Abstract
Phenotypic evolution and speciation depend on recombination in many ways. Within populations, recombination can promote adaptation by bringing together favorable mutations and decoupling beneficial and deleterious alleles. As populations diverge, crossing over can give rise to maladapted recombinants and impede or reverse diversification. Suppressed recombination due to genomic rearrangements, modifier alleles, and intrinsic chromosomal properties may offer a shield against maladaptive gene flow eroding coadapted gene complexes. Both theoretical and empirical results support this relationship. However, little is known about this relationship in the context of behavioral isolation, where coevolving signals and preferences are the major hybridization barrier. Here we examine the genomic architecture of recently diverged, sexually isolated Hawaiian swordtail crickets (Laupala). We assemble a de novo genome and generate three dense linkage maps from interspecies crosses. In line with expectations based on the species’ recent divergence and successful interbreeding in the laboratory, the linkage maps are highly collinear and show no evidence for large-scale chromosomal rearrangements. Next, the maps were used to anchor the assembly to pseudomolecules and estimate recombination rates across the genome to test the hypothesis that loci involved in behavioral isolation (song and preference divergence) are in regions of low interspecific recombination. Contrary to our expectations, the genomic region where a male song and female preference QTL colocalize is not associated with particularly low recombination rates. This study provides important novel genomic resources for an emerging evolutionary genetics model system and suggests that trait–preference coevolution is not necessarily facilitated by locally suppressed recombination.
Keywords: speciation, sexual selection, recombination, chromosomal rearrangements, genome, crickets
SPECIATION is spurred by the accumulation of genomic variation, resulting in the formation of barriers that prevent gene flow between populations. Genomes diverge under the influence of selection and drift, while gene flow counteracts this divergence by homogenizing the genome (Felsenstein 1981; Kirkpatrick and Ravigne 2002; Gavrilets 2003). To appreciate the speciation process and the origin of the fascinating diversity of life on earth, we need to understand the interaction between the mechanisms that change allele frequencies and the mechanisms that govern the association of beneficial and deleterious alleles with other alleles. A key process in this interaction is recombination, which creates new allelic combinations during meiosis in sexually reproducing organisms.
Any association between loci that underlies environmental adaptation or between loci underlying coevolving (sexual) signals and signal responses (i.e., coadapted gene complexes) will be affected by recombination (Felsenstein 1981). Within populations, recombination can mitigate Hill–Robertson interference by combining locally adaptive alleles from different genomic backgrounds and by decoupling beneficial and deleterious alleles (Hill and Robertson 1966). Recombination can also influence the covariance between sexual traits and preference across sexes (Smith and Haigh 1974; Smith 1978; Gillespie 2000; Otto 2009). As such, recombination might increase the efficiency of background selection (purging deleterious alleles), sexual selection (through signal-preference coevolution), and local adaptation (by linking locally adapted alleles) in the earliest stages of speciation (Kirkpatrick and Ravigne 2002; Butlin 2005; Kirkpatrick and Barton 2006; Yeaman and Whitlock 2011).
Between divergent populations with some (but incomplete) reproductive isolation, recombination can also counteract population divergence and prevent the closure of a reproductive boundary by creating combinations of alleles that are favorable in different contexts (Noor et al. 2001; Rieseberg 2001; Coyne and Orr 2004; Ortiz-Barrientos et al. 2016). Interspecific recombination is constrained both by intrinsic properties of the species’ genomes that also constrain intraspecific recombination, as well as by genetic and chromosomal changes between populations such as incompatibilities, locally adaptive alleles, and adaptive modifications of between-population recombination (Yeaman and Whitlock 2011; Feder et al. 2012). The most intensely studied modifier of recombination between divergent populations are inversions. Inversions can suppress recombination locally in the genome and thus promote reproductive isolation by trapping genetic incompatibilities in linkage blocks (Noor et al. 2001), acting synergistically with other genes causing isolation (Rieseberg 2001), or by linking locally adaptive alleles (Kirkpatrick and Barton 2006). Other chromosomal rearrangements, such as translocations and transposable elements, can likewise contribute to “chromosomal speciation” (Rieseberg 2001) as well as to preventing gene flow and furthering genetic divergence among heterospecifics.
Interestingly, there is ubiquitous among-species variation in recombination rates (Wilfert et al. 2007; Smukowski and Noor 2011). In insects, for example, rates vary from 16.1 cM/Mb in Apis melifera to 0.1 cM/Mb in the mosquito Armigeres subalbatus (Wilfert et al. 2007). There is also variation across the genome within individuals. For example, 50-fold differences have been observed within single chromosomes of humans and birds (Myers et al. 2005; Singhal et al. 2015). These patterns of variation underline that the efficacy of selection acting within species may differ across taxa and across genomes of the same species.
A major prediction following from theoretical work is that favorable allele combinations that promote ecological adaptation are more likely to reside in regions of low recombination. Recombination counteracts adaptation by natural selection by breaking up associations between segregating alleles that are locally adaptive within the resident population and counteracts divergent selection if there is gene flow between recently diverged populations (Bürger and Akerman 2011; Yeaman and Whitlock 2011; Yeaman 2013). So far, empirical evidence for the prediction that locally adaptive alleles reside in regions of low recombination is not conclusive (Roesti et al. 2013; Burri et al. 2015; Marques et al. 2016). However, a recent study indicated that the interaction between gene flow and divergent selection is a strong predictor for the association between adaptive alleles and regions of low recombination in multiple species of stickleback fish (Samuk et al. 2017).
It is less clear how these predictions apply to the evolution of behavioral isolation. Theoretical models of speciation by sexual selection depend on linkage disequilibrium between sexual signaling traits and corresponding preference genes (Fisher 1930; Lande 1981; Kirkpatrick 1982). Linkage disequilibrium between trait and preference genes can come about by assortative mating (Lande 1981; Andersson and Simmons 2006) or by physical linkage (Kirkpatrick and Hall 2004), either through closely linked loci or through pleiotropy (a single gene affecting both signal and preference phenotypes). On the one hand, recombination can help consolidate loci brought together by nonrandom mating and as such facilitate linkage disequilibrium between trait and preference (Kirkpatrick and Ravigne 2002). On the other hand, recombination can also tear apart coadapted trait and preference alleles if genes are exchanged between populations that differ in mating phenotypes. Therefore, recombination between sexually divergent populations in sympatry and parapatry often compromises differentiation in mating phenotypes and hinders speciation (Arnegard and Kondrashov 2004; Servedio 2009, 2015; Servedio and Bürger 2014). However, there has been limited empirical insight into the relationship between trait–preference coevolution and genome-wide variation in recombination rates (see Davey et al. 2017 for a recent exception).
