Although molecular and adaptive breeding strategies can buffer the effects of climatic stress and improve crop resilience5, these approaches require sufficient knowledge of the genes that underlie productivity and adaptation6—knowledge that has been limited to a small number of well-studied model systems. Here we present the assembly and annotation of the large and complex genome of the polyploid bioenergy crop switchgrass (Panicum virgatum). Analysis of biomass and survival among 732 resequenced genotypes, which were grown across 10 common gardens that span 1,800 km of latitude, jointly revealed extensive genomic evidence of climate adaptation. Climate–gene–biomass associations were abundant but varied considerably among deeply diverged gene pools. Furthermore, we found that gene flow accelerated climate adaptation during the postglacial colonization of northern habitats through introgression of alleles from a pre-adapted northern gene pool. The polyploid nature of switchgrass also enhanced adaptive potential through the fractionation of gene function, as there was an increased level of heritable genetic diversity on the nondominant subgenome. In addition to investigating patterns of climate adaptation, the genome resources and gene–trait associations developed here provide breeders with the necessary tools to increase switchgrass yield for the sustainable production of bioenergy.Long-term climate change and periodic environmental extremes threaten food and fuel security Panicum virgatum) reveals climate–gene–biomass associations that underlie adaptation in nature and will facilitate improvements of the yield of this crop for bioenergy production.The genome of the biofuel crop switchgrass ( P. virgatum) is both a promising biofuel crop and an important component of the North American tallgrass prairie. Historically, tallgrass prairies were one of the largest temperate biomes on Earth, and they remain important sinks for atmospheric carbon8. However, most extant natural switchgrass populations are restricted to ‘relic’ sites, which represent crucial but dwindling genetic resources for the future conservation and breeding of tallgrass prairie.Switchgrass ), repetitive (56.9% repeats) . We pruned the resulting large contigs (N50 = 5.5 Mb) to a single representative haplotype, and then oriented and ordered into chromosome pseudomolecules using the consensus of two high-density genetic maps (Supplementary Data Panicum rudgeii17 (the sister taxa to the K subgenome of P. virgatum), and via de novo repeat clustering. The final assembly contains only 0.4% gaps, a 75-fold decrease relative to a previous v4 release from 2016 (https://phytozome-next.jgi.doe.gov/info/Pvirgatum_v4_1). Importantly, the genome assembly was co-linear with three sources of genetic information, despite being assembled independently from all three: the assembly of a close diploid relative , the marker order of a pseudo-F2 genetic map and the gene order of the alternative subgenome  and PacBio Iso-Seq  and octoploid (8×)), tetraploids represent the majority of cultivars19 and span a broader geographical range than octoploids20. To investigate the genetic basis of climate adaptation, stress tolerance and biomass production, we therefore developed a diversity panel of 732 exclusively tetraploid genotypes  . Our resequencing yielded 33.8 million single-nucleotide polymorphisms (SNPs)  mapped against the genome. We also de novo-assembled a 252-genotype subset of these deeply resequenced libraries and called presence–absence and structural variants  on the resulting contigs. To connect trait and molecular variation with climate, we extracted 46 climate variables22 from the georeferenced collection location of each genotype and clustered these data into seven groups that explained the majority of climatic variation across the diversity panel  Fig.  and rese26. In silico classification from morphological data, coupled with ecotype assignments by experts across our diversity panel (Supplementary Data n = 268), lowland (n = 99) and a third, coastal ecotype (n = 184). The coastal ecotype was broadly sympatric with the lowland ecotype but displayed upland leaf characters and lowland plant architecture  more likely to survive the winter of 2018–2019 in the northern 4 sites than the southernmost 30% of the genotypes.We observed strong evidence that adaptive evolution has contributed to ecotype divergence. Whereas winter-kill mortality was rare among northern upland plants 2.4%), nearly half of all coastal (42.1%) and lowland (42.8%) genotypes perished during the winter of 2018–2019 across the 4 northernmost gardens Fig. . Winter .4%, nearThe latitude gradient across our common gardens also served as the major axis of biomass variation. Among the seven groups of correlated climatic variables, the strongest predictors of biomass variation were always related to temperature    Fig. . The clu30.Population genetic structure was discordant with variation in morphological ecotype, which segregated strongly within genetic subpopulations. Plants with upland ecotype traits were present in both the Atlantic (37%) and Midwest (63%) gene pools. Similarly, 54% and 46% of coastal ecotype accessions were assigned to Atlantic and Gulf subpopulations, respectively Fig. . All plaFST = 0.03), or between lowland and coastal ecotypes within the Gulf subpopulation (FST = 0.03) )  than for noncoastal Gulf and Atlantic plants (FST = 0.24). By contrast, the plants with upland morphologies in the Midwest and Atlantic subpopulations were no more similar than other plants from those subpopulations (FST for both = 0.30). This convergence of upland morphologies in two highly differentiated genetic subpopulations could be the result of independent genetic origins of the upland ecotype or rare but evolutionarily important31 admixture events. We evaluate these hypotheses below.Admixture appears to be common between the Gulf and Atlantic subpopulations; comparisons of plants with coastal ecotype traits from both of these subpopulations were molecularly more similar (32 on genome-wide association mapping (GWAS) results within and across genetic subpopulations. Multivariate adaptive shrinkage shares GWAS peak effect size and direction between univariate tests to improve power to detect significant, shared results. Multivariate adaptive shrinkage results were determined for both fitness GWAS ), and climate GWAS (which detected associations between SNP variation and the climate of origin (seven representative climate variables)). To make direct comparisons among subpopulations (which have different segregating SNPs), we summarized the 12,239 significant linkage-disequilibrium block ‘peaks’ of multivariate adaptive shrinkage (log10-transformed Bayes factor > 2)33 into 10,090 20-kb regions (20 kb represents the inflection point at which linkage disequilibrium decay flattens)  and fitness (nregions = 332) GWAS  GWAS . A weighh2= 51.5 ± 15.4% (mean ± s.e.m.)) and climate-associated peaks (h2 = 70.5 ± 14.0%) collectively explained over threefold-more variation than the polygenic background  (Extended Data Table GWAS peaks explained the majority of heritable phenotypic and climatic variation (SNP–heritability) both across and within gene pools Fig. . SNP–her34, and are strong targets for the breeding of locally adapted cultivars. Overall, we observed nearly 2× more overlap of 20-kb regions associated with both climate and fitness than expected by chance . This overlap was especially strong within the two northern subpopulations   Fig. , where w35 may have created these shared adaptive polymorphic regions.Many regions of climate and fitness overlap were polymorphic only within a single genetic subpopulation, which highlights several, possibly independent, genetic paths to climate adaptation in switchgrass. However, 9.5% (940) of the 20-kb climate intervals were polymorphic in several genetic subpopulations. Given the substantial evidence of admixture between the Gulf and Atlantic subpopulations Fig. , we expe36. Introgressions between subpopulations represented 2.98% of the content of our resequenced genomes , indicating that adaptive introgressions underlie at least a portion of heritable variants shared among subpopulations.To explicitly address how introgressions may have shaped the distribution of climate–SNP associations, we investigated physically contiguous regions of admixture across the genome using a hidden Markov modelmes Fig. , but wer37, these findings suggest that introgression of putatively northern-adapted alleles from the Midwest into the Atlantic subpopulation could have facilitated the post-glacial colonization by switchgrass of colder habitats in the northeastern coastal region of the USA. To test this hypothesis, we conducted redundancy analyses to relate the presence of introgression blocks with climatic, geographical and phenotypic factors. Overall, Midwest introgressions in the Atlantic subpopulation were over four times more strongly associated with climate (percentage of variance explained = 46.5%) than geography (11.5%). Although 532 and 651 introgressions from the Midwest to the Atlantic subpopulation were associated with climate of origin or biomass, respectively, 254 introgressions were outliers for both analyses—representing a nearly 7-fold enrichment over expectations of independence between each set . These results reinforce the hypothesis that Midwest introgressions have shaped the climatic niche and phenotypic distribution of the northern Atlantic genotypes and support a growing body of evidence that demonstrates that adaptive introgressions can facilitate both range expansion and ecotype evolution39.Of particular interest were a suite of introgressions from the Midwest to the Atlantic subpopulation that dated to about 8,700 generations before present (17–34 thousand years ago (ka)), which coincides with a northern range expansion after the Last Glacial Maximum (about 22 ka). Atlantic genotypes with higher levels of Midwest introgressions exhibited a more-upland suite of traits Fig.  and were41, which can lead to adaptive evolution or sorting that alters ecological niche characteristics42. This process may explain the generally greater prevalence of polyploids in poleward latitudes and higher elevations that were once covered by ice sheets during glacial cycles43.Polyploidy is common among lineages of flowering plants and can increase the genetic diversity available to selection44. Following polyploid speciation, one subgenome commonly retains more genes and exhibits, on average, higher expression levels than the other subgenome, a phenomenon known as subgenome dominance45. As with other polyploids48, subgenome dominance and subfunctionalization were clear in switchgrass. Relative to the N subgenome, the K subgenome had higher gene density , more upregulated genes  and lower rates of mutation accumulation  greater than that in the N subgenome, versus 6,751 genes in the N subgenome with Ks greater than that in the K subgenome, binomial P < 1 × 10−15). Combined, all 11 of our subgenome statistics , neofunctionalize  or simply be lost44, biomass and