Genomic linkage map of the human blood fluke Schistosoma mansoni
© Criscione et al.; licensee BioMed Central Ltd. 2009
Received: 3 February 2009
Accepted: 30 June 2009
Published: 30 June 2009
Schistosoma mansoni is a blood fluke that infects approximately 90 million people. The complete life cycle of this parasite can be maintained in the laboratory, making this one of the few experimentally tractable human helminth infections, and a rich literature reveals heritable variation in important biomedical traits such as virulence, host-specificity, transmission and drug resistance. However, there is a current lack of tools needed to study S. mansoni's molecular, quantitative, and population genetics. Our goal was to construct a genetic linkage map for S. mansoni, and thus provide a new resource that will help stimulate research on this neglected pathogen.
We genotyped grandparents, parents and 88 progeny to construct a 5.6 cM linkage map containing 243 microsatellites positioned on 203 of the largest scaffolds in the genome sequence. The map allows 70% of the estimated 300 Mb genome to be ordered on chromosomes, and highlights where scaffolds have been incorrectly assembled. The markers fall into eight main linkage groups, consistent with seven pairs of autosomes and one pair of sex chromosomes, and we were able to anchor linkage groups to chromosomes using fluorescent in situ hybridization. The genome measures 1,228.6 cM. Marker segregation reveals higher female recombination, confirms ZW inheritance patterns, and identifies recombination hotspots and regions of segregation distortion.
The genetic linkage map presented here is the first for S. mansoni and the first for a species in the phylum Platyhelminthes. The map provides the critical tool necessary for quantitative genetic analysis, aids genome assembly, and furnishes a framework for comparative flatworm genomics and field-based molecular epidemiological studies.
Linkage mapping has been very successful for mapping the genes underlying phenotypic variation in a number of parasitic organisms. In malaria parasites (Plasmodium falciparum) three genetic crosses have now been completed, and a detailed microsatellite based map generated. The linkage map has resulted in the identification of major genes underlying resistance to chloroquine, quinine, sulfadoxine, host specificity, and male gametocytogenesis . Similarly, linkage maps of the parasitic protozoans Toxoplasma  and Eimeria  have resulted in mapping of quantitative trait loci underlying acute virulence, while trypanosome linkage maps have also been created [9, 10]. Linkage maps have been developed for a number of plant parasitic nematodes [11, 12]. However, to date there are no genetic linkage maps for a helminth parasite of humans, or platyhelminths of any species.
We describe a genetic linkage map for S. mansoni, which we constructed for the following reasons. First, a map will aid in the assembly of the genome sequence. The present version (version 3.1) of the genome assembly contains 19,022 scaffolds, in part due to a highly repetitive genome (45%) that inhibits further assembly . Importantly, the largest 280 scaffolds comprise more than 70% of the 381 Mb in version 3.1 of the genome assembly; by placing markers in these scaffolds the majority of the genome sequence can be ordered on linkage groups by examining their segregation patterns. Second, a linkage map is the critical tool needed for quantitative trait mapping . There is a rich experimental literature demonstrating heritable variation in a wide variety of biomedically important traits of S. mansoni, such as host specificity  and virulence , and revealing co-evolutionary interactions with the snail host . Infections showing reduced cure rates following treatment with the first line drug praziquantel have been observed from multiple foci, and worms recovered from these infections show increased tolerance to praziquantel in the laboratory, leading to worries about the potential for spread of drug resistance . Furthermore, resistance to oxamniquine has been selected in natural parasite populations . We note that this parasite is particularly well suited to linkage mapping approaches because large numbers of progeny can be recovered from single crosses, allowing statistically powerful experimental designs. In addition, clonal amplification of larvae within the snail intermediate host generates hundreds of genetically identical individuals of each recombinant genotype, allowing for precise replicated measurement of phenotypes (Figure 1). Third, with the genomes of the Asian schistosome Schistosoma japonicum and the free living flatworm Schmidtea mediterranea in the pipeline [13, 20], comparative linkage mapping and synteny analysis among platyhelminths will be feasible. Given the medical and veterinary importance of many flatworm species and the diversity of life styles (parasitic, free-living, monoecious, dioecious, clonal propagation, regeneration), comparative flatworm genomics will provide a fundamental framework for tackling both applied and basic questions. Finally, the development of molecular markers spanning the genome will enable more accurate estimates of population genetic and recombination parameters from field collected parasites. In turn, a better understanding of parasite transmission among human or reservoir hosts will be gained from field-based molecular epidemiological studies of S. mansoni .
