Two Evolutionary Histories in the Genome of Rice: the
Roles of Domestication Genes
Ziwen He1., Weiwei Zhai2., Haijun Wen1., Tian Tang1, Yu Wang2,3, Xuemei Lu2, Anthony J. Greenberg4,
Richard R. Hudson5, Chung-I Wu1,5,6*, Suhua Shi1*
1State Key Laboratory of Biocontrol and Guangdong Key Laboratory of Plant Resources, Sun Yat-Sen University, Guangzhou, China, 2Laboratory of Disease Genomics and
Individualized Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China, 3Graduate University of the Chinese Academy of Sciences, Beijing,
China, 4Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, United States of America, 5Department of Ecology and Evolution, University
of Chicago, Chicago, Illinois, United States of America, 6CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of
Sciences, Beijing, China
Genealogical patterns in different genomic regions may be different due to the joint influence of gene flow and selection.
The existence of two subspecies of cultivated rice provides a unique opportunity for analyzing these effects during
domestication. We chose 66 accessions from the three rice taxa (about 22 each from Oryza sativa indica, O. sativa japonica,
and O. rufipogon) for whole-genome sequencing. In the search for the signature of selection, we focus on low diversity
regions (LDRs) shared by both cultivars. We found that the genealogical histories of these overlapping LDRs are distinct
from the genomic background. While indica and japonica genomes generally appear to be of independent origin, many
overlapping LDRs may have originated only once, as a result of selection and subsequent introgression. Interestingly, many
such LDRs contain only one candidate gene of rice domestication, and several known domestication genes have indeed
been ‘‘rediscovered’’ by this approach. In summary, we identified 13 additional candidate genes of domestication.
Citation: He Z, Zhai W, Wen H, Tang T, Wang Y, et al. (2011) Two Evolutionary Histories in the Genome of Rice: the Roles of Domestication Genes. PLoS Genet 7(6):
Editor: Rodney Mauricio, University of Georgia, United States of America
Received December 10, 2010; Accepted April 7, 2011; Published June 9, 2011
Copyright: ? 2011 He 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.
Funding: This study is supported by grants from National Basic Research Program of China (2007CB815701), National Natural Science Foundation of China
(30730008, 40976081, 31000957, 31071914 and 30970208), National S&T Major Project of China (2009ZX08010-017B, 2009ZX08009-149B), Yat-Sen innovation
project (SYSU), Beijing Institute of Genomics, SRF for ROCS, and Chinese Academy of Sciences (KSCX1-YW-22). The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org (C-IW); email@example.com (SS)
. These authors contributed equally to this work.
A main objective in the study of natural and domesticated
species is to systematically identify genomic regions that have been
influenced by selection. A strategy that is effective but not
commonly used is to search for genomic regions with an unusual
genealogical history [1,2]. During speciation or domestication, if
gene flow continues between diverging populations, selection may
play a large role in shaping the genealogies of different parts of the
same genome. For example, mutations that contribute to local
adaptation may spread in some populations but not others, leading
to a higher level of differentiation at and near the genes for local
adaptation [3–5]. In contrast, mutations that are universally
selected may spread among populations more rapidly than neutral
variants resulting in reduced differentiation.
The joint action of gene flow and selection could be even
stronger in domesticated species than in natural populations as
breeders might cross varieties between subspecies that do not
readily interbreed in nature. Furthermore, human selection for
desired traits is often intense. In this context, Asian cultivated rice
(Oryza sativa) is of particular value as there are two subspecies, indica
and japonica, which are partially reproductively isolated . The
origin of cultivated rice is therefore a question of how human
selection created the two types of rice . Because phylogenetic
studies tend to support the independent domestication hypothesis
[8–11], we may have the unusual opportunity to analyze the
course of evolution twice from the same common ancestor, the
Asian wild rice O. rufipogon .
If indica and japonica were independently domesticated, then a
genome-wide pattern is expected. However, some loci in rice show
patterns of variation inconsistent with the independent domesti-
cation hypothesis. For example, the sh4 locus which is responsible
for the reduction in grain shattering among cultivars is fixed in
both subspecies for the same allele [12–14]. The genealogy
suggests a single domestication event with respect to the sh4 locus
and the new allele subsequently spread to all cultivars. In this
study, we take a whole-genome approach to sequencing 66
accessions of rice in order to answer these questions: i) which
genomic regions in rice exhibit a genealogy distinct from the rest of
the genome? ii) how do these regions reflect the process of
domestication under artificial selection? and iii) how many
domestication genes can be identified in these regions?
In this study, we first surveyed genome wide diversity pattern by
sequencing multiple lines of O. rufipogon, O. sativa indica and O. sativa
japonica. While second generation technologies, such as Illumina-
PLoS Genetics | www.plosgenetics.org1June 2011 | Volume 7 | Issue 6 | e1002100
Solexa-GA and ABI-SOLiD make the task feasible, they are more
error-prone than the conventional Sanger method [15,16].
Therefore, to distinguish true polymorphisms from sequencing
errors, we used both platforms for sequencing and retained only
the polymorphic sites identified by both methods and discarded
singletons, a procedure that is quite effective at significantly
driving down false positives  (see Materials and Methods and
We sequenced pooled DNA samples of each subspecies (21–23
accessions per subspecies used, Table S1) with the coverage about
30X for each sample, or 1.5X per accession (Table S2). Although
it may seem more informative to sequence each accession
individually, the gain in information, for example about linkage
disequilibrium, is achieved only when the coverage is deep for
each line . In fact, if the objective is to estimate genetic
diversity in the population, data from mixed samples can often be
as informative as data from individual lines .