Here, we examine the genomic architecture, specifically structural variation and heterogeneity in interspecific recombination, of four closely related, sexually isolated species of Hawaiian swordtail crickets from the genus Laupala. Laupala is one of the fastest speciating taxa known to date (Mendelson and Shaw 2005). The 38 morphologically cryptic species, each endemic to a single island of the Hawaiian archipelago (Otte 1994; Shaw 2000a), are the product of a recent evolutionary radiation. Evidence suggests that speciation by sexual selection on the acoustic communication system has driven this rapid diversification, as both male mating song and female acoustic preferences have diverged extensively among Laupala species (Otte 1994; Shaw 2000b; Mendelson and Shaw 2002). Sexual trait evolution strongly contributes to the onset and maintenance of reproductive isolation (Mendelson and Shaw 2002; Grace and Shaw 2011). Quantitative variation in one key temporal property of male song (pulse rate) and corresponding female preference strongly covaries across species and across populations within species (Shaw 2000b; Grace and Shaw 2011). Although the mechanisms of trait–preference coevolution require further study, there is evidence that both are associated with a polygenic basis and that genetic loci controlling quantitative variation in traits and preferences are physically linked in the genome (Shaw and Lesnick 2009; Wiley et al. 2012). Notably, one of the major song QTL (haploid effect size ∼9%) colocalizes with the first mapped preference QTL (haploid effect size ∼14%). Directional effects of song QTL provide additional evidence that (sexual) selection is driving divergence between species (Shaw et al. 2007).
The species pairs involved in this study—Laupala kohalensis and L. pruna, and L. paranigra and L. kona—are endemic to the Big Island, the youngest island of the Hawaiian archipelago (Figure 1, A and B). Although these species pairs have apparently diverged in allopatry within the Big Island, past or future migration is likely given their geographic proximity. Indeed, although allopatric and more closely related to L. kohalensis, L. pruna currently overlaps in distribution with L. paranigra (Figure 1B). The discordance between nuclear and mitochondrial phylogenies (Shaw 2002) and the limited degree of postzygotic isolation between some species pairs further emphasize the possibility of gene flow across natural populations. Together, the biogeography and the genetics of song and preference variation in this system provide a unique opportunity to explore the interaction between interspecific recombination rate variation, coevolution of mating traits, and speciation.
Figure 1.
Study design. (A) The phylogenetic relationships of studied Laupala species based on a neighbor-joining tree generated from genetic distances among the parental lines used in this study. Dashed gray lines connect species pairs that were crossed. (B) Approximate distributions of the studied species on the Big Island of Hawaii. (C) Hypothetical segregation and linkage map construction for five genetic loci A, B, C, D, and E in three crosses of four species. The genetic distance between the loci is 5 cM in each of the four species. Loci [B, C, D] are inverted in the green and black species. When two species that have alternative karyotypes for the inversion are crossed (pair 2), loci in the inversion will not recombine in the first-generation hybrid, resulting in reduced genetic (map) length in the second-generation hybrid. Other chromosomal rearrangements will have similar effects. Only if two crosses involve homokaryotypic species pairs that have alternative karyotypes can an inversion be detected in a comparison of intercross linkage maps.
We first assemble a de novo L. kohalensis draft genome and then obtain thousands of SNP markers for heterogeneously hybrid offspring from three laboratory-generated interspecific crosses. We then generate three dense linkage maps and compare these maps to test the hypothesis that the genomic architectures of young, sexually differentiated species are largely collinear (similar marker order) and have few genetic incompatibilities and conserved interspecific recombination frequencies (similar marker distances). There is some variation in the level of overall differentiation in the species pairs studied here, but all lineages are young (∼0.5 million years or less; Figure 1). It is commonly expected that strong prezygotic isolation can evolve rapidly and largely in the absence of intrinsic postzygotic isolating mechanisms (Coyne and Orr 2004), but explicit comparisons of chromosomal architectures across behaviorally isolated species are rare. We compare the maps visually and use variation in maker order and length (measured in genetic distance, or cM) as indicators of possible chromosomal rearrangements affecting the recombination rates differently in different crosses (Figure 1C). Using the large amount of information on linkage across many genomic markers from three hybrid crosses, we then anchor the draft genome assembly to pseudomolecules and estimate the landscape of recombination across the genome. Finally, using an additional map that integrates the amplified fragment length polymorphism (AFLP) markers from previous QTL studies of the L. kohalensis and L. paranigra species pair, we approximate the location of known male song QTL, including one colocalizing with a female acoustic preference QTL, on the pseudomolecules. We examine local variation in recombination rates across the genome and in relation to the location of the song and preference QTL to test the hypothesis that song–preference coevolution is facilitated by suppressed interspecific recombination. This study provides important insights into the role of the genomic architecture during divergence of closely related species separated by premating barriers.
Materials and Methods
De novo genome assembly
The L. kohalensis draft genome (estimated genome size ∼1.9 Gb; Petrov et al. 2000) was sequenced using the Illumina HiSequation 2500 platform. DNA was isolated with the DNeasy Blood and Tissue Kits (QIAGEN, Valencia, CA) from six immature female crickets (∼5 months of age) chosen randomly from a laboratory stock population (approximate laboratory generation 14). Females were chosen to balance the DNA content of sex chromosomes to autosomes (female crickets are XX; male crickets are XO). DNA was subsequently pooled for sequencing. Four different libraries were created: a paired-end library with an estimated insert size of 200 bp (sequenced by Cornell Biotechnology Resource Center), a paired-end library with an estimated insert size of 500 bp, and two mate-pair libraries with insert sizes of 2 and 5 kb (sequenced by Cornell Weill College Genomics Resources Core Facility).
Reads were processed using Fastq-mcf from the Ea-Utils package (Aronesty 2011) with the parameters -q 30 (trim nucleotides from the extremes of the read with qscore <30) and -l 50 (discard reads with lengths <50 bp). Read duplications were removed using PrinSeq version 0.20.4 (Schmieder and Edwards 2011) and reads were corrected using Musket version 1.0.8 with the default parameters (Liu et al. 2013).
Reads were assembled using SoapDeNovo2 (Luo et al. 2012). The reads were assembled using different k-mer sizes (k = 31, 39, 47, 55, 63, 71, 79, and 87). The 87-mer assembly produced the best assembly (based on N50/L50, assembly size, and number of scaffolds). Scaffolds and contigs were renamed using an in-house Perl script. Gaps were filled using GapCloser from the SoapDeNovo2 package.
The gene space covered by the assembly was evaluated using three different approaches: (1) L. kohalensis unigenes (Danley et al. 2007) were mapped using Blat version 35x1 (Kent 2002), where only unigenes mapping with ≥90% of their length were considered; (2) 50-bp paired-end RNA-sequencing (RNA-seq) reads from a congeneric species, L. cerasina, were mapped using Tophat2 (Kim et al. 2013), and reads were processed using the same methodology described above, but using a minimum length of 30 bp; and (3) using Benchmarking Universal Single-Copy Orthologs (BUSCO) version 2.0.1 (Simão et al. 2015) to search for conserved eukaryotic and arthropod genes.
