Population genomic analyses of schistosome parasites highlight critical challenges facing endgame elimination efforts

Abstract

Schistosomiasis persists in Asian regions despite aggressive elimination measures. To identify factors enabling continued parasite transmission, we performed reduced representation genome sequencing on Schistosoma japonicum miracidia collected across multiple years from transmission hotspots in Sichuan, China. We discovered strong geographic structure, suggesting that local, rather than imported, reservoirs are key sources of persistent infections in the region. At the village level, parasites collected after referral for praziquantel treatment are closely related to local pre-treatment populations. Schistosomes within villages are also highly related, suggesting that only a few parasites from a limited number of hosts drive re-infection. The close familial relationships among miracidia from different human hosts also implicate short transmission routes among humans. At the individual host level, genetic evidence indicates that multiple humans retained infections following referral for treatment. Our findings suggest that end-game schistosomiasis control measures should focus on completely extirpating local parasite reservoirs and confirming successful treatment of infected human hosts.

Full text

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
Scientic Reports | (2021) 11:6884 | 
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Population genomic analyses
of schistosome parasites highlight
critical challenges facing endgame
elimination eorts
Jonathan A. Shortt1,2,7, Laura E. Timm1,7, Nicole R. Hales3, Zachary L. Nikolakis3,
Drew R. Schield3,4, Blair W. Perry3, Yang Liu5, Bo Zhong5, Todd A. Castoe3,7,
Elizabeth J. Carlton6,7 & David D. Pollock1,7*
Schistosomiasis persists in Asian regions despite aggressive elimination measures. To identify
factors enabling continued parasite transmission, we performed reduced representation genome
sequencing on Schistosoma japonicum miracidia collected across multiple years from transmission
hotspots in Sichuan, China. We discovered strong geographic structure, suggesting that local, rather
than imported, reservoirs are key sources of persistent infections in the region. At the village level,
parasites collected after referral for praziquantel treatment are closely related to local pre-treatment
populations. Schistosomes within villages are also highly related, suggesting that only a few parasites
from a limited number of hosts drive re-infection. The close familial relationships among miracidia
from dierent human hosts also implicate short transmission routes among humans. At the individual
host level, genetic evidence indicates that multiple humans retained infections following referral
for treatment. Our ndings suggest that end-game schistosomiasis control measures should focus
on completely extirpating local parasite reservoirs and conrming successful treatment of infected
human hosts.
Schistosomiasis is a neglected tropical disease that impacts an estimated 200 million people globally1–3 causing
brosis of the liver and bladder, anemia, and in some species, cancer1,2,4,5. Schistosomiasis control programs in
China, beginning in the 1950s, are responsible for a 99% reduction in schistosomiasis infection prevalence, with
approximately 54,000 infections in China in 20166–8. e modern schistosomiasis control program in China is a
multi-pronged strategy including health education, testing and treatment, application of molluscicides to snail
habitat, and treatment of bovines9,10. While control programs are generally eective9,10, transmission hotspots
remain for reasons that are not well understood11,12. Several regions, including regions outside of China13, have
experienced re-emergence of schistosomiasis or no further declines in prevalence11, and our team, among oth-
ers, has found high infection rates in recent years14. ese infections are perplexing partly because they arise in
areas where control programs are ongoing and infected snails are not readily identied10.
e persistence of infection despite ongoing control measures highlights gaps in our knowledge of the natural
history of schistosomes and the epidemiology of schistosome infection. As China continues towards the goal of
schistosomiasis elimination by 202515, new insights into factors aecting schistosome transmission are needed.
Evolutionary and population genetic studies can yield insights that can be used to ll these gaps and increase
the eectiveness of control programs, such as how parasite populations change in response to mass administra-
tion of chemotherapy16. An in-depth understanding of S. japonicum population structure in Sichuan, China—a
region currently experiencing schistosomiasis re-emergence despite on-going, aggressive control measures10—can
OPEN
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provide crucial and actionable insights into how a parasite population on the brink of elimination is able to
persist.
