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MALARIA
Major subpopulations of Plasmodium
falciparum in sub-Saharan Africa
Alfred Amambua-Ngwa
1
, Lucas Amenga-Etego
2
, Edwin Kamau
3,4
, Roberto Amato
5,6
,
Anita Ghansah
7
, Lemu Golassa
8
, Milijaona Randrianarivelojosia
9
, Deus Ishengoma
10
,
Tobias Apinjoh
11
, Oumou Maïga-Ascofaré
12
, Ben Andagalu
3
, William Yavo
13
,
Marielle Bouyou-Akotet
14
, Oyebola Kolapo
1,15
, Karim Mane
1
, Archibald Worwui
1
,
David Jeffries
1
, Vikki Simpson
4,6
, Umberto D’Alessandro
1
,
Dominic Kwiatkowski
5,6
, Abdoulaye A. Djimde
5,16
*
Understanding genomic variation and population structure of Plasmodium falciparum
across Africa is necessary to sustain progress toward malaria elimination. Genome
clustering of 2263 P. falciparum isolates from 24 malaria-endemic settings in
15 African countries identified major western, central, and eastern ancestries, plus a
highly divergent Ethiopian population. Ancestry aligned to these regional blocs,
overlapping with both the parasite’s origin and with historical human migration. The
parasite populations are interbred and shared genomic haplotypes, especially across drug
resistance loci, which showed the strongest recent identity-by-descent between
populations. A recent signature of selection on chromosome 12 with candidate resistance
loci against artemisinin derivatives was evident in Ghana and Malawi. Such selection
and the emerging substructure may affect treatment-based intervention strategies
against P. falciparum malaria.
The worldwide decline in malaria prevalence
is now stalling and additional knowledge,
new tools, and intervention strategies
will be needed for global malaria elimi-
nation and eradication (1). The burden of
Plasmodium falciparum malaria in particular
remains substantial in sub-Saharan Africa (sSA),
where it involves various vectors and human
populations (2,3). Although interventions have
reduced and disconnected malaria parasite pop-
ulations, they may be driving selection, adapta-
tion, and population fragmentation. Population
fragmentation and reduced diversity can be as-
sessed for refining approaches or tools for elim-
ination (4). Therefore, it is important to determine
the effect of large-scale control interventions on
the structure of the parasite population, which
until recently was considered to be highly diverse
and homogeneously interconnected in sSA (5).
The ancestry, current structure, and gene flow
between different P. falciparum populations
across sSA remain unclear. Previous studies
have used single-nucleotide polymorphism (SNP)
markers to characterize specific geographic
populations and describe genomic variation
and signatures of selection in sSA (6,7). Re-
cent higher-density genomic polymorphisms
from next-generation sequencing technologies
can further resolve African P. falciparum sub-
populations and population-specific genomic
signatures.
The Plasmodium Diversity Network Africa
(PDNA) conducts P. falciparum genomic sur-
veillance across sSA, from the West Atlantic
coastal regions with their high rainfall and
perennial transmission; the Sahel with its short
rainy seasons and seasonal transmission; Central
Africa with its forest-covered areas and perennial
transmission; Eastern Africa with its perennial
and seasonal transmission; to Ethiopia and the
RESEARCH
Amambua-Ngwa et al., Science 365, 813–816 (2019) 23 August 2019 1of4
1
Medical Research Council Unit The Gambia at LSHTM,
Banjul, The Gambia.
2
West African Centre for Cell Biology of
Infectious Pathogens (WACCBIP), University of Ghana, Accra,
Ghana.
3
United States Army Medical Research Directorate-
Africa, Kenya Medical Research Institute/Walter Reed
Project, Kisumu, Kenya.
4
Walter Reed Army Institute of
Research, U.S. Military HIV Research Program, Silver Spring,
MD, USA.
5
Wellcome Sanger Institute, Hinxton, UK.
6
MRC
Centre for Genomics and Global Health, Big Data Institute,
University of Oxford, Oxford, UK.
7
Noguchi Memorial Institute
for Medical Research (NMIMR), Accra, Ghana.
8
Aklilu Lemma
Institute of Pathobiology, Addis Ababa University, Addis
Ababa, Ethiopia.
9
Institut Pasteur of Madagascar, Antanarivo,
Madagascar.
