Schistosomiasis is a widespread parasitic disease, affecting over 200 million people in tropical countries. It is caused by schistosome worms, which are carried by freshwater snails. These snails release worm larvae into the water, where they can infect humans – for example, after bathing or swimming.

Treatment options for schistosomiasis are limited. Eliminating the freshwater snails is one way to control the disease, but this is not always effective in the long term and the chemicals used can also harm other animals in the water.

Another way to manage schistosomiasis could be to stop the worms from infecting their snail host by breaking the parasites’ life cycle without killing the snails. It is already known that some snails are naturally resistant to infection by some strains of schistosomes. Since this immunity is also inherited by the offspring of resistant snails, there is likely a genetic mechanism behind it. However, very little else is known about any genes that might be involved. Tennessen et al. therefore set out to identify what genes were responsible for schistosome resistance and how they worked.

The experiments used a large laboratory colony of snails, whose susceptibility to schistosome infection varied among individual animals. To determine the genes behind this variation, Tennessen et al. first searched for areas of DNA that also differed between the immune and infected snails. Comparing genetic sequences across over 1,000 snails revealed a distinct region of DNA that had a large effect on how likely they were to be infected.

This section of DNA turned out to be highly diverse, with different snails carrying varying numbers and different forms of the genes within this region. Many of these genes appear to encode proteins found on the surface of snail cells, which could affect whether snails and worms can recognize each other when they come into contact. This in turn could determine whether or not the worms can infect their hosts.

These results shed new light on how the snails that carry schistosomes may be able to resist infections. In the future, this knowledge could be key to controlling schistosomiasis, either by releasing genetically engineered, immune snails into the wild (thus making it harder for the parasites to reproduce) or by using the snails’ mechanism of resistance to design better drug therapies.

Schistosomiasis is a chronic and debilitating disease suffered by over 200 million people worldwide (Evan Secor, 2014; Lo et al., 2018). It is caused by infection with schistosome trematode parasites that are transmitted by aquatic snails (Lo et al., 2018). Infection can be treated with regular doses of a single drug, praziquantel (Doenhoff et al., 2009). However, it has become increasingly clear that mass drug administration alone will not adequately control schistosomiasis, and that successful elimination of transmission requires intervention at the snail stage (Lo et al., 2018; Sokolow et al., 2018).

Immunogenetic interactions between snails and schistosomes represent a crucial stage in the parasite life cycle that can be targeted to block transmission. Parasite resistance is highly heritable in snails, and there is substantial strain-by-strain interaction between hosts and parasites (Richards and Shade, 1987; Richards et al., 1992; Knight et al., 1999; Webster et al., 2004; Webster and Woolhouse, 1998; Theron et al., 2014). Genetically diverse parasite cultures can infect nearly all snails, while bottlenecked laboratory strains of parasite can only infect a subset (Theron et al., 2008), suggesting a ‘trench warfare’ model in which numerous alleles are maintained in both host and parasite populations because each matches a different phenotype in the other species (Seger, 1988; Stahl et al., 1999). There are likely to be snail genes with large effects on resistance to particular parasite genotypes (Lewis et al., 2001). Finding these genes will open two potential avenues for disease mitigation. First, they may uncover mechanisms of infection by the parasite that could be therapeutically targeted. Second, they may facilitate genetic manipulation of wild snail populations so as to reduce parasite transmission by eliminating alleles that permit infection by certain schistosome genotypes (Theron et al., 2014; Reardon, 2016; Famakinde, 2018; Maier et al., 2019).

For invertebrates, highly strain-specific responses to parasites remain unexplained (Schmid-Hempel, 2005; Schulenburg et al., 2007), as these taxa lack the combinatorial immune system found in vertebrates with its vast and adjustable repertoire of recognition molecules. Invertebrate resistance to macroparasites or parasitoids often involves large-effect loci (Carton et al., 2005), but most of these are poor candidates for mediating strain specificity, as they reflect generic enhanced defenses conveying a constitutive fitness cost (Kraaijeveld and Godfray, 1997; Webster and Woolhouse, 1999; Koella and Boëte, 2002), or encode signaling molecules (Hita et al., 2006) or effectors (Goodall et al., 2006) rather than recognition molecules. Strain specificity may be conveyed by suites of highly diverse host genes that act synergistically, especially if these interact with similarly varying and coevolving sets of parasite genes to mediate host-parasite recognition (Schmid-Hempel, 2005; Schulenburg et al., 2007; Cerenius and Söderhäll, 2013). Such loci will not necessarily produce a large phenotypic signal unless several are clustered in the same genomic region.

The neotropical snail Biomphalaria glabrata has been the focus of recent efforts to develop genomic resources for schistosomiasis vector biology, although the 916 Mb, repetitive genome remains poorly assembled (reference genome BglaB1 N50 = 48 kb; Adema et al., 2017; Tennessen et al., 2017). B. glabrata snails can be readily challenged with Schistosoma mansoni miracidia under controlled laboratory conditions, with successful infections diagnosed by subsequent shedding of cercariae (Bonner et al., 2012). To date, four genomic regions have been identified in which allelic variation influences resistance to infection by S. mansoni (Knight et al., 1999; Goodall et al., 2006; Tennessen et al., 2015a; Tennessen et al., 2015b). An F2 mapping cross between resistant B. glabrata strain BS-90 and a susceptible strain yielded two RAPD markers linked to resistance (Knight et al., 1999). One of those was subsequently aligned to a contig on Linkage Group (LG) XII (Tennessen et al., 2017), although the identities of any candidate genes to which it may be linked remain unclear (the other could not be uniquely mapped). Two other genomic regions (sod1 and RADres; Goodall et al., 2006; Tennessen et al., 2015a) influence S. mansoni infection in B. glabrata population 13–16-R1 which is admixed from Caribbean and Brazilian populations and has been maintained free from parasites for several decades as a large laboratory population with substantial segregating variation. However, together those two regions explain only 7% of the variance in resistance in 13-16-R1, suggesting that other resistance loci remain undiscovered in this population. In B. glabrata from Guadeloupe, the Guadeloupe Resistance Complex (GRC) shows an 8-fold effect on the odds of S. mansoni infection (Tennessen et al., 2015b; Allan et al., 2017) via a hemocyte-mediated mechanism (Allan et al., 2018a) that also affects the proteome (Allan et al., 2019) and microbiome (Allan et al., 2018b). Seven clustered GRC genes encode hyperdiverse single-pass transmembrane (TM1) proteins that appear to recognize parasite-associated molecules (Tennessen et al., 2015b), likely including saccharides (Allan and Blouin, 2018).

Here, we use a genome-wide association study to pinpoint a new resistance region in snails, with a very large (over 15-fold) effect on odds of infection by schistosomes. It comprises a cluster of highly polymorphic transmembrane genes, and as GRC (=PTC1) was the first such cluster described in Biomphalaria, we designate this second region as Polymorphic Transmembrane Cluster 2 (PTC2). Using PacBio, we have vastly improved the assembly of the B. glabrata genome, allowing us to fully characterize the chromosomal vicinity of PTC2. Transcriptomic data show that, like GRC, PTC2 harbors exceptionally divergent suites of TM1 genes, suggestive of coevolutionary dynamics. These results support a general immunogenetic scenario in which clusters of highly polymorphic TM1 genes mediate host-parasite interaction.

