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\begin{document}

\title{Genome-wide analysis of the human head louse (\emph{Pediculus humanus
capitis}) reveals geographically structured genetic populations}



\author[1]{Niyomi House}%
\author[1]{Aida Miro-Herrans}%
\author[2]{Julie Allen}%
\author[3]{Muhammad Ashfaq}%
\author[4]{Bret Boyd}%
\author[5]{Henk Braig}%
\author[6]{Oleg Mediannikov}%
\author[7]{Kosta Mumcuoglu}%
\author[8]{Alejandra Perotti}%
\author[9]{Ramírez-Rozzi Fernando}%
\author[6]{Didier Raoult}%
\author[10]{Lajos Rozsa}%
\author[11]{Jan Štefka}%
\author[12]{Ariel Toloza}%
\author[13]{Natacha Wirdemark}%
\author[1]{David Reed}%
\affil[1]{University of Florida}%
\affil[2]{University of Nevada Reno}%
\affil[3]{University of Guelph}%
\affil[4]{Virginia Commonwealth University}%
\affil[5]{Bangor University}%
\affil[6]{Aix-Marseille Universite}%
\affil[7]{Hebrew University Hadassah Medical School}%
\affil[8]{University of Reading}%
\affil[9]{Centre National de la Recherche Scientifique}%
\affil[10]{Centre for Ecological Research}%
\affil[11]{University of South Bohemia in Ceske Budejovice}%
\affil[12]{Centro de Investigaciones de Plagas e Insecticidas}%
\affil[13]{ LusFri Inspagat AB}%


\vspace{-1em}



  
  \date{May 27, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
The human head louse (\emph{Pediculus humanus capitis}) is an obligate
ectoparasite of humans and has the potential to uncover aspects of human
history that cannot be directly inferred from genetic data derived from
humans. Previous studies have shown that global louse populations
exhibit restricted patterns of genetic variation. However, these studies
were restricted both genetically and lacked a global sampling. With the
aim of capturing the genetic diversity of head louse populations from
around the world, we generated whole genome sequences of human head lice
from 43 countries, spanning five continents and Oceania, to determine if
louse nuclear diversity mirrors its mitochondrial haplotypes or if
population genetic structure, genetic diversity, and population
connectivity are associated with geographical regions or host behavior.
Here we show that there are five nuclear genetic clusters that are
associated with large geographical regions, either at continental or
intercontinental levels. High genetic variation was found between
African and non-African individuals and the highest genetic diversity
was found in samples from sub-Saharan Africa, similar to that of humans.
Unlike the mitochondrial clades examined in previous studies, nuclear
genetic clusters of lice examined here are highly structured based on
geography (continentally and major regions within continents). Results
from our genome analyses revealed that host-mediated global dispersal as
the likely primary process in shaping diversity and maintaining genetic
population boundaries within the nuclear genome of the human head louse.%
\end{abstract}%



\sloppy


\textbf{Genome-wide analysis of the human head louse (\emph{Pediculus
humanus capitis}) reveals geographically structured genetic populations}

Running title: Population genomics of human head lice

Niyomi House\textsuperscript{1,2,4 *}, Aida T.
Miro-Herrans\textsuperscript{2,3}, Julie M. Allen\textsuperscript{4},
Muhammad Ashfaq\textsuperscript{5}, Bret M. Boyd\textsuperscript{6},
Henk Braig\textsuperscript{7,7a}, Oleg Mediannikov\textsuperscript{8},
Kosta Y. Mumcuoglu\textsuperscript{9}, Alejandra
Perotti\textsuperscript{10}, Ram\selectlanguage{ngerman}írez-Rozzi V.
Fernando\textsuperscript{11}, Dider Raoult\textsuperscript{8}, Lajos
Rozsa\textsuperscript{12,13}, Jan Stefka\textsuperscript{14}, Ariel C.
Toloza\textsuperscript{15}, Natacha V. Wirdemark\textsuperscript{16} and
David L. Reed\textsuperscript{2}

\textsuperscript{1} Department of Biology, University of Florida,
Gainesville, Florida, USA

\textsuperscript{2} Florida Museum of Natural History, University of
Florida, Gainesville, Florida, USA

\textsuperscript{3} George Smathers Libraries, University of Florida,
Gainesville, Florida, USA

\textsuperscript{4} Department of Biology, University of Nevada, Reno,
Nevada, USA

\textsuperscript{5} Centre for Biodiversity Genomics and Department of
Integrative Biology, University of Guelph, Ontario, Canada

\textsuperscript{6} Center for Biological Data Science, Virginia
Commonwealth University, Richmond, Virginia, USA

\textsuperscript{7} School of Natural Sciences, Bangor University,
Wales, UK

\textsuperscript{7a} Institute and Museum of Natural Sciences, National
University of San Juan, San Juan, Argentina

\textsuperscript{8} Aix Marseille Univ, IRD, AP-HM, MEPHI, Marseille,
France.

\textsuperscript{9} Department of Microbiology and Molecular Genetics,
The Hebrew University-Hadassah Medical School, Jerusalem, Israel

\textsuperscript{10} Ecology and Evolutionary Biology Section, School of
Biological Science, University of Reading, UK

\textsuperscript{11}UMR 7206 Ecoanthropologie, MNHN, CNRS, UP

\textsuperscript{12} Institute of Evolution, ELKH Centre for Ecological
Research, Hungary

\textsuperscript{13} Centre for Eco-Epidemiology, National Laboratory
for Health Security, Hungary

\textsuperscript{14} Faculty of Science, University of South Bohemia in
Ceske Budejovice, CZ and Institute of Parasitology, Biology Centre CAS,
CZ

\textsuperscript{15} Centro de Investigaciones de Plagas e Insecticidas
(CONICET-UNIDEF), Villa Martelli, Buenos Aires, Argentina

\textsuperscript{16} LusFri Inspagat AB, Stockholm, Sweden

\textsuperscript{*} Address correspondence to Niyomi House,
niyomiw88@gmail.com

Abstract

The human head louse (\emph{Pediculus humanus capitis} ) is an obligate
ectoparasite of humans and has the potential to uncover aspects of human
history that cannot be directly inferred from genetic data derived from
humans. Previous studies have shown that global louse populations
exhibit restricted patterns of genetic variation. However, these studies
were restricted both genetically and lacked a global sampling. With the
aim of capturing the genetic diversity of head louse populations from
around the world, we generated whole genome sequences of human head lice
from 43 countries, spanning five continents and Oceania, to determine if
louse nuclear diversity mirrors its mitochondrial haplotypes or if
population genetic structure, genetic diversity, and population
connectivity are associated with geographical regions or host behavior.
Here we show that there are five nuclear genetic clusters that are
associated with large geographical regions, either at continental or
intercontinental levels. High genetic variation was found between
African and non-African individuals and the highest genetic diversity
was found in samples from sub-Saharan Africa, similar to that of humans.
Unlike the mitochondrial clades examined in previous studies, nuclear
genetic clusters of lice examined here are highly structured based on
geography (continentally and major regions within continents). Results
from our genome analyses revealed that host-mediated global dispersal as
the likely primary process in shaping diversity and maintaining genetic
population boundaries within the nuclear genome of the human head louse.

