Calamus simplicifolius and Daemonorops jenkinsiana are two representative rattans, the most significant material sources for the rattan industry. However, the lack of reference genome sequences is a major obstacle for basic and applied biology on rattan.C. simplicifolius and D. jenkinsiana using Illumina, Pacific Biosciences, and Hi-C sequencing data. A total of ∼730 Gb and ∼682 Gb of raw data covered the predicted genome lengths (∼1.98 Gb of C. simplicifolius and ∼1.61 Gb of D. jenkinsiana) to ∼372 × and ∼426 × read depths, respectively. The two de novo genome assemblies, ∼1.94 Gb and ∼1.58 Gb, were generated with scaffold N50s of ∼160 Mb and ∼119 Mb in C. simplicifolius and D. jenkinsiana, respectively. The C. simplicifolius and D. jenkinsiana genomes were predicted to harbor  51,235 and  53,342 intact protein-coding gene models, respectively. Benchmarking Universal Single-Copy Orthologs evaluation demonstrated that genome completeness reached 96.4% and 91.3% in the C. simplicifolius and D. jenkinsiana genomes, respectively. Genome evolution showed that four Arecaceae plants clustered together, and the divergence time between the two rattans was ∼19.3 million years ago. Additionally, we identified 193 and 172 genes involved in the lignin biosynthesis pathway in the C. simplicifolius and D. jenkinsiana genomes, respectively.We produced two chromosome-level genome assemblies of de novo assemblies of two rattan genomes (C. simplicifolius and D. jenkinsiana). These data will not only provide a fundamental resource for functional genomics, particularly in promoting germplasm utilization for breeding, but also serve as reference genomes for comparative studies between and among different species.We present the first  Calamus  and Daemonorops (NCBI Taxon ID: 93268) are the most diverse, accounting for ∼65% and ∼20% of rattan species [Rattan is one of the world's most important nontimber forest products and represents a major lineage of climbing palms occurring naturally in the Old World . A recenCalamus simplicifolius (NCBI Taxon ID: 746888) is a deeply developed rattan species indigenous to China , a representative species of high-climbing evergreen rattan, is one of the rattan species in the Daemonorops genus  and mapping (Hi-C) technologies. With the availability of these two chromosome-level reference genomes in rattan, many comparative genome analyses and other downstream applications will become feasible, such as the development of biomarkers, the identification of functional genes, and molecular design breeding. Additionally, high-quality genome assemblies of rattan will facilitate genomic, transcriptomic, and metabolomic analyses of its material traits. As genes of possible specific interest for material improvement, members of gene families involved in lignin biosynthesis in rattan are identified here. These studies lay a foundation for future research on the utilization of these genes to improve rattan quality and diversity within rattan germplasm.C. simplicifolius and D. jenkinsiana in Spring 2015 at the Research Institute of Tropical Forestry of the Chinese Academy of Forestry in the city of Guangzhou, Guangdong Province, China . Total DNA was isolated and extracted using DNeasy Plant Mini Kits (Qiagen) based on the manufacturer's instructions. Genomic DNA was purified according to the isolation protocol for high-molecular-weight nuclear DNA. Multiple DNA libraries were constructed [C. simplicifolius genome, and 426.17 Gb of clean data , representing 266 × coverage of the D. jenkinsiana genome.Young leaves at the vegetative growth stage were collected from C. simplicifolius and D. jenkinsiana using the same young leaves in BGI-Qingdao [MboI restriction enzyme to digest the DNA after its conformation was fixed by formaldehyde and then repaired the 5′ overhangs using biotinylated residues. Following the ligation of blunt-end fragments in situ, the isolated DNA was reverse-crosslinked, purified, and filtered for biotin-containing fragments. Subsequently, DNA fragment end repair, adaptor ligation, and polymerase chain reaction were performed, in that order. Then, the standard circularization step of BGISEQ-500 was carried out, and sequencing was performed using BGISEQ-500 sequencing with 100PE reads [C. simplicifolius and D. jenkinsiana, respectively  were filtered out using NGS QC Toolkit (version 2.3.3) [C. simplicifolius and ∼1.61 Gb for D. jenkinsiana, and the related heterozygosity was estimated at 1.32%∼1.52% and 1.19∼1.31%, respectively. Thus, the genome survey suggested that these two rattan genomes might be suitable for a hybrid sequencing strategy using the Illumina and PacBio data.An understanding of the genomic characteristics of a given new species, i.e., genome size and heterozygosity, facilitates the development of a customized sequencing and assembly strategy. Thus, the genome size was estimated using four independent methods: a script of KmerSpectrumPlot.pl in ALLPATHS-LG (version r52488) , GCE Ge. In our n 2.3.3)  with theC. simplicifolius and D. jenkinsiana, respectively. For the PacBio data, we used MECAT (released 27 June 2017) to correct errors [C. simplicifolius and D. jenkinsiana, respectively. Subsequently, FALCON (version 0.3) [C. simplicifolius genome: a 1.59 Gb assembly with a contig N50 of 67.2 kb (∼80% of the estimated genome size) and a 1.53 Gb assembly with a contig N50 of 66.7 kb (∼77% of the estimated genome size). Additionally, a 1.27 Gb assembly with a contig N50 of 81.5 kb (∼79% of estimated genome size) was obtained for D. jenkinsiana. The performance of MECAT for the two rattans was still not of sufficiently high quality. Thus, we considered that the incompleteness of the assembled scaffolds and low contig N50 might be due to high heterozygosity (1.32%∼1.52% for C. simplicifolius and 1.19∼1.31% for D. jenkinsiana), a high proportion of repeat sequences , and inadequate sequencing depth, which was ∼26 × and ∼20 × of effective PacBio data after error correction, respectively. Therefore, taking the above findings into account, we conducted hybrid de novo genome assembly of C. simplicifolius and D. jenkinsiana using the Illumina and PacBio sequencing data. First, Platanus (version 1.2.4) [de novo genome assembler for highly heterozygous data, was carried out to assemble the fragment PE reads into contigs by constructing De Bruijn graphs with an automatically optimized k-mer size. Second, the corrected PacBio reads and the assembled contigs were subjected to DBG2OLC (released 11 July 2015) [C. simplicifolius and D. jenkinsiana, respectively. Third, a polishing process before the SSPACE process was performed with reference to the consensus analysis of DBG2OLC   was chos DBG2OLC ; this stC. simplicifolius assembly and the 13 for the D. jenkinsiana assembly are presented in C. simplicifolius and D. jenkinsiana genomes, with scaffold N50 values of 169 Mb and 119 Mb, respectively.Subsequently, the valid Hi-C data together with the above assembly were processed by the 3D-DNA pipeline (version 170123)  to produC. simplicifolius and D. jenkinsiana assemblies. First, two genome features were summarized: the percentage of ambiguous bases (Ns) and Guanine and Cytosine (GC) content. The results showed a low percentage of Ns (∼0.6% for C. simplicifolius and ∼0.7% for D. jenkinsiana) in each genome, and the overall GC contents (41.07% for C. simplicifolius and 41.78% for D. jenkinsiana) were similar to those of the related transcriptomic data (41.68% for C. simplicifolius and 41.89% for D. jenkinsiana). Then, the unigenes assembled from the RNA sequencing (RNA-seq) data were aligned to the assembly using the Basic Local Alignment Search Tool (BLAST)-like alignment tool (version 1.0) [C. simplicifolius and 81.81% of D. jenkinsiana)   with theinsiana) . Last, tectively .C. simplicifolius and D. jenkinsiana assemblies. We adopted two independent approaches to predict repetitive elements: homology-based annotation and de novo methods. In the homology-based annotation, TEs were identified using RepeatMasker (v4.0.5) and RepeatProteinMasker (v4.0.5) [de novo annotation, a de novo repeat library was constructed using RepeatModeler (v1.0.8) [de novo repeat library. Additionally, tandem repeat sequences were identified by Tandem Repeat Finder (version 4.09) [C. simplicifolius and of D. jenkinsiana assemblies, respectively, and the sequence divergence of TEs indicated that the de novo-predicted repeats were more recently active than the Repbase-predicted repeats  and tandem repeats were identified in the (v4.0.5)  via sear(v4.0.5) . In the (v1.0.8)  and LTR_(v1.0.8)  after elon 4.09)  with theats Fig. .C. simplicifolius and D. jenkinsiana. Each sample had three biological replicates  with the default parameters [de novo predictor based on a self-trained model. After the training data were optimized and multiple trainings were performed,  85,246 and  87,613 gene models were predicted for C. simplicifolius and D. jenkinsiana, respectively. In the homology-based prediction, we used the following seven species as reference datasets: Elaeis guineensis, Phoenix dactylifera, Brachypodium distachyon, Oryza sativa, Setaria italica, Sorghum bicolor, and Zea mays . Their protein sequences were downloaded from the ENSEMBL database [C. simplicifolius and D. jenkinsiana assemblies using TBLASTN (version 2.2.26) [C. simplicifolius and D. jenkinsiana, respectively   with an  2.2.26) . In the  2.2.26)  was used 2.2.26)  to definectively .C. simplicifolius and D. jenkinsiana, respectively.Last, we integrated the evidence from the three above independent predictions using MAKER (version 2) . The finC. simplicifolius and D. jenkinsiana, respectively. These protein databases included the NCBI nonredundant protein database (released 13 March 2018) [C. simplicifolius and D. jenkinsiana, respectively. Among the conserved BUSCO proteins, 76.2% and 81.2% were complete. Furthermore, the four types of noncoding RNA genes, i.e., tRNA, rRNA, miRNA, and snRNA, were also predicted  , SWISS-Pch 2018) , Gene Onch 2018) , Kyoto Ech 2018) , and Intch 2018) . Additioredicted .C. simplicifolius and D. jenkinsiana with an E-value cutoff of 1e-5 and a percent match cutoff of 80 . Markov chain clustering was also used with a default inflation parameter in an all-to-all BLASTP analysis of entries for the other eight plants, i.e., Amborella trichopoda, E. guineensis, A. thaliana, B. distachyon, O. sativa, Spirodela polyrhiza, P. dactylifera, and S. bicolor . Among the  30,936 gene families identified in all 10 species,  44,700 and  44,537 orthologous genes were detected in the C. simplicifolius and D. jenkinsiana genomes, respectively. Approximately 6,132 (19.8%) gene families common to all 10 species as well as 2,366 and 2,707 specific gene families were detected in C. simplicifolius and D. jenkinsiana, respectively  , was useely Fig. . Additioely Fig.  and in Kely Fig. .C. simplicifolius and D. jenkinsiana and the other containing E. guineensis and P. dactylifera  ; then, aera Fig. .C. simplicifolius, D. jenkinsiana, and the other eight species with the following parameters: “-nsample 200000 -burnin 40000.” The calibration times were derived from published times for the divergences of the reference species [P. dactylifera separated from E. guineensis at ∼40.8 Mya   to estim species . The resMya Fig. .p-hydroxyphenyl (H), vanillin (G), and syringaldehyde (S) [A. thaliana, B. distachyon, O. sativa, S. bicolor, Phyllostachys edulis, Populus trichocarpa, D. jenkinsiana, and C. simplicifolius. Most genome sequences  were downloaded from the