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dnasp 5 manualDnaSP can estimate several measures of DNA sequence variation within and between populations in noncoding, synonymous or nonsynonymous sites, or in various sorts of codon positions), as well as linkage disequilibrium, recombination, gene flow and gene conversion parameters. Moreover, DnaSP can conduct a number of neutrality tests, such as (among other), the Hudson, Kreitman and Aguade (1987), Tajima (1989), McDonald and Kreitman (1991), Fu and Li (1993), and Fu (1997), Ramos-Onsins and Rozas (2002), Achaz (2009) tests, and compute their confidence intervals by the coalescent. The results of the analyses are displayed on tabular and graphic form. Since almost all computers should work with the 64-bit version, it is better to install the most powerful 64-bit version. If it does not work, try the 32-bit one (x86).Bioinformatics 25: 1451-1452. Bioinformatics 19: 2496-2497. Bioinformatics 15: 174-175. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets.In this context, estimating the impact of natural selection (both positive and negative) is of major interest. Furthermore, DNA polymorphisms are relevant as a tool for a broad range of life science disciplines. Consequently, many high-throughput sequencing, genotyping and polymorphism detection systems have been developed and are currently publicly available (Shendure and Ji, 2008 ). These new technologies are generating massive amounts of data that need to be processed, analyzed and transformed effectively into knowledge. These technological advances have largely stimulated the development of both analytical methods and computer applications. DnaSP (DNA Sequence Polymorphism) is a software package that allows for extensive DNA polymorphism analyses using a friendly graphical user interface (GUI) (Rozas et al., 2003 ).http://canadianriversafety.com/userfiles/cambridge-audio-t500-tuner-manual.xml
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Version 5 extends the capabilities of the software, allowing comprehensive DNA polymorphism analyses on multiple data files and on large datasets. Altogether, the present version of DnaSP has the appropriate features for exhaustive exploratory analyses using high-throughput DNA polymorphism data. 2 FEATURES The new version currently allows for the handling and analysis of multiple data files in batch, and implements new algorithms and methods; among other things (see below) includes a new module to identify conserved DNA regions, this feature might be useful for phylogenetic footprinting-based analyses (Vingron et al., 2009 ). DnaSP provides a convenient GUI facilitating all data management and analytical tasks; the results can be visualized graphically as well as in a text report. DnaSP accepts multiple DNA sequence alignment file formats (Rozas et al., 2003 ), including NEXUS (Maddison et al., 1997 ), and HapMap3 files with phased haplotypes (The International HapMap Consortium, 2003 ). The software allows exhaustive DNA polymorphism analyses, including those based on coalescent theory (Rozas et al., 2003; Wakeley, 2009 ). 2.1 Haplotype reconstruction DnaSP implements statistical methods to infer haplotype phase, and prepares adequately the phased data for subsequent analyses. The input data (unphased genotype data) are required in FASTA format using IUPAC nucleotide ambiguity codes to represent heterozygous sites. DnaSP reconstructs the phase by applying various algorithms (PHASE v2.1, fastPHASE v1.1 and HAPAR) differing in the underlying population genetic assumptions.This algorithm is faster and allows for the handling of larger datasets than PHASE, while being slightly less accurate. This information, however, has been rarely used. One obstacle has been the difficulty of defining clearly homologous states (Young and Healy, 2003 ). Specifically, only indels with the same 5? and 3?http://afzaliqbal.org/userfiles/cambridge-azur-540r-manual.xml termini are considered homologous (resulted from a single event), and indels of different lengths (even in the same position of the alignment) are treated as different events. DnaSP, nevertheless, uses a slightly different method for coding completely overlapping gaps, and allows the user to choose the level of overlap to be coded. Subsequently, DnaSP estimates a number of DIP summary statistics, such as the average indel length, indel diversity, as well as Tajima's D (Tajima, 1989 ) based on indel information. Additionally, it exports the recoded data in the NEXUS format file. 2.3 Analysis of multiple data files These data files may contain a varying number of sequences (from within one species, or from one species as well as one outgroup), or represent diverse genomic regions. The program estimates the most common DNA polymorphism and divergence summary statistics (such as the nucleotide and haplotype diversity, the population mutation parameter, the number of nucleotide substitutions per site, etc.), and neutrality tests (such as Tajima's, Fu and Li's and Fu's tests). 