Keywords

host-mediated dispersal, nuclear, genome, population structure, genetic
diversity

\textbf{Introduction}

The study of organisms that are obligately or facultatively associated
with humans can provide insight into the history of its host. For
example, human head lice have been used as markers of the past to
uncover prehistoric contact between extinct and anatomically modern
humans (AMH) (Reed et al., 2004), the timing of the split between human
and chimpanzees (5-7 Ma) (Reed et al., 2007), and the origin of clothing
use by AMH (Kittler et al., 2003; Toups et al., 2011). The human head
louse \emph{Pediculus humanus capitis} De Geer, 1778 (Phthiraptera:
Anoplura) is a permanent, blood-feeding ectoparasite of humans
completing their entire life cycle on their human host. Lice cannot live
off their host for more than 36 hours (Burkhart \& Burkhart, 2007),
making their life cycle closely tied to their hosts for thousands of
generations. Head lice are primarily found among school-aged children
and dispersal (transmission) from one head to another occurs mainly when
the host individuals have close physical contact (Mumcuoglu et al.,
2021). Ancient human populations also carried lice, and this is
demonstrated by the finding of nits still attached to ancient human hair
as well as trapped in the combs used (Amanzougaghene et al., 2016;
Arriaza et al., 2014; Pedersen et al., 2022). Therefore, host migration
and contact should define the population structure of lice.
Historically, this appears to be true, that the evolutionary history of
human lice appears to have been shaped by human migration (Ewing, 1926;
Pedersen et al., 2022) that facilitated host-switching (Reed et al.,
2007). However, the increase in ease of global migration and travel
could break down geographical barriers and erase historical population
structure. The question remains, is louse population structure defined
by geographic regions and historic migration or recent host movement.

Anatomically modern humans migrated out of Africa 60-70 thousand years
ago (Carto et al., 2009; Stringer, 2000; Walter et al., 2000). A growing
number of studies now support two main dispersal events out of Africa: a
Southern route to Southeast Asia and a Northern route to Eurasia
(Reyes-Centeno et al., 2014; Tassi et al., 2015). As AMHs dispersed
throughout Eurasia they interbred with other species of archaic humans
including Neanderthals and Denisovans who migrated out of Africa much
earlier (Reich et al., 2011; Sankararaman et al., 2014; Villanea \&
Schraiber, 2019). While genetic and archeological evidence has increased
our understanding of human evolution, a complete picture remains elusive
because behavior and dispersal are hard to discern from fossilized
remains.

As speciation happens in the host lineage, host-specific parasites like
lice often speciate in tandem as a result of imposed isolation (Reed et
al., 2007). Therefore, the processes shaping human evolution may leave a
signal in the genome of human lice. To date, genetic diversity and
population structure of head lice has mostly been examined in terms of
mitochondrial clade diversity and distribution (Al-Shahrani et al.,
2017; Amanzougaghene et al., 2016; Ashfaq et al., 2015; Boumbanda Koyo
et al., 2019). Mitochondrial genes; partial cytochrome b and cytochrome
oxidase I of lice showed six genetically distinct, geographically unique
clades (clades A-F). Clade A is distributed world-wide, while the other
clades are more geographically restricted. Clade B is found in Europe,
the Americas, and Australia. Clade C is found in Africa and southern
Asia. Clades D and E are found only in Africa, while Clade F has been
found on individuals in Argentina and Mexico (Amanzougaghene et al.,
2019). Of these clades, only A and D include both head and body lice,
while the rest of the clades have consisted only of head lice.

Although identifying the distribution of mitochondrial clades through
only fragments of genes has helped elucidate the possibility of a
demographic history of human head lice, these patterns are only vaguely
indicative of just the maternal louse lineages, and longer term
co-speciating and coevolutionary events. An understanding of genome-wide
patterns of genetic diversity and population structure across global
head louse populations are needed to characterize the complete picture
of louse evolutionary history. For example, considering that lice are
horizontally transmitted, it is unclear if the nuclear genetic structure
of the lice mirrors that of its host genetic population structure, or if
human migrations and more recently the ease of global travel has erased
these patterns by transporting lice around the world. The question
remains whether lice are more defined by geography or host movement.
Given the long history of human migration across the globe, and the
co-demography observed in lice (Reed et al., 2007), it is useful to
evaluate worldwide louse populations as geographical units (i.e.,
countries or continents) rather than genetic clades. Investigating
population structure across geographically separated populations using
neutral nuclear loci can show interactions between genetic drift and
gene flow (Slatkin, 1987) that may not be captured by uniparentally
inherited markers (i.e., mitochondrial DNA) (Ebert \& Fields, 2020).

In attempts to understand the nuclear genetic diversity of human lice, a
previous study used microsatellite markers from 93 specimens in 11
countries and found no relationship between the mitochondrial clades and
the nuclear population clusters (Ascunce et al., 2013). Additionally,
these microsatellite markers were able to detect geographically
structured genetic population clusters indicating low levels of gene
flow between global louse populations (Ascunce et al., 2013). Further
investigations with a much broader sampling regime including genome-wide
representation are needed to have the resolution to identify the
distribution of genetic variants as well as the genetic population
structure of the global human head louse populations.

Using the human body louse, \emph{Pediculus humanus humanus} genome as a
reference, we analyzed whole nuclear genomes of the human head
louse\emph{P. humanus capitis} from across the world to overcome the
current lack of geographic and genome-wide representation. The main
goals of this study were to: 1) investigate nuclear genetic diversity
across the globe ; 2) examine the geographic distribution of genetic
variants worldwide ; 3) identify the population structure based on
nuclear markers; and 4) investigate possible modes of gene flow and
historical population connectivity between global louse populations. Our
main questions are, does the population structure of human lice match
that of its host migration patterns or does it reflect the same
evolutionary history shown through its mitochondrial genome?

