Glossina fuscipes fuscipes (Gff) is the insect vector of the two forms of Human African Trypanosomiasis (HAT) that exist in Uganda. Understanding Gff population dynamics, and the underlying genetics of epidemiologically relevant phenotypes is key to reducing disease transmission. Using ddRAD sequence technology, complemented with whole-genome sequencing, we developed a panel of ∼73,000 single-nucleotide polymorphisms (SNPs) distributed across the Gff genome that can be used for population genomics and to perform genome-wide-association studies. We used these markers to estimate genomic patterns of linkage disequilibrium (LD) in Gff, and used the information, in combination with outlier-locus detection tests, to identify candidate regions of the genome under selection. LD in individual populations decays to half of its maximum value (r2max/2) between 1359 and 2429 bp. The overall LD estimated for the species reaches r2max/2 at 708 bp, an order of magnitude slower than in Drosophila. Using 53 infected (Trypanosoma spp.) and uninfected flies from four genetically distinct Ugandan populations adapted to different environmental conditions, we were able to identify SNPs associated with the infection status of the fly and local environmental adaptation. The extent of LD in Gff likely facilitated the detection of loci under selection, despite the small sample size. Furthermore, it is probable that LD in the regions identified is much higher than the average genomic LD due to strong selection. Our results show that even modest sample sizes can reveal significant genetic associations in this species, which has implications for future studies given the difficulties of collecting field specimens with contrasting phenotypes for association analysis.The tsetse fly  Trypanosoma (Kinetoplastida), species T. brucei rhodesiense (Tbr) and T. b. gambiense (Tbg), while Animal African Trypanosomiasis (AAT or Nagana) is caused by T. b. brucei (Tbb), T. congolense, and T. vivax. HAT caused by Tbg is a chronic disease with asymptomatic periods lasting several years, while Tbr infection results in an acute disease with over 80% mortality within the first 6 months if untreated. Over 90% of HAT cases are due to Tbg, which occurs in the northwest regions of Uganda and extends from Central African Republic to Equatorial Guinea. More than 12 million people in eastern and southern Africa, including Uganda, Tanzania, Malawi, Zambia, and Zimbabwe, are at risk for Tbr infection  is the vector for both forms of HAT in Uganda. To understand the biological processes behind disease spread, especially in light of the impending disease merger, we had previously performed population genetics studies on Ugandan populations of Gff and identified three genetically and geographically distinct population clusters , based on 12–15 microsatellite loci , typically associated with insects that differ at the subspecies level, or with the possibility of coexistence of distinct species  using next-generation DNA sequencing (NGS) can be used to detect signatures of adaptive genetic variation in wild populations without a priori identification of candidate genes : Masindi (MS), Otuboi (OT), Namutumba (NB), and Kalangala Island (KG). Flies from each site included in this study had been previously analyzed using microsatellite and/or mtDNA loci , following the manufacturer’s protocol.Trypanosoma infection twice via PCR amplification using trypanosome alpha-tubulin primers . This assay was not specific for trypanosomes that are human infective , but rather detects the presence of any Trypanosoma species, including T. brucei, T. congolense, and T. vivax. DNA from legs was extracted using the DNeasy Blood and Tissue kit (Qiagen) according to the manufacturer’s instructions. All extractions were stored at –20° until further processing. Each sample was tested for Gff individual was digested with restriction enzymes NlaIII and MluCI (NEB). Each individual sample was then labeled with custom Illumina adaptors carrying a unique barcode at the 5′ end of the digested fragments. The fragments were amplified by eight cycles of polymerase chain reaction (PCR) to select for those fragments containing different restriction sites at each end, and increase the total concentration of the desired fragments. After PCR amplification, a library was constructed by pooling the products of 24 individuals and later size-selecting them to a fragment size of 215 bp under the “tight” setting of a Blue Pippin electrophoresis platform (Sage Science). Libraries were sent to the Yale Center for Genome Analysis for 75 bp paired-read sequencing with the Illumina Hi-Seq platform. To achieve the best sequence quality, the complexity of the sequencing lanes was increased by spiking the libraries with a secondary library whose fragments did not begin with the restriction sites used by our ddRAD library. Data are available via BioSample project accession number PRJNA303153, with linked associated short-read sequences and variation data.Double-digest restriction site-associated DNA sequencing libraries (ddRAD) were prepared using a modified version of the protocol described in Table S1). All sequence data for each of the 16 samples was trimmed using flexbar version 2.4  were calculated using vcftools v. 0.1.12b  as 100-bp reads using the Illumina HiSeq2000 platform at ∼40 × coverage . The software PGDSpider v. 2.0.5.2  by mapping them against the 2395 supercontigs of the assembly  in the “ 0.1.12b . Tajima’ttleneck .P ≤ 0.05)  among loci. In addition to detecting existing population structure, fastStructure provides ancestry estimates of each of the sampled individuals. Ten independent fastStructure runs were conducted with K = 1–5 on all individuals of all populations. The optimal number of K clusters was then determined using the chooseK.py program of fastStructure. Results were plotted with the program DISTRUCT v.1.1  . fastStrCT v.1.1 . Second,CT v.1.1  availablCT v.1.1 . PCA pror2 (N = 2395). The KG population was omitted from the analysis due to its low sample size. Unless stated otherwise, all subsequent analysis pertaining to LD used r2.Vcftools version 0.1.12  of the Beta distribution. A PDF describes the relative likelihood that a random variable will take a particular value, or range of values, given the type of probability distribution. Using the cumulative distribution function (CDF) of the Beta distribution, we can describe the probability that a binned SNP-pair will have an r2 less than or equal to x. It follows that 1 − CDF represents the probability of observing a more extreme value than a particular r2 value. For each set of binned r2 values, the SNP-pairs deemed worthy of further investigation were defined as those where 1 − CDF ≤ 0.01 after BH correction for multiple testing.LD values of all SNP-pairs were compared after binning SNP-pairs by physical distance (bp) to control for unknown rates of recombination in exactly 0 or 1. However, there are data in each bin that have been assigned values of 0 or 1. It is likely that these values are not truly 0 or 1 in the discrete binary sense that a coin-flip can produce only a heads or tails result. Therefore, to satisfy the expectations of the Beta distribution, all 2r values for each bin were scaled according to the scheme:r2 values to fit between 0 and 1, while not including any values equal to 0 or 1. In the scheme above, let xi stand for the r2 of each SNP-pair in an arbitrary bin, and θ stand for the scaling factor. The results in this paper used θ = 0.999.The Beta distribution is bounded on the noninclusive interval between 0 and 1, meaning no value is expected to equal Trypanosoma infected vs. uninfected), or with local environmental adaptation, determined by the PCA loadings of the environmental parameters corresponding to each geographic location obtained from the WorldClim database (File S1).As we hypothesize that SNP variation is associated with phenotypic and environmental variation, we carried out genome scans to identify possible genomic regions under selection associated with the infection status of the fly, assessed via PCR .We used multiple complementary methods to detect signatures of adaptive change while accounting for drift, geographic structure, and a small sample size. At the individual loci level, we used BayeScan , and the hidden Markov models (HMM) of the combined protein-domain sets on the Pfam databases , respectively. Settings used were as dictated by the ARGOT2 site. The blastp and hmmscan results were uploaded to ARGOT2 servers for analysis after being split into 10 groups (∼2330 peptides per group) to prevent overloading the remote ARGOT2 cluster. The functional annotations were then downloaded and joined back together.All putative peptides annotated for Figure S1, Figure S2, Figure S3, Figure S4, Figure S5, and Figure S6, and their legends, as well as Table S4 and the legends of all supplementary tables and supplementary files. File S1 contains the data and analysis of the bioclimatic parameters. File S2 contains details of the selection analyses. File S3 lists the genes located within 1000 bp of SNPs identified by the selection analysis in BayeScan, associated to local environmental adaptation and susceptibility to trypanosome infection. File S4 lists the SNPs identified as outliers in BayeScan during the pairwise population comparisons. The Gff Illumina read sequences produced in this study are available as part of BioSample project accession number PRJNA303153 with linked variation data (ddRAD), and project accession number SAMN02742630 (WGS). Data were deposited at BioSample project under accession numbers PRJNA303153 and SAMN02742630.The Supplementary Material files contain supplementary in vitro digestion of Gff genomic DNA using the restriction enzymes NlaIII and MluCI were 172 to 216 bp in length (193 bp on average), the size chosen for constructing the ddRAD libraries. Downstream analysis of the generated ddRAD reads indicates that we achieved an average coverage of 32 × for each individual fly.Approximately 15% of the fragments generated after Materials and Methods) allowed us to increase the coverage of individuals of this population that were underrepresented in the ddRAD libraries due to low quality samples. With this strategy we recovered 37 × more SNPs for the NB population than those obtained from the ddRAD seq alone, after the appropriate filtering was performed. The dataset containing the filtered ddRAD reads for all four Ugandan populations, and the filtered WGS reads for the NB population was subsequently used to call variant sites.We recovered a total of 448,881,370 reads from the 48 individuals subjected to ddRAD sequencing. Quality processing of these reads with the software Stacks . Further filtering for biallelic SNPs, with a minimum depth of coverage of 7 ×, and present in at least 80% of the samples, resulted in a set of 153,663 SNPs. Individuals KG10_030, MS11_0017, MS11_0050, NB11_056, and NB11_062 were removed from subsequent analyses because they genotyped for < 80% of the SNPs called. This dataset, containing 53 individual flies, was filtered once more to remove any sites that were polymorphic only in the individuals removed. The final set included 73,297 SNPs, with an average coverage of 41.89 × ± 17.46 × per site, ranging from 18.76 × to 102.78 × (median = 40.31 ×). The final dataset was used for the downstream analyses described below.The Gff supercontigs , with an average of two SNPs for every 10,000 bp. The number of SNPs per supercontig ranged from zero to 25 at the 10,000 bp window size . Analysis using a window size of 1000 bp yielded an average of 0.2 SNPs / 1000 bp, with some supercontigs having no SNPs, and 26 contigs having 10 or more.We