Salmonella serovars.Many of the bacterial genomic studies exploring evolution processes of the host adaptation focus on the accessory genome describing how the gains and losses of genes can explain the colonization of new habitats. Consequently, we developed a new approach focusing on the coregenome in order to describe the host adaptation of Salmonella enterica subsp. enterica to mammalian- (S. Dublin), multi- (S. Enteritidis), and avian-  hosts.In the present work, we propose bioinformatic tools allowing (i) robust phylogenetic inference based on SNPs and recombination events, (ii) identification of fixed SNPs and InDels distinguishing homoplastic and non-homoplastic coregenome variants, and (iii) gene-ontology enrichment analyses to describe metabolic processes involved in adaptation of S. Dublin diverged from the polyphyletic clade S. Enteritidis which includes the divergent clades S. Pullorum and S. Gallinarum (i). The scripts ‘phyloFixedVar’ and ‘FixedVar’ detected non-synonymous and non-homoplastic fixed variants supporting the phylogenetic reconstruction (ii). The scripts ‘GetGOxML’ and ‘EveryGO’ identified representative metabolic pathways related to host adaptation using the first gene-ontology enrichment analysis based on bacterial coregenome variants (iii).The ‘VARCall’ workflow produced a robust phylogenetic inference confirming that the monophyletic clade Salmonella serovars Dublin , Enteritidis (i.e. multi-hosts), Pullorum (i.e. avian-hosts) and Gallinarum (i.e. avian-hosts) at the coregenome scale. All these polyvalent Bioinformatic tools can be applied on other bacterial genus without additional developments.We propose in the present manuscript a new coregenome approach coupling identification of fixed SNPs and InDels with regards to inferred phylogenetic clades, and gene-ontology enrichment analysis in order to describe the adaptation of The online version of this article (10.1186/s12866-017-1132-1) contains supplementary material, which is available to authorized users. Recent advances in bacterial genomics aim to identify small insertions/deletions (InDels) of the coregenome  in additRepresenting several tens of Bioinformatics tools developed since the beginning of the twenty-first century, the gene-ontology enrichment analysis allows identification of relevant biological processes among large lists of genes . CoverinSalmonella enterica, especially Salmonella enterica subsp. enterica serovars, is the main cause of foodborne gastroenteritis [Salmonella is related to acquired pathogenicity islands (PAIs) called Salmonella pathogenicity islands (SPIs), and genes involved in intestinal phase of infection, colonization of deeper tissues, and expansion in host range, successively [S. enterica subsp. enterica serovars, especially those causing severe human infections (e.g. S. Typhimurium and S. Enteritidis), while the reduction of host range  would mainly be explained by the losses of genes due to large deletions and accumulation of pseudogenes (i.e. start/stop gained codons and/or frameshift mutations) [Salmonella serovars [With around 80 million cases annually, nteritis . Host adessively . Severaltations) . The fewtations) –22 or inserovars , 23–25, serovars .Salmonella and other Enterobacteriaceae (e.g. locus ccm of 7.5 kb in S. Typhimurium) [S. enterica serovars, as Abortusequi, Abortusovis, Choleraesuis, Dublin, Enteritidis, Gallinarum, Pullorum, Sendai, Paratyphi C, and Typhimurium, are known to harbor different virulence plasmids [spvR, spvA, spvB, spvC, and spvD) in the 7.8-kb spv region  which are required for bacterial multiplication in the reticulo-endothelial system [The evolution of serovars, as Agona, Dublin, Hadar, Heidelberg, Javiana, Kentucky, Newport, Saintpaul, Schwarzengrund, Virchow, Weltevreden, Choleraesuis, Enteritidis, Gallinarum, Paratyphi A, Paratyphi B, Paratyphi C, Typhi and Typhimurium, seems to be highly mediated by losses of coding sequences which lead to functional reduction, and horizontal acquisitions of plasmid and phage sequences, hypothetically under regulation of a defense mechanism against exogenous invading sequences, particularly through clustered regularly interspaced short palindromic repeats (CRISPR) . With thimurium) , and manplasmids  heterogel system  and cytol system .S. Enteritis is introduced in the food chain by products from diverse hosts [S. Dublin is mainly due to contaminated cow’s milk and cheese [S. Gallinarum [S. Pullorum [S. Enteritidis into two major clades [S. Enteritidis strain P125109, NC_011294.1) [S. Dublin and S. Enteritidis diverged from a most recent common ancestor (MRCA), and that S. Enteritidis was constituted of two clades , one of them being the origin of the avian-adapted serovars S. Gallinarum and S. Pullorum [Salmonella pathogenicity is strongly associated with the acquisition of SPIs [S. Gallinarum contributes to the colonization of the gastrointestinal tract of chickens, as demonstrated with chickens orally infected by bacterial mutants [S. Enteritidis, but were intact in the mammalian-adapted serovar S. Dublin (i.e. cattle and humans), as well as the avian-adapted serovars S. Gallinarum and S. Pullorum [std.∆, stiC, stfF, safC, stbC, pegC, lpfC, sefD, sefC, sthB, sthA, sthE in S. Gallinarum [ssf and saf operons are highly specific to avian adaptation of S. Gallinarum and S. Pullorum [Known as a common serovar involved in domestically acquired foodborne illness in humans, se hosts . In contd cheese , while Sllinarum  and S. PPullorum  infect ar clades , a recen11294.1)  demonstrPullorum . Focusin of SPIs , especia of SPIs . While T mutants , severalPullorum . Among mPullorum .Salmonella serovars: Enteritidis (i.e. multi-hosts), Dublin , Gallinarum (i.e. avian-hosts) and Pullorum (i.e. avian-hosts) [Here, we propose a novel approach coupling identification of clade specific SNPs and InDels , 2 and an-hosts) , 22, 25.S. Enteritidis (strain P125109) [S. Typhimurium reads against the reference LT2 genome  [After trimming and mapping of paired-end reads, the previously published genome dataset  . This deP125109) , and thi8% max.) .Salmonella genomes was analyzed through the variant calling workflow ‘VARCall’ where high-confidence variants were retained [Salmonella dataset falls inside the boundaries of 0.5–2 InDels / 10 SNPs which have been previously observed in other prokaryotic species  and eukhimurium , e.g. Huhimurium ).S. Enteritidis chromosome  [S. Agona serovar, we also detected 35% of intergenic InDels (i.e. 411/1157) across the 59 studied genomes.We found that 12,031 variants  were intragenic, corresponding to 85% of the 14,086 coregenome variants . As the coding regions represent about 88% of the p < 5.0×10−2, Wilcoxon rank sum or Kolmogorov-Smirnov tests). For instance, the average pairwise distances of S. Enteritidis clade  were significantly different than those calculated for Pullorum , Gallinarum  and Dublin  serovars .Interestingly, the average pairwise distances of SNPs or InDels were significantly different Fig.  between 6 for 1000 computed trees [The phylogenetic inference converged after 200 bootstrap replicates  with a led trees , resultiS. Dublin as a distinct clade with differences of 4945 ± 378 SNPs and 296 ± 17 InDels from the S. Enteritidis clade, as confirmed by our genetic structure analysis [S. Enteritidis described as classic and second clades by Langridge et al. [S. Enteritidis by microarray [The phylogenetic tree Fig.  showed tanalysis  and accoanalysis . Exceptie et al.  Fig. 2)S. Dublincroarray  and SNP-croarray , we suppS. Pullorum and S. Gallinarum are highly specialized toward avian-hosts; they are non-motile members of serogroup D, indistinguishable by serotyping and historically recognized as distinct biotypes considering several criteria: the distinct septocaemic diseases they cause in avian species, biochemical characteristics and multilocus enzyme electrophoresis [S. Pullorum and S. Gallinarum isolates arose from the second clade of S. Enteritidis , while by contrast the S. Pullorum clade displayed a larger significant diversity with regards to SNPs  or InDels . These results confirm at the genetic level the biochemical classification performed by Crichton and Old [phoresis . As prevphoresis , our phydis Fig. . Interesdis Fig. : the S.  and Old .S. Dublin and S. Enteritidis have a most recent common ancestor, and that the clade S. Pullorum/S. Gallinarum diverged from S. Enteritidis. According to Langridge et al. [S. Enteritidis , as well as significantly higher than those of the mammalian-adapted serovar S. Dublin and avian-adapted serovar S. Gallinarum .All together, these observations are in accordance with Langridge et al.  who conce et al. , we alsodis Fig. . In oppoudogenes , we obsedis Fig. , expressSalmonella, the PAIs called SPIs have long been recognized to play a determinant role in the virulence properties of the pathogen [Following lateral transfer of genetic material, recombination events are a common modality of evolution of bacterial genomes . In Salmpathogen . These rpathogen . We perfn = 91) were detected in S. Dublin, the most distant clade of the collection .Surprisingly, most of these recombination events  either of branches in the phylogenetic tree (i.e. ‘PhyloFixedVar’) or between groups defined by the user (i.e. ‘FixedVar’).S. Typhimurium estimating non-synonymous SNPs at 44% [p < 1.0×10−8; Wilcoxon signed rank test) variants . According to a study on s at 44% , most ofS. Dublin, S. Enteritidis, S. Gallinarum and S. Pullorum , divergence of S. Pullorum/Gallinarum from S. Enteritidis , divergences of S. Pullorum  and S. Gallinarum  with each other   and est (SPAdes ), is curAlthough the removal of variants from recombination events must be theoretically performed when the phylogenetic inference assumes  or requires (i.e. Maximum Likelihood) a Markov chain model of nucleotide substitution , we obseS. Dublin, S. Enteritidis, S. Gallinarum and S. Pullorum genomes. Considering all leaves of the phylogenetic inference, the non-homoplastic fixed variants represented 65% of all fixed variants. It should however be mentioned that it is fully possible to use the scripts ‘phyloFixedVar’, ‘FixedVar’, ‘GetGOxML’ and ‘EveryGO’ to select homoplastic variants for studies that would focus on coevolution [Because of our objective to detect clade specific traits related to host adaptation, we decided to exclude the homoplastic variants in the analyses of the volution .The gene-ontology enrichment analysis was applied to synonymous and non-synonymous fixed variants because synonymous variants may be involved in regulation of gene expression or level of protein synthesis even if they do not impact the protein sequence .In order to browse easily between tree branches, genes and their annotations, the xml file produced by the scripts ‘phyloFixedVar’ or ‘FixedVar’ centralizes the annotations of variants including the homoplastic variants for all the combinations of genomes present in the phylogenetic inference. The polyvalent scripts ‘phyloFixedVar’, ‘FixedVar’, ‘GetGOxML’ and ‘EveryGO’ were developed to perform the first gene-ontology enrichment analysis based on bacterial coregenome variants and can be applied to all kind of bacterial genome collection.Salmonella displayed a decreasing in coregenome diversity  may correspond to frameshift insertions of T or TT in S. Enteritidis, or frameshift deletions of T or TT in S. Dublin  are essential reactions during mice infection by S. Typhimurim [S. Typhimurium in mice requires also the twin-arginine translocation system (Tat) which exports across the cytoplasmic membrane numerous cofactors containing virulence factors and anaerobic respiratory chain proteins [astB) (GO:0009015) is indeed the second enzyme of the arginine succinyltransferase pathway involved in arginine catabolism as a sole nitrogen source [The results of the present study highlighted the accumulation of fixed variants in intragenic regions connected to metabolic pathways of amino acids Table . The mosars Fig. . Concernin Table , the glyphimurim . Aerobicproteins   is in fact a key enzyme in the biosynthetic pathway from aspartate to homoserine, which is a common precursor for the synthesis of amino acids methionine, threonine and isoleucine.With regard to the GO-terms of interest for in Table , the accmolality  and aspamolality . Also inS. Dublin , hyperosmotic stress and proline limitation in host compartment is indeed known to lead S. Typhimurium responds to a decrease in the levels of proline-charged tRNAPro by promoting expression of the mgtCBR virulence operon [S. Dublin (GO:0003842) is encoded in the putA gene of S. Typhimurim by a bifunctional membrane-associated dehydrogenase which oxidizes proline to glutamate for use as the sole carbon, nitrogen or energy source [Regarding the proline metabolic and catabolic processes impacted by