survival among SNPs on the K subgenome. Instead, 75.9% of biomass SNP–heritability was attributable to the N subgenome, and only 24.1% to the K subgenome across the 10 common gardens , even when correcting for the 7.5% expansion of the N subgenome . The abundance of introgressions and heritable biomass variation attributable to the N subgenome may appear to be at odds with subgenome evolutionary biases towards the K subgenome. One potential explanation for this counterintuitive finding is that relaxed evolutionary constraint (reduced purifying selection) on the N subgenome may have allowed for accumulation of adaptive genetic variation through directional or diversifying selection. As such, the N subgenome has accumulated heritable variation49 that future breeding regimes can target to shape natural switchgrass populations and improve biofuel yield.Given the evolutionary biases towards retention of the K subgenome, we expected to see stronger signals of climate adaptation50. Indeed, plant genomes offer glimpses into the past and future of crop and wild plant populations. Adaptation to glacial–interglacial cycles offers an instructive analogue for current and future environmental change, one that we explore here to investigate the past, present and future genomic mechanisms of climate adaptation and yield improvement in switchgrass.As the climate and the natural environment change, it is increasingly critical to qualify expectations of genetic improvements in domesticated species and the adaptive potential of wild populations37. Such adaptive introgressions and heritable subgenome-specific genetic variation49 may provide the genetic paths of least resistance that permit colonization of novel habitats during periods of environmental variability. Combined, obligate outcrossing and polyploidy—traits that are often consciously avoided when selecting genomic study systems—are the primary drivers of switchgrass adaptation in nature and the sources of genetic variation available for selection to improve biofuel yield through a changing future.However, the complexity of plant genomes has also presented a major barrier to the development of genetic resources that facilitate fast and effective molecular breeding. Our methodology and success in sequencing the complex genome of switchgrass will facilitate ecological and agricultural genomics in nearly any system. For example, our results demonstrate that adaptation to northern climates has been facilitated by introgressions between anciently diverged subpopulations, which provides further support for the hypothesis that admixture between divergent genomes can enhance adaptation to novel environmentsNo statistical methods were used to predetermine sample size. The experiments were completely randomized, and investigators were not aware of genotype identifiers while conducting experiments or sequencing.16. In brief, 10–15 seeds were sown in 9-cm square pots containing a mixture of ProMix BX potting soil (Premier Tech Horticulture) and Turface MVP calcined clay (Turface Athletics) and vernalized for 7 days at 4 °C. Pots were then placed in a lit greenhouse with 14-h day length and 30-°C/22-°C day/night temperature. Seedlings were thinned at the 3-leaf stage to 1 plant per pot and allowed to grow until the 5-tiller stage. Rhizome propagules and 5-tiller seedlings were transferred to 5-gallon pots containing finely ground pine bark mulch (Lone Star Mulch) and time-release fertilizer . All individual plants were propagated in Austin by clonal division from 2016 to 2018, targeting >10 clones per unique accession. Cleary 3336F systemic fungicide  was applied to the plants as necessary to control fungal pathogens. Plants were placed in 1-gallon pots for the final propagation.To form the diversity panel, seeds, rhizomes and clonal propagules from natural and common garden sources were collected from 2010 to 2018. Plants grown from seed followed a standard growth procedure16. In brief, plants were transported to each site by truck, where each field was covered with one layer of DeWitt weed cloth. Plants were placed in holes that were cut into the weed cloth into a honeycomb design in which each plant had four nearest neighbours, all located 1.56 m from one another. To prevent edge effects, the lowland Blackwell cultivar was planted at every edge position. Plants were hand-watered following transplantation. Aboveground portions of all plants were left to stand over the winter of 2018–2019 and removed in the spring of 2019 before spring tiller emergence. At the end of the 2019 season, plants were tied upright as a bunch and harvested with sickle bar mowers.Planting in the field sites occurred from 15 May to 10 July 2018 and followed previously published methodsWe generated two measures of fitness for the 2019 growing season: log-transformed biomass (kg) and proportion of winter survival . Biomass52 and ARROW polisher53. Final genome polishing and error correction was conducted with one 400 bp insert 2 × 150 bp Illumina HiSeq fragment library (177.1×). Reads with >95% simple sequence repeats and reads <50 bp after trimming for adaptor and quality  were removed. The final read set consisted of 1,259,053,614 reads for a total of 168× coverage of high-quality Illumina bases. This produced an initial diploid assembly of 6,600 scaffolds , with a contig N50 of 1.1 Mb, 3,489 scaffolds larger than 100 kb and a total 2C (diploid) genome size of 2,013.4 Mb.We sequenced the Alamo switchgrass genotype AP13 using a whole genome shotgun sequencing strategy and standard sequencing protocols at the Department of Energy Joint Genome Institute and the HudsonAlpha Institute for Biotechnology. The genome was assembled and polished from 4,520,785 PacBio reads   genome size of 1.2 Gb. Therefore, to detect putative meiotically homologous haplotypes, we identified and counted shared 24-mers that occurred exactly twice in the assembly and binned contigs accordingly. A total of 3,152 shorter and redundant alternative haplotypes and 2,387 overlapping contig ends were identified, comprising a total sequence of 871.2 Mb. The remaining 1,142.2 Mb of sequence was ordered and oriented into 18 chromosomes by aligning genetic markers from 2 available maps  by aligning the Illumina 2 × 150 bp library to the release consensus sequence using bwa mem58 in LTR regions of Gypsy, Copia and Pao insertions (identified by RepeatMasker59 and LTRHarvest60) that distinguished each set of homologous chromosomes (≤1 hit in one homologue and ≥100 in the other). The LTR sequences that shared common 15-mers were grouped as superfamilies and were aligned within each superfamily by BLAST. Superfamily members with significant BLAST hits  were assigned into families and aligned by Mafft61. Jukes–Cantor distances between LTR families were computed by the R ape package62, and clustered into two distinct sets of subgenomes. Clustering was identical between LTRs and alignments to P. rudgei , which is an ancient relative of the K subgenome17, giving high confidence that we have effectively assigned all chromosomes to the correct subgenomes. Finally, we assigned chromosome identifiers and oriented each chromosome pseudomolecule via synteny with Setaria italica63. The final haploid version 5.0 release contained 1,125.2 Mb of sequence, consisting of 626 contigs with a contig N50 of 5.5 Mb and a total of 97.2% of assembled bases in chromosomes.To distinguish the N and K subgenomes, we used a de novo repeat-clustering method and validated this with phylogenetic distances to a related species. We searched for ‘diagnostic’ 15-mers via Jellyfish64). In brief, PERTRAN conducts genome-guided transcriptome short-read assembly via GSNAP65 and builds splice alignment graphs after alignment validation, realignment and correction. In total, around 4.5 million PacBio Iso-Seq circular consensus sequences66 were corrected and collapsed, resulting in approximately 677,000 putative full-length transcript assemblies. Subsequently, 668,176 transcript assemblies were constructed using PASA67 from RNA-seq reads, full-length cDNA, Sanger expressed sequence tags, and corrected and collapsed PacBio circular consensus sequence reads. Loci were determined by EXONERATE68 alignments of switchgrass transcript assemblies and proteins from Arabidopsis thaliana69, soybean70, Kitaake rice71, Setaria viridis72, P. hallii var. hallii64, Sorghum bicolor73, Brachypodium distachyon74, grape and Swiss-Prot75 proteomes. These alignments were accomplished against a repeat-soft-masked switchgrass genome using RepeatMasker59 (repeat library from RepeatModeler76 and RepBase77) with up to 2,000-bp extension on both ends unless extending into another locus on the same strand. Incomplete gene models, which had low homology support without full transcriptome support, or short single exon genes (<300-bp coding DNA sequences (CDS)) without protein domain or good expression were removed.Transcript assemblies were made from about 2 billion pairs of 2 × 150-bp stranded paired-end Illumina RNA-seq reads, about 1 billion pairs of 2 × 100-bp paired-end Illumina RNA-seq reads and 454 reads  using PE64, using default parameters and two outgroups: P. hallii var. hallii64 and S. bicolor73. In brief, GENESPACE parses protein similarity scores into syntenic blocks and runs orthofinder78 on synteny-constrained blast results. The resulting block coordinates and syntenic orthology networks give high-confidence anchors for evolutionary inference.Syntenic orthologues and paralogues were inferred for the two switchgrass subgenomes via the GENESPACE pipeline79. The final number of hits to the switchgrass genome were 38,960 and 33,772 for P. hallii, and S. bicolor, respectively. For any given orthology network, we built two multiple sequence alignments in mafft61, one excluding the focal switchgrass sequence (msa0) and one forcing msa0 to align to the coordinate system of the focal sequence via the --keeplength parameter. We then extracted marginal character states with the maximum likelihood algorithm in Phangorn80. For each reconstruction, only the internal node closest to the switchgrass branch was used as the ancestral state. Overall, we analysed 40,943 switchgrass gene models  covering 54.95 Mb  from the cross-species analysis. We aligned the translated CDS of these sequences using Dialign-TX81. The aligned CDS sequences were concatenated and fed to Gblocks82 using default parameters. Gblocks filtered the alignment of 18,044,244 CDS nucleotides to 16,321,302 positions, in 50,334 blocks. The resulting alignment was then used in PhyML83 to build a maximum-likelihood tree using the general-time reversible model. This tree was used as an input to r8s84, to compute a time tree and calibrate the Panicum–Setaria node of the tree to 13.1 Ma63. To date subgenome divergence and therefore the timing of polyploid switchgrass speciation, we leveraged burst distances, which refer to all distances within an LTR family (whereas pairwise distances refer to the distance between the 5′ and 3′ LTRs of the same insertion). The 5′ versus 3′ distances of the N- or K-subgenome-specific retrotransposons were used