identified SNPs in 1389 of the assembled Ho) ranged from 0.1111 to 0.3435 (mean Ho = 0.2156 ± 0.0477). Average heterozygosities per population were HoKG = 0.4631, HoOT = 0.3404, HoNB = 0.3203, and HoMS = 0.2968. Average Tajima’s D across all populations and considering 1000 bp nonoverlapping windows (regardless of whether they contain SNPs or not) was 0.0526. Mean Tajima’s D values considering only those 1000 bp windows that contain at least one SNP was 0.4055, with absolute values larger than |2| detected in 2684 of the windows, and 77 of them displaying values larger than |3| .Observed individual heterozygosities  are shown in Fst = 0.3214); and between NB and OT (Fst = 0.3015). The lowest degree of differentiation was found between KG and NB (Fst = 0.0853). The average pairwise Fst = 0.2877 (weighted Fst = 0.3409).Values of between-population genetic differentiation (PBf ≤ 0.05), and 25,300 loci that deviated from HWE when BH correction was applied instead (PBH ≤ 0.05). These latter loci were not considered for the Bayesian clustering analysis in fastStructure, which assumes HWE (see next section). When populations were analyzed individually, no loci deviated from HWE in KG, MS, and OT after either Bf or BH correction was applied. However, in the NB population, 278 (Bf) or 936 (BH) loci were out of HWE.We identified 6180 loci that deviated from HWE after Bonferroni (Bf) correction . PCA of the data shows the same pattern as the Bayesian clustering  near 708 bp . When considering individual populations with smaller sample size, r2max increases : NB = 1359 bp, OT = 2334 bp, and MS = 2429 bp; The mean bin-wise LD in ncreases , and r2 P values ≤ 0.01 . Rather than looking at each of these individual genomic locations, we then used this information to sort potential SNPs of interest from the genome screen conducted in BayeScan, as relevant SNPs might have not been identified as outliers in this analysis due to the lack of statistical power consequence of the small sample size (see following section).After applying an LD filter based on a Beta probability distribution to identify potential LD outliers, we obtained 24,372 out of 6,454,294 SNP-pairs (0.38%) with corrected Table S1 shows the Trypanosoma spp. infection status of the flies from the four Gff collection sites included in this study. BayeScan analysis . Additional LD filtering of the top 10% BayeScan alpha values from this comparison, followed by a genome scan for genes located within 1000 bp of any SNP in the pair, identified a total of 340 genes . Among the most prevalent biological functions of these genes were: trans-membrane transport, transcription regulation, rRNA processing, DNA-replication, and metabolic processing. In the molecular function (MF) domain, some of the frequently occurring motifs were related to zinc ion binding, DNA binding, transferase activity, and oxidoreductase activity.Table S3). We identified 10 and 43 SNPs at the 5% and 10% alpha cutoff, respectively, and carried out a screen for putative genes under selection based on proximity, using a window of 1000 bp. This screen identified 20 genes, including transcription factors, and genes associated with circadian rhythms and membrane integrity. The genes and their distance to each SNP are found in File S3.When each location was analyzed individually , we founFst outlier loci in pairwise comparisons contrasting samples from the three distinct genetic clusters from northern, southern, and western Uganda, as these populations experience different environmental regimes that differ mainly in precipitation and seasonality (File S1). Using BayeScan we did not identify any SNPs between NB and OT, but found 40 outlier loci between MS and NB, and 119 outlier loci between MS and OT (File S4). Two of these outliers were common to both pairwise comparisons  and are adjacent to each other, falling within a 100 bp window that includes three SNPs, and has a Tajima’s D value of 1.25 (Table S2). A genomic scan around these SNPs identified genes GFUI009284 and GFUI009279 within a region 1000 bp; unfortunately, no functional annotations were available for any of these genes (File S3). Eight of the 40 outlier loci between MS and NB also passed the LD filter, with two of these SNPs located within genes, and one located 405 bp apart, none of them with functional annotations (File S3). Of the 119 outlier SNPs from the MS vs. OT comparison, 60 had LD levels above the threshold, with 35 genes located within 1000 bp of them (File S3). We could not carry out this analysis for the NB vs. OT comparison because no outliers were detected by the BayeScan method.We searched for SNPs associated with local environmental adaptation by searching for S and OT ; File S4Table S4 and Figure S5), clusters that are consistent with the ones detected by the fastStructure and the PCA analyses (File S3). Only one of the six SNPs identified by PCadapt was part of an SNP-pair that passed the LD filter (Scaffold368: 310507), but no genes were located within 1000 bp of either SNP of the LD pair.PCadapt .The haplotype-based analysis conducted in Hapflk  samples while adding data from 10 additional individuals of the NB population to the dataset. The depth of coverage achieved by our method (∼40 ×) provides us with a high probability of observing both alleles of an individual, if in a heterozygote state, assuming random detection of each allele. The use of two restriction enzymes during the ddRAD library preparation, in contrast to similar techniques . Average LD in Gff decays in half (r2max / 2 = 0.2009) at a distance of 708 bp. In comparison, Drosophila melanogaster achieves this r2 value (0.2) at 10 bp in autosomes, and 30 bp in the X chromosome , and higher level of genetic structuring compared to either Anopheles or Drosophila populations. The average genome-wide genetic diversity (π) of the NB population, calculated from WGS data, is consistent with a small population size. The value of π for this population is one or two orders of magnitude lower (π = 0.00056) than reported values for Drosophila , or An. Arabiensis . This may be explained if the smaller sample size of the individual populations results in the estimation of more homogeneous allele frequencies. The data also shows that LD decayed faster in NB than in OT and MS (r2max at 2429 bp (compared to r2max / 2 = 1359 bp in NB and 2334 bp in OT). A plausible explanation for this pattern could be that MS has undergone a recent bottleneck. This is consistent with the bottleneck evidence found by Gff in Uganda migrates from the southeast to the northwest , would both increase the power of the analysis and provide specific information on human disease risk.Another caveat is the effect of age of the fly at the moment of capture, as younger flies are less likely to have ingested an infected blood meal compared to older flies and thus might not have been exposed to the parasite yet. Although all the flies included in this study were adults, we did not control for age. Future studies should account for these parameters in order to increase the probability of identifying genetic variants related to susceptibility of infection by trypanosomes. Furthermore, establishing an accurate diagnostic assay for the specific trypanosome species infecting the flies (\ure S5), we identified regions under selection between populations in the west (MS), and east (NB and OT) of Uganda. Of particular interest is the genomic region around Scaffold150, positions 13771–13773, where three SNPs were identified as potential targets of selection. These SNPs are proximate to genes GFUI009284 and GFUI009279, which unfortunately lack homologs or a functional annotation .The second genomic screen seeking loci involved in local environmental adaptation identified over 150 SNPs associated with geographic location. Using two independent methods, pairwise-population comparison in BayeScan  and PCadFile S1) indicates that the seasonality of precipitation as well as temperature and precipitation during the wettest month of the year are different in MS compared to the eastern populations (NB and OT). Tsetse flies are associated with riverine habitats and vegetation thickets along rivers, which they use to get relief from heat and desiccation and to seek hosts upon which to feed  if different Gff genotypes vary in their transmission ability of Tbg and Tbr parasite species. This in turn could provide an immediate mechanism of action against the spread of the disease via the introduction of refractory genes into wild populations. Knowledge of the environmental parameters involved in generating and maintaining the genetic differences among Gff populations will contribute to develop more realistic suitability maps for this vector than currently available, as the integration of the ecological and evolutionary axes of divergence will likely increase their predictive power, and thus our ability to forecast changes in the vector distribution in response to impeding change in climatic conditions. The results of this study demonstrate the efficacy of our approach, and provide baseline data for future work to look at the genetic underpinning of these epidemiologically important traits.Specifically, we searched for genomic regions responsible for tsetse’s resistance/susceptibility phenotype to trypanosome infections and to different environmental adaptations. Identifying genetic regions associated with Trypanosoma infections could inform about (a) the genetic basis for resistance to trypanosomes in natural


Fusarium virguliforme. We compared the genomes of 11 isolates comprising six Fusarium species that cause soybean sudden death syndrome (SDS) or bean root rot (BRR), and identified significant levels of genetic variation in A+T-rich repeat blocks of the essential chromosomes and in A+T-neutral regions of the supernumerary segments. The A+T-rich repeat blocks in the essential chromosomes were highly variable between F. virguliforme and non-F. virguliforme isolates, but were scarcely variable between F. virguliforme isolates. The A+T-neutral regions in the supernumerary segments, however, were highly variable between F. virguliforme isolates, with a statistically significant number (21 standard deviations above the mean) of single nucleotide polymorphisms (SNPs). And supernumerary sequence types and rearrangement patterns of some F. virguliforme isolates were present in an isolate of F. cuneirostrum but not in the other F. virguliforme isolates. The most variable and highly expressed region in the supernumerary segments contained an active DNA transposon that was a most conserved match between F. virguliforme and the unrelated fungus Tolypocladium inflatum. This transposon was absent from two of the F. virguliforme isolates. Furthermore, transposons in the supernumerary segments of some F. virguliforme isolates were present in non-F. virguliforme isolates, but were absent from the other F. virguliforme isolates. Two supernumerary P450 enzymes were 43% and 57% identical to their essential counterparts. This study has raised the possibility that transposons generate genetic variation in supernumerary chromosome segments by frequent horizontal transfer within and between closely related species.Supernumerary chromosome segments are known to harbor different transposons from their essential counterparts. The aim of this study was to investigate the role of transposons in the origin and evolution of supernumerary segments in the asexual fungal pathogen  Mycosphaerella graminicola was three times higher than in the essential chromosomes  plates and grown on 1/3 PDA  to harvest conidial spores. The 1/3 PDA grown Fv Mont-1 became blue two weeks later when masses spores were produced. Harvested conidial