In pooled whole-genome sequencing of 600 infected and 600 uninfected 13–16-R1 snails (298x and 333x coverage, respectively), a single genomic region showed by far the greatest difference in allele frequencies between pools (Figure 1A; Supplementary file 1A). The highest outliers occurred in a 450 kb section of LG XII, here called PTC2. Genetic divergence between pools (F_ST) at numerous PTC2 variants exceeds 0.1, a value unobserved among one billion simulated neutral variants, which is therefore significant even if corrected for the nearly 7 million empirical variants examined (p<0.01). Many variants even show F_ST over 0.2, more than twice the F_ST at sod1 and RADres. By subsequently genotyping indel polymorphisms at PTC2 in individual snails, we observed three alleles at intermediate frequency (R: 44%, S1: 24%, and S2: 32%). Infection was rare for RR homozygotes (12.9%), and much more common for S1S1 (75.3%) and S2S2 (29.6%) homozygotes, a difference in infection odds of over 15-fold (i.e. infection odds of 0.15 vs. 3.0; Figure 1B). Heterozygotes showed intermediate phenotypes. There was weak partial dominance of S1 over R (observed Clopper-Pearson 95% confidence interval of infection probability for S1R = 51.0–61.8%; expected intermediate phenotype = 44.1%), such that relative to RR, carrying an S1 allele increases the odds of infection 5.9-fold (p=6 × 10⁻⁴²) while a second S1 allele further increases the odds of infection 2.7-fold for a 15.9-fold difference (p=1 × 10⁻⁴). The S2 allele acts additively, such that each S2 allele increases the odds of infection 1.5-fold (p=6 × 10⁻⁵). We confirmed the PTC2 signal using an independent set of 392 snails from 13-16-R1 that had previously been phenotyped (Tennessen et al., 2015a) (p=7 × 10⁻¹² for R vs. S1; p=4 × 10⁻⁵ for R vs. S2; Figure 1—figure supplement 1). These snails had also been genotyped at sod1 and RADres, revealing that all three loci had significant independent associations when included together in the same model (p≤10⁻⁴ for each), with no evidence for epistasis (p>0.05 for interaction terms). Segregating variation at PTC2 has a stronger association with odds of infection than that of any other known B. glabrata locus (Tennessen et al., 2015a; Tennessen et al., 2015b). The BS-90 RAPD marker (Knight et al., 1999) is only 5 Mb and 23 cM from PTC2 (Figure 1—figure supplement 2; Tennessen et al., 2017). This marker is predicted to be 17 cM (range 6–33 cM) from a causal locus, which could therefore plausibly be PTC2 (Supplementary file 1B).

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Genetic divergence between infected and uninfected snail pools in 10 kb windows.

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Using PacBio whole-genome assemblies from snails homozygous for each of the three PTC2 alleles (Supplementary file 1C), we find striking sequence and structural divergence among the haplotypes (Figure 2). Alignable regions show 3.3% nucleotide divergence on average (SD = 2.1%). A majority of PTC2 sequence shows no similarity among alleles; the percentage of sequence that could even be aligned ranged from 12.9% (R onto S2) to 40.0% (S1 onto R). All three PTC2 haplotypes harbor unique insertions tens of thousands of bp in size, some of which contain complete coding genes, such that each genotype carries a distinct combination of genes (Figure 2). Shared orthologous genes at PTC2 show many nonsynonymous differences and in some cases homology can only be identified at the protein, not DNA, level (Figure 2—figure supplement 1; Figure 3). This degree of polymorphism is unusually high for conspecific haplotypes in most genomic regions in any taxon (Leffler et al., 2012). In contrast to PTC2, 89.5% of sequence on other contigs can be aligned between assemblies, with a mean of 0.4% nucleotide divergence. It is not obvious how a chromosomal rearrangement (e.g. inversion) could maintain more than two distinct haplotypes, and in any case we see no evidence for one in our assemblies.

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Using RNA-Seq data from homozygotes of each genotype to identify expressed genes, we fully annotated PTC2 (Supplementary file 1D,E). Of the eleven PTC2 genes, eight are predicted to be TM1 genes, including all five genes that are shared among the three haplotypes (genes 1, 2, 4, 5, and 9; Figure 2; Figure 3). Of the three non-TM1 genes, gene 3 and gene 11 show homology to TM1 genes 2 and 8, respectively, but without the TM1 domains. Gene 6 contains a conserved protein domain of unknown function (DUF2732). Only 11% of B. glabrata genes are TM1 genes, so they would be unlikely to constitute eight of eleven genes by chance alone (p<10⁻⁵). PTC2 TM1 genes are all between 166 and 530 codons, have TM1 domains that are displaced from the N-terminus (Figure 3—figure supplement 1), and like the rest of PTC2 they are highly polymorphic (Figure 3), with amino acid level divergence exceeding 50% in several cases. Sequences similar to both R and S haplotypes are present in other B. glabrata populations without admixed histories (genomic and transcriptomic sequence from Brazilian strain BB02, Adema et al., 2017; transcriptomic sequence from Guadeloupe, Tennessen et al., 2015b; Figure 3—figure supplement 2), suggesting that they co-occur throughout the species range and the polymorphism is old. Synonymous divergence among alleles is higher than nonsynonymous, and a phylogeny of concatenated genes shows 24% synonymous divergence from the midpoint root. Thus, haplotypes are more consistent with an ancient origin (24 million years assuming a neutral mutation rate of 10⁻⁸ per year) rather than recent divergence via selection for protein diversity. Other than the transmembrane segment, these genes contain no known protein domains or homology to sequences outside of gastropods, nor are they homologous to GRC genes. Some show homology to each other and/or to other genes near PTC2 or elsewhere in the genome, but amino acid level sequence similarity among paralogs is low (<50%). The phenotypic effects of individual genes and polymorphisms will be an exciting subject for future work involving knockdowns or knockouts (Allan et al., 2017; Abe and Kuroda, 2019) and/or additional RNA-Seq from multiple individuals allowing quantification of expression differences.

Both GRC and PTC2 suggest a model of snail-schistosome interaction via molecular recognition (either of the parasite by the host, or of the host by the parasite) that is mediated by TM1 gene polymorphism. Across metazoans, TM1 genes often play a role in immunological recognition, and include B- and T-cell receptors, Toll-like receptors, major histocompatibility complex genes, and similar host defense genes (Pahl et al., 2013). Other polymorphic clusters of host transmembrane genes are used by parasites as receptors for host recognition and invasion (e.g. human glycophorins and Plasmodium; Malaria Genomic Epidemiology Network et al., 2017), and at least one of the GRC TM1 genes controls shedding of S. mansoni cercariae (Allan et al., 2017). One PTC2 TM1 gene is present only on the R haplotype and is an obvious candidate if it functions to recognize the parasite. However, allelic divergence among shared genes could also be important, and an R-specific gene alone would not explain the difference between S1 and S2. In contrast to GRC, in which a completely dominant allele confers resistance, all three alleles in PTC2 differ in their susceptibility, and allelic associations are additive or show partial dominant susceptibility (Figure 1B). This pattern suggests that multiple loci along the haplotypes may jointly contribute to phenotype by interacting with different combinations of parasite molecules such as SmPoMucs (Roger et al., 2008) or other glycoproteins (Allan and Blouin, 2018) to determine the outcome of infection.

