Feijoo et al. 2005), was also considered to be
a candidate of the selected genes (supplementary data S8,
Supplementary Material online). These genes may be involved in the slow growth, short stature, and lightweight
characteristics of New Guineans. Future association studies
between genotypes and phenotypes are indispensable.
1760 Kimura et al.
Other candidates of selective sweeps in Oceanic populations included several interesting genes such as DDX58,
SIAT4A (supplementary data S7, Supplementary Material
online), and IVNS1ABP (supplementary data S8, Supplementary Material online), which code molecules related
with infection of the influenza A viruses (Wolff et al.
1998; Shinya et al. 2006; Mibayashi et al. 2007; Nicholls
et al. 2007). If we could identify a protective effect of the
selected allele against the influenza, these kinds of signatures may therefore suggest evidence for the epidemic history of the virus in Oceania and human conquest of the
disease by genetic adaptation.
We observed the candidates of selective sweeps that
include no gene or genes whose functions have not been
known yet. The selected loci should have some phenotypic
functions because natural selection acts on phenotypes.
Therefore, the scans for signatures of selective sweeps
can be a trigger to identify genes or DNA sequences with
some important function as well as to determine the functional difference between alleles. Such an approach based
on evolutionary genetics, which thus provide clues to understand how humans have adapted to our environments, are
therefore also expected to help elucidate the genomic functions if further functional and association studies on the candidates are carried out. Conversely, this approach may also
shed some light on the invisible phenotypic difference between populations. Our study demonstrated that genomewide SNP typing systems, which have exerted their power
for identifying disease-associated polymorphisms (The
Wellcome Trust Case Control Consortium 2007), are also
useful for evolutionary study on human populations.
Supplementary Material
Supplementary table S1, figures S1–S8, and data S1–S9
are available at Molecular Biology and Evolution online
(http://www.mbe.oxfordjournals.org/).
Acknowledgments
We are grateful to the Gidra people in Papua New
Guinea and the Tongan people for their kind cooperation
in providing blood samples. We thank the staff of the Department of Health, Western Province of Papua New
Guinea, Dr Tetsuro Hongo at Yamanashi Institute of Environmental Sciences, Dr Taniela Palu at Ministry of Health,
Kingdom of Tonga, Dr Viliami Tangi at Diabetes Clinic,
Kingdom of Tonga, Dr Kazumichi Katayama at Kyoto University for help in sample collection, and 2 anonymous reviewers for helpful comments. This study was partly
supported by a Grant-in-Aid for Scientific Research from
the Ministry of Education, Culture, Sports, Science, and
Technology of Japan. This research was done mainly at
the Department of Human Genetics, Graduate School of
Medicine, The University of Tokyo.
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Yoko Satta, Associate Editor
Accepted May 26, 2008