2.4 Sliding window results visualization With the use of Windows emulators, DnaSP can also run on Apple Macintosh platforms, Linux and Unix-based operating systems. The software has been tested in all three platforms. ACKNOWLEDGEMENTS Special thanks to the numerous users who tested the software with their data, and particularly to all members of the Molecular Evolutionary Genetics group at the Departament de Genetica, Universitat de Barcelona. Funding: Spanish Direccion General de Investigacion Cientifica y Tecnica (grants BFU2004-02253 and BFU2007-62927); the Catalonian Comissio Interdepartamental de Recerca i Innovacio Tecnologica (grant 2005SGR00166). Conflict of Interest: none declared.Computer programs for population genetics data analysis: a survival guide, Nat. Rev. Genet., 2006, vol. 7 (pg. 745 - 758 ) Google Scholar Crossref Search ADS PubMed WorldCat Hudson RR.https://www.becompta.be/emploi/4-speed-manual-transmission-advantages Gene genealogies and the coalescent process, Oxf. Surv. Evol. Biol., 1990, vol. 7 (pg. 1 - 44 ) OpenURL Placeholder Text WorldCat Hutter S, et al. Genome-wide DNA polymorphism analyses using VariScan, BMC Bioinformatics, 2006, vol. 7 pg. 409 Google Scholar Crossref Search ADS PubMed WorldCat Kent WJ, et al. The Human Genome Browser at UCSC, Genome Res., 2002, vol. 12 (pg. 996 - 1006 ) Google Scholar Crossref Search ADS PubMed WorldCat Maddison WP, et al. NEXUS: an extendible file format for systematic information, Syst. Biol., 1997, vol. 46 (pg. 590 - 621 ) Google Scholar Crossref Search ADS PubMed WorldCat Nielsen R. Molecular signatures of natural selection, Annu. Rev. Genet., 2005, vol. 39 (pg. 197 - 218 ) Google Scholar Crossref Search ADS PubMed WorldCat Rosenberg NA, Nordborg M. Genealogical trees, coalescent theory, and the analysis of genetic polymorphisms, Nat. Rev. Genet., 2002, vol. 3 (pg. 380 - 390 ) Google Scholar Crossref Search ADS PubMed WorldCat Rozas J, et al. DnaSP, DNA polymorphism analyses by the coalescent and other methods, Bioinformatics, 2003, vol. 19 (pg. 2496 - 2497 ) Google Scholar Crossref Search ADS PubMed WorldCat Scheet P, Stephens M. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase, Am. J. Hum. Genet., 2006, vol. 78 (pg. 629 - 644 ) Google Scholar Crossref Search ADS PubMed WorldCat Shendure J, Ji H. Next-generation DNA sequencing, Nat. Biotechnol., 2008, vol. 26 (pg. 1135 - 1145 ) Google Scholar Crossref Search ADS PubMed WorldCat Simmons MP, Ochoterena H. Gaps as characters in sequence-based phylogenetic analyses, Syst. Biol., 2000, vol. 49 (pg. 369 - 381 ) Google Scholar Crossref Search ADS PubMed WorldCat Stephens M, Donnelly P. A comparison of Bayesian methods for haplotype reconstruction from population genotype data, Am. J. Hum. Genet., 2003, vol. 73 (pg.http://completedetailspainting.com/images/brothers-4100e-fax-manual.pdf 1162 - 1169 ) Google Scholar Crossref Search ADS PubMed WorldCat Stephens M, et al. A new statistical method for haplotype reconstruction from population data, Am. J. Hum. Genet., 2001, vol. 68 (pg. 978 - 989 ) Google Scholar Crossref Search ADS PubMed WorldCat Tajima F. VariScan: analysis of evolutionary patterns from large-scale DNA sequence polymorphism data, Bioinformatics, 2005, vol. 21 (pg. 2791 - 2793 ) Google Scholar Crossref Search ADS PubMed WorldCat Vingron M, et al. Integrating sequence,evolution and functional genomics in regulatory genomics, Genome Biol., 2009, vol. 10 pg. 202 Google Scholar Crossref Search ADS PubMed WorldCat Wang L, Xu Y. Haplotype inference by maximum parsimony, Bioinformatics, 2003, vol. 19 (pg. 1773 - 1780 ) Google Scholar Crossref Search ADS PubMed WorldCat Wakeley J., Coalescent Theory. An Introduction., 2009 Greenwood Village Roberts and Company Publishers Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Young ND, Healy J. GapCoder automates the use of indel characters in phylogenetic analysis, BMC Bioinformatics, 2003, vol. 4 pg. 6 Google Scholar Crossref Search ADS PubMed WorldCat Published by Oxford University Press. All rights reserved.It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. I need to know which data is required to perform different test with DNAsp: intra or interspecies sequences It appears that interspecies data are allowed. If you're still unclear, give the manual a(nother) read. I'd like to do Mcdonald-Kreitman test by DnaSP in command line. But I don't know how t. I calculated nucleotide diversity between two sets of sequences by Dnasp. The set o. I want to look at. I am new to the plink SNPs data (genotypes) as I am used to work with markers.Sorry if this is a duplicate question. I have searched but have not found a solution. I am looking at 5 isolates of a diploid fungus, and have been able to align the genomes