\textbf{Materials and Methods}

\textbf{Ethics Statement}

The Institutional Review Board of the University of Florida exempted the
study from review (Exemption of Protocol \#2009-U-0422) and waived the
need for written informed consent of the participants. This exemption is
issued based on the United States Department of Health and Human
Services (HHS) regulation 45 CFR part 46, because louse removal was
voluntary and no identifiable information was recorded and so the need
for written informed consent from the participants was waived.

\textbf{Sample Collection}

Head louse specimens were collected through collaborations including
both scientific researchers and the non-scientific community (head louse
removing clinics). In this study, we included a total of 377 specimens
collected worldwide and DNA extractions that were previously collected
and stored in Reed lab, Florida Museum of Natural History at University
of Florida, USA from Ascunce et al. 2013 publication. The samples that
were included in this study are summarized in Table 1. A special focus
was made in the classification and selection of individual lice
collected from different human heads, to maximize the genetic sampling
within an area. All specimens were stored in 95\% ethanol at -80ºC prior
to DNA extractions.

Table 1: Geographic localities and the number of head louse samples
included in this

study\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Continent & Region & Country & Number of Head lice\tabularnewline
\midrule
\endhead
Asia & West Asia & Turkey & 16\tabularnewline
& & Israel & 9\tabularnewline
& South Asia & Pakistan & 12\tabularnewline
& & India & 3\tabularnewline
& & Nepal & 6\tabularnewline
& Southeast Asia & Cambodia & 19\tabularnewline
& & Philippines & 9\tabularnewline
& & Laos & 4\tabularnewline
& & Thailand & 3\tabularnewline
& & China & 4\tabularnewline
& East Asia & Mongolia & 7\tabularnewline
Africa & Sub-Saharan Africa & Ethiopia & 11\tabularnewline
& & Burundi & 1\tabularnewline
& & Democratic Republic of Congo & 12\tabularnewline
& & Senegal & 16\tabularnewline
& & Guinea & 4\tabularnewline
& & Rwanda & 1\tabularnewline
& & Gabon & 6\tabularnewline
& & Cameroon & 3\tabularnewline
& North Africa & Egypt & 3\tabularnewline
& & Algeria & 8\tabularnewline
Europe & Europe & United Kingdom & 23\tabularnewline
& & Czech Republic & 3\tabularnewline
& & Norway & 3\tabularnewline
& & Netherlands & 9\tabularnewline
& & France & 5\tabularnewline
& & Sweden & 10\tabularnewline
& & Spain & 10\tabularnewline
& & Hungary & 3\tabularnewline
& & Greece & 4\tabularnewline
& & Italy & 14\tabularnewline
North America & North America & United States of America &
48\tabularnewline
& & Mexico & 15\tabularnewline
& Central America & Guatemala & 12\tabularnewline
& & Honduras & 13\tabularnewline
& & Panama & 4\tabularnewline
South America & South America & Colombia & 5\tabularnewline
& & Ecuador & 5\tabularnewline
& & Argentina & 18\tabularnewline
& & Venezuela & 1\tabularnewline
& & Chile & 2\tabularnewline
& Oceania & Papua New Guinea & 10\tabularnewline
& & Cook Islands & 3\tabularnewline
& Total & & 377\tabularnewline
\bottomrule
\end{longtable}

\textbf{DNA Extractions}

We extracted louse DNA using the MasterPure\textsuperscript{TM} Complete
DNA and RNA Extraction Kit (Lucigen, Middleton, WI, USA). First, each
louse specimen was prepared for DNA extraction by making a small
incision on their abdomen under a dissecting microscope. Each specimen
was then incubated overnight (\textless{}24hrs) at
52\textsuperscript{0}C in a solution of Tissue and Cell Lysis Solution
mixed with 1ul of 20ug/ul Proteinase K. Occasional vortexing of the
tubes facilitated cell lysis by allowing the solution to move into the
abdominal incision. After the incubation period, the exoskeletons of the
lice were removed from the tubes and stored in 70\% ethanol for future
morphological analyses. We followed the manufacturer's protocol to
purify the remaining digestion buffer. Using QUBIT dsDNA HS Assay Kit
(Thermo Fisher Scientific), we quantified DNA concentrations. As a
quality control step, to confirm the extraction of louse DNA, we ran a
polymerase chain reaction (PCR) using published louse cytochrome B
primers (Raoult et al., 2008) and PCR products were run on a 2\% agarose
gel. Samples that failed to amplify (resulting in no visual bands on the
gel) were removed from any further analyses.

\textbf{Library Construction and Sequencing}

For whole genome resequencing, libraries were built using unique in-line
indexed adapters designed by~Rohland \& Reich, 2012. First, 100 ng of
DNA per sample were sheared to 400bp on the~Covaris~E220
focused-ultrasonicator~using recommended factory settings for
400bp.~Library construction included blunt-end repair, adapter ligation,
and adapter fill-in~that followed the protocol by Rohland \& Reich,
2012. The individual samples were then PCR amplified with
Pre-hyb~primers following Rohland et al., 2015 and PCR products~were
purified using bead purification~and pooled (Rohland \& Reich,
2012).~Sample pools were reamplified with indexed Illumina-compatible
primers and they were size selected for fragments between 400-1000 bp
and sequenced at 2x150 on the Illumina~NovaSeq~6000 at~the
Interdisciplinary Center for Biotechnology Research (ICBR), University
of Florida.