As with GRC, we suspect non-neutral host-parasite coevolutionary processes have shaped sequence polymorphism at PTC2. The inferred ancient origin (>20 million years) of PTC2 is inconsistent with a neutral coalescent process. Because 13–16-R1 is admixed from geographically isolated populations, we can’t infer natural allele frequencies or compare the site frequency spectrum to a neutral expectation for a randomly-mating population, as was possible for GRC (Tennessen et al., 2015b), though these allelic lineages do segregate in natural populations (Figure 3—figure supplement 2). Therefore, while the remarkable structural and nonsynonymous polymorphism appears adaptive, it is difficult to distinguish among plausible scenarios including overdominance (Woolhouse et al., 2002), negative-frequency dependent selection (Woolhouse et al., 2002; Koskella and Lively, 2009; Bento et al., 2017), adaptive introgression from distantly related species (Hedrick, 2013), or selection for an epistatically-interacting supergene (Thompson and Jiggins, 2014). Introgression appears unlikely, as it would have had to occur twice independently to generate three distinct haplotypes, and all of the closest relatives of B. glabrata occur allopatrically in Africa (DeJong et al., 2001). Therefore, the most plausible explanations involve some form of long-term balancing selection. Although S. mansoni is not native to the neotropics (Desprès et al., 1993), selection may have been driven by other trematodes, a clade that has ubiquitously infected snails for millions of years (Blair et al., 2001) and which can be a strong selective force favoring rare alleles (Koskella and Lively, 2009). Schistosomes castrate snails (Faro et al., 2013) but wild snails show no sign of evolving universal resistance, suggesting that the R haplotype is either specific to parasite genotype or else costly to fitness in some undetected manner. The R allele has persisted within the 13–16-R1 population for decades in the absence of challenge by parasites, so any fitness cost must be relatively weak or context-dependent.

We have thus far only observed an effect on one parasite strain, PR-1, precluding inferences about gene-for-gene interaction. Nevertheless, a system of polymorphic matching alleles could explain the substantial schistosome-strain by snail-strain interaction in compatibility that is often observed (Richards and Shade, 1987; Richards et al., 1992; Knight et al., 1999; Webster et al., 2004; Webster and Woolhouse, 1998; Theron et al., 2014), including schistosome-infection dose-response curves that fit a simple phenotype-matching model (Theron et al., 2008; Theron et al., 2014). If more than one PTC2 gene contributes to resistance, then synergistic interactions among these genes and other unlinked loci could begin to explain the pronounced variation in host-parasite compatibility (Schulenburg et al., 2007). In other invertebrates, highly polymorphic haplotypes can be major-effect loci for infection and show striking coevolutionary signatures including variable presence/absence of genes. For example, the PR-locus mediating bacterial resistance in Daphnia via matching-allele interactions also features haplotypes of vastly different sizes (differences > 60 kb at both this locus and PTC2) with large non-homologous sections, and these contain glycosyltransferase genes that could mediate host-pathogen compatibility (Bento et al., 2017). Similarly, the APL1 immune factor impacting Plasmodium development in Anopheles consists of adjacent paralogs that differ in copy number among species and show extreme diversity within species (Rottschaefer et al., 2011; Mitri et al., 2020). Thus, while invertebrates lack the acquired immunity of vertebrates and its associated adaptive genetic variation (Spurgin and Richardson, 2010), their defenses can show similar nuance conveyed by molecular diversity (Loker et al., 2004; Cerenius and Söderhäll, 2013). However, the evolutionary consequences may not match those for vertebrate acquired immunity loci like the major histocompatibility complex or immunoglobulins, where sequence diversity per se tends to enhance immune effectiveness though perhaps at the cost of autoimmunity. For invertebrates, increased numbers of distinct immunogenetic sequences may not necessarily lead to increased resistance if parasites also use these sequences to recognize hosts or mount evasion strategies (Schmid-Hempel, 2005). If discarding immune genes is often as advantageous as gaining them, the result could be a patchwork of genes as observed at PTC2. A more appropriate vertebrate analog might be blood groups used by parasites to invade host cells, for which haplotypic differences often include loss of functional genes, and diversity has been maintained for millions of years by balancing selection (e.g. Dantu group, Malaria Genomic Epidemiology Network et al., 2017; ABO group, Ségurel et al., 2013). The generation and maintenance of such divergent haplotypes remain to be fully explained and could reflect long-term fluctuating selection among alleles with different combinations of specificity and cost (Seger, 1988; Ashby and Boots, 2017).

As with mosquitoes (Marshall et al., 2019), a promising strategy for disease control involves recruiting the natural immunogenetic variation of vectors (Reardon, 2016). The successful implementation of CRISPR/Cas9 in gastropods (Abe and Kuroda, 2019) will facilitate the creation of genetically modified snails having enhanced immunity to block disease transmission (Maier et al., 2019). As large-effect loci, the TM1 clusters are excellent candidates to target in such efforts. However, more work is needed to characterize the functional effects of these genes, as well as the molecular and evolutionary dynamics between hosts and parasites. For example, if host polymorphism is adaptive, it may not be readily replaced in natural populations. Furthermore, gene-by-gene interactions between snail and schistosome genotypes could permit the rapid evolution of parasite counterstrategies. In the context of ancient trench warfare coevolution, it is unlikely that a universally resistant snail could be generated by a single genetic change, although successive changes could enhance parasite resistance enough to impact patterns of transmission (Theron et al., 2014). As an alternative to genetic modification, future work could leverage the hypothesis that the snail TM1 proteins bind to key schistosome molecules that mediate invasion of the host. One could use TM1 proteins to find such molecules, as with snail fibrinogen related proteins and schistosome SmPoMucs (Roger et al., 2008), or GRC and galactose (Allan and Blouin, 2018). More broadly, clusters of immune recognition loci with elevated functional diversity have long been used to track and predict patterns of adaptive variation across populations and species (Sommer, 2005; Spurgin and Richardson, 2010). Thus, we anticipate that this class of genes will play a central role in disease control as the molecular aspects of vector biology are fully brought to bear on schistosomiasis.

All data not included in the manuscript are available at NCBI. PacBio genome assemblies are available under BioProject Accession PRJNA639204. Pooled whole-genome sequencing reads are available under BioProject Accession PRJNA638474. RNA-Seq reads are available under BioProject Accession PRJNA639026. Assembled transcripts are on Genbank, Accessions MT787302-MT787323.

