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\begin{document}

\title{Identification of a rapidly-spreading triple mutant for high-level
metabolic insecticide resistance in Anopheles gambiae provides a
real-time molecular diagnostic for anti-malarial intervention
deployment.}



\author[1]{Harun Njoroge}%
\author[1]{Arjen van't Hof}%
\author[1]{Ambrose Oruni}%
\author[1]{Dimitra Pipini}%
\author[1]{Sanjay C Nagi}%
\author[1]{Amy Lynd}%
\author[1]{Eric Lucas}%
\author[1]{Sean Tomlinson}%
\author[1]{Xavier Grau-Bové}%
\author[1]{Daniel McDermott}%
\author[2]{Francis t Wat'senga}%
\author[2]{Emile Z Manzambi}%
\author[3]{Fiacre R Agossa}%
\author[4]{Arlette Mokuba}%
\author[5]{Seth Irish}%
\author[6]{Bilali Kabula}%
\author[7]{Charles Mbogo}%
\author[8]{Joel Bargul}%
\author[1]{Mark JI Paine}%
\author[1]{David Weetman}%
\author[1]{Martin Donnelly}%
\affil[1]{Liverpool School of Tropical Medicine}%
\affil[2]{Institut National de Recherche Biomédicale}%
\affil[3]{USAID President’s Malaria Initiative }%
\affil[4]{USAID President’s Malaria Initiative, }%
\affil[5]{CDC}%
\affil[6]{National Institute for Medical Research Amani Centre}%
\affil[7]{Kenya Medical Research Institute}%
\affil[8]{Jomo Kenyatta University of Agriculture and Technology}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
Insecticide resistance provides both a pressing threat to the control of
vector-borne diseases and insights into the remarkable capacity of
natural populations to show rapid evolutionary responses. Malaria
control remains heavily dependent on deployment of insecticides,
primarily in long lasting insecticide treated nets (LLINs), but
resistance in the major malaria vectors has increased over the last 15
years. Identifying genetic mechanisms causing high-level resistance in
mosquitoes, which may almost entirely overcome pyrethroid efficacy, is
crucial for the development and deployment of potentially
resistance-breaking tools. Using the Anopheles gambiae 1000 genomes data
we identified a very recent selective sweep in Ugandan mosquitoes which
localized to a cluster of cytochrome P450 genes. Further interrogation
revealed a haplotype involving a trio of mutations, a point mutation in
Cyp6p4, an insertion of a partial Zanzibar transposable element (TE) and
a duplication of the Cyp6aa1 gene. The mutations appear to have
originated recently in An. gambiae from the Kenya-Uganda border region,
with stepwise replacement of the double-mutant (Zanzibar TE and
Cyp6p4-236M) with the triple-mutant haplotype (including Cyp6aa1
duplication), which has spread into the Democratic Republic of Congo and
Tanzania. The triple-mutant haplotype is strongly associated with
increased expression of genes able to metabolise pyrethroids; is
strongly predictive of resistance to pyrethroids but importantly,
appears less effective against LLINs co-treated with the synergist
piperonyl butoxide (PBO). Frequencies of the triple-mutant haplotype
remain spatially variable even within countries, suggesting an effective
marker system to guide deployment decisions for limited supplies of
PBO-pyrethroid co-treated LLINs across African countries.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Insecticide resistance in disease vectors has become an influential
model for understanding rapid contemporary evolution but, more
importantly, identifying how resistance arises and spreads is crucial
for disease control. Resistance to pyrethroid insecticides in African
malaria vector mosquitoes has spread to near ubiquity {[}1, 2{]} and,
though it is often difficult to demonstrate its impact on malaria
infections {[}3{]}, in some cases it has reached levels that threaten
the effectiveness of vector control programmes {[}4, 5{]}. A better
understanding of resistance distribution and mechanisms will permit a
more informed selection and deployment of insecticides to combat
evolving mosquito populations. Whilst our understanding of the genetic
basis of insecticide resistance in mosquitoes has advanced substantially
{[}6{]}, especially in the important vector \emph{Anopheles
funestus}{[}7{]}, molecular diagnostics for the major vectors in the
\emph{An. gambiae} complex remain limited to a handful of
mutations{[}8{]} which explain a relatively small fraction of the
variance in phenotype {[}9, 10{]} or which are now at such high
frequency as to provide limited diagnostic resolution {[}11{]}.

Long-lasting insecticidal nets (LLINs) are the principal tool for vector
control to combat malaria, especially in sub-Saharan Africa {[}12{]}.
The majority of LLINs are treated only with pyrethroid insecticides, to
which resistance is now widespread {[}13{]}. Though behavioural
variation and physical or physiological modifications affecting
insecticide uptake may sometimes play a role, pyrethroid resistance is
caused predominantly by two distinct mechanisms. The first is resistance
via point mutations in the target-site of the insecticide, for
pyrethroids the Voltage-gated sodium channel (\emph{Vgsc} ), which
results in decreased sensitivity to the insecticide {[}14{]}; the second
is metabolic resistance due to over-expression or altered activity of
detoxification enzymes, of which the cytochrome P450 family is commonly
considered most important {[}15, 16{]}. Cytochrome P450 activity is
inhibited by the synergist piperonyl-butoxide (PBO), and bed nets
incorporating PBO are effective against P450-mediated resistance, as
demonstrated by large-scale field trials {[}4, 17{]}. Given the
continued operational use of pyrethroid-containing nets, it is vital
that we understand the genetic mechanisms that may impact their
efficacy, to optimise bednet deployment, preferably using information
from rapidly-applied DNA markers. In advance of a randomized control
trial of PBO-LLINs {[}4{]}, we sought to characterise pyrethroid
resistance mechanisms in the primary malaria vector \emph{An. gambiae
s.s.} {[}18{]} in Uganda and Kenya.