We first estimate genetic diversity (h) genome-wide using a
method we describe in detail in another paper (He et al, in
submission). We use Watterson’s estimator of h , which is
based on the number of sites that are polymorphic. In Table 1, S is
the number of such segregating sites in a given region, while S.1is
the number of sites excluding singletons; S.2estimates further
exclude doubletons. The estimates from the combined data are
lower than those based on either SOLiD or GA data alone and are
close to previous estimates based on conventional sequencing of
selected genes [20,21]. Overall, indica retains much more genetic
diversity than japonica, as has been reported in the literature.
For the rest of this study, we use h estimates based on the
combined GA/SOLiD data with S.1. A detailed comparison of
various procedures of h estimation can be found in Table S3.
Figure 1 shows diversity estimates from a sliding-window
analyses across each genome, with 100 kb windows and steps of
10 kb (See Materials and Methods for details; Window size was
chosen based on typical levels of linkage disequilibrium in these
species) . Figure 1 gives two example profiles of h, 5 Mb each.
Panel (A) is a region with normal diversity. Genome wide low
diversity cutoffs are plotted as the dashed lines for three species
respectively. For each genome, in order to explore the heteroge-
neity in local variation, we chose a cutoff to identify regions of low
diversity based on the characteristics of each genome. While there
are many potential ways to select a cutoff value, a simple one
determined by shuffling 1 kb segments of the entire genome will be
used in our analysis. By this method, the lowest value among all
windows was chosen as the cutoff (see Materials and Methods).
Selection, demography and selfing may all generate genomic
regions of unexpectedly low genetic diversity. We used other
means of selecting the cutoffs and, as shown in Text S1 (section F),
the main conclusions remain the same. Panel (B) shows the
position of PROG1 which controls a key transition from prostrate
to erect growth during domestication . The PROG1 locus falls
into a region of low polymorphism in both indica and japonica. A
plot for the entire genome is given in Figure S1.
Low diversity regions (LDRs) in domesticated rice
Table 2 summarises the number of genomic regions with lower
diversity than genome wide cutoff values for each of the three taxa.
The number of such low diversity regions (LDRs) in O. rufipogon
decreases quickly if we increase window size. Only four LDRs in
O. rufipogon are larger than 200 kb, accounting for 0.25% of the
genome. In contrast, 6.15% of the indica genome falls in LDRs
larger than 200 kb and more than 25% of the japonica genome
appears to have too little polymorphism. Large genomic segments
devoid of genetic diversity are observed in multiple domesticated
animals . The excess of LDRs in the cultivated rice is
presumably attributable to domestication, which includes artificial
selection, population size reduction, introgression and selfing.
While it is tempting to associate LDRs with selective sweeps
under artificial selection, other forces of domestication must be
considered as well. In particular, since both cultivars are self-
pollinators whereas O. rufipogon is largely an outcrossing species
[6,26], population bottlenecks together with selfing are likely to
generate genomic segments with reduced polymorphism. To assess
whether these forces are sufficient to explain the excess of LDRs in
the domesticated cultivars, we performed a series of simulations
(see Text S1 section B for details). These simulations indeed
indicate that for plausible levels of population-size reduction and
effect of selfing on recombination, it is possible to observe the
patterns of genomic diversity we see in the data.
Since demography and selfing are confounding factors,
inference of selective sweeps cannot be justified solely by the
prevalence of low diversity regions. If selection has affected the
genomes of cultivated rice, this will have to be determined from
the patterns of genetic variation within LDRs.
To tease apart the evolutionary forces that influence LDRs, we
took advantage of the existence of two subspecies of domesticated
rice. Since both domesticated sub-species were selected for a
similar suite of characteristics, it was reasonable to hypothesize
that the same genes might be affected. We therefore identified
LDRs that are spatially overlapping between indica and japonica
(referred to as ‘‘overlapping LDRs’’). Overlapping LDRs could
Table 1. Estimated h per kb for O. rufipogon, indica, and
japonica under different schemes of site selection.
PlatformSites used japonicaindica O. rufipogon
GAS (All sites) 8.5510.13 11.53
SOLiDS (All sites) 13.89 13.98 12.46
1.64 4.24 4.64
Only sites whose coverage in GA and SOLiD platform are both 6X or more are
used. S is the number of segregating sites in a given region and S.1 counts the
same sites but excludes singletons. S.2 excludes doubletons in addition. For
the ‘‘combined’’ (GA plus SOLiD) data, S.1 represents keeping sites whose
variant appear more than once in both GA and SOLiD data. For a comparison,
estimates based on all polymorphic sites are also given (‘‘All sites’’). These
estimates are greatly inflated due to the excesses in singletons and doubletons,
many of which are sequencing errors (See Table S3 for more information).
The origin of two cultivated rice Oryza sativa indica and O.
sativa japonica has been an interesting topic in evolution-
ary biology. Through whole-genome sequencing, we show
that the rice genome embodies two different evolutionary
trajectories. Overall genome-wide pattern supports a
history of independent origin of two cultivars from their
wild population. However, genomic segments bearing
important agronomic traits originated only once in one
population and spread across all cultivars through
introgression and human selection. Population genetic
analysis allows us to pinpoint 13 additional candidate
Using Whole-Genome Resequencing Data
PLoS Genetics | www.plosgenetics.org2June 2011 | Volume 7 | Issue 6 | e1002100
Figure 1. The sliding window profiles of h in two 5 Mb regions. The window size is 100 kb and step size is 10 kb. The horizontal lines are the
cutoffs determined for each subspecies by whole-genome random shuffling. A) A typical region on chromosome 11 where no sub-region is lower
than the cutoff in all species. B) A region on chromosome 7 that contains PROG1, a locus known to be associated with domestication . Both the
indica and japonica genomes are below the cutoff in the neighborhood (300 kb and 780 kb, respectively) of PROG1.