non-homoplastic fixed variants in y source .alr of S. Typhimurim, also called dal genes, encode alanine racemases which are biosynthesis sources of D-alanine for cell wall formation and also necessary to the catabolism of L-alanine as a source of carbon, energy and nitrogen [S. Pullorum  may consequently be due to his adaptation to this avian alimentation.The genes nitrogen . L-arginnitrogen , the sto+-dependent D-glucose transporter SLGT1), whereas enzymes required for solid food and pinocytotic uptake capacity are produced during weaning (e.g. fructose and starch), and activities of sucrase-isomaltase, maltase-glucoamylase, trehalase and fructose transport (i.e. GLUT-5) increase when adult diet switches from lactose to sucrose and starch. In contrast, the pre- and post-natal periods of birds and chicks are associated with a switch of gastrointestinal functions driven by the transition from a lipid-rich yolk diet inside the egg  to a carbohydrate- and protein-based diet after hatch in young chickens and house sparrows  [Compared to similar sized nonflying mammalian, the avian gastrointestinal tracks present a typical shorter retention time and quantitatively much more important passive adsorption of L-glucose. During ontogeny, the architecture and functioning of mammalian and avian gastrointestinal tracts are closely related with their food diet and intake rate. The gestational phase of mammalian is dominated by production of gastrointestinal enzymes required for digestion and absorption of milk (e.g. amino acid transporters and the Naivities) . Severalivities) .S. Dublin  acid resistance system to prepare for the stresses of host-cell interactions [Further with respect to the GO-terms of interest related to metabolic and catabolism processes of amino acids in in Table , S. Typht pH 2.5 . Salmoneractions .ileS encoding the essential enzyme isoleucine-tRNA ligase (IleRS) were observed in S. Enteritidis under selection pressure induced by the mupirocin, an inhibitor of this enzymatic activity [S. Pullorum  may consequently also be linked to other antibiotics  . For instance, plasmids encoding aerobactin (i.e. iucABCD and iutA) and Sit iron transport operons (i.e. sitABCD), as well as other iron acquisition genes (e.g. iss) play indeed a major role in survival abilities of S. Kentucky (e.g. IncFIB plasmid) and some S. Heidelberg (e.g. pSH163_120 and pSH696_117 plasmids) in the extraintestinal environments of poultry where iron is in limited supply [Concerning the loss or modification of potassium ion transmembrane transporter activity in the avian-adapted serovar 9) Table , the modd supply .Salmonella serovars Dublin , Enteritidis (i.e. multi-hosts), Pullorum (i.e. avian-hosts) and Gallinarum (i.e. avian-hosts) at the coregenome scale. Among the multiple metabolic pathways impacted by fixed variants during host adaptation of Salmonella serovars, our main observation emphasized that glutamate metabolism could play a major role in adaptation of S. Dublin to mammalian-hosts.In conclusion, we proposed the first validated procedure to identify fixed SNPs and InDels according to inferred phylogenetic clades and performed the associated gene-ontology enrichment analysis in order to describe the adaptation of Salmonella strains and was constituted by Langridge et al. [Salmonella serovars S. Enteritidis (i.e. multi-hosts), S. Dublin , S. Gallinarum (i.e. avian hosts) and S. Pullorum (i.e. avian-hosts)  rather than to perform time consuming variant calling across reference genome alignments (UnifiedGenotyper) [We develop currently a new version of the ‘VARCall’ workflow  based on the HaplotypeCaller algorism (GATK) in order to call SNPs and InDels at the same time by local notyper) .Based on the pseudogenomes, the phylogenetic inference was performed with the multi-core architecture of RAxML . FollowiA script called ‘phyloFixedVar’ uses the annotated vcf file from the variant calling analysis and the nwk file from a binary tree. This script was developed in order to detect sensitive and specific variants at each branch of the phylogenetic inference. The variants are defined with a biological point of view either as fixed (i.e. variants with sensitive and specific genotypes at intermediate branches) or transient . The script ‘phyloFixedVar’ (a) identifies all comparisons of genome groups referring to all nodes, (b) selects sensitive and specific variants, and (c) defines if these variants are homoplastic (i.e. convergence of genotypes between independent phylogenetic clades), successively. More precisely, right child leaves are listed with corresponding leaves of left child at each node and reversely, then both comparisons (i.e. right versus left children and reversely) are connected with comparison numbers which are associated to identifiers of single nodes, all together grouped into node labels in a new nwk file (a). On the one hand the sensitive variants are detected as presenting common genotypes into leaves of right or left child of previously listed comparisons, and on the other hand the specific variants are identified as presenting different genotypes in corresponding right or left children (b). With regard to these sensitive and specific variants, the genotypes of all the other genomes are screened in order to tag homoplastic variants (i.e. common genotypes between variants of independent phylogenetic clades) (c). The selected variants are finally written in the xml file with their related annotations  for each node and all comparisons of lists of leaves. Similarly to ‘phyloFixedVar’, another script called ‘FixedVar’ requires a vcf file and lists of genomes IDs that have to be compared. This script was developed in order to detect sensitive and specific variants independently of the phylogenetic inference.http://geneontology.org/page/download-ontology), and the script ‘GOxML’ associates gene identifiers (i.e. NP or WP) from the xml file with GO-terms available in the QuickGO browser (http://www.ebi.ac.uk/GOA) of the UniProt GO annotation program (https://www.ebi.ac.uk/QuickGO/). In addition, the script ‘GOxML’ allows comparison of these GO-terms in order to exclude potential eukaryote GO-terms from the dataset and retains prokaryote GO-terms. Finally, the script ‘GOxML’ integrates the curated GO-terms (i.e. prokaryote GO-terms) and related biological processes to the common xml file in order to centralized the GO-terms and functional annotations of variants . With a view to select intragenic variants (SNPs and InDels), and distinguish between GO-terms from the variants of interest (i.e. tested sample) and all variants (i.e. universe) which are used for the hypergeometric test of the gene-ontology enrichment analysis based on the parent-child approach [p-values of the hypergeometric tests versus the branch levels from the GO-terms of the DAG).With the objective to associate the selected variants to corresponding prokaryotic GO-terms, a driver script called ‘GetGOxML’ invokes the scripts ‘GOSlimer_XML’ and ‘GOxML’, successively Fig. . More prapproach , the driapproach  implemenapproach . Finallyhttp://www.paidb.re.kr) from KonKuk University  [S. Enteritidis .Candidates of PAI-like region overlapping genomic islands (cPAIs) of the Pathogenicity Island Database (h Korea)  were useAdditional file 1:Salmonella enterica subsp. enterica serovars Enteritidis, Pullorum, Gallinarum and Dublin were described by Langridge et al. . (XLSX 15 kb)Genome dataset used in the present study. The genomes of Additional file 2:Salmonella enterica subsp. enterica serovars Dublin, Enteritidis, Pullorum and Gallinarum were described by Langridge et al. . (XLSX 16 kb)Statistical report of the ‘VARCall’ workflow. The serovars Additional file 3:Salmonella enterica subsp. enterica serovars Dublin, Enteritidis, Pullorum and Gallinarum. The variants were identified by the ‘VARCall’ workflow against the reference genome S. Enteritidis . The positions of recombination events detected by Maximum Likelihood and default gamma priors of ClonalFrameML are removed with a script ‘Clonal_VCFilter’ in order to compute phylogenetic inference based on pseudogenomes excluding variants linked to recombination events. The produced pseudogenomes  were inferred with RAxML based on a bootstrap analysis and search for best-scoring Maximum Likelihood tree with General Time-Reversible model of substitution and the secondary structure 16-state model. The color legend corresponds to phylogenetic clustering performed by Langridge et al. . The trees are rooted on the branches of S. Dublin before comparison. The comparison of the tree topologies were performed using the cophylo function of ‘phytools’ R package. (PDF 1733 kb)Phylogenetic inferences performed based on coregenome single nucleotide polymorphisms (SNPs) excluding (A) or including (B) variants from recombination events detected in Additional file 4:Salmonella enterica subsp. enterica serovars Dublin, Enteritidis, Pullorum and Gallinarum. The color legend corresponds to serovars presented by Langridge et al. . The variants were identified by the ‘VARCall’ workflow against the reference genome S. Enteritidis . The produced pseudogenomes  were inferred with RAxML based on a bootstrap analysis and search for best-scoring Maximum Likelihood tree with General Time-Reversible model of substitution and the secondary structure 16-state model. The phylogenetic inference converged after 200 bootstrap replicates with a log likelihood score of −8.106 for 1000 computed trees. The tree is rooted on the branch of S. Dublin. The pseudogenomes and the RAxML inference were used to perform detection of recombination events based on default gamma priors of ClonalFrameML. The number of recombination events is defined closed to white circles which represent recombination events occurred on a branch of the phylogenetic tree. The recombination events with sizes higher than 400 bp are presented. (PDF 752 kb)Phylogenetic inference based on coregenome single nucleotide polymorphisms (SNPs) and recombination events identified in Additional file 5:Salmonella enterica subsp. enterica serovars Dublin, Enteritidis, Pullorum and Gallinarum. The variants were identified by the ‘VARCall’ workflow against the reference genome S. Enteritidis . The produced pseudogenomes  were inferred with RAxML based on a bootstrap analysis and search for best-scoring Maximum Likelihood tree with General Time-Reversible model of substitution and the secondary structure 16-state model. The pseudogenomes and the RAxML inference were used to perform detection of recombination events based on default gamma priors of ClonalFrameML. The recombination events with sizes higher than 400 bp are presented. (XLSX 16 kb)List of recombination events identified in Additional file 6:Salmonella enterica subsp. enterica serovars Enteritidis (n = 33), Pullorum (n = 5), Gallinarum (n = 8) and Dublin (n = 13) diverged. The variant calling analysis was performed with the ‘VARCall’ workflow . The fixed non-homoplastic variants are defined by common genotypes across the considered group of genomes, as well as different genotypes in all the others compared genomes. The fixed homoplastic variants are defined by common genotypes across the considered group of genomes and genomes of independent phylogenetic clades, as well as different genotypes in genomes of the compared child-leaves. The variants were annotated with SnpEff against reference genome S. Enteritidis . (XLSX 13 kb)Phylogenetically relevant single nucleotide polymorphisms (SNPs) and small insertions/deletions (InDels) fixed at phylogenetic branches where genomes of Additional file 7:Salmonella enterica subsp. enterica serovars Dublin versus all the others genomes , Pullorum/Gallinarum versus Enteritidis , Pullorum versus Gallinarum , and Gallinarum versus Pullorum . The variant annotation was performed with SnpEff against reference genome S. Enteritidis . The identification of variants, detection of fixed variants, assignment of GO-terms to variants, and gene-ontology enrichment analysis were performed with the scripts ‘VARCall’, ‘phyloFixedVar’, ‘GetGOxML’, and ‘EveryGO’, respectively. The level, biological process (BP), molecular function (MF), and cellular component (CC) of GO-terms are represented. The p-values of hypergeometric tests were adjusted by Bonferroni correction. (XLSX 327 kb)Gene-ontology (GO) terms of intragenic and non-homoplastic variants (SNPs and InDels) fixed in Additional file 8:Salmonella enterica subsp. enterica serovars Dublin versus all the others genomes , Pullorum/Gallinarum versus Enteritidis , Pullorum versus Gallinarum , and Gallinarum versus Pullorum . Pathogenicity Island Database from KonKuk University  were used to detect Salmonella Pathogenic Islands (SPIs) SPI-1 , SPI-2 , SPI-4 , SPI-5 , SPI-6 , SPI-11 , SPI-12  and PAI III 536  of the reference genome S. Enteritidis . (XLSX 251 kb)Candidates of PAI-like region overlapping genomic islands (cPAIs) impacted by intragenic and non-homoplastic variants (SNPs and InDels) fixed in Additional file 9:Salmonella enterica subsp. enterica serovars Dublin, Enteritidis, Pullorum and Gallinarum. The variants were identified by the workflow ‘VARCall’ against the reference genome S. Enteritidis . The produced pseudogenomes  were inferred with RAxML based on a bootstrap analysis and search for best-scoring Maximum Likelihood tree with General Time-Reversible model of substitution and the secondary structure 16-state model. The pseudogenomes and the RAxML inference were used to perform detection of recombination events based on default gamma priors of ClonalFrameML. The recombination events with sizes higher than 400 bp are presented. Pathogenicity Island Database from KonKuk University  were used to detect Salmonella Pathogenic Islands (SPIs) SPI-1 , SPI-2 , SPI-4 , SPI-5 , SPI-6 , SPI-11 , SPI-12  and PAI III 536  of the reference genome S. Enteritidis . (XLSX 19 kb)Candidates of PAI-like region overlapping genomic islands (cPAIs) impacted recombination events identified in