1. 1. Tennessen JA
   2. Bollmann SR
   3. Peremyslova E
   4. Kronmiller BA
   5. Sergi C
   6. Hamali B
   7. Blouin MS

   (2020) NCBI BioProject

   ID PRJNA639204. PacBio assemblies of Biomphalaria glabrata snails derived from lab strain 13-16-R1.

   http://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA639204
2. 1. Tennessen JA
   2. Bollmann SR
   3. Peremyslova E
   4. Kronmiller BA
   5. Sergi C
   6. Hamali B
   7. Blouin MS

   (2020) NCBI BioProject

   ID PRJNA638474. Genome-wide association study of Biomphalaria glabrata resistance to Schistosoma mansoni.

   http://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA638474
3. 1. Tennessen JA
   2. Bollmann SR
   3. Peremyslova E
   4. Kronmiller BA
   5. Sergi C
   6. Hamali B
   7. Blouin MS

   (2020) NCBI BioProject

   ID PRJNA639026. RNAseq of Biomphalaria glabrata 1316 inbred lines.

   http://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA639026

1. 1. Adema CM
   2. Warren W
   3. Wilson RK
   4. Hillier LW
   5. Minx P

   (2017) NCBI Genome

   ID APKA00000000.1. Biomphalaria glabrata genome BglaB1.

   https://www.ncbi.nlm.nih.gov/genome/?term=APKA00000000.1

1. 1. Abe M
   2. Kuroda R

   (2019) The development of CRISPR for a mollusc establishes the formin Lsdia1 as the long-sought gene for snail dextral/sinistral coiling

   Development 146:dev175976.

   https://doi.org/10.1242/dev.175976

   • PubMed
   • Google Scholar
2. 1. Adema CM
   2. Hillier LW
   3. Jones CS
   4. Loker ES
   5. Knight M
   6. Minx P
   7. Oliveira G
   8. Raghavan N
   9. Shedlock A
   10. do Amaral LR
   11. Arican-Goktas HD
   12. Assis JG
   13. Baba EH
   14. Baron OL
   15. Bayne CJ
   16. Bickham-Wright U
   17. Biggar KK
   18. Blouin M
   19. Bonning BC
   20. Botka C
   21. Bridger JM
   22. Buckley KM
   23. Buddenborg SK
   24. Lima Caldeira R
   25. Carleton J
   26. Carvalho OS
   27. Castillo MG
   28. Chalmers IW
   29. Christensens M
   30. Clifton S
   31. Cosseau C
   32. Coustau C
   33. Cripps RM
   34. Cuesta-Astroz Y
   35. Cummins SF
   36. di Stephano L
   37. Dinguirard N
   38. Duval D
   39. Emrich S
   40. Feschotte C
   41. Feyereisen R
   42. FitzGerald P
   43. Fronick C
   44. Fulton L
   45. Galinier R
   46. Gava SG
   47. Geusz M
   48. Geyer KK
   49. Giraldo-Calderón GI
   50. de Souza Gomes M
   51. Gordy MA
   52. Gourbal B
   53. Grunau C
   54. Hanington PC
   55. Hoffmann KF
   56. Hughes D
   57. Humphries J
   58. Jackson DJ
   59. Jannotti-Passos LK
   60. de Jesus Jeremias W
   61. Jobling S
   62. Kamel B
   63. Kapusta A
   64. Kaur S
   65. Koene JM
   66. Kohn AB
   67. Lawson D
   68. Lawton SP
   69. Liang D
   70. Limpanont Y
   71. Liu S
   72. Lockyer AE
   73. Lovato TL
   74. Ludolf F
   75. Magrini V
   76. McManus DP
   77. Medina M
   78. Misra M
   79. Mitta G
   80. Mkoji GM
   81. Montague MJ
   82. Montelongo C
   83. Moroz LL
   84. Munoz-Torres MC
   85. Niazi U
   86. Noble LR
   87. Oliveira FS
   88. Pais FS
   89. Papenfuss AT
   90. Peace R
   91. Pena JJ
   92. Pila EA
   93. Quelais T
   94. Raney BJ
   95. Rast JP
   96. Rollinson D
   97. Rosse IC
   98. Rotgans B
   99. Routledge EJ
   100. Ryan KM
   101. Scholte LLS
   102. Storey KB
   103. Swain M
   104. Tennessen JA
   105. Tomlinson C
   106. Trujillo DL
   107. Volpi EV
   108. Walker AJ
   109. Wang T
   110. Wannaporn I
   111. Warren WC
   112. Wu XJ
   113. Yoshino TP
   114. Yusuf M
   115. Zhang SM
   116. Zhao M
   117. Wilson RK

   (2017) Whole genome analysis of a schistosomiasis-transmitting freshwater snail

   Nature Communications 8:15451.

   https://doi.org/10.1038/ncomms15451

   • PubMed
   • Google Scholar
3. 1. Allan ER
   2. Tennessen JA
   3. Bollmann SR
   4. Hanington PC
   5. Bayne CJ
   6. Blouin MS

   (2017) Schistosome infectivity in the snail, Biomphalaria glabrata, is partially dependent on the expression of Grctm6, a guadeloupe resistance complex protein

   PLOS Neglected Tropical Diseases 11:e0005362.

   https://doi.org/10.1371/journal.pntd.0005362

   • PubMed
   • Google Scholar
4. 1. Allan ERO
   2. Gourbal B
   3. Dores CB
   4. Portet A
   5. Bayne CJ
   6. Blouin MS

   (2018a) Clearance of schistosome parasites by resistant genotypes at a single genomic region in Biomphalaria glabrata snails involves cellular components of the hemolymph

   International Journal for Parasitology 48:387–393.

   https://doi.org/10.1016/j.ijpara.2017.08.008

   • PubMed
   • Google Scholar
5. 1. Allan ERO
   2. Tennessen JA
   3. Sharpton TJ
   4. Blouin MS

   (2018b) Allelic variation in a single genomic region alters the microbiome of the snail Biomphalaria glabrata

   Journal of Heredity 109:604–609.

   https://doi.org/10.1093/jhered/esy014

   • PubMed
   • Google Scholar
6. 1. Allan ERO
   2. Yang L
   3. Tennessen JA
   4. Blouin MS

   (2019) Allelic variation in a single genomic region alters the hemolymph proteome in the snail Biomphalaria glabrata

   Fish & Shellfish Immunology 88:301–307.

   https://doi.org/10.1016/j.fsi.2019.02.065

   • PubMed
   • Google Scholar
7. 1. Allan ERO
   2. Blouin MS

   (2018) Allelic variation partially regulates galactose-dependent hydrogen peroxide release from circulating hemocytes of the snail Biomphalaria glabrata

   Fish & Shellfish Immunology 72:111–116.

   https://doi.org/10.1016/j.fsi.2017.10.037

   • PubMed
   • Google Scholar
8. 1. Altschul SF
   2. Gish W
   3. Miller W
   4. Myers EW
   5. Lipman DJ

   (1990) Basic local alignment search tool

   Journal of Molecular Biology 215:403–410.

   https://doi.org/10.1016/S0022-2836(05)80360-2

   • PubMed
   • Google Scholar
9. 1. Ashby B
   2. Boots M

   (2017) Multi-mode fluctuating selection in host-parasite coevolution

   Ecology Letters 20:357–365.

   https://doi.org/10.1111/ele.12734

   • PubMed
   • Google Scholar
10. 1. Bao W
    2. Kojima KK
    3. Kohany O

    (2015) Repbase Update, a database of repetitive elements in eukaryotic genomes

    Mobile DNA 6:11.

    https://doi.org/10.1186/s13100-015-0041-9

    • PubMed
    • Google Scholar
11. 1. Bento G
    2. Routtu J
    3. Fields PD
    4. Bourgeois Y
    5. Du Pasquier L
    6. Ebert D

    (2017) The genetic basis of resistance and matching-allele interactions of a host-parasite system: the Daphnia magna-Pasteuria ramosa model

    PLOS Genetics 13:e1006596.