Table 2. Numbers of contigs in different size categories where h is lower than the cutoff.
Contig size (kb)
Number of contigs (% genome)
japonica (J) indica (I) overlapping regions (I and J)O. rufipogon
,10064 6040 77
100,20096 4427 31
200,30049 28 114
,200 kb 160 (4.90%) 104 (2.75%) 67 (1.60%) 108 (2.45%)
$200 kb 166 (26.38%)59 (6.15%)23 (2.35%) 4 (0.25%)
Common regions are windows overlapping between indica and japonica. The cutoff is determined for each subspecies by whole-genome random shuffling of 1 kb
segments (see Materials and Methods). The cutoff values (h per kb) are 0.215 for japonica, 2.153 for indica and 2.343 for O. rufipogon.
Using Whole-Genome Resequencing Data
PLoS Genetics | www.plosgenetics.org3 June 2011 | Volume 7 | Issue 6 | e1002100
happen by chance, by independent but convergent selection in the
two subspecies, or by introgression from one subspecies to the
other. The genealogical patterns of these overlapping LDRs, in
comparison with the genomic background, should be informative.
For convenience, we will use R for O. rufipogon, I for O. sativa
indica and J for O. sativa japonica to indicate the genomic
background, and R*, I*, J* to indicate overlapping LDRs. To
explore potentially different genealogical histories between differ-
ent parts of the genome, we first used a simplest method by
calculating genetic distances for overlapping LDRs and for whole-
genome sequences, respectively. The genetic distance is the
average distance between two sequences, each randomly chosen
from the populations of interest (see Materials and Methods), and
is a simple and well characterised method for assessing
relationships among populations.
Figure 2 displays the cumulative distributions for the distances.
For the genomic background, the genetic distances are very similar
in the three pair-wise comparisons (solid lines). In light of the
independent history of the two cultivars generally accepted in the
literature, similar distances between wild species and cultivars are
expected. The genetic distance between R and I is slightly larger
than those of the other two comparisons because these two
subspecies are the more polymorphic ones. (Hence, the coales-
cence time of some alleles from R and I could be older than the
divergence time of the two subspecies.)
Interestingly, in the LDRs, I and J are genetically closer to each
other than each is to O. rufipogon (Figure 2A, dashed lines).
Moreover, this observation that I and J are unusually closely
related appears to be a general property of regions of reduced
genetic diversity. For example, the lowest 5% LDRs chosen from
indica alone, exhibit very similar patterns as the overlapping LDRs
(see Figure 2B). The divergence patterns in Figure 2 suggest
different evolutionary histories between genomic background and
overlapping LDRs. More specifically, divergence in the genomic
background among the three subspecies appears to be commen-
surate with the widely-held view of independent domestication of I
and J from O. rufipogon (Figure 3A). However, the closer
relationship between the two cultivars in overlapping LDRs hints
support for sequential domestication (Figure 3B). These hints are
examined closely below.
Different evolutionary histories in the same genome—
observations versus simulations
The analysis above did not incorporate within-subspecies
polymorphism. To take into account polymorphisms in the
analysis, we used the Fst statistic . Fst reflects the proportion
of total genetic diversity that is due to among-population
differentiation. Polymorphic sites with Fst
differentiation, while those with Fst =1 show complete differen-
tiation with no common alleles among populations. Since mosaic
genealogies can be statistically complex, we determined the
statistical confidence by comparing the observation with extensive
coalescence simulations, which use information on standing
The observed cumulative distributions of Fst are shown in
Figure 4A (for I vs. J) and Figure 4B (for R vs. J). In each panel, the
distributions of the whole genome and overlapping LDRs are
represented by the solid and dotted line, respectively. Similar to
what we observe in Figure 2, overlapping LDRs show a pattern of
population differentiation distinct from that of the genome
background. We measured the largest distance between the dotted
curve (for overlapping LDRs) and the solid curve (genomic
background), marked D in Figure 4A and 4B. The observed D
value is given in the upper left corner of each panel.
To find out whether the observed D’s in Figure 4A and 4B are
compatible with neutral demographical models, we performed
coalescent simulations. The simulations were done under either
the independent domestication model of Figure 3A or the
sequential domestication model of Figure 3B. We explored a wide
range of parameter combinations. The simulation scheme and
=0 exhibit no
Figure 2. Distributions of genetic distances between populations in the genomic background and LDRs. A) The cumulative
distributions at overlapping LDRs (dashed curves) and genome background (solid curves). B) The cumulative distributions at bottom 5% of LDRs in
indica (dashed curves) and genome background (solid curves). We use R for O. rufipogon, I for indica and J for japonica to indicate the genomic
background. Overlapping LDRs (in panel A) or bottom 5% of LDRs (in panel B) in these species are designated by I*, J* and R* respectively.
Using Whole-Genome Resequencing Data
PLoS Genetics | www.plosgenetics.org4 June 2011 | Volume 7 | Issue 6 | e1002100
parameters chosen are described in detail in Text S1 (section D).