Sex chromosomes evolve once recombination is halted between a homologous pair of chromosomes. The dominant model of sex chromosome evolution posits that recombination is suppressed between emerging X and Y chromosomes in order to resolve sexual conflict. Here we test this model using whole genome and transcriptome resequencing data in the guppy, a model for sexual selection with many Y-linked colour traits. We show that although the nascent Y chromosome encompasses nearly half of the linkage group, there has been no perceptible degradation of Y chromosome gene content or activity. Using replicate wild populations with differing levels of sexually antagonistic selection for colour, we also show that sexual selection leads to greater expansion of the non-recombining region and increased Y chromosome divergence. These results provide empirical support for longstanding models of sex chromosome catalysis, and suggest an important role for sexual selection and sexual conflict in genome evolution. It has been suggested that sex chromosomes arise as a result of sexual conflict, resulting in selection against recombination between chromosomes. Here, the authors resequence laboratory and wild guppy populations with differing levels of sexual antagonism, providing support for this long-held view. Sex chromosomes are typically thought to evolve as recombination is halted between a homologous pair of chromosomes in one sex. Although we have a detailed understanding of the evolutionary consequences of the loss of recombination for sex chromosome evolution1346Poecilia reticulata) have been of interest for more than a century, following early reports that many sexually selected colour traits are passed through the patriline on the Y chromosome8341011The sex chromosomes of the guppy  in regions that retain substantial sequence similarity to the X, resulting in higher average male heterozygosity for the sex chromosomes32In order to determine the relative divergence between X and Y chromosomes, we plotted coverage and SNP density differences between males and female on our scaffolds against physical position on the guppy genome assembly. We detected significantly reduced male coverage outside the autosomal 95% confidence interval from 22–25 Mb . This reP<0.001.Our coverage and SNP analysis suggest that although male-specific SNPs have accumulated, the Y chromosome has not degenerated significantly. Because loss of gene activity often quickly follows loss of recombination on the Y chromosomedN/dS is greater though marginally non-significant for X-linked genes  relative to the autosomes , due to a marginally significant increase in dN  (X chromosomes are predicted in many circumstances to show elevated rates of evolution3536P=0.014) . This paPredation pressures vary substantially for natural guppy populations, with generally lower predation pressures upstream compared with downstream2425P=0.011, Quare P=0.046, Aripo P=0.017). Expansion of the non-recombining region and corresponding X-Y divergence has occurred repeatedly and independently across populations, as the phylogeny of these populations reveals that in each watershed, upstream populations are consistently derived from downstream populations  in the Northern Range Mountains of Trinidad in February 2015 (see ref. Nucleic acids were extracted with RNAeasy Kit (Qiagen) and DNeasy Blood and Tissue Kit (Qiagen) using the manufacturer protocols. The libraries were prepared and barcoded at The Wellcome Trust Centre for Human Genetics, University of Oxford using standard protocols. RNA was sequenced on an Illumina HiSeq 2500 resulting in on average 32 million 100 bp paired-end reads per sample. DNA was sequenced on an Illumina HiSeq 4000, resulting in on average 269 million 100 bp paired-end reads per individual sampled from a single large, outbred laboratory population, and 123 million 100 bp paired-end reads per sample for individuals caught in the wild in Trinidad .www.bioinformatics.babraham.ac.uk/projects/fastqc) and quality trimmed using Trimmomatic v0.35  and the longest isoform picked for each. Coding sequences were BLASTed against the De novo scaffolds were assigned to the guppy reference chromosomes and oriented using the chromosomal location and start position of mapped guppy genes. If multiple genes mapped to a given scaffold, the scaffold was assigned to the reference chromosome that the majority of genes were located on. Specifically, at least 70% of genes mapping to a given scaffold must be located on the same chromosome in the reference genome otherwise the scaffold was discarded. The degree of concordance in assigned chromosome position using this approach is high  was used to extract coverage of scaffolds in every individual. For each scaffold, coverage was defined as the total number of times each site was sequenced divided by the number of sites that were sequenced.Male and female trimmed DNA-seq reads were separately mapped to the 2 0. Male:female coverage was calculated for each scaffold as log2 – log2.For lab populations, average coverage values were calculated for females and males separately. We added 1 to each value to avoid infinitely high numbers associated with log2 coverage for each gene was normalized by the median log2 coverage of X chromosome (log2 coverage—median log2 coverage of X chromosome). Male:female coverage was estimated for each population relative to the normalized coverage of the female lab population.For upstream and downstream wild populations, coverage was estimated using Bwa v0.7.15 aln/sampe and the same pipeline as the lab populations. Average coverage was calculated for each gene separately across each population. To account for differences in sequencing depth across populations, the logde novo genome assembly using Bowtie1 v1.1.2 (ref. http://guanine.evolbio.mpg.de/) was used to generate a profile for each sample. Sites with coverage <10 were excluded from the analysis and SNPs were called when a site had a major allele frequency of 0.3 times the site coverage. SNPs were only included in further analyses if they were located within genic regions . Average SNP density for each gene was calculated as sum(SNPs) divided by sum(no. of filtered sites). We added 1 to each value to avoid infinitely high numbers associated with log2 0. Genes were excluded if zero sites remained after filtering.Male and female trimmed DNA-seq reads from both wild and lab populations were separately mapped to the 1.2 ref. , specify2−log2.For lab populations, average SNP density was calculated separately for males and females. Male:female SNP density was calculated for each gene as log2 SNP density for each gene was normalized by the median log2 SNP density of X chromosome (log2 SNP density—median log2 SNP density of X chromosome). Male:female SNP density was estimated for each population relative to the normalized SNP density of the female lab population.For upstream and downstream wild populations, average SNP density was calculated for each gene separately across each population. To account for differences in overall genetic diversity across populations, the logP-values<0.05 and the median SNP density in the lab population was greater than the 95% autosomal confidence interval.To calculate the probability that the convergence in patterns of SNP density across populations we observe is due to chance, we randomly sampled 10 Mb windows across the autosomes 1,000 times. For each window, we tested whether the upstream normalized male:female SNP density was greater than the downstream population in each river using a one-tailed Wilcoxon ranked sum test. We looked for windows where all three rivers had de novo genome assembly using HISAT2 v2.0.4 , Gasterosteus aculeatus (BROADS1), Poecilia formosa (PoeFor_5.1.2) and Danio rerio (GRCz10) ncRNA downloaded from Ensembl 84  and transcripts less than 50 bp in length. Specifically, transcript sequences were extracted using bedtools getfasta 84 ref. . Transcr2 0. Male:female expression was calculated for each gene as log2 —log2.Expression was normalized using EdgeRDanio rerio (GRCz10) coding sequences were downloaded from Ensembl 84  and compared with the remainder of the genome using GOrilla72We tested whether there was an enrichment of GO terms in the X-Y diverged region compared with the rest of the genome.  84 ref.  and the 3.0 ref.  with an Transcriptional similarity of normalized count data for female and male individuals was assessed using a multi-dimensional scaling plot (MDS) with default settings in EdgeRMoving averages of coverage/polymorphism/expression were calculated in ROryzias latipes (MEDAKA1), Xiphophorus maculatus (Xipmac4.4.2), Poecilia formosa (PoeFor_5.1.2) were downloaded from Ensembl 84 , Xiphophorus maculatus), Oryzias latipes). SWAMP v 31-03-14 , Xiphophorus maculatus, Oryzias latipes). The branch model was used to calculate mean dN/dS across the Poecilia reticulata branch. As mutational saturation and double hits can lead to inaccurate divergence estimatesdS >2.We used the branch model  in the CODEML package in PAML v4.8 ref.  to obtaidN and mean dS were calculated as the sum of the number of substitutions across all orthologues divided by the number of sites . This approach prevents disproportionate weighting of shorter genes by avoiding the problems of infinitely high dN/dS estimates arising from sequences with extremely low dS  using Stampy v1.0.28 ref.  with a sAs part of the GATK variant calling pipeline, v3.4.46 ref. , we firsZ score for mapping quality, base quality or read position<-1.96, or strand bias Fisher exact test P>0.05. We also removed SNPs with missing genotype in any individual. This yielded 4.6 million high-quality SNPs.After variant calling we removed indels, intersected the GATK and Platypus SNP sets, and applied stringent quality filtering. We removed singleton SNPs, multiallelic SNPs and SNPs failing the following quality thresholds: quality by depth>2, coverage>0.5x and<2x mean coverage, >2 reads for the alternative allele, mapping quality>40, allele bias Next, we used R package adegenet v2.0.1 ref.  to constRNA and DNA reads have been deposited at the NCBI Sequencing Read Archive, BioProject ID PRJNA353986.How to cite this article: Wright, A. E. et al. Convergent recombination suppression suggests role of sexual selection in guppy sex chromosome formation. Nat. Commun.8, 14251 doi: 10.1038/ncomms14251 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures and Supplementary Tables