Detailed insight into schistosome transmission patterns in response to treatment could inform future control
programs implemented in other parts of the world where parasitic helminths are endemic. Most population
genetic studies in schistosomes have been limited by the number of loci, small sample sizes, or both17–21, and
thus provided limited resolution in answering questions about population structure. However, recent advances
in genomic technologies are making it possible to address previously inaccessible questions and promise to grant
greater insight into the persistence of schistosome infections. Here, we apply a reduced representation genome
sequencing approach22–24 to sample tens of thousands of single nucleotide polymorphisms (SNPs) from hun-
dreds of miracidia (the ospring of infective schistosome mating pairs) longitudinally collected across nearly a
decade. ese data provide unprecedented resolution of patterns of schistosome population structure across a
geographically small area in Sichuan, China that highlight key features of regional infection hotspots. We further
describe an approach to discern between dierent degrees of relatedness, enabling the inference of source infec-
tions using high-resolution genomic data.
Results
In total, 272 miracidia preserved on FTA indicator cards were sequenced using double digest restriction-site asso-
ciated DNA sequencing (ddRADseq)25. is reduced representation genome sequencing approach was applied
following whole genome amplication, and generated a total of 1.8B reads. Aer ltering sequences for quality,
mapping reads to the S. japonicum reference genome, and excluding both low-coverage loci (Supplementary
Fig.S1) and miracidia with excess missing genotypes, there were 72,797 variable sites in 200 miracidia. e
details of the distribution of these miracidia across hosts and villages are provided in Supplementary TableS1. We
further ltered out low-condence SNP calls as missing data, resulting in a nal set containing 33,901 variants.
Population analyses. To determine whether schistosome infections are acquired from local or regional
sources, we evaluated the spatial distribution of schistosome genetic diversity across the study region. Genetic
structure indicates that the parasites are more related within villages than between villages, with allele sharing
decreasing signicantly with geographic distance between villages (Fig.1a,b, Supplementary Fig.S2). Population
structure is strong enough that most villages have a unique, discernible population of miracidia. For example,
the rst two principal components in principal component analysis (PCA) of genetic variation across samples
distinguish miracidia in the two most distant villages, C (the northernmost village sampled in this study) and D
(the southernmost village), from other villages (Fig.1c); additional principal components separate most other
villages into clear clusters based on genetic similarity (Supplementary Fig.S3). Phylogenetic analysis of mira-
Figure1. Genetic and geographic structure of Schistosoma japonicum miracidia sampled in Sichuan, China.
(a) Map showing locations of the 12 villages sampled, indicated by colored dots. Yellow lines represent major
roads and blue lines indicate rivers and major streams. e map was created with ArcGIS ArcMap52 (version
10.6; https:// deskt op. arcgis. com/ en/ arcmap/). (b) Proportion of rare alleles shared among villages with mean,
interquartile ranges, and outliers beyond the 2.5th percentile shown. Inter-village distances are Euclidean. All
comparisons were signicantly dierent (all p < 2.2 × 10–16; Mann–Whitney U test). (c) Principal component
analysis (PCA) of genetic variation from 200 miracidia across all 12 villages. e rst two principal components
(PC1 and PC2) respectively account for 4.2% and 2.5% of the genetic variation among individuals. (d)
Neighbor-joining tree of miracidia colored by village (top) and sampling timepoint (bottom). (e) ADMIXTURE
plot showing optimal k = 4 genetic clusters grouped by village and sampling timepoint. Timepoints are labeled
with year of collection (e.g., 2008 in d or 08 in e); Summer and late Fall 2016 collections are labeled with small s
or f, respectively.
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cidia also clusters villages, with most villages occupying their own clade (Fig.1d, Supplementary Fig.S4). is
trend was not seen when neighbor-joining trees were labeled by timepoint (Fig.1d). Estimates of population
structure using ADMIXTURE26 support this nding and identify further substructure within villages, particu-
larly village C (Fig.1e).
e genetic structure of schistosomes within villages indicates that local infection sources were not fully
eliminated by whole-village praziquantel treatments between sampling points. Miracidia from the same village
fall into characteristic ADMIXTURE clusters regardless of sampling timepoint (Fig.1e, villages A, B, C, E, and H;
see also Supplementary Figs.S4, S5), and miracidia collected from the same timepoint fall into multiple clades
on the phylogenetic tree (Fig.1d). However, there is a notable dierence in genetic structure in village J (within
the eastern cluster of villages) between 2010 and 2016, the largest time span present in the data (Fig.1d,e). While
structure appears to be retained over time in many cases, our ability to conduct longitudinal sampling for every
village was limited and the extent to which population structure is conserved is variable. Our resolution is also
limited by limited sampling of hosts in particular villages, with some villages represented by a single individual
host.
To conrm that broad patterns in our results were robust when sibling miracidia were removed, a sibling-
pruned dataset was generated and analyzed in the same way as the full dataset. Results of these analyses were
indeed qualitatively similar to those based on the full dataset and are presented in Supplementary Figs.S6–S10.