\textbf{Bioinformatic Pipeline}

To analyze the raw whole genome sequencing reads, we used a custom made,
bioinformatic pipeline for our study organism following the GATK best
practices method (Van der Auwera et al., 2013) (Figure 1). Because the
whole genome analysis of each louse was computationally expensive and
time consuming, we employed a GPU-accelerated software system utilizing
NVIDIA RTX6000 GPU nodes on University of Florida's high performance
computing cluster, HiPerGator, to run the software suite, Clara
Parabricks (https://github.com/clara-parabricks) and significantly
accelerated the processing time. The first 3 steps of the pipeline:
reference mapping, coordinate sorting and mark duplicates were performed
in Parabricks under BWA-MEM (H. Li, 2013) and GATK4 (McKenna et al.,
2010; see Supplementary Materials for parameter details) .\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Post-process Filtering}

The resulting SNP dataset consisted of 19,256,706 SNPs. To ensure the
quality of our dataset, we applied additional filters to the VCF
(Variant Calling Format) file. First, we removed all the singleton SNPs
from our dataset (as these carry no useful information to address our
main questions). Singleton detection was carried in vcftools under
--singletons and the variants were extracted from the dataset. We then
analyzed the dataset in vcftools (--freq2, --depth, --site-mean-depth,
--site-quality, --missing-individuals, --missing-site, --het) to
identify additional quality filters. Based on the results, we applied
the following filters using vcftools; --minQ 20 (minimum quality score
of 20), --min-meanDP 10 (minimum read depth), --max-meanDP 50 (maximum
read depth), --maf \textless{}0.05 (minor allele frequency cutoff at
0.05). We then used PLINK v2.0 (Chang et al., 2015) to apply ``missing
data'' filters; --mind 0.2 (filter out individuals that have
\textgreater{}20\% missing data), --geno 0.2 (filter out variants that
have \textgreater{}20\% missing data). The resulting dataset consisted
of 575,096 biallelic SNPs. This SNP set was tested for Linkage
Disequilibrium (LD) to remove SNPs that were in complete or in very high
LD. We used PLINK --indep-pairwise with a window of 50kbp, sliding by
10kbp with a r\textsuperscript{2} \textless{} 0.7. This resulted in a
final set of 366,078 SNPs for all downstream analyses.

\textbf{Population-specific Summary Statistics}

To better understand the genetic diversity and demographics of the
worldwide head louse populations, we first calculated summary
statistics. We grouped our individuals by continent and calculated the
nucleotide diversity (pi) and Tajima's D values from SNPs across the
entire nuclear genome using the sliding window approach using the R
package \emph{PopGenome} (Pfeifer et al., 2014). We then calculated SNP
heterozygosity for each country (observed;H\textsubscript{o} and
expected heterozygosity;H\textsubscript{e}), to identify populations and
loci that significantly deviated from Hardy Weinberg Equilibrium (HWE).
By grouping individuals into their respective countries, we considered a
spatial based population substructure to account for the Wahlund effect
(Wahlund,1928) that would otherwise have an impact on the countrywide
HWE calculations. All locus-by-locus HWE analyses were done using
Arlequin 3.5 (Excoffier \& Lischer, 2010) with 1,000,000 steps in Markov
chain after 100,000 steps discarded as burnin. We also calculated
statistical significance of the deviations at \emph{p} \textless{}0.05
and proportion of loci that significantly deviated from HWE for each
population. In addition to SNP heterozygosity, we also calculated
autosomal heterozygosity for each country which indicates the proportion
of heterozygous sites in each country. SNP heterozygosity and autosomal
heterozygosity differ in that both polymorphic and monomorphic sites are
included in autosomal heterozygosity calculations. Polymorphic sites are
those which at least two individuals in each population had in common.
We eliminated the monomorphic sites for our HWE analyses because they
provided no useful information about the evolutionary forces acting upon
the population.

To examine the distribution of genetic variability within and among
groups and populations, we ran an Analysis of Molecular Variance (AMOVA)
by using Arlequin 3.5 (Excoffier \& Lischer, 2010). Hierarchical groups
were defined as follows: samples were first grouped into their
respective populations (by countries) and then they were further grouped
into the five geographic continents (North America, South America, Asia,
Europe, Africa, and Oceania. The significance of the variance components
was tested using 1000 permutations. Lastly, we calculated the pairwise
genetic differentiation (F\textsubscript{ST}) between all pairs within
and between countries and continents. The analysis was run in Arlequin,
and significant genetic differentiation was determined after 1000
permutations.

\textbf{Population Structure Analyses}

We employed several methods to investigate head louse genetic population
structure, we first conducted a Principal Component Analysis (PCA) using
the R package \emph{SNPRelate} (Zheng et al., 2012). Secondly, a
Discriminant Analysis of Principal Components (DAPC) was performed using
the R package \emph{Adegenet} (Jombart, 2008). Unlike PCA, which
summarizes the overall variability among individuals, DAPC maximizes the
between group variation while minimizing within group variation (Jombart
et al., 2010). In the first DAPC analysis, we used an a priori
population assignment by continents and used these as the population
identifiers. In the second analysis, we used the find.cluster() method
without prior population assignment. Both DAPC analyses were run with
500 Principal Components (PC) and retained the first 5 discriminant
functions. Optimum.a.score() was used to assess the optimum number of
PCs to retain after 100 simulations (Figure S2). We tested K values from
1 to 50 and selected the optimum number of K based on the lowest
Bayesian Information Criterion (BIC) for each value of K (Figure S1).
Results for both PCA and DAPC were visualized in a scatterplot in R.

We used a model-based Bayesian clustering analysis, fastSTRUCTURE v 1.0
(Raj et al., 2014) to infer ancestral population structure. We ran 10
iterations each for K=1 through K=10 with a 10-fold cross-validation and
selected the optimum K value through the ``choose K'' method. All the
input files were generated using PLINK v 2.0 (Chang et al., 2015) and
the results were visualized using CLUMPAK (Kopelman et al., 2015) and
DISTRUCT (Rosenberg, 2004). We also used the program ADMIXTURE to
implement a maximum-likelihood approach to infer population structure.
ADMIXTURE was run using the cross-validation flag (--cv) to select the
best K with the lowest cv error. Additional ADMIXTURE analyses were run
on each major genetic cluster that resulted from the main population
structure analyses through fastSTRUCTURE that included all samples. For
the population sub-structure analysis, individuals were selected if
their genetic ancestry was greater than 0.8 in the resulting q matrix,
for a given cluster.

\textbf{Gene flow and phylogeny}

To detect gene flow and possible migration events between louse
populations, we used TREEMIX (Pickrell \& Pritchard, 2012) to build a
population tree and add on likely migration events. TREEMIX can model
gene flow between populations and its direction by comparing the
covariance modeled by the bifurcating tree to the observed covariance
between populations. We ran the TREEMIX analysis with m=1 to m=14
migration events with four replicates for each migration edge (m) with
varying SNP window sizes. We assessed the variance explained by the
models, standard error and likelihood scores using the Evanno method
(Evanno et al., 2005) implemented in the \emph{optM} R package (Fitak,
2021). Delta ``m'' was calculated for each run and the optimum number of
migration edges that fit the data were chosen accordingly. A summary of
the percentage of variance explained by each model, significance of the
migration events, and the associated likelihood scores can be found in
Figure S3.