Samples
We generated three F2 interspecies hybrid families to estimate genetic maps. Multiple F1 male and sibling females were intercrossed to generate F2 mapping populations for the following species crosses: (1) a L. kohalensis female and L. paranigra male [“ParKoh”; 178 genotyped F2 hybrid offspring; previously reported in Shaw et al. (2007)], (2) a L. kohlanesis female and a L. pruna male (“PruKoh”; 193 genotyped F2 hybrid offspring), and (3) a L. paranigra female and a L. kona male (“KonPar”; 263 genotyped F2 hybrid offspring). These four species are part of a recently radiated clade showing conspicuous mating song divergence (Mendelson and Shaw 2005). Approximate geographic distributions of the species, phylogenetic relationships, and parent collection localities are shown in Figure 1 and in Supplemental Material, Table S1. Crickets used in crosses were a combination of laboratory stock and outbred individuals [L. kohalensis (for ParKoh) and L. paranigra (for ParKoh and KonPar) were both reared in the laboratory for 3–15 generations; L. kohalensis (for PruKoh), L. pruna, and L. kona were wild caught]. All parental and hybrid generations were reared in a temperature-controlled room (20°) on Purina cricket chow and were provided with water ad libitum.
Genotyping
DNA was extracted from whole adults using the DNeasy Blood and Tissue Kits. Genotype-by-sequencing library preparation and sequencing were done at the Genomic Diversity Facility at Cornell University in 2014 following Elshire et al. (2011). The PstI restriction enzyme was used and DNA was sequenced (1 × 100 bp) on the Illumina HiSequation 2000 platform.
Reads were trimmed and demultiplexed using Flexbar version 2.5 (Dodt et al. 2012) and then mapped to the L. kohalensis de novo draft genome using Bowtie2 version 2.2.6 (Langmead and Salzberg 2012) with default parameters. We then called SNPs using two different pipelines: the Genome Analysis Toolkit (GATK) version 3.6.0 (DePristo et al. 2011; Van der Auwera et al. 2013) and FreeBayes version 0.9.13 (Garrison and Marth 2012). For GATK, we used individual BAM files to generate gVCF files using “HaplotypeCaller” followed by the joint genotyping step “GenotypeGVCF.” We then evaluated variation in SNP quality across all genotypes using custom R scripts (R Development Core Team 2016) to determine appropriate settings for hard filtering. The following metrics (based on the recommendations for hard filtering at https://gatkforums.broadinstitute.org/gatk/discussion/6925/understanding-and-adapting-the-generic-hard-filtering-recommendations) were used: quality-by-depth, Phred-scaled P-value using Fisher’s exact test to detect strand bias, root mean square of the mapping quality of the reads, u-based z-approximation from the Mann–Whitney rank sum test for mapping qualities, and u-based z-approximation from the Mann–Whitney rank sum test for the distance from the end of the read for reads with the alternate allele. For FreeBayes, we called variants from a merged BAM file using standard filters. After variant calling, we filtered the SNPs using “vcffilter,” a Perl-library part of the VCFtools package (Danecek et al. 2011), based on the following metrics: quality (>30), depth of coverage (>10), and strand bias for the alternative and reference alleles (SAP and SRP, both >0.0001). Finally, the variant files from the GATK pipeline and the FreeBayes pipeline were filtered to only contain biallelic SNPs with <10% missing genotypes using VCFtools version 0.1.15.
We retained two final variant sets: a high-confidence set, including only SNPs with identical genotype calls between the two variant discovery pipelines; and the full set of SNPs, which included all variants called using FreeBayes but limited to positions that were shared among the GATK and FreeBayes pipelines.
Linkage mapping
The genotype information from the parental lines was used to assign ancestry to the SNP loci. The parents of the crosses were heterogeneously heterozygous and only ancestry-informative loci were retained, i.e., all loci for which one or both parents were heterozygous were discarded. We were unable to obtain sequence data from the parents for PruKoh but we used sequence data from a single, nonparental L. pruna female and three available L. kohalensis females, which were all from the same populations as the parents. Ancestry was inferred if all three L. kohalensis individuals were homozygous for one allele and the L. pruna individual was homozygous for the alternative allele. All other loci were discarded. The loci were then further filtered based on genotype similarity and segregation distortion (see below for details).
The linkage maps deriving from the three species crosses were generated independently and by taking a three-step approach, employing both the regression mapping and the maximum likelihood (ML) mapping functions in JoinMap 4.0 (Van Ooijen 2006) as well as the three-point, error-corrected ML mapping function in MapMaker 3.0 (Lander et al. 1987; Lincoln et al. 1993). We used an intercross (F2) scheme in both programs because we limited our SNP markers to those that are fixed between the parents (i.e., the only genotypes are A, B, and H).
In the first step, we estimated “initial” maps that are relatively low resolution (5 cM) but with high marker-order certainty. For initial maps, we first grouped (3.0 ≤ LOD score ≤ 5.0) and then ordered the high-confidence markers that showed no segregation distortion (markers with chi-square-associated P-values for deviation from Mendelian inheritance of <0.05 were discarded) and for which no marker had >95% similarity in genotypes across individuals compared to other markers (otherwise, one of each pair was excluded). When excluding similar loci, we favored those marker loci shared among the three mapping populations over markers unique to one or two crosses. We then checked for concordance among the three mapping algorithms. In most cases, the maps had highly concordant marker orders; any (rare) marker-order discrepancies between maps for the same cross were resolved by choosing the order that resulted in the highest map likelihood, shortest total map length, and the best fit of markers based on pairwise recombination frequencies with neighboring markers (see also JoinMap 4.0 manual).
These initial maps were then filled out using MapMaker with marker loci passing slightly more lenient criteria: markers were drawn from the full set of SNPs if they had false discovery rate (FDR)-corrected (Benjamini and Hochberg 1995) P-values for chi-square tests of deviation from Mendelian inheritance of ≤0.05 and if <99% of their genotypes were in common with other markers. First, more informative markers (no missing genotypes, >2.0-cM distance from other markers) were added, satisfying a log-likelihood threshold of 4.0 for the positioning of the marker (i.e., assigned marker position is 10,000 times more likely than any other position in the map). Remaining markers were added at the same threshold, followed by a second round for all markers at a log-likelihood threshold of 3.0. We then used the ripple algorithm on five-marker windows and explored alternative orders.