spores were grown for 12 h in liquid modified Septoria medium (MSM) to obtain germinated spores [The isolate d spores . The gerμg each from germinating conidial spores and mycelia, were used to purify poly (A)+ RNAs using oligo (dT) attached to the magnetic beads . RNA samples were reverse transcribed using a cDNA synthesis kit from Illumina , and cDNAs of an individual RNA sample were sequenced on Illumina Genome Analyzer II  at the DNA Facility, Iowa State University. Over 16 million single reads of 83 bp were generated using Solexa GA pipeline 1.6. No read trimming was performed; only reads with a high percent identity to the reference were selected in read mapping.Total RNAs, 10 A SNP between the reference isolate and another isolate (query) has two or more alleles termed REF and ALT. The REF allele refers to the allele in the reference and ALT alleles refer to alternate non-reference alleles. A SNP is of type 2 if both the REF allele and the ALT allele are present in the query isolate, and of type 1 if only the ALT allele is present in the query isolate. Because all the isolates in the vegetative state have haploid nuclei, the presence of type-2 SNPs in any of them indicates the presence of paralogous sequences in it. Thus, in some cases, type-2 SNPs were excluded in the calculation of the SNP rate between the reference and each query isolate.The sets of Illumina paired-end reads for each query isolate were mapped onto the reference genome assembly with Bowtie2 . The outWe used the following procedures to ensure that SNPs detected in this study were not due to errors in the sequence data or methods. First, the entire read must align to the reference with at least 95% percent identity. Second, a mapping quality cutoff of 40  was used. The use of this low read mapping error rate means that if a read was sufficiently identical to two or more regions of the reference, then the read was rejected by the mapping process. As a result, nearly identical regions of the reference were not covered by reads; we confirmed this mapping feature by finding such regions and checking on their read coverage depths. Note that reads with type-2 SNPs were still mapped to a unique region of the reference. Either there was only one copy of this element in the reference isolate, or highly identical copies (with over 99% identity) of the element were collapsed into one reference region during the assembly. For this reason, type-2 SNPs were not used in SNP rate calculations. Third, the SNP calls were filtered with a minimum quality value of 80 and a minimum read coverage depth of 10. Errors in SNP detection were unlikely to occur because of the high depths of coverage (over 100X) for the reference by reads from each isolate. In addition, we also used the FreeBayes-based SNP call component of SpeedSeq  to detecFv Mont-1 was evaluated by mapping Illumina reads from isolate Fv 34551; a read of 102 bp can be mapped to a region of the 454 assembly if they had at most 5 base differences. About 97.8% of the assembly was covered by reads at an average depth of 167. The genome-wide nucleotide difference rate for the 454 assembly was about 1 in 10,000 bp. This rate was an upper bound on the genome-wide error rate for the 454 assembly because the nucleotide differences comprised both errors and SNPs.To ascertain that SNPs were not due to errors in the reference assembly by Srivastava et al. , this 45F. virguliforme isolates, were evaluated to make sure that they had an error rate much lower than the variation rates. About 98% of contig mc28.2 was covered by mapped Illumina reads from isolate Fv 34551 to an average depth of 207  with the following data and options: a pair of mate files in fastq format; a minimum insert length of 100 bp and a maximum insert length of 700 bp; an average insert length of 400 bp with a standard deviation of 100. The minimum length of overlaps with no base mismatch match was set to 84 bp, and that of overlaps with up to three base mismatches was set to 90 bp. No overlap with more than three base mismatches was accepted. Each data set was of size up to 49 Gb, and each assembly could be produced in a day on a processor with 100 Gb of main memory.About a dozen PCAP.Solexa contigs were selected for analysis in this study. These contigs were further checked by comparing them with the contigs produced by a popular short read assembler named SPAdes . All butPCAP.Solexa still follows the overlap-layout-consensus strategy of PCAP . It compEach assembly of Illumina reads was mapped to the reference genome assembly by BWA-MEM  with theFusarium genomic sequence was performed using Augustus [F. graminearum. A non-redundant protein sequence database at National Center for Biotechnology Information was searched using Blastx [Ab initio gene identification in a Augustus  with trag Blastx  with a gg Blastx  on the sg Blastx .This was done by computing the duplication depth for each position in the genome assembly, where the duplication depth of a position was defined as the number of times a sufficiently long region containing the position was similar to another region in the genome assembly. The region length cutoff was set to 400 bp as duplications of lengths above this cutoff would be affected by RIP  in the lThe duplication depths for the genome assembly were computed as follows. Initially, the duplication depth for each position was set to 0. A file of contig sequences for the genome assembly was compared with itself by modifying the DDS2 program  so that Fusarium isolates was inferred from genome-wide SNP data. The data were produced by mapping reads from each of 10 of the 11 isolates onto a genome assembly of the reference Fv Mont-1. A covered SNP position was a position of the reference that was sufficiently covered by reads from each isolate and had an alternative allele (a SNP) in the read coverage of this position from one of the 10 isolates. A total of 297,076 covered SNP positions were aligned in the 11 isolates. The multiple sequence alignment was analyzed to infer the tree with 200 bootstrap samples.A maximum-likelihood tree of the 11 SDS/BRR Fv Mont-1 [F. virguliforme isolates possessed a low genome-wide SNP rate of less than 1 in 10,000 bp, which is consistent with an asexual mode of reproduction. Isolate Fv 34551 collected in South America in 2002 was closer to Fv Mont-1 collected in the USA in 1991 than the other two F. virguliforme isolates collected in the USA. Second, the genome-wide SNP rate of about 1 in 200 bp between the reference and each non-F. virguliforme isolate was at least 80 . Third, the genome-wide SNP rate of 1 in 200 bp between the reference and each non-F. virguliforme isolate was not high enough to explain why a large portion (∼10 Mb) of the reference genome was covered by reads from every F. virguliforme isolate, but not covered by reads from any non-F. virguliforme isolate.We mapped short reads from each of the ten isolates onto a 50-Mb genome assembly of isolate v Mont-1  as a refFc 31157 as well as that not covered by reads from this isolate. The size of the covered portion was 39.5 Mb; that of the uncovered portion was 10.9 Mb. The uncovered portion was A+T rich (68%), whereas the covered portion was A+T poor (45%). The content of duplicated sequences in the uncovered portion was 70%, with 48% containing sequences with copy numbers above 20. In sharp contrast, the content of duplicated sequences in the covered portion was 3.8%, with 0.56% containing 20-plus-copy sequences. These results indicate that F. virguliforme and F. cuneirostrum were more diverged in the uncovered portion rich in duplication and A+T content. For example, we observed C-T/G-A substitutions at a rate of 98% (871/887) in a 10-kb alignment (with 91% identity) of two adjacent contigs in the uncovered portion, where the integrity of both Fv Mont-1 contigs were verified based on their nearly complete and SNP-free coverage at a depth above 100 by reads from each of the other three F. virguliforme isolates. Each contig had a best match (with 90% identity) to the Fc 31157 genome assembly and to the Fp 31156 genome assembly, which were 99.95% identical over the match. Moreover, a maximum likelihood tree of 13 duplicated sequences in the Fv Mont-1 assembly illustrates that the more recently duplicated sequences had a higher A+T content  between the f ∼0.005 ), but weF. virguliforme isolate and each of the other three F. virguliforme isolates was at most 0.00005, we found high levels of variation among three of the four F. virguliforme isolates in a small portion (≤ 4%) of the genome; the maximum SNP rate between the reference F. virguliforme isolate and each of the two F. virguliforme isolates (Fv Clinton-1B and Fv LL0009) was at least 21 standard deviation units above the mean SNP rate. In addition, the maximum SNP rate for isolate Fv LL0009 was close to that for each of the non-F. virguliforme isolates, three of which belonged to the sexual species F. tucumaniae. This suggests that different evolutionary forces may have shaped their genomes.Although the genome-wide SNP rate between the reference Fv Clinton-1B and Fv LL0009 with the reference assembly were observed in one of the two contigs  in scaffold 28 of the reference assembly. Scaffold 28 contained 12 contigs  that were ordered and oriented using 454 read pairs with two insert sizes of 3 kb and 20 kb [The maximum SNP rates for both nd 20 kb . The twoF. virguliforme isolates was at least 0.00117. The high type-2 SNP rates indicated that two or more sequence types  were present in each F. virguliforme isolate.  in contig mc71.1. Like mc28.4, mc71.1 was rich in SNPs for some F. virguliforme isolates (see below). The unique significant matches between scaffold 28 of Fv Mont-1 and chromosome 14 of N. haematococca MPVI suggest that scaffold 28 was supernumerary. Thus, scaffold 28 is called a supernumerary segment.Scaffold 28 of the ing DDS2 ) with thF. virguliforme isolates, with several presence/absence polymorphisms. For example, contig mc28.3 was fully covered by reads from Fv 34551 with no SNPs, mostly covered by reads from Fv LL0009 with many SNPs, but barely covered by reads from Fv Clinton-1B. In addition, mc28.3 was highly variable among Fc 31157, Fp 31156, and Fb 31757. Similarly, contigs mc28.8 of 5 kb, mc28.11 of 8 kb, and mc28.12 of 8 kb were highly variable among the F. virguliforme isolates.Scaffold 28 was also highly variable among the Fv 34551. Corresponding contigs were found in each of the top six isolates in Fv 34551. For Fv 34551, the major differences in read coverage depth and type-2 SNP number between mc28.2 and mc28.4 indicate the presence of a long segment and a short segment in Fv 34551 that were highly polymorphic over mc28.4.Contigs mc28.2 (of 52 kb) and mc28.4 (of 68 kb) were compared with a genome assembly of each isolate to find corresponding contigs in the assembly with unique significant matches (with ≥ 94% identity over ≥ 5 kb), where the cutoffs for sequence similarity were selected based on the identity and length ranges of the unique significant matches between mc28.4 and the genome assembly of Fv Clinton-1B by finding unique significant matches in a comparison of these contigs with the Fv Clinton-1B genome assembly. Some of the matches suggest a chromosomal rearrangement between the reference isolate and Fv Clinton-1B, and the presence of two genomic segments in the reference isolate that were 95% identical over some of their lengths but were quite different over the rest , Fv LL0009 (319), Fc 31157 (722), Fp 31156 (494), and Fb 31757 (231). However, cc26.1 was not covered at the breakpoint by any reads from Fv 34551, although cc26.1 was deeply covered before and after the breakpoint by these reads. Therefore, the