ENSEMBL database [Ph. edulis was downloaded from the Bamboo Genome Database [C. simplicifolius and D. jenkinsiana, respectively. The total numbers of genes in the lignin biosynthesis pathway were 193 and 172 genes in C. simplicifolius and D. jenkinsiana, respectively. Peroxidase, as the most common gene, was detected in both rattans. Among the least common genes, phenylalanine ammonia-lyase was identified in C. simplicifolius, and coumarate 3-hydroxylase and cinnamate 4-hydroxylase were detected in D. jenkinsiana. The observed expansion of lignin biosynthesis genes in rattan could be due to the occurrence of a whole-genome duplication (WGD) event, since a WGD could provide more gene copies, which facilitates the evolution of genes with new functions [Lignins are a class of complex aromatic heteropolymers of monolignols that encrust and interact with the cellulose/hemicellulose matrix of the secondary cell wall. The aromatic lignin polymers are commonly composed of three monolignols, i.e., hyde (S) . Thus, wdatabase . The genDatabase . Based oDatabase , were usunctions .C. simplicifolius and D. jenkinsiana) using multiple types of sequencing data and assembly technologies. These C. simplicifolius [D. jenkinsiana [de novo genome assembly and resequencing of other rattan species and serve as essential resources to identify regions that provide suitable resolution in the evolutionary landscape by performing comparative studies between and among different species. The availability of two high-quality rattan genomes simplifies the identification of critical genes involved in the lignin biosynthesis pathway, which have potential importance for rattan growth and development. Therefore, these data pave the way for additional genomic studies in rattan and related species.Here, we report two chromosome-level reference genome sequences of rattan , downloaded from the Amborella Genome Database (amborella.huck.psu.edu), and E. guineensis (version GCF_000 442705.1), downloaded from NCBI. The remaining genomes were downloaded from the ENSEMBL database, including E. guineensis (version GCF_000 442705.1), Ph. dactylifera (version 1.0), B. distachyon (version 3.1), O. sativa (version R498), S. italica (version 9.0), S. bicolor (version 3.1), Z. mays (version B73_RefGen_V4), Ph. edulis (version 2), Po. trichocarpa (JGI2.0.31), and A. thaliana (version: TAIR10).The datasets and materials supporting the results presented in this article are available in the pository . All rawAdditional Table S1: Evaluation of genome sizes of C. simplicifolius and D. jenkinsianaAdditional Table S2: The 17-mer frequency method estimated genome sizes of C. simplicifolius and D. jenkinsianaAdditional Table S3: Statistics of the assemblies using different assembly strategiesAdditional Table S4: BUSCO evaluation of the polishing processAdditional Table S5: The chromosome-level lengths of the Hi-C assemblies for C. simplicifolius and D. jenkinsianaAdditional Table S6: Statistics of the quality assessment of the C. simplicifolius and D. jenkinsiana genomesAdditional Table S7: BUSCO evaluation of the C. simplicifolius and D. jenkinsiana genomesAdditional Table S8: Statistics of the predicted repetitive sequences in the C. simplicifolius and D. jenkinsiana genomesAdditional Table S9: Statistics of RNA libraries in the transcriptome assembliesAdditional Table S10: Statistics of the predicted protein-coding genes in the C. simplicifolius and D. jenkinsiana genomesAdditional Table S11: Statistics of functional annotations of the C. simplicifolius and D. jenkinsiana genomesAdditional Table S12: Statistics of the predicted noncoding RNAs in the C. simplicifolius and D. jenkinsiana genomesAdditional Table S13: GO analysis of rattan-specific gene familiesAdditional Table S14: KEGG analysis of rattan-specific gene familiesAdditional Table S15: A total of 140 genes in the lignin biosynthetic pathway experimentally validated in previous studiesAdditional Figure S1: Evaluation of the genome sizes of C. simplicifolius and D. jenkinsiana by K-merAdditional Figure S2: Evaluation of the genome size of C. simplicifolius and D. jenkinsiana by flow cytometryBLAST: Basic Local Alignment Search Tool; BUSCO: Benchmarking Universal Single-Copy Ortholog; CDS: coding DNA sequence; GC:Guanine and Cytosine; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; LINE: long interspersed nuclear element; LTR: long terminal repeat; MP: mate pair; Mya: million years ago; NCBI: National Center for Biotechnology Information; Ns: ambiguous base; PacBio: Pacific Biosciences; PE: paired end; RNA-Seq: RNA-sequencing; SINE: short interspersed nuclear element; TE: transposable element; WGD: whole-genome duplication.The authors have declared that they have no competing interests.This work was supported by the Sub-Project of the National Science and Technology Support Plan of the Twelfth Five-Year Plan in China (2015BAD04B03 and 2015BAD04B01), Fundamental Research Funds for the International Center for Bamboo and Rattan (1632017018), and the Science Technology and Innovation Committee of Shenzhen Municipality (JCYJ20160331190123578).H.S.Z. and R.S.L. collected the samples; J.L.W., H.Y.S., S.N.W., H.X., K.B.Y., X.R.X., X.M.S., and J.J.S. constructed libraries; and H.S.Z., S.B.W., C.H.C., L.F.C., A.Q.F., C.Z., and Q.G. performed the genome assembly. S.J.H., K.H., C.C.S., and G.Y.F. performed the Hi-C analysis. S.B.W. and L.F.C. performed the genome annotation and H.S.Z., S.B.W., L.F.C., and X.L. analyzed the genome data. H.S.Z. and S.B.W. wrote the manuscript and H.S.Z., S.B.W., X.L., Z.M.G., and Z.H.J. reviewed the manuscript. All of the above authors read and approved the final manuscript.As a part of the Genome Atlas of Bamboo and Rattan (GABR), we acknowledge the GABR Consortium members, partners, advisors, and supporters who have helped to make this project run smoothly.GIGA-D-18-00152_Original_Submission.pdfClick here for additional data file.GIGA-D-18-00152_Revision_1.pdfClick here for additional data file.GIGA-D-18-00152_Revision_2.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision1.pdfClick here for additional data file.Reviewer_1_Report_ -- Yinping Jiao5/17/2018 ReviewedClick here for additional data file.Reviewer_1_Report_(Revision_1) -- Yinping Jiao06/22/2018 ReviewedClick here for additional data file.Reviewer_2_Report_ -- Denis Murphy05/29/2018 ReviewedClick here for additional data file.Reviewer_2_Report_(Revision_1) -- Denis Murphy6/9/2018 ReviewedClick here for additional data file.Supplemental FilesClick here for additional data file.


Odocoileus hemionus hemionus) and black-tailed deer (O. h. columbianus) formed a hybrid swarm along the Cascade mountain range despite substantial differences in body size (up to two times) and habitat preference. In this study, we examined genetic population structure, extent of introgression, and selection pressures in freely interbreeding populations of mule deer and black-tailed deer using mitochondrial DNA sequences, 9 microsatellite loci, and 95 SNPs from protein-coding genes.Secondary contact between closely related lineages can result in a variety of outcomes, including hybridization, depending upon the strength of reproductive barriers. By examining the extent to which different parts of the genome introgress, it is possible to infer the strength of selection and gain insight into the evolutionary trajectory of lineages. Following secondary contact approximately 8000 years ago in the Pacific Northwest, mule deer , and indicated that the center of the hybrid swarm is 20-30 km to the east of the Cascades ridgeline, where there is a steep ecological transition from wet, forested habitat to dry, scrub habitat.Our data are consistent with a genetic boundary between mule deer and black-tailed deer that is porous but maintained by many loci under weak selection having a substantial cumulative effect. The absence of clear reproductive barriers and the consistent centering of geographic clines at a sharp ecotone suggests that ecology is a driver of hybrid swarm dynamics. Adaptive introgression in this study (and others) promotes gene flow and provides valuable insight into selection strength on specific genes and the evolutionary trajectory of hybridizing taxa.The online version of this article (10.1186/s12862-019-1497-x) contains supplementary material, which is available to authorized users. Hybrid zones, once thought to be “exceptional or absent in animals” , have beHybrid speciation can occur when hybrids are more fit than parental lineages and hybrids preferentially mate with each other , though The dynamics of hybrid zones are generally fluid, changing in response to selection pressures. Selection can act evenly across traits or differ for particular phenotypes, resulting in variable rates of gene introgression. Alleles that reduce viability or fertility in hybrids or contribute to assortative mating are expected to be under stronger selective pressures and therefore show limited introgression. Conversely, alleles that confer higher fitness in hybrids should spread quickly –27. The Drosophila [Identifying which regions of the genome are under selection can help explain overall patterns of introgression and provide insight into the structure of hybrid zone. By quantifying selection strength on individual regions or genes, we can assess to what extent they contribute to reproductive isolation. Strong selection on even a small portion of the genome can have a large impact on hybrid zone dynamics , 34, 35.osophila . Genes uosophila , 38. Wheosophila –41. Highosophila .Not only can selection pressures differ across the genome, they can also vary over the landscape. In hybrid zones, positive selection on ecological adaptations in only part of the landscape can cause the hybrid zone to move in the direction of overall greater selection until selection is counter-balanced by selection for the opposite trait or a barrier to gene flow is reached , 43. ForOdocoileus hemionus columbianus (BTD) and mule deer O. h. hemionus . These nus  from protein-coding regions, microsatellite loci, and mtDNA sequences – to identify signatures of hybrid zone stability and predict the future trajectory of the hybrid swarm. Second, we quantified the strength of selection on protein-coding divergence. We predicted that genes involved with disease resistance and mate discrimination in ungulates (e.g. olfaction) would be important for maintaining species boundaries . Individuals assigned to a parental lineage typically belong to that mtDNA lineage while hybrids had both BTD and MD mtDNA. We did observe some evidence of mitochondrial capture. Three individuals assigned as pure BTD using both microsatellites and SNPs had MD mtDNA and two pure MD had BTD mtDNA. Assignments based on microsatellite and SNP datasets were the same for 100 of 172 individuals . All mismatches occurred when an individual was classified as a BTD or MD for either microsatellites or SNPs and a hybrid in the other genetic dataset. Though there was some disparity between datasets, a paired t-test showed that the Q values for the microsatellite and SNP analyses were not significantly different (p = 0.09).Admixture analysis of the microsatellite and SNP data also showed strong support for two clusters corresponding to BTD and MD, with individuals consistently more clearly delineated using SNPs Fig. . There wNewHybrids analyses were generally concordant with the STRUCTURE and fastSTRUCTURE analysis when the Uniform prior was applied Fig. . There wST and Dest did not differ significantly between any