In the second step, “comprehensive” maps were obtained in MapMaker by sequentially adding markers from the full set of SNPs that met the more lenient criteria described above to the initial map. Markers were added if they satisfied a log-likelihood threshold of 2.0 for the marker positions, followed by a second round with a log-likelihood threshold of 1.0. We then used the ripple algorithm again on five-marker windows and explored alternative orders. Typically, MapMaker successfully juxtaposes SNP markers from the same scaffold. However, in marker-dense regions with low recombination rates, the likelihoods of alternative marker orders coalesce. In such regions, when multiple markers from the same genomic scaffold were interspersed by markers from a different scaffold, we repositioned the former markers by forcing them in the map together. If the log likelihood of the map decreased by >3.0 (factor 1000), only one of the markers from that scaffold was used in the map. The comprehensive maps provide a balance between marker density and confidence in marker ordering and spacing.
The third step was to create “dense” maps. We added all remaining markers that were not yet incorporated in step two, first at a log-likelihood threshold of 0.5, followed by another round at a log-likelihood threshold of 0.1. We then used the ripple command as described above. The dense maps are useful for anchoring of scaffolds and for obtaining the highest possible resolution of variation in recombination rates, but with the caveat that there is some uncertainty in marker order. Uncertainty is expected to be higher toward the centers of the linkage groups (LGs) where crossing-over events between adjacent markers become substantially less frequent (see Results).
Comparative analyses
Based on the recent divergence times and high interbreeding successes, we predict a large degree of collinearity of the linkage maps. We note that interpretations must take into account the nonindependence of the ParKoh and PruKoh/KonPar maps, as a fully independent contrast is only obtained by comparing PruKoh and KonPar. We first examined whether inversions or other chromosomal rearrangements were common (affecting linkage map lengths and marker orders) or whether maps were generally collinear by comparing among the initial and comprehensive linkage maps visually, using map graphs from MapChart version 2.3 (Voorrips 2002). Inverted or transposed markers present in two or all maps can be detected by connecting “homologs” in MapChart (a homolog in this case means a scaffold that is represented in two or more maps). We then tested whether linkage maps are generally collinear across the species pairs quantitatively. We calculated the Spearman’s rank order correlation test statistic (ρ) and corresponding P-value (the probability of observing the measured or stronger correlation given no true correlation exists) to examine the correlation between the order of shared markers in homologous LGs using the cor.test() function in R version 3.3.1.
We then tested for genetic incompatibilities among the genomes of the four species by measuring segregation distortion in sliding, 10-cM windows. Although we filtered out markers with very high levels of segregation distortion (using a 5% FDR cutoff) to purge markers with potential sequencing errors, groups of distorted markers in a single region of a LG represent genomic regions with biased parental allele contributions, suggesting genetic incompatibilities (or, less commonly, selfish alleles and other active segregation distorters). Because L. kohalensis and L. paranigra are more distantly related to each other (and thus there has been more time for genetic incompatibilities to accumulate) than they are to L. pruna and L. kona, respectively (Mendelson and Shaw 2005; see Figure 1), we expected more regions with significant segregation distortion in the ParKoh map relative to the KonPar and PruKoh maps. We calculated genotype frequency and the negative 10-base logarithm of the P-value for the chi-square test of deviation from Mendelian inheritance across the LGs in R using the R/qtl package version 1.42 (Broman et al. 2003). Windows with P < 0.01 were considered to have significant segregation distortion, and thus potentially reflect genetic incompatibilities.
After establishing that the linkage maps were generally collinear (see Results), we merged the maps and examined patterns of variation in crossing over along the Laupala genome. Maps were consolidated using ALLMAPS version 0.7.1 (Tang et al. 2015). We then calculated species-specific average recombination rates for the LGs by dividing the total length of the LG (in cM) by the physical length of the pseudomolecule (in Mb) obtained by merging homologous LGs using ALLMAPS. Lastly, to evaluate recombination rate variation along the LGs, we fitted smoothing splines (with 10 d.f., based on the fit of the spline to the observed data) in R to describe the relationship between the consensus physical distance (as per the anchored scaffolds) and the genetic distance specific to each linkage map. Variation in the recombination rate was then assessed by taking the first derivative (i.e., the rate) of the fitted spline function. The estimated recombination rates are likely to be an overestimate of the true recombination rate, because unplaced/unordered parts of the assembly do not contribute to the physical length of the pseudomolecules but are reflected in the genetic distances obtained from crossing-over events in the recombining hybrids.
To test the hypothesis that linked trait and preference genes reside in low recombination regions, we integrated the AFLP map and song and preference QTL peaks identified in previous work on L. kohalensis and L. paranigra (Shaw and Lesnick 2009) with the current ParKoh SNP map and projected the QTL peaks onto the anchored genome. The SNPs used in the present study were obtained from the same mapping population (same individuals) as in the 2009 AFLP study. Therefore, we combined the high-confidence SNPs described above (for the initial map) with the AFLP markers reported in Shaw et al. (2007), which were of the same individuals as the SNP markers used in this study, and created a new linkage map using the same stringent criteria as for the initial maps described above. We projected this map onto the anchored draft genome based on common markers (scaffolds). We then approximated the physical location of the QTL peaks by looking for SNP markers on scaffolds present in the draft genome flanking AFLP markers underneath the QTL peaks identified in the 2009 study.
Data availability
Raw data (genotype data, linkage maps, pseudomolecule agp file) and supplemental files are available at https://doi.org/10.25386/genetics.6429494. Supplemental data consists of Table S1, geographic locations of sampled populations; Table S2, segregation distortion (count of heterozygotes per genotype) statistics; Table S3, summary statistics for anchored assembly; Table S4, integrated AFLP and SNP map for the L. kohalensis by L. paranigra cross; Figure S1, comprehensive linkage maps; Figure S2, ALLMAPS output; and Figure S3, coverage per cross per LG. Custom R scripts are also available at https://doi.org/10.25386/genetics.6392243. The L. kohalensis genome assembly and sequencing reads are available on National Center for Biotechnology Information’s (NCBI’s) GenBank under BioProject number PRJNA392944. The genotype-by-sequencing reads are available at NCBI’s short-read archive under BioProject number PRJNA429815. Supplemental material available at Figshare: https://doi.org/10.25386/genetics.6429494 and https://doi.org/10.25386/genetics.6392243.
Results
De novo genome assembly
The sequencing of the four libraries yielded 162.5 Gb of raw sequences (Table 1). After read processing, 145.5 Gb was used for the sequence assembly. We compared among assemblies resulting from different k-mer sizes (k = 31, 39, 47, 55, 63, 71, 79, and 87). Based on the N50/L50 and the total assembly size, the assembly produced with k = 87 was retained for the final draft genome. Despite a large number of scaffolds in the final assembly (149,424), the median length of the scaffolds was high and the total length of the assembly covers ∼83% of the expected complete genome in Laupala (Table 1).
