Review Article Importance of Genetic Diversity Assessment...

Review ArticleImportance of Genetic Diversity Assessment in Crop Plants andIts Recent Advances An Overview of Its Analytical Perspectives

M Govindaraj12 M Vetriventhan12 and M Srinivasan3

1Centre for Plant Breeding and Genetics Tamil Nadu Agricultural University Coimbatore 641 003 India2International Crops Research Institute for the Semi-Arid Tropics Patancheru Telangana 502324 India3School of Life Science Bharathidasan University Tiruchirappalli 620 024 India

Correspondence should be addressed to M Srinivasan mahasiva2gmailcom

Received 17 July 2014 Revised 24 November 2014 Accepted 27 November 2014

Academic Editor Igor B Rogozin

Copyright copy 2015 M Govindaraj et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The importance of plant genetic diversity (PGD) is now being recognized as a specific area since exploding population withurbanization anddecreasing cultivable lands are the critical factors contributing to food insecurity in developingworld Agriculturalscientists realized that PGD can be captured and stored in the formof plant genetic resources (PGR) such as gene bankDNA libraryand so forth in the biorepository which preserve genetic material for long period However conserved PGR must be utilized forcrop improvement in order tomeet future global challenges in relation to food and nutritional securityThis paper comprehensivelyreviews four important areas (i) the significance of plant genetic diversity (PGD) and PGR especially on agriculturally importantcrops (mostly field crops) (ii) risk associated with narrowing the genetic base of current commercial cultivars and climate change(iii) analysis of existing PGD analytical methods in pregenomic and genomic era and (iv) modern tools available for PGD analysisin postgenomic era This discussion benefits the plant scientist community in order to use the new methods and technology forbetter and rapid assessment for utilization of germplasm from gene banks to their applied breeding programs With the adventof new biotechnological techniques this process of genetic manipulation is now being accelerated and carried out with moreprecision (neglecting environmental effects) and fast-track manner than the classical breeding techniques It is also to note thatgene banks look into several issues in order to improve levels of germplasm distribution and its utilization duplication of plantidentity and access to database for prebreeding activities Since plant breeding research and cultivar development are integralcomponents of improving food production therefore availability of and access to diverse genetic sources will ensure that the globalfood production network becomes more sustainable The pros and cons of the basic and advanced statistical tools available formeasuring genetic diversity are briefly discussed and their source links (mostly) were provided to get easy access thus it improvesthe understanding of tools and its practical applicability to the researchers

1 Introduction

Diversity in plant genetic resources (PGR) provides oppor-tunity for plant breeders to develop new and improvedcultivars with desirable characteristics which include bothfarmer-preferred traits (yield potential and large seed etc)and breeders preferred traits (pest and disease resistanceand photosensitivity etc) From the very beginning ofagriculture natural genetic variability has been exploitedwithin crop species to meet subsistence food requirementand now it is being focused to surplus food for growing

populations In the middle of 1960s developing countrieslike India experienced the green revolution by meeting fooddemand with help of high-yielding and fertilizer respon-sive dwarf hybridsvarieties especially in wheat and rice(Figure 1) These prolonged activities that lead to the hugecoverage of single genetic cultivars (boom) made situationagain worse in other forms such as genetic erosion (loss ofgenetic diversity) and extinction of primitive and adaptivegenes (loss of landraces) Today with an advancement ofagricultural and allied science and technology we still askourselves whether we can feed the world in 2050 this

Hindawi Publishing CorporationGenetics Research InternationalVolume 2015 Article ID 431487 14 pageshttpdxdoiorg1011552015431487

2 Genetics Research InternationalRe

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30

35

25

20

15

10

05

1960 1970 1980 1990 2000 2010

Main grains (wheat barley maize rice and oats)Coarse grains (millet and sorghum)Root crops (cassava and potato)