Philaenus spumarius (L.) , a widespread insect species in the Holarctic region, exhibits a striking dorsal colour/pattern balanced polymorphism. Although experimental crosses have revealed the Mendelian inheritance of this trait, its genetic basis remains unknown. In this study we aimed to identify candidate genomic regions associated with the colour balanced polymorphism in this species.Colour polymorphisms are common among animal species. When combined with genetic and ecological data, these polymorphisms can be excellent systems in which to understand adaptation and the molecular changes underlying phenotypic evolution. The meadow spittlebug, typicus, marginellus, and trilineatus). Single and multi-association analyses identified a total of 60 SNPs associated with dorsal colour morphs. The genome size of P. spumarius was estimated by flow cytometry, revealing a 5.3 Gb genome, amongst the largest found in insects. A partial genome assembly, representing 24% of the total size, and an 81.4 Mb transcriptome, were also obtained. From the SNPs found to be associated with colour, 35% aligned to the genome and 10% to the transcriptome. Our data suggested that major loci, consisting of multi-genomic regions, may be involved in dorsal colour variation among the three dorsal colour morphs analysed. However, no homology was found between the associated loci and candidate genes known to be responsible for coloration pattern in other insect species. The associated markers showed stronger differentiation of the trilineatus colour phenotype, which has been shown previously to be more differentiated in several life-history and physiological characteristics as well. It is possible that colour variation and these traits are linked in a complex genetic architecture.By using restriction site-associated DNA (RAD) sequencing we were able to obtain a set of 1,837 markers across 33 individuals to test for associations with three dorsal colour phenotypes (P. spumarius.The loci detected to have an association with colour and the genomic and transcriptomic resources developed here constitute a basis for further research on the genetic basis of colour pattern in the meadow spittlebug The online version of this article (doi:10.1186/s12863-016-0455-5) contains supplementary material, which is available to authorized users. With the latest advances in sequencing technologies, the relationships between genetic variation and adaptive traits can now be investigated in a broader range of species for which, in some cases, there is extensive knowledge of ecological and evolutionary history, but few genomic resources [Understanding the genetic basis underlying phenotypic variation responsible for evolutionary change and adaptation in natural populations remains a major goal and one of the most interesting challenges in evolutionary biology. Not long ago, despite the development of new molecular tools, establishing genotype-phenotype associations, mapping adaptive loci, and identifying gene function, was limited to a few esources –7. Moreoesources , 9.taxa, including mammals [Intraspecific colour variation is commonly found in many different  mammals , fishes  mammals , amphibi mammals , reptile mammals , 14, bir mammals , 16, and mammals  for refe mammals . Through mammals . When co mammals . Negativ mammals –23, hete mammals , and dis mammals , 26 are  mammals .Philaenus spumarius  , a widespread and highly polyphagous sap-sucking insect species in the Holarctic region, shows a well studied balanced polymorphism of dorsal colour/pattern variation [populi, typicus, vittatus, trilineatus and praeustus) and melanic forms  [trilineatus phenotype in laboratory conditions, which was also found to have the highest reflectance [p with seven alleles, with complex dominance and co-dominance relationships, being likely regulated by other loci [typicus phenotype is the most common (over 90% frequency in most populations) and it is the bottom double recessive form. It is believed to be the ancestral form because its main colour pattern characteristics are shared with several other cercopid species [leucopthalmus is dominant over typicus, and several other forms, with pale heads and/or spots, are dominant over the completely dark form. The trilineatus phenotype, pale with three dark stripes, is controlled by the top dominant allele pT [P. spumarius the genetic architecture of its balanced dorsal colour polymorphism and the possible existence of a supergene remain to be investigated.The meadow spittlebug, ariation . It is tariation . Sixteenariation  but onlythalmus) , 30–32. thalmus)  have sholectance  and to blectance , supporther loci , 36. Thelele pT . Halkka lele pT  suggestelele pT , has beelele pT , 41, lanlele pT  and birdlele pT . In P. sP. spumarius. The phenotypes used were: typicus (TYP), the most common and non-melanic recessive phenotype; trilineatus (TRI), the non-melanic dominant phenotype; and marginellus (MAR), the most common melanic phenotype found in the population from which samples were collected. The first partial draft genome and transcriptome of P. spumarius are presented here and were used to help the characterisation of the genomic regions found to be associated with colour variation. The size of the genome of this insect species was also estimated by flow cytometry.A genome-wide association study has the potential to identify the genetic and/or genomic region(s) associated with these dorsal colour patterns. In this study we used restriction site-associated DNA (RAD) sequencing  to obtaiThis research does not involve any endangered or protected species and did not require any permits to obtain the spittlebug individuals.P. spumarius from three different colour phenotypes – 12 typicus (TYP), 12 trilineatus (TRI), and 12 marginellus (MAR) – were collected from a Portuguese population near Foz do Arelho locality  in 2011. Adult insects were captured using a sweep net suitable for low-growing vegetation and an entomological aspirator (pooter). Specimens were preserved in absolute ethanol and stored at 4°C. The wings and abdomen were removed to avoid DNA contamination by endosymbionts, parasitoids and parasites and only the thorax and head were used. Genomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen).A total of 36 female specimens of Three RAD libraries with twelve individuals each were prepared following a modified RAD sequencing protocol , using Pr2) in VCFtools. In association analysis, the detection of statistical associations may be affected when a marker is replaced with a highly correlated one [r2 = 1) were randomly eliminated and only one of them was retained in the final VCF matrix. The filtered VCF file with the genotypes for each individual was converted into the file formats needed for further analyses using PGDSpider v 2.0.4.0 [Raw reads were trimmed, demultiplexed and aligned using the pyRAD software pipeline v3.0.5 , which fated one . Taking  2.0.4.0 , fcGENE  2.0.4.0  and/or up-values were obtained for each SNP and a false discovery rate (FDR) of 5% was applied [For the SNPs dataset, single-SNP associations between allele frequencies and dorsal colour phenotypes were tested using a Fisher’s exact test of allelic association in PLINK v 1.07 . Three p applied  to each 0.99 SNPs) were considered as highly associated with colour morphs. For all PIP0.99 SNPs we reported their PIP and the estimates of their phenotypic effect (β). A positive β in the pairwise morph1-morph2 (e.g. MAR-TRI) analysis means that the frequency of the minor allele (maf) is higher in morph2 (TRI in the example) and a negative β means that maf is higher in morph1 (MAR in the example). Thus, to investigate the phenotypic effect size of each PIP0.99 SNP, the | β | was considered. The model contains additional parameters that are estimated from the data: proportion of variance explained by the SNPs (PVE), the number of SNPs in the regression model (nSNPs) and the average phenotypic effect of a SNP that is in the model (σSNP). For all pairwise analyses, we obtained 4 million Markov Chain Monte Carlo samples from the joint posterior probability distribution of model parameters  and discarded the first 100,000 samples as burn-in. piMASS also outperforms a single-SNP approach to detect causal SNPs even in the absence of interactions between them [0.95 SNPs) were considered to be strongly associated to the colour phenotypes. Those above 99% empirical quantile for BF (BF0.99 SNPs) were considered to have the strongest associations. Imputation of the missing genotypes was performed in BIMBAM v1.0 [To test for single and multi-SNP correlations between SNPs and colour morphs, a Bayesian Variable Selection Regression (BVSR) model proposed by  was alsoeen them . For sinBAM v1.0 .G–test [FST were obtained following the method of [Genetic differences among populations were tested using a G–test  and estiethod of  implemenethod of . To bettethod of .de novo assembly of the genome, genomic DNA of one P. spumarius individual from Quinta do Bom Sucesso, Lagoa de Óbidos  was extracted using the DNeasy Blood & Tissue Kit (Qiagen) and sequenced externally in GenoScreen  (http://www.genoscreen.fr/). A whole-genome shotgun sequencing approach using one lane of Illumina HiSeq 2000 to generate a paired-end library of approximately 366 million 100 bp reads was carried out. After sequencing, the quality of the sequence reads was assessed in FastQC v0.10.1 [De novo assembly of large genomes tends to be computationally demanding, requiring very large amounts of memory to facilitate successful assembly. Taking these conditions into account, the assembler SOAPdenovo2 [P. spumarius genome. This assembler implements the de Bruijn graph algorithm tailored specifically to perform the assembly of short Illumina sequences and is optimised for large genomes. A k-mer parameter of 33 was used for this assembly. The quality of the assembly results was investigated through several metrics: N50, percentage of gaps, number of contigs, number of scaffolds and genome coverage .To attempt potential  v0.10.1  and low  v0.10.1  and the Pdenovo2 , 63 was P. spumarius were obtained from Lexington, Fayette Co., Kentucky, USA in July 2013 and frozen at −80°C. Total RNA was extracted from 6 adult specimens by first grinding the entire body using a 1 mL glass tissue grinder with 1 mL Trizol (Invitrogen). This was followed by passing the homogenate over a Qiagen Qiashredder column. The eluate was extracted with 200 μL chloroform, and the RNA was precipitated with 500 μL isopropanol. Pellets were resuspended in RNAse-free water.Fresh adult specimens of Paired-end RNA libraries were prepared using Illumina’s TruSeq Stranded RNA sample preparation kit with an average cDNA size of 250 bp (range 80–550 bp). These libraries were sequenced using an Illumina HiSeq2500 machine with a TruSeq SBS sequencing kit version 1 analysed with Casava v1.8.2. Raw reads were filtered for duplicates using a custom script and trimmed for 5′ bias and 3′ quality using the FASTX-toolkit . TranscrP. spumarius, and nine females of P. maghresignus, a closely related species of the same genus. A suspension of nuclei from both the Philaenus sample and a reference standard  and 50 μg mL−1 of RNAse  were added to stained DNA and avoid staining of double stranded RNA, respectively. After 5 minutes of incubation, the nuclear suspension was analysed in a Partec CyFlow Space flow cytometer . Data was acquired using the Partec FloMax software v 2.4d  in the form of four graphics: histogram of fluorescence pulse integral in linear scale (FL); forward light scatter (FS) vs. side light scatter (SS), both in logarithmic (log) scale; FL vs. time; and FL vs. SS in log scale. To remove debris, the FL histogram was gated using a polygonal region defined in the FL vs. SS histogram. At least 1,300 nuclei were analysed per Philaenus’ G1 peak [Philaenus below 5% were accepted [1 peak mean/S. lycopersicum G1 peak mean) * genome size of S. lycopersicum. The obtained values were expressed in picograms (pg) and in giga base pairs (Gb), using the formula by [Genome size estimates were obtained through flow cytometry . A total1.96 pg; ) were pr at 4°C; ). The nu G1 peak . Only CVaccepted . The homaccepted ) was assrmula by  .Differences in genome size between males and females were evaluated using a one-way analysis of molecular variance (ANOVA), followed by a Tukey test for multiple comparisons at A consensus sequence, with IUPAC ambiguity codes for variable sites, was generated for each RAD locus across individuals using the python script loci_consensus.py .Homology to non-coding and coding regions was investigated for the inferred loci by locally querying consensus sequences against Arthropoda sequences available in the NCBI nucleotide database , using BLASTN 2.2.28+ . A proteP. spumarius genome and transcriptome assembled in this study. In this case, an E-value threshold of 1e-15 was chosen as the cutoff for restricting the alignments to the most significant ones. The top five contigs and/or scaffolds were subsequently investigated by querying them using BLASTN against Arthropoda sequences available in nucleotide and protein databases of NCBI.RAD loci were also queried using BLASTN against the drafts of the The sequencing set produced a total of 341 million reads. After filtering reads based on quality scores, 269 million reads were retained, corresponding to an average of 7.4 million reads per individual. Before filtering, individuals yielded 335,767 to 12,711,816 sequenced reads of 90 bp each  were randomly excluded.The average number of reads per locus per individual used to estimate a consensus sequence was 51.0  associated with dorsal colour phenotypes, corresponding to 12.52% of the analysed markers. When a more strict, 99% quantile, threshold was applied 50 BF0.99 SNPs (2.7%) showed the strongest associations to colour morphs, including eight shared among colour morph comparisons   , intermediate between TRI and TYP (FST = 0.2125) and the lowest between MAR and TYP (FST = 0.1787)  while the lowest proportion was found in MAR-TYP