, 79 (2012);
et al.Ian H. Cheeseman
A Major Genome Region Underlying Artemisinin Resistance in
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Acknowledgments: We thank E. Meyerowitz for comments
on the manuscript, E. Centeno for help with ChIP-Seq analysis,
O. Casagran for help with the microfluidic arrays, M. Perales
for help with the ChIP, L. Rico for help with the inducible
constructs, and L. Schaeffer and D. Trout (Jacobs Genetics
and Genomics Laboratory at Caltech) for the primary
sequence data processing. This work was supported by
grants to P.M. from the Ramón Areces Foundation, the
Spanish Ministry of Science and Innovation (MICINN), the
European Molecular Biology Organization (EMBO) Young
Investigators Program, and from the European Heads of
Research Councils and the European Science Foundation
through the European Young Investigator Award; to J.L.R.
from the European Comission (EC) Marie Curie program
and MICINN; and to A.J.M. and others from the EC FP7
Collaborative Project TiMet. The Centre for Systems
Biology at Edinburgh is a Centre for Integrative and
Systems Biology supported by the Biotechnology and
Biological Sciences Research Council and the Engineering
and Physical Sciences Research Council award D019621.
W.H. is supported by a Juan de la Cierva contract (MICINN)
and P.P.-G. by a Formación de Personal Investigador
fellowship (MICINN). Sequencing data have been deposited
with the National Center for Biotechnology Information
Gene Expression Omnibus under accession number
Supporting Online Material
Materials and Methods
Figs. S1 to S11
Tables S1 to S7
12 January 2012; accepted 27 February 2012
Published online 8 March 2012;
A Major Genome Region Underlying
Artemisinin Resistance in Malaria
Ian H. Cheeseman,1Becky A. Miller,2Shalini Nair,1Standwell Nkhoma,1Asako Tan,2
John C. Tan,2Salma Al Saai,1Aung Pyae Phyo,3Carit Ler Moo,3Khin Maung Lwin,3
Rose McGready,3,4,5Elizabeth Ashley,3,4,5Mallika Imwong,4Kasia Stepniewska,4,5,7Poravuth Yi,8
Arjen M. Dondorp,4,5Mayfong Mayxay,6Paul N. Newton,5,6Nicholas J. White,4,5
François Nosten,3,4,5Michael T. Ferdig,2Timothy J. C. Anderson1*
Evolving resistance to artemisinin-based compounds threatens to derail attempts to control
malaria. Resistance has been confirmed in western Cambodia and has recently emerged in
western Thailand, but is absent from neighboring Laos. Artemisinin resistance results in reduced
parasite clearance rates (CRs) after treatment. We used a two-phase strategy to identify genome
region(s) underlying this ongoing selective event. Geographical differentiation and haplotype
structure at 6969 polymorphic single-nucleotide polymorphisms (SNPs) in 91 parasites from
Cambodia, Thailand, and Laos identified 33 genome regions under strong selection. We
screened SNPs and microsatellites within these regions in 715 parasites from Thailand,
identifying a selective sweep on chromosome 13 that shows strong association (P = 10−6to
10−12) with slow CRs, illustrating the efficacy of targeted association for identifying the
genetic basis of adaptive traits.
and are central to the current success of global
efforts to control and eliminate Plasmodium fal-
ciparum malaria (2). Resistance to artemisinin
(ART) in P. falciparum has been confirmed in
Southeast Asia (3), raising concerns that it will
spread to sub-Saharan Africa, following the path
of chloroquine and anti-folate resistance (4).
ART resistance results in reduced parasite clear-
ance rates (CRs) after treatment (Fig. 1A) and is
principally due to parasite genetics, which de-
termines 58 and 64% of the variance in parasite
rtemisinin-based combination therapies
(ACTs) are the first-line treatment in
nearly all malaria-endemic countries (1)
CRs in western Cambodia and western Thailand,
respectively (5, 6). The resistance mechanism is
asites (7) and quiescence have been implicated
(8). The genetic basis is likely to be simple. A
single mutation in the ubp1 gene confers ART
(9). Similarly, resistance to other antimalarials in
P. falciparum involves single major-gene effects
or is oligogenic (10).