10
National Insti tute for Medical Research
(NIMR), Tanga, Tanzania.
11
Department of Biochemistry and
Molecular Biology, University of Buea, Buea, Cameroon.
12
Bernhard Nocht Institute for Topical Medicine (BNITM),
Hamburg, Germany.
13
Unite des Sciences Pharmaceutiques
et Biologiques, University Félix Houphouët-Boigny, Abidjan,
Côte d’Ivoire.
14
Faculty of Medicine, University of Health
Sciences, Libreville, Gabon.
15
Department of Zoology,
University of Lagos, Lagos, Nigeria.
16
Malaria Research and
Training Centre, University of Science, Techniques and
Technologies of Bamako, Bamako, Mali.
*Corresponding author. Email: adjimde@icermali.org
Fig. 1. Sites, sample sizes,
and genetic groupings
of P. falciparum isolates
across PDNA and Pf3K
studies in Africa.
(A)Sites,P. falciparum
(Pf) prevalence rate, and
studies from which
SNP data of 2263 isolates
were accessed. Map
was extracted from a
malaria atlas showing
P. falciparum prevalence
as brown density within
the ranges of the key
(https://map.ox.ac.uk/
explorer/#/). (B)Com-
plexity of infections
by inbreeding coefficient
(Fws). (C) Scatter plot
from multidimensional
scaling of tess3r
ancestry coefficients
for six predicted
ancestral populations.
on August 24, 2019 http://science.sciencemag.org/Downloaded from
island of Madagascar with their cotransmission
of P. vivax (8). Using high-resolution genome-
wide SNP variants of P. falciparum isolates
across sSA, we reveal the population structure,
admixture, markers of identity-by-descent (IBD),
differentiation, and signatures of selection.
SNP variants (29,998) were extracted from
whole-genome sequences of 2263 P. falciparum
isolates sampled from across 15 African coun-
tries (Fig. 1A and tables S1 and S2). At least 55%
of infections were polygenomic, with up to nine
clones in some infections from Ghana, Guinea,
and Malawi (fig. S1). The proportion of complex
infections [i.e., lower mean inbreeding coefficient
(Fws)] was highest in Kenya and lowest in
Ethiopia (Fig. 1B). Malaria transmission around
the sampling site in Kenya (Kisumu, Western
Kenya) was stable and high (9), probably driving
the high infection complexity. In West Africa,
isolates from The Gambia and Senegal were the
least complex, confirming earlier reports of a
decline in complexity with decreasing preva-
lence, probably due to the scale-up of inter-
ventions (10).
Standard principal components analysis, using
imputed genome haplotypes (fig. S2), resolved
three major groups: western (West Africa and the
more-central countries of Cameroon and Gabon),
eastern [Democratic Republic of the Congo (DR
Congo) and all other sites in East Africa], and a
Amambua-Ngwa et al., Science 365, 813–816 (2019) 23 August 2019 2of4
Fig. 2. Genome-wide ancestry proportions. Ancestry proportions for P. falciparum isolates (admixture-like bar plots) or populations (pie charts)
modeled to include donors from all sites (incl. self) or excluding isolates from recipient sampling site (without self). (A) Ancestry per isolate (rows) from
each sampling site (left column). (B) Median ancestry from each sampling site. (C) Median ancestry proportions between isolates from each sampling
site, excluding donors from same site. Country colors are the same as in Fig. 1.
Fig. 3. Genome-wide ancestry proportions for P. falciparum populations in sSA. (A) Ancestry proportions for regional genetic blocs (left column).
Ancestry proportions for each genetic cluster (B) including self-copying and (C) without self-copying.
RESEARCH |REPORT
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distinct Ethiopian population (fig. S3). This sub-
structure was refined to six distinct clusters from
multidimensional scaling of ancestral member-
ship coefficients, splitting DR Congo from East
African populations (Fig. 1C and fig. S4). The six
retained genetic clusters were West African (WAF;
Senegal, Gambia, Guinea, Mali, Côte d’Ivoire,
Ghana, and Nigeria), Central African (CAF;
Cameroon and Gabon), South Central African
(SCAF; DR Congo), East African (EAF; Kenya
and Tanzania), Southeast African (SEAF; Malawi
and Madagascar), and the Horn of Africa (HAF;
Ethiopia).