Finally, to evaluate the evolutionary relationships among the lice from
the countries in our dataset, we constructed a neighbor-joining tree
that uses Euclidean genetic distances for each population using the R
package \emph{dartR}(Gruber et al., 2018). To root the phylogenetic tree
with an outgroup and evaluate ancestral populations, we identified SNPs
from 16 chimpanzee lice (\emph{Pediculus schaeffi} ) using the body
louse genome as the reference (Kirkness et al., 2010) and following the
same bioinformatic pipeline as outlined in Figure 1. The resulting
dataset underwent the same post-process filtering steps as the head lice
(see methods above). The final filtered SNPs from both species were
intersected using bcftools -isec to identify variants that were common
to both species. The final dataset for the phylogenetic analysis
including the outgroup contained 889 variants. The resulting
neighbor-joining tree using these 889 SNPs was imported into Figtree for
visualization.

\textbf{Results}

\textbf{Population specific summary statistics}

The nucleotide diversity (pi) of lice from each continent revealed
African lice had the highest diversity (0.318) compared to all other
non-African lice. Asian lice had the second highest diversity (0.175)
followed by South America (0.125), North America (0.110), Europe (0.099)
and Oceania (0.073). Hardy Weinberg Equilibrium (HWE) tests using SNP
heterozygosity revealed a pattern where most of the European and North
American countries had a higher percentage of loci that were
significantly out of HWE compared to Asian and African countries (Table
2). Each country had a different number of polymorphic sites.
Noticeably, African countries had the highest number of polymorphic
sites. Several countries had all polymorphic loci in HWE, which was
unexpected. Each of these were instances where the lice used are
suspected of coming either from a single human host or from a relatively
few. Based on the Hardy-Weinberg Equilibrium (HWE) calculations, whether
a specific locus was in/out of HWE seems to be a population-specific
phenomenon rather than a locus-specific event. The fraction of loci that
were out of HWE was different in each population. Summarized HWE results
can be found in Table 2.

Table 2: Population summary statistics by country. Columns include major
geographic regions, country, number of individuals, total number of
polymorphic SNPs in each population, total SNPs that are significantly
out of HWE, mean observed SNP heterozygosity (H\textsubscript{o}), mean
expected SNP heterozygosity (H\textsubscript{e}) and percentage of SNPs
that deviates from HWE. SNP heterozygosity mean values were calculated
per country. Countries in each region are arranged by percentage out of
HWE from high to low where it was calculated as, number of loci
with\emph{p} \textless{}0.05/total polymorphic loci*100.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Region & Population (Country) & Number of individuals & Total
polymorphic SNPs & Total SNPs significantly out of HWE & Mean Ho & Mean
He & Percentage out of HWE\tabularnewline
\midrule
\endhead
Europe & Sweden & 10 & 62,330 & 41,877 & 0.047 & 0.379 &
67.1\tabularnewline
& UK & 23 & 100,938 & 66,416 & 0.091 & 0.267 & 65.7\tabularnewline
& Italy & 14 & 103,001 & 66,596 & 0.094 & 0.319 & 64.6\tabularnewline
& Netherlands & 9 & 68,826 & 22,808 & 0.106 & 0.343 &
33.1\tabularnewline
& Spain & 10 & 100,419 & 16,432 & 0.171 & 0.313 & 16.3\tabularnewline
& France & 5 & 23,974 & 1,248 & 0.442 & 0.410 & 5.2\tabularnewline
& Norway & 3 & 53,975 & 0 & 0.096 & 0.512 & 0\tabularnewline
& Greece & 4 & 66,624 & 0 & 0.172 & 0.436 & 0\tabularnewline
& Czech Republic & 3 & 31,291 & 0 & 0.234 & 0.516 & 0\tabularnewline
& Hungary & 3 & 78,039 & 0 & 0.441 & 0.461 & 0\tabularnewline
North America & USA & 48 & 145,883 & 120,077 & 0.052 & 0.240 &
82.3\tabularnewline
& Mexico & 15 & 101,635 & 54,292 & 0.113 & 0.309 & 53.4\tabularnewline
Central America & Guatemala & 12 & 121,137 & 26,900 & 0.184 & 0.315 &
22.2\tabularnewline
& Honduras & 13 & 90,922 & 13,998 & 0.245 & 0.342 & 15.3\tabularnewline
& Panama & 4 & 76,950 & 0 & 0.262 & 0.432 & 0\tabularnewline
South America & Argentina & 18 & 166,011 & 38,168 & 0.170 & 0.258 &
22.9\tabularnewline
& Colombia & 5 & 93,602 & 7,521 & 0.225 & 0.391 & 8.0\tabularnewline
& Ecuador & 5 & 115,355 & 4,794 & 0.259 & 0.372 & 4.1\tabularnewline
& Chile & 2 & 75,427 & 0 & 0.429 & 0.559 & 0\tabularnewline
West Asia & Turkey & 16 & 153,718 & 56,627 & 0.132 & 0.269 &
36.8\tabularnewline
& Israel & 9 & 79,396 & 21,006 & 0.156 & 0.347 & 26.4\tabularnewline
South Asia & Pakistan & 12 & 181,009 & 34,422 & 0.207 & 0.324 &
19.0\tabularnewline
& Nepal & 6 & 171,734 & 6,383 & 0.277 & 0.363 & 3.7\tabularnewline
& India & 3 & 94,357 & 0 & 0.428 & 0.469 & 0\tabularnewline
Southeast Asia & Cambodia & 19 & 137,931 & 19,388 & 0.204 & 0.259 &
14.0\tabularnewline
& Philippines & 9 & 87,813 & 3,439 & 0.305 & 0.340 & 3.9\tabularnewline
& Laos & 4 & 81,663 & 0 & 0.308 & 0.410 & 0\tabularnewline
& Thailand & 3 & 71,761 & 0 & 0.261 & 0.478 & 0\tabularnewline
Oceania & Papua New Guinea & 10 & 69,088 & 1,576 & 0.232 & 0.261 &
2.2\tabularnewline
& Cook Islands & 3 & 62,413 & 0 & 0.337 & 0.468 & 0\tabularnewline
East Asia & Mongolia & 7 & 147,135 & 14,560 & 0.197 & 0.337 &
9.8\tabularnewline
& China & 4 & 38,980 & 0 & 0.368 & 0.410 & 0\tabularnewline
North Africa & Algeria & 8 & 85,068 & 21,205 & 0.102 & 0.319 &
24.9\tabularnewline
& Egypt & 3 & 115,388 & 0 & 0.320 & 0.475 & 0\tabularnewline
Sub-Saharan Africa & Senegal & 16 & 265,774 & 65,235 & 0.2334544 &
0.3778581 & 24.5\tabularnewline
& D.R.Congo & 12 & 221,449 & 29,496 & 0.287 & 0.379 &
13.3\tabularnewline
& Ethiopia & 11 & 239,829 & 16,785 & 0.305 & 0.375 & 6.9\tabularnewline
& Gabon & 6 & 194,737 & 12,751 & 0.305 & 0.415 & 6.5\tabularnewline
& Guinea & 4 & 179,765 & 0 & 0.224 & 0.456 & 0\tabularnewline
& Cameroon (lice from Baka Pygmy) & 3 & 182,616 & 0 & 0.394 & 0.480 &
0\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Observed autosomal heterozygosity (including both polymorphic and
monomorphic sites) varied across all populations. Sub-Saharan African
countries had the highest heterozygosity values observed (Figure 2).
Asian and South American countries had the second highest heterozygosity
estimates, followed by North American and European countries (Figure 2).
Across all the countries, the observed heterozygosity was lower than the
expected heterozygosity for all populations. Tajima's D values for each
continent ranged from -0.0001-0.0003, indicating no demographic changes
within populations among continents (Figure S4).