Cross-population genomic comparisons offer
a means to identify putative targets of natural
selection (11–14). Targeted association analyses
of genome regions under selection can then be
ive traits (13, 14), minimizing the multiple-testing
penalties constraining standard genome-wide
association studies. We compared three neigh-
boring Southeast Asian P. falciparum populations
(in Laos, Thailand, and Cambodia) with low lev-
els of genetic differentiation (Fig. 1B), but differ-
ences in CRs after ART treatment (Fig. 1C), to
detect recent selective sweeps that may underlie
resistance.Parasites from Laos are clearedrapid-
decline = 2 hours], parasites from western Cam-
bodia clear slowly [median –log(CR) 2.15, half-
1.4, half-life 3 hours]. CR distributions are sig-
nificantly different between all locations (Thai-
Cambodia, D = 0.58, P < 0.001; Thai-Laos, D =
0.68, P < 0.001; Cambodia-Laos, D = 0.93, P <
0.001; two-sided Kolmogorov-Smirnov test).
We genotyped 91 genetically unique single-
clone parasites (27 from Laos, 30 from Cambo-
dia, and 34 from Thailand) by hybridization to a
custom Nimblegen genotyping array that scores
per 500 base pairs (bp)] and copy number
variation (CNV) (16, 17). Principal-components
analysis (PCA) and global fixation indices (FST)
confirmed low but significant differentiation be-
tween the three populations (Fig. 1, B and D).
We characterized CNV across the three pop-
ulations, identifying 78 common CNVs [minor
allele frequency (MAF) > 5%] containing 209
For each SNP (MAF > 5%, n = 6969), we cal-
culated two statistics to identify genome regions
under strong selection for our three populations,
measuring differentiation in haplotype structure
[XP-EHH (18)] and allele frequency (FST) and
classifying selected regions using a 10-kb sliding
three nonoverlapping regions, comprising 2.4%
of the 23-Mb genome, showed evidence for
selection (top 1% of genome-wide values in
both tests) in one or more populations and were
ranked by the proportion of significant tests
Known antimalarial resistance genes ac-
Three genes ( pfcrt, dhps, and dhfr) were iden-
evidence of selection within 5 kb (Fig. 2B). pfmdr1
was not identified, most likely because the am-
plicon containing pfmdr1 has multiple origins
(19). An additional 23 genome regions showed
strong signatures of selection (table S1 and fig.
dows and ranked eighth, first, and second, respec-
tively (table S1 and Fig. 2C). We did not observe
evidence for selection at two proposed candidate
loci, atpase6 (Fig. 2A) (20) or Part (21). Two
1Texas Biomedical Research Institute, San Antonio, TX 78245,
USA.2The Eck Institute for Global Health, Department of
Biological Sciences, University ofNotre Dame, Notre Dame, IN
46556, USA.3Shoklo Malaria Research Unit, Mae Sot, Tak,
Thailand.4Faculty of Tropical Medicine, Mahidol University,
Bangkok, Thailand.5Centre for Tropical Medicine, Churchill
Hospital, Oxford, UK.
Oxford Tropical Medicine Research Collaboration, Mahosot
wide Antimalarial Resistance Network, Oxford, UK.8The Na-
tional Center for Parasitology, Entomology, and Malaria
Control, Phnom Penh, Cambodia.
*To whom correspondence should be addressed. E-mail:
6Wellcome Trust–Mahosot Hospital–
VOL 336 6 APRIL 2012
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chr-12 CNV containing GTP-cyclohydrolase I
(FST= 0.6; Thailand versus Laos) is driven by
anti-folate treatment (22); whereas a deletion of
surfin4.1 (FST= 0.51; Cambodia versus Laos) is
not a strong candidate, because this is present in
>20% of Lao infections and in African samples.
The 78 CNVs did not overlap with the 33 regions
under selection and were not considered further.
We examined the association of each of these
33 regions with parasite CRs in an independent
parasite population.Targeted associationallowed
us to exploit an extensive archive of blood-spot
DNA samples from Thai patients with detailed
(6-hourly) CR data. Between 2001 and 2010,
3202 patients with uncomplicated malaria were
treated with ART in four clinics on the Thai-
Burmese border (5). The proportion of parasites
with slow clearance [–log(CR) > 1.89] rose from
<5% in 2001 to >50% in 2010, with resistance
spreading more rapidly north of Mae Sot as
all genetically unique infections containing a sin-
gle parasite clone [n = 715; 417 from the north
(96 from 2001–2004 and 321 from 2007–2010)
and 298 from the south (121 from 2001–2004
and 177 from 2007–2010)]. We genotyped 90
SNPs targeting the 33 selected regions and 4
SNPs in atpase6 and pfmdr1 (table S2), in addi-
tion to the 93 genome-wide control SNPs geno-
typed previously (5, 17).