Figure 1 Changes in the relative global production of crops since1961 (when relative production scaled to 1 (mt) in 1961) (sourcehttpfaostatfaoorgdefaultaspx (2010))

question was recently sensitized at the world food prizeevent in 2014 and remains that unanswered in every onehands since global population will exceed 9 billion in 2050The per capita availability of food and water will becomeworse year after year coping with the undesirable climatechange Therefore it becomes more important to look atthe agriculture not only as a food-producing machine butalso as an important source of livelihood generation bothin the farm and nonfarm sectors Keeping the reservoirfor cultivated and cultivable crops species is a principle forfuture agriculture just like keeping a museum of culturaland spiritual specialty of diverse civilized humans in variousgeography for their historical evidence for futureThe formercan play a very important role in providing adaptive andproductive genes thus leading to long-term increases in foodproductivity which is further associated with environmentaldetrimentThis paper will indicate the significance of geneticconservation and its analytical tools and techniques that aremade widely available for utilization in postgenomic eraPlant and animal breeders introduced desirable genes andeliminated undesirable ones slowly altering in the process ofunderlying heredity principle for several decades [1] Withthe advent of new biotechnological tools and techniques thisprocess of genetic manipulation is being accelerated and itshortened the breeding cycles and it can be carried out withmore precision (neglecting environmental effects) and fast-track manner than the classical breeding techniques

2 Significance of GeneticConservation of Crop Plants

The growing population pressure and urbanization of agri-cultural lands and rapid modernization in every field of our

day-to-day activities that create biodiversity are getting tooeroded in direct and indirect way For instance land degra-dation deforestation urbanization coastal development andenvironmental stress are collectively leading to large-scaleextinction of plant species especially agriculturally importantfood crops On the other hand system driven famine such asIrish potato famine and Southern corn leaf blight epidemic inUSA are the two instances of food crises caused by large-scalecultivation of genetically homogenous varieties of potatoand corn respectively Even after these historical events theimportance of PGR had only got popular recognition whenthe spread of green revolution across cultivated crops threat-ened the conservation of land races [2] Green revolutiontechnologies introduced improved crop varieties that havehigher yields and it was hoped that theywould increase farm-ersrsquo income Consequently the Consultative Group of Inter-national Agricultural Researches (CIGAR) initiated genebanks and research centers of domestication for conservingPGR in most of the stable food crops around the worldCenter for domestication maize (Mexico) wheat and barley(middlenear East and North Africa) rice (North China)and potatoes (Peru) for further information see httpwwwcigarorgcenterindexhtml) The Food and AgricultureOrganization (FAO) supported the International Treaty onPlant Genetic Resources (ITPGR) and UN supported theConvention on Biological Diversity (CBD) which are theinternational agreements that recognize the important role ofgenetic diversity conservation Such treaty still plays in cur-rent and future food production as one of the major supremo[3]

Genetic diversity is the key pillar of biodiversity anddiversity within species between species and of ecosystems(CBD Article 2) which was defined at the Rio de JaneiroEarth Summit However the problem is that modern cropvarieties especially have been developed primarily for highyielding potential under well endowed production condi-tions Such varieties are often not suitable for low incomefarmers in marginal production environments as they arefacing highly variable stress conditions [4] Land races ortraditional varieties have been found to have higher stabil-ity (adaptation over time) in low-input agriculture undermarginal environments thus their cultivation may con-tribute farm level resilience in face of food production shocks[5 6] This is especially true in some part of Ethiopiawhere agroclimatic conditions are challenging technologicalprogress is slow andmarket institutions are poorly developedand have no appropriate infrastructure [7 8]

Why is genetic diversity important The goal of conser-vation genetics is to maintain genetic diversity at many levelsand to provide tools for population monitoring and assess-ment that can be used for conservation planning Every indi-vidual is genetically unique by nature Conservation effortsand related research are rarely directed towards individualsbut genetic variation is always measured in individuals andthis can only be estimated for collections of individuals ina populationspecies It is possible to identify the geneticvariation from phenotypic variation either by quantitativetraits (traits that vary continuous and are governed by manygenes eg plant height) or discrete traits traits that fall into

Genetics Research International 3

discrete categories and are governed by one or few majorgenes (eg white pink or red petal color in certain flowers)which are referred to as qualitative traits Genetic variationcan also be identified by examining variation at the level ofenzymes using the process of protein electrophoresis Fur-ther genetic variations can also be examined by the order ofnucleotides in the DNA sequence