pair of transects, permitting transects to be combined in subsequent analyses. There were more private alleles for pure BTD than pure MD or hybrids (non-overlapping 95% CIs) in the microsatellite dataset but the total number of alleles was comparable across groups (Table O) and expected heterozygosity (HE) for microsatellites were similar for pure BTD, pure MD and hybrids. For SNPs, HE was significantly higher than HO for MD based on 95% CIs. Microsatellite analysis showed a significant deficiency of heterozygotes in all groups (FIS (BTD) = 0.126, 95% CIs 0.037–0.202; FIS (MD) = 0.084, 95% CIs 0.018–0.173; FIS (hybrids) = 0.109, 95% CIs 0.069–0.155; p < 0.01 for all groups). For SNPs, only FIS for hybrids had a confidence interval that did not include 0 . Heterozygote deficiencies are likely due to nonrandom mating; we found no evidence for null alleles in this study or in other studies using these markers [IS values can reflect cryptic substructure, there was little support for substructure in the mtDNA data.Estimates of Fps Table . Observe markers , 61, andST were significantly lower for microsatellites  than SNPs  only identified a single SNP likely to be under selection. This SNP represents a non-synonymous mutation and was found within the gene EIF4G3, a likely component of the protein complex EIF4F which is involved in the recognition of the mRNA cap, ATP-dependent unwinding of the 5′ terminal secondary structure and recruitment of mRNA to the ribosome , 66. ThiST and Dest between pure parental lineages for each locus . Regardless of whether SNPs were ranked by FST or Dest, the top 10 % of SNPs included the same set of nine loci (Table https://genome.ucsc.edu/cgi-bin/hgGateway). Assuming linkage groups in Odocoileus are similar to those of Bos taurus, these outlier loci were scattered across five of the 29 chromosomes sampled. It is unlikely that any of these nine SNPs are linked because they are located at least 10 megabases apart , and, iWe fit geographic cline models to the mtDNA, microsatellite and SNP datasets in order to characterize cline shape. Based on AICc, models with fixed maximum and minimum values of 1 and 0 were selected for all datasets and cline tails were estimated for the SNP dataset only. Models predicted that the cline center for all datasets was significantly to the east of the Cascade ridgeline, with average cline center varying between + 20 and + 30 km Fig. a. All inST: r2 = 0.0148; Dest: r2 = 0.0005).Within the SNP dataset, we could not reject the null model  for 21 SNPs. For the remaining 74 SNPs, the change in allele frequency from west to east occurred over a relatively narrow range, with an average slope of 0.10 ΔP/km  for only a few SNPs and only a single locus with significant rates of change in allele frequency (β) across the hybrid zone. Using 95% confidence intervals, 14 loci generally had weak evidence of excess BTD ancestry, with mean values for α between − 5 and 0 , one on either side of a boundary located just east of the Cascades mountain range. The Cascade mountain region also serves as a genetic boundary for other species , 77. As ocoileus , 50, 51,ocoileus , 75.Bos taurus) to develop baits for exon capture. Using highly conserved genes between such closely related lineages can lead to an underestimation of genetic divergence. However, within these exons, we chose SNPs that showed high variability between lineages [We also observed a high degree of genetic differentiation between BTD and MD lineages for microsatellites and SNPs. Overall, both datasets yielded consistent assignments, with differences exclusively between pure and hybrid categories and not between parental categories. Disparate assignments of individuals for the two datasets could arise if hybridization frequency varied over time, for example if hybridization occurred thousands of years ago but not recently of vice versa. The concordant assignments we observed, using rapidly evolving microsatellites and more slowly-evolving SNPs, suggest stability of genetic structure over time. In this study, SNPs were chosen from conserved exons, a consequence of using the genome of a distantly related species  and does from the lineage with smaller body size (BTD). Mating success has been correlated with larger male body size in cervids –81, inclocoileus –84. Howeocoileus , 85–87. ocoileus , 89.Alternatively, observed hybridization rates may be driven by population demography. Previous work on hybridization in cervids has attributed higher realized rates of hybridization between does of the larger species and bucks of the smaller species to differences in migration rates and population densities , 90–92. Odocoileus.Though signatures of selection can be difficult to disentangle from the effects of demographic processes, the consistently high effective population sizes of BTD and MD  make thibgc analysis, eight exhibited non-synonymous mutations and four were in putative mate choice genes. Since mutations in ROPN1L affect sperm motility and can cause male infertility in mammals [Odocoileus could represent a post-mating reproductive barrier. Genome-wide association studies would help reveal how mutations in these genes affect deer survival. The gene SCRG1 is associated with prion infection response  exists at coding or non-coding loci we did not sample . However mammals , it is p disease ); howeveBos taurus reference genome [Odocoileus that would be expected to be under selection. A complete cervid genome and a higher density of SNPs distributed across the genome, including non-coding regions, would allow us to better understand the processes driving selection and test whether genes involved in mate choice or environmental adaptation are overrepresented among loci under selection.Contrary to our predictions, signatures of selection were also observed for genes associated with general cell processes. One explanation for selection on general cell process genes is environmental adaptation. For example, the pattern of divergence at the metabolism gene PLIN2 mimics the sharp ecological transition along the contact zone and could reflect the significant differences in diet that exist between BTD and MD , 102. Gee genome . It is pWhile the presence of distinct lineages on either side of the hybrid zone indicates that partial barriers to gene flow do exist, it is unclear what specific barriers are driving hybrid zone dynamics. We predicted that reproductive barriers would be important for restricting introgression across the hybrid zone but did not find strong evidence to support this hypothesis. Selection on individual putative mate discrimination genes was weak or non-existent. Moreover, if selection on reproductive barriers were strong, then we would expect these highly mobile bucks to disperse to mate with a doe from the same lineage. Instead, we found evidence of extensive bi-directional introgression, suggesting that reproductive barriers have been eroded or never existed between these lineages.Physical geography also does not appear to have a strong effect on gene flow. If the Cascades range functioned as a significant barrier, then genetic cline centers would have aligned with ridgeline. Instead, the observed cline centers correspond with the interface between two distinct habitats , suggestThe Cascade Mountain region is a hybrid zone hot spot , and phyc. 8000 BP [The co-localization of genetic clines among markers suggests long-term stability of the BTD-MD hybrid swarm. Mitochondrial DNA, microsatellites, and exonic SNPs have different mutation rates and therefore provide insight into the genetic structure of the swarm at different time periods. If the position of the swarm had changed over time, we would have expected to see a shift in cline center between types of genetic markers. We did observe some variation in cline center among individual SNPs but the majority of SNPs showed a distinct change in allele frequency that coincides with the average cline center across SNPs. The sharp ecological transition across the hybrid zone may prove to be such a strong barrier that the swarm is trapped in a habitat suitability trough . Habitat 8000 BP . No majo 8000 BP  and clim 8000 BP . AssuminThere is conflicting evidence in regards to whether the width of the hybrid swarm has changed over time. The significantly wider cline width for microsatellite markers compared to SNPs suggests that the swarm is expanding. Microsatellites reflect recent population structure; whereas SNPs in protein-coding genes tend to be more conserved and provide insight into more distant evolutionary history. Alternatively, swarm width may be stable and wider microsatellite clines might reflect poorer resolution or mutation model differences compared to SNPs , 113. BeStable hybrid swarms provide an excellent opportunity to investigate long term gene flow between genetically, and often morphologically, distinct lineages. In the case of the BTD-MD hybrid swarm, the boundary between these highly divergent lineages is porous, and is more closely aligned with the sharp ecological transition than the physical ridgeline of the Cascade range. Ecology can be a strong driver of hybrid swarm dynamics, and in this system ecologically-based selection is presumably acting on many genes, each with a small effect. Multivariate tests for polygenic selection on a set of high-density, genome-wide SNPs (Genome wide association study (GWAS) approach) could facilitate further testing of our polygenic selection hypothesis (e.g. ). A GWASOdocoileus hemionus spp.  along three latitudinal transects, spanning the state of Oregon in the United States . Haplotypes matched those in GenBank previously observed in the Pacific Northwest  [n = 17) to quantify sequencing error rates. We observed no differences in base calls between replicated sequences.All samples were sequenced for a 583 bp portion of the mitochondrial control region. Following the protocol detailed in Latch et al. , we usedJ189323) . We re-aO. h. hemionus (FJ188901 and FJ18911) and O. virginianus (JQ037851 and JQ037857) from outside the hybrid zone to more accurately designate mitochondrial clades as O. h. hemionus and O. h. columbianus and potentially identify individuals admixed with O. virginianus. Three published sequences of O. h. columbianus were already part of the original dataset. The following outgroups were selected from within Cervidae: Alces alces (JN632595); Cervus elaphus (NC007704); Dama dama (NC020700).We analyzed mitochondrial DNA sequences using Bayesian and Maximum Likelihood methods of phylogenetic reconstruction in MrBayes version 3.2.5 and RAxML version 8.2.9 , respectThe Bayesian analysis was run for 6 million generations with a 20% burn-in. We performed two independent runs, each with four Markov chain Monte Carlo (MCMC) chains that were sampled every 500 generations. Average standard deviation of split frequencies (< 0.01) was used to confirm chain convergence. The Maximum Likelihood analysis was conducted with 100 bootstraps using RAxML-HPC2 on XSEDE  on the CO. hemionus  to quantify our genotyping error rate. We recorded a single instance of allelic dropout in 153 repeated genotypes, for a microsatellite genotyping error rate of 0.65%.We amplified nine microsatellite loci previously used by Latch et al.  to charad Odh O: , BM848: d Odh O: , C273 and Odh O: , RT24: [d Odh O: ). We fold Odh O:  and amplBos taurus genome [ST [An initial exon capture was performed on three BTD and four MD showing no admixture based on microsatellite analysis . Due to s genome  was useds genome ). Sequens