There were no associations in 2001–2004
(Fig. 3B) or at either pfmdr1 or atpase6. Two ad-
association (P= 5.0 × 10−6to 6.5 × 10−7, Fishers’
exact test) in the north (2007–2010, Fig. 3D). The
strongest signals of association in the 2007–2010
southern samples lay adjacent to these SNPs
(Fig. 3C). Quantile-quantile plots indicate mod-
erate inflation of association P values (inflation
factor = 1.24 to 1.27, fig. S2), but the two SNPs
remain significantly associated after adjustment
for this inflation.
We fine-mapped this region using 19 micro-
satellite markers spanning 550 kb surrounding
the strongest association signal in 417 northern
Thai, 88 Lao, and 83 Cambodian parasites. Four
microsatellites spanning ~35 kb showed strong
c2test) in 2007–2010 but no association in 2001–
2004 (Fig. 4A). This analysis uses a threshold to
define “resistant” parasites. Reanalysis of quantita-
tive CR data using a general linear model (23) con-
10−5to 1.6 × 10−10), and use of a more stringent
boosted maximal significance to P = 9 × 10−16.
Alleles at the marker showing maximal associa-
tion show a threefold difference in CR (Fig. 4D).
Assuming a recent selective sweep, we ex-
pected reduced allelic variation in Cambodia and
Thailand relative to Laos. We observed maximal
diversity reduction in Cambodia [expected het-
erozygosity (He) = 0.24 T 0.07 (1 SD)] at eight
markers spanning 105 kb (Fig. 4B). In contrast,
diversity is high across this regioninLaos(He=
0.79 T 0.17 SD), whereas Thai parasites showed
intermediate levels of diversity (He= 0.63 T
0.09 SD). There was no separation between
highly resistant and sensitive parasites, perhaps
due to multiple origins of resistance alleles or
evolution from standing variation (24). Analy-
sis of haplotype structure provides strong
evidence for recent selection in Thailand (Fig.
4C). Extended haplotype homozygosity (EHH)
decays to background levels (0.05) over 99 kb
(range = 24 to 190 kb) in sensitive [–log(CR) <
1.3] parasites, whereas in resistant [–log(CR) >
1.89] parasites, decay to background levels is
over 375 kb (range = 140 to >550 kb). Permu-
tation testing reveals this difference to be highly
significant (P = 0.0003, (17) and fig. S5).
Using a general linear model (23), we es-
timated that 22.5% of the variation in CRs was
determined by this region in 2007–2010. Given
the heritability of CR in this population [64%
(5)], we conservatively estimate that this locus
explains at least 35.2% (22.5/64) of the heritable
component of CR, suggesting that it is a major
determinant of ART resistance.
Within this 35-kb region there are multiple
include the genes encoding lipoate synthase (lip-
oic acid salvage/biosynthesis), aminomethyl-
transferase (glycine cleavage pathway), and heat
shock protein 70 (stress response/molecular
chaperone). We sequenced the coding regions
of six of the seven genes (18,747 bp) in 8 Lao, 8
Cambodian,and 24to 31 Thaiparasites (fig. S3).
Six nonsynonymous, derived mutations in 3/6
genes are at high frequency in ART-resistant pop-
ulations including SNPs 1 and 2 (Fig. 3F and
from Southeast Asia before the origin of ART
resistance, suggesting that they are associated
with but do not directly underlie CR and that non-
coding regulatory mutations may be involved.
Analysis of transcriptional changes in resistant
lines offers a means to further prioritize genes
significant changes in transcript levels during at
least one life-cycle stage in ART-resistant lines
profiled in a recent transcriptomic study (25)
The spread of ART-resistant parasites would
of variation in CR. Future functional dissection of
loci within this region will be dependent on the
Time since treatment (hrs)
Parasitaemia half-life (hrs)
Fig. 1. PhenotypicandgeneticdifferentiationbetweenSoutheastAsianparasites.(A)Patternsofparasite
clearance from two Thai patients (black, slow CR; red, fast CR) after treatment. (B) FSTbetween locations
n = 64), and Thailand (black, n = 3202). (D) PCA of parasites; 1770 SNPs were used, with MAF >5% and
no missing data. PC1 and -2 are the first two principal components of the data.
6 APRIL 2012 VOL 336
on April 11, 2012
Fig. 3. Associationtesting.(A)Studysitesonthe
Thai-Burma border. Northern populations were
Maela Camp and Wang Pha, and southern pop-
ulations were Mae Kong Khen and Mawker Thai.