3 Erosion of Genetic Diversity due toPopulation Size A Bottleneck Concept

It is well known that inbreeding is the most common phe-nomena in cross-pollinated crops and in small outcross pop-ulations it has resulted in deleterious effects and loss of fitnessof the population due to recombination between undesirablegenes (recessive identical alleles) In natural population toosevere reductions in population size the so-called geneticbottleneck leads to loss of genetic diversity and increasedsusceptibility to infectious pests and diseases that superveneincreased chances of extinction of an individual crop in ques-tion Genetic models that predict the proportion of initialheterozygosity retained per generation is [1minus (12119873e)]where119873e is the effective population size usually less than 119873 theactual population size Thus a population of 119873e = 10 indi-viduals loses 5 of its heterozygosity per generation Thisindicates that severe bottlenecks degrade heterozygosity andgenetic diversity [9] Therefore plant breeders have beenadvised tomaintain the optimumpopulation size for any traitconservation for specific purpose and its utilization for cropimprovement Thus before quantifying the genetic diversityit is essential to know the optimum population size and itsrepresentatives to ensure no biasness in diversity assessmentthat leads to wrong prediction of its value

4 Climate Change and Its Impact onPlant Genetic Resources

Themost profound and direct impacts of climate change overprevious decade and the next few decades will surely be onagriculture and food security The effects of climate changewill also depend on current production conditions The areawhere already being obstructed by other stresses such as pol-lution and will likely to have more adverse impact by chang-ing climate Food production systems rely on highly selectedcultivars under better endowed environments but it mightbe increasingly vulnerable to climate change impacts such aspest and disease spread If food production levels decreasesover the year there will be huge pressure to cultivate the cropsunder marginal lands or implement unsustainable practicesthat over the long-term degrade lands and resources andadversely impact biodiversity on and near agricultural areasIn fact such situations have already been experienced bymostof the developing countries These changes have been seento cause a decrease in the variability of those genetic loci (alle-les of a gene) controlling physical and phenotypic responsesto changing climate [10] Therefore genetic variation holdsthe key to the ability of populations and species to persist overevolutionary period of time through changing environments

[11] If this persists neither any organism can predict itsfuture (and evolutionary theory does not require them to)nor can any of those organisms be optimally adapted for allenvironmental conditions Nonetheless the current geneticcomposition of a crop species influences how well its mem-bers will adapt to future physical and biotic environments

The population can also migrate across the landscapeover generations By contrast populations that have a narrowrange of genotypes and aremore phenotypically uniformmaymerely fail to survive and reproduce at all as the conditionsbecome less locally favorable Such populations are morelikely to become extirpated (locally extinct) and in extremecases the entire plant species may end up at risk of extinctionFor example the Florida Yew (Torreya taxifolia) is currentlyone of the rarest conifer species in North America But inthe early Holocene (10000 years ago) when conditions insoutheastern North America were cooler and wetter thantoday the species was probably widespread The reasons forthat are not completely understood but T taxifolia failed tomigrate towards the northward as climate changed duringthe Holocene Today it is restricted to a few locations in theApalachicola River Basin in southernGeorgia and the Floridapanhandle As the T taxifolia story illustrates once plantspecies are pushed into marginal habitat at the limitationsof their physiological tolerance they may enter an extinctionvortex a downward cycle of small populations and so on [1213] Reduced genetic variability is a key step in the extinctionvortex Gene banks must be better to respond to novel andincreased demands on germplasm for adapting agriculture toclimate change Gene banks need to include different char-acteristics in their screening processes and their collectionsneed to be comprehensive including what are now consid-ered minor crops and that may come with huge impact onfood baskets

5 Assessment of Genetic Diversity inCrop Plants

The assessment of genetic diversity within and between plantpopulations is routinely performed using various techniquessuch as (i) morphological (ii) biochemical characteriza-tionevaluation (allozyme) in the pregenomic era and(iii) DNA (or molecular) marker analysis especially singlenucleotide polymorphism (SNPs) in postgenomic era Mark-ers can exhibit similarmodes of inheritance as we observe forany other traits that is dominantrecessive or codominant Ifthe genetic pattern of homozygotes can be distinguished fromthat of heterozygotes then amarker is said to be codominantGenerally codominant markers are more informative thanthe dominant markers

Morphological markers are based on visually accessibletraits such as flower color seed shape growth habits andpigmentation and it does not require expensive technologybut large tracts of land area are often required for these fieldexperiments making it possibly more expensive than molec-ular assessment in western (developed) countries and equallyexpensive in Asian and Middle East (developing) countriesconsidering the labour cost and availability These marker