rearrangement type in cc26.1 of Fv Clinton-1B was not present in Fv 34551; the rearrangement type in mc28.2 and mc28.4 of Fv Mont-1 was present only in Fv 34551 based on the deep read coverage of mc28.2 around the breakpoint (at a depth of 240) and of mc28.4 around the breakpoint (231). Furthermore, a type-2 SNP G/A  was found near the breakpoint in cc26.1 in Fv Clinton-1B , Fc 31157 (567/179), and Fp 31156 (278/224), a sign that two polymorphic segments were present in each of these three isolates.By contrast, we detected significant variation in mc28.2 and mc28.4 between the reference isolate and the rest . The seqthe rest  was confFv Clinton-1B are shown in Fv Clinton-1B. We found additional types of polymorphic sequences by examining the read coverage of each contig sequence. Thus, some alignments in Fv Clinton-1B and Alignment A3). The most likely explanation for the large difference in read count between the three types in isolate Fv Clinton-1B is that at least 4 copies of the supernumerary segment were present in this isolate, with type A3.Tb present in more copies than types A3.Ta and A3.Tc. In light of this observation, a segment with two or more highly polymorphic copies in an isolate is also called supernumerary. Thus, the detection of a significant number of type-2 SNPs in an isolate indicates the presence of a supernumerary segment.A total of eight contig sequence alignments showing SNPs and small indels between the reference isolate and Fb 31757 contained two or more polymorphic sequence types, i.e., two or more copies of an element. A separate analysis of Fb 31757 revealed that it contained two alleles at each SNP position in its deep read coverage (≥ 500) of two large regions of mc28.4, a sign that the isolate contained two copies of an element. These observations suggest the possibility that copies of the element in scaffold 28 were transferred horizontally.Fv Clinton-1B and Fc 31157, we checked to see if the two isolates were closer in the whole contigs than the other isolates. Contig cc26.1 was completely covered at a high depth by reads from Fc 31157, but only partially at a high depth by reads from each of the other four isolates. Thus, Fv Clinton-1B was most similar to Fc 31157 in this contig, which is another species, and less similar to Fv LL0009 and Fv 34551 of its own species. We also made a similar observation regarding contig cc440.1. These observations also suggest the possibility that the segment  in contigs cc26.1 and cc440.1 of Fv Clinton-1B was horizontally acquired from another species. The presence of two or more DNA segments homologous to scaffold 28 and with numerous small and large variations in each of the top six SDS/BRR isolates  had unique significant matches (with 99% identity over 10 kb) to two contigs  in the Fv LL0009 genome assembly. Contig lc220.1 was a nearly perfect match over its whole length (except its short ends) to a region of mc74.1. However, contig lc25.1 was only a local match to mc74.1; a region of lc25.1 from positions 9,409 to 27,568 bp was 99% identical to a region of mc74.1 from positions 41,232 to 23,076 bp (in reverse orientation). Moreover, only this region of lc25.1 was covered in high depth by reads from Fv Mont-1, Fv 34551, Fc 31157, and Fp 31156.We discovered another reference contig (mc74.1 of 75 kb) in which a high SNP rate between the reference isolate and Fv Clinton-1B; contig lc25.1 from positions 4,830 to 13,682 bp was 99% identical to contig bc299.1 from positions 8,853 to 1 bp of Fb 31757. The two strong matches confirmed the integrity of the region of contig lc25.1 from positions 4,830 to 9,408 bp. In addition, a region of lc25.1 from positions 606 to 4,153 bp was 99% identical to contig bc2776.1  of Fb 31757. This region of lc25.1 was not covered by any read from the other isolates. Contig cc714.1 was another contig in which not all of the six SDS/BRR isolates were the same in their read coverage of this contig.On the other hand, contig lc25.1 from positions 4,845 to 12,262 bp was 99% identical to contig cc714.1  of We screened the reference assembly for additional contigs with a high SNP rate or contigs in which some of the isolates were different in their read coverage of these contigs. A total of 18 scaffolds with such contigs were found . These sWe annotated genes in two supernumerary segments by combining ab initio gene structure prediction with protein database matching. A list of proteins along with their functions in each segment are shown in Ft 31096, but not in the other three isolates including Ft 31781 and Ft 34546 . In addition, in Ft 31096, 12 SNPs were found, of which 8 were nonsynonymous.We examined variation in some of the genes among the isolates. Contig mc74.1 harbors a gene encoding a cytochrome P450 (CYP) enzyme of 643 residues. This enzyme, a member of family CYP53 , is capable of detoxifying plant defensive compounds, including benzoic acid derivatives . The genFt 34546 . No SNPsCYP53 gene was 43% identical at the amino acid level to another region (contig mc2.51) in the essential (core) genome, where the two genes shared the same 4-exon gene structure with two short exons followed by two long exons. The core CYP53 gene was present in all of the isolates with no SNPs among the F. virguliforme isolates and a total of 12 SNPs between the F. virguliforme isolates and the non-F. virguliforme isolates. Of the 12 SNPs, 3 were present in all the non-F. virguliforme isolates, 2 were in all the F. tucumaniae isolates, 3 were in Fc 31157 and Fb 31757, 3 were only in Fa 54364, and 1 was only in Fp 31156. The core CYP53 enzyme was 90% identical to a CYP53 enzyme of 635 residues from N. haematococca MPVI; which was also the best match (at 43% identity) for the supernumerary CYP53 enzyme when searched against all of the N. haematococca MPVI proteins.The supernumerary CYP53 gene in contig mc74.1 was a 2-exon gene encoding a 514-residue protein with a heterokaryon incompatibility (HET) domain . Variation in the supernumerary HET gene among the isolates was similar to that of the supernumerary CYP53 gene. The HET gene was present in the top seven isolates  to a 2-exon gene in the essential genome (contig mc29.14), which was present in all of the isolates with no SNPs in each F. virguliforme isolate but with several SNPs in each non-F. virguliforme isolate.Downstream of the supernumerary lates in  with no F. oxysporum (FOXG_17536) in subfamily CYP567F; family CYP567 was merged with family CYP60 [CYP567 gene was present with 11 to 21 SNPs in each of the top six isolates in Fv LL0009 with 6 in introns and 15 in coding exons, of which, 7 were nonsynonymous and 8 synonymous. The six isolates contained 5 common SNPs, which were all in coding exons with 4 of them being nonsynonymous. In each of the top five isolates in CYP567 gene were present in the isolate. As an example, in isolate Fc 31157, a type-2 SNP C/T (357/904) was found at position 45,570 bp in mc28.4. The frequency for the allele T was twice that for the allele C, suggesting that at least three copies of the gene were present in this isolate, with one copy having the C allele and the other two having the T allele. The supernumerary CYP567 enzyme was a unique strong match with 57% identity (298/517) to a P450 enzyme in the essential genome (contig mc11.8).We found another P450 enzyme (of 517 residues) in the supernumerary genome. The enzyme was encoded by a 4-exon gene in contig mc28.4, with two short exons followed by two long exons. The supernumerary P450 enzyme was 68% (342/497) identical to a P450 enzyme of ly CYP60 , a grouply CYP60 . This CYFv Clinton-1B, Fv 34551, Fc 31157 and Fp 31156, based on the complete coverage of the reference gene sequence by reads from each isolate and the presence of at least 9 type-2 SNPs. For example, we observed a SNP C/T (at codon position 2 for residue 211) in each isolate’s read coverage of the reference gene sequence with the following read counts for each allele: 141/106 (Fv Clinton-1B), 0/132 (Fv LL0009), 384/147 (Fv 34551), 471/215 (Fc 31157), 417/239 (Fp 31156), and 0/172 (Fb 31757). Note that the count for the reference allele was about twice that for the alternate allele in three isolates, suggesting that there were three copies of this gene: two copies of the reference type and one copy of the alternate type. We found 9 type-2 SNPs in the Fv Clinton-1B read coverage of the reference gene sequence; most of the SNPs were present in the other isolates. Of the 9 substitutions, 6 were nonsynonymous and 3 were synonymous.We found a single-exon gene coding for a 248-residue G1/S-specific cyclin. This gene was present in each of the top six isolates in  with at RVT1 and RVT2 genes  domain, a reverse transcriptase (3.1e-27), and an RNase H (8.9e-18). The endonuclease/exonuclease domain was in exon 4 encoding 430 residues, and the other two domains were mostly in exon 5 encoding 692 residues, with the two exons separated by an intron of 58 bp. The RVT2 gene was predicted to encode a protein of 957 residues with an integrase core domain (4.6e-18) and a reverse transcriptase domain (2.9e-88) but without an endonuclease/exonuclease or RNase domain. The two domains were in exon 2 encoding 710 residues. Scaffold 74 had a G+C content of 52%. We searched the rest of the reference genome for strong matches to either RVT protein and found 7 additional RVT1 regions and 3 additional RVT2 ones.The supernumerary segment in scaffold 74 carried both T2 genes , which wT2 genes  based onRVT ORFs in the region was correlated with the presence (or absence) of type-1/2 SNPs in the read coverage of this region by some isolates. The results are shown in RVT1 match  contained shorter RVT ORFs with a lower SNP rate.For each region, we checked whether its scaffold was variable among the isolates, and if so, we checked whether the presence (or absence) of long Fv Mont-1 and Fv Clinton-1B with the Repbase database [Fv Mont-1 assembly and 36 matches in the Fv Clinton-1B assembly. All but one of these 54 matches were in supernumerary regions; the only match (of 642 bp and with an A+T content of 53%) not in the supernumerary regions was 91% identical (with two in-frame stop codons) to a 236-residue region of a 626-residue transposase (GenBank accession: AAC16005) from F. oxysporum f. sp. lycopersici. Nearly all of the matches in each assembly were with DNA transposons, and nearly each match had an A+T content between 46% and 50%. Below are reports of the presence/absence polymorphisms and SNP rates in these transposons among some of the isolates.In addition, we compared the genome assemblies of isolates database  , but not in any of the five isolates Fv LL0009, Fv 34551, Fc 31157, Fp 31156 and Fb 31757 (with 0% of the transposon covered by reads from the isolate). The percent coverage of each of the remaining 21 transposons by reads from each of the six isolates is shown in F. virguliforme isolates and some non-F. virguliforme isolates, but a low coverage breadth by reads from at least one F. virguliforme isolate, which raised the possibility that the transposons were transferred horizontally.We checked if these transposons were present in each isolate by calculating their coverage breadths by reads from the isolate. We observed presence/absence polymorphisms in 29 of the 54 transposons among the three F. virguliforme isolates. The DNA transposon in mc28.3 was 91% identical over a region of 1,227 bp to the Restless transposon in Repbase, which came from the fungus Tolypocladium inflatum with a genome size of about 30 Mb. This match was one of the top three matches between the F. virguliforme and Tolypocladium inflatum genomes; the average percent identity of the 306 most significant matches of lengths over 1,000 bp between the two