o.https://careerhack.net/wp-content/plugins/formcraft/file-upload/server/content/files/1628fffa3793fb---Case-cx210b-manual.pdf Tajima's D: -1.02669 Statistical significanc. I have an excel file which contains DNA sequence information (over 7000 nucleotides) f. I have SNPs data in several vcf files and I would like to compute diversity stats like Pi. I have only on. I used DnaSp but. Structure Among these characteristicsMesseguer, X. and Rozas, R. (2003). DnaSP, DNA polymorphismBioinformatics 19:Arlequin ver. 3.0: An integrated software package for populationEvolutionary Bioinformatics OnlineP. (2000). Inference of population structure using multilocusMolecular Ecology. Notes. Introduction Arlequin, and Structure. These three programs provide a very usefulDnaSP is probably the most directlyIt is a popular program for carrying out. AMOVA, or Analysis of Molecular Variance. We will then use sample data sets thatThey can be found under course software in the. ZOL855 folder. Notepad). Save each file as a text file. This gives a menuIf not, you will needIntraspecific Data command. Bias command. Pomonella mtDNA data file (note: positions 180-261 areYou may findExplain any differences in these parametersExamine the contents of each. What'sIn what ways is it the same? Jiri to help you find pathways to explore. Documentation while you are doing thisJiri knows how to findUse the default values at first, includingThe run will takeStructure 2.2 for now. I am new to this program myself. So, once I've. In the latter case, pleaseHow are we doing. Europe PMC is part of the ELIXIR infrastructureEurope PMC is a service of theIt includes content provided to the. By continuing to browseFind out about Lean Library here Find out about Lean Library here Download PDFThis product could help you Lean Library can solve it Content ListSimply select your manager software from the list below and click on download.Simply select your manager software from the list below and click on download.For more information view the SAGE Journals Sharing page.www.fruko-schulz.com/upload/files/89-dodge-d150-repair-manual.pdf Search Google ScholarSearch Google ScholarSearch Google ScholarArlequin can handle several data types like DNA sequences, microsatellite data, or standard multi-locus genotypes. A Windows version of the software is freely available on. Keywords Computer package, population genetics, genetic data analysis, AMOVA, EM algorithm, gametic phase estimation, spatial expansion Introduction Most genetic studies on non-model organisms require a description of the pattern of diversity within and between populations, based on a variety of markers often including mitochondrial DNA (mtDNA) sequences and microsatellites. The genetic data are processed to extract information on the mating system, the extent of population subdivision, the past demography of the population, or on departure from selective neutrality at some loci. A series of computer packages have been developed in the last 10 years to assist researchers in performing basic population genetics analyses like Arlequin2 ( Schneider et al. 2000 ), DNASP ( Rozas et al. 2003 ), FSTAT ( Goudet 1995 ), GENEPOP ( Raymond and Rousset 1995b ), or GENETIX ( Belkhir et al. 2004 ). These programs have been widely used in the molecular ecology and conservation genetics community ( Labate 2000; Luikart and England 1999; Schnabel et al. 1998 ). Among these, Arlequin is a very versatile (though not universal) program, and complements the other programs listed above. It can handle several data types like RFLPs, DNA sequences, microsatellite data, allele frequencies, or standard multi-locus genotypes, while allowing the user to carry out the same types of analyses irrespective of the data types. We present here the version 3 of Arlequin with additional methods extending its capacities for the handling of unphased multi-locus genotypes and for the estimation of parameters of a spatial expansion. Note that these new developments are mainly implementations of new methodologies developed in our lab.https://www.dekleinewerf.nl/wp-content/plugins/formcraft/file-upload/server/content/files/1628fffaadbc7a---case-cx210-manual.pdf We believe these methods will be useful to the research community, but we do not claim that alternative methods implemented by other groups in other programs are inadequate. A new graphical interface has been developed to provide a better integration of the different analyses into a common framework, and an easier exploration of the data by performing a wide variety of analyses with different settings. The tight coupling of Arlequin with the simulation programs SIMCOAL2 ( Laval and Excoffier 2004 ) and SPLATCHE ( Currat et al. 2004 ) should also make it useful to describe patterns of genetic diversity under complex evolutionary scenarios. Methods implemented in Arlequin Arlequin provides methods to analyse patterns of