4 Genetics Research International

traits are often susceptible to phenotypic plasticity con-versely this allows assessment of diversity in the presence ofenvironmental variation which cannot be neglected from thegenotypic variation These types of markers are still havingadvantage and they are mandatory for distinguishing theadult plants from their genetic contamination in the field forexample spiny seeds bristled panicle and flowerleaf colorvariants

Second type of genetic marker is called biochemicalmarkers allelic variants of enzymes called isozymes that aredetected by electrophoresis and specific staining Isozymemarkers are codominant in nature They detect diversity atfunctional gene level and have simple inheritance It requiresonly small amounts of plant material for its detection How-ever only a limited number of enzymes markers are availableand these enzymes are not alone but it has complex structuraland special problems thus the resolution of genetic diversityis limited to explore

The third and most widely used genetic marker type ismolecularmarkers comprising a large variety ofDNAmolec-ular markers which can be employed for analysis of geneticand molecular variation These markers can detect the varia-tion that arises from deletion duplication inversion andorinsertion in the chromosomes Such markers themselves donot affect the phenotype of the traits of interest because theyare located only near or linked to genes controlling the traitsThese markers are inherited both in dominant and codomi-nant patterns Different markers have different genetic qual-ities (they can be dominant or codominant can amplifyanonymous or characterized loci can contain expressed ornonexpressed sequences etc) A molecular marker can bedefined as a genomic locus detected through probe or spe-cific starter (primer) which in virtue of its presence distin-guishes unequivocally the chromosomic trait which it repre-sents as well as the flanking regions at the 31015840 and 51015840 extremity[14] Molecular markers may or may not correlate with phe-notypic expression of a genomic trait They offer numerousadvantages over conventional phenotype-based alternativesas they are stable and detectable in all tissues regardless ofgrowth differentiation development or defense status of thecell Additionally they are not confounded by environmentalpleiotropic and epistatic effects We are not describing muchabout the pregenomic era tools since our paper deals withgenomic advances and its assistance in crop genetic diversityassessment

6 Analyses of Genetic Diversity inGenomic Era

A comprehensive study of the molecular genetic variationpresent in germplasm would be useful for determiningwhether morphologically based taxonomic classificationsreveal patterns of genomic differentiation This can also pro-vide information on the population structure allelic richnessand diversity parameters of germplasm to help breeders to usegenetic resources with less prebreeding activities for cultivardevelopment more effectively Now germplasm characteriza-tion based on molecular markers has gained importance due

to the speedy and quality of data generated For the readersbenefit the availability of different DNA markers acronymsis given in Abbreviations section

61 Molecular Markers DNA (or molecular) markers are themost widely used type of marker predominantly due to theirabundance They arise from different classes of DNA muta-tions such as substitution mutations (point mutations) rear-rangements (insertions or deletions) or errors in replicationof tandemly repeated DNA [15]Thesemarkers are selectivelyneutral because they are usually located in noncoding regionsof DNA in a chromosome Unlike other markers DNAmarkers are unlimited in number and are not affected byenvironmental factors andor the developmental stage of theplant [16] DNAmarkers have numerous applications in plantbreeding such as (i) marker assisted evaluation of breedingmaterials like assessing the level of genetic diversity parentalselection cultivar identity and assessment of cultivar purity[16ndash26] study of heterosis and identification of genomicregions under selection (ii) marker assisted backcrossingand (iii) marker assisted pyramiding [27]