Results and discussion
We developed a genetic map by crossing a female S. mansoni from the NMRI (Puerto Rico) line to a male S. mansoni from the LE (Brazil) line (that is, P1 grandparents). Subsequently, 2 F1 parents were crossed to generate 88 F2 progeny (41 males and 47 females). We initially designed 376 primer pairs (microsatellite loci) with at least 1 marker in the largest 283 scaffolds. Additional markers were placed in 73 of the largest 94 scaffolds to verify contig assembly and to obtain direct estimates of the recombination rate (physical distance/map distance). Screening of the grandparents and F1 parents with all 376 loci revealed that 251 loci (Additional data file 1) could be scored reliably and were informative in male and/or female meioses. All 92 individuals (88 progeny, 2 F1 parents, and 2 P1 grandparents) were genotyped with these 251 microsatellite markers. The data set was of good quality, with only 324 missing genotypes out of 22,088 possible (88 offspring × 251 loci). Each locus had an average of 86.7 offspring scored (range 80 to 88), while for each offspring an average of 247.3 loci were scored (range 221 to 251). We used the regression mapping algorithm and Kosambi mapping function implemented in JoinMap version 4 to construct the linkage map .
Anchoring linkage groups to chromosomes
Summary of linkage groups
Chromosome size (Mb)†
Map length (cM)
Adjusted map length (cM)
Interval spacing (cM/interval)¶
Recombination parameters and map length
The final genetic map of the 8 major linkage groups (Table 1) contained 210 loci because 33 loci showed identical segregation to other loci (Figure 2). The 8 chromosomal linkage groups spanned 1,134.8 cM with an average marker spacing of 5.6 cM per interval. To account for linkage group ends beyond terminal makers, we used the methods in [23, 24], which calculate an expected map length for the terminal regions of the linkage groups (see Materials and methods). These adjustments yielded a total adjusted genome length of 1,228.6 cM (Table 1). Linkage groups ranged in (adjusted) size from 84 to 244 cM. The expected distance of a gene, E(m), from the nearest random marker (n = 210) is 2.9 cM with an upper 95% confidence interval (CI) of 8.7 cM .
We were also able to compare 78 autosomal intervals between male and female meioses (Figure 4b) to obtain sex-specific recombination rates. Over these homologous regions, the average female interval (9.42 cM) was significantly longer than the average male interval (7.42 cM) (P = 0.019, Wilcoxon signed-rank test). Sex-biased recombination rates (heterochiasmy) have been reported in many organisms (reviewed in [27, 28]). The evolutionary hypotheses and mechanistic processes put forth to explain sex differences in recombination can be difficult to disentangle . However, as the female, the heterogametic sex, had 1.27-fold higher recombination than the male, we can rule out the Haldane-Huxley rule. This rule predicts lower recombination among autosomes in the heterogametic sex because selection acts against recombination between different sex chromosomes. Our data provide a second and phylogenetically independent example of a ZW system that is inconsistent with the Haldane-Huxley rule (the other is in the passerine bird Acrocephalus arundinaceus ).