Each cluster suggests an ancestral or trans-
mission connectivity supported by geographic
proximity and confirmed by significant isola-
tion by distance (P= 0.03, Mantel test) (fig. S5).
The major population continuums were within
West Africa and East Africa, with several-fold
difference in genetic distance [all fixation index
(F
ST
) values > 0.1] between them and Ethiopia.
Differentiation might also result from differences
in human and vector populations, the history
of interventions on spatial separation, and geo-
graphic barriers (e.g., western Cameroon forest,
the equatorial forest, Congo Basin rivers, and
highlands of Ethiopia). Isolates from DR Congo
and Ethiopia clustered away from geographically
proximal sites in CAF and EAF, respectively.
Human populations from Ethiopia and other
HAF sites, such as Djibouti, have a distinct an-
cestry from the rest of Africa, allowing sympat-
ric transmission of P. vivax, with earlier reports
of divergent P. falciparum populations (11,12).
As in Madagascar, HAF human populations have
higher frequencies of the Duffy antigen, allowing
P. vivax cotransmission. However, isolates from
Madagascar clustered with those from Malawi,
indicating mainland ancestry despite a high pro-
portion of human populations originating from
Southeast Asia and being separated by 1400 km
of land and the Indian Ocean. Therefore, it is not
likely that the divergence of HAF isolates is due
to co-prevalence with P. vivax but might be
driven by other factors such as differences in
vector populations. This could also explain the
differentiation between Congolese and other CAF
isolates where vector populations differ, with
Anopheles funestus being relatively dominant in
DR Congo (13).
Recent studies have shown that P. falciparum
from western great apes jumped into humans
about 10,000 years ago, prior to major human
migrations (14,15). The donation of ancestral
genome chunks from CAF to both western and
eastern P. falciparum populations aligns with
such an origin and the spread of malaria through
historical and more recent human migration in
Africa. Recent human migration brought on by
colonization and slavery may have resulted
in P. falciparum ancestral chunks shared be-
tween distal French colonies like Cameroon,
Mali, and Senegal, whereas ancestry from WAF
sites of Mali, Guinea, and Senegal are present in
DR Congo (Fig. 2 and fig. S6). However, historical
links prior to dispersal of humans and parasites
to West and East Africa may also account for the
shared ancestry between all major population
blocs (Fig. 3). The early human migration from
Central Africa, after the emergence of malaria
in humans, was dominated by Bantu popula-
tions moving westward and southeastward (16).
T-SNE and fineSTRUCTURE clustering of an-
cestral chunk matrices also maintained the
major West and East African subpopulations,
further indicating that isolates from DR Congo
share more eastern ancestry (figs. S7 and S8). Hu-
man population mixing could have facilitated
P. falciparum gene flow, IBD signatures, and
spread of adaptive alleles across Africa (17).
The proportions of isolates sharing IBD (<3%)
was weak and uneven across the genome, as ex-
pected for intensely recombining parasite pop-
ulations (Fig. 4A and fig. S9). However, relatively
high IBD proportions spanned 12 segments of the
genome, including regions coding for candidate
drug resistance loci; Pfaat1 (PF3D7_0629500) on
chromosome 6; known drug resistance genes
Pfmdr1, Pfcrt, and Pfdhps; anda cluster of genes
on chromosome 12 (Pfap2mu, PfATPase, and
Pfap2g2). These genes are involved in drug re-
sponses, transportation, and metabolism (fig. S10).