Molecular markers may be broadly divided into threeclasses based on themethod of their detection hybridization-based polymerase chain reaction- (PCR-) based and DNAsequence-based Restriction fragment length polymorphisms(RFLPs) are hybridization-based markers developed first inhuman-based genetic study during 1980s [28 29] and laterthey were used in plant research [30] RFLP is based on thevariation(s) in the length of DNA fragments produced bya digestion of genomic DNAs and hybridization to specificmarkers of two or more individuals of a species is comparedRFLPs have been used extensively to compare genomes inthe major cereal families such as rye wheat maize sorghumbarley and rice [31ndash33] The advantages of RFLPs includedetecting unlimited number of loci and being codominantrobust and reliable and results are transferable across popula-tions However RFLPs are highly expensive time consuminglabour intensive larger amounts of DNA required limitedpolymorphism especially in closely related lines [34] Atpresent polymerase chain reaction- (PCR-) basedmarker sys-tems are more rapid and require less plant material for DNAextraction Rapid amplified polymorphic DNAs (RAPDs)were the first of PCR-based markers and are produced byPCR machines using genomic DNA and arbitrary (random)primers which act as both forward and backward primersin creation of multiple copies of DNA strands [35 36] Theadvantages of RAPDs include being quick and simple andinexpensive and the facts that multiple loci from a singleprimer are possible and a small amount of DNA is requiredHowever the results from RAPDs may not be reproduced indifferent laboratories and only can detect the dominant traitsof interest [34] Amplified fragment length polymorphisms(AFLPs) combine both PCR and RFLP [37] AFLP is gener-ated by digestion of PCR amplified fragments using specificrestriction enzymes that cut DNA at or near specific recog-nition site in nucleotide sequence AFLPs are highly repro-ducible and this enables rapid generation and high frequencyof identifiable AFLPs making it an attractive technique foridentifying polymorphisms and for determining linkages by


analyzing individuals from a segregating population [37]Another class of molecular markers which depends on theavailability of short oligonucleotide repeats sequences in thegenome of plants such as SSR STS SCAR EST-SSR and SNPMany authors reviewed in detail differentmarkers techniques[38 39] In this paper we are presenting the most widelyused molecular markers and next generation sequencingtechnologies in detail in the following section

62 Simple Sequence Repeat or Microsatellite Microsatellites[40] are also known as simple sequence repeats (SSRs) shorttandem repeats (STRs) or simple sequence length polymor-phisms (SSLPs) which are short tandem repeats their lengthbeing 1 to 10 bp Some of the literatures define microsatellitesas 2ndash8 bp [41] 1ndash6 bp [42] or even 1ndash5 pb repeats [43]SSRs are highly variable and evenly distributed throughoutthe genome and common in eukaryotes their number ofrepeated units varying widely among crop species Therepeated sequence is often simple consisting of two three orfour nucleotides (di- tri- and tetranucleotide repeats resp)One common example of a microsatellite is a dinucleotiderepeat (CA)119899 where 119899 refers to the total number of repeatsthat ranges between 10 and 100 These markers often presenthigh levels of inter- and intraspecific polymorphism partic-ularly when tandem repeats number is 10 or greater [44]PCR reactions for SSRs are performed in the presence offorward and reverse primers that anneal at the 51015840 and 31015840 endsof the template DNA respectively These polymorphisms areidentified by constructing PCR primers for the DNA flankingthe microsatellite region The flanking regions tend to beconserved within the species although sometimes they mayalso be conserved in higher taxonomic levels

PCR fragments are usually separated on polyacrylamidegels in combination with AgNO

3staining autoradiography

or fluorescent detection systems Agarose gels (usually 3)with ethidium bromide (EBr) can also be used when differ-ences in allele size among samples are larger than 10 bp How-ever the establishment of microsatellite primers from scratchfor a new species presents a considerable technical challengeSeveral protocols have been developed [43 45ndash47] and detailsof the methodologies are reviewed by many authors [48ndash50]The loci identified are usually multiallelic and codominantBands can be scored either in a codominant or as presentor absent The microsatellite-derived primers can often beused with many varieties and even other species because theflanking DNA is more likely to be conserved These requiredmarkers are evenly distributed throughout the genome easilyautomated and highly polymorphic and have good analyticresolution and high reproducibility making them a preferredchoice of markers [51] most widely used for individualgenotyping germplasm evaluation genetic diversity stud-ies genome mapping and phylogenetic and evolutionarystudies However the development of microsatellites requiresextensive knowledge of DNA sequences and sometimes theyunderestimate genetic structure measurements hence theyhave been developed primarily for agricultural species ratherthan wild species [39]