Hierarchical population structure analysis (AMOVA) revealed significant
variation partitioned at each population hierarchical level (Table 3).
The variation among continents was 25\%, among countries within
continents was 21\%, among individuals within countries was 24\% and
lastly the individual variation among all individuals was 29\%.
F\textsubscript{ST} values between continents were also relatively high.
The highest F\textsubscript{ST} values were found between Africa vs
non-African countries and Oceania vs Europe (0.441) (Table 4). Low
F\textsubscript{ST} values between Asia and other continents suggest
that it is the least differentiated continent relative to others. We
also observed the lowest F\textsubscript{ST} values between
geographically proximal continents (e.g.: between North and South
America; between Asia and Oceania). The
pairwise-F\textsubscript{ST}values between countries further explains
the overall continental differentiation patterns in Table 4.

Table 3: Global AMOVA results as a weighted average across all
polymorphic loci the SNP dataset. Significant p values
(p\textless{}0.00005) are indicated by an asterisk (*)\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Source of variance & Sum of squares & Variance components & Percentage
variation (\%)\tabularnewline
\midrule
\endhead
Among continents & 4087677.400 & 5560.54718 & 24.72825*\tabularnewline
Among countries within continents & 3672984.746 & 5373.26137 &
23.89538*\tabularnewline
Among individuals within countries & 5393862.265 & 4986.70023 &
22.17630*\tabularnewline
Within individuals & 2431033.500 & 6566.10712 & 29.20007*\tabularnewline
Total & 15585557.911 & 22489.08438 & 100\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& \textbf{North America} & \textbf{Asia} & \textbf{Africa} &
\textbf{Europe} & \textbf{South America}\tabularnewline
\textbf{Asia} & 0.174 & & & &\tabularnewline
\textbf{Africa} & 0.455 & 0.325 & & &\tabularnewline
\textbf{Europe} & 0.061 & 0.202 & 0.458 & &\tabularnewline
\textbf{South A.} & 0.039 & 0.142 & 0.376 & 0.103 &\tabularnewline
\textbf{Oceania} & 0.385 & 0.149 & 0.343 & 0.441 & 0.364\tabularnewline
\bottomrule
\end{longtable}

Between countries, some F\textsubscript{ST} values were as high as 0.83,
indicating strong genetic differentiation at the country level (Figure
3). Geographically proximal countries, that is, countries within a
continent, and those that are in close proximity to one another, show
less differentiation than those farther apart. One important pattern
that emerges as we examine country-level variation is that South Asian
countries (India, Pakistan, and Nepal) and Mongolia seem to be
genetically similar (low F\textsubscript{ST} values) compared to African
countries. This same pattern does not seem to be apparent when other
countries are compared to African countries. Non-significant
F\textsubscript{ST} values (``X'' marks on the heatmap; Figure 3) were
between populations that had three or fewer samples.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Population Structure}

At K=2, fastSTRUCTURE results show a clear separation of lice collected
in Africa from the rest of the regions, including a few admixed samples
in Asia (Figure 4). As the number of K increases, the genetic clusters
start to separate the samples by continents and further subdividing into
sub-continents. Out of the 10 K-values tested, the optimum value of K
for this dataset was K=5. The five genetic clusters (Figure 4) separate
out individuals as follows: Sub-Saharan African (orange cluster),
Southeast Asian and Oceanian (dark blue cluster), South Asia (green
cluster), Central and South America (maroon cluster), and Europe with
the majority of North America (light blue cluster). There are a few
exceptions to these general categorizations of individuals. Some that
stand out are samples from Hungary, Egypt, Mongolia, Turkey, and Israel.
Both fastSTRUCTURE and ADMIXTURE analyses produced identical results.
Therefore, we only report fastSTRUCTURE results on the overall genetic
population structure.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\par\null\par\null

\textbf{Principal Component Analysis}

The principal component analysis showed similar population structure as
the fastSTRUCTURE analysis. PC1 and PC2 explain 29.68\% and 6.41\% of
variation, respectively, and separated the sub-Saharan African samples
from the rest very clearly (Figure 5). In addition, we observed the same
clustering patterns of lice in the fastSTRUCTURE results, including the
same outliers (Figure 4). For example, in the PCA, Asia is subdivided
into South Asia and Southeast Asia (See table 2 for details on countries
that belong to each region), where the Southeast Asian cluster includes
individuals from Oceania. Similarly, the main outliers detected in the
fastSTRUCTURE analysis were Hungary, Mongolia and Egypt, clustering
together with South Asian individuals, and all of the Algerian lice
clustering with Europeans. These were also observed in the PCA clusters.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\par\null