genome ). For eanome [ST  was calcnome [ST . Allele n = 60) or both subspecies (n = 2) and SNPs that had FST > 0.25 between lineages (n = 36). We conservatively chose FST > 0.25 in order to reliably identify hybrids [ST > 0.25 (n = 5). We also chose transversions if all seven individuals were genotyped, if fewer than 10 Ns were observed on the consensus sequence, and if no Ns were observed within 50 nucleotides of the respective SNP (n = 35). Lastly, SNPs with expected heterozygosity between 0.2 and 0.6 that were deemed potentially informative for population assignment, population structure analysis, and estimation of relatedness were chosen for which all seven individuals were genotyped, if the minor allele was observed more than twice, if there were fewer than 5 Ns on the consensus sequence, and if no Ns were observed within 50 nucleotides of the SNP (n = 121).We used multiple methods to select SNPs from the subset of seven individuals for downstream analysis. First, to enrich for loci that were informative for species delineation, we chose SNPs that were fixed in one , prioritizing loci showing evidence of being informative for species diagnostics and transversions. We tested an initial set of 95 assays by genotyping 47 samples from across the hybrid zone in duplicate. DNA concentration and quality was standardized in order to eliminate any effects associated with low sample quality or variable DNA concentration. Concentration and quality were assessed using a Nanodrop 2000 spectrophotometer . For each sample, if the DNA concentration was less than 40 ng/μl or the 260/280 ratio was less than 1.8, then the DNA was precipitated using isopropanol, rinsed three times with 500 μl of 80% ethanol, then re-suspended in 15% of the original elution volume.Samples were standardized to a concentration of 60 ng/μl prior to genotyping with Fluidigm 96.96 Dynamic Arrays and the Fluidigm EP1 Genotyping System  using recommended reaction conditions for KASP KBD-Fluidigm Assays. Fluorescence intensity plots were examined for each assay using Fluidigm SNP Genotyping Analysis software. Genotypes were called using the no template control (NTC) normalization method to normalize the data against background noise and a 60% confidence threshold in the genotype assignment. Assays were retained if duplicate genotypes were concordant, genotype clusters were easily distinguishable, and assays yielded polymorphic genotypes. The remaining set of 35 assays was tested by genotyping 95 samples from across the hybrid zone in duplicate. If assays from the initial set of 95 were monomorphic or lacked homozygotes for the minor allele, they were re-tested using the same set of 95 samples used to test the second set of assays.Of the 130 assays tested, 111 were variable and yielded high confidence genotypes with a high degree of concordance between duplicate PCRs. From the 111 assays that were successful, we included 95 SNPs for our final SNP genotyping panel that had well defined clusters (easy to score genotypes) and at least one occurrence of each possible genotype. We preferentially included assays targeting transversions, potential species diagnostic SNPs, and SNPs in genes of known function. This final set of assays was used to genotype all samples from across the hybrid zone using the same reaction conditions used for testing. To verify that assays were consistently yielding correct genotypes, we checked for concordance between exon capture and assay derived genotypes for four individuals that were genotyped using both technologies.4 iterations followed by 106 iterations and performed ten replicate runs for K = 1 through K = 8. For the STRUCTURE analyses, we set the parameters to allow for admixture between clusters and selected the correlated allele frequency model. We assessed stationarity by ensuring that MCMC runs yielded a Gelman-Rubin statistic of less than 1.1 , fed in R; ).Using STRUCTURE HARVESTER , we combWe established thresholds for ‘pure’ parentals using simulated data. Since microsatellite and SNP data was not available for BTD and MD outside of the hybrid zone, we used individuals that met a stringent threshold of Q ≥ 0.95 for either the black-tailed deer or mule deer cluster as proxies for allopatric populations. These individuals were used to simulate 500 genotypes of ‘pure’ parentals in HYBRIDLAB . The simFor each of the three groups , we performed AMOVAs between pairs of transects using GENODIVE. The microsatellite and SNP data was analysed separately. We analyzed each locus separately and assessed significance at α = 0.05, following false discovery rate correction .6 sweeps and a burn-in of 104 sweeps. This process was repeated using Uniform priors. All results were summarized in CLUMPP and visualized in Microsoft Excel. We ran the simulated individuals used in the STRUCTURE and fastSTRUCTURE analyses in NewHybrids using the same parameters. We then calculated the 95% confidence intervals of the distribution of probabilities that simulated ‘pure’ individuals belonged to one of the parental groups. These confidence intervals were used to establish cut-offs for parental group assignment for the empirical data. Remaining individuals were considered hybrids.As with STRUCTURE and fastSTRUCTURE, NewHybrids uses a clustering algorithm to calculate the probability of an individual belonging to either parental group. Additionally, NewHybrids calculates the probability that an individual belongs to one of four hybrid classes . Models for allele frequencies and mixing proportions were implemented with a Jeffrey’s-like prior and run ten times with 10IS). We calculated FST and Jost’s D [S and HT across loci as recommended by Meirmans and Hedrick [IS which was calculated in Genodive [For both the microsatellite and SNP datasets, we quantified allelic diversity for putative parental subspecies and hybrids based on the corresponding STRUCTURE and fastSTRUCTURE results (see below). This included the number of alleles and private alleles, expected and observed heterozygosity, and deviation from random mating  or allowed to vary (free) and tails were either not estimated (none) or both estimated independently (both). The three models tested  were compared to the null model using corrected Akaike information criterion (AICc). The model with the lowest AICc was considered the best model and used for further analysis. When the AICc for the null model was 2 or more units lower than the AICc for the best model, the null model was rejected. Using the selected models, we calculated cline center and width for all markers and determined coincidence using confidence intervals of two log-likelihood scores. Slope was calculated by dividing the change in actual or estimated allele frequency by cline width. Sharper slopes are associated with stronger selection whereas shallower slopes are indicative of weaker selection.To complement the geographic cline analysis, we also performed genomic cline analyses specifically test for outlier loci. We used the software bgc , 148 to For the genomic cline analysis, the parental populations comprised individuals identified as putative parentals in the SNP fastSTRUCTURE analysis . All other individuals were considered hybrids. Following the protocol of Trier et al. , we ran Additional file 1:Figure S1. Maximum likelihood tree based on mitochondrial DNA control region haplotypes. Bootstrap values > 0.70 are provided next to internal nodes. The bar chart indicates the assignment of each individual as a black-tailed deer (blue), hybrid (purple), or mule deer (red) based on data from microsatellites (column 1) and SNPs (column 2). Outgroups and mule deer samples collected outside the hybrid zone are designated by GenBank numbers. MD = Odocoileus hemionus hemionus; WTD = Odocoileus virginianus. (PDF 469 kb)Additional file 2:Figure S2. Collection localities for all Odocoileus individuals. Individuals are classified as black-tailed deer (blue circles), hybrids (purple squares), or mule deer (red triangles) based on A) mitochondrial clades and B) microsatellite STRUCTURE analysis. The Cascades ridgeline is indicated by the bold black line. Map source: Esri. (PDF 326 kb)Additional file 3:Table S1. Sample information for all samples used in this study. Data include the group to which each sample was assigned, using mtDNA, microsatelite, or SNP data. (PDF 55 kb)


PbAcE) from the psychrophilic soil microbe Paenibacillus sp. R4. The enzyme hydrolyzes glucose penta-acetate and xylan acetate, reversibly producing acetyl xylan from xylan, and it shows higher activity at 4°C than at 25°C. We solved the crystal structure of PbAcE at 2.1-Å resolution to investigate its active site and the reason for its low-temperature activity. Structural analysis showed that PbAcE forms a hexamer with a central substrate binding tunnel, and the inter-subunit interactions are relatively weak compared with those of its mesophilic and thermophilic homologs. PbAcE also has a shorter loop and different residue composition in the β4–α3 and β5–α4 regions near the substrate binding site. Flexible subunit movements and different active site loop conformations may enable the strong low-temperature activity and broad substrate specificity of PbAcE. In addition, PbAcE was found to have strong activity against antibiotic compound substrates, such as cefotaxime and 7-amino cephalosporanic acid (7-ACA). In conclusion, the PbAcE structure and our biochemical results provide the first example of a cold-active acetyl xylan esterase and a starting template for structure-based protein engineering.Cold-active acetyl xylan esterases allow for reduced bioreactor heating costs in bioenergy production. Here, we isolated and characterized a cold-active acetyl xylan esterase ( Reactions containing 10 μg of PbAcE and 250 μM of substrate were incubated for 5 min at room temperature. The amount of p-nitrophenol released from the hydrolysis of pNP ester derivatives by PbAcE was quantified by measuring absorbance at 405 nm. For α-β-naphthyl ester derivatives, reactions containing 10 μg of PbAcE and 50 μM of substrates were incubated for 5 min at room temperature. The hydrolase activity was monitored by measuring absorbance at 315 nm. The hydrolase activity of PbAcE toward various substrates was assessed using colorimetric analysis with phenol red as a pH indicator. Substrates included carbohydrate esters , tertiary alcohol esters , lipids , and antibiotic-related compounds . Each reaction mixture containing the above substrates was incubated with 100 μg of PbAcE at 37°C for the indicated time. The temperature-specific active properties of PbAcE were studied by incubating 10 μg of PbAcE at temperatures ranging from 4 to 37°C. After 1 h of incubation, pNP-C2 was added to the incubated mixture at a final concentration of 250 μM. The thermal stability and chemical stability of PbAcE were investigated by incubating 10 μg of PbAcE at various temperatures  with various chemical compounds . After 1 h of incubation, pNP-C2 was added at final concentration of 250 μM. The kinetic parameters of PbAcE were investigated using pNP-C2 and pNP-C4 as substrates with 5 μg of PbAcE. The absorbance at 405 nm was monitored for 10 min, and the initial linear measurements were used for determining the slope of the initial velocity. The molar extinction coefficient for p-nitrophenol was 16,400 M-1 cm-1 at pH 8.0. The Michaelis–Menten constant [Vmax), turnover rate (kcat), and catalytic efficiency (kcat/KM) were calculated from double reciprocal plots (GraphPad Prism 6.0 software). For comparing the thermal