Identication of family clusters and relatedness estimates. Measures of relatedness among mira-
cidia allow inference of ne-scale transmission patterns. In the absence of reliable allele frequencies and/or
robust linkage information, we used the proportion of rare alleles shared between all pairs of miracidia to cal-
culate the posterior probabilities of rst-, second-, third-, or fourth-degree relationships between members of a
pair (Fig.2a; Supplementary TableS2). We nd evidence that miracidia from the same village tend to be closely
related (Figs.1b and 2b). Posterior probabilities of relatedness calculated from allele sharing (“Methods”) indi-
cate that schistosome rst cousins (3rd degree relatives) are extremely common within villages, but much rarer
between villages (Fig.2b). Because we only sample the progeny of adult mating schistosomes, a rst-degree
relationship between a pair of miracidia indicates that members of the pair are siblings, and as expected, pairs
of miracidia collected from the same human host are oen siblings (1st degree relatives; Fig.2b). However, we
also nd a large number of 2nd degree relatives within villages (Fig.2c, village D). It seems reasonable that most
of these are double rst cousins, given the high frequency of rst cousins within villages. Separate clutches of
parasite siblings were identied within individual human hosts (Fig.3a), indicating infection by multiple mating
pairs. We also found multiple examples of human hosts with sibling clusters that span multiple sampling time-
points (Fig.3b); while the possibility that a human host was reinfected with clones cannot be discounted, this is
preliminary evidence of retained infection despite the host being referred for intervening treatment protocols
(Fig.3b). One instance of a cross-timepoint sibling cluster was sampled in 2016, when the region implemented
directly observed treatment (DOT). Infections detected prior to 2016 could have, in principle, been retained
due to non-compliance with treatment. However, the retained infection detected aer DOT raises questions
about the eectiveness of treatment protocols and concerns that human hosts who failed to clear their infections
despite drug treatment may have served as sources of new infections to other community members.
High levels of allele sharing within villages (Fig.3c) indicate that parasite mate choice is oen limited to rela-
tives during the reinfection process. is limitation implies that following treatment, infections in a village may
have been re-established by a small number of genetically unique schistosomes, likely reecting the eectiveness
of local schistosomiasis control programs10,27. However, these results also suggest that long-term elimination
may require identication and targeted treatment of remaining local parasite sources. e genetic structure of
schistosomes within villages indicates that local infection sources were not fully eliminated by whole-village
praziquantel treatments between sampling points. For example, we inferred two possible treatment failures based
on the identication of apparent siblings collected from the same host before and aer treatment cycles, one of
which was sampled at two timepoints in 2016, when treatment was directly observed (Fig.3b).
e existence of clones could produce false inferences of sibling relationships (and thus retained infections)
within individual hosts across timepoints, and cannot be ruled out entirely if the number of cercaria-emitting
snails in the environment is small enough that individuals are reinfected with identical worms from the same
snails, or if identical juvenile worms residing in the liver survive treatment. We discount this partly due to the
~ 6-month lifespan of infected Oncomelania snails28 and the months required to develop worms from cercariae
and form mating pairs within a human host. Time-separated clonal double infections would require the envi-
ronmental condition that the individual snails produce clonal cercariae from the time of the initial infection
(followed by development, mate pairing, detection, and treatment—a minimum of 40 days29) until the time of the
second infection. In contrast, the retention of infections over time, due either to non-compliance with treatment
or treatment failure, is a more obvious explanation and supported by prior evidence14. Furthermore, cross-host
siblings indicating clonal pairs are rare (Fig.3c). us, we generally expect that clonality has had little impact on
our within-host inferences of retained infection. Instead, the detection of schistosome clones between human
hosts suggests that schistosomiasis control eorts have been highly eective in reducing local snail populations
required for producing cercariae.
Discussion
Our results highlight the important role of epidemiological and genomic data to resolve transmission patterns in
areas approaching elimination. ree major trends apparently contributed to the persistence of schistosomiasis
in the residual transmission hotspots we studied. First, local parasite reservoirs were a major contributor to
local re-introduction of schistosome infections. is is demonstrated by the nding that village miracidia are
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Figure2. Genetic relatedness of Schistosoma japonicum miracidia within and between villages. (a) Heatmap of allele sharing between all
sampled Schistosoma japonicum miracidia. Rows and columns are ordered using hierarchical clustering and annotated with village and
timepoint. (b) Distributions are shown for allele sharing between miracidium pairs sampled from dierent villages (blue), within villages
but dierent hosts (grey), and within hosts (green). e posterior probabilities for dierent degrees of relatedness are indicated by width
for 1st to 4th degree relatives in the lower plot. (c) Distributions of within-village (top panel) and within-host (lower panel) allele sharing
are shown for villages C (green) and D (orange). e total number of comparisons underlying each distribution is shown on the right.