Table 1. L. kohalensis sequencing, assembly, and gene space evaluation statistics.
Sequencing statistics | Raw data | Processed data | ||
---|---|---|---|---|
Library | Size (Gb) | Coveragea | Size (Gb) | Coveragea |
Paired-end 0.2-kb inserts | 28.9 | 15 | 26.1 | 14 |
Paired-end 0.5-kb inserts | 63.1 | 33 | 59.8 | 31 |
Mate-pair 2-kb inserts | 36.2 | 19 | 31.8 | 17 |
Mate-pair 5-Kb inserts | 34.3 | 18 | 27.8 | 14 |
Total | 162.5 | 85 | 145.5 | 76 |
Assembly statistics | Contigs | Scaffolds | ||
---|---|---|---|---|
Total assembly size (Gb) | 1.6 | 1.6 | ||
Total assembled sequences | 219,073 | 148,874 | ||
Longest sequence length (kb) | 465 | 4,541 | ||
Average sequence length (kb) | 7.2 | 10.7 | ||
N90 indexb | 40,926 | 3,505 | ||
N90 length (kb) | 7.7 | 67.7 | ||
N50 index | 9,917 | 756 | ||
N50 length (kb) | 43.6 | 583 | ||
GC content (%) | 34.9 | 34.9 |
Gene space statistics | Mapping percentage | |||||
---|---|---|---|---|---|---|
Laupala unigenes from the Gene Index | 95 | |||||
Laupala RNA-seq reads | 92 |
BUSCO database | Complete (%) | Single copy (%) | Duplicated (%) | Fragmented (%) | Missing (%) | Total |
---|---|---|---|---|---|---|
Eukaryota_odb9 | 98.7 | 93.7 | 5.0 | 0.3 | 1.0 | 303 |
Arthropoda_odb9 | 99.3 | 96.8 | 2.5 | 0.1 | 0.6 | 1066 |
Coverage is based on an estimated genome size of 1.91 Gb (Petrov et al. 2000).
When ordering all contigs (or scaffolds) by size, the N50 or N90 index indicates the number of the longest sequences (contigs or scaffolds) that contain 50 or 90% of the total assembled sequence, respectively. The N50 and N90 length indicate the length of the shortest sequence in the set of the largest contigs (or scaffolds) that contain 50 or 90% of all the sequence in the assembly, respectively.
Gene space coverage in the assembly was evaluated using the L. kohalensis cricket Gene Index (Danley et al. 2007) (release 2.0), RNA-seq from L. cerasina (Blankers et al. 2018), and by performing a BUSCO search using eukaryotic and arthropod-specific conserved genes. A total of 95 and 92% of the Laupala Gene Index and RNA-seq sequences mapped to the assembled genome, respectively. In addition, the BUSCO search using either database found most genes in the Laupala assembly (Table 1).
Collinear linkage maps
We obtained 815,109,126; 522,378,849; and 311,558,401 reads after demultiplexing for ParKoh, KonPar, and PruKoh, respectively. Average sequencing depth ± SD across all individuals in the F2 mapping population after filtering, (before and) after marker selection based on segregation distortion and ancestry information for linkage mapping was (62.4× ± 162.5 SD) 52.2× ± 31.4 SD, (44.3× ± 58.5 SD) 38.1× ± 23.8 SD, and (56.1× ± 105.7 SD) 41.8× ± 29.3 SD, respectively.
In the initial maps, 158 (ParKoh), 170 (KonPar), and 138 (PruKoh) markers were grouped into eight LGs at a LOD score of 5.0, corresponding to the seven autosomes and one X chromosome in Laupala. Markers group in eight LGs for a range of LOD scores: 5.0 < LOD score < 15.0 for ParKoh (no markers unlinked), 5.0 < LOD score < 42.0 for KonPar (no markers unlinked), and 4.0 < LOD score < 14.0 for PruKoh (between four and seven markers are unlinked depending on the LOD score threshold). The corresponding marker spacing was 5.14, 4.85, and 7.33 cM. The comprehensive maps contained 526, 650, and 325 markers with an average marker spacing of 1.91, 1.37, and 3.25 cM, respectively; and on the dense maps we placed 608, 823, and 383 markers with on average 1.69, 1.17, and 3.81 cM, respectively, between markers.
The recent divergence times and the limited levels of postzygotic isolation observed in this system led us to hypothesize that the linkage maps would show a high degree of collinearity. The visual comparison of marker positioning showed that the relative locations of shared scaffolds were similar across the linkage maps in both the initial and the comprehensive maps (Figure 2 and Figure S1). However, we also observe substantial variation in the total genetic length of homologous LGs, indicating recombination rate variation (Figure 2 and Figure S1). This variation may in part result from chromosomal rearrangements. However, we can only reliably detect rearrangements in our maps if they are alternatively fixed between L. pruna and L. kohalensis on the one side and L. paranigra and L. kona on the other side. In that specific scenario, the inverted marker order is visible when contrasting the PruKoh and KonPar maps, while the ParKoh map would show reduced recombination in that area (Figure 1C). Despite the apparent variation in recombination rates among homologous LGs, Spearman’s rank correlation of pairwise linkage-group comparisons was high (ρ varied between 0.91 and 1.00) and similar to values seen in comparisons of intraspecific linkage maps (e.g., Poursarebani et al. 2013); the quantitative measure of collinearity was largely consistent across LGs and across cross types (Table 2). Finally, merging the maps into a consensus pseudomolecule assembly allowed us to measure the error between individual maps and the merged assembly. Correlations between linkage maps and the pseudomolecule assembly were generally high (>0.95), indicating substantial synteny (Figure S2).
Figure 2.
Initial linkage maps. Bars represent LGs for ParKoh, KonPar, and PruKoh. Lines within the bars indicate marker positions. The scale on the left measures marker spacing in cM. Blue lines connect markers on the same scaffold between the different maps. The map for ParKoh is shown twice to facilitate comparison across all three maps. See Figure S1 for comprehensive maps.
Table 2. Linkage map comparison.
corr(ParKoh,KonPar) | corr(ParKoh,PruKoh) | corr(KonPar,PruKoh) | |
---|---|---|---|
1 | 0.99** | 0.90** | 0.97** |
2 | 0.99** | 0.96** | 0.93** |
3 | 1.00** | 0.98** | 0.95** |
4 | 0.99** | 1.00** | 0.97** |
5 | 0.97** | 0.98** | 0.95** |
6 | 0.99** | 0.94** | 0.94** |
7 | 0.96** | 0.93* | 0.91** |
X | 0.92** | 0.96** | 0.99** |
Spearman’s rank correlation (ρ) is shown for each pairwise comparison of linkage maps across all eight LGs.