stabilities of wild-type PbAcE and the L144R mutant, 1 mg/ml of each enzyme was incubated at 70°C for 1 h. Heated samples of 10 μl were collected every 20 min, and the residual activity was measured using pNP-C2 as a substrate. All reactions mentioned above were carried out in 20 mM Tris-HCl and 150 mM NaCl, pH 7.4. Absorbances were measured using an Epoch 2 microplate reader .Assays of the activity and thermal stability of sterases , 12, 13.Xylan (10 g) from beechwood  was dissolved in 250 ml of DMSO at 55°C for 24 h. Next, 0.4 g of potassium borate was added to the xylan solution while stirring at 55°C, followed by 200 ml acetic anhydride, which was added slowly over 5 min. After 4 h of incubation at 55°C, the mixture was dialyzed against tap water at 4°C for 5 days, followed by dialysis against distilled water for 1 day.PbAcE was precipitated with 80% ammonium sulfate and cross-linked by 25 mM glutaraldehyde, followed by gentle agitation for 12 h. After centrifugation, the pellet was resuspended, washed repeatedly, and finally stored in 20 mM Tris-HCl and 150 mM NaCl, pH 7.4, for further analysis. For reusability assay, PbAcE CLEAs were repeatedly used in a new enzyme reaction after extensive washing until no activity was detected from the supernatant. For mCLEA preparation, different amounts of PbAcE were precipitated and cross-linked using the method described above but in the presence of 500 μg of magnetic nanoparticles (MNPs). PbAcE mCLEAs were recovered from the reaction solution by magnet and washed by gentle agitation in 20 mM Tris-HCl and 150 mM NaCl, pH 7.4. For assessment of the hydrolase activity of PbAcE CLEAs and PbAcE mCLEAs, pNP-C2 was used as a substrate.For cross-linked enzyme aggregate (CLEA) preparation, 500 μg PbAcE CLEAs prepared from 500 μg PbAcE were dissolved in hexane and 1 M acetic acid to a final volume of 1 ml. After incubation at 37°C with continuous shaking, 1 μl of synthesized acetyl xylan was directly analyzed by gas chromatography . The gas chromatographer was installed with a HP-5 capillary column . The injector and detector temperatures were 190°C. Samples (1 μl) were injected with a 1:20 split ratio. The initial oven temperature was set to 35°C (1 min) and then programmed to increase to 160°C at a 10°C/min ramping rate.To synthesize acetyl xylan, 2% (w/v) xylan from beechwood (Sigma-Aldrich) and PbAcE protein was expressed and purified to apparent homogeneity as described in the Materials and methods. The purified PbAcE showed a homogenous band with a molecular mass of ~36 kDa following SDS-PAGE  as a search model [PbAcE belongs to the C2 space group and contains six molecules in the asymmetric unit, forming a donut-shaped hexamer. The central tunnel of the donut-shaped hexamer has a diameter of about 18.5 Å. Each monomer of PbAcE is composed of 11 α-helices and 9 β-strands. In detail, the central large β-sheet consists of an antiparallel β-sheet (β1–β3) and a parallel β-sheet (β4–β6), and α-helices surround the central β-sheet. Moreover, PbAcE also has an N-terminus extended by α1–α2 helices. These features of the α/β hydrolase fold are common for other CE7 members  and acetyl xylan esterase from T. maritima  returned the top Z scores of 46.7  and substrate analog 2-acetate (OIA)-bound TmAcE (PDB id: 5JIB), several different residues were identified [PbAcE molecule is located at the entrance of the tunnel. For this structural reason, the N-terminus acts as gatekeeper for various substrates.The conserved catalytic triad residues of Ser185, Asp274, and His303 are located inside the hexamer tunnel, with a neighboring putative substrate binding site. This hydrophobic putative substrate binding site is composed of β4, β6, and β7 strands and α3 and α11 helices . It consentified , 32. TheTmAcE, which has high thermal stability, deletion of the β-interface results in a significant reduction in thermal stability [PbAcE  and TmAcE (PDB id: 5JIB) have lysine and arginine residues in the β5–α4 loop region, respectively [PbAcE, this residue is substituted for leucine  contain 354 and 58 ionic side-chain interactions, respectively, whereas the hexamer complex and monomers of PbAcE contain only 256 and 38, respectively. This indicates that the structure of TmAcE contains many more salt bridges than that of PbAcE. These numerous interactions in TmAcE make it highly thermostable, while the reduced interactions in PbAcE make it more flexible, which may allow it to function at low temperatures.In the hexamerization of CE7 family proteins, there is a key interface between each pair of monomers called the β-interface , 17. Thitability , 33. They [PbAcE . This inectively , 32. The leucine . The leuflexible , 35. Thuflexible . The hexPbAcE with other AcEs showed significant differences in the size of the cleft area located near the catalytic triad  and β5–α4 (residues 119–152) loop regions were lower than the average for the whole PbAcE structure . Notably, the PbAcE β4–α3 and β5–α4 loop structures are one and six residues shorter than those of TmAcE, respectively. These shortened gatekeeper loops may expand the substrate binding site area and create a more open active site conformation. Collectively, the results of this structural analysis suggest that the β4–α3 and β5–α4 loop regions could act as gatekeepers in AcEs. In particular, the relatively large cleft of PbAcE should facilitate substrate binding at low temperatures. In addition, it offers a spatial advantage in accommodating a wider variety of substrates.Structural comparisons of ic triad . PbAcE htructure . In contPbAcE was examined for its ability to remove acetyl groups from four acetylated carbohydrate substrates: glucose penta-acetate, cellulose acetate, N-acetyl glycosamine, and acetyl xylan. A colorimetric assay based on a pH indicator, phenol red, was performed. In this assay system, color changes to yellow are induced when the pH decreases due to the release of acetic acid from the substrate. As shown in PbAcE was active on glucose penta-acetate, one of the simplest acetylated carbohydrates, which is consistent with other previously reported acetyl esterases [PbAcE showed activity toward acetyl xylan, clearly demonstrating that PbAcE is indeed an acetyl xylan esterase, not an acetyl esterase. However, in contrast to acetyl xylan esterases belonging to families CE1, CE4, and CE5, PbAcE did not show activity toward cellulose acetate, indicating that PbAcE likely does not belong to those families and has different substrate specificity [N-acetyl glycosamine was used as a substrate, no color change was observed, probably because PbAcE selectively hydrolyzes ester bonds but not amide bonds.sterases , 11, 12.cificity . When N-PbAcE towards glyceryl esters (glyceryl tributyrate and glyceryl trioleate), oils (olive oil and fish oil), and tertiary alcohol esters  was investigated. The enzyme hydrolyzed all tested substrates, with particularly efficient hydrolysis of glyceryl tributyrate and tertiary-butyl acetate , and cephalosphorin C  esters with varying acyl chain lengths, from C2 to C8 (PbAcE strongly prefers pNP-acetate (C2), followed by pNP-butyrate (C4), while no or little activity was detected against pNP-esters with acyl chain lengths longer than C6. Similar observations have been reported in previous studies on other members of the CE7 family [PbAcE on naphthyl derivatives  more than 2000 times higher for pNP-C2 compared with those of pNP-C4.To obtain more information regarding the substrate specificity of C2 to C8 . PbAcE s7 family , 12. Nexivatives . The higd pNP-C4 . The valPbAcE activity was investigated using pNP-acetate as a substrate (PbAcE are moderately to highly thermostable (temperature optima: 30–90°C) [PbAcE, which is derived from a psychrophilic microorganism, showed the highest activity at 4°C, suggesting that its structural and biochemical properties are optimized to low temperatures. The thermal stability of PbAcE was investigated by measuring the residual activity after incubation of the enzyme for different time intervals at temperatures ranging from 37 to 70°C  , 19. How to 70°C . The enzesterase . It was R mutant . After 2PbAcE was also investigated, with the enzyme retaining over 95% of its original activity in the presence of 30% isopropanol and 1% Triton X-100, a nonionic detergent  with higher activity than those of free PbAcE and PbAcE CLEAs Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S1 File(PDF)Click here for additional data file.


Human population history in the Holocene was profoundly impacted by changes in lifestyle following the invention and adoption of food-production practices. These changes triggered significant increases in population sizes and expansions over large distances. Here we investigate the population history of the Fulani, a pastoral population extending throughout the African Sahel/Savannah belt.LCT gene enabling carriers to digest lactose throughout their lives. The lactase persistence (LP) trait in the Fulani is conferred by the presence of the allele T-13910, which is also present at high frequencies in Europe. We establish that the T-13910 LP allele in Fulani individuals analysed in this study lies on a European haplotype background thus excluding parallel convergent evolution. We furthermore directly link the T-13910 haplotype with the Lactase Persistence phenotype through a Genome Wide Association study (GWAS) and identify another genomic region in the vicinity of the SPRY2 gene associated with glycaemic measurements after lactose intake.Based on genome-wide analyses we propose that ancestors of the Fulani population experienced admixture between a West African group and a group carrying both European and North African ancestries. This admixture was likely coupled with newly adopted herding practices, as it resulted in signatures of genetic adaptation in contemporary Fulani genomes, including the control element of the MCM6/LCT locus by reporting the first GWAS of the lactase persistence trait. We also explored other signals of recent adaptation in the Fulani and identified additional candidates for selection to adapt to herding life-styles.Our findings suggest that Eurasian admixture and the European LP allele was introduced into the Fulani through contact with a North African population/s. We furthermore confirm the link between the lactose digestion phenotype in the Fulani to the  Fulanisation, that led to shifts in ethnic identity of some sedentary peoples, as has been described in North Cameroon [fulfulde Niger-Congo west-Atlantic language , consistent with their postulated Western African ancestry [The Fulani are a large and widely dispersed group of both nomadic herders and sedentary farmers living in the African Sahel/Savannah belt. Currently, they reside mostly in the western part of Africa, but some groups are dispersed up to the Blue Nile area of Sudan in the east , 2. AlthCameroon . HoweverCameroon , Niger [Cameroon , CentralCameroon  and BurkCameroon , 14. Allancestry .LCT gene on chromosome 2 [LCT gene during adulthood and confer the ability to digest lactose after weaning [Similar to other pastoralists, the Fulani experienced specific selection pressures probably associated with a lifestyle characterized by transhumance and herding , 17. Lacmosome 2 , 23–29.  weaning , 20, 29. weaning –33. To dLCT gene expression in European adults. This variant is generally not detected in most East African and Middle Eastern populations, where other LP variants are observed instead [The T-13910 allele is reported to be the key variant regulating maintenance of  instead , 33, 34. instead , 35–37.  