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comprised of closely related populations of S. japonicum across timepoints, despite prompt referral for treatment
of all positive infections and complementary eorts to eliminate schistosomiasis from these villages during the
study period. Second, there is apparent retention of infection in individual hosts despite referral for treatment.
Strong evidence for this is provided by identication of sibling clusters from the same human host during
sampling events separated by seasons or years. ird, the high degree of relatedness of miracidia from dierent
hosts suggests that humans likely participate in maintaining local schistosomiasis reservoirs and amplify local
transmission events, although the participation of non-human mammals cannot be excluded.
We nd clear evidence for the successful impact of control measures on population dynamics in S. japonicum.
is result is somewhat dierent from some studies in S. mansoni and S. haematobium/S. bovis that observed
high gene ow among neighboring populations30–32. It seems reasonable to suppose that the dierence may lie in
the long-term, focused, and comprehensive nature of Chinese schistosome control eorts (which have induced
extremely low observed snail abundance), as well as the rural, mountainous topography of our study region. We
note that it is not possible to draw denitive conclusions about village-wide population structure in ve villages
where miracidia were collected from a single host (Supplementary TableS1), however based on the observation
Figure3. Relatedness of Schistosoma japonicum miracidia within and between hosts. Examples highlight
relatedness structures indicating multiple infections, retained infections, evidence for clones, and inbreeding
within villages. Hosts are indicated with human gures, with dierent miracidia collected from a single
host connected by thin dark grey lines. Ribbons between miracidia show posterior probabilities of degree of
relatedness through color (scale to side) and by ribbon width. (a) Two examples of multiple sibling clusters (2
and 4) within hosts are shown. In the second example, 8 miracidia are not in sibling clusters (all connections are
in light grey), indicating a high multiplicity of infection sources (a minimum of 12 mating pairs) in this host. (b)
Two examples of likely retained infections over time are inferred from the sibling-level miracidia sampled from
the same host at dierent timepoints separated by ve months and two years. (c) Miracidia from multiple hosts
living in villages B, J, and D and sampled at the same timepoint are shown with gaps between dierent villages.
In the le graph, sibling-level relatedness is shown, and a case of sibling-level relatedness between miracidia
across two individuals indicates clonal parents. In the right graph, cousin-level relatedness emphasizes that
strongly supported rst-cousin relationships are common among miracidia within villages and sparse between
villages.
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that population structure in the more densely-sampled villages is generally stronger between villages than within
villages, it seems reasonable to expect that denser sampling within these villages would reveal similar patterns.
Evidence of retained individual infections across sampled timepoints in our local study system raises ques-
tions about the negative impact of occasional treatment failures on the eectiveness of control measures. Spe-
cically, human hosts who fail to clear their infections may serve as sources of new infections to other humans.
e extent to which human vs. non-human mammalian hosts serve as sources of new infections is an extremely
important factor for guiding control eorts. If human hosts sometimes fail to clear their infections following
treatment and subsequently serve as sources of infection to others, the eectiveness of treatment protocols
should be reviewed and improved. We caution that the frequency and causes of retained infection remain
uncertain—including the extent to which treatment failure is due to drug resistance33, suboptimal dosing34, or
non-compliance with treatment35—and warrant further investigation. Furthermore, S. japonicum is a zoonosis
and it is dicult to eliminate non-human mammalian hosts as local reservoirs and ampliers of human infec-
tions. Now that the importance of local reservoirs has been established, ongoing sampling eorts will include a
variety of such alternate hosts.
Furthermore, the evidence for inbreeding among schistosomes complicates the evaluation of short inter-
human infection pathways. Such evaluation is also complicated because the human-infective cercaria stage
of schistosomes that originates from snails is clonal, and genetically identical cercariae may produce multiple
infections in one or more human hosts36. e most direct human-to-human infection pathway, involving only a
snail intermediate host, would yield avuncular relationships between miracidia from each host (Fig.4). However,
because of inbreeding and clonality, we were unable to dierentiate between the types of 2nd degree relations
(double rst cousins, half-siblings, or avuncular). Due to the high frequency of rst-cousin level relationships
within villages, we suspect that many, if not most, of the 2nd degree relations observed are double rst cousins.