genomes was 79%. The Restless transposon in mc28.3 was highly expressed; its expression level ranked in the top 2% of all the genes. The G+C content of this transposon was 51%.We examined the DNA transposon Restless in the contig mc28.3, which was downstream of the contig mc28.2 and upstream of the contig mc28.4; as noted above, both mc28.2 and mc28.4 showed significantly higher SNP rates between some of the F. virguliforme isolates Fv Mont-1, Fv Clinton-1B, Fv LL0009 and Fv 34551. The two most significant examples were a SNP rate of 0.004 in contig mc28.3 with the Restless transposon between Fv Mont-1 and Fv LL0009, and a SNP rate of 0.006 in contig cc32.1 with an NHT1 transposon between Fv Clinton-1B and Fv LL0009.We also observed significantly higher SNP rates in or around 20 of the 54 transposons between some of the four RVT genes, which polymerize DNA via an RNA intermediate. The expression level of each contig in the Fv Mont-1 reference assembly was estimated by totaling the coverage depths of each contig position by short transcriptomic reads and dividing the sum by the contig length. The 11 most highly expressed contigs of ≥ 1 kb were mc66.3 (at an average depth of 144), mc4.6 (137), mc28.4 (130), mc28.8 (95), mc420.2 (88), mc79.8 (83), mc28.7 (81), mc54.5 (80), mc28.10 (74), mc28.12 (72), and mc28.2 (70). All the contigs except mc4.6 were supernumerary. Moreover, 6 of them came from scaffold 28, and 3 of the rest  were linked to scaffold 28 by a significant number of read pairs. In addition, we estimated the expression level of each gene in the same reference assembly. Half of the top 12 most highly expressed genes were located in the contigs mc66.3 (3 genes) or mc28.4 (3 genes); the rest were located in different contigs. Thus, scaffold 28 was the most variable and most highly expressed segment in the genome.We examined the expression levels of supernumerary segments because they contained F. virguliforme. Transposons are known to play a major role in building two-speed genomes, with their variable compartments enriched in transposons [F. virguliforme was drastically expanded through the generation of variable A+T-rich repeat blocks, as happened to the L. maculans genome [F. virguliforme was enriched in A+T-neutral transposons, one of which was active in a supernumerary segment whose SNP rate (between some F. virguliforme isolates) was 120 times higher than the average rate across the whole genome including the A+T-rich blocks. This complements the previous observation that the average non-synonymous substitution rate in the supernumerary chromosomes of Mycosphaerella graminicola was three times higher than in the essential chromosomes [We used a comparative genomics approach to investigate the role of transposons in shaping the genome of the asexual fungal pathogen nsposons –4. The es genome , 6. Mostomosomes .F. virguliforme isolates; during that period, the A+T-rich repeat blocks in the essential genome, which were highly variable between closely related species, still had an extremely low SNP rate between the F. virguliforme isolates. Thus, the origin of the supernumerary genome was partly due to the origin of its transposons, which we addressed by looking at common sequences and transposons within and between F. virguliforme and its related species. Our results suggest that some transposons in the supernumerary genome moved between these species after the divergence of the F. virguliforme isolates. Moreover, an active DNA transposon was found in scaffold 28 with the most variable and highly expressed genes. This provides an explanation to the persistence of transposons as selfish DNA: transposons carry important genes (like ones encoding for P450 enzymes) and evolve them rapidly for their host without silencing them.The scarceness of variation and the absence of A+T-neutral transposons in the essential genome contrast with the abundance of variation and the presence of A+T-neutral transposons in the supernumerary genome. This indicates that the activity of A+T-neutral transposons was inhibited much more strongly in the essential genome than in the supernumerary genome; the supernumerary genome provided places where A+T-neutral transposons could generate genetic variation much more rapidly without causing damage to the essential genome. Note that this rapid evolution occurred after the recent divergence of the Supernumerary chromosomes are common in fungi, and they are hypothesized to originate by horizontal transfer . TranspoFv Mont-1 assembly was the most highly expressed as well as the most variable. This variation-expression connection suggests that variation is important to the success of this pathogen. Future studies on the functions of the genes in scaffold 28 might be able to shed light on the control and management of this pathogen.The supernumerary segment in scaffold 28 of the S1 FigThe figure consists of ten horizontal panels, one for each of the ten isolates in the same order as in (PNG)Click here for additional data file.S2 Fig(PNG)Click here for additional data file.S3 FigF. tucumaniae isolate but not in the other F. tucumaniae isolates.The contig region containing a gene encoding a cytochrome P450 enzyme was present in one (PNG)Click here for additional data file.


Mungbean yellow mosaic India virus (MYMIV) is a bipartite Geminivirus, which causes severe yield loss in soybean (Glycine max). Considering this, the present study was conducted to develop large-scale genome-wide single nucleotide polymorphism (SNP) markers and identify potential markers linked with known disease resistance loci for their effective use in genomics-assisted breeding to impart durable MYMIV tolerance. The whole-genome re-sequencing of MYMIV resistant cultivar ‘UPSM-534’ and susceptible Indian cultivar ‘JS-335’ was performed to identify high-quality SNPs and InDels (insertion and deletions). Approximately 234 and 255 million of 100-bp paired-end reads were generated from UPSM-534 and JS-335, respectively, which provided ~98% coverage of reference soybean genome. A total of 3083987 SNPs (1559556 in UPSM-534 and 1524431 in JS-335) and 562858 InDels (281958 in UPSM-534 and 280900 in JS-335) were identified. Of these, 1514 SNPs were found to be present in 564 candidate disease resistance genes. Among these, 829 non-synonymous and 671 synonymous SNPs were detected in 266 and 286 defence-related genes, respectively. Noteworthy, a non-synonymous SNP  at the 149th base-pair of LEUCINE-RICH REPEAT RECEPTOR-LIKE PROTEIN KINASE gene responsible for a G/C transversion [proline (CCC) to alanine(GCC)] was identified and validated in a set of 12 soybean cultivars. Taken together, the present study generated a large-scale genomic resource such as, SNPs and InDels at a genome-wide scale that will facilitate the dissection of various complex traits through construction of high-density linkage maps and fine mapping. In the present scenario, these markers can be effectively used to design high-density SNP arrays for their large-scale validation and high-throughput genotyping in diverse natural and mapping populations, which could accelerate genomics-assisted MYMIV disease resistance breeding in soybean. Mungbean Yellow Mosaic Virus is one of the most destructive as well as widely distributed plant pathogenic viruses belonging to the family Geminiviridae. This virus causes yellow mosaic disease in legumes and particularly in India, the yields of several legume crops including blackgram (Vigna mungo), mungbean (Vigna radiata), cowpea (Vigna unguiculata) and soybean (Glycine max) are challenged by Mungbean Yellow Mosaic India Virus (MYMIV). Among legumes, soybean is an economically important crop in which MYMIV causes devastating yield losses  belonging to disease resistant QTLs were retrieved from PRG database . The conserved domains in the proteins were predicted by searching against Pfam database using HMMscan v3.0  by Sanger sequencing. Approximately 500 bp flanking sequences of the selected SNPs were extracted using in-house perl programming and then flanking forward and reverse primers were designed. The sequences were amplified by PCR using genomic DNA of UPSM-534 and JS-335 along with 10 cultivars. The PCR amplification reactions were performed in a 20 μl reaction volume containing 100 ng of genomic DNA, 1x Taq buffer, 2 mM of MgClThe present study has direct relevance to the current scenario of identifying prominent breeding strategies for MYMIV resistance. Despite the efforts of soybean breeders, no major breakthrough has been achieved for imparting MYMIV resistance in soybean. Hence, molecular marker-based approaches will offer innovative avenues to breeders, geneticists and researchers to investigate the MYMIV resistance in soybean. Further, this will enable the improvement of horizontal resistances that are polygenetically inherited and for which screening and selection is cumbersome. Whole genome re-sequencing is widely practised for large-scale identification of molecular markers and their utilization in mapping of disease resistant loci. This would serve as a valuable resource for fine mapping the disease resistant loci in crop plants, including soybean. Further, high-throughput DNA sequencing and generation of large-scale expression data will provide the opportunity to explore the genetic and genomic variations at resistance genes/QTLs and pave way for conducting further downstream functional assays to clarify their mechanisms of action.MYMIV infected soybean seedlings initially showed chlorosis of leaves, which is the characteristic symptom of the disease . The disG. max var. Williams 82). Approximately 97% of the reads were mapped to the reference genome with a coverage of 95% in both the cultivars  and transversion (Tv) type of SNPs were 1042619 and 527093, respectively, with a Ts/Tv ratio of 1.9781 for JS-335 . SimilarGlycine max var. Williams 82 genome with 950068807 bases contained in twenty chromosomes was used as reference. The distribution of DNA polymorphisms detected between JS-335 and UPSM-534 was analysed with the reference genome. The frequency of SNPs and InDels identified in both the cultivars varied across twenty chromosomes  and chromosome 14 of UPSM-534 (606.68 SNPs per Mb). Similarly, higher InDel density was observed in chromosome 18 of JS-335 (441.89 InDels per Mb) and chromosome 16 of UPSM-534 (447.79 SNPs per Mb), whereas lower InDels density was detected in chromosome 4 of JS-335 (158.28 InDels per Mb) and chromosome 14 of UPSM-534 (157.58 InDels per Mb) . FurtherThe annotation of soybean reference genome was used to reveal the distribution of SNPs and InDels within various genomic regions such as intergenic and intragenic. Approximately 50% of SNPs were identified in intergenic regions in both the cultivars. Overall, a similar distribution pattern of SNPs and InDels was observed in the entire chromosome for both the cultivars. Only 10–12% of total SNPs were detected in genic regions, whereas upstream (promoter) and downstream regulatory regions had significant number of SNPs (18–22%). Within the genic region, more than 6% of SNPs were present in the introns . The 3′ Amino acid substitution was analysed to investigate the effect of SNPs in coding sequences. In JS-335, 28267 and 25533 SNPs showed non-synonymous and synonymous type modifications, respectively. Similarly, 29012 non-synonymous and 26195 synonymous changes were observed in UPSM-534. The ratio of non-synonymous to synonymous SNPs was about 1.11 for JS-335 and 1.10 for UPSM-534 . FunctioFunctional annotation of the genes showing SNP variations revealed the participation of these genes in defense response. A total of 1514 SNPs were detected in 564 defence response-related genes in both the soybean cultivars  Tables aAlthough the use of next-generation sequencing and high-throughput analysis platforms in re-sequencing of soybean cultivars and identification of QTLs related to biotic stress tolerance have been demonstrated –27, no aLEUCINE-RICH REPEAT RECEPTOR-LIKE PROTEIN KINASE (LRR-RP) gene (Glyma18g02850)  . The G tIn the present study, SNPs and InDels were generated in large-scale by re-sequencing two soybean cultivars with contrasting tolerance characteristics to MYMIV infection. In addition to the usefulness of these resources in genomics-assisted breeding for MYMIV resistance, the present study will facilitate the dissection of complex traits through construction of high-density linkage map and fine mapping. Further, these markers would be useful for high-density SNP array designing and high-throughput genotyping for variety identification, gene tagging, molecular mapping of genes/QTLs, construction of linkage map, association mapping and marker-assisted selection. Of note, the SNP identified in chromosome 18 (18–1861613) with G/C transversion at 149 bp could be used in marker-assisted breeding for MYMIV resistance after large-scale validation.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 FigThe SNP 18–1861613 is highlighted in red. Non-synonymous SNPs are indicated in bold, synonymous SNPs are underlined.(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S1 Table(XLS)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