Representative results are shown in Figure. 4C–4F.
As shown in Figure 4C–4F, the dotted and solid curves are not
very different under one single evolutionary history, regardless of
the particular model of demography. The simulated D’s are much
smaller than those observed in Figure 4A and 4B. For a statistical
test of DFst, we simulated 4000 replicates from a set of 8 parameter
combinations (see Text S1 section D). The maximal DFstfrom the
4000 simulations is given in each panel as well. In all cases, the
maximal DFstis far smaller than the observed value. Therefore, the
genealogy of overlapping LDRs as observed in Figure 4A and 4B
is not likely to result from the same evolutionary history as that of
the rest of the genome (Text S1 section D) and is robust to possible
ancestral structure in rufipogon population (Text S1 section G).
What then might account for the different evolutionary histories
in the same genome? The solid curves in the observation
(Figure 4A and 4B) appear to agree with the simulations under
the independent domestication model of Figure 4C and 4D. In
contrast, the dotted curves for the observations seem to follow the
sequential domestication model of Figure 4E and 4F. In short,
while the genomic background follows the independent domesti-
cation model, consistent with the accepted view of rice
domestication, the genealogy of overlapping LDRs follows the
sequential domestication model.
There may be two explanations for the observed closer
relationship between two cultivars in overlapping LDRs. In the
first explanation, independent selection for the same trait drove
the same set of alleles in rufipogon to high frequency in the
domesticated species. However, since linkage disequilibrium in
the wild species is limited, typically spanning only a few kilobases
 and is much less than the length of overlapping LDRs,
selection for the same focal allele is not likely to drag the same set
of nearby variants to fixation in the two subspecies. A second, and
perhaps more likely, explanation is that genomic segments were
selected in one subspecies and subsequently introgressed into the
other. It seems plausible that breeders through the ages
hybridized varieties in order to introduce desired traits from
one variety to others .
We should note that the observed and simulated results of
Figure 4 is based on sites where Fst (R, I) $0.5. At sites where R
and I (the two more highly polymorphic taxa) are not strongly
differentiated, there is little statistical resolution in genealogies
between models of Figure 3A and 3B. At those sites, the difference
in genealogies between LDRs and the genomic background
cannot be easily observed. Hence, we focused on sites that are
sufficient differentiated between R and I with Fst (R, I) $0.5 and
asked if J is significantly more closely related to I (Figure 3B) or
nearly equally related to R and I (Figure 3A). The conclusions are
the same when all sites are used (see Figure S2), but the resolution
is lower, as expected. We also note that a separate analysis that
switches I and J yields the same conclusion as Figure 4. That
analysis asks whether I is closer to J or R at sites where Fst (R, J)
$0.5. We prefer the analysis presented in Figure 4 because I and
R are comparably polymorphic and much more so than J. This
property makes it easier to see the predicted outcome in Figure 4
under either model of Figure 3A or Figure 3B.
Genomic regions enriched for genes of domestication
If the hypothesis of frequent introgressions between indica and
japonica [28,29] is correct, then overlapping LDRs may have
played an important role in rice domestication. These overlapping
LDRs may be enriched for genes underlying interesting traits in
both indica and japonica. Therefore, we focused on the 61 genomic
regions where Fst(I*, J*)’s are significantly smaller than Fst (R*,
I*)’s and Fst (R*, J*)’s at the 5% nominal level by the Kolmogorov-
Smirnov test  (Table S4). These 61 genomic segment account
for about 3% of the rice genome and 86.7% of all the overlapping
LDRs (Table 2 and Table S4).
Figure 3. Two models for the domestication of indica (I) and japonica (J). A) Independent domestication – In the simplest form of
independent domestication, indica and japonica were separately domesticated from O. rufipogon at about the same time, resulting in a trifurcation
phylogeny. The most recent common ancestor of three taxa was time T from present. The two dashed circles highlight the coalesced lineages (xIand
xJ, respectively) at the time of domestication, Td. Branch widths reflect the relative population sizes (NI, NRand NJ) of the three taxa. B) Sequential
domestication – In this model, indica and japonica share a common history of domestication (Td’), and they are most closely related to each other.
Using Whole-Genome Resequencing Data
PLoS Genetics | www.plosgenetics.org5 June 2011 | Volume 7 | Issue 6 | e1002100
For a positive control, genes that are known to delineate
domesticated rice from their wild progenitors by an important trait
should fall in these regions (Figure 5 and Table S4). The sh4 gene,
responsible for seed shattering [12–14], and PROG1, associated
with the transition from the prostrate growth in the wild rice to the
erect growth of cultivars , are the two best examples (Figure 5).
Both are indeed in one of the 61 regions (Table S4). A third gene
(Rc) responsible for the white grain pericarp in cultivars [28,31] is
another possibility although the association between the pheno-
type and the cultivars is incomplete. Rc is also close to one of the
61 overlapping LDRs identified (Figure 5 and Text S1 section E).
We wished to identify, from this analysis of LDRs, new
candidate genes of rice domestication. We chose candidate genes
within the 61 regions that have at least one nonsynonymous
Figure 4. Cumulative plots for Fst distributions in observed data and two example simulations. A) Observed cumulative plot for Fst
between I and J; Fst distribution for overlapping LDRs are plotted in dashed lines. Solid lines are used for genome background. B) Observed
cumulative plot for Fst between R and J. C) Simulated cumulative plot for Fst between I and J under an independent domestication history. D)
Simulated cumulative plot for Fst between R and J under an independent domestication history. E) Simulated cumulative plot for Fst between I and J
under a sequential domestication history. F) Simulated cumulative plot for Fst between R and J under a sequential domestication history. D measures
the maximal distances between the two plotted cumulative distributions in each panel (see main text). Both observed D value in real data and
maximal value across simulated replicates are shown in left upper corner of each panel.