Although rare, observations of sibling-level relatives across human hosts demonstrate that clonal infections
occurred in our samples (Fig.3c), and so clonal infections could also explain some 1st and 2nd degree relatives
observed between human hosts.
We expect that some of these questions can be resolved by the sampling and acquisition of denser variant
information with more loci per Mbp. Increased directed sampling will enable the estimation of key epidemio-
logical parameters such as the frequency of treatment failure, the number of active mating pairs within a human
host, and the frequency of clonal infections. Sampling of non-human mammalian hosts can potentially establish
a role for such hosts as both reservoirs and ampliers of re-introduced human infections. It is worth noting that
it may not be possible to eliminate a role for non-human hosts. If non-human hosts contribute low-frequency
infection rates or if a non-human host type is unidentied, such sources become nearly impossible to detect.
Denser variant information, such as that obtained by whole genome sequencing, along with recent improvements
in the S. japonicum reference genome37, will allow construction of extended haplotypes (local linkage groups)
that should be able to better distinguish among types of 2nd degree relatives and potentially extend pedigrees.
Such denitive inference of infection pathways would allow the establishment of frequencies of transmission
routes in the local schistosome re-establishment.
e work presented here exemplies how population genomic studies can illuminate factors underlying
transmission of macroparasites and provide strategic and precise advice to direct control eorts. We nd that
there are high levels of schistosome inbreeding within villages, that there are consistent, local sources of infection
through time, and that some human hosts appear to retain infections despite treatment referral. ese ndings
indicate that the persistence of schistosomiasis in residual transmission hotspots is primarily driven by local
transmission and reinfection, with at least some contribution from humans. Based on our ndings, end-game
Figure4. Avuncular relationships among schistosome miracidia. Diamonds indicate schistosomes, with those
surrounded by a gray box indicating an adult mating pair, and those surrounded by a dashed box indicating
miracidia sibling clutches. Chromosomes within diamonds are colored to indicate dierent haplotype
combinations that could be inherited from parents. e arrow points to a sibling of a miracidia clutch collected
from Host 1 that became a parent of another sibling clutch collected from Host 2. is worm is the link that
creates the depicted avuncular relationship between the ospring of schistosomes within Host 1 and Host 2.
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control measures should focus on conrmation of schistosome elimination from infected human hosts and
complete extirpation of local infection reservoirs.
Materials and methods
Miracidia collection and sample selection. Miracidia, the rst schistosome larval stage, were collected
from 12 villages in Sichuan, China (see Fig.1a). Infection surveys took place in 2007, 2008, 2010, and in both
the summer and fall of 2016. During each survey, village residents submitted fecal samples for three consecutive
days and each sample was tested for S. japonicum infection using the miracidium hatching test as described in
the literature23. Individual miracidia were collected from the top of the hatching test ask, rinsed three times
in autoclaved, de-ionized water and transferred to Whatman FTA indicator cards using a hematocrit tube or
Pasteur pipette drawn to a narrow bore with a ame.
A subset of collected samples were selected for inclusion in the study. is subsampling was designed to
include 10–15 miracidia from every village and across multiple timepoints. When possible, we tried to include
multiple samples from the same human host and multiple human hosts from each village. However, ve villages
(E, F, I, K, and L) presented here are represented by multiple miracidia collected from a single host (Supple-
mentary TableS1).
e research involving human subjects was approved by the Sichuan Institutional Review Board, the Uni-
versity of California, Berkeley, Committee for the Protection of Human Subjects, and the Colorado Multiple
Institutional Review Board. Participants provided written, informed consent. All experiments were performed
in accordance with relevant guidelines and regulations. Anyone testing positive for Schistosoma japonicum was
informed of their infection status and referred to the local anti-schistosomiasis control station for treatment.