    https://doi.org/10.1371/journal.pgen.1006596

    • PubMed
    • Google Scholar
12. 1. Berriman M
    2. Haas BJ
    3. LoVerde PT
    4. Wilson RA
    5. Dillon GP
    6. Cerqueira GC
    7. Mashiyama ST
    8. Al-Lazikani B
    9. Andrade LF
    10. Ashton PD
    11. Aslett MA
    12. Bartholomeu DC
    13. Blandin G
    14. Caffrey CR
    15. Coghlan A
    16. Coulson R
    17. Day TA
    18. Delcher A
    19. DeMarco R
    20. Djikeng A
    21. Eyre T
    22. Gamble JA
    23. Ghedin E
    24. Gu Y
    25. Hertz-Fowler C
    26. Hirai H
    27. Hirai Y
    28. Houston R
    29. Ivens A
    30. Johnston DA
    31. Lacerda D
    32. Macedo CD
    33. McVeigh P
    34. Ning Z
    35. Oliveira G
    36. Overington JP
    37. Parkhill J
    38. Pertea M
    39. Pierce RJ
    40. Protasio AV
    41. Quail MA
    42. Rajandream MA
    43. Rogers J
    44. Sajid M
    45. Salzberg SL
    46. Stanke M
    47. Tivey AR
    48. White O
    49. Williams DL
    50. Wortman J
    51. Wu W
    52. Zamanian M
    53. Zerlotini A
    54. Fraser-Liggett CM
    55. Barrell BG
    56. El-Sayed NM

    (2009) The genome of the blood fluke Schistosoma mansoni

    Nature 460:352–358.

    https://doi.org/10.1038/nature08160

    • PubMed
    • Google Scholar
13. 1. Blair D
    2. Davis GM
    3. Wu B

    (2001) Evolutionary relationships between trematodes and snails emphasizing schistosomes and paragonimids

    Parasitology 123:229–243.

    https://doi.org/10.1017/S003118200100837X

    • Google Scholar
14. 1. Bolger AM
    2. Lohse M
    3. Usadel B

    (2014) Trimmomatic: a flexible trimmer for Illumina sequence data

    Bioinformatics 30:2114–2120.

    https://doi.org/10.1093/bioinformatics/btu170

    • PubMed
    • Google Scholar
15. 1. Bonner KM
    2. Bayne CJ
    3. Larson MK
    4. Blouin MS

    (2012) Effects of Cu/Zn superoxide dismutase (sod1) genotype and genetic background on growth, reproduction and defense in Biomphalaria glabrata

    PLOS Neglected Tropical Diseases 6:e1701.

    https://doi.org/10.1371/journal.pntd.0001701

    • PubMed
    • Google Scholar
16. 1. Burge C
    2. Karlin S

    (1997) Prediction of complete gene structures in human genomic DNA

    Journal of Molecular Biology 268:78–94.

    https://doi.org/10.1006/jmbi.1997.0951

    • PubMed
    • Google Scholar
17. 1. Cantarel BL
    2. Korf I
    3. Robb SM
    4. Parra G
    5. Ross E
    6. Moore B
    7. Holt C
    8. Sánchez Alvarado A
    9. Yandell M

    (2008) MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes

    Genome Research 18:188–196.

    https://doi.org/10.1101/gr.6743907

    • PubMed
    • Google Scholar
18. 1. Carton Y
    2. Nappi AJ
    3. Poirie M

    (2005) Genetics of anti-parasite resistance in invertebrates

    Developmental & Comparative Immunology 29:9–32.

    https://doi.org/10.1016/j.dci.2004.05.004

    • PubMed
    • Google Scholar
19. 1. Cerenius L
    2. Söderhäll K

    (2013) Variable immune molecules in invertebrates

    Journal of Experimental Biology 216:4313–4319.

    https://doi.org/10.1242/jeb.085191

    • PubMed
    • Google Scholar
20. 1. Chin CS
    2. Peluso P
    3. Sedlazeck FJ
    4. Nattestad M
    5. Concepcion GT
    6. Clum A
    7. Dunn C
    8. O'Malley R
    9. Figueroa-Balderas R
    10. Morales-Cruz A
    11. Cramer GR
    12. Delledonne M
    13. Luo C
    14. Ecker JR
    15. Cantu D
    16. Rank DR
    17. Schatz MC

    (2016) Phased diploid genome assembly with single-molecule real-time sequencing

    Nature Methods 13:1050–1054.

    https://doi.org/10.1038/nmeth.4035

    • PubMed
    • Google Scholar
21. 1. DeJong RJ
    2. Morgan JA
    3. Paraense WL
    4. Pointier JP
    5. Amarista M
    6. Ayeh-Kumi PF
    7. Babiker A
    8. Barbosa CS
    9. Brémond P
    10. Pedro Canese A
    11. de Souza CP
    12. Dominguez C
    13. File S
    14. Gutierrez A
    15. Incani RN
    16. Kawano T
    17. Kazibwe F
    18. Kpikpi J
    19. Lwambo NJ
    20. Mimpfoundi R
    21. Njiokou F
    22. Noël Poda J
    23. Sene M
    24. Velásquez LE
    25. Yong M
    26. Adema CM
    27. Hofkin BV
    28. Mkoji GM
    29. Loker ES

    (2001) Evolutionary relationships and biogeography of Biomphalaria (Gastropoda: Planorbidae) with implications regarding its role as host of the human bloodfluke, Schistosoma mansoni

    Molecular Biology and Evolution 18:2225–2239.

    https://doi.org/10.1093/oxfordjournals.molbev.a003769

    • PubMed
    • Google Scholar
22. 1. Desprès L
    2. Imbert-Establet D
    3. Monnerot M

    (1993) Molecular characterization of mitochondrial DNA provides evidence for the recent introduction of Schistosoma mansoni into America

    Molecular and Biochemical Parasitology 60:221–229.

    https://doi.org/10.1016/0166-6851(93)90133-I

    • PubMed
    • Google Scholar
23. 1. Doenhoff MJ
    2. Hagan P
    3. Cioli D
    4. Southgate V
    5. Pica-Mattoccia L
    6. Botros S
    7. Coles G
    8. Tchuem Tchuenté LA
    9. Mbaye A
    10. Engels D

    (2009) Praziquantel: its use in control of schistosomiasis in sub-Saharan Africa and current research needs

    Parasitology 136:1825–1835.

    https://doi.org/10.1017/S0031182009000493

    • PubMed
    • Google Scholar
24. 1. Evan Secor W

    (2014) Water-based interventions for schistosomiasis control

    Pathogens and Global Health 108:246–254.

    https://doi.org/10.1179/2047773214Y.0000000149

    • PubMed
    • Google Scholar
25. 1. Famakinde D

    (2018) Treading the path towards genetic control of snail resistance to schistosome infection

    Tropical Medicine and Infectious Disease 3:86.

    https://doi.org/10.3390/tropicalmed3030086

    • Google Scholar
26. 1. Faro MJ
    2. Perazzini M
    3. Corrêa LR
    4. Mello-Silva CC
    5. Pinheiro J
    6. Mota EM
    7. de Souza S
    8. de Andrade Z
    9. Júnior AM

    (2013) Biological, biochemical and histopathological features related to parasitic castration of Biomphalaria glabrata infected by Schistosoma mansoni

    Experimental Parasitology 134:228–234.