Using Whole-Genome Resequencing Data
PLoS Genetics | www.plosgenetics.org6 June 2011 | Volume 7 | Issue 6 | e1002100
mutation distinguishing (I, J) from R. Specifically, we required
both Fst (I, R) and Fst (J, R) to be .0.8 but Fst (I, J) ,0.1 at these
sites. It should be noted that such a gene is not expected in every of
the 61 regions since adjacent regions may not have been
independently derived. For example, a large portion of a
chromosome could have been introgressed initially when a single
gene of domestication spread among cultivars. This large region
was then broken into many smaller LDRs by recombination
(Figure S1). In that case, several LDRs may have resulted from one
Among the 61 regions, 20 regions contain at least one gene
fulfilling the Fst criteria. Interestingly, 13 of these 20 regions are
represented by a single candidate gene. Table S5 presents these 13
genes with their putative functions listed; two are of special
interest. LOC_Os01g36640 is a candidate gene of disease
resistance. Its expression level increases sharply after treatment
with Magnaporthe grisea suggesting its functional role in blast fungus
resistance . Similar to a previously cloned gene Pi-ta, this gene
also has one single amino acid difference between the resistant and
susceptible alleles . LOC_Os03g44710 encodes a YABBY
domain-containing protein. In Arabidopsis, members of the
YABBY gene family specify abaxial cell fate. Thus LO-
C_Os03g44710 may contribute to the architectural difference
between wild and cultivated rice .
In all, we have identified 13 genes that bear the population
genetic signature of having been selected in one domesticated
subspecies and introgressed to the other subsequently. Each of
these genes is embedded in an overlapping LDR between the two
subspecies. To ensure that the inference of these 13 candidate
gene regions was not biased by the relatively small sample size of
roughly 22 accessions in each subspecies, we examined a much
larger collection of accessions published recently . This
collection consists of 373 indica and 131 japonica lines, each of
which lightly sequenced (about 1 X coverage). In this large
dataset, the average diversity of these 13 regions in indica is
0.00074 in a genomic background of 0.0016. In japonica, the
corresponding values are 0.0001 and 0.0006, respectively.
Therefore, these 13 candidate regions are indeed much lower
in genetic diversity than the genomic background across a very
large number of accessions. We should note that, in the larger
collection, one of the 13 regions in indica shows a relatively high
diversity that is twice higher than the average of the rest. This
outlier region is marked out in Table S5. Several of these genes
are currently being tested for their functional role in delineating
cultivars from their wild progenitors.
In this study, we surveyed whole-genome DNA polymorphisms
in rice. It is commonly accepted that LDRs are a possible signature
of selective sweep and LDRs are indeed more common in the
cultivars than in the wild rice in our study. However, because of
population bottleneck and selfing, the prevalence of LDRs in the
cultivars is also compatible with many purely demographic
To address the issue of selection versus demography, we took
advantage of the independent domestication of indica and japonica.
We showed, by two different approaches, that some LDRs have an
evolutionary history distinct from the rest of the genome. These
LDRs, overlapping between the two subspecies and accounting for
about 3% of the genome, bear the signature of introgression from
one subspecies to the other (Table 2 and Table S4). Such
introgressions imply human selection and become the target
regions in the search for genes of rice domestication.
Because this analysis aimed at identifying genetic changes that
distinguish cultivars, be they landraces or elite accessions in indica
or japonica, from O. rufipogon. it would have missed variations that
delineate different groups of cultivars, such as Phr-1 . We
Figure 5. Genetic diversity and population differentiation at chromosome 4 and 7. A) Genetic diversity at chromosome 4 for three species.
B) Population differentiation at chromosome 4 for all three pair-wise comparisons C) Genetic diversity at chromosome 7 for three species. D)
Population differentiation at chromosome 7 for all three pair-wise comparisons. Brown horizontal bars are the overlapping low diversity regions
identified in this study.
Using Whole-Genome Resequencing Data
PLoS Genetics | www.plosgenetics.org7 June 2011 | Volume 7 | Issue 6 | e1002100
suspect that the changes identified here may tend to be associated
with earlier events in domestication. In general, these genes may
be difficult to identify by the conventional means of mapping and
cloning. To do that, it would be necessary to show that the traits
differentiate most O. rufipogon lines from indica and japonica lines.
This requirement would entail laborious and extensive genetic
mapping. Hence, a pre-screen for candidate domestication genes
by the population genetic analyses shown here could be
The criteria used to construct the list of overlapping LDRs yield
both sh4 and PROG1 (Table S4), the two best known genes that
distinguish wild rice from the cultivars. This predicted gene list
(Table S5) should therefore be enriched for domestication genes.
As the number of candidate genes associated with each
overlapping LDR is often small (one single candidate in many
cases), direct testing by transgenic means is well justified.
The main point of this study is that certain LDRs appear to be
introgressions driven by positive selection. An interesting, but
secondary point, concerns the direction of introgression, i.e., from
japonica to indica or vice versa . While the two types of
introgressions may leave different footprints in the polymorphism
patterns, the statistical resolution is too weak to be conclusive (Text
S1, Section H). Further studies of the haplotype structure near the
focal sites may provide an answer to this question [e.g. 29].