Arachis hypogaea L.) is an allotetraploid (AABB) species derived from the A-genome (Arachis duranensis) and B-genome (Arachis ipaensis) progenitors. Presence of two versions of a DNA sequence based on the two progenitor genomes poses a serious technical and analytical problem during single nucleotide polymorphism (SNP) marker identification and analysis. In this context, we have analyzed 200 amplicons derived from expressed sequence tags (ESTs) and genome survey sequences (GSS) to identify SNPs in a panel of genotypes consisting of 12 cultivated peanut varieties and two diploid progenitors representing the ancestral genomes. A total of 18 EST-SNPs and 44 genomic-SNPs were identified in 12 peanut varieties by aligning the sequence of A. hypogaea with diploid progenitors. The average frequency of sequence polymorphism was higher for genomic-SNPs than the EST-SNPs with one genomic-SNP every 1011 bp as compared to one EST-SNP every 2557 bp. In order to estimate the potential and further applicability of these identified SNPs, 96 peanut varieties were genotyped using high resolution melting (HRM) method. Polymorphism information content (PIC) values for EST-SNPs ranged between 0.021 and 0.413 with a mean of 0.172 in the set of peanut varieties, while genomic-SNPs ranged between 0.080 and 0.478 with a mean of 0.249. Total 33 SNPs were used for polymorphism detection among the parents and 10 selected lines from mapping population Y13Zh (Zhenzhuhei × Yueyou13). Of the total 33 SNPs, nine SNPs showed polymorphism in the mapping population Y13Zh, and seven SNPs were successfully mapped into five linkage groups. Our results showed that SNPs can be identified in allotetraploid peanut with high accuracy through amplicon sequencing and HRM assay. The identified SNPs were very informative and can be used for different genetic and breeding applications in peanut.The cultivated peanut ( Among all the structural variations in the genomes of animals and plants, single nucleotide polymorphisms (SNPs) provide the most frequent polymorphism having potential to be used as genetic markers. The SNP markers have proven their usefulness in conducting different genetic and breeding studies in a large number of organisms including several crop plants. The SNPs are now preferred by geneticists and breeders in conducting large scale genome-wide association studies (GWAS) and modern breeding such as marker-assisted backcrossing (MABC), marker-assisted recurrent selection (MARS), and genomic selection (GS). To a large extent, these studies have been driven by technology which made available a number of cost-efficient and robust high throughput SNP genotyping platforms. Initially all the analytical softwares for highthrouput genotyping data were designed keeping diploid human genome in mind. Lack of appropriate analytical tools/softwares for polyploidy was the main bottleneck which restricted their use in only major diploid crop plants. Nevertheless, diploid crop plant species benefitted hugely by these new genotyping platforms  arise between corresponding nucleotide coordinates of subgenomes  technologies together with improved and customized analytical tools for allotetraploids facilitated successful discovery of large scale SNPs in allopolyploid taxa  curve analysis is a highly sensitive and efficient method for mutation theory and SNP genotyping , a self-pollinating allotetraploid , is a major oilseed and food crop in semi-arid tropic regions of the world and serve as a significant source of oil and protein to the consumers. The cultivated peanut is derived from a fusion of the Arachis duranensis  and the Arachis ipaensis  ~3500 years ago   and A. ipaensis (BB). All these genotypes were accessed from Oil Crops Research Institute (OCRI), Chinese Academy of Agricultural Sciences (CAAS), Wuhan, China. Ninety six peanut varieties in south China including the above 12 varieties were investigated for allele frequency (Addition File 1). In addition, the RIL population Y13Zh (Zhenzhuhei × Yueyou13) described by Hong et al.  peanut varieties (Addition File 1) and two diploid progenitor's i.e., g et al.  was usedn et al. .A. hypogaea available at the NCBI database (www.ncbi.nlm.nih.gov) were accessed for analysis. The primer pairs were designed using these sequences with the Primer Premier 5 program . The main parameters for designing primers include (1) 55–65°C melting temperature (Tm) with 60°C as optimum; (2) 450–750 bp product size; (3) 18–24 bp primer length with predicted amplification rate >80%; and (4) 40–60% GC content. The PCR amplification was carried out in a 50 μl volume reaction containing 2 μl of DNA (~100 ng), 1 × Power Pfu buffer , 1 × enhancer solution , 0.2 mM dNTPs, 0.25 μM primers, and 1.25 U Power Pfu DNA Polymerase . The PCR profile had 1 cycle of 5 min at 94°C, an annealing temperature of 55°C for 35 cycles  and an additional cycle of 10 min at 72°C. The presence of a single defined fragment in the test panel was visualized in agarose gel electrophoresis.The expressed sequence tags (ESTs) from peanut EST database developed by Guo et al.  and genoThe PCR products with a single fragment from the 12 peanut varieties and putative donors of subgenomes were purified using the Bioteke PCR Purification Kit  and sent to BGI  for sequencing. Each PCR product was sequenced from both sides using the Big Dye terminator system, version 3.1 Mix (Applied Biosystems) by an ABI 3730XL DNA sequencer. Sequence alignments and SNP identification were carried out using Mutation Surveyor®  80–250 bp of product size; (2) only one SNP from each amplified fragment; (3) primer binding region containing HSV and PSV for designing subgenome specific primer. PCR was performed in a 10 μl reaction under the same conditions as mentioned above, except two modifications. The first modification was addition of 2.5 μM CYTO®9  to the reactions while using either the 1 μl of a 100X dilution of the first PCR (unpurified) or 1 μl of 20X dilution of purified first PCR product as the template. The PCR and HRM analysis were carried out using the LightCycler® 96 Real-Time PCR System . The conditions used had 1 cycle at 95°C for 3 min; 40 cycles at 95°C for 10 s, 60°C for 15 s, 72°C for 10 s; 1 cycle at 72°C for 90 s followed by melting at 72°C to 90°C with increase of 0.1°C and waiting time of 2 s each step. After monitoring the amplification status, the significantly early and late amplifications were not considered for HRM analysis to avoid rising of aberrant melting curves. The selected samples were checked on 2% agarose gels for their specificity.k is the total number of alleles and p is the frequency of the ith allele at a given locus.The formula suggested by Botstein et al.  and AndeThree RIL (recombinant inbred line) populations namely Y13ZH, Y13FU, and Y13J11 were developed from three crosses for conducting genetic mapping . The 82% of the 100 EST-derived amplicons and 77% of 100 GSS-derived amplicons were successfully sequenced. Most of the amplicons from A. duranensis and A. ipaensis were homozygous and the homozygosity of EST-derived amplicons (87.8%) was higher than that of GSS-derived amplicons (75.3%). On the contrary, most of the amplicons from A. hypogaea were heterozygous and the heterozygosity of GSS-derived amplicons (76.6%) was higher than that of EST-derived amplicons (62.2%).Amplicon sequences derived from ESTs and GSS were analyzed to identify SNPs in a panel of 12 peanut varieties and two putative diploid progenitors of A. hypogaea made the composition of heterozygous amplicon more complicated. It may contain homoeologous and (or) paralogous sequence. Sequence alignment between A. hypogaea and diploid progenitors showed that most of the PCR fragments in A. hypogaea were derived from both A and B genome , yielding a total of 18 SNPs averaging one SNP in every 2557 bp in the 12 peanut varieties . SNPs in the nucleotide variant classes C/T (G/A) and A/C (T/G) were most common (both were 38.9%), while A/T (T/A) and C/G (G/C) variants were less prevalent . Of the 77 sequenced GSS-derived amplicons, 20 were polymorphic, yielding a total of 44 genomic-SNPs in the investigated panel . The frequency of genomic-SNPs was one SNP/1011 bp, which was much higher than that of the EST-SNPs. Nucleotide variant classes of genomic-SNPs was similar to that of EST-SNPs i.e., C/T (G/A) and A/C (T/G) were most common with a frequency of 68.2 and 22.7%, respectively, while A/T (T/A) and C/G (G/C) were the least prevalent with a frequency of 6.8 and 2.3%, respectively.A. hypogaea, which were distributed in 43 heterozygous EST-derived amplicons with one SNP in every 76.9 bp. EST-HSVs in the nucleotide variant classes C/T (G/A) were most common (71.6%), while C/G (G/C) (13.7%), A/C (T/G) (7.8%), and A/T (T/A) (6.9%) variants were less prevalent. Meanwhile, 869 genomic-HSVs were identified from 48 heterozygous GSS-derived amplicons. The frequency of genomic-HSV was higher than that of EST-HSVs, averaging one SNP in every 32.2 bp. Genomic-HSVs in nucleotide variant classes C/T (G/A) were most common (61.4%), while A/C (T/G), A/T (T/A), and C/G (G/C) were less prevalent with18.1, 15.8, and 4.6%, respectively.A total of 306 EST-HSVs were identified in A. hypogaea, averaging one SNP in every 86.8 bp, while 11 GSS-derived amplicons containing 277 PSVs were identified in A. hypogaea, averaging one SNP in every 27 bp. Nucleotide variant classes of EST-PSVs and genomic-PSVs were similar. The C/T (G/A) was most common, while A/C (T/G), A/T (T/A) and C/G (G/C) were less prevalent.Total eight EST-derived amplicons containing 54 PSVs were identified in r2 = 1). For GSS-56 and GSS-67, both r2 < 1, two SNPs were determined. Therefore, 34 SNPs were available for PIC estimation in the 96 peanut varieties. A total of 34 nest primers were used for PCR amplification and HRM analysis. The results indicated that except GSS-4, HRM melting curve of the other 33 amplicons were well distinguished in the investigated panel, and the genotypes identified by HRM analysis was consistent with that by amplicon sequencing  and (or) between closely related copies of genes that are members of gene families. Methods that rely on the comparison of ESTs and GSS from different varieties face a bioinformatic challenge, because SNPs between