The recent development of the \emph{Anopheles gambiae} 1000 genomes
project (Ag1000g) has led to a step change in our ability to identify
DNA variation driven by selection pressure. We have been able to perform
genome-wide searches for regions under recent natural selection in
insecticide resistant populations across Africa, and work has shown that
the strongest selective sweeps in the genome are all found around genes
known to be important for resistance {[}6, 19{]}. Furthermore, a
whole-genome scan of copy number variants (CNVs) in the Ag1000g data
revealed that increases in gene copy number were highly enriched in
clusters of detoxification genes, pointing to a potentially widespread
mechanism for increased gene expression {[}20{]}, which, in some cases,
may elevate resistance. A number of gene duplications were observed
around the \emph{Cyp6aa} /\emph{Cyp6p} gene family cluster on chromosome
2R, and the majority of these duplications included the
gene\emph{Cyp6aa1} (Figure 1). \emph{Cyp6aa1} has been found to be
overexpressed in pyrethroid resistant populations in congeneric species
{[}21-23{]}, but it has received very little attention compared to known
insecticide-metabolizing genes such as \emph{Cyp6m2} {[}24,
25{]},\emph{Cyp6p3} {[}15{]} and \emph{Cyp9k1} {[}26{]} and its
importance in resistance in \emph{An. gambiae} remains unknown.

In this study, we examine a strong selective sweep detected in
the\emph{Cyp6aa/Cyp6p} genomic region in samples of \emph{An. gambiae
s.s.}from Uganda and Western Kenya. We find that the sweep is closely
associated with three mutations (a SNP in \emph{Cyp6p4} , a duplication
of \emph{Cyp6aa1} and a partial transposable element insertion termed
ZZB-TE) in tight physical and statistical linkage. The triple-mutant
haplotype is associated with a high-level of pyrethroid resistance, most
notably to deltamethrin. The three mutations appeared sequentially,
leading to successive selective sweeps, with the triple-mutant haplotype
replacing earlier variants and then spreading rapidly across East and
Central Africa. We show that this haplotype is under positive selection
and causes increased expression of key cytochrome P450s and through
recombinant protein expression using both an \emph{E. coli} and
an\emph{Sf9} -baculovirus system we show that both CYP6AA1 and CYP6P4
are capable of metabolizing pyrethroid insecticides.

\textbf{Methods}

\textbf{Interrogation of the Ag1000g dataset and identification of
tagging markers}

Whole genome sequence data in the Ag1000g data set have previously
revealed a strong selective sweep in Ugandan populations around
the\emph{Cyp6aa/Cyp6p} cluster {[}19{]} (Figure 1). An isoleucine to
methionine substitution in codon 236 of CYP6P4 (see extended data Figure
10b in {[}19{]}) was identified provisionally as a swept haplotype
tagging SNP. Previous work {[}20{]} had shown that a duplication of
the\emph{Cyp6aa1} gene was also observed in these samples (previously
termed \emph{Cyp6aap-Dup1} ). To objectively determine how these
mutations segregated with the observed selective sweep {[}19{]} we
grouped the 206 Ugandan haplotypes (n=103 diploid individuals) by
similarity using the 1000 SNPs located immediately upstream and
downstream of the start of the \emph{Cyp6aa/Cyp6p gene} cluster (500
non-singleton SNPs in each direction from position 2R:28,480,576).
Distances were calculated with the \emph{pairwise\_distance} function
in\emph{scikit-allel} {[}27{]} and converted to a nucleotide divergence
matrix (\emph{Dxy} statistic) by correcting the distance by the number
of sequencing-accessible bases in that region. We defined clusters of
highly similar haplotypes by hierarchical clustering with a cutoff
distance of 0.001. This resulted in the identification of a cluster of
122 highly similar haplotypes. To determine whether the haplotype
cluster showed signs of a selective sweep, we estimated the extended
haplotype homozygosity (EHH) decay within each of the haplotype
groupings, around two different focal loci: (i) the putative sweep SNP
marker \emph{Cyp6p4-236M} (2R:28,497,967 +/- 200 kbp; total 14,243
phased variants), and (ii) the 5' and 3' breakpoints of
the\emph{Cyp6aap-Dup1} duplication (2R:28,480,189 - 200 kbp and
2R:28,483,475 + 200 kbp; total 14,398 phased variants). We used
the\emph{ehh\_decay} function of \emph{scikit-allel}{[}27{]}.