* P < 0.001,
** P < 0.0001.
Limited heterogeneity in segregation distortion
We expected genetic incompatibilities to be more likely to occur in the ParKoh cross than in the KonPar and PruKoh cross, because L. kohalensis and L. paranigra are more distantly related than any of the other species pairs (Figure 1). We tested this hypothesis by examining the degree of segregation distortion in markers within 10-cM sliding windows across the linkage maps. Overall, segregation distortion was limited and average genotype frequencies were close to Mendelian expectations (Figure 3). However, LG3 showed a bias against L. kohalensis homozygotes in the ParKoh cross but not in any of the other crosses. Additionally, there was significant variation in the frequency of heterozygotes across the LGs [linear model: frequency(heterozygotes) ∼ LG × cross: R2 = 0.21, F20,1547 = 20.7, P < 0.0001]. The post hoc Tukey honest significant differences test revealed that LG7 had the lowest abundance of heterozygotes overall and within each of the intercrosses, and that levels of heterozygosity on LG7 were similar across the maps (Table S2). Together, these results show that for some LGs and in some crosses, certain genotype combinations were less common than expected, potentially as a result of genetic incompatibilities or meiotic drive.
Figure 3.
Segregation distortion. For each of the seven autosomal LGs within the three comprehensive linkage maps (from top to bottom: ParKoh, KonPar, and PruKoh), a sliding window of the negative log-transformed P-values for the χ2-square test for deviation from a 1:2:1 segregation ratio is shown across markers with black lines in the top panels. In the panel below, the trace of the frequency of heterozygote genotypes (blue lines) and homozygote genotypes for both parental alleles (black and red lines, respectively) is shown. For each intercross, dashed gray lines indicate P = 0.01 (top panels) or expected allele frequencies based on 1:2:1 inheritance (bottom panels).
Variable recombination rates across the genome
We anchored a total of 1054 scaffolds covering 720 million bp, a little below half the current genome assembly. The pseudomolecule length ranges from 25,387,579 bp (LG6) to 137,279,277 bp (LG3), with N50 values between 569,059 bp (LG6) and 1,249,941 bp (LGX) (see Table S3 for scaffold number, N50, and assembly size per LG; and Figure S3 for coverage variation across the LGs). This gives us enough power to make inferences about broadscale recombination rate variation, but not about the existence of small-scale recombination hotspots. Average recombination rates varied from between 0.75 (KonPar) and 0.93 cM/Mb (ParKoh) on the X chromosome to between 3.12 (KonPar) and 4.24 cM/Mb (PruKoh) on LG6 (Table 3). We note that the recombination rate for LG6 might be artificially inflated because of lower assembly quality (expressed as N50) of this LG relative to the other LGs in all linkage maps and in the pseudochromosomes (Table S3). Both including and excluding the sex chromosome, there is a significant linear relationship between chromosome size and genetic length (linear mixed effect model with cross as random variable; with X: β = 0.62, F1,23 = 14.95, P = 0.0008; without X: β = 0.69, F1,20 = 29.7, P < 0.0001) and between chromosome size and broadscale recombination rate (with X: β = −34.1, F1,23 = 29.1, P < 0.0001); without X: β = −24.0, F1,23 = 63.7, P < 0.0001).
Table 3. Linkage map summary statistics.
LG | Length (Mb) | ParKoh | KonPar | PruKoh | |||
---|---|---|---|---|---|---|---|
Map length (cM) | Recombination rate (cM/Mb) | Map length (cM) | Recombination rate (cM/Mb) | Map length (cM) | Recombination rate (cM/Mb) | ||
1 | 117 | 207 | 1.77 | 156 | 1.33 | 156 | 1.33 |
2 | 102 | 167 | 1.64 | 128 | 1.25 | 205 | 2.01 |
3 | 137 | 169 | 1.23 | 167 | 1.22 | 173 | 1.26 |
4 | 90 | 100 | 1.11 | 117 | 1.30 | 99 | 1.10 |
5 | 62 | 91 | 1.47 | 84 | 1.35 | 103 | 1.66 |
6 | 25 | 85 | 3.40 | 106 | 4.24 | 78 | 3.12 |
7 | 53 | 78 | 1.47 | 84 | 1.58 | 139 | 2.62 |
X | 134 | 124 | 0.93 | 101 | 0.75 | 114 | 0.85 |
Total | 720 | 1021 | 1.42 | 943 | 1.31 | 1067 | 1.48 |
Most LGs showed wide regions of strongly reduced recombination rates in the center of the LGs (Figure 4). The general pattern of peripheral peaks in recombination rates juxtaposing large recombination “deserts” was observed in all three intercrosses, but some additional cross-specific peaks in recombination rates were observed on almost all LGs (Figure 4).
Figure 4.
Recombination and Marey maps. Grayscale symbols and lines indicate the relationship between the physical distance (scaffold midposition) in Mb on the x-axis and the genetic distance in cM for each of the eight LGs on the left y-axis. ○’s represent the dense ParKoh linkage map, ▵’s and ⋄’s represent that of the KonPar and PruKoh cross, respectively. The corresponding lines (ParKoh: solid; KonPar: dashed; PruKoh: dotted) indicate the fitted smoothing spline (10 d.f.). The red lines (same stroke style) show the first order derivative of the fitted splines and represent the variation in recombination rate as a function of physical distance (cM/Mb, on the right y-axis). Gray bars indicate the approximate location of male song rhythm QTL peaks. The yellow ☆ in the LG1 panel highlights the QTL peak that colocalizes with a female preference QTL peak (Shaw and Lesnick 2009).
Trait–preference coevolution despite high recombination
Contrary to our expectation, the approximate location of the colocalizing song and preference QTL peak from (Shaw and Lesnick 2009) was associated with average recombination rates in the ParKoh and KonPar map and low recombination rates in the PruKoh map (Figure 4; Table S4). However, most other QTL peaks are located in regions of low recombination (Figure 4; Table S4).
Discussion
The evolutionary trajectory of diverging populations and the likelihood of speciation can be heavily influenced by recombination. Within species, recombination can create favorable combinations of alleles or decouple deleterious from beneficial alleles. Among species, regions with low recombination can provide a genetic shield against introgression of maladaptive loci (Noor et al. 2001; Rieseberg 2001; Butlin 2005; Slatkin 2008; Noor and Bennett 2009; Cutter and Payseur 2013; Ortiz-Barrientos et al. 2016). Understanding recombination is thus critical to understanding adaptation and speciation. Recombination also has important implications for the analysis of genotype–phenotype relationships (Mackay 2001), demographic inference (Li and Durbin 2011), and analyses of genomic variation (Cutter and Payseur 2013; Wolf and Ellegren 2016). However, we still have limited insight into the patterns of recombination rate variation among species and across genomes, in particular for radiations powered largely by behavioral isolation.