genetic diversity within and between population samples. The ELB algorithm ( Excoffier et al. 2003 ) is a pseudo-Bayesian approach aiming at reconstructing the gametic phase of multi-locus genotypes, and the estimation of the haplotype frequencies are a by-product of this process. Phase updates are made on the basis of a window of neighbouring loci, and the window size varies according to the local level of linkage disequilibrium. ? The EM zipper algorithm, which is an extension of the EM algorithm for estimating haplotype frequencies ( Excoffier and Slatkin 1995 ), aims at estimating the haplotype frequencies in unphased multi-locus genotypes. The estimation of the gametic phases are a by-product of this process. It proceeds by adding loci one at a time and progressively extending the length of the reconstructed haplotypes. With this method, Arlequin does not need to build all possible genotypes for each individual like in the conventional EM algorithm, but it only considers the genotypes whose sub-haplotypes have non-null estimated frequencies. It can thus handle a much larger number of polymorphic sites than the strict EM algorithm.https://www.scmphotography.co.uk/wp-content/plugins/formcraft/file-upload/server/content/files/1628fffb3b08b5---Case-95xt-operators-manual.pdf It also gives final haplotype frequencies that often have a higher likelihood than those estimated under the strict EM algorithm, due to the difficulty in exploring the space of all possible genotypes when the number of polymorphic loci in the sample is large. The estimation is based on a simple model of instantaneous and infinite range expansion, where some time ago, a single deme instantaneously colonized an infinite number of demes subsequently interconnected by migration (as under an infinite-island model) ( Excoffier 2004 ). The parameters are obtained by a least-square approach maximizing the fit between the observed and expected distribution of pairwise differences (the mismatch distribution) computed on DNA sequences. Availability A Windows executable version Arlequin ver 3 can be freely downloaded on, together with an up-to-date user manual in Adobe Acrobat PDF format incorporating more technical details on the methods used in Arlequin 3, as well as several example files. Laboratoire Genome, Populations, Interactions, CNRS UMR 5000, Universite de Montpellier II, Montpellier. Google Scholar Chakraborty, R. 1990. Mitochondrial DNA polymorphism reveals hidden heterogeneity within some Asian populations. In Balding, D, Bishop, M., and Cannings, C., eds. Handbook of Statistical Genetics, 2nd Edition. In Futuyma, D.J., and Antonovics, J.D., eds. Oxford Surveys in Evolutionary Biology. In Carvalho, G., eds. Advances in Molecular Ecology. Amsterdam: IOS Press. User manual ver 2.000. Genetics and Biometry Lab, Dept.Sinauer Assoc., Inc.: Sunderland, MA, USA. Google Scholar Scientific Reports Dec 2020 Show details Hide details Hurricane-induced disturbance increases genetic diversity and populati. Crossref J. Andres Pagan and more. Scientific Reports Dec 2020 Show details Hide details Manuscript content on this site is licensed under Creative Commons Licenses By continuing to browse.fruits-kiyoka.com/upload/newsfiles/89-crx-si-manual.pdf We sequenced two chloroplast DNA (cpDNA) fragments ( ndh J- trn F and trn D- trn T) and one nuclear DNA ( Pgk1 ) of 472 individuals from 51 populations of such a group, the Indigofera bungeana complex. We used population genetic data as well as ecological niche modelling to examine the evolutionary history and glacial refugia during the Last Glacial Maximum (LGM) of this group. We recovered 133 cpDNA and 68 nuclear haplotypes. The star-phylogeny of the recovered cpDNA and nuclear haplotypes and demographic analyses suggested distinct range expansion of I. bungeana complex have occurred during the early and middle Pleistocene. The climate change of the LGM might have affected little on the distribution of this complex based on the niche modelling. However, these climate changes and geographic isolation probably resulted in fixtures of the private haplotypes and genetic differentiations between regions. Our results suggested that this arid-tolerant species complex may have different responses to the Quaternary climate changes with those climate-sensitive species. Two general hypotheses on forest responses to the Quaternary climate changes in East Asia have been proposed 2, 3. Conversely, Harrison et al.A limited number of phylogeographic studies appear to support the latter hypothesis. For example, in northern and northeast China, two species of shrubs, Ostryopsis davidiana Decne.In subtropical China, the evergreen broad-leaved forest constituents conform to either an in situ survival model or an expansion-contraction model, such as Castanopsis tibetana, Machilus thunbergii and Schima superba Four species have been ascribed to this