63 EST-SSRs An alternative source of SSRs development isdevelopment of expressed sequence tag- (EST-) based SSRsusing EST databases has been utilized [52ndash58] With theavailability of large numbers of ESTs andotherDNAsequencedata development of EST-based SSR markers through datamining has become fast efficient and relatively inexpensivecompared with the development of genomic SSRs [59] Thisis due to the fact that the time-consuming and expensiveprocesses of generating genomic libraries and sequencing oflarge numbers of clones for finding the SSR containing DNAregions are not needed in this approach [60] However thedevelopment of EST-SSRs is limited to species for which thistype of database exists as well as being reported to have lowerrate of polymorphism compared to the SSR markers derivedfrom genomic libraries [61ndash64]

64 Single Nucleotide Polymorphisms (SNPs) Single nucleo-tide polymorphisms (SNPs) are DNA sequence variationsthat occur when a single nucleotide (A T C or G) in thegenome sequence is changed that is single nucleotide vari-ations in genome sequence of individuals of a populationThese polymorphisms are single-base substitutions betweensequences SNPs occurmore frequently than any other type ofmarkers and are very near to or even within the gene of inter-est SNPs are themost abundant in the genomes of themajor-ity of organisms including plants and are widely dispersedthroughout genomes with a variable distribution amongspecies SNPs can be identified by using either microarrays orDHPLC (denaturing high-performance liquid chromatogra-phy) machines They are used for a wide range of purposesincluding rapid identification of crop cultivars and construc-tion of ultrahigh-density geneticmapsThey provide valuablemarkers for the study of agronomic or adaptive traits in plantspecies using strategies based on genetic mapping or associ-ation genetics studies

65 Diversity Arrays Technology (DArT) A DArT markeris a segment of genomic DNA the presence of which ispolymorphic in a defined genomic representation A DArTwas developed to provide a practical and cost-effectivewhole genomefingerprinting toolThismethodprovides highthroughput and low cost data production It is independentfrom DNA sequence that is the discovery of polymorphicDArT markers and their scoring in subsequent analysis doesnot require any DNA sequence data The detail of methodol-ogy forDArT is described by Jaccoud et al [65] and Semagn etal [38] as well as in website httpwwwdiversityarrayscom

To identify the polymorphicmarkers a complexity reduc-tionmethod is applied on themetagenome a pool of genomesrepresenting the germplasm of interest The genomic repre-sentation obtained from this pool is then cloned and individ-ual inserts are arrayed on a microarray resulting in a ldquodiscov-ery arrayrdquo Labelled genomic representations prepared fromthe individual genomes included in the pool are hybridizedto the discovery array Polymorphic clones (DArT markers)show variable hybridization signal intensities for differentindividuals These clones are subsequently assembled into aldquogenotyping arrayrdquo for routine genotyping DArT is one of


the recently developed molecular techniques and it has beenused in rice [66] wheat [38 67 68] barley [69] eucalyptus[70] Arabidopsis [71] cassava [72] pigeon-pea [73] and soforth

DArT markers can be used as any other genetic markerWith DArT comprehensive genome profiles are becomingaffordable regardless of the molecular information availablefor the crop DArT genome profiles are very useful forcharacterization of germplasm collections QTL mappingreliable and precise phenotyping and so forth HoweverDArT technique involves several steps including preparationof genomic representation for the target species cloningdata management and analysis requiring dedicated softwaresuch as DArTsoft and DArTdb DArT markers are primarilydominant (present or absent) or differences in intensitywhich limits its value in some application [38]

7 Next Generation Sequencing

DNA sequencing is the determination of the order of thenucleotide bases A (adenine) G (guanine) C (cytosine) andT (thymine) present in a target molecule of DNA DNAsequencing technology has played a pivotal role in theadvancement of molecular biology [74] Next generationsequencing (NGS) or second generation sequencing tech-nologies are revolutionizing the study of variation amongindividuals in a population Most NGS technologies reducethe cost and time required for sequencing than Sanger-style sequencing machines (first generation sequencing)Thefollowing is the list of NGS technologies available at presentnamely the Roche454 FLX the IlluminaSolexa GenomeAnalyzer the Applied Biosystems SOLiD System the Helicossingle-molecule sequencing and pacific Biosciences SMRTinstruments These techniques have made it possible toconduct robust population-genetic studies based on completegenomes rather than just short sequences of a single gene