These results confirm links between Pfcrt and
Pfaat1, which together with Pfap2g2 and PfAT-
Pase2 have been identified as part of the malaria
druggablegenome(18). Pfap2mu in particular
has been linked to artemisinin tolerance in
Africa (19). Strong IBD around Pfap2mu in Ghana
and Malawi (Fig. 4B) may have emerged inde-
pendently and calls for increased vigilance
against artemisinin-based combination therapy
(ACT) efficacy. The introduction or local emer-
gence and sharing of candidate drug resistance
haplotypes would be recent, as IBD detection
was limited to 25 generations. Haplotype paint-
ing across drug resistance loci (table S6) empha-
sized bidirectional gene flow across these loci
(fig. S11). Multiple origins of antifolate markers
were confirmed (20) but also seen for Pfmdr1,
which showed two ancestral lineages dominant
in West and East African populations, respec-
tively (fig. S12). Multiple emergence for a major
quinolone resistance mediator such as Pfmdr1
Amambua-Ngwa et al., Science 365, 813–816 (2019) 23 August 2019 3of4
Fig. 4. Pairwise IBD between isolates across sites. (A) Manhattan plot of median IBD between
pairs of P. falciparum isolates, showing each chromosome as numbered on the xaxis. IBD segment
peaks labeled for dihydrofolate reductase (dhfr), multidrug resistance protein 1 (mdr1), amino
acid transporter 1 (aat1), chloroquine resistance transporter (crt), dihydropteroate synthetase
(dhps), AP2 domain transcription factors (ap2-g2 and ap2-mu), and aminophospholipid-
transp orting P-ATPase (atpase2). (B) Heatmap of pairwise IBD between sampled populations
clustered on rows for similar patterns between populations. SNP values are in columns
separated by chromosomes for each pair of populations in rows. Low to high values are color
graded from blue to red on RGB color wheel.
RESEARCH |REPORT
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has not been previously reported. Selection,
emergence, and spread of resistance to drugs is
therefore possible in all malaria endemic sites
across sSA. These findings are important because
artemisinin resistance may emerge independently
in sSA and not necessarily spread from Southeast
Asia. This calls for careful surveillance of artemis-
inin resistance in sSA, where drug pressure from
ACT and seasonal malaria chemoprevention with
sulfadoxine-pyrimethamine and amodiaquine are
being scaled up for elimination. These would also
lead to population differentiation (fig. S13) and
positive selection that could facilitate the devel-
opment of clinical drug resistance.
SNPs related to drug resistance, erythrocyte
invasion, gametocytogenesis, oocyst development,
and antigenic loci were the most differentiated
between populations (fig. S14, A and B, and tables
S7 and S8). These could be due to different envi-
ronmental conditions and varying human and
mosquito populations. Known drug loci (Pfaat1,
Pfmdr1, Pfcrt, Pfdhfr, and Pfdhps) and the IBD
cluster on chromosome 12 showed signatures of
positive selection and haplotype differentiation
across sampled populations (figs. S14, C and D,
S15, and S16, and tables S9 and S10). It would be
important to determine whether variants at these
loci can compromise the efficacy of artemisinins
and/or ACTs.
P. falciparum in sSA is clustered into major
western, central, and eastern subgroups and a
highly divergent Ethiopian subpopulation. These
endogenous genomic lineages are the ancestral
backbone on which adaptive loci such as drug
resistance mutations may have emerged, recom-
bined, and been shared both westerly and easterly
across sSA. This may occur again against current
artemisinin-based treatments, which are already
directionally selecting loci on chromosome 12.
These signal the need for broader molecular
and phenotypic surveillance of P. falciparum in
sSA, including the large swathes of endemic pop-
ulations in Central Africa, where civil strife and
other global health pathogen epidemics could
maintain malaria and threaten elimination efforts.
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ACKNOW LEDGM ENTS
We thank the participants and local health workers from PDNA
sites. Special thanks to G. Busby for discussion and advising on
admixture analyses. Genome sequencing was done at the
Wellcome Sanger Institute as part of the MalariaGEN Plasmodium
falciparum Community Project (www.malariagen.net/projects).
We thank the MalariaGEN P. falciparum Community Project and
Pf3K Project for allowing access to non-PDNA data. We thank
K. Rockett, J. Stalker, R. Pearson, and other members of the
MalariaGEN resource center and the staff of Wellcome Sanger
Institute Sample Logistics, Sequencing, and Informatics facilities
for their contributions to sample processing, sequence data
generation, and variant calling pipelines. Funding: A.A.-N., L.A.-E.,
A.G., L.G., D.I., T.A., O.M.-A., B.A., Y.W., M.B.-A., and A.A.D. are
currently supported through the DELTAS Africa Initiative, an
independent funding scheme of the African Academy of Sciences
(AAS)’s Alliance for Accelerating Excellence in Science in Africa
(AESA), and are also supported by the New Partnership for Africa’s
Development Planning and Coordinating Agency (NEPAD Agency)
with funding from Wellcome (DELGEME grant 107740/Z/15/Z) and
the U.K. government. Sample collection in Kenya was funded by
Armed Forces Health Surveillance Center (AFHSB) and its Global
Emerging Infections Surveillance (GEIS) Section, Grant P0209_15_
KY. The views expressed in this publication are those of the
authors and not necessarily those of AAS, NEPAD Agency,
Wellcome, the U.S. Army or the Department of Defense, or the U.K.