Genome assembly by linkage
Of the 243 markers assembled on the 8 chromosomes, there are 203 unique scaffolds (totaling 209 Mb) represented. Thus, the linkage map contains 70% of the estimated 300 Mb physical genome and 55% of the 381 Mb currently in version 3.1 of the genome assembly. However, the current genome assembly contains considerable redundancy and overestimates genome size and the 55% is thus likely to underestimate true coverage. Furthermore, if the total genetic map length is calculated from the direct estimate of the recombination rate (300 Mb/227.2 kb/cM = 1,320 cM), then the unadjusted map length accounts for 86% (1,134 of 1,320 cM) of the total genetic map length. The genome assembly will benefit from the broad coverage of the map, high density of markers, and placement of previously unanchored and unordered scaffolds. The map data also provide a means to assess the quality of the current assembly. There were 37 scaffolds with 2 or more markers located < 2.2 Mb apart (Additional data file 3), which equates to about 8 to 10 cM. Markers from 21 of the scaffolds were consistent with this pattern. However, there were 16 scaffolds where markers mapped to different linkage groups or had map distances that were much greater than expected based on the recombination rate (Additional data file 3; Figure 2). These data suggest that a substantial portion (43% of our sample) of the current assembly is incorrect. However, given the highly repetitive nature of the genome, it is encouraging that 57% of the scaffolds were valid and that many of the mapped markers show congruence with FISH-mapped BACs (Figure 3). These results also illustrate the utility of linkage maps in correcting genome assembly errors. Thus, the map will provide a platform for the continued assembly for the genome.
Marker segregation on sex chromosomes
Two regions in the linkage map showed strong deviations from Mendelian inheritance (χ2-test, α = 0.01): 12 markers between sc300 and sc481 on LG2_ChrZ, and 9 markers between sc221 and sc26 on LG1_Chr1 (Figures 2 and 5a). The remaining 222 markers did not deviate from Mendelian expectations. The 2 regions displayed different patterns of distortion. From sc481 to sc126 on LG2_ChrZ (Figure 2), there was an excess of heterozygous genotypes of an allele from the NMRI female and LE male. From sc305 to sc120, however, there was a major decrease in the NMRI female homozygote genotype (only one to six individuals). The pattern on LG1_Chr1 was uniform across loci in having a decreased NMRI female homozygote genotype and one heterozygote combination, whereas the other heterozygote combination was normal and the LE male homozygote was increased (Figure 2). It is not uncommon to find genomic regions with segregation distortion when crossing diverged populations due to the evolution of coevolved gene complexes or of incompatible regions [24, 30, 31]. The NMRI and LE lines have been separated well over 250 generations in the laboratory (see Materials and methods). Genetic load from inbreeding depression that may build up in laboratory maintained lines is another plausible explanation . If loci between the two regions were interacting (for example, an allele is deleterious at one locus only in the presence of a particular allele at another locus), we would expect genotypic associations between markers in the two regions. However, pairwise comparisons failed to detect any genotypic associations (P > 0.14 in all comparisons). Thus, the cause of distortion at the two regions appears to be independent.