analysis (PVE = 0.6018) (Table 10(BF) > 10) only, the mean number of SNPs included in the model (nSNPs_BF) for each comparison decreased up to values between nine and 12 while the mean effect size of the SNPs (σSNP_BF) increased ranging between 2.4 and 4.1 (Table 0.99 SNPs) were identified for each analysis and investigated  , intermediate between TRI and MAR (FST = 0.1907) and the lowest genetic differences were observed between MAR and TYP (FST = 0.1650)  Table . Estimat8) Table . However8) Table . When costs Fig.  and for sts Fig.  showed tr2 > 0.7) were found for five pairs of SNPs within MAR and for two pairs in TYP phenotypes  and 8.90 ± 0.20 pg (8.90 Gb), respectively. In P. spumarius, males and females differed significantly in genome size , with males presenting on average a lower genome size  than females   were retained  were found against Arthropoda sequences of NCBI nr database , 18 of which were associated with colour morphs (34.62% of the colour-associated loci sequences)  . Some RAD loci had more than one contig/scaffold hit  aligned to the draft of Transcriptome and genome scaffolds/contigs with RAD loci alignments, ranging from 100 to 12,325 bp (gaps included), were queried against Arthropoda nt and nr databases using BLASTN and BLASTX. Out of 210 transcriptome sequences, 22  had homology with the nucleotide database , as estimated here by flow cytometry. This large size is among the largest genomes reported in insects [typicus, marginellus, and trilineatus), we were able to detect association with colour in 3% of the analysed SNPs . These phenotypes did not reveal significant genome-wide differences but when considering only the associated SNPs, the three colour morphs were differentiated and the trilineatus phenotype showed the highest genetic differentiation. Interestingly, greater differences involving life-history traits such as longevity, number of eggs, and number of oviposition events are also known to occur in trilineatus [In this study, we aimed to identify candidate genomic regions associated with colour polymorphism in the meadow spittlebug  insects , making lineatus . It may lineatus . Our fin0.99 SNPs) showed moderate phenotypic effects (0.10 < | β | < 0.15). In the multi-SNP association analyses, 50 SNPs with posterior inclusion probabilities at quantile 99% (PIP0.99 SNPs) showed large effects for pairs of colour phenotypes . From these, 40 were common to the SNPs identified in single SNP analyses (BF0.99 SNPs), increasing the confidence for the detected associations. Although inferences about the genetic architecture are only tentative in this study, due to the relatively small proportion of the genome covered, our results suggest that differences among the three dorsal colour phenotypes are associated with several loci with large effects. However, it is still not entirely clear if these constitute the major locus determining dorsal colour pattern, revealed by Mendelian crosses in P. spumarius [Heliconius species [Cepaea nemoralis [Timema cristinae stick insects [Heliconius melpomene, a cluster of three tightly linked loci , lying just a few centimorgans apart, as well as other unlinked loci have been shown to control distinct wing colour pattern elements in this species [Heliconius numata, polymorphic colour variation is controlled by a single locus P, forming a supergene, resulting from chromosomal rearrangements [doublesex, with closely linked mutations, also controls supergene mimicry in Papilio polytes [In the single-SNP association analyses, the 50 individual SNPs found to be associated at 99% quantile , suggesting that there is no enrichment/depletion of coding regions in the associated loci in relation to the total number of loci. Around 35% of the colour-associated loci aligned with the genome and 22% also aligned with the transcriptome. If we assume a good representation of the total transcriptome, this result point to the majority of the associated loci being in non-coding regions. Considering that our assembled genome represents only 24% of the total genome size, the low percentage of hits in the genome was expected. Also, the low number of nucleotide and protein matches of genome and transcriptome sequences is certainly due to the degree of similarity of P. spumarius to other available Arthropoda sequences being too low to allow significant matches. Increasing the genomic resources for this or related species will allow exploring the candidate loci here described and provide insight into some of the key questions that remain to be answered. What are the specific genes contributing to this balanced colour polymorphism? What mutations cause allelic differences in these genes and how do they contribute to the different colour phenotypes? Are there epistatic or additive effects among the alleles responsible for the polymorphism? Does this constitute a supergene? Are coding or regulatory mutations involved? In the future, it would also be interesting to investigate the evolutionary history of the colour polymorphism within Philaenus since identical variation in dorsal colour/pattern can be observed in the other species of the genus, suggesting an ancestral polymorphism maintained through the speciation process.Various genes and pathways have been reported to be involved in insect coloration and pigmentation. These pathways comprise genes regulating the distribution of pigments in space and time, as well as genes that are involved in the synthesis of pigments . Severalpp. (see  for a rebyx mori  and Papilio spp. , 89, 90 or optix , which iory loci . Anotheridoptera . Regulatla flies , 95. HowP. spumarius dorsal colour polymorphism, by studying single and multi-SNP association with three of the colour phenotypes. We detected several loci with large effects occurring in multiple genomic regions. Trilineatus was found to be the most differentiated colour phenotype for these loci, and since it is also the most differentiated for several life-history and physiological traits, we suggest that genetic bases of colour and of these traits are linked. The development of genomic and transcriptomic resources in this work was a first step toward characterizing these loci and will be very useful for further research on the genetic basis of dorsal colour pattern variation in P. spumarius.This work was a first approach to investigate the genetic architecture of


Ctenopharyngodon idella) is an important economic species in freshwater aquaculture and its industry has been confined due to variety degeneration and frequent diseases. Marker-assisted selection is a feasible method for selective breeding of new varieties. Transcriptome data have greatly facilitated high-throughput single nucleotide polymorphism (SNP) marker discovery and phenotype association study. In this study, we gained a total of 25,981 and 5,775 high quality SNPs in two transcriptomes from individuals and cell lines, respectively. Comparative transcriptome analysis identified 413 and 832 grass carp reovirus (GCRV)-resistant-association SNPs as well as 1,381 and 1,606 GCRV-susceptible-association SNPs in individuals and cell lines, respectively. Integrated analysis indicated 22 genes with single SNP share common resistant/susceptible traits in two transcriptomes. Furthermore, we infected grass carp with GCRV, genotyping and association analyses were performed, and 9 in 22 SNPs were confirmed by PCR-RFLP. Meanwhile, mRNA expression profiles of 6 genes containing confirmed SNPs were examined by qRT-PCR. The results demonstrated that mRNA expressions were significant differences in resistant/susceptible individuals and cell lines. The present study develops an important strategy for high throughput screening of phenotype association genetic markers and the results will serve in grass carp breeding for GCRV resistance.Grass carp ( Ctenopharyngodon idella) is an important economic freshwater fish in China and accounted for 15.6% of global freshwater aquaculture production in 20111. However, frequent diseases and growth degradation have restricted its development in aquaculture3. Grass carp reovirus (GCRV) is a disastrous pathogen causing hemorrhagic disease, mainly infecting young fingerlings and yearlings of C. idella and black carp (Mylopharyngodon piceus)4. This virus is widespread in south China and results in severe economic losses to aquaculture industry5. Numerous researches devote to find an effective approach to prevent this disease, i.e. drug screening, vaccine and RNAi6. However, comparing with those therapies, molecular breeding may be more environmentally friendly and sustainable. Inspiringly, the success of marker-assisted breeding (MAB) of a lymphocystis disease-resistant Japanese flounder  presents the prospect of anti-disease breeding7. As the primary task of MAB is to put plenty of genetic markers of immunity-associated genes on records8. Since the 1960s, various intra- and interspecific hybridization have been carried out and progresses have been made in the breeding of disease resistance in C. idella. As an example, specimens selected from a population in Heilongjiang River in China increase the GCRV resistance by 54.7%, which are harvested from the hybrid offspring of common carp (Cyprinus carpio) and C. idella, but has not been commercialized due to the genetic instability of hybrid offspring2. In summary, due to the long sexual maturation period (4–5 years) of C. idella, the traditional breeding method is inefficient and this is a common problem in fish breeding. Thus, it is crucial to improve the breeding efficiency to promote the cultivation of fish varieties.Grass carp (9. SNPs are the most abundant type of DNA sequence polymorphisms whose applications have been proved in genetic studies10. They have been applied in quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS) in model organisms and humans12. In aquaculture species, SNP markers are becoming the important genetic resources in linkage map construction and association studies. In recent years, more efforts have been made for SNP discovery in fish14. However, the number of SNPs is still insufficient for high density SNP chip construction and GWAS.Genetic single nucleotide polymorphisms (SNPs) in innate and adaptive immunity have aroused much attention and a number of studies have been conducted to identify SNPs in the genomes of diverse species14. Recently, transcriptome sequencing has become an important method for SNP discovery15. Through transcriptome sequencing, functional genes can be sequenced at high coverage, which ensures full-scale SNP discovery in protein-coding genes with high accuracy. Massive SNPs have been identified by transcriptome sequencing in aquaculture species such as catfish (Clarias gariepinus), Atlantic cod (Gadus morhua), oyster (Crassostrea gigas), shrimp (Litopenaeus vannamei), half-smooth tongue sole (Cynoglossus semilaevis), Atlantic herring (Clupea harengus), Atlantic salmon , silver carp  and C. carpio16. These data supply a large amount of genetic information related to development and disease resistance in aquaculture species. However, SNP discovery associated with resistance/susceptibility to virus has not been reported in the transcriptome of fish.Next-generation sequencing (NGS) technologies have made high-throughput SNP discovery feasible for non-model speciesSNPs applied in MAB is a potent method for selective breeding of disease resistance varieties. Herein, the transcriptomes of individuals and cell lines were employed to identify SNPs that involved in resistance/susceptibility to GCRV. Two transcriptomes were integrated to look for their communal variation locus to improve the SNPs accuracy, additionally, the candidate SNPs were confirmed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) experiments. Furthermore, genotyping and association analyses were conducted to study the relationship between SNPs and antiviral activity by an independent infection experiment. Meanwhile, mRNA expression profiles of genes corresponding to confirmed SNPs were examined by quantitative real-time RT-PCR (qRT-PCR) as well as transcriptome data analysis in resistant and susceptible groups. These results provide precious resources for molecular and genetic breeding as well as immune researches.17. In the present study, SNP only refers to single base substitution (transition and transversion), InDel represents single base insertion and deletion. Meanwhile, in order to improve the accuracy of SNP analysis, those with total read depth over 20 and mutation read depth over 10 are regarded as high quality SNPs and InDels. A total of 33,433 and 12,081 high quality SNPs were picked out from SS1, SR2, KS3 and KR4 libraries of individuals and C1, R2 and S3 libraries of cell lines, respectively  in individuals and Illumina HiSeq2500 2 × 125 bp) in cell lines generated 20.04 Gb (89.07% of raw data) and 