paralogous sequences and the transcripts from the two genomes have to be clearly identified. In order to achieve good results by keeping all the above considerations, a very high redundancy of sequences are required from each of the identified varieties. The available ESTs and GSS in the NCBI database are at present not providing the necessary sequence depth. The Zhou et al.  verified that cultivated tetraploid peanut has also undergone DNA sequence elimination in its early stage of formation. The sequencing traces indicated that multiple-copy sequences were more easily eliminated than the single-copy sequence in A. hyopgaea genome, similar to other species (Ma et al., Evolutionary studies have suggested elimination of DNA sequence in the early stage after the formation of allopolyploids and was observed more for the multiple-copy sequences (Rieseberg, As a positive development, recently few studies in plants demonstrated utility of HRM in detection of genetic variation and genotyping (Chateigner-Boutin and Small, A. hypogaea. The results indicate that the genomic-SNPs are distributed widely and evenly in the peanut genome of A. hypogaea and, therefore, the genomic-SNPs will be more efficient than EST-SNPs in genetic mapping, association analysis and in other breeding applications. In this study, nine SNP markers showed polymorphism in the RIL population Y13Zh, and two SNPs cannot be mapped into the linkage groups of peanut. Since the two SNP markers did not show segregation distortion in the mapping population, it can be inferred that SNP genotyping was correctly completed by HRM, and their long linkage distance with the other markers may have caused the failure of mapping into the linkage groups.In the present study, the frequency and PIC value of genomic-SNPs was significantly higher than the EST-SNPs in The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Tropical forests are believed to be very harsh environments for human life. It is unclear whether human beings would have ever subsisted in those environments without external resources. It is therefore possible that humans have developed recent biological adaptations in response to specific selective pressures to cope with this challenge. To understand such biological adaptations we analyzed genome-wide SNP data under a Bayesian statistics framework, looking for outlier markers with an overly large extent of differentiation between populations living in a tropical forest, as compared to genetically related populations living outside the forest in Africa and the Americas. The most significant positive selection signals were found in genes related to lipid metabolism, the immune system, body development, and RNA Polymerase III transcription initiation. The results are discussed in the light of putative tropical forest selective pressures, namely food scarcity, high prevalence of pathogens, difficulty to move, and inefficient thermoregulation. Agreement between our results and previous studies on the pygmy phenotype, a putative prototype of forest adaptation, were found, suggesting that a few genetic regions previously described as associated with short stature may be evolving under similar positive selection in Africa and the Americas. In general, convergent evolution was less pervasive than local adaptation in one single continent, suggesting that Africans and Amerindians may have followed different routes to adapt to similar environmental selective pressures. Tropical forests are characterized by a high diversity of plants, with tall trees, dense canopies and low light penetration . Their cBesides food limitation, other characteristics of tropical forests may also contribute to the hostility of these environments. For instance, tropical areas harbor on average 70% higher human pathogen diversity as compared to more temperate areas . As a coHomo ).The six populations were combined into four distinct population sets (PS), each including one tropical forest and one control population per continent as follows:PS1: West Africa, Biaka; Pima, Surui.PS2: West Africa, Mbuti; Pima, Surui.PS3: West Africa, Biaka; Pima, Karitiana.PS4: West Africa, Mbuti; Pima, Karitiana.Each PS was analyzed separately to find loci and genomic regions that would putatively be under natural selection. The rationale behind the use of different population sets is to look for congruent signals of adaptation across data sets, and thus to eliminate signals potentially due to particular tropical forest populations, which have been shown to present high rates of genetic drift due to their small effective population sizes , 17.The results of this analysis are the core of this study, but since we had a single non-forest dwelling Amerindian population, we performed a supplementary analysis with a similar sampling design as described above, except that we replaced the Mexican Pima by the Zapotec. The rationale behind this approach is to check whether the signal observed using the core analyses is not related to any adaptive evolutionary phenomenon that would be restricted to Pima (non-forest) rather than to the forest dwellers Karitiana and Surui. Thus, by replacing Pima by Zapotec—another non-forest agriculturalist population—we should be able to check if the reported signals hold. This analysis is performed separately, because the number of shared SNP for this dataset  is much lower than that of the HGDP database . We therefore studied the following supplementary population sets (SPSs):SPS1: West Africa, Biaka; Zapotec, Surui.SPS2: West Africa, Mbuti; Zapotec, Surui.SPS3: West Africa, Biaka; Zapotec, Karitiana.SPS4: West Africa, Mbuti; Zapotec, Karitiana.ftp://ftp.cephb.fr/hgdp_supp1/>). We initially discarded 250 markers that were monomorphic in all populations, those that presented only missing data, and those located on either the Y-chromosome (due to low SNP density in the chip), the pseudoautosomal region of the sex chromosomes, or the mtDNA, leaving us with 660,668 SNPs. Within each PS defined above, those markers with minor allele frequency below 5% in all populations joined into one were discarded, yielding 582,074, 581,855, 584,205, and 577,345 SNPs for population sets PS1–PS4, respectively.Data on 660,918 SNPs (Illumina HumanHap 650K Beadchips) were downloaded from the Stanford University HGDP-CEPH SNP genotyping data supplement 1 , shared by all loci, and a locus-specific component (α), shared by all the populations [A modified version of BayeScan , 21 was ulations , 21, 23.ulations , 21 and ulations , 21. Thuulations –23.SC coefficient measures the differentiation of each population within the continental pool of migrants and an FCT coefficient measures the differentiation of each continent within the overall meta-population [q-values for each marker in order to control for the False Discovery Rate  considete  and disregard the results obtained for the highest hierarchical level , which could be due to other environmental differences between these two continents. By doing so, we still take into account the divergence between the two continents, but we avoid any particular bias or power decrease that would result in ignoring them [For an Fn humans –18 and Bn humans . In thising them . As for ing them , which iST-based natural selection tests are those based on Linkage Disequilibrium (LD). Those tests use information on homozygosity in single populations, which makes it difficult to formally test for differences in levels of LD and the length of homozygosity tracts over all populations. This is precisely the strength of the current approach, the integration of all populations in a single statistical analysis. A detailed comparison of this approach to other available methods can be found in ref [Alternatives to Fd in ref .q-values lower than 0.1 as potentially significant. This procedure yielded a list of outlier SNPs for each PS, which were then considered as candidate loci for natural selection targets. We finally assume hereafter that convergent selection occurred when selection presents a higher probability than neutrality and model 4 has a higher posterior probability than models 2 and 3.In this analysis, we considered all SNPs with http://pngu.mgh.harvard.edu/purcell/plink). SNPs not present in coding regions were assigned to a given gene if located less than 50 kb away from it. When more than one gene was within this range, the closest gene was chosen for the subsequent analyses. This software was also used for indicating the attributes of the functional SNPs, i.e. if they are nonsense (stop codon), missense (non-synonymous), frameshift or splice-site mutations, based on information from dbSNP build 129 available with PLINK v1.07. The amount of outlier SNPs falling in genic regions (or < 50 kb away from them) was compared to the amount of outlier SNPs in non-genic regions first taking into account only protein coding genes and then all regulatory elements as described below. This proportion was then compared to the distribution of the 660,668 HGDP SNPs in genic or non-genic regions with a Fisher exact test using R [All SNPs were assigned to genes using PLINK v1.07  sampled randomly the same number of SNPs as the counts of outlier SNPs from the population sets PS1-PS4 and counted the number of overlapping SNPs. We assessed the p-value by taking the proportion of occasions that resulted in an overlap equal or larger than the overlap we found. We followed the same procedure when testing the overlap of outlier genes.We tested whether the number of outlier SNPs and genes shared among the population sets was higher than expected. For this we repeatedly . Seventeen genes were annotated with multiple locations (caused by merging of gene records in the NCBI database); in these cases we took the outermost start and end positions.The hg19 assembly coordinates (NCBI Build 37.3) of 19,683 protein-coding genes located on the human autosomal and X chromosomes were obtained from the NCBI Entrez Gene website  were remapped on hg19 with the NCBI Genome Remapping Service (<http://www.ncbi.nlm.nih.gov/genome/tools/remap>). In doing so, we were not able to remap 74 SNPs, which were then excluded from the following analyses.The original positions of the SNPs on the hg18 reference genome (NCBI Built 36.3) obtained from the original dataset  for the hg19 assembly and added to the annotation file. These clusters show DNaseI hypersensitive areas assayed in 125 human cell types by the ENCODE Project [Information on the DNaseI Hypersensivity sites were obtained online  was plotted with R taking into account the physical position of the SNPs, their particular q-value, the window q-value, and the best supported model of selection  for each outlier SNPs. For the outlier SNPs, we also included information on their nearest gene if it was at most 50 kb apart.A sliding-window approach was implemented in R to identify significant clusters of candidate SNPs and remove isolated loci. We considered consecutive windows of size 500 kb wide with a shifting increment of 25 kb at each step. Despite the acknowledged differences in the extent of LD in the analyzed populations due to different time-scales and other particularities of the demographic history of the continent they are located –18, a siThe sliding-window approach yielded a second set of outlier markers. This SNP set is more refined than the one with the first candidates, since it ignores the low SNP density regions of the genome and those candidates that are isolated, highlighting genomic regions with a higher density of outlier SNP.paf), the Americas (pam), and both continents . We also added the score psl, which is the probability of any selection (psl = paf + pam +pco), which means selection in only one or both continents. As