Here, we study four species of sexually divergent Hawaiian swordtail crickets and generate the first pseudomolecule-level assembly for Orthoptera and the first published genome assembly for crickets, an important model system in neurobiology, behavioral ecology, and evolutionary genetics (Horch et al. 2017). Below, we discuss how our results provide insight into the potential for structural variation (linkage map collinearity) and genetic incompatibilities to drive reproductive isolation among closely related Laupala species. We also elaborate on the patterns of variation in recombination rates across the genome. We then discuss the surprising finding that colocalizing male song and female preference QTL did not fall in a region with particularly low recombination. This is important because it challenges the hypothesis that coevolution of traits and preferences is facilitated by locally reduced recombination between recently diverged populations.
Collinearity of genetic maps
Based on the recent divergence (Mendelson and Shaw 2005) and strong premating isolation of Laupala species in the absence of conspicuous morphological and ecological differences (Otte 1994; Shaw 1996; Mendelson and Shaw 2005), we expected limited variation in chromosome structure and few signatures of genetic incompatibility between the species. In line with these expectations, we found that LGs are collinear across interspecies crosses (Figure 2). This was true both for comparisons of nonindependent species pairs (between ParKoh and the other two crosses), as well as for the independent contrast of the PruKoh map vs. the KonPar map. We saw some instances where markers occupied regions that may have been translocated or inverted. However, these instances were rare (Figure 2 and Figure S1) and recombination rates were similar among homologous LGs across the hybrid families (Figure 4). Moreover, the quantitative measures of collinearity, Spearman’s rank correlation among maps and between maps and the pseudomolecule assembly, as well as limited segregation distortion (but see discussion of one exception below) both supported the collinearity hypothesis; if there were larger or many small inversions between the species pairs, the order of markers in the different maps would be more variable, in which case significantly lower values for Spearman’s rank correlation would be expected than the uniformly high correlations (most values are between 0.95 and 0.99) we observe here.
Variation in the organization and structure of chromosomes can contribute to postzygotic reproductive isolation after speciation as well as to the speciation process directly (Noor et al. 2001; Rieseberg 2001). We conclude that, at least for the Laupala group that radiated on the Big Island of Hawaii in the last 500,000 years, structural rearrangements have not played a major role in the evolution of reproductive isolation. This is because, similar to two hybridizing Heliconius species (Davey et al. 2017), we observed that chromosome-wide recombination rates are relatively conserved and large chromosomal rearrangements are absent. We hypothesize that, for Laupala on the Big Island, premating isolation combined with (partial) geographic separation (i.e., low migration rates) provides a sufficiently strong barrier to gene flow between sister species. Indeed, as has been shown in recent models of the role of inversions in speciation, genomic rearrangements can only invade and spread in diverging populations if levels of gene flow and the contribution of structural variation to isolation (by linking adaptive alleles or incompatibilities) is high relative to the strength of assortative mating (Feder et al. 2014; Dagilis and Kirkpatrick 2016).
We acknowledge that the power to detect rearrangements and recombination rate variation between the genomes of the species studied here is limited by the resolution of the maps, the sample size, and the error rate of the sequencing platform. The average spacing of markers is between 1.37 and 3.25 cM; the resolution at which we can detect rearrangements is on the order of 105 and 106 bp. Therefore, it is difficult to attribute subtle variation in marker order and genetic distance (<1–2 cM) between the maps to genomic rearrangements, as opposed to mapping errors and sampling variance, because variation in recombination frequencies between closely related species might be detected only at finer scales (Stevison et al. 2017). However, the overall collinearity of the maps supports our conclusion that chromosomal architectures are broadly conserved between the species studied here.
Genetic incompatibilities
We expected genetic incompatibilities to be more likely between genomes of more distantly related species. Accordingly, we discovered a single region covering approximately half of LG3 with high segregation distortion in ParKoh; we found no such deviations in the other two crosses (Figure 3). Inspection of genotype frequencies indicated that fewer individuals than expected were homozygous for L. kohalensis alleles at loci in this region. In a controlled cross, segregation distortion can be caused by prezygotic effects such as meiotic drive of selfish genetic elements and distorter genes [e.g., like sd in Drosophila melanogaster (Larracuente and Presgraves 2012)], and by postzygotic genetic incompatibilities (Dobzhansky 1937; Muller 1942; Burt and Trivers 2006; Presgraves 2010). Genotypic errors may produce superficially similar patterns but are unlikely to distort segregation over large genomic regions and with consistent bias toward the same genotypes. Although meiotic drive is a possible alternative to genetic incompatibilities, we do not see the same effect in the other cross involving L. paranigra, where selfish genetic elements or segregation distorters ought to have a similar effect. Overall, the large region on LG3 reveals a potential local postmating barrier to gene flow that could contribute to strengthening existing prezygotic barriers in secondary contact zones or following episodes of migration.
Recombination landscape
Chromosomal rearrangements influence genomic divergence by locally altering recombination rates within and among species. Felsenstein (1974, 1981) illuminated the role of intraspecific recombination in purging deleterious alleles and the role of interspecific recombination in decoupling coadapted alleles. In recent years, the role of recombination and its interaction with divergent selection and adaptation on genomic scales have received considerable attention (e.g., Yeaman and Whitlock 2011; Feder et al. 2012; Samuk et al. 2017) and technological advances are shifting focus toward characterizing recombination rates across genomes (Butlin 2005; Slatkin 2008; Noor and Bennett 2009; Barb et al. 2014; Burri et al. 2015). Comparisons of genomic recombination landscapes illuminate the role of recombination in speciation, but focus has been on systems where environmental and ecological pressures are driving divergence between locally adapted populations.
Here, we show for species separated by conspicuous sexual barriers that there is limited variation in recombination rates across the maps of three interspecific crosses (Figure 4), but strong heterogeneity in recombination rates across the genome. Genome-wide average interspecific recombination rate varied between 1.3 and 1.5 cM/Mb (Table 3), similar to intraspecific rates observed in dipterans and substantially lower than social hymenopterans and lepidopterans (Wilfert et al. 2007). We note that our estimates are derived from interspecific maps, which may lead to somewhat lower estimates compared to intraspecific maps (e.g., Beukeboom et al. 2010), because genetic incompatibilities and rearrangements may reduce rates of crossing over; however, differences between intra- and interspecific recombination might be negligible if rearrangements are rare (e.g., Davey et al. 2017). Moreover, we anchored ∼50% of the nucleotides in the draft assembly to LGs, and there remain many scaffolds not mapped to a genomic position. These “missing” scaffolds are expected to add to the physical length of the chromosomes more so than to the genetic length of the chromosomes, thus lowering the overall recombination rate and increasing heterogeneity in recombination rates among the crosses. However, our study emphasized relative patterns of recombination, which should not be affected by our sampling. While we can only approximate intraspecific recombination rates at this point, we note that recent divergence of the study species and collinearity of the linkage maps support conservation of recombination landscapes across intraspecific and interspecific comparisons.