instead , 37 and  instead , 38.Alu insertions did not lead to similar results, connecting instead the Fulani with East African pastoralists [Details surrounding the European admixture event and the post-admixture selection of the European LP mutation in Fulani genomes remain unclear. Studies based on uni-parental markers reported higher frequencies of western Eurasian and/or North African mitochondrial DNA (mtDNA) and Y chromosome haplogroups in the Fulani than in neighbouring populations –42. Howeoralists .In this study we analyse genome-wide single nucleotide polymorphisms (SNP) data from 53 Fulani pastoralists from Ziniaré, Burkina Faso to investigate the history of the Fulani population and the patterns of Eurasian admixture in their genomes, and to uncover the origin of the LP variant they carry. We perform genome-wide selection scans to investigate the strength of selection on the LP region and to identify other additional genomic regions that experienced selection during processes of adaptation to herding lifestyles in the Fulani. Lastly, we attempt to identify additional genomic regions associated with the ability of digesting milk during adult life by performing, for the first time in published research, a genome-wide association study (GWAS) on the lactose tolerance phenotype in adults.We started by investigating the genetic affinities of the Fulani from Ziniaré in Burkina Faso using a set of comparative populations from Africa, Europe and Near East  in the Fulani, Czech and Slovak individuals, using Sanger sequencing. Of the known LP mutations in intron 13 of the MCM6 gene, the Fulani from Ziniaré, Burkina Faso, only have the "European" LP T-13910 variant. We observed a T-13910 allele frequency of 48.0%, while the genome-wide European admixture fraction in the Fulani is 21.4% at K = 3. The notable European admixture fraction in the Fulani coupled with the high frequencies of the LP T-13910 allele suggests the possibility of adaptive gene flow into the Fulani gene pool.In addition to the SNP typing we sequenced the LP region in intron 13 of the LCT and MCM6 , assuming either two or three ancestral sources: West African and European from the high density dataset A; and West African, European and North African from the lower density dataset B . These results suggest that it is unlikely that both ancestries would have been introduced by separate gene-flow events. To further test this, we simulated admixture scenarios  and inferred the expected proportion of European haplotypes surrounded by North African ancestry in case of independent admixture events. If the European and North African segments were introduced by independent contact with a European and North African groups, respectively, we would expect on average that admixed segments would follow a random distribution across the genomes. In the 100 simulated populations we did not observe similar frequencies of European segments being surrounded by North African segments at the frequency we observe in the Fulani from Ziniaré, Burkina Faso , indicating that the two ancestries, at least in this Fulani population from Ziniaré, were not introduced by two separate events.To further investigate the origin of the European ancestry segments in the Fulani, we analysed the flanking regions of European segments in their genomes. We observed a significant enrichment of North African ancestry in regions flanking European fragments. On average, European fragments in Fulani genomes are flanked by North African segments with a frequency of 0.302. To test for enrichment, we performed a bootstrapping test by randomly drawing fragments in the genome and recording their flanking regions . Surprisingly, however, the region on chromosome 13 showed a slightly higher association with the phenotype in our GWAS analysis, with the highest association for the rs6563275 SNP . The rs6563275 SNP had an effect size of 38.7% . For the rs6563275 and − 13,910 SNPs together, a combined effect size of 59.2%  was estimated. The regions seam to act independent of each other and controlling for one SNP in the GWAS did not affect the other peak  [rs6563275 SNP on chromosome 13, however, did not display any signal of recent selection in our scans   and croses (iHS)  with theMCM6/LCT region. This signal seems to correspond to the PTPRM gene that encodes a tyrosine phosphatase enzyme highly expressed in adipose tissues and associated with HDL cholesterol levels, body weight and type 2 diabetes [MAN2A1 gene to be under selective pressure . This gene encodes a glycosyl hydrolase found in the gut that functions in liberating α-glucose and β-glucose. Both these selection signals could represent additional indicators of dietary adaptation in the Fulani population.A number of other potential selection signals were observed across Fulani genomes  in a very large area that extends from the Fouta Djallon in Guinea to the Blue Nile in Ethiopia and Sudan. Even though an origin in the central Sahara has been suggested on archaeological grounds , we founWe inferred that the non-West African proportion in the Fulani were introduced through two admixture events  estimates in the Fulani  we show that present-day Fulani genomic diversity developed from admixture between a West African group and a group/s that carried European and North African ancestry. The European LP variant was likely introduced through this admixture event, and was strongly selected in successive generations, in a similar way as the e family . Our resCentre national de la recherche scientifique et technologique) institution in Ouagadougou. Measurements of LP phenotypes and collection of saliva  of the Fulani  were carried out in three nomadic camps located, at the time, northeast of Ziniaré  with research permit No 0495 and help of local assistants. Informed consent was obtained from all the participants included in the study before samples were collected. Additional DNA and LP phenotype measures were collected for a comparative dataset of 63 unrelated volunteers based in Prague with Czech or Slovak (CS) nationality. All Czech and Slovak volunteers signed informed consent on anonymous use of their sample. This study was approved by the Ethical Committee of the Charles University in Prague  and by the Swedish Ethical Review Authority .Sample collection in Burkina Faso was conducted in collaboration with the Burkinabe CNRST , which is based on the measurement of an increase of blood glucose (glycemia) after consumption of 50 g of lactose on an empty stomach , 65. BloMCM6 gene with a previously reported set of primers [To detect which LP mutations were present in the 56 Fulani and 63 Czech/Slovak genomes, we Sanger sequenced intron 13 of the  primers . The priA subset of 55 Fulani and 7 Czech/Slovak individuals were selected for genome-wide genotyping on the Illumina Omni2.5-Octo BeadChip (which contains the T–13910 SNP). The data was aligned to the Human Genome built version 37.p ≤ 1e-4). AT and CG SNPs were excluded to prevent strand flipping errors when merging with comparative datasets. Relatedness was measured by Identity-By-State analysis and two Fulani individuals were excluded due to potential genetic relatedness. A total of 2,359,821 SNPs and 60 individuals were kept for the study.Data management and quality filtering was carried out using PLINK v.1.90 software . A totalWe merged the new data with published comparative datasets following the same quality control criteria as described above. We added 1295 samples from 39 populations  we compiled selected groups from the 1000 genomes project  and a SaPopulation structure analyses were performed on dataset B. We generated a Principal Component Analysis (PCA) that compares Fulani individuals to comparative groups of dataset B. The PCA analyses were performed with EIGENSOFT v.7.2.1 , 72 undeTo estimate the time of possible admixture events we used a linkage disequilibrium (LD) decay based method. The date estimations were done for dataset B using Malder v.1.0, ADMIXTOOLS package v.5.0 . The HapWe use the RFMix software v.1.5.4  to identWe extracted the haplotypes of approximately 1.1 Mb  surrounding the T–13910  on chromosome 2 from our phased dataset. The selected region was chosen based on RFMix results and the haplotypes were sorted by position of T/A–13910. We used the same selected region to construct a median joining network using the NETWORK software package v.5.0.0.1 employing the median joining (MJ) algorithm  and the f3-outgroup and pairwise fst were obtained using ADMIXTOOLS v.5.0. To avoid affinity bias towards CEU , the individuals previously selected for representing the European source in RFMix were replaced by the remaining 50 individuals of the CEU population in the 1000 genomes project.To test the affinity of the European-specific fragments in the Fulani genomes we firstly kept only the European-like regions of the Fulani genomes based on the output of RFMix for dataset B. To inspect the frequency of a European-like (CEU) fragment being flanked by North-African-like (Mozabite) fragment we recorded the number of European fragments flanked by a North-African ancestry region in relation to the total number of European fragments across all Fulani datasets. To test whether North African regions flank European regions more often than expected, we performed a bootstrap test by randomly selecting fragments across the genome  and test the likelihood of a random fragment being flanked by a North African ancestry region. This analysis was repeated 9999 times.We also tested if the different ancestry distributions across the genome could be explained by two independent admixture events. We simulated 53 phased individuals containing a conservative number of 10,000 haplotypes. These individuals hold similar admixture fractions to the Fulani . We tested if the three different ancestries follow a random distribution across the genomes, as expected under a neutrality model assumption. This simulation test was performed 100 times and the frequency of a European-like fragment being surrounded by North African-like fragments was recorded.Lastly we tested population models with specific admixture events using qpGRAPH, within the ADMIXTOOLS v5.0 package . The modp-value for the HWE of 1e-08 and false discovery rate of 0.95 were implemented in the analysis. We classified the phenotypic trait into lactose intolerant (glucose level < 1.1 mmol/l), intermediate tolerance (1.1 mmol/l < glucose level < 1.7 mmol/l) and lactose tolerant (glucose level > 1.7 mmol/l), as implemented in previous studies [2 to infer the percentage of the phenotype explained by each SNP and both SNPs together.The Fulani (FZR) and Czech & Slovakian (CS) individuals were used in a genome-wide association scan (GWAS) analysis between their LTT phenotype and their genomic composition (samples extracted from dataset A). The analysis was performed using the GenABEL v. 1.7–6 R package . A SNP c studies , 29, 65.p-value) every 10 SNPs and top 5 regions were inspected on Genome Browser to identify possible genes in the target region. Selection coefficient estimates were calculated using formula by Ohta and Kimura [Scans for signals of selection in the Fulani genomes were done using the integrated haplotype score (iHS) and Cross-population Extended Haplotype Homozygosity (XP-EHH) analyses within the R package REHH v.2.0.2 . In bothd Kimura .Additional file 1. An excel spreadsheet file containing supplementary Tables S1-S6.Additional file 2. A pdf file containing supplementary Figures S1-S17.