    https://doi.org/10.1016/j.exppara.2013.03.020

    • PubMed
    • Google Scholar
27. 1. Giraldo-Calderón GI
    2. Emrich SJ
    3. MacCallum RM
    4. Maslen G
    5. Dialynas E
    6. Topalis P
    7. Ho N
    8. Gesing S
    9. Madey G
    10. Collins FH
    11. Lawson D
    12. VectorBase Consortium

    (2015) VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases

    Nucleic Acids Research 43:D707–D713.

    https://doi.org/10.1093/nar/gku1117

    • PubMed
    • Google Scholar
28. 1. Goodall CP
    2. Bender RC
    3. Brooks JK
    4. Bayne CJ

    (2006) Biomphalaria glabrata cytosolic copper/zinc superoxide dismutase (SOD1) gene: association of SOD1 alleles with resistance/susceptibility to Schistosoma mansoni

    Molecular and Biochemical Parasitology 147:207–210.

    https://doi.org/10.1016/j.molbiopara.2006.02.009

    • PubMed
    • Google Scholar
29. 1. Grabherr MG
    2. Haas BJ
    3. Yassour M
    4. Levin JZ
    5. Thompson DA
    6. Amit I
    7. Adiconis X
    8. Fan L
    9. Raychowdhury R
    10. Zeng Q
    11. Chen Z
    12. Mauceli E
    13. Hacohen N
    14. Gnirke A
    15. Rhind N
    16. di Palma F
    17. Birren BW
    18. Nusbaum C
    19. Lindblad-Toh K
    20. Friedman N
    21. Regev A

    (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome

    Nature Biotechnology 29:644–652.

    https://doi.org/10.1038/nbt.1883

    • PubMed
    • Google Scholar
30. 1. Haas BJ
    2. Papanicolaou A
    3. Yassour M
    4. Grabherr M
    5. Blood PD
    6. Bowden J
    7. Couger MB
    8. Eccles D
    9. Li B
    10. Lieber M
    11. MacManes MD
    12. Ott M
    13. Orvis J
    14. Pochet N
    15. Strozzi F
    16. Weeks N
    17. Westerman R
    18. William T
    19. Dewey CN
    20. Henschel R
    21. LeDuc RD
    22. Friedman N
    23. Regev A

    (2013) De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

    Nature Protocols 8:1494–1512.

    https://doi.org/10.1038/nprot.2013.084

    • PubMed
    • Google Scholar
31. 1. Hedrick PW

    (2013) Adaptive introgression in animals: examples and comparison to new mutation and standing variation as sources of adaptive variation

    Molecular Ecology 22:4606–4618.

    https://doi.org/10.1111/mec.12415

    • PubMed
    • Google Scholar
32. 1. Hita M
    2. Espagne E
    3. Lemeunier F
    4. Pascual L
    5. Carton Y
    6. Periquet G
    7. Poirie M

    (2006) Mapping candidate genes for Drosophila melanogaster resistance to the parasitoid wasp Leptopilina boulardi

    Genetical Research 88:81–91.

    https://doi.org/10.1017/S001667230600841X

    • PubMed
    • Google Scholar
33. 1. Kent WJ

    (2002) BLAT--the BLAST-like alignment tool

    Genome Research 12:656–664.

    https://doi.org/10.1101/gr.229202

    • PubMed
    • Google Scholar
34. 1. Knight M
    2. Miller AN
    3. Patterson CN
    4. Rowe CG
    5. Michaels G
    6. Carr D
    7. Richards CS
    8. Lewis FA

    (1999) The identification of markers segregating with resistance to Schistosoma mansoni infection in the snail Biomphalaria glabrata

    PNAS 96:1510–1515.

    https://doi.org/10.1073/pnas.96.4.1510

    • PubMed
    • Google Scholar
35. 1. Koella JC
    2. Boëte C

    (2002) A genetic correlation between age at pupation and melanization immune response of the yellow fever mosquito Aedes aegypti

    Evolution 56:1074–1079.

    https://doi.org/10.1111/j.0014-3820.2002.tb01419.x

    • Google Scholar
36. Book

    1. Korber B

    (2000) HIV signature and sequence variation analysis

    In: Rodrigo A. G, Learn G. H, editors. Computational Analysis of HIV Molecular Sequences, 4. Dordrecht, Netherlands: Kluwer Academic Publishers. pp. 55–72.

    https://doi.org/10.1007/b112102

    • Google Scholar
37. 1. Korf I

    (2004) Gene finding in novel genomes

    BMC Bioinformatics 5:59.

    https://doi.org/10.1186/1471-2105-5-59

    • PubMed
    • Google Scholar
38. 1. Koskella B
    2. Lively CM

    (2009) Evidence for negative frequency-dependent selection during experimental coevolution of a freshwater snail and a sterilizing trematode

    Evolution 63:2213–2221.

    https://doi.org/10.1111/j.1558-5646.2009.00711.x

    • Google Scholar
39. 1. Kraaijeveld AR
    2. Godfray HC

    (1997) Trade-off between parasitoid resistance and larval competitive ability in Drosophila melanogaster

    Nature 389:278–280.

    https://doi.org/10.1038/38483

    • PubMed
    • Google Scholar
40. 1. Leffler EM
    2. Bullaughey K
    3. Matute DR
    4. Meyer WK
    5. Ségurel L
    6. Venkat A
    7. Andolfatto P
    8. Przeworski M

    (2012) Revisiting an old riddle: what determines genetic diversity levels within species?

    PLOS Biology 10:e1001388.

    https://doi.org/10.1371/journal.pbio.1001388

    • PubMed
    • Google Scholar
41. 1. Lewis FA
    2. Patterson CN
    3. Knight M
    4. Richards CS

    (2001) The relationship between Schistosoma mansoni and Biomphalaria glabrata: genetic and molecular approaches

    Parasitology 123 Suppl:169–179.

    https://doi.org/10.1017/s0031182001007831

    • PubMed
    • Google Scholar
42. 1. Li H
    2. Handsaker B
    3. Wysoker A
    4. Fennell T
    5. Ruan J
    6. Homer N
    7. Marth G
    8. Abecasis G
    9. Durbin R
    10. 1000 Genome Project Data Processing Subgroup

    (2009) The sequence alignment/Map format and SAMtools

    Bioinformatics 25:2078–2079.

    https://doi.org/10.1093/bioinformatics/btp352

    • PubMed
    • Google Scholar
43. 1. Li H
    2. Durbin R

    (2009) Fast and accurate short read alignment with Burrows-Wheeler transform

    Bioinformatics 25:1754–1760.

    https://doi.org/10.1093/bioinformatics/btp324

    • PubMed
    • Google Scholar
44. 1. Lo NC
    2. Gurarie D
    3. Yoon N
    4. Coulibaly JT
    5. Bendavid E
    6. Andrews JR
    7. King CH

    (2018) Impact and cost-effectiveness of snail control to achieve disease control targets for schistosomiasis

    PNAS 115:E584–E591.

    https://doi.org/10.1073/pnas.1708729114

    • PubMed
    • Google Scholar
45. 1. Loker ES
    2. Adema CM
    3. Zhang SM
    4. Kepler TB

    (2004) Invertebrate immune systems--not homogeneous, not simple, not well understood

    Immunological Reviews 198:10–24.