The clearance rate declined between 2001 and
percentage of parasites with –log(clearance rate)
> 1.89. (B to D) Association of 94 SNPs from
selected regions (black) and 93 genome-wide
SNPs (red). Two neighboring SNPs in the northern
population from 2007 to 2010 showed strong as-
4 indicate the SNPs showing the strongest asso-
ciations in regions under selection. (E) Box plot of
the CR for each allele of SNP 1. (F) The chromo-
somal context surrounding these SNPs.
Fig. 2. Evidence for selection. Plots show FSTand XP-EHH for
each SNP (n = 6969) in a pairwise comparisons of countries.
Dashed lines represent the 1% level from the genomic empir-
ical distribution. Significant windows are shown by black bars
above each plot. Red, Cambodia versus Laos; blue, Thailand
versus Laos; black, Cambodia versus Thailand. (A) Chr 1. There
selection surrounding five known drug-resistance genes
(marked by arrows). (C) Three loci putatively under selection.
VOL 336 6 APRIL 2012
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CR phenotype. The mapping approach we have
used—targeted association of selected genome
regions—has broad utility for researchers wishing
to map variants responsible for traits under strong
recent positive selection.
and Chromosomal Microdeletions
in Normal Tissues
Yoshiyuki Shibata,1* Pankaj Kumar,1* Ryan Layer,1Smaranda Willcox,2Jeffrey R. Gagan,1
Jack D. Griffith,2Anindya Dutta1†
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Acknowledgments: Clinical work was funded by the Wellcome
Trust. Molecular work was funded by the National Institutes
of Health (grant R01 AI048071/AI075145) in facilities
constructed with support from Research Facilities Improvement
Program grant no. C06 RR013556 from the National Center
for Research Resources. We thank patients and staff who
contributed to data collection in Thailand, Cambodia
(C. Nguon, C. Meng Chuor, and D. Socheat), and Laos
(M. Khanthavong, O. Chanthongthip, B. Soonthornsata,
T. Pongvongsa, S. Phompida, and B. Hongvanthong);
K. Burgoine, P. Singhasivanon, P. Ringwald, M. Zlojutro Kos, and
J. Currans’ lab. Microarray data have been submitted to the
National Center for Biotechnology Information’s Gene Expression
Omnibus under accession nos. GSM818073 to GSM818239.
The authors declare no competing financial interests.
Materials and Methods
Figs. S1 to S5
Tables S1 to S4
31 October 2011; accepted 2 March 2012
We have identified tens of thousands of short extrachromosomal circular DNAs (microDNA) in mouse
tissues as well as mouse and human cell lines. These microDNAs are 200 to 400 base pairs long, are
derived from unique nonrepetitive sequence, and are enriched in the 5′-untranslated regions of genes,
exons, and CpG islands. Chromosomal loci that are enriched sources of microDNA in the adult brain are
somatically mosaic for microdeletions that appear to arise from the excision of microDNAs. Germline
microdeletions identified by the “Thousand Genomes” project may also arise from the excision of
cells and provide evidence that their generation leaves behind deletions in different genomic loci.
ingle-nucleotide polymorphisms and copy-
number variations are known sources of
but there is also great interest in variations that
arise during generation ofsomatic tissueslike the
mammalian brain, leading to genetic mosaicism
between somatic cells. To identify sites of intra-
al circular DNA (eccDNA) derived from excised
chromosomal regions in normal mouse embry-
We purified eccDNA from nuclei of embry-
linear DNA by digestion with an adenosine 5′-
triphosphate (ATP)–dependent exonuclease (6)
(fig. S1,table S1,and SOM methods). Multiple
displacement amplification (MDA) with random
primers (7, 8) enriched circular DNA by rolling-
circle amplification.The linear productsof MDA
were sheared to 500–base pair (bp) fragments
Fig. 4. Finemappingusing19microsatellites.(A)AssociationPvaluesfromtheearly(2001–2004,reddots)
is shown by a horizontal dashed line. (B) Comparison of Hein Thailand, Laos, and Cambodia. (C) EHH
surrounding a microsatellite (position 1,763,950) in slow-clearing (half-life >4.6 hours) and fast-clearing
(half-life <2.3 hours) parasites. (D) Phenotypic distribution at this locus in 2007–2010 (P = 4 × 10−12).
1Department of Biochemistry and Molecular Genetics, Uni-
versity of Virginia SchoolofMedicine,Charlottesville,VA,USA.
2Lineberger Cancer Center,University of North Carolina,Chapel
Hill, NC, USA.
*These authors contributed equally to this work.
†To whom correspondence should be addressed. E-mail:
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