Materials and Methods
Sample preparation and sequencing
We used 43 lines of Oryza sativa including 21 japonica and 22
indica accessions and 23 lines of O. rufipogon in this study (Table S1).
Total DNA was extracted from leaves using the CTAB method
. For each taxon (japonica, indica, and O. rufipogon), we pooled
equal amount of total DNA from all individuals of that taxon for
sequencing. Pooled samples were processed with the Illumina
Genome Analyser at the Beijing Genomics Institute (Shenzhen),
following the manufacturers’ instructions. We sequenced each
sample using a full run and generated paired-ends reads. We also
sequenced the same samples using the ABI SOLiD sequencing
platform at Beijing Institute of Genomics (Beijing) (two slides per
sample) and generated single-end reads.
Mapping of sequencing data
Short reads generated by the two platforms were mapped to the
reference genome (MSU Rice Genome Annotation Project
Release 6.0, http://rice.plantbiology.msu.edu/) using MAQ .
Only uniquely mapped reads were used for subsequent analysis.
The main parameters (-n 2 -a 400 -m 0.002(J)/0.01(I,R) -C 20 -e
200 -N) were used in mapping and parameters (-m 3 -q 20) were
used to filter low quality reads in GA data. For SOLiD data, we
used parameters (-n 3 -c -m 0.005(J)/0.01(I, R) -C 20 -e 200 -N) in
color spaces mapping and parameters (-m 5 -Q 1000 -q 20) to filter
low quality reads. To reduce the error rate caused by the low
quality sites in reads, we discarded bases where quality values were
lower than 15.
Method of estimating h
To accurately estimate h, we had to filter out sequencing errors.
We accomplished this by using only variant sites detected by both
sequencing platforms and estimating Watterson’s h , which
does not require knowing allele frequencies (E(S) = anh, where S is
the number of segregating sites, an = (1+1/2+1/3+….+1/[n-1])
and n is the sample size (n=21, 22, and 46 in japonica, indica and O.
rufipogon, respectively). Many singletons and doubletons are caused
by sequencing errors. To minimize the confounding effects of
these errors, we used S.1(segregating sites excluding singletons)
and S.2 (excluding doubletons in addition) to estimate h. We
describe the method in detail in another paper (He et al, in
Identification of LDRs (low diversity regions)
h was estimated from the combined GA/SOLiD data across
the whole-genome using a sliding window approach. The window
size was 100 kb and step size was 10 kb. To identify windows
with unusually long stretches of low polymorphism, we calculated
cutoff h values for each of the three taxa separately. We broke the
genomes into 1 kb units and randomly shuffled these pieces 200
times, rendering thediversity at
independently. For each shuffled genome, we calculated h in
each 100 kb window and recorded the lowest h (hmin). Among the
200 hmin, we selected 10th smallest as the cutoff (hence, P=0.05).
The cutoff is defined as the level at which 95% of the simulations
do not yield a single 100 kb segment with a h value below it. Note
that in the 5% of the cases where simulations yielded some
100 kb segments below the cutoff the number of such segments is
never greater than 2.
each adjacent segment
Sliding-window calculations of h
We set the window size at 100 kb, in keeping with average levels
of linkage disequilibrium in the cultivars, or larger when specified.
We then let the windows slide along each chromosome by 10 kb
steps. We used the S.1of combined data to calculate h of every
window which has 10,000 sites covered at least four reads from
both platforms. Most of the 10 kb region is covered by 10 windows
and some are not. We thus only retained regions covered by four
or more windows, and chose the median h of these windows to
represent each region. If its median h value was lower than the
cutoff, we treated it as a low polymorphism region.
For a polymorphic SNP position, allele frequencies in
population one are p1 and q1. In population two, the
corresponding frequencies are p2 and q2 respectively. Then
genetic distance between two populations at this position is p1*q2
+p2*q1. The distance for a genomic segment is the average
distance across all SNP positions within this region. This genetic
distance measures the average distances for all pairwise compar-
isons between two sequences each taken at random from two
populations. It has range between 0 and 1.
We used the method described by Weir  to estimate Fst. For
each taxon, we combined the reads from both platforms (Table
S2). For a more accurate estimation, we used only high quality
bases covered by at least 10 reads in all three taxa (see Mapping of
sequencing data). We discarded all sites that had a single mutation
in the combined three-species data set.
Coalescent simulations under different demographic
We take two different approaches to the simulations of sequence
evolution under either model of rice domestication (Figure 2). In
the first approach, we directly simulate gene genealogies for our
samples and then overlay mutations on the simulated gene
genealogy. Coalescent process is partitioned into two phases
(before domestication where recombination happened freely and
after domestication when recombination is greatly reduced due to
selfing) . For each focal genomic segment, we first simulated
Using Whole-Genome Resequencing Data
PLoS Genetics | www.plosgenetics.org8June 2011 | Volume 7 | Issue 6 | e1002100
genealogical history for a non-recombining loci until we reach the
time of domestication, then we approximate the coalescent process
in the ancestral population by partitioning the focal segment into
different sizes of non-recombining small segments (corresponding
to different recombination rate in the wild population).
In order to explore a wider range of demographic histories, we
employ the ms program  to simulate the evolution of genome
sequences under both the independent and sequential domestica-
tion models (Figure 2). The demographic histories we explored
include a range of values for population bottleneck and divergence
time. The exact details of the simulations are presented in Text S1.