to date the insertion times of those elements. This method cannot be used for the P. virgatum-specific or Panicum-specific families because the more recent expansions of those elements dominate the distributions. Instead, we relied on comparing the best cross-species alignments to estimate the LTR distances of the P. virgatum–P. hallii and Panicum–Setaria nodes. This way, we have calibration points to compare the LTR distances to the more confident protein-coding gene divergences between species.To infer the ages of the subgenomes and tetraploid switchgrass, we took a conservative set of orthologues with simple 2:1:1 networks between n ≥ 5) collected at two sites (TX2 and MI). Illumina paired-end RNA-seq 150-bp reads were quality trimmed (Q ≥ 25) and reads shorter than 50 bp after trimming were discarded. High-quality sequences were aligned to P. virgatum v5.1 reference genome using GSNAP65 and counts of reads uniquely mapping to annotated genes were obtained using HTSeq v.0.11.285. The test for differential expression was conducted through a likelihood ratio test in DESeq286. Library sizes were calculated before splitting the reads by subgenome; these sizes were used as the size factors in the analysis of differential expression. Subfunctionalization was defined as a significant subgenome-by-environment interaction from the likelihood ratio test. Subgenome expression bias was tested for both the field gardens and annotation libraries using post hoc Wald-test contrasts between subgenomes within conditions. Significant bias was defined as differential expression false-discovery-rate-adjusted P < 0.05. Weighted gene coexpression clustering of AP13 gene annotation RNA-seq libraries was conducted with WGCNA87 with a power of 6. Raw counts can be found in To assess whether the subgenome evolution biases observed at the protein-coding sequence scale were manifest in phenotypes, we explored gene expression biases between homologues from biologically replicated AP13 leaf tissue (P. virgatum accessions of known ploidy.We used a LSRFortessa SORP Flow Cytometer (BD Biosciences) to determine ploidy levels of the resequenced accessions. For each plant, 200–300 mg of young leaf tissue was macerated in a Petri dish with a razor blade and treated for 15 min with 1 ml Cystain PI Absolute P nuclei extraction buffer (Sysmex Flow Cytometry) mixed with 1 μl 2-mercaptoethanol. Samples were filtered to isolate free nuclei with a CellTrics 30-μm filter (Sysmex) and treated for 20 min on wet ice with 2 ml of Cystain PI Absolute P staining buffer (Sysmex), 12 μl of propidium iodide and 6 μl of RNase A. Samples were run on the flow cytometer to determine nuclei size with a minimum of 10,000 nuclei analysed per sample. Output from the flow cytometer was analysed with FlowJo software (BD Biosciences) and samples were binned into three categories on the basis of the average units of fluorescence per nuclei  that had flow cytometry data matched with these results.We also assessed ploidy of the samples via the distribution of variant allele frequency at biallelic SNPs . This method assumes that tetraploids and octoploids follow different allele frequency distribution patterns, with tetraploids having 0.5/0.5 (reference and variant depths) and octoploids having a mixture of 0.75/0.25 and 0.5/0.5. If the proportion of hits with 0.48 ≤ 55.A total of 789 tetraploid diversity samples were resequenced at a median depth of 59× (range 20×–140×). Of these, 732 were used for further analysis after filtering for missing data, outlier elevated heterozygosity and collection site discrepancies. The samples were sequenced using Illumina HiSeq X10 and Illumina NovaSeq 6000 paired-end sequencing (2 × 150 bp) at HudsonAlpha Institute for Biotechnology and the Joint Genome Institute. To account for different library sizes, reads were pruned to ≤50× coverage, then mapped to the v5 assembly using bwa-memhttp://broadinstitute.github.io/picard) and realigned around indels using GATK 3.056. Multi-sample SNP calling was done using SAMtools mpileup88 and Varscan V2.4.089 with a minimum coverage of eight and a minimum alternate allele count of four. Genotypes were called via a binomial test. SNPs within 25 bp of a 24-mer repeat were removed from further analyses. Only SNPs with ≤20% missing data and minor allele frequencies >0.005 were retained, resulting in 33,905,042 SNPs across 75% of the genome at a coverage depth between 8× and 500×. Phasing was performed using SHAPEIT390. FST calculations were accomplished via vcftools91. We tested for subgenome read-mapping bias by generating mean coverage per Mb for each of the 732 libraries and 18 chromosomes. We then fit a mixed effects linear model to these data in lme492 in which the chromosome number (1–9) was a random effect, to test the main effect of subgenome. Models with and without the main effect term were compared via a likelihood ratio test.SNPs were called by aligning Illumina reads to the AP13 reference with BWA-mem. The resulting .bam file was filtered for duplicates using Picard  based on total assembly size, contig N50 length and total gene alignments per library.Individual de novo assemblies for the 732 short read libraries were constructed using HipMerOropetium thomaeum 94, S. italica 63, Setaria viridis (n = 891)72, P. hallii var. filipes 64, S. bicolor (n = 878)95 and P. hallii var. hallii (n = 772)64. These unique genes were extracted from single-copy orthology networks inferred via orthofinder78 and selection owing to a lack of orthology to switchgrass. All proteins (≥100 amino acids) were aligned to all de novo assemblies using BLAT54. Gene alignments from AP13 proteins were considered present if they aligned with greater than or equal to 80% identity and 75% coverage, whereas other grass proteins were considered present with alignments greater than 70% identity and 75% coverage . Variable (pan-genome shell) genes  were extracted from the presence–absence variation matrix and used to visualize differences among non-admixed individuals from the Atlantic, Gulf and Midwest subpopulations. Testing genes that were significantly over- or under-represented within each subpopulation was conducted with a χ2 test with a Benjamini–Hochberg multiple testing correction (P ≤ 0.05).To assess presence–absence variation of genes across the pan-genome, we aligned all AP13 proteins and a unique set of 6,161 proteins from 96 with default settings for PacBio reads. The resulting .bam file was sorted using samtools88 and used for calling structural variants with sniffles96. Individual structural variant calls were merged across samples using SURVIVOR97, with a maximum allowed distance of 1 kb. The resulting .vcf file was filtered using bcftools88 using a minimum minor allele frequency of 0.1, and considering only insertions and deletions between 100 and 1,500 bp in length.To detect structural variants across the pan-genome, contigs (≥2 kb) from each library were aligned to the AP13 reference genome using ngmlrr| ≤ 0.6, resulting in 59,789 sites for downstream analyses in the R package SNPRelate98.To assess the genetic population structure of the 732 tetraploid libraries (Supplementary Data 99 in PLINK100. Linkage disequilibrium (r2) was calculated using plink (--ld-window 500--ld-window-kb 2000). The r2 value was averaged every 500 bp. A nonlinear model was fit for this data in R using the nls function, and the extent was determined as to when the linkage disequilibrium (r2) nonlinear curve stabilized.The extent of linkage disequilibrium for the population was determined from SNPs101 in adegenet v.2.0.1102. This method does not rely on common assumptions  that underlie many population clustering approaches and therefore provides a valuable tool to look at broad structural divisions. DAPC demonstrated a strong set of gene pools and separated Midwest genotypes from all others. We then evaluated the genetic population structure and potential admixture of the remaining non-Midwest individuals using a Bayesian clustering algorithm implemented in STRUCTURE v.2.3.4103 via the admixture model with correlated allele frequencies. The analysis consisted of 20,000 burn‐in steps and 30,000 replicates of 1–6 genotypic groups, each of which was run 10 times. Ancestry coefficients across all subpopulations were assigned post hoc through eigenvector decomposition in SNPRelate.Population genetic structure was assessed hierarchically. Given the presence of highly divergent ecotypes across the study range, we first analysed the broadest genetic population structure using discriminant analysis of principal components (DAPC)104), which is a population genetic method used to infer demographic history and population structure through time from sequence data. This method models an approximate version of the coalescent under recombination, and produces tests of both population size and divergence time. MSMC was run using four haplotypes for each subpopulation, skipping ambiguous sites, an estimated rhoOverMu of 0.25 and a time segment pattern of 10 × 2 + 20 × 5 + 10 × 2. We estimated rhoOverMu as 0.25 as the mean value from 100 iterations without the fixed recombination parameter for 5 sets of 4 haplotypes in each subpopulation and averaged them. To estimate scaled divergence time in generations, we assumed a mutation rate of 6.5 × 10−8. To make estimates of initial divergence time, we compared adjacent relative cross-coalescence rate (RCCR) values (past to present)  . If ther105.Population structure was visualized across SNPs, structural variants and presence–absence variants via eigenvector decomposition of a distance matrix. First, a Euclidean distance matrix was calculated among 0/1/2  library × marker matrices for each of the three variant call types. The Euclidean matrix was then scaled and centred to remove among-library coverage variance via Gower’s centred similarity matrix, implemented in the R package MDMR2  and Michigan State University Kellogg Biological Station ) common gardens (see Supplementary Data 2) (Licor 3100C leaf area meter). In addition to these quantitative traits, we also generated a qualitative upland–lowland index for both the leaf and whole-plant appearance, collected at the end of the summer 2019 in Austin (TX2 site). Each plant characteristic was assessed on a 1–5 scale from most lowland-like to most upland-like. The established cultivars Alamo and Dacotah were used for baseline measurements of lowland and upland characters, respectively. Plant characters assessed included: tiller appearance, from thickest and most lowland-like to thinnest and most upland-like; leaf appearance, from widest, longest and most lowland-like to shortest, thinnest and most upland-like; canopy colour from bluest and most typically lowland to darkest green and most typically upland. This visual approach is akin to basic selection criteria often used by switchgrass breeders.Mature switchgrass accessions at or near anthesis were surveyed for 16 plant traits  to determine ecotype identity during the summer of 2019 at the University of Texas J. J. Pickle Research Campus , then the first 10 principal components were used