We also used the haplotype groupings identified above to calculate the
Garud \emph{H} statistics {[}28{]} and the haplotypic diversity at the
\emph{Cyp6aa/Cyp6p} cluster locus (coordinates 2R:28,480,576 to
2R:28,505,816). Specifically, we used the\emph{moving\_garud\_h} and
\emph{moving\_haplotype\_diversity}functions in \emph{scikit-allel} to
obtain a series of estimates for each statistic in blocks of 100
variants located within the cluster, and used a block-jackknife
procedure to calculate averages and standard errors of each estimate
(\emph{jackknife} function in\emph{scikit-allel} \emph{misc} module).

\textbf{Molecular screening of colony and wild caught mosquitoes}

Locked-nucleic acid (LNA) probe-based PCR diagnostics were designed for
all three mutations (Supplementary materials Appendix 1).

Genotype:phenotype association testing was performed using two colonies
of \emph{An. gambiae s.s.} , BusiaUG (resistant) and Mbita
(susceptible). The BusiaUG strain was established in the lab in November
2018 from Busia, eastern Uganda and exhibits high resistance to
pyrethroids and organochlorines (\textless{}10\% mortality following WHO
exposure test), but full susceptibility to organophosphates and
carbamates (Oruni\emph{et al.} unpublished). The Mbita strain was first
colonised from Mbita Point, Kenya in 1999, and is fully susceptible to
pyrethroids. Colonies were reared in insectaries targeted to
25-27\textsuperscript{o}C and 70-80\% relative humidity.

Freshly emerged females from the Mbita line were mated with 3-5 day-old
BusiaUG males and then blood fed. The offspring from this cross were
then crossed back to the parental BusiaUG line. This design was chosen
as resistance variants are often recessive. The resultant backcrossed
3-5-day-old females were exposed for one hour to deltamethrin,
permethrin or \selectlanguage{greek}α-\selectlanguage{english}cypermethrin (the three insecticides most commonly used
on LLINs) or DDT (a non-pyrethroid sodium channel antagonist), following
WHO standard procedures. Mosquitoes were maintained on a 10\% sugar
solution after exposure and mortality was recorded 24 hours post
exposure.

\emph{Democratic Republic of Congo.} \emph{Anopheles gambiae s.s.} were
obtained from the President's Malaria Initiative supported entomological
surveillance project {[}29{]} and from collections conducted by
Lynd\emph{et al} {[}30{]}. Mosquitoes were collected by human landing
catch or pyrethrum spray collection from 15 locations between 2013 and
2018. Resistance-phenotyped individuals were also obtained from Pwamba,
Bassa and Fiwa in Nord Ubangi Province in 2016. These mosquitoes were
assessed for susceptibility to deltamethrin or permethrin using a
standard WHO tube assay or a cone bioassay where Permanet 3.0
(deltamethrin plus PBO) or Olyset Plus (permethrin plus PBO) were the
test nets {[}23{]}.\emph{Kenya and Uganda.} Collection details for
specimens from contiguous areas of western Kenya and eastern Uganda have
been published previously {[}20, 24, 25{]}.\emph{Tanzania.} Mosquito
collections were conducted in Geita, Bagamoyo and Muleba districts of
Tanzania in 2018.

Mosquitoes from all collections were genotyped at the
ZZB-TE,\emph{Cyp6p4-236M} and \emph{Cyp6aap-Dup1} loci and
genotype:phenotype association testing was performed by Fisher's exact
tests.

\textbf{Analysis of temporal change}

The most complete time series of ZZB-TE, \emph{Cyp6p4-236M}
and\emph{Cyp6aap-Dup1} allele frequencies were available from Kabondo,
DRC; western Kenya and eastern Uganda. To model allele frequency changes
over time we estimated the three parameters of the standard recursive
population genetic model (allele frequency at time zero, selection
coefficient and dominance coefficient) using a maximum likelihood
approach assuming a binomial distribution: an approach previously
applied to insecticide target-site resistance mutations {[}31{]}. The
analysis was performed in R (\emph{http://www.r-project.org}). An
estimated generation time of one calendar month was used as in previous
studies {[}31{]}.

\textbf{Recombinant protein and insecticide metabolism}

The CYP6P4-236M variant, and its redox partner cytochrome P450 reductase
gene (CPR), were expressed in \emph{E. coli} as per standard protocols
{[}32{]} (Supplementary materials Appendix 2). Initial efforts to
generate recombinant \emph{CYP6AA1} in an \emph{E. coli} system with
optimised codon usage failed, and we therefore used an
Sf9-baculovirus-based expression system. Since P450 catalytic activity
is dependent on electrons supplied by NADPH via CPR, insecticide
metabolism was assayed with cell pellets of CYP6P4/CPR or CYP6AA1/CPR in
the presence or absence of NADPH. The depletion of the substrate and the
appearance of metabolites were monitored by reverse-phase HPLC
(Supplementary materials Appendix 3).