17.23 Gb (96.74% of raw data) clean data bulk, respectively  and 413 identical SNPs between SR2 and KR4 (excluding SS1 and KS3) in individuals, 832 specific SNPs in R2 (excluding S3) and 1,606 specific SNPs in S3 (excluding R2) in cell lines Fig. . Meanwhi18, statistical analyses of read depth for each SNP were performed in the transcriptomes of individuals  and cell lines , respectively  categories and kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed to annotate genes containing resistant/susceptible SNPs in individuals. Totally, 1,024 transcripts  consist of 272 resistant transcripts 413 SNPs) and 752 susceptible transcripts , in which, 129 resistant transcripts (192 SNPs) and 401 susceptible transcripts (783 SNPs) can be blasted to the corresponding genes according to the genome annotation of  SNPs andIn addition, the top 20 assignments to KEGG pathways in resistant and susceptible groups are shown in Fig. Gsto, Yes, C7N1 and Napi-llb2 genes contain three genotypes, Hnrpa and Chsg have two genotypes, while Hiat, Mef2d and Zfyve26 are heterozygote and possess just one genotype . Aside from this SNP, there was no other SNP that was significantly related to the resistance/susceptibility to GCRV (P > 0.05)  is almost 100%. Interestingly, D. rerio post GCRV challenge is nearly 100% survival20. In order to investigate the differences among them, comparisons of genomic base types of the corresponding SNP positions among C. idella, G. rarus and D. rerio were listed in Supplementary Table C. idella were listed in Supplementary Table C7N1, which can cause a change of amino acid (M/I). C7N1 is a novel gene that is homologous with human C15orf39 and locates on chromosome 7 (named as C7N1). Interestingly, comparative genomic analysis revealed that corresponding base of C7N1 SNP in G. rarus and D. rerio were G and A, respectively (Supplementary Table http://www.expasy.org/tools/). But these results revealed that there were no differences among them.The mortality of 21. To investigate mRNA expression levels of genes corresponding to resistant/susceptible SNPs, we selected 6 genes with validated genetic variation to examine their mRNA expression levels in spleen tissue and CIK cells. The results showed that their expression patterns were accordant between RNA-Seq and qRT-PCR. Meanwhile, mRNA expression profiles were similar between individuals and cell lines except for Hnrpa  have been reported in the C. idella genome1. However, SNPs and InDels associated with resistance/susceptibility to GCRV were not reported by omics sequencing till now. Population differentiation has also been observed in Streblospio benedicti28. Abundant genetic diversity in wild C. idella germplasm resources is observed and the breeding of improved C. idella varieties has a good genetic basis2.It is notable that substitution is conserved in the process of evolution18. One advantage of Illumina sequencing platform is the higher read depth comparing to 454 sequencing platform29, which could ensure that most of expected SNPs in the sequenced population could be detected30. It’s worth noting that SNPs with much higher read depth should be excluded since too high read depth might be caused by paralogous sequence variants31. In this study, read depth of SNP positions between 10 and 59 accounted for majority, meanwhile, all read depth did not surpass 300. These were enough to ensure the accuracy of predicted SNPs. What’s more, genes with single SNP were more common, which increases the coverage of SNPs associated with resistance/susceptibility to GCRV in C. idella genome. In view of the whole genome assembly, the exact assessment pattern of the SNP distribution in the genome is possible. For this reason, when these genes containing SNPs associated with resistance/susceptibility to GCRV were plotted to the C. idella genome by BLAST analysis, they had a good coverage of all 24 chromosomes. Some SNPs were not plotted to the corresponding chromosomes due to the gaps in genome sequence, which is acceptable at the genomic scale. These sequences and SNPs will provide novel materials for genome sequence amendment and facilitate the studies on selective breeding and immune mechanisms.Owing to the read depth play important roles in prediction accuracy of SNPs33. GO subcategories or KEGG pathways with high ratio of SNPs are the major concerns. Our results revealed that GO subcategory with the highest SNP enrichment ratio was “binding” in resistant and susceptible groups. GO analysis also showed that some cellular components tended to be less polymorphic. Whereas KEGG pathway analysis showed that some pathways tended to be more polymorphic. Because disease-related mutations are unequally distributed throughout protein sequences, having a higher occurrence in structurally/functionally important sites, we can expect the number of localization mutations to be higher than that of the calculated. Localization mutations are rare events, but they should be taken into account when predicting consequences of mutations34. Biological systems are defined by their components and the interactions among these components. Likewise, mutations can affect the components and their interactions. Mutations that alter interactions are most likely to be detrimental36. These observations should be explored and verified in future studies.In this study, we identified many differential genes between resistant and susceptible population in individuals. To better understand the molecular functions of these candidate genes, we performed GO classification and KEGG pathway analyses. The ratio of differentially expressed sequences to the total sequences of corresponding GO categories or KEGG pathways were regarded as the major criteria for enrichment assessment37. Therefore, genotyping-by-sequencing (GBS) may be employed for further gene fine-mapping and allele analysis38. All the high-quality sequence reads were aligned to the C. idella whole genome1, with the objective of SNP identification. In this work, we picked up 22 SNPs associated with resistance/susceptibility to GCRV infection for verification and 9 SNPs were validated by PCR-RFLP. With SHEsis genotyping and association analysis in 30 resistant and 30 susceptible individuals, the mutation (4807309 A/G) in C7N1 gene was significantly associated with resistance/susceptibility to GCRV infection in individuals. Our study preliminarily showed that heterozygous genotype A/G was significantly resistant to GCRV infection than homozygous genotype A/A and G/G (P < 0.05). Furthermore, comparative genomic analysis showed that the mutation site (4807309 A/G) in C7N1 gene corresponds to A and G in D. rerio (resistant to GCRV) and G. rarus (susceptible to GCRV) respectively. These results may suggest that the base G was associated with susceptibility to GCRV and the heterozygous genotype can improve the ability of resistant to GCRV in C. idella. Similarly, heterozygous SNP variation can contribute to increase latex yield in the hybrid21. The molecular functions of C7N1 have not been studied in fish and even higher vertebrates. The relationship between above significant SNPs and resistance/susceptibility to GCRV was preliminarily verified by an independent infection experiment, these significant SNPs can be considered as candidate genetic markers and further verification needs to be done in other natural populations in the future. In the present study, SNPs were identified at the antiviral transcriptome level, which will enable us to further understand the roles of SNPs in antiviral responses.A higher number of short-read alignments at regions of interest may be helpful in more precisely resolving the real allele frequency of mutant allele in bulked DNA39. Several studies on closely related species indicate that there is a genetic basis for differences in transcript levels40, which could lead to adaptive divergence in the wild42. In this study, Hnrpa has different expression patterns between individuals and cell lines, which may indicate the difference in antiviral immune mechanism between in vivo and in vitro. Quantitative estimate of gene expression can be associated with change in nucleotide sequence43. Most SNPs controlling gene expression occur outside the coding regions of genes, so finding relationship between SNP genotype and expression level can provide an indirect link between SNP and phenotype44. Other methods can furthermore be used to study regulatory network changes by analysing co-expression patterns and associating with nucleotide changes and phenotypic traits45. The superiority of this approach is that experiments on natural selection for gene expression differences can now be monitored in a more effortless way than that in the past. Finally, cross-species comparisons of transcriptomes have recently shown promise for conservation genetics of endangered animals46 and also for enhanced understanding of the fundamental principles of population genomics47, allowing us to potentially predict the responses of natural populations to future environmental perturbations.A powerful feature of transcriptome is fully examining changes of gene expression levels among individuals or populations. There has long been a realization that gene expression differences play vital roles in species differentiation and population adaptationC. idella is labor-intensive and time-consuming48. Therefore, it is necessary for breeders to use an effective selection method to increase breeding efficiency as opposed to the traditional pure phenotype-based selection process. Natural germplasm resources of wild C. idella were genetically diverse, which provides the basis for constructing basal populations for QTL and GWAS analysis2. This study is an important step towards the generation of SNPs in specific feature and provides precious SNP resource for the selective breeding of resistant varieties. In addition, our surveys provide valuable information that will facilitate the studies on genomic variation underlying traits of interest in fish, including immune responses, regulatory mechanisms and environmental adaptability.SNP as molecular marker plays vital roles in animal breeding. As an important economic fish with long growing and breeding cycle, conventional breeding in All experiments were approved by the Animal Care Committee of Huazhong Agricultural University. The Administration of Affairs Concerning Animal Experimentation Guidelines stated approval from the Science and Technology Bureau of China. The methods were carried out in accordance with the approved guidelines. Total 200 experimental individuals were fed in four 300-liter aquaria with suitable illumination, water temperature, dissolved oxygen content, and adequate forage in the Huazhong Agricultural University, China. Approval from the Department of Wildlife Administration is not required for the experiments conducted in this paper. All surgery was performed to minimize suffering by using 3-Aminobenzoic acid ethyl ester methanesulfonate (MS-222) anesthesia.C. idella were infected with GCRV ). In this process, the moribund fish with hemorrhagic symptom between 24 and 72 hours post challenge were regard as susceptible individuals and the surviving fish after 10 days post challenge were resistant individuals. The spleen and head-kidney tissues from 12 resistant and 12 susceptible individuals were collected and their RNA-Seq libraries were constructed and sequenced, which were divided into 4 groups: spleen tissue from susceptible fish (SS1) and resistant fish (SR2), head-kidney tissue from susceptible fish (KS3) and resistant fish (KR4). These transcriptome data were deposited in the NCBI with the Sequence Read Archive (SRA) accession number of SRP0490818. Meanwhile, to obtain monoclonal cells, CIK cells were digested and then filtered twice with a 150 μM nylon mesh. 100 μL Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 7% fetal bovine serum (FBS), 100 U/mL of penicillin and streptomycin sulfate as well as 5 μL/mL insulin  were added to each well of the 96-well culture plates beforehand, the single cell was ultimately instilled utilizing the BD FACSAria™ III Cell Sorter (USA) and subcultured up to being the monoclonal cells with a cell density of 5 × 104/well in 96-well plates. Identified as resistant, susceptible or ambiguous by three strategies: (1) CPE analysis. (2) Cell proliferation assay. (3) Antiviral activity assay. These samples were divided into control , resistant (R2) and susceptible (S3) groups. The RNA-Seq data were obtained by Illumina HiSeq2500 sequencing technology and deposited in the NCBI Gene Expression Omnibus with accession number of GSE87414. All reads were filtered with NGS QC Toolkit for further analysis.For the viral challenge, healthy C. idella genome using BWA version 0.5.9 (http://bio-bwa.sourceforge.net/) with the default settings except for