complex 17, I. bungeana Walpers, I. amblyantha Craib, I. silvestrii Pampanini, and I. ramulosissima Hosokawa. Species delimitations between them remains unclear due to the lack of clear morphological and genetic gaps. Our unpublished phylogenetic analyses suggested that numerous individuals of each species intermixed, but formed a highly supported monophyletic clade sister to the species of Indigofera from the Cape region of South Africa. We therefore treated them as a single evolutionary lineage in our phylogeographic analyses. Their widespread distribution provides a unique opportunity to examine how plants responded to past climate changes over a large region in East Asia. We sequenced two types of DNA fragments with contrasting backgrounds of inheritance. First, two maternally inherited chloroplast DNAs (cpDNAs) were used, as in most phylogeographic studies 16, 18, due to the merits of rare recombinations and smaller effective population size 19. This type of population genetic data allows an inference of historical range shifts and recolonization routes 20, 21, 22. Second, we also sequenced one nuclear DNA fragment. Population data from nuclear genetic polymorphisms can confirm the phylogeographic inferences from cpDNA 23, 24, 25, 26. Sequence variation data from a single nuclear locus is becoming popular for such an aim 23, 27. We finally used ecological niche modelling to infer the possible distributions of this complex during the LGM in East Asia. We expected that the simulated distributions should be consistent with the phylogeographic inferences of the population genetic data. We aimed to address the following questions: (1) Are phylogeographic inferences from cpDNA data consistent with those from nuclear DNA data? (2) When and how did this complex obtain its widespread distribution in East Asia? (3) Did the I. bungeana complex retreat southward or survive in situ during the LGM. The most common haplotypes, C5, C23 and C24, were found in 5 (9.8) populations, respectively. Total haplotype ( H. The haplotypes found in more than one population are color-coded, while private haplotype particular to each population are shown in white. Figure was generated in DIVA-GIS 7.5 ( ). Full size image. Full size table Clade C included all the remaining numerous chlorotypes with unresolved relationships, indicating radiative diversification. Figure 2 The evolutionary relationships among cpDNA haplotypes of Indigofera bungeana complex. ( a ) NJ phylogenetic tree of the 133 cpDNA haplotypes. The size of circles corresponds to the frequency of each haplotype and black dots represent missing haplotypes (not sampled or extincted). Full size image Total haplotype ( H. The haplotypes found in more than one population are color-coded, while private haplotype particular to each population are shown in white. Figure was generated in DIVA-GIS 7.5 (.). Full size image. Figure 4 The evolutionary relationships among Pgk1 haplotypes of Indigofera bungeana complex. ( a ) NJ phylogenetic tree of the 68 Pgk1 haplotypes. The size of circles corresponds to the frequency of each haplotype and black dots represent missing haplotypes (not sampled or extincted). Full size image. The level of total genetic diversity H For both cpDNA and Pgk1 datasets, a significantly larger N Table 2 Estimates of average gene diversity within populations ( H Full size table. Variations among regions accounted for 5.26 and 23.53 of the total genetic variation for cpDNA and Pgk1 datasets, respectively. Complex and heterogeneous climate and topography may serve as a favorable condition for isolation, drift and barriers of gene flow in the HMR. When each species was analyzed separately, differences among species explained 7.56 ( F. Table 3 Hierarchical analysis of molecular variance (AMOVA) of cpDNA and Pgk1 for Indigofera bungeana complex, partitioned by species and region, respectively. Full size table Full size table Figure 5 Modelled climatically suitable areas for Indigofera bungeana complex at different times using Maxent. Full size image. The small genetic differentiations seem not to support the previous taxonomic delimitations within this complex 17. This phylogeographic pattern may be better explained by the hypothesis that all examined populations have experienced a common expansion followed by the fast isolations 29. The BSP analysis, which is based on coalescent methods, revealed that the effective population sizes ( N All these available evidences seem to support that a common expansion have occurred within I. bungeana complex. The range expansions detected in cpDNA clade C and nuclear clade III were estimated to have occurred approximately between 60 and 961 Kya, in the early and middle Pleistocene. Although we could not pinpoint the expansion accurately, it is highly possible that climate change of the Quaternary might have facilitated this expansion 30. Some cold-tolerant