DNA library preparation and sequencing. DNA library preparation followed a previously published
methodology24. Briey, discs containing individual miracidia were excised from Whatman FTA cards using a
2mm card punch (Whatman WB100029) and DNA from the disc was whole-genome-amplied by isothermal
genome amplication, termed “multiple displacement amplication” (MDA), using GenomiPhi v3 (GE Health-
care Biosciences 25660124) amplication tubes with modications as described in the literature24. Amplied
DNA was digested for > 8h with PstI-HF and Sau3AI at 37°C followed by a 65°C heat deactivation step. Fol-
lowing solid phase reverse immobilization (SPRI) DNA extraction, custom adaptors containing an 8-bp unique
molecular identier (UMI) and sequences corresponding to the single-stranded DNA sticky ends generated by
digestion and a 6-bp barcode were ligated to digested fragments. Adaptors ligating to PstI-HF cuts also contained
6-bp barcodes. Following ligation, sets of 6–8 samples were pooled such that no barcode was used twice within
the same pool, and underwent size selection for fragments sizes either 300–600bp (including adaptor sizes) or
390–690bp (including adaptor sizes) using a PippinPrep with a 1.5% agarose gel. Following size selection, sam-
ples underwent 15 cycles of PCR amplication. Primers used in amplication also contained index sequences
and sequences used for Illumina-based sequencing cluster formation (sequences for all adaptors and primers are
shown in Supplementary TableS3). Sample pools were then combined in equimolar ratios such that no index
sequence was used more than once within each pool. Samples were sequenced on an Illumina HiSeq using v4
chemistry.
Fastq processing and variant identication. In total, 272 samples were sequenced: 124 samples with
125-bp single end reads each, and 148 samples with 150-bp paired end reads each, resulting in 1,799,089,548
total reads generated. PCR clones were ltered from the reads with the clone lter tool in stacks38 using the
UMIs contained in each barcode. Sequences were then quality ltered and divided by barcode using the pro-
cess_radtags tool in stacks38 with restriction enzymes and barcodes supplied as arguments. We ‘rescued’ reads
with a single base mutation in the 8-bp barcode or restriction sites (-r). Low-quality reads were removed (-q) to
a separate le (-D) and excluded from downstream analysis. On average, 5.61% of reads from each library were
identied as clones and removed. An average of 31.37% of reads were ltered from each library due to clonality,
ambiguous barcodes/restriction site, or quality, though one library containing barcoded DNA from eight mira-
cidia contained an abnormally high number of reads missing restriction sites in the correct place. is library
was retained, with the lters above applied. Excepting this library, an average of 26.96% of total reads were
ltered from all reads. Reads passing this series of lters were mapped to the S. japonicum reference genome
(downloaded from schistodb.net39,40) using bwa mem41. Variants were called from .bam les using Haplotype
Caller in the Genome Analysis Toolkit42–44 with gvcf mode and GenotypeGVCFs. Over 4 million variant sites
were found, however most of these sites were sequenced in just one or very few miracidia. Demultiplexed fastq
les, as well as bam and bam index les, are available through the NCBI Sequence Read Archive (SRA) database
under BioProject PRJNA349754.
Dening sets of ddRADseq loci. Although most ddRADseq reads mapped reliably to expected ddRAD-
seq loci24, loci from o-target reads may add noise to subsequent analyses. To de-noise our data, we identied
a set of loci that were reliably recovered at ≥ 20 × depth across the majority of samples in order to retain only
those variants that map to ddRADseq loci. Using a custom perl script (cutgenome.pl; github.com/PollockLabora-
tory/Schisto), we identied the expected mapping locations of ddRADseq reads in the S. japonicum reference
genome40, with each expected individual ddRADseq locus having two dierent locations: one for the forward
read and one for the reverse read, if applicable.
To identify the subset of these expected loci that could be reliably recovered, we rst eliminated miracidia
that had fewer than 500K reads post-ltering or less than 20K reads that map to the reference genome with a
mapq ≥ 20. We obtained the sequencing depth of each expected ddRADseq locus in each of these 156 remaining
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‘high-depth’ miracidia using bedtools45 intersect. We recorded the coverage of each expected read locus (-c) and
required that mapped loci overlapped by at least 50% of an expected read length before incrementing the depth
count (-f 0.5). For miracidia that were sequenced with single end sequencing, the cumulative depth of each of the
fragment’s possible reads was used as the depth for the locus; for miracidia sequenced with paired end sequenc-
ing, the mean depth of the two read loci was used for the fragment’s depth.
From this data set, we identied 9637 expected ddRADseq loci sequenced at ≥ 20 × depth in ≥ 75% of ‘high-
depth’ miracidia (see Supplementary Fig.S1). To further restrict variants to the most stringent loci, analyses
reported here used only variants from the 6990 expected ddRADseq loci that were close to the target size selection
range (170–500bp). Once these high-condence loci were identied, they were called across the entire dataset,
resulting in reads across 200 samples.