in a k-means algorithm to classify individuals into 3 possible groupings aiming to maximize the variation between groups. Next, DAPC was implemented on the 10 retained principal components to provide an efficient description of the ecotypic clusters using two synthetic variables, which are linear combinations of the original phenotypic variables that have the largest between-group variance and the smallest within-group variance .To assess phenotypic structure in these data, we used a DAPC2 gardens  to 1 of the 3 ecotypes through a low-capacity neural network with 1 hidden layer and 5 units  and 78 additional genotypes that were in the same SNP-based genetic cluster , then to |r| < 0.95  in snpRelate. This process resulted in the following SNP and library counts for each subpopulation: Atlantic, 579,468 SNPs and 284 libraries; GULF, 641,975 SNPs and 215 libraries; and Midwest, 481,563 SNPs and 196 libraries.We built a database of admixture-informative SNPs through a two-step pipeline. First, ancestry coefficients were calculated as in ‘Population Genomics’ from fourfold degenerate sites that had associated ancestral-state calls. The 30 samples with the least missing data and proportion of genome-wide admixture ≤ 0.001 for each subpopulation were used to define subpopulation-specific allele frequencies. These libraries were used to find SNPs with at least one pairwise 107. This approach leverages allele frequencies in putative parental populations to determine regions of likely introgressions in a test population. For each of the three subpopulations, we sought to determine the timing, extent and current positions of admixture block introgressions. In each case, we permitted two pulses from each of the other two subpopulations. Ancestry_HMM can optimize the number of generations before present when an ancestry pulse occurred and the proportion of individuals involved in the admixture pulse. However, 8-parameter optimization with >480,000 sites and >150 libraries was not computationally feasible. Therefore, we optimized parameters using 40 randomly sampled libraries with admixture coefficients within the 0.2–0.8 quantiles of the admixture proportion distribution and SNPs only on chromosome 4 of the N subgenome. We chose this chromosome as representative of others because of a lack of obvious large high-frequency introgressions. The resulting ancestry pulse parameter optimizations were founded on an initially unadmixed population 10,000 generations before present, and two subsequent admixture pulses for each of the other two subpopulations; the optimized pulses are as follows (source–reference): Midwest–Atlantic , Gulf–Atlantic , Atlantic–Gulf , Midwest–Gulf , Atlantic–Midwest , and Gulf–Midwest . These pulses were supplied to the full model with all individuals and chromosomes, along with an error probability of 0.001, maximum number of generations before present of 10,000 and effective population size of 100,000. Posterior ancestry probabilities were decoded into haplotype blocks and blocks were binned into clusters of similarly positioned blocks.To test for the physical locations of admixture blocks between each pair of subpopulations, we used Ancestry_HMMhttps://www.naturalearthdata.com/). Various plotting routines rely on the sf108 and raster109 packages in the R environment for statistical computing110. Climate data were downloaded from WorldClim22  and ClimateNA21. The distribution of climate variables across collections sites was explored via dynamic clustering111 followed by partitioning around medoids clustering112 with k = 7. The most representative climate variables were defined as those most correlated with the first eigenvector of variation within each cluster. Six of the seven clusters included WorldClim variables.Geographical maps were made with publicly available layers downloaded from Natural Earth (1 (USC00414810), TX2 (USC00410433), TX3 (USC00418862) and TX4 (USW00003901).Weather data were downloaded from the NOAA portal for the most proximate weather station to each garden site that had complete daily temperature (minimum–maximum), and precipitation data from 1 September 2018 to 31 October 2019. The NOAA weather station identifiers used for each garden are as follows: IL (USC00110338), MI (USW00014815), MO (USW00003945), NE (USC00255362), OK (USW00053926), SD (USC00391076), TXk = 5), were used exclusively for tests of the rank order of gardens  to a reduced model without the climate distance fixed effect using a likelihood ratio test.Climate–phenotype associations across gardens were conducted on both raw data and imputed data. Latitude–survival associations Fig.  were accens Fig. . ClimateP. virgatum. The final datasets used to build the SDMs comprised 277 (upland), 199  and 121 (lowland) occurrence records. Six environmental predictors were used in our final SDM modelling . SDMs were then generated with BIOMOD2 v.3.3113 with seven modelling algorithms: generalized linear models, boosted regression trees, artificial neural networks, flexible discriminant analysis, random forest, classification tree analysis and multivariate adaptive regression splines. For each model, the occurrence data were coupled with 500 pseudo-absence data generated randomly within the modelled study area with equal weighting for presences and pseudo-absences114. Models were trained with 80% of the coupled occurrences and pseudo-absence data and tested with the remaining 20%. Each modelling algorithm was run 100 times for a total of 700 models, which were evaluated via true skill statistics (TSS)115. TSS values ranging from 0.2 to 0.5 were considered poor, from 0.6 to 0.8 useful, and >0.8 good to excellent116. Unique ensemble SDMs were computed from approximately the 50 best SDMs out of 700 models for the three ecotypes on the basis of TSS threshold values . The final ensemble SDMs were projected onto present climate layers to visualize modern-day potential ranges (Supplementary Data Species distribution modelling (SDM) was used to simulate modern-day potential ranges for all ecotypes  of 120. To partition explainable variance in introgression presence attributable to climate, kinship and geography, we ran four models: one full model with introgression presence  explained by climate , kinship  and geography (latitude and longitude), and three models for each of these three factors conditioned on the other two. The inertia  values from the constrained matrix of each model were compared to determine the relative importance of climate, kinship, geography and their joint effect. Furthermore, to find introgression regions strongly linked to climate and survival-corrected biomass, we extracted the loadings for the redundancy analysis axes from two additional models: (1) one predicted by only climate and (2) one predicted only by survival-corrected biomass. Both models were significant according to permutation tests , and all axes were approximately normally distributed. SNPs loading at the tails of each axis were more likely to indicate selection related to the predictors , so we identified all markers that were at least 2.5 s.d. (two-tailed P = 0.012) from the centre as introgressions putatively under selection119.We examined how the presence of Midwest introgressions in the Atlantic subpopulation were associated with the independent and joint influences of climate, geography and kinship, by implementing redundancy analysis in veganhttps://github.com/Alice-MacQueen/switchgrassGWAS)—to allow fast, less-memory-intensive GWAS on the diversity panel, and to analyse the extent to which SNP effects were similar or different for phenotypes measured at different sites. This package leverages bigsnpr121 to perform fast (>300× faster than TASSEL) statistical analysis of massive SNP arrays encoded as matrices. It also incorporates current gold standards in the human genetics literature for SNP quality control, pruning and imputation, as well as population structure correction in GWAS. To test the significance of many effects in many conditions , we used mashr32, a flexible, data-driven method that shares information on patterns of effect size and sign in any dataset for which effects can be estimated on a condition-by-condition basis for many conditions and SNPs. We determined which SNPs had evidence of significant phenotypic effects using local false sign rates, which are analogous to false discovery rates but more conservative 122. We used these values to find SNPs with log10-transformed Bayes factors > 2. Here, the Bayes factor was the ratio of the likelihood of one or more significant phenotypic effects at a SNP to the likelihood that the SNP had only null effects. Following previous work33, a Bayes factor of >102 is considered decisive evidence in favour of the hypothesis that a SNP has one or more significant phenotypic effects.Owing to the large sizes of our common garden datasets, we developed a pipeline—the switchgrassGWAS R package , using genomic relationship matrices calculated using the van Raden method124. Genomic relationship matrices were calculated within each subpopulation and for the full diversity panel. A kinship matrix based on all SNPs used in the univariate GWAS was calculated (G), as well as a kinship matrix based on SNPs significantly associated with climate in that subpopulation  and a kinship matrix based on SNPs significantly associated with biomass or winter survival in that subpopulation . These kinship matrices were used for regional heritability mapping123 as in a previous publication125, using mixed models of the form:y represents the biomass values, Z is the design matrix for random effects, u is the whole genomic additive genetic effect, v is the regional genomic additive genetic effect and e is the residual. Matrix G is the whole genomic relationship matrix using all SNPs for the whole genome additive effect. Matrix Q is the regional genomic relationship obtained as above: one of Qclimate or Qfitness.  