\textbf{Estimation of gene expression of key P450s in triple mutant
haplotype}

To determine whether the presence of the triple mutant haplotype type
was associated with differential expression of genes, we examined
individual females from the BusiaUg colony (Triple mutant Freq. 0.297;
95\%CI 0.233-0.370). Two legs were removed from individual mosquitoes
for DNA analysis with the remaining mosquito kept for RNA analysis. DNA
was extracted from legs by boiling in STE buffer at 95\selectlanguage{ngerman}°C for 90 minutes
and individuals were genotyped using the LNA qPCR assays. RNA was then
extracted individually from 8 mosquitoes in each genotypic group -
homozygotes for the triple mutant haplotype, wild-type homozygotes, and
heterozygotes, using the Arcturus Picopure RNA isolation kit
(Thermofisher). We then performed SYBR green based qPCR to measure the
expression of \emph{Cyp6aa1} and \emph{Cyp6p4} together with the known
resistance-linked variant \emph{Cyp6p3} using the housekeeping genes 40s
ribosomal protein S7 (AGAP010592) and elongation factor Tu (AGAP005128)
for normalisation. The \selectlanguage{greek}ΔΔ\selectlanguage{english}CT values were tested for normality and
homogeneity of variances using the Shapiro-Wilks test, and the Bartlett
test, respectively. A significant difference in gene expression between
the genotypic groups was determined by a two-tailed two-sample Student's
t-test on \selectlanguage{greek}ΔΔ\selectlanguage{english}CT values, with a threshold of P=0.05.

\textbf{Results}

Hierarchical clustering of 206 Ag1000g haplotypes from \emph{An. gambiae
s.s.} from Uganda resulted in identification of a cluster of 122
haplotypes around the \emph{Cyp6aa} /\emph{Cyp6p} gene cluster,
putatively representing a swept haplotype in this region. To
characterise the signatures of selection in Ugandan haplotypes around
this cluster, we examined the profile of extended haplotype homozygosity
around the position of the \emph{Cyp6p4-236M} SNP and around the CNV
in\emph{Cyp6aa1} . In both cases, we found that the putative swept
haplotype had longer stretches of homozygosity than wild-type haplotypes
(Figure 2). In addition, we found that \emph{An. gambiae s.s.} from
Uganda had reduced haplotypic diversity along the
entire\emph{Cyp6aa/Cyp6p} gene cluster (\emph{h} = 0.339746 +/- 0.005664
standard error) and a combination of Garud's \emph{H} statistics that
was indicative of a hard selective sweep in this region
(high\emph{H\textsubscript{12}} = 0.821867 +/- 0.006308 SE;
low\emph{H\textsubscript{2}/H\textsubscript{1}} = 0.016779 +/- 0.000228
SE){[}28{]}. These results confirm that the haplotypes we have
identified have undergone a selective sweep. We then used iterative read
mapping of individuals homozygous for the sweep to search for additional
mutations that might be distinctive of the haplotype. This revealed that
a partial copy of a Ty3/Gypsy Zanzibar transposon insertion (termed
ZZB-TE), lacking functional open reading frames, was linked to
\emph{Cyp6p4-236M} and \emph{Cyp6aap-Dup1} (Figure 2).

\textbf{The evolution of the ZZB-TE, \emph{Cyp6p4-236M}
and\emph{Cyp6aap-Dup1} haplotypes}

Based upon Ag1000g data and a time series of collections from Central
and East Africa, we were able to trace the sequence of mutational events
(ZZB-TE, \emph{Cyp6p4-236M} and \emph{Cyp6aap-Dup1} ) and reconstruct
the evolutionary history of the swept haplotype. Among the Ag1000g data
{[}19{]}, the \emph{Cyp6p4-236M} mutation was only observed in
collections from eastern Uganda (collected in 2012) suggesting that this
mutation originated in the eastern Ugandan/western Kenyan region. In a
screen of collections from Uganda and Kenya predating the Ag1000g
collections by eight years (2004) (Fig 3) only the ZZB-TE insertion was
detected, although the sample size was too small (n=4) to conclude that
the \emph{Cyp6p4-236M} allele was absent. The \emph{Cyp6p4-236M}mutation
was first observed in this region in 2005 (frequency\emph{Cyp6p4-236M}
=0.10) in individuals carrying the ZZB-TE mutation, whilst the
\emph{Cyp6aap-Dup1} CNV was first recorded in 2008 (proportion of
individuals with Dup1=0.8\%). This inferred sequence of events may
explain why ZZB-TE and \emph{Cyp6p4-236M} mutations are in tighter
statistical linkage with each other than with\emph{Cyp6aap-Dup1} (Figure
2), despite the closer proximity of ZZB-TE and \emph{Cyp6aap-Dup1}
(Figure 1). Given the very tight association between the ZZB-TE
insertion and the \emph{Cyp6p4-236M} SNP, we will henceforth refer to
the \emph{Cyp6p4-236M} (double mutant) haplotype and the
\emph{Cyp6aap-Dup1} (triple mutant). The double mutant haplotype shows a
steady increase in frequency between 2004 and 2011 in Kenya (Figure 3);
possibly in response to the introduction and subsequent intensification
of bednet distribution programmes {[}29, 33, 34{]}. Following its
appearance in 2008, the triple mutant haplotype, rapidly increased
towards fixation in both collections from Uganda and Kenya, replacing
the double mutant. This haplotype replacement and the observation that
the triple mutant is the only non-wildtype haplotype observed outside
Kenya/Uganda (such as in Tanzania and DRC, Figure 1 and 3) strongly
implies an additional selective advantage to the triple mutant. The time
series data from across DRC are particularly striking both in terms of
the speed of increase of the triple mutant but also the north-south
heterogeneity, with very low frequencies in the more southerly provinces
(Figure 3).