\textbf{Discriminant Analysis of Principal Components (DAPC)}

We ran a DAPC analysis to optimize the variation between populations
while reducing within population variation. When grouping individuals by
continent, we were able to detect the variation among samples from
different continents (Figure 6A). Both the African and Asian groups were
separated from the rest of the samples, indicating greater variation
among those lice. Even though European, South + Central American and
North American samples formed different clusters, the variation among
those was small as compared to the African and Asian groups (Figure 6A).
We also ran the DAPC analysis without adding any prior information about
the sampling localities and tested putative genetic populations K=1
through K=50. We retained K=5 (Figure 6B) as the optimum K after using
the find.clusters parameter and calculating the BIC scores for each K
tested. This K=5 genetic clustering agrees with the results obtained
from both the fastSTRUCTURE and PCA analyses, and the distribution of
individuals (Figure 6B) was also similar to that of the PCA and fast
STRUCTURE results. In addition, like the PCA results, discriminant
component 1 separated the sub-Saharan African samples from the rest.
North African (Algeria and Egypt) samples were separated from this main
sub-Saharan African cluster\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\textbf{Phylogenetic relationships and Gene Flow}

The neighbor-joining SNP phylogeny provided additional support for
continental and regional division among the sampled populations (Figure
7). Results show that phylogenetic relationships among worldwide louse
populations appear to be correlated with geography. With \emph{Pediculus
schaeffi} (chimpanzee louse) as an outgroup, sub-Saharan African
populations were sister to all other populations.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

Population relationships were further assessed by modeling migration
events with TREEMIX given a number of migration edges from m=1 to m=14.
Using the Evanno method we identified that 10 migration events best
explained the sample covariance of allele frequencies among louse
populations (Figure 8). Gene flow with the highest migration weight
across continents was between Hungary and Egypt. The analysis also
showed a second gene flow event between Hungary and Turkey; however, the
migration weight was lower. Hungary and Egypt still appear to be an
outlier (not geographically clustered) in the ML tree generated through
TREEMIX. Another intercontinental gene flow event was found between the
Philippines and Colombia. All other admixture events were between
populations within continents (Figure 8). Overall, the population
relationships between the TREEMIX ML tree and SNP phylogeny are in
agreement with each other.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image13/image13}

\end{center}
\end{figure}

\textbf{Discussion}

\textbf{Diversity and Population Structure}

In this study, we focused on worldwide populations of head lice to
understand their genetic diversity and distribution, which may help shed
light on major events of their human host dispersal throughout the
recent past. We investigated the worldwide population structure of human
head lice using whole genome nuclear SNPs. Results of multiple
clustering analyses agreed with an overall population structure that
showed five genetically distinct nuclear clusters. In all our analyses
that evaluated population structure, sub-Saharan African individuals
separate out from the rest of the individuals when considering only two
populations (Africa and the remainder of the world) up to as many as
five genetic populations (Figure 4). This African cluster is also the
most genetically diverse possessing a greater number of polymorphic
sites compared to other non-African countries. Through comparison to an
outgroup (the chimpanzee louse) we were able to confirm that this
sub-Saharan African population is sister and basal to the rest of the
non-African individuals (Figure 7).These results are consistent with
Yong et al., 2003 who also found a clear geographic separation between
African and non-African lice using partial nuclear genes 18S rRNA and
EF1-alpha. Human genetics and history present a similar pattern, with a
great deal of genetic diversity being found in sub-Saharan African
populations (Campbell \& Tishkoff, 2008). Some of these characteristics
include highest nucleotide diversity, highest observed heterozygosity,
and high percentage variation separating them out from all other
populations. In human population genetics, all of these are
characteristics of Africa being the source population to all modern-day
humans (J. Z. Li et al., 2008; Xing et al., 2010).

Upon closer investigation of the five nuclear genetic clusters that we
uncovered in human head lice, we detected some global patterns arising
that are similar to its host. In Asia + Oceania, lice split among two
genetic clusters consisting of Southeast Asia and South Asian
individuals (Figure 4). This geographical split between South and
Southeast Asia is consistent with the southern expansion route proposed
for human dispersals into Asia (Macaulay et al., 2005; Reyes-Centeno et
al., 2014; Tassi et al., 2015). Southeast Asia further divided into four
geographically structured sub-clusters comprised of a Thailand + Laos +
Cambodia genetic cluster, and China, Papua New Guinea, and Philippines
each forming separate genetic clusters (Figure S7), reflecting the fine
scale population structure of humans in that area (Henn et al., 2010).
In contrast, the South Asian genetic cluster includes lice from
locations that are geographically separated from it (i.e., Mongolia,
Hungary, and Egypt). The genetic affinity of the samples from Hungary
and Egypt could be the result of sampling from recent immigrants or
travelers (in both cases, the samples were obtained from a single
individual). However, our TREEMIX analysis showed gene flow between
Hungary and Egypt with a relatively high migration weight which was
highly significant (p\textless{}10\textsuperscript{-308}). This gene
flow event may explain the similarity between Hungary and Egypt. While
the genetic affinity among these groups may be reflective of louse
demographic history, further research is needed to better understand
these relationships. Furthermore, due to the limited number of sampling
in Hungary and Egypt, these samples may not necessarily reflect the
genetic structure found across these countries and any interpretations
taken from a sample derived from a single host should be done with
caution.

The samples from Europe and the Americas showed little differentiation,
grouping together in the PCA, DPAC and fastSTRUCTURE analyses. Nuclear
diversity, heterozygosity, and F\textsubscript{ST} values were similarly
low between the Europe, North America, and South American samples, which
is unexpected given the host population structure and dispersal history.
In humans, African populations have the highest genetic diversity,
followed by Europeans and Asians, with the lowest genetic diversity in
indigenous American populations (Rosenberg et al., 2002). However, one
key difference in host and parasite diversity patterns is the sampling
strategies. In this study, lice were not collected from isolated ethnic
groups or from aboriginal Americans like in human genetic diversity
studies. Therefore, genetic similarity between European and American
louse populations could be due to more recent gene flow (e.g., during
European colonization of the Americas). Alternatively, the low genetic
diversity and similarity of European and American (North and South
America) lice could be due to selective pressures from insecticide use.
The high use of pyrethroid insecticides to control louse infestations in
Europe and the Americas (Diamantis et al., 2009) may have reduced the
genetic variation among these populations but further investigation is
needed, and is currently underway, to test this alternative explanation.
In addition, it could also be the case that our lice were sampled from
European descendants in cities outside of Europe (i.e in the Americas) .
At K=5 clusters, a subset of the continental North American samples,
primarily from Central America, separate out from the Europe + Americas
cluster. The populations in this cluster also had moderate levels of
observed heterozygosity (0.05-0.08), greater than in European and other
American populations. It could be that these Central American louse
populations may have experienced less insecticide exposure or the
genetic variation may reflect earlier louse demographic history that
could not be observed in the other American louse populations. For
example, our TREEMIX analysis using allele frequency distributions shows
a potential ancestral gene flow event between Philippines and the
countries in this Central American cluster (Figure 8), suggesting
inter-continental mixing at some point in time.