I is the rank-y identity matrix, in which y is equal to the number of biomass values. Whole genomic, regional genomic and residual variances are To calculate regional heritability for climate- and fitness-associated SNPs we followed a previously described two-step methodG only and a G + Q model), for 10 phenotypes .These models were run for the three locations where subpopulation GWAS were conducted: Columbia, Missouri; Hickory Corners, Michigan; and Austin, Texas. This resulted in 80 models: 4 sets of populations (the full diversity panel and 3 subpopulations), 2 model types , resulting in 18 kinship matrices. These kinship matrices were used for regional heritability mapping, using mixed models of the form:y represents the biomass values, Z is the design matrix for random effects, v1K (to v9K) or v1N (to v9N) (collectively designated vi) are the chromosome-specific genomic additive genetic effects and e is the residual. Matrices Qi are the chromosome-specific genomic relationship matrices for the nine chromosomes of the N and K subgenomes. Chromosome-specific and residual variances are Variance component analyses were also used to partition variance between the K- and N-subgenomes. Only SNPs with ancestral state calls  were use126 and the weighted sum of three main categories of variants  for each gene were calculated as SNPeff_score = high × 20 + moderate × 5 + low × 1. Third, for each gene, we calculated the minor allele frequency of structural and presence–absence variants. Fourth, we include a vector of the identity of the WGCNA clusters for each gene. Finally, if the candidate was a homologue of flowering-time GWAS candidate genes from a previous publication127, the identity of the overlapping interval or gene is included.We integrated multiple data structures to rank and provide meaningful culling criteria for candidate genes within introgression intervals and physical proximity to quantitative trait loci peaks. In the case of GWAS peaks, candidate genes were defined as those loci within a 20-kb interval surrounding the mashr peak. Candidate genes for genomic introgressions must have at least partially overlapped the introgression interval. As inference of GWAS and introgressions were conducted within genetic subpopulations, all statistics reported in Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-020-03127-1.Supplementary FigureExamples of gating strategy in estimates of ploidy from flow cytometry.Reporting SummarySupplemental Data 1Mapping positions of two genetic maps. The genetic linkage group (LG) and centimorgan (cM) mapping positions are in the first two columns. The v5 genome physical chromosome and basepair (bp) positions are the third and fourth columns. The identity of the marker and genetic map is found in the fifth and sixth  column. The two maps are stacked, where the Noble map  is above the UGA map .Supplemental Data 2P. hallii and S. bicolor outgroups. This is an annotated blast formatted file, where each pairwise combination of (sub)genomes is stacked. In addition to standard blast format columns, the following data are presented for both the query (1) and target (2): genome, gene ID (‘id’), chromosome (‘chr’), and gene ‘start’ (bp), ‘end’ (bp), ‘strand’ and gene ‘order’ within each genome. The last two columns are ‘orthology.network’, which defines the subgraph (orthogroup), and ‘hit.type’, which here, specifies the 418,768 orthologous and 58,430 paralogous hits.Syntenic orthology database for the switchgrass K and N subgenomes, and the Supplemental Data 3Metadata for the 84 RNA-sequencing libraries. Here, we present accession numbers, experimental design, coverage and related statistics. The experiment, tissue and replicate are specified in the ‘Sample’ column. The ‘Library’ column matches the raw read counts in Supplementary Data 10. SRA identifiers can be found in the last two columns.Supplemental Data 4Metadata for the 732 DNA resequencing libraries. Here, we present accession numbers, georeferenced collection locations, subpopulation classifications, ecotype assignments, coverage and related statistics. SRA identifiers are presented in the final column.Supplemental Data 5in silico ecotype classification. Genotypes used for training (first two columns), neural network layers (columns 5-6) and variable dictionary (4th column) were extracted from the neural network model training. LDA and DAPC variable loadings accompany the neural network results.Model specification, trait descriptions and variable importance for Supplemental Data 6Mash output statistics of GWAS peak positions and effects for 627,563, 523,501 and 541,889 GWAS hits within the ATLANTIC, GULF and MIDWEST subpopulations, respectively. The physical chromosome and position are found in the first two columns. Test classifications of subpopulation, are in the third, fourth and fifth columns and mash effect estimates (‘Bhat’), standard errors (‘Shat’) and significance (‘log10bf’) are presented in the last three columns.Supplemental Data 7th column. Those genes with orthologs to candidate genes in a recent flowering time GWAS paper are flagged in the 15th column.Composite database of candidate genes for introgression intervals and GWAS peaks. All genes within 10kb of a GWAS peak or within an introgression interval are listed along with their genome coordinates (columns 1-5) along with the subpopulation in which the significant association was found, the statistical test, the start and end coordinates of the candidate interval and the physical distance between the candidate gene and the center of the interval (columns 6-10). Columns 11-13 contain subpopulation-specific statistics for the weighted SNPeff score, proportion of libraries with gene absences and the size and type of SVs within 100bp (if any) of the candidate gene. The gene coexpression module (from WGCNA) identity is printed in the 14Supplemental Data 8Biomass and survival by genotype and common garden. The first column (‘plant_id’) corresponds to the ‘plant_ID’ column in Supplemental Data 4. The other column names follow garden ID _ phenotype. Garden IDs correspond to the first column in Extended Data Fig. 2a. phenotypes are either ‘BIOMASS’  natural log of biomass “LOGBIOMASS” or survival “SRV” . Missing values indicate cases where that plant was not grown in that particular site. Where multiple clonal replicates of a genotype were grown at a single site, the values represent a mean of all observed data. Hence, some survival values are > 0 or < 1.Supplemental Data 9Gene sequence ancestral state reconstructions. File format is gzip-compressed fasta, where each sequence header is the primary transcript ID for single-copy genes in the switchgrass genome.Supplemental Data 10RNA-sequencing raw counts. Libraries (columns) match those in the metadata file . Counts for the 80,278 genes are presented in each row.Supplemental Data 11th column) and relative cross coalescence (RCCR) are presented. These are transformed into differences between steps and the divergence time is flagged in the seventh column.Relative cross-coalescence data from MCMS runs. For each step (row) in the run, the calculated generations (4Supplemental Data 12Spatial distribution models for each switchgrass ecotypes. These are saved as R objects compressed into a single archive.Peer Review File


Piaractus mesopotamicus) is one of the most important Neotropical aquaculture species from South America. Disease outbreaks caused by Aeromonas hydrophila infection have been considered significant contributors to the declining levels of pacu production. The current implementation of genomic selection for disease resistance has been adopted as a powerful strategy for improvement in fish species. This study aimed to investigate the genetic architecture of resistance to A. hydrophila in pacu via Genome-Wide Association Study (GWAS), the identification of suggestive Quantitative Trait Loci (QTLs) and putative genes associated with this trait. The genetic data were obtained from 381 juvenile individuals belonging to 14 full-sibling families. An experimental challenge was performed to gain access to the levels of genetic variation for resistance against the bacteria using the following trait definitions: binary test survival (TS) and time of death (TD).Pacu (h2) for both resistance traits: 0.20 (± 0.09) for TS and 0.35 (± 0.15) for TD. A linkage map for pacu was developed to enable the GWAS, resulting in 27 linkage groups (LGs) with 17,453 mapped Single Nucleotide Polymorphisms (SNPs). The length of the LGs varied from 79.95 (LG14) to 137.01 (LG1) cM, with a total map length of 2755.60 cM. GWAS identified 22 putative QTLs associated to A. hydrophila resistance. They were distributed into 17 LGs, and were considered suggestive genomic regions explaining > 1% of the additive genetic variance (AGV) for the trait. Several candidate genes related to immune response were located close to the suggestive QTLs, such as tbk1, trim16, Il12rb2 and lyz2.The analyses of genetic parameters estimated moderate heritability (A. hydrophila was found to be moderately heritable but with a polygenic architecture suggesting that genomic selection, instead of marker assisted selection, might be useful for efficiently improving resistance to one of the most problematic diseases that affects the South American aquaculture.This study describes the development of the first medium density linkage map for pacu, which will be used as a framework to study relevant traits to the production of this species. In addition, the resistance to  Aeromonas septicemia is a common infectious disease in aquaculture caused by Aeromonas hydrophila, a gram-negative bacterial pathogen, which is generally associated with symptoms such as reddened or rotten fins, external/internal septicemia, and hemorrhage of aquatic organisms [Bacterial diseases are responsible for the loss of billions of dollars to aquaculture production every year, and may be considered a major threat to the fish farm’s sustainability , 2. Motirganisms .A. hydrophila infection that involves important farmed fish species from South America, such as pacu (Piaractus mesopotamicus) [A. hydrophila outbreaks at the pacu farms, non-official communications of Brazilian farmers have reported that A. hydrophila infection is responsible for total mortalities ranging between 20 to 30% of the annual production [The incidence of sanitary problems associated to the intensification of fish production have caused large mortalities by tamicus) , 5. Althoduction .A. hydrophila by GWAS-based conclusions have already been performed in some fish species, including carp Labeo rohita [Ictalurus species [A. hydrophila were identified, containing candidate genes related to the immune responses.While practically all Neotropical fish production is still carried out based on unselected stocks, large-scale genetic studies performed on global aquaculture species, such as carp –9, tilapo rohita  and catf species . In bothA. hydrophila when binary survival status and time of death were used as trait definitions, which have demonstrated the potential of the genetic improvement for Aeromonas resistance in this species [A. hydrophila have not yet been performed in pacu. Moreover, there is neither a dense SNP platform nor a genetic map available to perform GWAS analyses in pacu. Based on this proposal, restriction site-associated DNA sequencing (RAD-Seq) represents an affordable and viable technique for the discovery and genotyping of several sets of SNPs, through a reduced representation of the genome belonging to multiplexed libraries of samples [Experimental challenges that consisted of an infection induced on pacu families have shown significant genetic variation for resistance against  species . However samples –24, partA. hydrophila infection. These data were used to investigate the genetic architecture of resistance to this disease generating a medium density linkage map and GWAS analyses for host resistance, following survival and time of death as trait definitions. The presented results will be fundamental to evaluate the potential of genome-wide molecular information for obtaining subsidies on application of genetic selection studies aiming at resistance against A. hydrophila, and accelerating the genetic improvement of this trait on pacu aquaculture.The present