The linkage map complements the genome sequence and other tools such as RNA interference, and adds to a growing toolkit for genomic analyses in S. mansoni. We anticipate that next generation sequencing and rapid single nucleotide polymorphism typing methods will be used to build on the foundation provided by this microsatellite-based map. In particular, next generation sequencing of single parasite genotypes (rather than pooled individuals from laboratory parasite lines) will allow rapid improvements in the genome assembly that can be verified by genotyping single nucleotide polymorphisms in the genetic cross. The combination of these tools will improve the genome assembly and provide markers for fine mapping of genes that underlie traits of biological or biomedical interest. We also foresee that provision of these tools will invigorate research on this pathogen and attract researchers from other fields. A great advantage to studying S. mansoni over other human helminths is that the complete life cycle can be maintained in the laboratory using mice or hamsters as the definitive host, thus allowing experimental investigation of life cycle traits (for example, [15, 16, 33]). Such studies have demonstrated that numerous phenotypic traits of S. mansoni vary within and between parasite populations and that many of these traits have a genetic basis. Linkage mapping, utilizing the 5 cM map described here, provides a means to investigate the underlying basis of traits of medical and epidemiological relevance, such as virulence, host specificity, and drug resistance. For example, different strains of B. glabrata and S. mansoni have been shown to have different compatibilities in terms of infectivity or virulence [15, 17]. Drug resistance is a trait of particular biomedical interest and this trait can readily be measured both in vivo in infected rodents and in vitro using adult worms maintained in culture media. Resistance to oxamniquine has been demonstrated as a double recessive trait in S. mansoni , and there is clear evidence that parasites with increased tolerance to the first-line drug praziquantel occur in natural populations . Linkage mapping will allow identification of the genes responsible for resistance to these drugs. For mapping the genes underlying these traits additional crosses will need to be conducted. The microsatellite markers used for map construction are highly variable, so the majority of markers are likely to be informative in additional crosses. The map also has multiple applications for developmental and evolutionary biology. Provision of hundreds of molecular markers and recombination parameters will facilitate high resolution population genetic studies of S. mansoni, which will improve our understanding of transmission patterns in endemic areas. The S. mansoni linkage map presented expands the genetic toolkit for S. mansoni, providing opportunities to understand fundamental features of S. mansoni biology, and opening doors to new advances in combating this human pathogen.
Materials and methods
We crossed a NMRI female to an LE male to generate F1 progeny. Subsequently, a male and female from the F1 were crossed to generate 88 F2 progeny (reared to the adult stage). The NMRI line originated in the early 1940s from human isolates in Puerto Rico and the LE line was established from a human isolate in 1965 in Belo Horizonte, Brazil . At each stage in the cross, we conducted monomiracidial infections of snails (B. glabrata). Because sex is determined in the zygote (which develops into a miracidium) by a chromosomal mechanism, monomiracidial infections allowed us to be certain that we were using single clonal types (that is, single genetic individuals of the same sex) in the crosses. After 28 days (the last 3 under darkness), snails were exposed to light to shed cercariae. Cercariae were sexed with the following protocol. We collected 20 to 50 cercariae of one clonal genotype from each infected snail. For DNA extractions, samples were placed in 50 μl of 5% chelex containing 0.2 mg/ml of proteinase K, incubated for 2 h at 56°C, and boiled at 100°C for 8 minutes. PCR with the W1 primers , which are specific to a repetitive region on the W chromosome in females, was used to discriminate between males and females. PCR was performed with 15 μl reactions containing 2.4 μl of extraction supernatant, 1× PCR buffer, 1.5 mM MgCl2, 0.2 mM of each dNTP, 0.4 μM of each primer, and 0.75 units (0.15 μl) Taq DNA polymerase (Takara Shuzo Co., Otsu, Shiga, Japan). PCR cycling was 95°C for 3 minutes, once; 94°C for 45 s, 54°C for 30 s, 72°C for 45 s, 35 times; 72°C for 7 minutes, once. Because this test depends on the failed amplification in males, we ran a concurrent PCR under the same conditions with the autosomal locus sc18 (see Additional data file 1 for primers) to ensure that the DNA had successfully been extracted from each sample. Results were visualized on a 2% agarose gel containing GelStar® nucleic acid gel stain (Lonza, Basel, Switzerland).
Upon identification of gender, snails were shed again to collect cercariae for infections. We exposed a hamster to 300 female cercariae (one genetic individual) and 300 male cercariae (one genetic individual) for the parental cross. After 45 days, the hamster was euthanized and perfused to collect adult worms. Eggs were collected from the liver and hatched under light to obtain miracidia for the next generation of monomiracidial snail infections. This process was repeated to stage the F1 cross. In the F2 generation we reared worms to the adult stage in mice (BALB/c). Mice were exposed to 200 female cercariae (one genetic individual) and 200 male cercariae (one genetic individual) or with 200 cercariae of a single sex. F2 worms were collected from mice after 40 days.