Machine learning (ML) is poised as a transformational approach uniquely positioned to discover the hidden biological interactions for better prediction and diagnosis of complex diseases. In this work, we integrated ML-based models for feature selection and classification to quantify the risk of individual susceptibility to asthma using single nucleotide polymorphism (SNP). Random forest (RF) and recursive feature elimination (RFE) algorithm were implemented to identify the SNPs with high implication to asthma. K-nearest neighbor (kNN) and support vector machine (SVM) algorithms were trained to classify the identified SNPs whether associated with non-asthmatic or asthmatic samples. Feature selection step showed that RF outperformed RFE and the feature importance score derived from RF was consistently high for a subset of SNPs, indicating the robustness of RF in selecting relevant features associated with asthma. Model comparison showed that the integration of RF-SVM obtained the highest model performance with an accuracy, precision, and sensitivity of 62.5%, 65.3%, and 69%, respectively, when compared to the baseline, RF-kNN, and an external MeanDiff-kNN models. Furthermore, results show that the occurrence of asthma can be predicted with an Area under the Curve (AUC) of 0.62 and 0.64 for RF-SVM and RF-kNN models, respectively. This study demonstrates the integration of ML models to augment traditional methods in predicting genetic predisposition to multifactorial diseases such as asthma. Risk prediction of complex diseases such as asthma proves to be challenging due to the combinatorial effects of environmental and genetic factors. The major challenges are: 1) obtaining a sufficient sample size to develop univariate or multivariate predictor and, 2) equipping the predictor with biological variables associated with complex disease. In general, biological modelling studies for disease risk assessment using genetic information have been supplemented by several technologies and study designs. For example, genome-wide association studies (GWAS) use single-nucleotide polymorphism (SNP), a single-base pair change in the DNA sequence, as biomarkers to locate genetic regions associated with a particular trait. Recent GWAS  identifiOne possible explanation for the “mystery of missing heritability” is the compounding biological interactions inherent to a complex disease . Most ofDue to the well-known curse of dimensionality inherent to the SNP data, i.e., higher feature size compared to sample size, constructing accurate machine learning-based predictive models for complex disease presents a challenge . One wayIn this work, we developed a novel machine learning-based approach to quantify the individual’s susceptibility to asthma based on SNP data. We implemented a two-step approach which involves feature selection and classification steps. The RF and RFE were utilized to select SNPs highly associated to asthma, while SVM and kNN were employed to quantify the propensity of an individual to develop asthma. The predictive models were evaluated using the following metrics: accuracy, sensitivity, precision, and area under the curve (AUC). In the next section, we describe the procedure of data collection, preprocessing, SNP selection, and classification. Finally, the results of integrating feature selection and classification methods will be compared with baseline and external models.The SNP data was obtained from openSNP database , a publip2 is the frequency of homozygotes for allele p, 2pq is the frequency of heterozygotes for alleles p and q, and q2 is the frequency of homozygotes for allele q. SNPs that deviated from Hardy-Weinberg equilibrium due to selection, population admixture, cryptic relatedness, genotyping error and genuine genetic association [p − value < 0.001 were discarded. After data preprocessing and quality control, 128 samples (57 samples with asthma condition during childhood and adulthood and 71 control samples are unaffected with asthma) were retained. A total of 176,288 SNPs was retained with genotypes comprised of minor and major alleles. Finally, genotypic encoding was implemented wherein homozygous, heterozygous, and variant homozygous SNPs were represented by 1, 2, 3, respectively.We adopted similar quality control procedure from  which inociation  were excociation  on allelN is the number of samples, and Xi and Yi are the SNP values and the output class of ith sample, respectively. For each data point, an out-of-bag error (OOBE) and the mean over the forest were calculated. After training, the jth SNP was permuted among the training data and the difference the OOBE before and after permutation was calculated. To obtain the importance score of the jth SNP, the mean of the differences were calculated and normalized.Disease detection driven by high-dimensional data entails the use of feature selection method to identify the features highly associated to the phenotype . In thisTo predict the individual’s susceptibility to asthma, two different integrated machine learning models were constructed. These models are RF-kNN and RF-SVM which both employed RF as the feature selection algorithm to identify the relevant SNPs. Subsequently, the selected SNPs were used as inputs to kNN and SVM classifiers.k nearest neighbors. For further discussion of kNN using SNP data, refer to [kNN algorithm is a nonparametric lazy learner due to the absence of a training phase when performing classification tasks. In this work, the kNN algorithm was implemented on the SNP data to construct a predictive model that classifies non-asthmatic and asthmatic samples. A particular set of SNPs associated to a sample is represented as a continuous variable that can be plotted into the feature space. Hence, given a sample with SNP as features and an unknown label, the kNN algorithm gives prediction by performing a majority voting on the output class of the sample’s refer to .Xi be the p-dimensional input vector of the ith sample, and is defined as,p is the total number of SNPs. The corresponding output vector Yi is given by,N is the total number of samples. The dataset are plotted on a multi-dimensional feature space with their corresponding class label. To determine the class of an unseen sample U, the k nearest neighbor is determined by calculating the Euclidean distance d relative to the other labeled samples. The Euclidean distance between U and Xi is given by,j represents a certain SNP. We note that our selection of Euclidean distance as a similarity measure is motivated by numerous literatures [To implement kNN, we define eratures , 15, 20 SVM is a learning algorithm used in tasks such as non-linear classification , 13, 14,N is the total number of samples, and Xi and Yi represent the SNP values and class label of the ith sample, respectively. The algorithm minimizes,w is learning weight vector, b is the bias, ε is the error term, and C is the tradeoff parameter between the error and margin. After finding the optimal solution, the function,The construction of the classification model can be viewed as an optimization problem. To implement SVM, the training vector representing SNP values and its corresponding label is given by,One crucial step in constructing a machine learning predictive model is the selection of hyperparameters to achieve the highest model performance. Shown in k = 10) which ensures that each fold contains roughly the same number of samples per class. RF-SVM and RF-kNN were evaluated using leave-one-out cross-validation (LOOCV), which discards a single sample to be used as a test set before training the model on the remaining samples; hence, k is equal to the number of samples (k = 128). The performance metrics used to evaluate the models were accuracy , precision , and sensitivity . The AUC—receiver operating characteristic (ROC) curve is another measure of performance of a machine learning classifier model. The ROC is a probability curve while the AUC represents the capability of the model to distinguish among classes. The ROC curve is constructed by plotting the true positive rate versus false positive rate.Cross-validation techniques were employed to estimate the model performance on the unseen data. The RF classifier was evaluated using stratified k-fold cross validation  in combination with SVM and kNN. As a feature selection method, RFE initially builds a model on the entire dataset and computes feature importance score for each predictor. For the next iteration, the predictor with the lowest feature importance score is omitted from the dataset that will be used to rebuild the model. Result shows see  that RF Shown in While the asthma-associated SNPs selected by RF has low feature importance scores, it is interesting to note that prior studies have not identified these SNPs to be associated to asthma, nor included in the 19 asthma-associated SNPs  present The SVM and kNN were implemented on both entire and selected dataset to establish baseline models. Furthermore, a more stringent test on the performance of SVM and kNN classifiers were implemented on the 19 asthma-associated SNPs. Considering the higher accuracy of RF as a feature selection method when integrated to SVM and kNN as discussed above, we compare the performance of RF-SVM and RF-kNN with the baseline model. To further evaluate the performance of the integrated models, MeanDiff-kNN derived from  was implWe developed machine learning-based predictive models which utilized SNP data to quantify the individual susceptibility to asthma. More precisely, we employed an integrated approach consisting of a feature selection step to identify the SNPs highly associated to asthma, and a classification step to predict asthma susceptibility. Results show that the integrated RF-SVM model achieves the highest accuracy, precision, sensitivity, and AUC compared to RF-kNN and baseline models. Furthermore, this study demonstrated that the integration of various machine learning methods is a well-suited approach to investigate high dimensional SNP data for genotype-phenotype association and phenotype prediction. To a large extent, we have shown how machine learning approach can augment the insights derived from GWAS in analyzing massive and complex biological data obtained from next generation sequencing (NGS) platforms. The integrated approach can be naturally extended on a wide variety of biological data such genetic mutations, copy number variations as well as clinical data which may take into account environmental factors to come up with a more holistic understanding of genetic etiology of complex diseases.github repository.The Python codes as well as the SNP data used in this study is available in this S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.


Candida albicans is both a frequent commensal organism and a prevalent opportunistic pathogen. A prevailing theory is that C. albicans evolves primarily through the gradual buildup of mitotic mutations, and a pressing issue is whether sexual or parasexual processes also operate within natural populations. Here, we establish that the C. albicans genome evolves by a combination of localized mutation and both short-tract and long-tract loss-of-heterozygosity (LOH) events within the sequenced isolates. Mutations are more prevalent within noncoding and heterozygous regions and LOH increases towards chromosome ends. Furthermore, we provide evidence for genetic exchange between isolates, establishing that sexual or parasexual processes have contributed to the diversity of both nuclear and mitochondrial genomes.Mutations introduce variation into the genome upon which selection can act. Defining the nature of these changes is critical for determining species evolution, as well as for understanding the genetic changes driving important cellular processes. The heterozygous diploid fungus  Candida albicans lacks a conventional sexual program and is thought to evolve, at least primarily, through the clonal acquisition of genetic changes. Here, we performed an analysis of heterozygous diploid genomes from 21 clinical isolates to determine the natural evolutionary processes acting on the C. albicans genome. Mutation and recombination shaped the genomic landscape among the C. albicans isolates. Strain-specific single nucleotide polymorphisms (SNPs) and insertions/deletions (indels) clustered across the genome. Additionally, loss-of-heterozygosity (LOH) events contributed substantially to genotypic variation, with most long-tract LOH events extending to the ends of the chromosomes suggestive of repair via break-induced replication. Consistent with a model of inheritance by descent, most polymorphisms were shared between closely related strains. However, some isolates contained highly mosaic genomes consistent with strains having experienced interclade recombination during their evolutionary history. A detailed examination of mitochondrial genomes also revealed clear examples of interclade recombination among sequenced strains. These analyses therefore establish that both (para)sexual recombination and mitotic mutational processes drive evolution of this important pathogen. To further facilitate the study of C. albicans genomes, we also introduce an online platform, SNPMap, to examine SNP patterns in sequenced isolates.The opportunistic fungal pathogen  A wide variety of genetic events contribute to the evolution of eukaryotic genomes. In asexual cells, haploid genomes evolve via the accumulation of point mutations as well as undergo recombination events that drive DNA expansions/contractions (indels). Heterozygous (het) diploid genomes also have the capacity to undergo loss-of-heterozygosity (LOH) events, in which genetic information is lost from one of the two chromosome (Chr) homologs. In addition, both haploid and diploid genomes may experience large-scale chromosomal changes such as gross rearrangements or acquisition of supernumerary chromosomes or other forms of aneuploidy , 2.Many eukaryotic species also generate genetic diversity via sexual reproduction. Here, recombination between individuals provides an efficient mechanism for producing diverse progeny. Sexual reproduction can therefore promote adaptation to new environments more rapidly than asexual propagation , 4. Howe5–Candida albicans is a prevalent opportunistic fungal pathogen that was long thought to represent an obligate asexual species  = 83118, P < 2.2E–16,  = 13825, P < 2.2E–16) . Th. ThC. al00 genes . Accordi10.1128/mBio.01205-18.5TABLE S1C. albicans isolates. Download Table S1, PDF file, 1 MB.Percentage of the genome