plants, such as the species of the fir genus, expanded extensively and continuously in high-elevation regions during the Quaternary 31, 32, 33, 34. It is highly likely that geographic isolations following the range expansions promoted the private haplotypes displaced the ancestral ones. Such scenarios were usually found for isolated species or populations 35, 36, 37. The Quaternary climate changes after the range expansions should have mainly accounted for such fixtures of the private haplotypes in the different regions and great among-regions differentiations. The limited seed and pollen dispersals of this specie complex may have also played an important role. The Quaternary climate changes have strongly affected distribution and genetic diversity of the temperate plants in East Asia, resulted in experienced glacial southward migrations, or in situ glacial survival 18, 26. Most private haplotypes in this complex derived from the range expansion in the early and middle Pleistocene, earlier than the LGM. Our simulations of the distributions of the I. bungeana complex during the LGM also indicated that the distribution did not migrate southward significantly although the core distributions shrank, which is also consistent with the species with similar distribution in East Asia, such as Juglans cathayensis However, this and other Quaternary climate oscillations might have together accelerated the regional isolations of the I. bungeana complex that promoted the fixture of the numerous private haplotypes. Our results seem to suggest not all woody species growing under the temperate deciduous forests in northern China migrated southward 3. However, these climatic changes might have promoted the species or genetic differentiations of plants occurring in East Asia as suggested by Qian and Ricklefs 2. We tentatively ascribed all collected materials to four species names in the Flora of China 17. Voucher specimens are deposited in Herbarium of Chengdu Institute of Biology, Chinese Academy of Sciences (CDBI).PCR products were purified using an E.Z.N.A gel extraction kit (OMEGA, Biotech., USA). The purified PCR products were sequenced by Life Technologies TM (Shanghai, China). Population genetic analyses Sequences were assembled and edited with Sequencher 4.1 (Gene Codes, Ann Arbor, MI), aligned using Clustal X 1.81 49 and subsequent manual adjustments. Nuclear ( Pgk1 ) allelic phases were resolved using the algorithm of PHASE 27 implemented in DnaSP 5.0 50, using 1,000 iterations with a 1,000 generation burn-in iterations and a thinning interval of 10. Indels were treated as single mutation events and coded as substitution (A or T). Haplotypes of cpDNA (chlorotypes) and Pgk1 were recognized using DNASP 5.0 50. Genealogical relationships among chlorotypes and nuclear ( Pgk1 ) haplotypes were constructed using a statistical parsimony algorithm 51 as implemented in Network v. 4.6 ( ). Population gene diversity ( H A higher N A comparison was made between N The spatial analysis of molecular variance (SAMOVA) was conducted using SAMOVA 1.0 53 to define the groups of populations that are geographically homogeneous and maximally differentiated. The SAMOVA analysis was conducted with the number of groups ( K ) ranging from 2 to 20. To verify the consistency, we ran the analysis five times for each K value with 1,000 independent iterations, starting from 100 random initial conditions. We assessed the optimal K as the one for which F Hierarchical analysis of molecular variance (AMOVA) was performed in ARLEQUIN 3.1 54 to estimate the partition of genetic variance among groups, within and among populations. In the AMOVA analysis, populations were partitioned by geography or species, respectively. Geographical groups were obtained from SAMOVA analysis. Phylogenetic analyses and divergence time estimation Two species of Indigofera ( I.Confidence values at the nodes were tested by performing 1,000 bootstrap replicates. Prior to BI analyses, the optimal nucleotide substitution model was determined using jModeltest 2.1.2 58 via the Akaike Information Criterion (AIC) 59. Four Markov chain Monte Carlo (MCMC) chains were run for 20,000,000 generations, starting from random trees and sampling one tree per 1,000 generations with the first 4,000,000 samples discarded as burn-in. The program Tracer 1.5 60 was used to check the parameter convergence and effective sample size. A 50 majority-rule consensus tree was summarized with posterior probabilities as nodal support.A mismatch distribution analysis 69 (MDA) was also conducted to explore the demographic history of major chlorotypes and nuclear ( Pgk1 ) haplotype clades. Populations that have experienced expansion are expected to have a unimodal shape, whereas stable populations are expected to have a bi- or multi-modal mismatch distribution. Statistical significance was determined by 1,000 bootstrap replicates.