Genomic DNA extraction and whole genome amplification
Individual adult worms were placed in 50 μl of 5% chelex containing 0.2 mg/ml of proteinase K, incubated for 2 h at 56°C, and boiled at 100°C for 8 minutes. The GenomiPhi V2 DNA amplification kit (GE Healthcare, Piscataway, New Jersey, USA) was used to amplify whole genomic DNA according to the manufacture's protocol.
Microsatellite markers and genotyping
Microsatellite markers were designed from the largest 283 supercontigs in version 3.0 of the genome assembly. These 283 supercontigs account for 72% of sequence data in version 3.1 of the genome assembly (available from the Sanger Institute ). The difference in the two versions is only the removal of approximately 720 kb of sequence in version 3.1, most of which (703 kb) was a single supercontig that was removed. The major change was the renaming of supercontigs to scaffolds without change to the actual sequence data. We provide this information in Additional data file 1. Markers were selected from a masked copy of the genome to avoid placing markers in repetitive DNA. Tandem Repeats Finder version 4  was used to search the contigs for microsatellite repeats. Only perfect di- and trinucleotide repeats were selected. Primer 3.0  was used to design all primers with an annealing temperature of 54 to 56°C.
We used the M13(-21) method for genotyping . The M13(-21) oligonucleotide was added to the 5' end of each forward primer. We also 'pig-tailed' the reverse primers by adding GTTTCTT to the 5' ends . PCR was performed in 5 μl reactions containing 15 ng of genome amplified template, 1× PCR buffer, 1.5 mM MgCl2, 0.2 mM of each dNTP, 0.08 μM of the forward primer, 0.16 μM of the reverse primer, 0.16 μM of the fluorescent labeled M13(-21) primer, and 0.15 units (0.03 μl) Taq DNA polymerase. PCR cycling was 94°C for 5 minutes, once; 94°C for 30 s, 56°C for 45 s, 65°C for 45 s, 30 times; 94°C for 30 s, 53°C for 45 s, 65°C for 45 s, 8 times; 65°C for 10 minutes, once. PCR products were run on an ABI 3100 with Genescan software and scored using Genotyper (Applied Biosystems, Foster City, CA, USA). LIZ500 size standard (Applied Biosystems) was used for all loci. All traces were visually examined and checked for correct peak labeling.
All loci are named for the supercontig (version 3.0 of the genome assembly) on which they reside. Additional data file 1 provides the cross reference information to the scaffold (version 3.1 of the genome assembly) that the markers are on. In addition, Additional data file 1 provides the information on repeat motifs, primer sequences, linkage map positions, scaffold (version 3.1) length, physical position of the microsatellite repeat motif on the scaffold (version 3.1), and physical positions of the flanking sequences used to design primers. Marker names with lower case letters indicate that more than one marker was placed on that supercontig. This lettering does not indicate physical ordering of markers on supercontigs. For example, sc5, sc5b, sc5c, and sc5d are all markers on Supercontig_0000005 (Smp_scaff000005), but not necessarily in that physical order. For simplicity, we abbreviate marker names in the text (for example, sc5b); however, the names are written in full in the figure maps and tables to facilitate queries that match the genome database. A list of the 37 scaffolds that have more than one mapped marker is in Additional data file 3.
Linkage map construction
We used JoinMap  both to assign markers to linkage groups and then to order markers on each linkage group. The F1 parents and F2 offspring were coded according to the CP population type, a population resulting from a cross between two heterogeneously heterozygous and homozygous diploid parents. We input the phase of the F1 genotypes based on the genotypes of the grandparents. Z-specific markers, which were identified by the fact that all females were hemizygous with an allele inherited from their male parent, were coded as nnxnp (F1 female × F1 male). We generated a sex-combined map irrespective of whether the locus was informative in one or both of the F1 parents.