    https://doi.org/10.1111/j.0105-2896.2004.0117.x

    • PubMed
    • Google Scholar
46. 1. Maier T
    2. Wheeler NJ
    3. Namigai EKO
    4. Tycko J
    5. Grewelle RE
    6. Woldeamanuel Y
    7. Klohe K
    8. Perez-Saez J
    9. Sokolow SH
    10. De Leo GA
    11. Yoshino TP
    12. Zamanian M
    13. Reinhard-Rupp J

    (2019) Gene drives for schistosomiasis transmission control

    PLOS Neglected Tropical Diseases 13:e0007833.

    https://doi.org/10.1371/journal.pntd.0007833

    • PubMed
    • Google Scholar
47. 1. Malaria Genomic Epidemiology Network
    2. Leffler EM
    3. Band G
    4. Busby GBJ
    5. Kivinen K
    6. Le QS
    7. Clarke GM
    8. Bojang KA
    9. Conway DJ
    10. Jallow M
    11. Sisay-Joof F
    12. Bougouma EC
    13. Mangano VD
    14. Modiano D
    15. Sirima SB
    16. Achidi E
    17. Apinjoh TO
    18. Marsh K
    19. Ndila CM
    20. Peshu N
    21. Williams TN
    22. Drakeley C
    23. Manjurano A
    24. Reyburn H
    25. Riley E
    26. Kachala D
    27. Molyneux M
    28. Nyirongo V
    29. Taylor T
    30. Thornton N
    31. Tilley L
    32. Grimsley S
    33. Drury E
    34. Stalker J
    35. Cornelius V
    36. Hubbart C
    37. Jeffreys AE
    38. Rowlands K
    39. Rockett KA
    40. Spencer CCA
    41. Kwiatkowski DP

    (2017) Resistance to malaria through structural variation of red blood cell invasion receptors

    Science 356:eaam6393.

    https://doi.org/10.1126/science.aam6393

    • PubMed
    • Google Scholar
48. 1. Marcel M

    (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads

    EMBnet.journal 17:10–12.

    https://doi.org/10.14806/ej.17.1.200

    • Google Scholar
49. 1. Marshall JM
    2. Raban RR
    3. Kandul NP
    4. Edula JR
    5. León TM
    6. Akbari OS

    (2019) Winning the tug-of-war between effector gene design and pathogen evolution in vector population replacement strategies

    Frontiers in Genetics 10:1072.

    https://doi.org/10.3389/fgene.2019.01072

    • PubMed
    • Google Scholar
50. 1. Mitri C
    2. Bischoff E
    3. Eiglmeier K
    4. Holm I
    5. Dieme C
    6. Brito-Fravallo E
    7. Raz A
    8. Zakeri S
    9. Nejad MIK
    10. Djadid ND
    11. Vernick KD
    12. Riehle MM

    (2020) Gene copy number and function of the APL1 immune factor changed during Anopheles evolution

    Parasites & Vectors 13:18.

    https://doi.org/10.1186/s13071-019-3868-y

    • PubMed
    • Google Scholar
51. Book

    1. Pahl MC
    2. Askinazi OL
    3. Hamilton C
    4. Cheng I

    (2013) Signalling via Single‐Pass Transmembrane Proteins

    John Wiley & Sons, Ltd.

    https://doi.org/10.1002/9780470015902.a0025160

    • Google Scholar
52. Software

    1. R Development Core Team

    (2020) R: A Language and Environment for Statistical Computing

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org
53. 1. Reardon S

    (2016) Welcome to the CRISPR zoo

    Nature 531:160–163.

    https://doi.org/10.1038/531160a

    • PubMed
    • Google Scholar
54. 1. Richards CS
    2. Knight M
    3. Lewis FA

    (1992) Genetics of Biomphalaria glabrata and its effect on the outcome of Schistosoma mansoni infection

    Parasitology Today 8:171–174.

    https://doi.org/10.1016/0169-4758(92)90015-T

    • PubMed
    • Google Scholar
55. 1. Richards CS
    2. Merritt JW

    (1972) Genetic factors in the susceptibility of juvenile Biomphalaria glabrata to Schistosoma mansoni infection

    The American Journal of Tropical Medicine and Hygiene 21:425–434.

    https://doi.org/10.4269/ajtmh.1972.21.425

    • PubMed
    • Google Scholar
56. 1. Richards CS
    2. Shade PC

    (1987) The genetic variation of compatibility in Biomphalaria glabrata and Schistosoma mansoni

    The Journal of Parasitology 73:1146–1151.

    https://doi.org/10.2307/3282295

    • PubMed
    • Google Scholar
57. 1. Roger E
    2. Grunau C
    3. Pierce RJ
    4. Hirai H
    5. Gourbal B
    6. Galinier R
    7. Emans R
    8. Cesari IM
    9. Cosseau C
    10. Mitta G

    (2008) Controlled Chaos of polymorphic Mucins in a metazoan parasite (Schistosoma mansoni) interacting with its invertebrate host (Biomphalaria glabrata)

    PLOS Neglected Tropical Diseases 2:e330.

    https://doi.org/10.1371/journal.pntd.0000330

    • PubMed
    • Google Scholar
58. 1. Rottschaefer SM
    2. Riehle MM
    3. Coulibaly B
    4. Sacko M
    5. Niaré O
    6. Morlais I
    7. Traoré SF
    8. Vernick KD
    9. Lazzaro BP

    (2011) Exceptional diversity, maintenance of polymorphism, and recent directional selection on the APL1 malaria resistance genes of Anopheles gambiae

    PLOS Biology 9:e1000600.

    https://doi.org/10.1371/journal.pbio.1000600

    • PubMed
    • Google Scholar
59. 1. Schmid-Hempel P

    (2005) Natural insect host-parasite systems show immune priming and specificity: puzzles to be solved

    BioEssays 27:1026–1034.

    https://doi.org/10.1002/bies.20282

    • Google Scholar
60. 1. Schulenburg H
    2. Boehnisch C
    3. Michiels NK

    (2007) How do invertebrates generate a highly specific innate immune response?

    Molecular Immunology 44:3338–3344.

    https://doi.org/10.1016/j.molimm.2007.02.019

    • Google Scholar
61. 1. Seger J

    (1988) Dynamics of some simple host-parasite models with more than two genotypes in each species

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 319:541–555.

    https://doi.org/10.1098/rstb.1988.0064

    • PubMed
    • Google Scholar
62. 1. Ségurel L
    2. Gao Z
    3. Przeworski M

    (2013) Ancestry runs deeper than blood: The evolutionary history of ABO points to cryptic variation of functional importance

    BioEssays 345:862–867.

    https://doi.org/10.1002/bies.201300030

    • Google Scholar
63. 1. Seppey M
    2. Manni M
    3. Zdobnov EM

    (2019) BUSCO: assessing genome assembly and annotation completeness

    Methods in Molecular Biology 1962:227–245.

    https://doi.org/10.1007/978-1-4939-9173-0_14

    • PubMed
    • Google Scholar
64. Software

    1. Smit AFA
    2. Hubley R
    3. Green P

    (2013) RepeatMasker Open, version 4.0

    RepeatMasker Open.

    http://www.repeatmasker.org
65. 1. Sokolow SH
    2. Wood CL
    3. Jones IJ
    4. Lafferty KD
    5. Kuris AM
    6. Hsieh MH
    7. De Leo GA

    (2018) To reduce the global burden of human schistosomiasis, use 'old fashioned' snail control

    Trends in Parasitology 34:23–40.

    https://doi.org/10.1016/j.pt.2017.10.002

    • PubMed
    • Google Scholar
66. 1. Sommer S

    (2005) The importance of immune gene variability (MHC) in evolutionary ecology and conservation

    Frontiers in Zoology 2:16.

    https://doi.org/10.1186/1742-9994-2-16

    • PubMed
    • Google Scholar
67. 1. Sonnhammer EL
    2. von Heijne G
    3. Krogh A

    (1998)

    A hidden Markov model for predicting transmembrane helices in protein sequences

    Proceedings. International Conference on Intelligent Systems for Molecular Biology 6:175–182.