no gap tolerance. The data from individuals  and cell lines  were aligned for SNP and InDel identification. One reliable and frequently used software program, SAMtools, was independently applied for the identification of SNP and InDel with C. idella genome as reference sequences. InDel represents single base insertion and deletion. The software package SAMtools (http://samtools.sourceforge.net/) was used to convert sequence alignment/map (SAM) file to sort binary alignment/map (BAM) file. And the command dump was employed to remove duplicates. SNPs were further investigated by BCFtools. Those with total read depth more than 20 and mutation read depth over 10 were identified as high quality SNPs or InDels. Since accuracy of SNP prediction is dependent on sequence coverage30, we combined two transcriptomes to improve the prediction accuracy.Short reads of two transcriptomes were separately mapped to 8. SNP and InDel ratios were obtained by each type of DNA substitution and SNP read depth in the result file in SAMtools. SNPs involved in resistance/susceptibility to GCRV were analysed using the tool (venny 2.1) of BioinfoGP (http://bioinfogp.cnb.csic.es/index.html) in individuals and cell lines, respectively. Meanwhile, the number of these SNPs in genes  was analysed. And the BLAST was employed to analyse the chromosome location of genes with resistance/susceptibility SNP in individuals and cell lines.The ratio of mapped reads in each dataset was calculated by flagstaff command in SAMtools software49. After that, BLAST2GO software were employed to allot the genes with GO terms of biological process, molecular function and cellular component33. Subsequently, annotated information was imported into BGI WEGO program (http://wego.genomics.org.cn) in WEGO native format to plot GO annotation results. KEGG pathways were assigned to genes containing SNP associated with resistance/susceptibility to GCRV by the online KEGG Automatic Annotation Server (KAAS) (http://www.genome.jp/tools/kaas/). KEGG Orthology (KO) assignment was applied by Bi-directional Best Hit (BBH) method.The genes possessing SNPs associated with resistance/susceptibility to GCRV were annotated using NCBI non-redundant database by BLASTX C. idella was found in recent years, which were different population from RNA-Seq. Fish were injected with GCRV  at a dose of 3.63 × 107 TCID50/g as previous report6. The moribund fish were sampled between 24 h to 72 h post injection and the surviving fish after 10 days post-injection were serenely sacrificed. Spleen tissue was collected and kept at −80 °C until DNA and RNA isolation. These samples were divided into resistant and susceptible groups. DNA and RNA were prepared for SNP verification and gene expression analyses.For verification experiment, grass carp (approximate body length of 10 cm) were collected from three fish farms  where no hemorrhagic disease of 50 and sequencing all samples. The primers were designed to amplify the target sequence with fragment length of 100 ~300 bp containing polymorphism sites and synthesized in TsingKe Biotech   Table  accordinhttp://analysis.bio-x.cn) software were employed to estimate allele and genotype frequencies and analyse their relationship with resistance/susceptibility to GCRV. The logistic regression model was performed to verify the interaction by using SPSS 16.0 software, the odds ratio (OR) and 95% confidence interval (95% CI) were calculated. Propensity covariate adjustment was also performed to verify results. P-value less than 0.05 was considered to be statistically significant. HWE for genotypic frequencies was evaluated by the goodness-of-fit χ2-test for each genotyped SNP.Single site analysis and haplotype analysis of SHEsisPlus online version - beta in SHEsis . 18S rRNA and EF1α genes were employed as reference genes in individuals and cell lines separately. qRT-PCR and data analysis were performed according to the protocol and method as described previously51. Fold changes of gene expression levels were relative to corresponding susceptible samples (defined as 1). P-value less than 0.05 was accepted as significant difference .To confirm the expression profiles of genes containing resistant/susceptible SNPs, qRT-PCR was performed using a Roche LightCyclerSupplementary tablesDataset 1Dataset 2Dataset 3Dataset 4Dataset 5Dataset 6Dataset 7


Oryza glaberrima (African rice) and Oryza sativa (Asian rice) segregating for AC was analysed by sequencing bulks of individuals with high and low AC. SNP associated with the granule bound starch synthase (GBSS1) locus on chromosome 6 were polymorphic between the bulks. In particular, a G/A SNP that would result in an Asp to Asn mutation was identified. This amino acid substitution may be responsible for differences in GBSS activity as it is adjacent to a disulphide linkage conserved in all grass GBSS proteins. Other polymorphisms in genomic regions closely surrounding this variation may be the result of linkage drag. In addition to the variant in the starch biosynthesis gene, SNP on chromosomes 1 and 11 linked to AC was also identified. SNP was found in the genes encoding the NAC and CCAAT‐HAP5 transcription factors that have previously been linked to starch biosynthesis. This study has demonstrated that the approach of sequencing bulks was able to identify genes on different chromosomes associated with this complex trait.Amylose content (AC) is a key quality trait in rice. A cross between  Oryza glaberrima, commonly referred to as African rice, is one of the two independently domesticated rice species, the other one being Oryza sativa, which is also commonly referred to as Asian rice. African rice is a valuable source of genetic diversity for rice improvement. Its genetic potential in terms of resistance/tolerance to biotic and abiotic stresses has been well documented and deployed in rice improvement  , with several transcription factors (TFs) being reported to be involved in the transcriptional and post‐transcriptional regulation of this starch gene. Among these include OsBP‐5, a MYC TF  TF  . These include two SNPs that are located in the 5′ UTR and 2 nonsynonymous SNPs located in the exonic regions , Histone‐fold domain‐containing protein (LOC_Os01g01290) both of which are located on chromosome 1 and a NAC  TF located on chromosome 11 (Table glutelin subunit mRNA (LOC_Os01g55690) and basic helix‐loop‐helix (OsbHLH071) located on chromosome 1.In addition to the variants located on chromosome 6, other amylose‐linked SNPs were found on chromosome 1 and 11. Some of the amylose‐linked SNPs located outside chromosome 6 were found in genes that have previously been associated with starch biosynthesis. These include The identified marker sites had the alleles from the high amylose parent significantly over‐represented in the HAB. Under the BSA approach, it would be expected that approximately 50% of the reads in loci that have no genetic effect on the trait of interest would be derived from each of the two parental genomes. On the other hand, allele frequency would differ noticeably in genomic regions controlling the trait, with such regions showing significant over‐representation of reads from one of the parental genomes.et al., Studies have indicated that very low coverage in genetic marker sites may lead to sampling error which may cause spurious deviations in allele frequency from the expected 50% segregation pattern in regions not linked to the phenotype  and CCAAT_HAP5 (LOC_Os01g01290) TFs were co‐expressed with various starch genes. NAC TF was co‐expressed with six starch biosynthesis genes among them being GBSS1 and MADS29, both of which are involved in regulating amylose biosynthesis. Similarly, CCAAT_HAP5 TF was also co‐expressed with GBSS1 among other starch genes  and pullulanase also play minor roles in amylose synthesis  and CCAAT‐HAP5 (LOC_Os01g01290) families. Though both of these TFs have previously been reported to be co‐expressed with starch synthesis genes showing preferential expression in both the endosperm and vegetative tissues  was found to cause increased protein content, zinc and iron by accelerating senescence which subsequently increased nutrient remobilization , pullulanase, SSIIIa, starch branching enzyme 1 (SBE1) and SSI play minor roles in amylose synthesis by interacting with GBSS1. These starch genes were however not functionally associated with amylose in the mapping population studied here.This study used an integrative genetics approach that employed a combination of genetic mapping strategies to enhance the discovery and use of useful genetic variation. Among the approaches used is gene co‐expression analysis which has successfully been used in identifying regulatory factors involved in various metabolic pathways. The two candidate TFs that were found to be putatively associated with amylose were co‐expressed with various starch genes Table . The majn et al.  and Sun   which is among the genes located in this genomic region to be differentially expressed and to be associated with AC. However, although this study found this gene to have some association with AC, we identified a different SNP from the one reported by Butardo et al.  (BC2F8) may also have contributed to the increased resolution. In a simulation study, Takagi et al. . AC was measured using the Megazyme amylose/amylopectin assay kit  according to manufacturer's procedure.et al., et al., Bulk segregant analysis  protocol that was originally published by Carroll et al. . DNA quaO. sativa Nipponbare reference  using a quality limit of 0.01 . The trimmed reads were then separately mapped against the e IRGSP,  and the SNPs were called using the basic variant detection tool with a minimum coverage of 20, read count of 20 and allele frequency of 90% for the two bulks and a minimum coverage of 5, read count of 5 and allele frequency of 100% for the parents. After SNP calling, a two‐way comparison of SNPs identified in the two bulks was conducted, with SNPs present in both bulks being filtered out. SNPs with a phred score of <30 were filtered out. Additionally, positions with multi‐allelic nucleotide variations as well as those showing nonparental alleles were excluded as these were likely to be false positives which could have been caused by sequencing errors or errors in variant detection. The analysis pipeline that we used is shown in Figure Analysis for marker‐trait association was then conducted by quantitative evaluation of allele frequencies of the identified nonsynonymous SNPs. The contribution of the parental alleles in the bulked DNA was assessed with the focus being genetic marker sites where the allele from the high amylose parent (African rice) showed over‐representation in the HAB. A cut‐off of 90% allele frequency in the high bulk was used while over‐representation of the alternate allele in the LAB was assessed by a statistical test. The analysis for candidate markers was conducted in two steps. In the first step, the analysis focused on candidate marker positions where the parental genomes were fully homozygous. In the second step, the focus was on genetic marker sites where the parents were heterozygous but the two bulks were fully homozygous for different SNP alleles. Candidate marker positions obtained from this analysis were subject to further statistical analysis.P < 0.05 were retained for further analysis.A chi‐square test was carried out on the allele frequency of the identified SNPs in both bulks separately to assess the cosegregation pattern of the parental alleles in the two bulks. Specifically, the chi‐square test was used to determine the causativeness of the identified SNPs by assessing whether they follow a binomial distribution of 1 : 1 as would be expected of noncausative mutations. Only SNPs that did not follow a binomial distribution of 1 : 1 in both bulks at et al.,et al.,et al.,et al.,et al.,Using the principle of gene co‐expression, the current study sought to identify candidate genes with similar expression patterns with known starch biosynthesis genes. The principle of gene co‐expression is based on the assumption that genes with similar expression patterns are likely to be functionally related. A total of 23 starch synthesis‐related genes (Ohdan http://PlantPAN2.itps.ncku.edu.tw)  (LOC_Os06g04200).Figure S2 Spatial‐temporal expression of histone‐fold domain containing protein (LOC_Os01g01290).Figure S3 Spatial‐temporal expression of NAC Transcription factor (LOC_Os11g31330).Table S1 Sequencing statistics.Table S2 Statistics of reads uniquely mapped against O. sativa and O. glaberrima references.Table S3 Nonsynonymous candidate SNPs putatively associated with amylose content in African rice.Table S4 O. glaberrima and O. barthii accessions used in the analysis of GBSS1 gene.Table S5 Cis regulatory motifs present in the candidate genes.Click here for additional data file.