\textbf{Identifying potential drivers of haplotype frequency increase}

The genomic region into which the ZZB-TE inserted does not show histone
signals of regulatory activation (H3K27ac, H3K9ac) or repression
(H3K9me3), and ATAC-seq data suggests it is not in an open chromatin
region {[}35{]}. However we took two in silico approaches to determine
whether the ZZB-TE insertion (748bp) carried putative regulatory
variants. The inserted region had 98.5\% sequence identity to a putative
enhancer (2R:45966598-45966822) identified by homology
with\emph{Drosophila melanogaster} {[}35{]}. However, despite the
similarity, the insertion lacked enhancer-like combination of chromatin
marks identified in {[}35{]} and its potential regulatory role in nearby
genes is unclear. The second approach involved screening the ZZB-TE
inserted sequence for putative enhancers using iEnhancer-2L{[}36{]} and
iEnhancer-EL {[}37{]}. In a windowed analysis of 200bp with a 1bp step
across the entire length of ZZB-TE both predicted that some of the
windows would have strong enhancer activity, however the windows were
not-concordant, precluding further analysis.

Given that cytochrome P450 mediated resistance is commonly associated
with differential gene expression we performed transcription studies
within the \emph{Cyp6aa/Cyp6p} cluster between the most contrasting
haplotypes, wild-type and triple mutant, present in the BusiaUg colony.
The group homozygous for the triple mutant haplotype significantly
overexpressed both \emph{Cyp6aa1} (2.23-fold, 95\% CI: 1.73-2.90,
P=0.0003) and \emph{Cyp6p4} (2.57-fold, 95\% CI 1.25-5.93, P=0.039)
compared to wild-type individuals. The ratio of expression
of\emph{Cyp6aa} 1 broadly reflected the expected pattern based on
genotype (ie 2:1.5:1 for triple mutant homozygotes: heterozygotes: wild
type genotypes, respectively. Figure 4). As a control we examined a
neighbouring, very commonly resistance-associated gene, \emph{Cyp6p3} ,
but triple mutant and wild type homozygotes did not differ significantly
in expression (1.33 fold, 95\% CI 0.64-2.74, P\textgreater{}0.05).

To investigate whether resistance may be driven at least in part by an
effect of the allelic variant on metabolic activity of CYP6P4, we
expressed the wild-type (236I) and mutant (236M) forms in an \emph{E.
coli} based recombinant protein system (Supplementary materials
Appendices 2 and 4). Both alleles were shown to be capable of
metabolizing class I (permethrin) and II (deltamethrin) pyrethroids but
there was no evidence that the mutant (236M) or wildtype (236I) alleles
had different rates of pyrethroid depletion. We also expressed the
duplicated P450 CYP6AA1, in an \emph{Sf9} -baculovirus protein
expression system. Again, metabolism assays demonstrated that the enzyme
was capable of metabolizing both deltamethrin and permethrin
(Supplementary materials Appendices 3 and 4). Depletion of deltamethrin
was 36.6\% greater (SE= 3.79) in the presence of NADPH than in the
control (t-test: t=-9.67; d.f. = 8; P= 9.6 x10\textsuperscript{-6}),
demonstrating that CYP6AA1/CPR is capable of metabolizing deltamethrin
\emph{in vitro} . Similarly, permethrin was metabolised by CYP6AA1/CPR,
with permethrin being depleted by 22.4\% (SE = 0.63) compared to the
control without NADPH (t-test: t= -31.08; d.f. = 14; P= 2.55
x10\textsuperscript{-14}).

Given clear evidence of increased expression of both \emph{Cyp6aa1}
and\emph{Cyp6p4} in the triple mutant haplotype and the ability of both
enzymes to metabolise pyrethroids \emph{in vitro} , we investigated
whether the mutations were significantly associated with
resistance\emph{in vivo.} Exposure of \emph{An. gambiae} females from
Busia, Uganda and Nord Ubangi, DRC to new LLINs in cone assays resulted
in negligible mortality to the pyrethroid only LLINs, Olyset and
Permanent 2.0 (Figure 5). Simultaneous exposure to pyrethroid plus the
P450 inhibitor PBO in Olyset + and the top of Permanent 3.0 nets
resulted in a marked reduction in resistance, demonstrating that the
resistance phenotype is substantially mediated by P450s. We performed
laboratory backcrosses between additional mosquitoes from Busia with the
pyrethroid susceptible Mbita colony, and found that the triple mutant
haplotype was significantly associated with resistance to the most
commonly used type II pyrethroids in LLINs: deltamethrin (Fisher's exact
test p=3.2x10\textsuperscript{-6}) and alphacypermethrin (Fisher's exact
test p=5.9x10\textsuperscript{-7}) resistance although not to permethrin
(Fisher's exact test p=0.06) nor, as a control, DDT (Fisher's exact test
p=0.84) (Table 1) in WHO tube assays. Similarly, specimens collected in
2016 from the DRC showed a strong association between the triple mutant
genotype and survival rate 24 hours post-exposure to either 0.05\%
deltamethrin for 1 hour or 3-minute exposures to deltamethrin-treated
sides of a new PermaNet 3.0 net (Table 2). No association was found in
samples exposed to permethrin (24 hour WHO tube assay) or
permethrin-treated Olyset Plus nets (3-minute WHO cone assay) (Table 2).
Complete linkage of the three mutants in the BusiaUG colony and the DRC
wild caught collections precludes determination of the relative
contribution of each of the three mutations to the resistance phenotype
but taken together these results demonstrate a strong impact of the
triple mutant on the efficacy of pyrethroid resistance.