study aimed for the discovery and the genotyping of SNPs using RAD-Seq approach on 14 full-sib pacu families, which had been experimentally challenged with A. hydrophila infection was 93.1% (family 1); in contrast, the most resistant family presented 40.7% of cumulative mortality rate (family 14), which indicated a considerable phenotypic variation associated to resistance against A. hydrophila infection  and 0.35 (± 0.15) for TS and TD, respectively. Heritability using the genomic information was similar to the pedigree-based results (data not shown).Additive-genetic variation was observed in both traits. Variance components and estimated heritabilities for TS and TD are presented in Table Initially, 1,599,222,848 RAD-Seq reads with 150 bp were obtained. From the total, 795,613 low quality reads were excluded of the analysis. Additionally, 984,501,898 ambiguous barcodes and 11,344,436 ambiguous RAD tags were discarded, which resulted in 602,580,901 retained reads.The average number of putative RAD loci identified for each family ranged from 37,035.12 (SD 4657.77) to 31,382.57 (SD 6664.41), with an average coverage ranging from 19x (SD 8x) to 25x (SD 15x). A catalog containing 246,566 consensus loci was created, of which 26,597 polymorphic loci with 162,305 SNPs were obtained. Only 30,093 SNPs presenting MAF values higher than 0.05, and on Hardy-Weinberg equilibrium were retained. SNPs that exceeded 20% of missing genotype data were also removed, leaving 18,262 SNPs for downstream analyses. Furthermore, 46 individuals exceeded 30% of missing genotype data and were discarded, which resulted in 345 individuals for a correct assignment of true parents.The pedigree information provided by the SNP data was tested for each individual to confirm a correct parental assignment. Considering the genotype errors found, it was not possible to obtain a conclusive analysis in 13 individuals due to the lack of statistical criteria (low likelihood ratio) and, therefore, were disregarded. Additionally, Mendelian rate test was carried out in the offspring to discard alleles that could not have been received them from their putative biological parents. Then, this analysis discarded 300 SNPs with more than 10% of Mendelian error rate, remaining 17,962 SNPs to be included in the linkage map.As pacu has 27 pairs of chromosomes (2n = 54), 17,453 SNPs were assigned to 27 linkage groups. Initially, 652 SNPs presented none association to any linkage group. Posteriorly, 143 markers were joined to already existing linkage groups, and 509 markers were discarded because no association to the linkage map was detected.OrderMarkers2 module was repeated 5 times retaining the ordering with the highest likelihood for each linkage group. The marker orders with the highest likelihoods for each linkage group were combined to produce the final linkage map. In the ordering of markers, the number of SNPs varied from 262 (LG27) to 1504 (LG1)  to 137.01 (LG1) cM, with a total integrated map length of 2755.60 cM and an average distance between markers of 0.47 cM. Considering the density within linkage groups, the linkage group that presented the highest and lowest density of markers were LG8 and LG24, with an average distance of markers of 0.38 and 0.66 cM, respectively  species was investigated using all mapped loci on the LGs . Significant BLASTn hits to blind cave tetra genome matched 2851 pacu RAD tags (27.9%) wherein 2194 (21.5%) showed synteny for all 27 linkage groups , 3 only associated with TD , and 14 were registered in association for both traits   with A. hydrophila in pacu, as a key step to harness genomic information to improve selection processes directed to disease resistance in this Neotropical fish species. Recently, dense SNP arrays have facilitated researchers assessing the association between genome-wide markers and utilizing them for optimization of the disease resistance through genetic improvement in aquaculture [Studies on selection of superior genotypes related to resistance have been reported in several aquaculture species , 25, 26,aculture , 27–29. aculture . Recent aculture , as wellaculture .A. hydrophila resistance, with moderate heritability values (both to pedigree and genomic-based data). The heritability estimates were higher than those previously performed in different pacu populations (h2 = 0.15 for TS and h2 = 0.12 for TD) [A. hydrophila resistance in other species, such as Clarias macrocephalus, Megalobrama amblycephala, and Labeo rohita, ranging from 0.12 to 0.39 [A. hydrophila resistance in rohu carp (Labeo rohita) and common carp (Cyprinus carpio), with values of 0.02 and 0.04, respectively [The results of genetic parameters revealed significant genetic variation for  for TD) . In gene to 0.39 –34. Neveectively , 35. TheA. hydrophila in pacu. The linkage map was comprised of 27 linkage groups that correspond to the number of chromosomes of pacu, arranging 10,220 loci , which used the information of 7734 SNPs to obtain a map length of 2811 cM, with an average marker interval of 0.39 cM. Here, we prefer to use the genome of the blind cave tetra (A. mexicanus) as the reference genome to perform the synteny analysis instead of zebrafish, because the former is more related to pacu than the latter, which resulted in considerable homology between the LGs of pacu and chromosomes of A. mexicanus. A conserved synteny on the majority of LGs was identified between pacu and A. mexicanus genomes  by performing GWAS analysis and providing insights on the genetic architecture of these production traits, as proposed in this study approaching the resistance to cropomum , a speciA. hydrophila (AGV > 1%) were observed in 17 linkage groups  [A. hydrophila in the analyzed population of pacu, similarly to a previous QTL mapping study that associated 21 QTLs to 10 linkage groups for A. hydrophila resistance in rohu carp [Marker-assisted selection has been suggested as a viable approach for catfish breeding due to the limited number of QTLs involved in  groups) . Howeverohu carp . Therefoohu carp  could beohu carp , 28, 39.A. hydrophila infection (Table TANK-binding kinase 1-binding protein 1 (tbk1) gene had an essential role in the activation of pattern-recognition receptors, starting the innate immune responses against pathogens [Tripartite motif-containing protein 16 (trim16) gene is a part of tripartite motif family which has also been associated with QTL regions for A. hydrophila resistance in the hybrid catfish [Interleukin 12 Receptor Subunit Beta2 (Il12rb2) is responsible in regulation of receptors to interleukin-12, a proinflammatory cytokine produced by phagocytic cells already identified in response to A. hydrophila infection [Lysozyme C-2 precursor (lyz2) is another gene responsible to contribute to the inflammatory response, as it is associated to activity of immune modulating agents and digestive function against gram-negative bacteria [A. hydrophila [A. hydrophila.Among the QTLs with highest AGV, six were identified adopting both models GBLUP and wGBLUP   belonging to 14 full-sibling families of pacu. The induced reproduction was generated in 2016 by a hierarchical mating scheme using 4 dams and 14 sires (approximately 1 dam for each 4 sires). These breeders were originated from three different fish farms located at Sao Paulo State (Brazil). The names of the fish farms were kept confidential. Induced spawning was successfully carried out injecting carp pituitary extract, similarly to Mastrochirico-Filho et al. [After incubation of fish embryos and hatching process, artemia were offered to the larvae during 20 days in 20 l conical fiberglass incubators installed in the Laboratory of Genetics in Aquaculture and Conservation (LaGeAC), at the São Paulo State University (UNESP), Jaboticabal city . Posteriorly, fish food with 50% of crude protein were gradually incorporated into the diet. In the fingerling stage, 1.2 mm pelleted feeds were used (40% of crude protein), being gradually replaced by 2 to 3 mm pelleted feeds (36% of crude protein) provided twice daily in 60 l tanks. Passive integrated transponder (PIT) tags were inserted into the intraperitoneum of individuals when they reached at least 5 g to maintain the pedigree information during the challenge experiments. Laterally, fish were kept in 800 l fiberglass tanks up to about 6 months post hatch.Aeromonas hydrophila challenge, including the bacteria strain preparation, were similar to Mastrochirico-Filho et al. [A. hydrophila and other pathogenic bacteria. In relation to the challenge test design, 381 juvenile individuals were distributed among three communal tanks , where approximately 10 individuals from each family  were randomly distributed into each treatment tank (about 127 fish per tank), according to the sample size recommendation for disease resistance challenges proposed by Gjedrem and Baranski [The protocols for the o et al. . BrieflyBaranski .50 of A. hydrophila (8 × 105 CFU/g body weight) were injected, according to protocols carried out by Mastrochirico-Filho et al. [Before the bacteria inoculation, fish were placed for 1 min in a 3 l plastic washbasin containing benzocaine dissolved in water (10 mg/l), with continuous aeration, and weighed. The mean weight of the individuals prior to the bacteria inoculation time was 23.0 g SD 9.06 g). When the individuals reached the dormant state, intraperitoneal inoculation of the predefined LD g. When A. hydrophila infection  were recorded and removed immediately from the tanks. A subsample of dead fish was necropsied and suffered routine microbiological tests to confirm mortality by A. hydrophila and discard a possibility of infection caused by other pathogens.Fish mortality events were registered all the time in the initial 3 days, and in 8-h intervals in the remaining days of the challenge experiment. Susceptible fish with clinical signs of At the end of the challenge period, surviving fish were checked externally for detection of clinical signs of the disease, and posteriorly euthanized using benzocaine.yij was the phenotype for the fish j, in tank i; μ was the fixed effect of the overall mean; ti was the fixed effect of the tank i; wij was the covariate of weight prior bacteria inoculation for fish j, in tank i; aij was the random animal genetic effect of individual j, in tank i; and eij was the random residual effect for the fish j, in tank i.yij was the phenotype (TS) for the fish j, in the tank i; Φ(·) was the cumulative standard normal distribution and the other parameters were similar to those described above.Resistance was assessed as survival to the challenge test using the trait definitions of Mastrochirico et al. , such asN  were sampled for DNA extraction using the Dneasy Blood & Tissue QIAGEN kit. The quantification of extracted DNA (ng/μl) was measured by the Qubit fluorescence detector using the Qubit dsDNA BR Assay kit .Sbfl enzyme. For each 11.35 μl of DNA (concentration at 10 ng/μl), 0.25 μl of Sbfl and 1.3 μl of NEBuffer 4 (New England Biolabs) were used. The enzyme digestion was performed at 37 °C for 1 h, followed by an inactivation period of 20 min at 80 °C.Each library