Assignment and ordering of markers to linkage groups
Overall, there was strong support for each linkage group and the ordering of markers within each linkage group (Additional data file 2). Linkage groups were formed at a threshold pairwise recombination frequency of 30%. This threshold corresponded to an independence LOD (a description of this calculation is given in ) of 4 or greater for each linkage group except for LG5_Chr4 (Additional data file 2). LG5_Chr4 had 25 markers that were grouped at an independence LOD of 10 but markers sc475 and sc173 were not among them. However, these 2 markers were within the 25% threshold of the pairwise recombination frequency. Visual inspection of the estimated recombination frequencies and FISH data supported the inclusion of sc475 and sc173 in LG5_Chr4 (Figure 2).
Prior to ordering markers within linkage groups, we identified loci that had 0% recombination with one or more markers. In such cases, we retained only one marker for subsequent analyses (Additional data file 2), choosing the locus that was more informative and/or had fewer missing genotypes. We used the Kosambi mapping function to convert recombination frequencies into map distances. The regression mapping algorithm with the default settings (recombination frequency threshold < 0.4, LOD threshold > 1) was used to order loci within each linkage group. On LG3_Chr2, a reduced stringency (recombination frequency threshold < 0.49, LOD threshold > 0.1) was needed to include markers sc54 to sc466 (Additional data file 2). Visual inspection of the estimated recombination frequencies and FISH data supported the order of these markers. A ripple (all ordering permutations within a moving window of three adjacent markers) was performed after the addition of each new marker. When the best position of a marker decreased the goodness-of-fit too sharply (default jump = 5) or gave rise to negative distance estimates, the locus was removed. After all loci are handled once, a second round is made to add previously excluded loci using the added information of all pairwise markers included in the first round. In a third round, all loci previously removed are added to the map without constraints in order to obtain a general idea about where poorer fitting loci reside on the map. All linkage groups except LG2_ChrZ had a single round of mapping. Marker sc193 was the only marker in LG2_ChrZ that needed a second and third round. However, FISH data and visual inspection of the estimated recombination frequencies confirmed the relative position of this marker (Figure 5c). The overall map order can be evaluated by a goodness-of-fit measure between the direct pairwise estimates of recombination frequency and the frequencies obtained from the map (using the mapping function). This goodness-of-fit measure is roughly distributed as chi-square . Mean chi-square values (Chi-square test statistic divided by the degrees of freedom) well below 1 indicate good support for the ordering of markers .
Evaluation of double recombinants and mutations
With the exception of LG2_ChrZ, there were few improbable genotypes and suspect linkages (Additional data file 2). The genotype probabilities are calculated conditional on the map and genotypes of neighboring loci . These probabilities flag possible double recombinants or possible genotyping errors . There were 59 genotypes with P ≤ 0.01. We visually re-inspected all genotypes (n = 16) with P ≤ 0.001 and confirmed that the genotypes were correctly scored. Although these could represent double recombinants, we cannot rule out mutation (naturally, genome amplified, or PCR induced) as a possible cause. For example, one genotype, which had the only P < 0.0001, showed a double recombinant from both the male and female meioses. Re-inspection of this genotype showed that a possible 2 bp mutation in one of the alleles of this offspring could create this possible pattern. Removal or 'assumed correction' of a subset of these genotypes, including the latter, had little impact on the loci ordering or on map length of each linkage group. Thus, we did not remove these possible double recombinants (< 0.27% of the genotypes in the data set) from the final analysis. On LG2_ChrZ, 18 of the 25 improbable genotypes involved marker sc85c. In the main text, we discuss how the regions flanking sc85c represent possible host spots of recombination in the female meioses. Supporting this claim, a large number of suspect linkages (> 60% recombination) occur on LG2_ChrZ between markers that lie in the region from sc208 to sc312 with markers in the region of sc195 to 240.