\textbf{Discussion}

We have identified a sequential series of fitness-augmenting mutations
in \emph{An. gambiae} culminating in a triple-mutant haplotype with a
large effect on pyrethroid resistance and which is spreading rapidly
across East and Central Africa. The mutation that is probably the oldest
in this series, the insertion of ZZB-TE, was first detected in 2004 in
the malaria-endemic area around Lake Victoria, with
the\emph{Cyp6p4-236M} SNP evident in 2005 samples and the third, a
duplication in \emph{Cyp6aa1} detected in 2008. In samples collected
only five years later, the triple mutant was detected hundreds of
kilometres away in the DRC. These patterns suggest both a large fitness
advantage arising from the triple mutant, and a frightening speed at
which resistance-conferring mutations are able to spread within and
across populations.

Second generation nets treated with the synergist PBO were shown to be
much more effective than conventional nets in killing mosquitoes in
populations where the triple mutant haplotype is present (Figure 5).
Therefore the use of PBO bednets should be prioritised in the regions
where this mutation is present. A strong corollary of this finding comes
from the cluster randomised control trial conducted in Uganda, where the
mutation is at high frequency, which demonstrated that malaria parasite
prevalence in children \textless{}10 years old (12\% vs 14\%; Prevalence
ratio = 0\selectlanguage{ngerman}·84, 95\% CI 0·72--0·98; P=0·029) and mean number of mosquitoes
(Density ratio=0·25, 95\% CI 0·18--0·3; P \textless{}0·0001) per house
were significantly lower in villages that had received PBO LLINs
relative to standard LLINs {[}4{]}. There is some evidence that the
haplotype may be less strongly associated with resistance to permethrin
than deltamethrin (and perhaps also alphacypermethrin), although both
pyrethroids were metabolised by \emph{Cyp6aa1} and \emph{Cyp6p4} .

Our results highlight the importance of gene duplications for the
evolution of insecticide resistance. In \emph{An. gambiae} ,
duplications have recently been shown to be concentrated in regions
associated with metabolic resistance, and over 40 such duplications have
been described across the genome {[}20{]}. Thirteen different
duplications have so far been described that encompass \emph{Cyp6aa1}
(Figure 1), both in West and East Africa and in the two sister-species
\emph{An. gambiae} ss and \emph{An. coluzzii} {[}20{]}. It seems likely
that these other \emph{Cyp6aa1}duplications are also associated with
pyrethroid resistance. For example, in \emph{An. coluzzii} sampled from
a highly insecticide-resistant population from Cote d'Ivoire, greater
than 95\% of individuals had Cyp6aap duplications (Supplementary
materials Appendix 5). Five different duplications were observed in the
collections from 2012 and 2017 and, whilst we detected no association
with pyrethroid resistance, two duplications (\emph{Cyp6aap-Dup7}
and\emph{Cyp6aap-Dup14} ) showed significant increases in frequency over
time. Moreover the total number of CNVs per sample (measured as presence
of each of the 5 duplications, summed for each sample) increased
significantly from an average of 1.59/ individual in 2012 to 2.08 in
2017 (Mann Whitney U test; P\textless{}0.0001); the mean number of
duplications \textgreater{}2 indicates that there are multiple CNVs on
the same haplotype. Duplications of the \emph{Cyp6aa1} orthologue have
been found in another important malaria vector, \emph{An. funestus} ,
from west and central Africa {[}21{]}. The \emph{Cyp6aa1} orthologue
in\emph{An. funestus,} which shares an 87\% identity with \emph{An.
gambiae,} was also observed to metabolize permethrin and deltamethrin
and, when expressed in transformed \emph{Drosophila} , was associated
with significant increases in resistance to both permethrin and
deltamethrin relative to control mosquitoes {[}21{]}. This evolution of
multiple \emph{Cyp6aa1} duplications suggests this is an important
Africa wide resistance mechanism

There are now several cases of insecticide resistance evolution where an
initial mutation in a genomic region is followed by the spread of
additional mutations on the resistant haplotype background {[}14,
38-42{]}. It is not yet clear whether the sequence of mutations that we
have identified rely on each-other for effect, and thus could only have
spread sequentially, or whether each additional mutation coincidentally
appeared on the background of an already common mutant haplotype. In the
case of \emph{Cyp6p4-236M} and \emph{Cyp6aap-Dup1} it seems that the
latter is more likely, although we cannot exclude the possibility that
the duplication affects the regulation of \emph{Cyp6p4} . In contrast,
the putative enhancer inserted with the ZZB transposon may affect either
or both of \emph{Cyp6p4} and \emph{Cyp6aa1} , and may thus interact with
the other two mutations in ways that have yet to be determined.
Transposable elements can sometimes affect gene expression of
neighbouring genes {[}38{]} and are abundant in mosquito genomes {[}43,
44{]}. Interestingly, in the common house mosquito, \emph{Culex pipiens}
, TEs have been found in the flanking regions of the\emph{Ester} locus,
a genomic region in which many independent gene duplications have arisen
and spread worldwide in response to selection from organophosphates
{[}45{]}. Clearly, both TEs and gene duplications are an understudied,
yet common source of variation that may have important implications for
vector control efforts. The appearance and rapid spread of the three
mutations described here is broadly coincident with the scale up of LLIN
coverage in DRC, Kenya and Uganda. The haplotype is a strongly
predictive marker of high-level resistance to pyrethroids, is easily
screened with a single diagnostic assay and we suggest should be used
for both insecticide resistance monitoring strategies and for informing
LLIN selection.{[}6, 29, 46{]}.