was constructed with approximately 29 individuals identified with barcodes . Genomic DNA from each individual was digested by Sbfl, using the T4 DNA ligase (New England Biolabs). The ligation was incubated at 20 °C for 2 h, and then at 65 °C for 20 min. Following library construction, their quality was determined by PCR that combined 8.5 μl of water; 12.5 μl Phusion High-Fidelity Master Mix; 1 μl of RAD primer mix (forward and reverse) and 1 μl of digested DNA. The amplification was carried out with 18 cycles in a thermocycler  and 72 °C for 5 min. Size selection (300–500 bp) was carried out using excision of the appropriate band from gel electrophoresis. Purification was then carried out by the MinElute Gel Extraction kit, following the recommendations of the manufacturer. The concentration of each library was then normalized to 2.5 nm for sequencing by Edinburgh Genomics  on Illumina NovaSeq 6000 platform (flow cell Type S1 in 1 lane).Barcode adapters were ligated to the end of the DNA fragments following digestion with p-value of Hardy-Weinberg disequilibrium (PHWE) lower than 1 × 10− 6. Additionally, SNPs with more than 20% of genotyping error rate (geno 0.2) and more than 10% of Mendelian error rate were also discarded. In relation to filter out individuals, samples presenting more than 30% of missing genotypes (mind 0.3) were excluded for linkage map construction.RAD sequences were filtered to discard those of low quality and posteriorly trimmed to 150 bp. Stacks 2.0 software  maintainParentCall2 confirmed reliable parental genotypes using joint information on offspring and parents. Filtering2 was used to remove markers with significant segregation distortion (dataTolerance = 0.001). Markers were assigned into 27 linkage groups (LGs) (corresponding to the 27 pairs of chromosomes in pacu) by SeparateChromosomes2. In the LG assignment, an optimized LOD score of 8.6 was achieved binning the markers into LGs by adoption of LOD scores ranging from 4 to 15 [JoinSingles2 to maximize the map-abilities of the total input of markers, and ordered the binned markers within linkage groups using OrderMarkers2. The generated linkage map was drawn using the LinkageMapView [Astyanax mexicanus) and the LGs for pacu was performed adopting the circos plots by Circa software (http://omgenomics.com/circa/).Analysis of parentage assignment was performed by software Cervus3 , 54 usin 4 to 15 . The orpeMapView . In orden = 332 individuals) was considered. The animal models applied to the test was the same used for estimation of genetic parameters (above), but with addition of the genotype data. TD was analyzed as a linear trait using BLUPF90, whereas TS was analyzed as a threshold trait using THRGIBBS1F90. Gibbs sampling scheme was run considering one million iterations, of which the first 200,000 iterations were discarded. From the remaining 800,000 iterations one sample was saved from every 100 iterations.GWAS for TS and TD traits were performed by genomic BLUP (GBLUP) and weighted genomic BLUP (wGBLUP) methods, adopting windows of 20 adjacent SNPs, and analyzed by BLUPF90 family of programs . All avaα were a vector of random additive genetic effects with distribution ~N , D was a diagonal inverse matrix with the expected variance for all SNPs, and λ was a weighting vector derived from the observed SNP frequencies. For GBLUP, matrix D equals identity matrix (I), while in wGBLUP, matrix D was estimated using the SNP effects using PREGSF90 and POSTGSF90. For this, GEBV  were estimated, where matrix G , that waquencies . The equ}u^) was , 61:\domated as :\documejth SNP marker effect in the ith individual, and pj expressed the observed allele frequency for the second allele of the jth marker. After normalizing the matrix D and the weights of SNPs such that the total genetic variance remains constant, the percentage of additive genetic variance explained by each SNP window could be estimated as following:where, ith SNP window was plotted by R-qqman [In total, 12,657 SNPs and 268 genotyped animals which passed on the quality control  were analyzed. Manhattan plots based on the percentage of genetic variance explained by the  R-qqman  using R A. hydrophila resistance [Pygocentrus nattereri genome  using Nucleotide Basic Local Alignment Search Tool (BLASTn) to evaluate the presence of putative genes associated with the trait between the first and the last position of each 20 SNP window. The putative candidate genes identified were within or adjacent to each associated SNP.SNP windows that explained more than 1% of the AGV for the traits were defined as suggestive QTL associated with sistance , 64. RAD


Wolbachia are commonly found among arthropods, intraspecific infection rates can vary substantially across the geographic populations. Here we report nearly 100% prevalence of Wolbachia in the global populations of the yellow crazy ant, Anoplolepis gracilipes. To understand coevolutionary history between Wolbachia and A. gracilipes, we identified single nucleotide polymorphisms (SNPs) in Wolbachia from the ant across 12 geographical regions and compared the phylogeny of SNP-based Wolbachia to patterns of the ant’s mitochondrial DNA (mtDNA) variation. Our results revealed a strong concordance between phylogenies of Wolbachia and host mtDNA, providing immediate evidence of co-divergence. Among eight identified SNP loci separating the genetic clusters of Wolbachia, seven loci are located in potential protein-coding genes, three of which being non-synonymous SNPs that may influence gene functions. We found a Wolbachia hypothetical protein gene with signature of positive selection. These findings jointly allow us to characterize Wolbachia-ant coevolution and also raise a question about mechanism(s) underlying maintenance of high prevalence of Wolbachia during the colonization of this invasive ant.While  Wolbachia is an endosymbiont commonly found in arthropods and filarial nematodes . The contigs with e-value lower than 1e-20 were kept as our Wolbachia draft genome (hereinafter referred to as wAgra) except those with length <250 bp which were excluded from the analysis. The completeness of genome assembly was evaluated using the BUSCO v. 4.0.5 [Wolbachia genomes  for comparison purposes. The assembly statistics were calculated using QUAST [The MLST loci are generally conserved and may possess insufficient resolution for reconstructing recent evolutionary history of olbachia ,26. A Wo v. 0.36 . The whoa-BLASTn  against ng QUAST .The restriction site associated DNA sequencing (RAD-seq) has been demonstrated as a powerful population genomic approach for recovering high numbers of single nucleotide polymorphisms (SNPs) in various systems, especially for non-model organisms . We condwAgra genome using BWA v. 0.7.17 [Quality of generated raw reads was first examined using FastQC v. 0.11.8 . To keep. 0.7.17 , and var. 0.7.17 . To mini. 0.7.17 . Employi. 0.7.17 , the SNPwAgra genome instead of the host genome , we seler3-Plus , and theCOI sequences were aligned in MEGA 7 [The concatenated SNPs were generated by populations program in Stacks2 v. 2.5 . Since tn MEGA 7 . The hapn MEGA 7  using thwAgra were manually examined using IGV v. 2.3.71 [The genomic regions containing polymorphic SNPs in . 2.3.71 . The SNPWolbachia ORFs containing non-synonymous SNPs were expressed in wAgra, the transcriptome sequencing by RNA-Seq on wAgra was carried out. The RNA pool-seq was conducted on a mixture of total RNA from two adult worker ants of A. gracilipes collected in 2018. One adult worker ant was collected from Penang, Malaysia , whereas the other was from Okinawa, Japan . The entire worker ants were soaked in TRIzol™ RNA Extraction Reagent  and homogenized with a pestle, with the rest of the procedure following the standard TRIzol RNA extraction protocol. The RNA quality and quantity were measured with Nanodrop spectrophotometers, and then mixed at one to one ratio of the total RNA amount. The mixed sample was submitted to the Eurofins Genomics  where the total RNA was purified using polyA selection method, and sequencing library was constructed with the TruSeq® Stranded mRNA Library Prep (insert size: 200 bp). The library was sequenced using Illumina HiSeq 4000 platform in the pair-end mode (2 × 100 bp). Read adapters and low-quality bases  were trimmed using Trimmomatic v. 0.36 [wAgra genome used HISAT2 v2.1.0 [wsp gene were examined for confirming expression level of the selected Wolbachia genes (see Results 3.4).To verify if the  v. 0.36  and mapp2 v2.1.0 . The ali2 v2.1.0  and visu2 v2.1.0 . The RNAWolbachia genes with non-synonymous mutations , 83.33% , to 100% . Wolbachia detected in our A. gracilipes samples shared identical sequence at both MLST loci  and the wsp gene . All the sequences indicated that this Wolbachia strain belongs to supergroup A. We found no evidence for co-infection with more than one Wolbachia-variant (strain) in all infected individuals, suggesting single Wolbachia infection in this ant.We screened the presence of Wolbachia in A. gracilipes (wAgra) as the reference genome (GenBank accession: JACRYZ000000000). The draft genome assembly (96 contigs) contained 1,202,684 bp, a comparable size to other supergroup A Wolbachia genomes (genome size: 1.26–1.8 Mb) and within the range of other Wolbachia belonging to other supergroups discovered to date . Similar to other supergroup A Wolbachia, GC content in the wAgra assembly was 35.23% (35.2–35.3%); it, however, differed from the rest of contigs with the majority of which deriving from the host ant genome (GC: 33.78%). The average genome coverage was 278.22 reads per nucleotide.We assembled the draft genome of wAgra genome was 85.4%, as 187 out of 219 complete and single copy proteobacteria orthologs were found in our assembly. The same BUSCO analysis was used to calculate other complete Wolbachia supergroup A genomes. The BUSCO scores were 84.5% in wMel and wRi, 84% in wHa, and 84.9% in wAu, suggesting that our assembly, despite being fragmented, has recovered most of the proteobacteria orthologs. Thus, the assembly could serve as the reference genome with robustness to reveal the population genomic variations of this bacterium . We found 53 SNP loci that possess polymorphisms. To verify if the discovered SNPs were located in the Wolbachia genome, we randomly selected 10 SNP loci and PCR-amplified on both Wolbachia-infected and Wolbachia-free colonies. All 10 polymorphic SNP loci were successfully amplified only from Wolbachia-infected samples but not Wolbachia-free ones, suggesting that these SNPs originated from the wAgra genome.Our SNP calling and filtering yielded 129 SNP loci from 68 Wolbachia-origin SNPs and the network analysis based on partial mitochondrial COI sequence of the host  and found a high degree of concordance (wAgra from Thailand (Thailand_01) was among the most genetically divergent  to compare COI primers against the wAgra genome, and the result indicated no Wolbachia amplification was available. We also compared mitochondrial COI sequences against NCBI database with BLASTn and verified the COI sequences we amplified indeed derived from