Variant set creation. Variants then underwent a number of lters as follows: invariant sites were removed,
sites with more than two alleles or that contained an indel were removed, and variants that were not within
an expected ddRADseq locus were removed. To create our nal variant set, we re-coded any sites sequenced
at < 10 × coverage as missing data, recoded individual genotypes with GQ < 20 as missing, removed sites that
were missing more than 50% of genotype calls, and removed miracidia missing more than 90% of genotype calls
(Supplementary Fig.S11). is nal ltering resulted in 200 miracidia genotyped at 33,901 sites. e .bed le
and .vcfs from dierent stages of ltering can be downloaded from http:// www. Evolu tiona ryGen omics. com/
Progr amsDa ta/ Schis toGen omics.
Population analyses. e parametric tests for population structure we performed require that the pro-
vided loci be in linkage equilibrium, however missing genetic distances between neighboring sites and the cur-
rently highly fragmented reference genome makes linkage pruning dicult. is problem is compounded in
our dataset because a large proportion of the miracidia were suspected to be highly related, which could inate
linkage estimates between sites. Here, we outline the steps we performed to obtain a set of variants likely to be
unlinked (though we note that this pruning does not guarantee that all sites used are in linkage equilibrium).
We rst identied miracidia that are expected to be closely related by identifying clusters of miracidia that
share a proportion of rare alleles greater than 0.45 (see “Identication of family clusters and relatedness esti-
mates” below) between each pair of miracidia, and removed all but one miracidium from each cluster. A total of
83 miracidia remained following this step (see “Identication of family clusters and relatedness estimates” for
details). We then pruned linked variants in this putative unrelated set using plink’s –indep-pairwise command
(v1.90b4.6)46 with arguments 1000 100 0.1, which greedily prunes variants with r2 > 0.1 from overlapping win-
dows consisting of 1000 variants. Linkage pruning in this way reduced the number of variants in the putatively
unrelated set to 6642.
We used ADMIXTURE26 and these putatively unlinked variants with all 200 miracidia to determine the pro-
portion of each miracidium’s genome that can be attributed to one of k dierent populations. We tested k = 2–10,
with ten replicates for each k and default cross-validation to determine the k with the lowest cross validation
error (Supplementary Fig.S5).
Principal component analysis (PCA), as implemented in R’s (version 3.5.1)47 ‘SNPrelate’ package48, was applied
to the full variant set to assess how genotype dierences between miracidia contribute to region-wide variability
between samples and villages.
Using all variants, we calculated pairwise genetic distances between miracidia through the distance-based
bitwise.dist function implemented in R’s ‘adegenet’ package49,50 and used distances to construct a neighbor-joining
tree using the R’s ‘ape’ package51.
Identication of family clusters and relatedness estimates. To identify highly related samples in
the absence of reliable allele frequency estimates, we used pairwise comparison of shared rare alleles. Rare alleles
were dened as alleles whose minor allele frequency ≤ 0.1. Rare allele sharing was calculated between all pairs
of samples using only rare variants and a custom perl script (ndSibClusters.pl; github.com/PollockLaboratory/
Schisto) following
where
and
Pij
is the proportion of shared alleles between individuals i and j, L is the number of loci tested, and k is a
locus for which both individuals i and j have non-missing genotype calls and individual i has a rare variant. To
avoid overestimating relationships because of linked variants, we use the mean proportion of rare alleles shared
generated from 30 replicates of randomly sampling 2000 loci with replacement for each pairwise comparison.
We identied clusters of highly related miracidia such that each miracidium in a cluster shared ≥ 0.45 of its rare
alleles with at least one other miracidium in the cluster. Removal of all but one miracidium from each putative
sibling cluster (117 individuals) resulted in a data set of 83 miracidia. e sibling-pruned vcf le, which was used
to prune linked variants, is available at http:// www. E vo l u tiona ryGen omics. com/ Progr amsDa ta/ Schis toGen omics.