The geographically structured genetic clusters we uncovered here are
concordant with previous findings examining microsatellite markers from
eight localities around the world (Ascunce et al., 2013). Our current
dataset adds information about African populations and how the genetic
diversity of human head lice is distributed across the world by
ancestral demographic events. Based on our analyses of population
substructure, it is evident that the 5 major nuclear clusters that we
uncovered are further subdivided into major regions within continents
suggesting even more genetically structured louse populations (Figure
S5-S9).

\textbf{Parasite and Host Discordance}

Our results found that much of the genetic structure in lice was similar
to that of their human hosts, a few notable differences deserve further
exploration. The hierarchical structure analysis (AMOVA) found high
amounts of variation (\textgreater{}20\%) across multiple spatial
levels. This differs significantly from human genetic diversity where
\textasciitilde{}13\% of variation is among continents,
\textasciitilde{}1\% is among regions within continents and
\textasciitilde{}85\% is within-population among regions (Jorde et al.
2000). These differences point to lice maintaining higher isolation than
their hosts even with the ability of horizontal transfer compared to the
strict vertical transfer in the host. Additional research is needed to
understand the evolutionary forces acting upon local louse populations,
but not on the host, to achieve these levels of louse variation and
continental population genetic structure (High F\textsubscript{ST}values
between continents).

The host-mediated geographical structure of nuclear diversity in lice
contrasts with the documented distribution of mitochondrial clades in
lice. Three deeply divergent mitochondrial clades (A, B and C) that are
not geographically well defined have been hypothesized to be associated
with lice on now-extinct species of hominids (e.g., Neanderthals) that
dispersed onto modern humans during periods of contemporaneity and
survived to present day (Reed et al., 2004). In contrast, the more
geographically structured nuclear clusters mirror the human host
distribution following modern human dispersal out of Africa. This is
consistent with Ascunce et al. (2013) where they found no relationship
between the mitochondrial clade a given louse was assigned to and the
nuclear genetic clusters in which the individual was found.

\textbf{Mito-nuclear Discordance}

Reasons for the mitonuclear discordance include the different
inheritance patterns of mitochondrial and nuclear markers. Mitochondrial
DNA (mtDNA) markers track the maternal lineage of a species whereas
nuclear markers include DNA from both parents and undergo recombination,
which adds additional levels of complexity (Rubinoff \& Holland, 2005;
Zink \& Barrowclough, 2008). This discordance raises the question of
mtDNA's power to resolve population level relationships. If mtDNA
saturation is at play, which can happen due to the rapid evolutionary
rate, mtDNA markers could be inaccurately signaling to this deep louse
evolutionary history. This seems unlikely, however, because the primary
endosymbiont of lice, \emph{Candidatus Riesia} \emph{pediculicola} ,
which is maternally inherited, shows similar distribution patterns to
louse mitochondrial DNA (Hammoud et al., 2022) .

On the other hand, it might also be possible that the mito-nuclear
discordance is resulting from the unusual gene transfer of human head
lice where males preferentially eliminate their paternally inherited
chromosomes (PGE- paternal genome elimination) (de la Filia et al.,
2018). PGE coupled with sex ratio distortion of human head lice (Perotti
et al., 2004) could potentially lead to sex-biased gene flow that is
reflected through the differences in mitochondrial and nuclear
phylogenies.

Nevertheless, the different views from both nuclear and mitochondrial
DNA of lice suggest a rich evolutionary history of lice that we're only
beginning to understand. Despite the differences in inheritance, both
mtDNA and nuclear markers in human head lice broadly agree on an out of
Africa dispersal, and the differences in mitochondrial and nuclear
patterns between human and lice suggest that louse genetics may be
capturing human demographic events that are not detectable from human
genetics. Recent human dispersal across the world through global ease of
travel might be contributing to the continental and regional patterns
that we see through the nuclear genome of lice.

It has been suggested that within-species population structure of the
parasites have either an equal or higher structure than its host
(Criscione et al., 2006). If this was the case for humans and lice, what
we can infer from the population structure of lice can potentially be
applied towards understanding the history of humans. Therefore, we
uncovered some patterns of genetic variation distribution across the
world that can only be explained by the recent movements and settlements
of its host.

\textbf{Conclusion}

All analyses concluded that sub-Saharan African individuals are
ancestral and genetically distinct from all other non-African
individuals. Sub-Saharan samples also had the highest heterozygosity
mirroring its host. Unlike the mitochondrial clades, nuclear genetic
clusters of lice are highly structured based on geography (continental
and major regions within continents). This mito-nuclear incongruence is
possibly due to different evolutionary histories of the markers.

Although we do not know the exact timing of this genetic population
structure that we observed, it seems possible that these five nuclear
genetic clusters are a result of more recent events of human migration
(e.g European colonization) and settlement across the world (Ascunce
2013) or it is reflective of major historical events in the human
history such as anatomically modern humans moving out of Africa. Further
investigations on the timing of these nuclear population splits and
coalescent based approaches to investigate the most recent common
ancestor are needed to confirm the host influence on shaping the genetic
structure of this parasite.

\textbf{Acknowledgements}

We thank Mercedes Domingo and Francesca Reale from Pid free clinic
Milano, Italy for sending us louse specimens. We also thank Dr. Marina
Ascunce for her DNA extractions on some of the louse specimens that were
included in this study. This study was funded by the National Science
Foundation P0044766 to DLR and 2206735 and 1925312 to JMA.

\textbf{Data Accessibility}

Raw genomic variants used in this study are publicly available through
Dryad (link XXX). Parameter specifications used for processing raw data
can be found in the supplementary data file.

\textbf{Author Contribution}

D.L.R, A.M and N.H designed the study. A.M. prepared the genomic
libraries. N.H performed the DNA extractions, conducted the analyses,
and wrote the original draft. All other authors provided specimens for
the study, and everyone contributed to reviewing and editing the final
manuscript.

\textbf{Competing Interests}

The authors declare no competing interests.

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