we use one value per gene in the enrichment test, we transformed the SNP-based scores to gene-based scores. First, SNPs were assigned to genes as described above: a SNP was assigned to a gene if the SNP location was within a gene transcript; otherwise it was assigned to the closest gene within 50 kb distance. For each gene and each selection model, we took the highest selection score of all SNPs assigned to this gene. After removing 1,922 genes with no SNPs assigned, a list of 17,761 genes remained for PS1 and PS4, 17,766 for PS2, and 17,769 for PS3.We applied a gene set enrichment analysis as described by Daub et al.  to find http://www.ncbi.nlm.nih.gov/biosystems, downloaded 3 Sep 2013). After removing genes that were not part of the gene list mentioned above, excluding gene sets containing less than 10 genes and pooling (nearly) identical gene sets into union sets, 1,216 (PS1 and PS2) and 1,217 (PS3 and PS4) sets remained and they were used as input in the enrichment tests.We collected 2,336 gene sets from the NCBI Biosystems database  were reported.R [We used R  and CytoR  to visuaR .ST-based Bayesian approach implemented via BayeScan [According to the FBayeScan , 21 1,48BayeScan  or a posBayeScan , or to tBayeScan , but notABLIM3, ACSS2, AKAP6, ANKRD26, ARHGEF10, ATIC, BCAT1, C20orf111, C2orf73, CBLN1, CNTN4, CNTNAP5, COL22A1, CPA5, CRTC3, CWH43, DCUN1D4, DHCR7, EPHB4, FAM188B, FKBP6, GALNT16, GLIS3, GLRB, GPC6, GRIK2, HLA-DPA1, HMG20B, HSF2, IQGAP1, KIAA1598, KLHL29, LRRC66, MASTL, MPST, NAALADL2, NRG1, NRP2, NSUN5, PARK2, PKIB, PPP2R2C, RABGGTB, RAD51B, RBFOX1, RBM9, RBMS3, RFX3, ROBO2, SCP2, SGCB, SHISA6, SPAG16, SPATA13, SPATA18, ST6GAL1, and TRG@). We confirmed with a random permutation test  that the number of outlier SNPs (75) and genes (57) shared by all population sets is higher than expected by chance .Using a threshold of 50 kb, 568, 474, 620, and 517 genes were associated with those significant SNPs from PS1 to PS4 respectively, of which 57 genes were found to be co-occurring in all four PSs  for genic SNPs in comparison to the whole HGDP SNP set. On average 72.8% of the outlier SNPs after the sliding-window procedure are located in or less than 50 kb away from protein-coding regions considering all four PSs, while for all SNPs in the HGDP database this proportion is only 63.6%. This enrichment is not due to potential LD between outlier SNPs, as it still holds when randomly sampling one outlier SNP and one non-outlier SNP per 100 kb window . Moreover, all outliers were located at maximum 45.8 kb apart from a gene or from a putative regulatory element indicated by a hypersensibility to DNaseI.q-values for the different PSs, as well as the best model for selection (color-coded) in the sliding-window approach. It was possible to identify seven clusters of outlier SNPs co-occurring in all four PSs  includes two SNPs, one located in a FKBP6 intron and the other located 0.12 kb apart from NSUN5 and 3.55 kb from TRIM50. The abovementioned 14 genes that include or are close (<50 kb) to SNPs that present signals of positive selection in the sliding-windows approach for all four PSs are described in For the first two clusters, the most likely model of selection for the majority of outlier SNPs is positive selection in the Americas; however, for a few SNPs convergent evolution cannot be ruled out . While tSCP2 ., the seSCP2 .. The neSCP2 .. Clusteclusters –S6 Figs.lyzed PS .. ClusteFive of the seven clusters showing signs of positive selection with the HGDP data sets also show signals of positive selection in the supplementary analysis. The exceptions are cluster 3 on chromosome 4, which was not covered by any SNP in the smaller data set, and cluster 7 on chromosome 7, for which the signal of positive selection disappears after replacing Pima by Zapotec. In the supplementary analysis, no additional clusters with signal of positive selection were found in all SPSs. The type of natural selection—convergent evolution or selection in a single continent—inferred from the supplementary data set is generally concordant with the analysis done on HGDP data, except for cluster 6 on chromosome 6, where selection in Africa is more important in the supplementary analysis. After performing the sliding-window approach, cluster 1 appears for SPS1 and SPS3, clusters 4 and 5 for all SPSs, and cluster 6 for SPS2 and SPS4.q-values in our study  in TRIP4. In a third region, we found two genes with positive selection signals considering West Africans in comparison to Biaka instead of Mbuti: USP46 and MLL3. An additional region suggested by Mendizabal et al. [NNT, while we observed a more diffuse significant signal also including CCL28 in some cases .As selection is acting on phenotypes instead of genotypes, we applied a gene set analysis  to detecTropical forests are believed to be very harsh habitats for human beings . In addiABCA1, a gene encoding a cholesterol efflux regulatory protein, was evolving under positive selection due to limited food resources that Native Americans encountered during their history. We also found significant signals of positive selection in genes that are related to lipid circulation and metabolism, such as SCP2 and CWH43  and the second includes the genes DCUN1D4, LRRC66, and SGCB, which are found in a region known to be associated to severe limb-girdle muscular Duchenne-like dystrophy [It is generally accepted that the pygmy phenotype might have evolved as an adaptation to life in dense tropical forests, to thermoregulation, and to food scarcity  or as a PPP3CB, which encodes a subunit of calcineurin, a protein that regulates bone formation by osteoblast differentiation [TRIP4, a gene with positive selection in pygmy populations [MLL3, involved in histone modification, a category associated with height in a genome-wide association study [Other regions of interest for the study of the pygmy phenotype are those four in which we found positive selection signals and are associated with the pygmy phenotype according to another study . From thntiation ; TRIP4, ulations  that relulations ; and MLLon study .In general, non-convergent positive selection signals were more pervasive than signals of convergent evolution at the SNP, gene and pathway levels. This suggests that the similar selective pressures imposed by the tropical forest environment in Africa and the Americas have targeted different genes and pathways. This could be a result of different genetic background of the populations from these two continents and also a result of the differences in the time-scales that adaptation took place, since the Americas were peopled more recently than Africa –18. As iST-based hierarchical Bayesian method used in our study enabled us to detect a number of regions with positive selection, suggesting that the following biological functions and pathways may play a role in human adaptations to tropical forest: lipid metabolism, immunology, body development, and heat stress response. The same signals found in different population sets suggest that they are due to environmental adaptation rather than to the demographic history of the sampled populations. Further refinement of these analyses with e.g. full genome or exome sequence information could reveal which particular mutations are responsible for these adaptations. Moreover, the few cases in which convergent evolution could be inferred contrast with the larger amount of genes with non-convergent positive selection signals, suggesting that Africans and Amerindians may have followed different routes to adapt to similar environmental selective pressures.The FS1 Figq-value is indicated by a yellow continuous line. With a False Discovery Rate of 0.1 (dashed line), SNPs are color-coded according to the best supported model of selection, namely neutrality (black), selection in Africa (blue), selection in the Americas (green), and in both continents . When an outlier SNP was located less than 50 kb apart from a gene, the closest gene name was written next to it. Different sets of populations were used in the analysis yielding four different population sets .The sliding-window (TIFF)Click here for additional data file.S2 Figq-value is indicated by a yellow continuous line. With a False Discovery Rate of 0.1 (dashed line), SNPs are color-coded according to the best supported model of selection, namely neutrality (black), selection in Africa (blue), selection in the Americas (green), and in both continents . When an outlier SNP was located less than 50 kb apart from a gene, the closest gene name was written next to it. Different sets of populations were used in the analysis yielding four different population sets .The sliding-window (TIFF)Click here for additional data file.S3 Figq-value is indicated by a yellow continuous line. With a False Discovery Rate of 0.1 (dashed line), SNPs are color-coded according to the best supported model of selection, namely neutrality (black), selection in Africa (blue), selection in the Americas (green), and in both continents . When an outlier SNP was located less than 50 kb apart from a gene, the closest gene name was written next to it. Different sets of populations were used in the analysis yielding four different population sets .The sliding-window (TIFF)Click here for additional data file.S4 Figq-value is indicated by a yellow continuous line. With a False Discovery Rate of 0.1 (dashed line), SNPs are color-coded according to the best supported model of selection, namely neutrality (black), selection in Africa (blue), selection in the Americas (green), and in both continents . When an outlier SNP was located less than 50 kb apart from a gene, the closest gene name was written next to it. Different sets of populations were used in the analysis yielding four different population sets .The sliding-window (TIFF)Click here for additional data file.S5 Figq-value is indicated by a yellow continuous line. With a False Discovery Rate of 0.1 (dashed line), SNPs are color-coded according to the best supported model of selection, namely neutrality (black), selection in Africa (blue), selection in the Americas (green), and in both continents . When an outlier SNP was located less than 50 kb apart from a gene, the closest gene name was written next to it. Different sets of populations were used in the analysis yielding four different population sets .The sliding-window (TIFF)Click here for additional data file.S6 Figq-value is indicated by a yellow continuous line. With a False Discovery Rate of 0.1 (dashed line), SNPs are color-coded according to the best supported model of selection, namely neutrality (black), selection in Africa (blue), selection in the Americas (green), and in both continents . When an outlier SNP was located less than 50 kb apart from a gene, the closest gene name was written next to it. Different sets of populations were used in the analysis yielding four different population sets .The sliding-window (TIFF)Click here for additional data file.S7 FigNodes are gene sets that score a q-value ≤ 20% (before pruning). Gene sets are connected when at least one of them shares >33% of its genes with the other set. The width of the connecting lines represents the amount of similarity between sets; the size of a node scales with the size of the gene set. Each quadrant of a node represents results on one of the four selection scores, while the four rings in a node correspond to the four population sets Click here for additional data file.S1 Fileq-value, and nearest gene (NA stand for those cases where no gene was found in < 50 kb) is organized by sheet for each population set (PS).Information on SNP coordinates, α values for each model of selection , (XLSX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.