(1)
P
ij =
1
L
L

k=1
x
ijk
(2)
xijk =
1 if iand jhave the same genotype at locus k
0.5 if iand jshare one allele at locus k
0 if iand jshare no alleles at locus k
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Calculating posterior probabilities across degrees of relatedness. To generate posterior prob-
ability distributions for each degree of relatedness, we rst estimated mean levels of unrelated allele sharing,
ˆµunrelated =0.04
, as the average pairwise rare allele sharing between individuals from the most geographically
distant villages (n = 35.6km) in the full dataset of 200 miracidia. As analysis of pairwise rare allele sharing by
inter-village distance indicated a statistically signicant decrease in pairwise rare allele sharing as inter-village
distance increased, this was determined to be the best, data-driven estimate. To estimate allele sharing among
sibling miracidia, we began by identifying clusters of miracidia most likely to be siblings (1st degree relatives):
clusters of 3 or more miracidia from the same host and collection timepoint, all with pairwise rare allele sharing
proportionn ≥ 0.30 (45 miracidia in 13 clusters). ere were an additional 8 pairs of miracidia within the same
host that are likely siblings but not part of a big enough cluster. e estimated mean,
ˆµsibs =0.44
and variance,
ˆ
σ2
sibs
=
0.30
, of allele sharing were calculated from eligible pairs (n = 60). For intermediate degrees of related-
ness, means (
ˆµdegree
) were estimated by successively halving the distance from sibs to unrelated, and variances
( ˆ
σ2
degree
) were estimated by successively halving the sibling variance for each further degree of relatedness, which
will have had twice the number of meioses (e.g., ˆ
µ2
◦=
(ˆµ
unrelated
+ˆµ
sibs
)
2
and ˆ
σ
2
2
◦=ˆσ
2
sibs
2
). Posterior probabilities
were calculated roughly assuming even prior probabilities for each categorical degree of relatedness from sib-
lings to 5th degree relatives and unrelated, and assuming that allele sharing probabilities for each degree of relat-
edness were distributed normally, i.e.,
∼
N(ˆµdegree,ˆσ
2
degree)
, a reasonable large-sample approximation.
Analysis of non-sibling miracidia. As a safeguard against making conclusions about population struc-
ture using data that may violate assumptions of independence between samples, we used the posterior probabili-
ties of relatedness to identify sibling clusters (see “Identication of family clusters and relatedness estimates” and
“Calculating posterior probabilities across degrees of relatedness”) and generated a sibling-pruned dataset that
includes 81 non-sibling miracidia. We used the sibling-pruned dataset to repeat analyses described in “Popula-
tion analyses”, namely: ADMIXTURE, PCA, and construction of a neighbor-joining tree. Finally, we subset the
rare-allele sharing described in “Identication of family clusters and relatedness estimates” to include only the
81 non-sibling miracidia and compared the proportions of shared rare alleles between all pairs of remaining
miracidia to the distance between the two villages where members of the pair were collected.
Data availability
Sequences generated during this work have been deposited in the NCBI Sequence Read Archive under BioProject
PRJNA349754. e vcf le used in analysis is available at http:// www. Evolu tiona ryGen omics. com/ Progr amsDa
ta/ Schis toGen omics and custom scripts are available at github.com/PollockLaboratory/Schisto.
Received: 25 June 2020; Accepted: 9 March 2021
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Acknowledgements
We thank the members of the Sichuan Centers of Disease Control and the local county anti-schistosomiasis sta-
tions for their assistance in collecting parasite samples and related eld data. is work was supported by fund-
ing from the NIH (R21 AI115288 from National Institute of Allergy and Infectious Disease and R01 AI134673)
to EJC as principal investigator and YL, BZ, TAC, and DDP as co-investigators.LET wasfunded through the
Colorado Biomedical Informatics Training Program (T15 LM009451).
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Author contributions
T.A.C., E.J.C., and D.D.P. conceived and supervised the study, also serving as project administrators. J.A.S., T.A.C.,
E.J.C., and D.D.P. designed the experiment. J.A.S. and D.D.P. developed soware for data analysis. L.E.T. vali-
dated the data and results. J.A.S., L.E.T., N.R.H., Z.L.N., D.R.S., B.W.P., T.A.C., and D.D.P. executed the analyses.
Funding and other resources for this study were provided by Y.L., B.Z., T.A.C., E.J.C., and D.D.P. J.A.S., L.E.T.,
T.A.C., and D.D.P. ensured data was properly curated and made publicly available. e original dra of this
manuscript was prepared by J.A.S., E.J.C., and D.D.P. J.A.S., L.E.T., N.R.H., Z.L.N., D.R.S., B.W.P., T.A.C., and
D.D.P. contributed to data visualization. All authors reviewed the manuscript prior to submission.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary Information e online version contains supplementary material available at https:// doi. org/
10. 1038/ s41598- 021- 86287-y.
Correspondence and requests for materials should be addressed to D.D.P.
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