\textbf{Acknowledgements}

This work was supported by the National Institute of Allergy and
Infectious Diseases ({[}NIAID{]} R01-AI116811) with additional support
from the Medical Research Council (MR/P02520X/1). The latter grant is a
UK-funded award and is part of the EDCTP2 programme supported by the
European Union. HN and AO were supported by Wellcome Trust MSc Training
Fellowships in Public Health and Tropical Medicine. SI is funded by the
U.S. President's Malaria Initiative. CM was supported by the Bill and
Melinda Gates Foundation through the WHO (Award \#54497) and by the
Biovision Foundation (Grant No.BV HH-07). JLB is supported by DELTAS
Africa Initiative grant \# DEL-15-011 to THRiVE-2. The DELTAS Africa
Initiative is an independent funding scheme of the African Academy of
Sciences (AAS)'s Alliance for Accelerating Excellence in Science in
Africa (AESA) and supported by the New Partnership for Africa's
Development Planning and Coordinating Agency (NEPAD Agency) with funding
from the Welcome Trust grant \# 107742/Z/15/Z and the UK government. SI
is funded by the U.S. President's Malaria Initiative. The findings and
conclusions in this paper are those of the authors and do not
necessarily represent the official position of CDC, the NIAID, the
National Institutes of Health or the other donors.

\textbf{Author contributions}

HN, AvH, MJIP, DW and MJD designed the study; HN, AvT, AO, DP, SCN, AL
conducted lab and insectary experiments; HN, AvH, SCN, AL, ERL, ST,
XG-B, DM, MJD performed analysis; AO, AL FW, EM, FA, SI, BK, DW, CM
conducted field collections and phenotyping; HN,ERL, DW, MJD wrote the
manuscript with input from all authors; JLB, CM, MJIP, DW and MJD
supervised the study; all authors approved the final version of the
manuscript.

\textbf{Data Availability Statement}

Sequence read alignments and variant calls from Ag1000G are available
from the European Nucleotide Archive under study accessions PRJEB18691
and PRJEB36277 (ENA http://www.ebi.ac.uk/ena ). Python scripts to
reproduce all analyses detailed in this manuscript are available
at\emph{https://github.com/xgrau/cyp6-AgUganda}

\textbf{Tables and figures}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1. Schematic of the mutational events around
the\emph{Cyp6aa/Cyp6p} cluster on chromosome 2R in \emph{Anopheles
gambiae}} . Upper panel shows the three mutational events observed in
East and Central African samples. Orange indicates the genes/
transposable elements involved and the blue band the extent of
the\emph{Cyp6aap-Dup1} CNV. The lower panel, redrawn from {[}20{]},
shows the genomic extent and geographic distribution of the CNVs that
have been observed around the \emph{Cyp6aa/Cyp6p} cluster, 13 out of 15
include \emph{Cyp6aa1} .The dark blue shading on the map indicates the
geographic extent of \emph{Cyp6aap-Dup} 1. Data from Uganda, Kenya and
DRC are reported extensively in the main text, in addition 27.4\%
(n=84)\emph{of Anopheles gambiae} females sampled from northern Tanzania
in 2018 were triple mutant carriers.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 2. Selective sweep around the \emph{Cyp6aa/Cyp6p} cluster
in\emph{Anopheles gambiae} from Uganda.}