All the sequencing data from this study will be available at the
FTP server hosted by Beijing Institute of Genomics (BIG), Chinese
Academy of Sciences. Ftp address: ftp://ftp.big.ac.cn.
across the rice genome for three populations. The top panels show
the diversity for three rice populations. Brown horizontal segments
are overlapping LDRs identified in the current study. The bottom
panels show the sliding window (100 kb window stepping at 10 kb)
estimates of mean Fst values for three pair wise comparisons.
Brown segments display the locations for the overlapping LDRs.
Genome-wide diversity as well as mean Fst values
demography for all sites. A) Observed cumulative plot for Fst
between I and J; Fst distribution for overlapping LDRs are plotted
in dashed lines. Solid lines are used for genome background. B)
Observed cumulative plot for Fst between R and J. C) Simulated
cumulative plot for Fst between I and J under an independent
domestication history. D) Simulated cumulative plot for Fst
between R and J under an independent domestication history.
E) Simulated cumulative plot for Fst between I and J under a
sequential domestication history. F) Simulated cumulative plot for
Fst between R and J under a sequential domestication history.
This is the same plot as Figure 4 in main text, but plotted for all
sites rather than only sites where Fst(R, I).0.5.
Fst distributions from real data as well as simulated
Plant materials used in this study.
Summary of sequencing data and reads mapping.
data. Only sites whose coverage in GA and SOLiD platform are
both 6X or more are used. S is the number of segregating sites in a
given region and S.1 counts the same sites but excludes
singletons. S.2 excludes doubletons in addition. Estimates in
the ‘‘Mocked’’ row do not distinguish GA and SOLiD reads and
simply add up all reads. The numbers in this row show that sample
sizes do not make the estimates lower. In contrast, the estimates in
the ‘‘Combined’’ row take into consideration platform-dependent
errors. Sample sizes between the two rows are comparable.
Estimates of the ‘‘Literature’’ row were from Caicedo et al and
Tang et al. Since japonica lines in our collection are all from
the temperate zone, we used the corresponding number in the
h per kb estimated from single platform or combined
japonica and indica. P value are testing the hypothesis whether
Fst(I,J) is significantly shift to the left of Fst(R,J) or Fst(R,I) using
one sided Kolmogorov-Smirnov test with R package (http://www.
Overlapping low diversity regions shared between
Predicted candidate genes of domestication.
Supporting methods and discussion.
We thank IRRI and Y. Liu for the samples; J. Ross-Ibarra, S. Wright, J.
Doebley, R. Abbott, T. Izawa, Z. Yang, K. Zeng, and Y. Shen for helpful
comments and suggestions. We also want to thank B. Han and X. Huang
for sharing part of their published data and results. The high-performance
grid computing platform of Sun Yat-sen University provided technical
Conceived and designed the experiments: C-IW SS. Performed the
experiments: ZH HW. Analyzed the data: ZH WZ HW TT YW XL RRH
C-IW. Wrote the paper: ZH WZ HW TT AJG RRH C-IW SS.
1. Mekel-Bobrov N, Gilbert SL, Evans PD, Vallender EJ, Anderson JR, et al.
(2005) Ongoing adaptive evolution of ASPM, a brain size determinant in Homo
sapiens. Science 309: 1720–1722.
2. Yu F, Hill RS, Schaffner SF, Sabeti PC, Wang ET, et al. (2007) Comment on
"Ongoing adaptive evolution of ASPM, a brain size determinant in Homo
sapiens". Science 316: 370.
3. Patterson N, Richter DJ, Gnerre S, Lander ES, Reich D (2006) Genetic
evidence for complex speciation of humans and chimpanzees. Nature 441:
4. Ting CT, Tsaur SC, Wu CI (2000) The phylogeny of closely related species as
revealed by the genealogy of a speciation gene, Odysseus. Proc Natl Acad
Sci U S A 97: 5313–5316.
5. Zhou R, Zeng K, Wu W, Chen X, Yang Z, et al. (2007) Population genetics of
speciation in nonmodel organisms: I. Ancestral polymorphism in mangroves.
Mol Biol Evol 24: 2746–2754.
6. Oka H (1988) Origin of Cultivated Rice. Tokyo/Amsterdam: Japan Scientific
7. Sang T, Ge S (2007) Genetics and phylogenetics of rice domestication. Curr
Opin Genet Dev 17: 533–538.
8. Cheng C, Motohashi R, Tsuchimoto S, Fukuta Y, Ohtsubo H, et al. (2003)
Polyphyletic origin of cultivated rice: based on the interspersion pattern of
SINEs. Mol Biol Evol 20: 67–75.
9. Londo JP, Chiang YC, Hung KH, Chiang TY, Schaal BA (2006)
Phylogeography of Asian wild rice, Oryza rufipogon, reveals multiple
independent domestications of cultivated rice, Oryza sativa. Proc Natl Acad
Sci U S A 103: 9578–9583.
10. Ma J, Bennetzen JL (2004) Rapid recent growth and divergence of rice nuclear
genomes. Proc Natl Acad Sci U S A 101: 12404–12410.
11. Vitte C, Ishii T, Lamy F, Brar D, Panaud O (2004) Genomic paleontology
provides evidence for two distinct origins of Asian rice (Oryza sativa L.). Mol
Genet Genomics 272: 504–511.