harboring SNPs among the sequenced Copyright © 2018 Wang et al.2018Wang et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.01205-18.2FIG S1FIG S1, PDF file, 0.8 MB.The distribution of polymorphisms is nonrandom. The number of strains harboring each SNP (A) or indel (B) was determined and the frequency plotted. A best-fit line (red) was plotted for each distribution. Download Copyright © 2018 Wang et al.2018Wang et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the LOH events can distort the patterns of SNPs inherited from ancestral strains . To help10.1128/mBio.01205-18.6TABLE S2C. albicans isolates. Download Table S2, XLSX file, 1.9 MB.Strain-specific SNPs among sequenced Copyright © 2018 Wang et al.2018Wang et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.01205-18.7TABLE S3C. albicans isolates. Download Table S3, CSV file, 0.2 MB.Strain-specific indels among sequenced Copyright © 2018 Wang et al.2018Wang et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.01205-18.3FIG S2FIG S2, PDF file, 0.8 MB.LOH can obfuscate patterns of inheritance by descent of shared polymorphisms. LOH of opposing alleles in a common ancestor can make it appear that mutations arose independently despite the mutations sharing a common origin. LOH of homolog A in clade III produces a SNP pattern different from that of clade II although t arose from the same ancestral strain. Download Copyright © 2018 Wang et al.2018Wang et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the t = 11.64, df = 286, P  < 2.2E−16; https://drive.google.com/drive/u/0/folders/1u0LiWcB-yufoL4iQOn_bQQ2ercxB-F6R). Enrichment of SNPs was observed immediately adjacent to indels (within 10 bp) but not within indels  . This association highlights that these mutagenic events often occur in close proximity to one another, as has been observed in other eukaryotic species . In fact, the overwhelming majority of all long-tract LOH regions (155 of 170 regions larger than 50 kb) extended to the ends of the corresponding chromosomes  and homozygous (hom) regions using defined criteria for all SNPs , resulting in 468 het and 445 hom regions, respectively . Furthermore, all 21 isolates exhibited the same bias toward het regions containing more strain-specific SNPs than hom regions . This indicates that mutations preferentially accumulate in het regions rather than hom regions during natural evolution of C. albicans isolates, as was seen for het regions versus hom regions during meiotic propagation . Strikingly, however, certain regions of the genome exhibited clear violations of identity by descent (https://drive.google.com/drive/u/0/folders/1u0LiWcB-yufoL4iQOn_bQQ2ercxB-F6R). In heterozygous diploid genomes, these deviations could potentially arise through two mechanisms: (i) through sexual recombination between genetically distinct isolates or (ii) through multiple independent LOH events that obfuscate the actual pattern of descent . In the latter case, multiple LOH events could cause loss or retention of SNPs through homozygosis of one chromosome homolog or the other, thereby generating a subset of isolates that appeared “recombinant,” i.e., appeared to have intermixed genetic content from two different relatives. Such a history can sometimes be inferred by a comparison of SNP patterns within the region of interest in multiple extant strains . In line with this, we identified 514 nonoverlapping 25-kb windows across the 21 sequenced isolates that do not harbor similar polymorphisms to closely related strains and that could represent regions that had experienced LOH. Interestingly, two strains, P60002 and P94015, contained 390 (75.9%) of these regions, although only 214 (54.9%) of the 390 incongruent regions in these two strains overlapped with LOH tracts (hom regions) in these isolates. In contrast, the majority (117 of 124) of the incongruent regions in all other strains overlapped with LOH tracts. This suggests that incongruence in polymorphisms in most strains likely results from divergent LOH events but that LOH cannot easily explain the majority of incongruent polymorphic patterns observed in P60002 and P94015.We note that certain regions in the C. albicans (–http://snpmap.asc.ohio-state.edu/), which allows users to map the positions of individual mutations, mutation types, and het/hom tracts across user-defined regions of the 21 genomes.Previous studies provided conflicting messages regarding recombination in natural populations of albicans –23, 26, C. albicans phylogeny and that clustered with different strains by MLST or DNA fingerprinting analysis  and to those on homolog A from a clade III isolate  . Both thP = 1.14E−10). The majority of genomic regions in P94015 aligned with clade I , as expected for a population propagating clonally . In contC. albicans strains containing mosaic genomes should display evidence of classical admixture and allow an estimation of clade contributions to the mosaicism. To assess admixture, we restricted analysis to the four major clades represented here and assigned the fraction of SNPs within each genome to each of the clades. Consistent with the previous sliding approach to assign sequence homology, P60002 and P94015 were identified as the only strains with significant levels of admixture  were annotated within the 21 isolates, with an average SNP density of 1 polymorphism every 476 bp. The mitochondrial genome was highly heterogeneous and included areas of high SNP density  and regions devoid of polymorphisms . Furthermore, of the 1,847 annotated mitochondrial SNPs, only 39 were strain specific in the set of 21 sequenced genomes . We also found that maximum parsimony approaches mostly separated the P90145 mitochondrial genome into clade II and SA regions and the P60002 mitochondrial genome into clade I and II regions (https://drive.google.com/drive/u/0/folders/1u0LiWcB-yufoL4iQOn_bQQ2ercxB-F6R).ial SNPs . As withial SNPs . There, ial SNPs . An addiial SNPs . The rem regions , consist regions  and B. RC. albicans mitochondrial genomes and provide clear evidence that sexual/parasexual processes have occurred during C. albicans evolution.We further note that the single representative of clade E in our collection, strain P75010, also displays strong evidence of recombination in its mitochondrial genome . The firC. albicans genome structure reveals a number of important aspects concerning mutational patterns occurring during the natural evolution of the species. We highlight that (i) there is a significant association between the positions of emergent SNPs and indels; (ii) diverse LOH events contribute to genetic inheritance, including long-tract LOH events that extend to the ends of the chromosomes; (iii) heterozygous regions of the genome accumulate more mutations than coding and homozygous regions; (iv) a subset of strains exhibit mosaic nuclear and mitochondrial genomes; and (v) analysis of specific chromosomal regions reveals clear evidence for interclade recombination.Our analysis of the C. albicans populations similarly exhibit a nonrandom distribution across the genome. Our analysis focused on the distribution of strain-specific SNPs, as these SNPs are likely to have emerged since these strains diverged from one another. We found that the location of strain-specific SNPs was biased toward heterozygous rather than homozygous regions of the genome. This is consistent with recent studies that showed that mutations arising during meiosis occurred more frequently within heterozygous regions of eukaryotic genomes than within homozygous regions, although the mechanism driving this bias is unknown . Homozygosis of certain regions may confer a selective advantage given that LOH has been shown to alter C. albicans traits, from growth rates to virulence to drug resistance . We reveal that a subset of isolates contained mosaic genomes, consistent with those genomes being the products of mating between different C. albicans clades. Both nuclear and mitochondrial genomes of P60002 and P94015 show recombinant genotypes, supporting the idea of a (para)sexual origin for these strains. Genetic information from multiple clades contributed to these genomes, and recombination tracts ranged in length from a few kilobases to hundreds of kilobases sexual exchange.Critically, we identified regions in clade SA . MoreoveC. albicans genomes reveal multiple signatures of the forces that have shaped genetic diversity within the species. Both short and long LOH events have played a major role in increasing population diversity, with large tracts extending to chromosome termini that impact hundreds to thousands of polymorphisms. Base substitution mutations and indels cluster within heterozygous regions of the genome, suggestive of faster evolution of these regions. Finally, recombination between isolates has generated mosaic nuclear and mitochondrial genomes, which could have enabled adaptation in the species. The diploid heterozygous genome of C. albicans is therefore a highly dynamic platform on which selection can act.In summary, Whole-genome sequencing, variant identification, and loss-of-heterozygosity (LOH) windows were identified in a previous study . Brieflyhttps://drive.google.com/drive/u/0/folders/1u0LiWcB-yufoL4iQOn_bQQ2ercxB-F6R. Dividing the mean number of reads of the allele by the total number of reads resulted in a value of 0.4499 for heterozygous positions and a value of 0.9889 for homozygous regions. Thus, a 90% threshold was used for homozygous regions whereas heterozygous regions span from 10 to 50%.To assess heterozygous and homozygous variant calls, the number of reads at each variant position was divided by the total number of reads at that position in that strain. On average, each variant position had 52.17 reads, with an interquartile range of 33 to 71 reads. The distribution of the allelic ratio at each variant is plotted in Fig. S12 at https://snpmap.asc.ohio-state.edu/). Manual interrogation of 100 variants using the Integrated Genome Viewer  + 1] * homozygous SNPs}/length. This effectively doubles the number of homozygous SNPs in hom regions to account for heterozygous SNPs that are lost due to LOH. Even with this doubling, significantly more emergent SNPs occurred in het regions than in hom regions.The methods used to construct the phylogeny of the sequenced strains were previously described by Hirakawa et al. . BrieflyPreviously defined heterozygous and homozygous genomic regions were used as detailed previously . Brieflyclade score = abs)). The clade with the highest similarity score (and thus the greatest proportion of shared SNPs) was selected as the most similar clade for that window. This process was repeated across the full genome. As a follow-up, this process was repeated by removing all strains within the query strain’s clade and the scores were recalculated.Two independent methods were used to assign clade designations for nuclear and mitochondrial genomes in nonoverlapping 25,000-bp or 750-bp windows, respectively. The first process assigned the clade that most closely corresponded to the SNP pattern of the query strain. However, clade I contains fewer SNP calls due to alignment to the SC5314 reference genome , which can introduce an artificial bias. Therefore, a data frame was constructed where each row represented any SNP contained within any strain in the query window. A SNP could be counted only as a single row, so the identity of that SNP position was recorded as “0” for the absence of the SNP, “1” for a heterozygous position, and “2” for a homozygous position. The correlations between the target strain’s resulting numeric SNP profile and that of each of the other strains were individually calculated. Scores from strains within the same clade were averaged as follows and taken as the absolute value: The second phylogenetic approach used an expression matrix listing the 21 strains against all possible SNP positions present for each nonoverlapping window. For each cell, we assigned a value of 0 if the respective SNP was not present in the respective strain, a value of 1 if one copy of the SNP was present, and a value of 2 if both copies were present. A distance matrix was constructed from these data using a binary method (R dist), finally resulting in a phylogenetic tree (R hclust). The appropriate number of K clusters for the phylogenetic output of each window was estimated by traversing all 21 possible values (1 to 21) incrementally using R cutree. The first k-value that was chosen was the value that allowed the target strain to cluster with at least two other strains that were members of the same clade as defined by the current phylogenetic relationship among the sequenced strains. These criteria effectively eliminated clade E because it contains only one strain. Additionally, this approach was assessed manually for congruence across candidate regions.The NGSAdmix tool was used to perform measurements of genetic admixtures in the sequenced clinical isolates . The SNPThe association between the SNPs and indels identified in the next-generation-sequencing (NGS) data was tested by amplification of specific regions that were either shared among a number of strains (P75063) or strain specific (P60002). Two regions were PCR amplified using primers 5′ AGTCGGTGATGTCTATAGTG 3′ and 5′ GCTGTCCTTGGATCATTGAT 3′ to amplify Chr7:48017.48664 in P75063 and 5′ TTCTGCTGTTGCTGCTGCTA 3′/5′ CTGTCAACTGTCAACCAAAG 3′ to amplify ChrR: 19979596.1998145 in P60002. Amplicons were purified and subjected to Sanger sequencing.Mitochondrial SNPs were verified using 3 sets of primers to amplify different regions of ChrM across the 21 natural isolates. Two isolates were analyzed from each clade, including strains P60002, P75010, and P94015. Primers 5′ TTAGTAGTGTCGGTGTCTTC 3′ and 5′ AGAGAGGGTTTTGGTTAGGG 3′ were used to PCR amplify ChrM: 4899.6076, 5′ GAATCTCAGAGACTACACGT 3′/5′ GTGGTATACGACGAGGCATT 3′ were used for ChrM: 18265.20660, and 5′ TGGGAAGTAGAGGCTGAAGA 3′/5′ AGGGGCATTATAAGGAGGAG 3′ were used for ChrM: 28094.29548. PCR amplicons were purified and subjected to Sanger sequencing.t test unless otherwise indicated. All statistical tests were performed in the R 3.2.5 programming environment . SNP data are available from dbSNP (https://www.ncbi.nlm.nih.gov/projects/SNP/) under noninclusive accession no. 1456786277 to 1457237021.The genome sequences used in this study are available under BioProject identifier (ID) 10.1128/mBio.01205-18.1TEXT S1Text S1, PDF file, 0.3 MB.Supplemental results. Download Copyright © 2018 Wang et al.2018Wang et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the