    • PubMed
    • Google Scholar
68. 1. Spurgin LG
    2. Richardson DS

    (2010) How pathogens drive genetic diversity: MHC, mechanisms and misunderstandings

    Proceedings of the Royal Society B: Biological Sciences 277:979–988.

    https://doi.org/10.1098/rspb.2009.2084

    • Google Scholar
69. 1. Stahl EA
    2. Dwyer G
    3. Mauricio R
    4. Kreitman M
    5. Bergelson J

    (1999) Dynamics of disease resistance polymorphism at the Rpm1 locus of Arabidopsis

    Nature 400:667–671.

    https://doi.org/10.1038/23260

    • PubMed
    • Google Scholar
70. 1. Stamatakis A

    (2006) RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models

    Bioinformatics 22:2688–2690.

    https://doi.org/10.1093/bioinformatics/btl446

    • PubMed
    • Google Scholar
71. 1. Stanke M
    2. Steinkamp R
    3. Waack S
    4. Morgenstern B

    (2004) AUGUSTUS: a web server for gene finding in eukaryotes

    Nucleic Acids Research 32:W309–W312.

    https://doi.org/10.1093/nar/gkh379

    • PubMed
    • Google Scholar
72. 1. Sullivan JT
    2. Richards CS

    (1981) Schistosoma mansoni, NIH-SM-PR-2 strain, in susceptible and nonsusceptible stocks of Biomphalaria glabrata: comparative histology

    The Journal of Parasitology 67:702–708.

    https://doi.org/10.2307/3280445

    • PubMed
    • Google Scholar
73. 1. Tennessen JA
    2. Bonner KM
    3. Bollmann SR
    4. Johnstun JA
    5. Yeh JY
    6. Marine M
    7. Tavalire HF
    8. Bayne CJ
    9. Blouin MS

    (2015a) Genome-wide scan and test of candidate genes in the snail Biomphalaria glabrata reveal new locus influencing resistance to Schistosoma mansoni

    PLOS Neglected Tropical Diseases 9:e0004077.

    https://doi.org/10.1371/journal.pntd.0004077

    • PubMed
    • Google Scholar
74. 1. Tennessen JA
    2. Théron A
    3. Marine M
    4. Yeh JY
    5. Rognon A
    6. Blouin MS

    (2015b) Hyperdiverse gene cluster in snail host conveys resistance to human schistosome parasites

    PLOS Genetics 11:e1005067.

    https://doi.org/10.1371/journal.pgen.1005067

    • PubMed
    • Google Scholar
75. 1. Tennessen JA
    2. Bollmann SR
    3. Blouin MS

    (2017) A targeted capture linkage map anchors the genome of the schistosomiasis vector snail, Biomphalaria glabrata

    G3: Genes, Genomes, Genetics 7:2353–2361.

    https://doi.org/10.1534/g3.117.041319

    • PubMed
    • Google Scholar
76. 1. Theron A
    2. Coustau C
    3. Rognon A
    4. Gourbière S
    5. Blouin MS

    (2008) Effects of laboratory culture on compatibility between snails and schistosomes

    Parasitology 135:1179–1188.

    https://doi.org/10.1017/S0031182008004745

    • PubMed
    • Google Scholar
77. 1. Theron A
    2. Rognon A
    3. Gourbal B
    4. Mitta G

    (2014) Multi-parasite host susceptibility and multi-host parasite infectivity: a new approach of the Biomphalaria glabrata/Schistosoma mansoni compatibility polymorphism

    Infection, Genetics and Evolution 26:80–88.

    https://doi.org/10.1016/j.meegid.2014.04.025

    • PubMed
    • Google Scholar
78. 1. Thompson MJ
    2. Jiggins CD

    (2014) Supergenes and their role in evolution

    Heredity 113:1–8.

    https://doi.org/10.1038/hdy.2014.20

    • PubMed
    • Google Scholar
79. 1. UniProt Consortium

    (2019) UniProt: a worldwide hub of protein knowledge

    Nucleic Acids Research 47:D506–D515.

    https://doi.org/10.1093/nar/gky1049

    • PubMed
    • Google Scholar
80. 1. Webster JP
    2. Gower CM
    3. Blair L

    (2004) Do hosts and parasites coevolve? Empirical support from the Schistosoma system

    The American Naturalist 164 Suppl 5:S33–S51.

    https://doi.org/10.1086/424607

    • PubMed
    • Google Scholar
81. 1. Webster JP
    2. Woolhouse MEJ

    (1998) Selection and strain specificity of compatibility between snail intermediate hosts and their parasitic schistosomes

    Evolution 52:1627–1634.

    https://doi.org/10.1111/j.1558-5646.1998.tb02243.x

    • Google Scholar
82. 1. Webster JP
    2. Woolhouse MEJ

    (1999) Cost of resistance: relationship between reduced fertility and increased resistance in a snail-schistosome host-parasite system

    Proceedings of the Royal Society of London. Series B: Biological Sciences 266:391–396.

    https://doi.org/10.1098/rspb.1999.0650

    • Google Scholar
83. 1. Woolhouse ME
    2. Webster JP
    3. Domingo E
    4. Charlesworth B
    5. Levin BR

    (2002) Biological and biomedical implications of the co-evolution of pathogens and their hosts

    Nature Genetics 32:569–577.

    https://doi.org/10.1038/ng1202-569

    • PubMed
    • Google Scholar

Author details

1. Jacob A Tennessen

   1. Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, United States
   2. Department of Integrative Biology, Oregon State University, Corvallis, United States

   Contribution

   Data curation, Formal analysis, Visualization, Writing - original draft, Writing - review and editing

   For correspondence

   jtennessen@hsph.harvard.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5015-4740

2. Stephanie R Bollmann

   Department of Integrative Biology, Oregon State University, Corvallis, United States

   Contribution

   Data curation, Formal analysis, Investigation, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

3. Ekaterina Peremyslova

   Department of Integrative Biology, Oregon State University, Corvallis, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

4. Brent A Kronmiller

   1. Department of Integrative Biology, Oregon State University, Corvallis, United States
   2. Center for Genome Research and Biocomputing, Oregon State University, Corvallis, United States

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

5. Clint Sergi

   Department of Integrative Biology, Oregon State University, Corvallis, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Bulut Hamali

   Department of Integrative Biology, Oregon State University, Corvallis, United States

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

7. Michael Scott Blouin

   Department of Integrative Biology, Oregon State University, Corvallis, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8439-2878

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