A. gracilipes.We compared the phylogenetic trees based on 129 cordance . wAgra fivergent a, coinci samples b. IndiviWolbachia SNPs revealed that eight distinct SNP loci contributed to the separation of Wolbachia SNPs Clade I and Clade III. To further dissect the SNP polymorphisms associated with selection and/or demography, we analyzed the synonymous and non-synonymous mutations of these SNP loci. Seven of the eight SNP loci were located in ORFs, and three out of these seven were non-synonymous mutations, with two of which each residing in one of two Wolbachia hypothetical proteins and one in RluA-like gene. The RluA is a pseudouridine synthase gene involved in RNA binding and has been known to participate in the cellular information processing [Alignment of the ocessing .wAgra, we examined the transcriptome from a pooled RNA sample prepared from two yellow crazy ant workers. In total, 32,771,008 paired RNA-seq reads passed the quality control. Among these, 11,975 (0.04%) of reads mapped onto the wAgra draft genome assembly. First, we found the MLST genes had fewer reads  than the wsp gene (73 reads). We then inspected the non-synonymous mutation containing ORFs and found that the hypothetical protein  possessed 70 RNA reads, which is comparable to that of the wsp gene. Another hypothetical protein (scaffold_2912: 3397–3696 bp) had only 9 reads, whereas no evidence of expression of the RluA-like gene (GenBank accession: MT896124) was detected.To verify if the genes with non-synonymous mutation express in RluA-like gene from all Wolbachia-infected colonies. HP_12890 possessed four haplotypes . The haplotype III of HP_12890 is associated with individuals bearing mitochondrial COI haplotype H02-H04 and H12-H14, and the haplotype IV of HP_12890 was only harbored in Thailand_01 with a unique mitochondrial COI haplotype  and those from Clade III harboring the other haplotype (RluA_H02). One exception, however, involved an individual in China (Yunnan) with the RluA-like haplotype that was found in the individual belonging to Wolbachia SNPs Clade III but harboring mitochondrial COI haplotype H08 (Wolbachia SNPs Clade I). The only difference between the two RluA-like gene haplotypes was a non-synonymous mutation.A similar pattern was found in the haplotype distribution of p = 0.043) on the HP_12890 haplotype IV in Thailand (Thailand_01) compared to the most common HP_12890 haplotype . Signal of positive selection was not detected in the RluA-like gene ORF . We also found no evidence for purifying selection among all haplotype pairs for both genes .Results of the Z-test indicated the presence of signature of positive selection ; GenBank accession: CP021120.1) and the Asian citrus psyllid ; GenBank accession: CP051608.1). The results of interspecific Z-test indicated evidence of purifying selection for HP_12890 , while there was no evidence of positive selection between species . A similar pattern was found for RluA-like gene: we compared Wolbachia in hosts, including the horn fly ; GenBank accession: CP037426.1) and fruit fly ; GenBank accession: CP042445.1), and the interspecific Z-test was significant for purifying selection  but not positive selection .In addition, we compared HP_12890 against NCBI nucleotide collection (nr/nt) database with mega-BLASTn, and showed that orthologs of HP_12890 were present in Wolbachia with identical sequences of both housekeeping  and fast-evolving genes , suggesting that evolutionary history of wAgra should be recent and that wAgra may have derived from a single ancestral population. The MLST strain (ST52) in the yellow crazy ant (Formicinae) was also reported in an ant Lophomyrmex sp. (Myrmicinae) in Thailand [Lophomyrmex sp. and A. gracilipes is approximately 111 MYA (CI: 99-126 MYA) [Wolbachia and the ant’s mitochondrial COI mirror to each other, it is likely that there may have been no additional horizontal transfer of Wolbachia involving other ant species bearing different MLST strains along the evolutionary time. Tseng et al. [Wolbachia is prevailing between ant and ant guests with an intimate ecological association , except A. gracilipes and its closely-associated, host-specific ant cricket, M. albicinctus. Combined with near fixation of Wolbachia in all sampled A. gracilipes populations in this study, vertical transmission, rather than horizontal, appears to be the predominant route for the spread of Wolbachia within this ant species.Our study extended the findings of Tseng et al.  and SebaThailand . Conside126 MYA) , identicg et al.  showed tWolbachia is highly prevalent across sampled colonies of A. gracilipes in virtually all geographic regions. In some cases, Wolbachia infection rates in ants were found to be 100%, but most of these studies either were conducted at a rather local scale  [Wolbachia prevalence pattern has been reported in the parasitic wasp [Wolbachia for essential functions such as nutritional provision, metabolism facilitation, or oocyte maturation and thus engage in an exclusive mutualistic relationship with Wolbachia. Hence, the observed high prevalence of Wolbachia in A. gracilipes may imply an intimate association between Wolbachia and A. gracilipes, although more empirical tests are needed.One major finding of the present study is that on site)  or that on site) ,54,55,56tic wasp , bedbugstic wasp ,7. TheseSpiroplasma is observed in Drosophila populations with the presence of parasitic nematodes [A. gracilipes and their pathogenicity remain poorly described (as compared to other major pest insects), several potential pathogens  have been reported in Cooling et al. [A. gracilipes populations in Okinawa (Japan), Taiwan, and Penang  [Wolbachia may potentially be maintained by benefits of endosymbiotic-driven defense that provides A. gracilipes protection against these viral pathogens, as this is the case frequently observed in flies [The selection pressures exerted by natural enemies often lead to an increase of particular inherited symbionts in insect populations ,59. For ematodes . While ng et al.  and Lee g et al. . Evidencg et al. . TR44839alaysia) , it, howalaysia) . High prin flies  and mosqin flies .Wolbachia in A. gracilipes is “Jekyll and Hyde” infection of Wolbachia [Wolbachia may act as beneficial symbiont and reproductive parasite simultaneously, which is termed “Jekyll and Hyde” infection [Wolbachia in California Drosophila populations were shown to be associated with the dynamic interaction between parasitic and mutualistic life modes of Wolbachia [Wolbachia in ants seems uncommon , mobility, geographical distribution, body size, and/or population connectivity  [Wolbachia infection rate, and these traits may have facilitated the persistence of Wolbachia in the host [Wolbachia prevalence observed in A. gracilipes.Host life-history style may represent another potential reason for the extraordinarily high prevalence of solated) . In antsthe host . We, howWolbachia are in parasitic or mutualist association with A. gracilipes, Wolbachia–host coevolution should be expected, given the extremely high Wolbachia infection in the ant and the high level of concordance between Wolbachia SNPs phylogenetic tree and geographical distribution of mitochondrial COI haplotypes of A. gracilipes.While it remains uncertain that A. gracilipes is limited because a high level of aggression is expected between workers (and possibly between worker and gyne) originating from different supercolonies, and this pattern applies even within a fine geographical scale [A. gracilipes [Wolbachia at a local scale is either discouraged or highly dependent on supercolony structure. Nevertheless, human-mediated jump dispersal has been the primary mode of spread for many invasive ants [A. gracilipes. Indeed, historical records and our preliminary analyses  showed that A. gracilipes has been transported to multiple Indo-Pacific islands through anthropogenic activities [Wolbachia, to spread across different biogeographical regions. Co-divergence of Wolbachia and mitochondrial COI of A. gracilipes provides firm support to this prediction and suggests that this bacterium may have frequently spread with the host ant as a hitchhiker. An alternative possibility is that invasion success of this ant is at least partially associated with Wolbachia infection  of Wolbachia infection during colonization of invasive ants [Wolbachia and host shapes the invasion success of ant.Gene flow between different supercolonies of al scale ,17,64. Cacilipes , spread ive ants  includintivities ,76. Suchcephalum ). This pive ants ,78,79, yWolbachia and host mitochondria DNA from other samples of A. gracilipes  possesses a deep genetic divergence in both acilipes , suggest studies ,15 despi studies ,81, combcal MLST , charactwAgra and the host ant A. gracilipes raises a question of whether it is driven by the bacterium or simply reflects the demographic history of host population. We confirmed the presence of non-synonymous mutations in the two Wolbachia genes, and one of which, namely RluA-like gene, is crucial for bacterial cellular functions [Wolbachia. While we observed nucleotide polymorphisms in wAgra across our sampled populations of A. gracilipes, no evidence for expression nor signature of positive selection suggests that these nucleotide polymorphisms are most likely associated with demographic history of the host such as bottleneck during population expansion or genetic drift, although we cannot rule out the possibility that divergence time was not sufficient to accumulate synonymous mutations or that our RNA sequencing depth is insufficient to detect the evidence for expression.The observed signature of co-divergence between unctions . Signal wAgra and shows that HP_12890 possesses comparable RNA reads to the wsp gene that plays a key role in mediating Wolbachia–host interactions. One potential explanation for such high expression level is that HP_12890 may interact intimately with its host A. gracilipes. Alternatively, it is likely that HP_12890 acts as a selfish element , and the observed polymorphism may be the result of selfish gene replication, given that the selfish element usually can be highly expressed and spreads rapidly in the host genome [Wolbachia.We fully understand that the read number here serves an indicator of “relative” abundance of gene expression because our RNA sequencing read depth likely is below the level where gene expression pattern can be precisely characterized. However, our transcriptome analysis verifies the expression of HP_12890 in t genome . We, howWolbachia strain that is nearly fixed in a globally distributed invasive ant species and adds to the existing literature showing Wolbachia–ant coevolution. More importantly, our data may represent a rare case involving Wolbachia as a potential mutualist in ants and raises an invasion biology-centered question: how this bacterium is maintained at high prevalence during the colonization of this ant. Genetic and functional characterization of Wolbachia and its phenotypic effect on A. gracilipes would offer additional insights into the dynamics of the Wolbachia–ant coevolution.This study reports a single