Capsicum annuum domestication. In this study, we used a diverse collection of C. annuum to generate 66,960 SNPs using genotyping by sequencing. The study identified 1189 haplotypes containing 3413 SNPs. Length of individual linkage disequilibrium (LD) blocks varied along chromosomes, with regions of high and low LD interspersed with an average LD of 139 kb. Principal component analysis (PCA), Bayesian model based population structure analysis and an Euclidean tree built based on identity by state (IBS) indices revealed that the clustering pattern of diverse accessions are in agreement with capsaicin content (CA) and fruit weight (FW) classifications indicating the importance of these traits in shaping modern pepper genome. PCA and IBS were used in a mixed linear model of capsaicin and dihydrocapsaicin content and fruit weight to reduce spurious associations because of confounding effects of subpopulations in genome-wide association study (GWAS). Our GWAS results showed SNPs in Ankyrin-like protein, IKI3 family protein, ABC transporter G family and pentatricopeptide repeat protein are the major markers for capsaicinoids and of 16 SNPs strongly associated with FW in both years of the study, 7 are located in known fruit weight controlling genes.Accumulated capsaicinoid content and increased fruit size are traits resulting from  Capsicum originated in Bolivia and consists of 25 to 30 species; five are domesticated: C. annuum, C. baccatum, C. chinense, C. frutescens, and C. pubescens1C. annuum is the most popular and was first introduced from the West Indies to Europe in March 1493, with the first travels of Christopher Columbus356The pepper genus C. annuum cv. CM334 is 650.2 Gb (186.6× genome coverage)Zunla-1 (C. annuum L.) and its wild progenitor Chiltepin (C. annuum var. glabriusculum) were made available by Qin et al.C. annuum cultivarsC. annuum genome will help group SNPs into haplotypes and, their use in genome analysis can lead to understanding the consequences of selection and breeding histories across the collections of C. annuum L.For many crop species, identification and utilization of single nucleotide polymorphisms (SNPs) have become economical because of the availability and abundance of various high-throughput technologies. Recently, three whole-genome sequence (WGS) drafts of hot pepper were released for public use, which opened up unique opportunities for pepper research. The WGS for C. annuum. Despite several QTL studies of pepper1516171819C. annuum populations by using SNPs identified by GBS.Genome-wide association study (GWAS) with SNPs generated by genotyping by sequencing (GBS) has been widely used in all major crops including maize, rice, barley, tomato, wheat, sorghum, soybean, watermelon and several other important plant species10111213C. annuum accessions studied; 7678, 7751, 8201, 6285, 4724, 6087, 5230, 3873, 3826, 4279, 3768 and 5268 SNPs were mapped to the WGS draft  and FW classifications were created to understand the relationships of accessions. This analysis produced a close cluster of hot peppers on CA-PCA and bell peppers as separate cluster  coefficients among all pairs of accessions . Allele FST) between domesticated and wild accessions was 0.10. The FST between low and high, low and medium, and medium and high capsaicin types was 0.08, 0.02 and 0.03, respectively, and that between small and large, small and medium, and medium and large FW types was 0.15, 0.03 and 0.07, respectively. The highest FSTwas noted for SNPs on chromosomes 1, 2, 4, 6, 7 and 8, so these regions are under positive selection. We annotated 122 regions showing strong positive selection and identified important genes for FW  algorithm2  and D’ (LD estimate) values for the EM and CHM methods are in 2 and we compared the pattern of decay at different levels. First, with pair-wise analysis considering SNPs across chromosomes, we noted LD decay on average, with an average block size of 139 kb , S3_211558976 -log10 P = CA 4.0 and DCA 3.3), S5_215972421 (-log10 P = CA 3.6 and DCA 3.9), S5_227837981 (-log10 P = CA 3.4 and DCA 3.8), S5_229634509 (-log10 P = CA 3.6 and DCA 4.5), S6_203416571 (-log10 P = CA 3.9 and DCA 3.5), S10_156251204 (-log10 P = CA 3.0 and DCA 5.8), S10_172735351 (-log10 P = CA 3.1 and DCA 4.0), S10_221317647 (-log10 P = CA 4.0 and DCA 3.9), S10_225598553 (-log10 P = CA 3.9 and DCA 3.5), S11_83592400 (-log10 P = CA 3.4 and DCA 3.7), S11_85543247 (-log10 P = CA 4.2 and DCA 6.3), S11_85543251 (-log10 P = CA 3.6 and DCA 5.3), and S11_85543257 (-log10 P = CA 4.2 and DCA 6.3).+/H+ antiporter and lucose-6-phosphate 1-dehydrogenase, S3_230322338 (-log10 P 2011 = 3.3 and 2012 = 3.9) of the intergenic space between SNF1-related protein kinase and RAD50-interacting protein S3_230372266 (-log10 P 2011 = 3.5 and 2012 = 3.1) located in Ubiquitin-like modifier-activating enzyme 5-like (thiamine synthesis) and S5_131824978 (-log10 P 2011 = 3.9 and 2012 = 3.9) of an unknown protein. We also found an association of S6_202147247 (-log10 P 2011 = 3.9 and 2012 = 3.9), S6_202147285 (-log10 P 2011 = 3.1 and 2012 = 3.2), S6_202147337 (-log10 P 2011 = 3.1 and 2012 = 3.2) and S6_202147420 (-log10 P 2011 = 3.1 and 2012 = 3.2) located in the intergenic space between STYLOSA protein and flavin monooxygenase in both years. We found an association of S6_227195619 (-log10 P 2011 = 3.9 and 2012 = 3.8) of chloroplastic-FANTASTIC FOUR (FAF)-like protein, S8_132459145 (-log10 P 2011 = 6.8 and 2012 = 3.7) of DnaQ-like exonuclease, S9_250224149 (-log10 P 2011 = 6.8 and 2012 = 3.7) of mitochondrial-processing peptidase subunit alpha, S10_229225552 (-log10 P 2011 = 3.1 and 2012 = 5.0) of cell division control protein 45 (CDC45), S11_94177155 (-log10 P 2011 = 3.8 and 2012 = 3.0) of Clathrin assembly protein, and S12_72971688 (-log10 P 2011 = 7.06 and 2012 = 3.9) in the intergenic space between CLAVATA1 receptor kinase and pentatricopeptide repeat. S6_227195619, S6_204246361, S10_229225552 were nonsynonymous SNPs located in isopenicillin N epimerase (AAT_I superfamily), chloroplastic-FAF-like protein, cell division control protein 45 (CDC45) and TRS120 isoform, respectively, which have biological processes involving cell division and meristem organization.Despite the high variation noted for FW across the 2 years of the study, 15 common SNPs were found strongly associated  expression. Strong interaction between FAF and WUS determine the fate of meristem activityS12_72971688 is an intergenic SNP close to CLAVATA 1 receptor kinase that showed a strong association with FW in both seasons. Arabidopsis plants homozygous for mutations at the CLAVATA1 (CLV1) locus, a receptor kinase protein, accumulate excess undifferentiated cells37et al. 2011). The marker S12_72971688 linked to the locus Ca12g10030 (PPR) might play key roles in determining FW in pepper as QTL fw3.2 does in tomato.Fine mapping of the QTL fw3.2 controlling FW in tomato was accomplished recentlyet al.et al.394142Given our SNP density and sample size, this study is not sufficiently robust to find alleles of small effect among the pepper secondary centers of origin distributed across the world. However, we located SNPs with major effect in highly inbred, diverse and smaller population. Some of the strongest signals are quite far from known capsaicin genes possibly because of ascertainment bias. Also SNPs located in the candidate genes may not be in LD, which hampers their identity in association mapping strategies. Furthermore, stringent control for population stratification and IBS may eliminate useful SNPs located in the candidate genes. Such tradeoffs are part of GWAS, especially in reducing spurious associationsCapsicum.Our GWAS for capsaicin and dihydrocapsaicin content and FW identified SNPs located in the candidate genes known to have similar biological functions in tomato and other plants for these domestication related traits. Accessions containing higher minor allele frequencies from this study can be used to generate nested association mapping populations to validate GWAS results and further dissect the complex interaction among genes involved in the pleotropic effects on fruit size and capsaicin content evident in C. annuum belonging to various countries representing a wide geographical area of the world for the molecular diversity analysis .Genomic DNA was isolated using the DNeasy plant mini kit , and GBS was as described44Ks in each dataset was evaluated by ΔK values estimated and visualized with the software Structure Harvester, (www.taylor0.biology.ucla.edu/structureHarvester)www.goldenhelix.com).For quantitative assessment of the number of clusters in the GWAS panel, we used a Bayesian clustering analysis with a model-based approach implemented in STRUCTURE v2.2FST) estimation was based on Wright’s F statistichttp://goldenhelix.com/). Annotation and gene ontology terms for genes from the selective sweeps were identified with the WGS draft at http://peppergenome.snu.ac.kr.Fixation index .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.