Upper panel Haplotype contingency for the selective sweep
(``Sweep``),\emph{Cyp6p4-236M} SNP (''P4'') and the \emph{Cyp6aap-Dup1}
(''Dup1''). Each vertical bar represents a single haplotype,
colour-coded to show whether it is a copy of the swept haplotype (blue)
and whether it carries the SNP (purple), the TE insertion (green) and
the duplication (orange). The swept haplotype and the \emph{Cyp6p4-236M}
SNP overlap almost completely, while the duplication is found on a
subset of these haplotypes. Lower panel Extended Haplotype Homozygosity
(EHH) plots around the \emph{CYP6P4-236M} and \emph{CYP6AA1-Dup1}
variants show slower loss of homozygosity in the swept haplotypes than
in the wild-type.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 3. Observed and modelled changes in
\emph{Cyp6aa/Cyp6p}haplotype frequencies over time in \emph{Anopheles
gambiae s.s.}populations from DRC, Kenya and Uganda.} Upper panel shows
mutation frequency estimates derived from wild caught individuals from
Kabondo, DRC; western Kenya and eastern Uganda, The 95\% CIs for each
observed data point were calculated according to{[}47{]}. Expected data
generated from simultaneous maximum likelihood estimates of initial
frequency and selection and dominance coefficients which were then used
to parameterize standard recursive allele frequency change equation. The
lower panel shows data from DRC tracking the emergence and spread of the
triple mutant haplotype. Triangles indicate collection locations within
each district. The red triangle shows the Kabondo sample site for which
there was the most complete time series.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure 4. Gene expression analysis of \emph{Cyp6p4} and
\emph{Cyp6aa1} in genotyped \emph{Anopheles gambiae} females.} Relative
fold change for\emph{Cyp6p4} (Panel A) and \emph{Cyp6aa1} (Panel B),
comparing individuals homozygous for the swept triple mutant haplotype
and heterozygotes in the BusiaUG colony to wild-type individuals. 95\%
confidence intervals are shown. Asterisks indicate statistical
significance in two-tailed students t-tests; *** P [?] 0.001, ** P [?] 0.01,
** P [?] 0.05.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\textbf{Figure 5. Patterns of insecticide resistance in \emph{Anopheles
gambiae} following exposure to permethrin and deltamethrin treated
nets.} Female \emph{Anopheles gambiae s.s.} from the Democratic Republic
of Congo (DRC) and Uganda exhibited very high resistance (low \%
mortality) in WHO cone assays to permethrin (Olyset) and deltamethrin
(PermaNet 2.0) LLINs. There was an increase in mortality following
exposure to an increased concentration of deltamethrin (PermaNet 3.0
Side) and very high mortality following exposure to pyrethroid plus the
P450 inhibitor piperonyl butoxide (PBO) Olyset+ and PermaNet 3.0 Top.
95\% confidence intervals are shown.

\textbf{Table 1. Association between insecticide susceptibility as
determined by WHO tube bioassay and triple mutant genotype in
female\emph{Anopheles gambiae} from a cross between BusiaUG and Mbita
colonies. \textsuperscript{a}Odds ratios for allelic test and associated
95\%CI}\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\textbf{Insecticide}\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\textbf{Phenotype}\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
Wildtype homozygote\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
Heterozygote\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
Triple mutant homozygote\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\textbf{Test and p value}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\selectlanguage{greek}\textbf{α-Cypermethrin}\selectlanguage{english}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Alive}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
32\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
24\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Genotypic p=5.92x10\textsuperscript{-7}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{n=146}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Dead}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
14\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
68\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Allelic p=6.34x10\textsuperscript{-5}; OR\textsuperscript{a} 2.74
(1.63-4.69)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Deltamethrin}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Alive}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Genotypic p=3.21x10\textsuperscript{-6}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{n=60}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Dead}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
15\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
26\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Allelic- Cannot be calculated\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Permethrin}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Alive}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
18\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Genotypic p=0.059\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{n=53}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Dead}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Allelic p=0.08; OR 1.99 (0.86-4.67)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{DDT}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Alive}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
14\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Genotypic p=1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{n=65}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Dead}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
45\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Allelic p=0.84; OR 1.19 (0.48-2.94)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}



\textbf{Table 2. Association between insecticide susceptibility as
determined by WHO tube bioassay or WHO net bioassay and triple mutant
genotype in wild-caught, female \emph{Anopheles gambiae} from three
locations in Nord Ubangi Province, Democratic Republic of
Congo.\textsuperscript{a}Odds ratios for allelic test and associated
95\%CI}\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Site}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Insecticide}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Bioassay}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Phenotype}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Wildtype homozygote\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Heterozygote\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Triple mutant homozygote\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Test and p value}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Fiwa}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Permethrin}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Tube}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Alive}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
42\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
30\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Genotypic p=0.63\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{n=161}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Dead}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
49\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Allelic p=0.42; OR\textsuperscript{a} 1.23 (0.76-2.00)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Fiwa}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Deltamethrin}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Tube}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Alive}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
14\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
18\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Genotypic p=6.21x10\textsuperscript{-3}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{n=78}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Dead}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Allelic p=0.04; OR 2.01 (1.01-4.06)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Bassa}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Permethrin}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Tube}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Alive}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
19\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
18\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Genotypic p=0.79\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{n=95}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Dead}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
32\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
23\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Allelic p=0.63; OR 1.21 (0.61-2.43)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Bassa}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Deltamethrin}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Tube}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Alive}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
16\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Genotypic p=4.02x10\textsuperscript{-3}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{n=47}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Dead}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
16\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Allelic p=5.71x10\textsuperscript{-3}; OR 5.44 (1.41-31.28)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Pwamba}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Deltamethrin}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Tube}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Alive}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Genotypic p=0.066\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{n=40}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Dead}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Allelic p=0.02; OR 2.93 (1.07-8.39)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Fiwa}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Olyset Plus}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Net}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Alive}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
21\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
30\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Genotypic p=0.82\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{n=106}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Dead}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
15\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
28\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Allelic p=0.88; OR 0.93 (0.49-1.77)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Fiwa}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Permanet 3.0}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Net}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Alive}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
26\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
24\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Genotypic p=5.61x10\textsuperscript{-4}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{n=155}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Dead}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
30\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
51\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
19\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Allelic p=1.4x10\textsuperscript{-4}; OR 2.56 (1.54-4.31)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

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