Estimation of linkage map parameters
To account for the terminal parts of the linkage groups, an adjusted map length for each linkage group was calculated by averaging the results from the methods of Fishman et al.  and Chakravarti et al. . The Fishman et al.  method adds twice the average spacing of markers (across the entire map) to the lengths of each linkage group. Method 4 of Chakravarti et al.  expands each linkage group by (m + 1)/(m - 1), where m is the number of loci mapped. Formula 14.8 in  was used to calculate the expected distance of a gene, E(m), from the closest of n (= 210) random markers and the upper 95% confidence interval for this distance. The total adjusted map length of 1,228.59 cM was used as the estimate of L. Formula 14.7 in , which accounts for linear chromosomes, was in near agreement with formula 14.8, where 94.24% of the genome was within 8.7 cM of a marker assuming a random distribution of markers.
Sex specific recombination and segregation distortion
Parental meioses were examined by creating maternal and paternal population nodes in JoinMap. We only compared intervals between homologous loci that generated the same mapping order as the sex-combined map. Homologous map distances for all autosomal markers were compared with a Wilcoxon signed-rank test to test for a difference in male and female recombination rates. Segregation distortion (non-Mendelian inheritance) for all loci was tested in JoinMap (χ2-test, α = 0.01). To determine if there were interactions between the two distorted regions on LG1_Chr1 and LG2_ChrZ, we tested for an association of genotypes between pairs of markers from the two regions. We compared each of the seven mapped markers on LG1_Chr1 from sc221-sc26 to randomly chosen markers from the region of sc300-sc481 on LG2_ChrZ (that is, we conducted seven tests). To analyze the contingency tables of genotypes between loci, we used the program RxC . RxC employs the metropolis algorithm to obtain an unbiased estimate of the exact P-value (that is, Fisher's exact test) for any sized contingency table. The following Markov chain parameters were used to test significance: 2,500 dememorizations, 100 batches, and 2,500 permutations per batch.
Anchoring markers in the linkage map to chromosomes
Methods for FISH analysis are described in . BAC end sequences obtained from GenBank (see Additional data file 4 for accession numbers) were used in BLAST searches of the genome database. We only used BACs that FISH mapped to a single homologous pair of chromosomes (or Z and W). Linkage groups were anchored to chromosomes by the following. We first determined if the FISH mapped BAC BLAST matched to a scaffold. If the scaffold was one in which we had a mapped microsatellite maker, we considered that marker to belong on the chromosome to which the BAC was FISH mapped. Evidence from several of these matches allowed us to anchor the linkage groups to chromosomes (Additional data file 4; Figure 3). We also BLAST matched six genes with known chromosomal locations (Figure 2): 28s rDNA on chromosome 2, eggshell protein genes p14 and p48 on chromosome 2, SmTRα on chromosome 7, and SmHox1 and Smox1 on chromosome 3 [43–47].
Additional data files
The following additional data are available with the online version of this paper: primer, motif, and position information for each microsatellite marker (Additional data file 1); summary methods, grouping statistics, and ordering of markers used in the construction of the linkage map (Additional data file 2); a list of supercontigs where more than one marker was placed (Additional data file 3); a list of FISH-mapped BACs that BLAST matched to scaffolds with markers in the linkage map (Additional data file 4).
bacterial artificial chromosome
fluorescent in situ hybridization
Supported by NIH R21 AI072704 (TJCA), NIH Training Grant D43TW006580 (PTL) and NIH schistosome supply grant AI30026. This investigation was conducted in facilities constructed with support from Research Facilities Improvement Program Grant Number C06 RR013556 from the National Center for research Resources, NIH. FISH analyses were partially supported by JSPS (13557021) (HH), 21st century COE and global COE of MEXT (HH), and U01-AI48828. We thank the following: the Welcome Trust Sanger Institute for providing the genome sequence and repeat masked sequence; Matt Berriman and Najib El-Sayed for genome support; Guilherme Oliveira for supplying the LE line; Fred Lewis, Greg Sandland (Dennis Minchella lab), Conor Caffrey, Sam Loker, and John Sullivan for providing uninfected snails; Claudia Carvalho-Queiroz and Shalini Nair for assistance in the laboratory.
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