Interestingly, for all three species pairs we document high variability in interspecific recombination across genomic regions. We found a stereotypical U-shaped pattern: large regions of low recombination in the center, with recombination rates well below 1 cM/Mb and occasionally approaching 0, flanked by steep inclines reaching rates up to 6 cM/Mb (Figure 4). This pattern is consistent with earlier findings in plants (Anderson et al. 2003), invertebrates (Rockman and Kruglyak 2009; Niehuis et al. 2010), and vertebrates (Backström et al. 2010; Roesti et al. 2013; Singhal et al. 2015) including humans (Kong et al. 2002); but differs from observations in, for example, Drosophila (Kulathinal et al. 2008), which show heterogeneity in recombination rates, but not necessarily much higher rates on the periphery of the chromosomes. Commonly invoked drivers of local recombination suppression, such as selection against recombination due to negative epistasis or the maintenance of linkage disequilibrium between mutually beneficial alleles (Smukowski and Noor 2011; Stevison et al. 2011; Smukowski Heil et al. 2015; Ortiz-Barrientos et al. 2016), are not likely to leave chromosome-wide signatures. Rather, the observed U-shaped pattern is more likely attributable to structural properties of chromosomes, such as the location of the centromere and heterochromatin-rich regions (Copenhaver et al. 1999; Haupt et al. 2001). Roesti et al. (2013) observe similar recombination landscapes in stickleback fish and suggest it might be due to peripheral clustering during meiosis prophase I to facilitate homolog pairing (Harper et al. 2004; Brown et al. 2005). Regardless of the mechanism, the observed genomic architecture will drive substantial heterogeneity in the propensity of favorable and/or maladaptive alleles to come together, break apart, and introgress in heterospecific backgrounds.
Trait–preference coevolution
Recombination heterogeneity may be important in this study system as a facilitator of trait–preference coevolution. If trait and preference genes are coupled through physical linkage (Kirkpatrick and Hall 2004), linkage can be stronger and span wider physical distances in regions with reduced recombination. We hypothesized that recombination facilitates linkage between trait and preference genes in Laupala because a previous study showed that a major song QTL (∼9% of the parental difference in male song) colocalizes with a preference QTL (∼14% of parental difference for female preference) in a cross between L. kohalensis and L. paranigra (Shaw and Lesnick 2009). Contrary to our expectation, we show that the colocalizing QTL fall in a region with intermediate to high recombination rates (>2.0 cM/Mb) compared to chromosomal averages (typically 1–2 cM/Mb). This suggests that reduced recombination over larger physical distances is unlikely to facilitate trait–preference coevolution in this system. Importantly, a high speciation rate and widespread divergence in sexual signaling phenotypes suggest a primary role for trait–preference coevolution in Laupala speciation (Mendelson and Shaw 2005; Shaw et al. 2011). Additionally, although these species likely diverged in allopatry (Mendelson and Shaw 2005), some level of interspecific gene exchange is likely given historical biogeography, widespread secondary contact, and evidence derived from discordant nuclear and mitochondrial gene trees (Shaw 2002).
How then is linkage disequilibrium between traits and preferences maintained? First, QTL may colocalize due to very tight physical linkage or pleiotropy instead of looser linkage. Under pleiotropy, a lack of physical space for crossovers to occur—rather than locally low recombination rates—maintains linkage disequilibrium. Linkage disequilibrium might also persist in the face of recombination if strong assortative mating results from female mate preference. In this case, genetic correlations in signal and preference variation will evolve, independent of their genomic locations (Fisher 1930; Lande 1981). Concordantly, recent simulation studies showed that the probability with which recombination rate modifiers that link coadaptive alleles spread in a population is lower when assortative mating is strong, recombination between loci is low, and selection on the loci themselves is strong (Feder et al. 2014; Dagilis and Kirkpatrick 2016). Third, the current test involves only a single locus and additional tests are required to more robustly examine the relationship between recombination and trait–preference coevolution. We observed that several known male song QTL on other LGs fall in regions of low recombination. Additional female preference QTL covary with these song QTL as well (Wiley et al. 2012), although precise map locations are not yet known.
In summary, we find limited variation in chromosome structure among species, but strong heterogeneity in the recombination landscape across the genome. We present a de novo genome assembly for L. kohalensis and anchor a substantial part of the genome to pseudomolecules. Crickets are an important model system for evolutionary and neurobiological research (Horch et al. 2017), but limited genomic resources are available. The first Orthopteran pseudomolecule-level draft genome and recombination rate map are thus important new contributions to future speciation genomics research. This study further provides important insight into the extent to which structural variation and genetic incompatibilities contribute to reproductive barriers between closely related, sexually divergent species. We also shine light on the role of recombination in trait–preference coevolution and argue, at least in Laupala, that the evolution of behavioral isolation is not contingent on structural genomic variation and locally reduced recombination.
Acknowledgments
We thank Stephen Chenoweth and two anonymous reviewers for helpful comments that strongly improved the quality of this manuscript. We further thank the Shaw laboratory, in particular Mingzi Xu, as well as Michael Sheehan and other members from Cornell’s Department of Neurobiology and Behavior for input that contributed to the interpretation of the findings. This work was supported by the National Science Foundation (DEB 1241060, IOS 1257682, and IOS 0843528).
Footnotes
Supplemental material available at Figshare: https://doi.org/10.25386/genetics.6429494 and https://doi.org/10.25386/genetics.6392243.
Communicating editor: S. Chenoweth
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Raw data (genotype data, linkage maps, pseudomolecule agp file) and supplemental files are available at https://doi.org/10.25386/genetics.6429494. Supplemental data consists of Table S1, geographic locations of sampled populations; Table S2, segregation distortion (count of heterozygotes per genotype) statistics; Table S3, summary statistics for anchored assembly; Table S4, integrated AFLP and SNP map for the L. kohalensis by L. paranigra cross; Figure S1, comprehensive linkage maps; Figure S2, ALLMAPS output; and Figure S3, coverage per cross per LG. Custom R scripts are also available at https://doi.org/10.25386/genetics.6392243. The L. kohalensis genome assembly and sequencing reads are available on National Center for Biotechnology Information’s (NCBI’s) GenBank under BioProject number PRJNA392944. The genotype-by-sequencing reads are available at NCBI’s short-read archive under BioProject number PRJNA429815. Supplemental material available at Figshare: https://doi.org/10.25386/genetics.6429494 and https://doi.org/10.25386/genetics.6392243.