government. The investigators have adhered to the policies for
protection of human subjects as prescribed in AR-70. Sequencing
was undertaken in partnership with MalariaGEN and the
Parasites and Microbes program at the Wellcome Sanger Institute
with funding from Wellcome (206194; 090770/Z/09/Z) and by
the MRC Centre for Genomics and Global Health which is jointly
funded by the Medical Research Council and the Department
for International Development (DFID) (G0600718 to D.K.;
M006212). Author contributions: A.G., L.G., M.R., D.I., T.A.,
O.M.-A., B.A., Y.W., O.K., and M.B.-A. contributed samples and
reviewed the manuscript. A.A.-N. and L.A.-E. contributed samples,
conceived of the manuscript, executed data analysis, and
participated in the writing (A.A.-N.) and revision (L.A.-E.) of the
manuscript. E.K. reviewed the analysis and manuscript. R.A.
provided analytical support. K.M., A.W., and D.J. conducted data
analysis and reviewed the manuscript. V.S. coor dinated the
collaboration an d reviewed the m anuscript. U.D. read and
reviewed the manu script. D.K. led the team that generated data,
conceived of the m anuscript, and reviewed the analysis an d
manuscript. A.A .D. coordinated the consortium, contr ibuted
samples conceived of the manuscript, and read and reviewed the
manuscript. Competing interests: The authors declare no
competi ng interest. Data and materials availability: The short-
read sequences used in this publication are available in the ENA
and SRA databases (see table S2 for accession numbers). The
views expressed are those of the authors and should not be
construed to represent the positions of the U.S. Army or the
Department of Defense. The investigators have adhered to the
policies for protection of human subjects as prescribed in AR-70.
SUPPLEMENTARY MATERIALS
science.sciencemag.org/content/365/6455/813/suppl/DC1
Materials and Methods
Figs. S1 to S16
Tables S1 to S10
References (21–31)
27 September 2018; accepted 5 July 2019
10.1126/science.aav5427
Amambua-Ngwa et al., Science 365, 813–816 (2019) 23 August 2019 4of4
RESEARCH |REPORT
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in sub-Saharan AfricaPlasmodium falciparumMajor subpopulations of
Dominic Kwiatkowski and Abdoulaye A. Djimde
Bouyou-Akotet, Oyebola Kolapo, Karim Mane, Archibald Worwui, David Jeffries, Vikki Simpson, Umberto D'Alessandro,
Randrianarivelojosia, Deus Ishengoma, Tobias Apinjoh, Oumou Maïga-Ascofaré, Ben Andagalu, William Yavo, Marielle
Alfred Amambua-Ngwa, Lucas Amenga-Etego, Edwin Kamau, Roberto Amato, Anita Ghansah, Lemu Golassa, Milijaona
DOI: 10.1126/science.aav5427
(6455), 813-816.365Science
, this issue p. 813; see also p. 752Science P. vivax.malaria parasite, , which may be indicative of coexistence with anotherP. falciparumand that Ethiopia has a distinctive population of
slavery. Furthermore, whole-genome sequencing showed that there is extensive gene flow among the different regions
signatures of selection by antimalarial drugs were detected, along with indications of the effect of colonization and
within Africa that is consistent with human and vector population divergence (see the Perspective by Sibley). Specific
of the Plasmodium Diversity Network Africa found substantial population structureet al.genomics, Amambua-Ngwa
important to know for grasping the risks and dynamics of the spread of drug resistance. Harnessing the power of
across Africa is poorly understood butPlasmodium falciparumThe population genetics of the malaria parasite
Ebb and flow of parasite populations
ARTICLE TOOLS http://science.sciencemag.org/content/365/6455/813
MATERIALS
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REFERENCES http://science.sciencemag.org/content/365/6455/813#BIBL
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