12. Li C, Zhou A, Sang T (2006) Rice domestication by reducing shattering. Science
13. Onishi K, Takagi K, Kontani M, Tanaka T, Sano Y (2007) Different patterns of
genealogical relationships found in the two major QTLs causing reduction of
seed shattering during rice domestication. Genome 50: 757–766.
14. Zhang LB, Zhu Q, Wu ZQ, Ross-Ibarra J, Gaut BS, et al. (2009) Selection on
grain shattering genes and rates of rice domestication. New Phytol 184:
15. Druley TE, Vallania FL, Wegner DJ, Varley KE, Knowles OL, et al. (2009)
Quantification of rare allelic variants from pooled genomic DNA. Nat Methods
16. Shendure J, Ji H (2008) Next-generation DNA sequencing. Nat Biotechnol 26:
Using Whole-Genome Resequencing Data
PLoS Genetics | www.plosgenetics.org9 June 2011 | Volume 7 | Issue 6 | e1002100
17. Zhou R, Ling S, Zhao W, Osada N, Chen S, et al. (2011) Population genetics in Download full-text
non-model organisms: II. Natural selection in marginal habitats revealed by
deep sequencing on dual platforms. Mol Biol Evol doi: 10.1093/molbev/
18. Lynch M (2009) Estimation of allele frequencies from high-coverage genome-
sequencing projects. Genetics 182: 295–301.
19. Watterson GA (1975) On the number of segregating sites in genetical models
without recombination. Theor Popul Biol 7: 256–276.
20. Caicedo AL, Williamson SH, Hernandez RD, Boyko A, Fledel-Alon A, et al.
(2007) Genome-wide patterns of nucleotide polymorphism in domesticated rice.
PLoS Genet 3: e163. doi:10.1371/journal.pgen.0030163.
21. Tang T, Lu J, Huang J, He J, McCouch SR, et al. (2006) Genomic variation in
rice: genesis of highly polymorphic linkage blocks during domestication. PLoS
Genet 2: e199. doi:10.1371/journal.pgen.0020199.
22. Garris AJ, Tai TH, Coburn J, Kresovich S, McCouch S (2005) Genetic structure
and diversity in Oryza sativa L. Genetics 169: 1631–1638.
23. Mather KA, Caicedo AL, Polato NR, Olsen KM, McCouch S, et al. (2007) The
extent of linkage disequilibrium in rice (Oryza sativa L.). Genetics 177:
24. Tan L, Li X, Liu F, Sun X, Li C, et al. (2008) Control of a key transition from
prostrate to erect growth in rice domestication. Nat Genet 40: 1360–1364.
25. Lindblad-Toh K, Wade CM, Mikkelsen TS, Karlsson EK, Jaffe DB, et al. (2005)
Genome sequence, comparative analysis and haplotype structure of the domestic
dog. Nature 438: 803–819.
26. Oka H, Morishima H (1967) Variation in the breeding systems of a wild rice,
Oryza perennis. Evolution 21: 249–258.
27. Weir BS, Cockerham CC (1984) Estimating F-Statistics for the Analysis of
Population-Structure. Evolution 38: 1358–1370.
28. Sweeney MT, Thomson MJ, Cho YG, Park YJ, Williamson SH, et al. (2007)
Global dissemination of a single mutation conferring white pericarp in rice.
PLoS Genet 3: e133. doi:10.1371/journal.pgen.0030133.
29. Kovach MJ, Sweeney MT, McCouch SR (2007) New insights into the history of
rice domestication. Trends Genet 23: 578–587.
30. Conover WJ (1971) Practical Nonparametric Statistics. New York: John Wiley &
31. Sweeney MT, Thomson MJ, Pfeil BE, McCouch S (2006) Caught red-handed:
Rc encodes a basic helix-loop-helix protein conditioning red pericarp in rice.
Plant Cell 18: 283–294.
32. Brenner S, Johnson M, Bridgham J, Golda G, Lloyd DH, et al. (2000) Gene
expression analysis by massively parallel signature sequencing (MPSS) on
microbead arrays. Nat Biotechnol 18: 630–634.
33. Bryan GT, Wu KS, Farrall L, Jia Y, Hershey HP, et al. (2000) A single amino
acid difference distinguishes resistant and susceptible alleles of the rice blast
resistance gene Pi-ta. Plant Cell 12: 2033–2046.
34. Siegfried KR, Eshed Y, Baum SF, Otsuga D, Drews GN, et al. (1999) Members
of the YABBY gene family specify abaxial cell fate in Arabidopsis. Development
35. Huang X, Wei X, Sang T, Zhao Q, Feng Q, et al. (2010) Genome-wide
association studies of 14 agronomic traits in rice landraces. Nat Genet 42:
36. Yu Y, Tang T, Qian Q, Wang Y, Yan M, et al. (2008) Independent losses of
function in a polyphenol oxidase in rice: differentiation in grain discoloration
between subspecies and the role of positive selection under domestication. Plant
Cell 20: 2946–2959.
37. Doyle J, Doyle J (1987) A rapid DNA isolation procedure for small quantities of
fresh leaf tissue. Phytochem Bull 19: 11–15.
38. Li H, Ruan J, Durbin R (2008) Mapping short DNA sequencing reads and
calling variants using mapping quality scores. Genome Research 18: 1851–1858.
39. Hudson RR (2002) Generating samples under a Wright-Fisher neutral model of
genetic variation. Bioinformatics 18: 337–338.
Using Whole-Genome Resequencing Data
PLoS Genetics | www.plosgenetics.org10 June 2011 | Volume 7 | Issue 6 | e1002100