The Roche454 FLX based on sequencing-by-synthesiswith pyrophosphate chemistry was developed by 454 LifeSciences and was the first next generation sequencing plat-form available on the market [75] The Solexa sequencingplatformwas commercialized in 2006The working principleis sequencing-by-synthesis chemistry The Life TechnologiesSOLiD system is based on a sequencing-by-ligation technol-ogy This platform has its origins in the system describedby Shendure et al [76] and in work by McKernan et al[77] at Agencourt Personal Genomics (acquired by AppliedBiosystems in 2006) Helicos true singlemolecule sequencing(tSMS) technology is an entirely novel approach to DNAsequencing and genetic analysis and offers significant advan-tages over both traditional and ldquonext generationrdquo sequencingtechnologies Helicos offers the first universal genetic analysisplatform that does not require amplification Pursuing asingle molecule sequencing strategy simplifies the DNAsample preparation process avoids PCR-induced bias anderrors simplifies data analysis and tolerates degraded sam-ples Helicos single-molecule sequencing is often referred toas third generation sequencing The detailed methodologyadvantages and disadvantages of each NGS technology werereviewed by many authors [78ndash81]

8 Analysis of Genetic Diversity fromMolecular Data

It is essential to know the different ways that the data gen-erated by molecular techniques can be analyzed before theirapplication to diversity studies Two main types of analysisare generally followed (i) analysis of genetic relationshipsamong samples and (ii) calculation of population geneticsparameters (in particular diversity and its partitioning atdifferent levels) The analysis of genetic relationships amongsamples starts with the construction of a matrix sample timessample pair-wise genetic distance (or similarities)

The advent and explorations ofmolecular genetics led to abetter definition of Euclidean distance to mean a quantitativemeasure of genetic difference calculated between individualspopulations or species at DNA sequence level or allelefrequency level Genetic distance andor similarity betweentwo genotypes populations or individuals may be calculatedby various statistical measures depending on the data setThecommonly usedmeasures of genetic distance (GD) or geneticsimilarity (GS) are (i) Nei and Lirsquos [82] coefficient (GDNL)(ii) Jaccardrsquos [83] coefficient (GDJ) (iii) simple matchingcoefficient (GDSM) [84] and (iv) modified Rogersrsquo distance(GDMR) Genetic distance determined by the abovemeasurescan be estimated as follows

GDNL = 1 = [211987311

(211987311+ 11987310+ 11987301)]

GDJ = 1 = [11987311

(11987311+ 11987310+ 11987301)]

GDSM = 1 = [(11987311+ 11987300)

(11987311+ 11987310+ 11987301+ 11987300)]

GDMR = [(11987310+ 11987301)

2119873]

05

(1)

where 11987311

is the number of bandsalleles present in bothindividuals 119873

00is number of bandsalleles absent in both

individuals 11987310

is the number of bandsalleles present onlyin the individual 119894119873

01is the number of bandsalleles present

only in the individual 119895 and119873 represents the total number ofbandsalleles Readers are requested to readMohammadi andPrasanna [85] review paper for more details about differentGD measures

There are two main ways of analyzing the resultingdistance (or similarity) matrix namely principal coordinateanalysis (PCA) and dendrogram (or clustering tree dia-gram) PCA is used to produce a 2 or 3 dimensional scatterplot of the samples such that the distances among the samplesin the plot reflect the genetic distances among them with aminimum of distortion Another approach is to produce adendrogram (or tree diagram) that is grouping of samplestogether in clusters that are more genetically similar to eachother than to samples in other clusters Different algorithmswere used for clustering but some of the more widely usedones include unweighted pair group method with arithmetic


Table 1 Some basic statistical concept on genomic data for genetic diversity assessment

Concept terms Descriptionfeatures FormulaeprosconsBand-basedapproaches

Easiest way to analyze and measure diversity byfocusing on presence or absence of banding pattern

Routinely use individual levelTotally relay on marker type and polymorphism

(1) Measuringpolymorphism

Observing the total number of polymorphic bands (PB)and then calculating the percentage of polymorphicbands

This ldquoband informativenessrdquo (Ib) can be represented ona scale ranging from 0 to 1 according to the formulaIb = 1 minus (2 times |05 minus 119901|)where 119901 is the portion of genotypes containing theband

(2) Shannonrsquosinformation index (119868)

It is called the Shannon index of phenotypic diversityand is widely applied

119868 = minussum119901119894log2119901119894

These methods depend on the extraction of allelicfrequencies

(3) Similaritycoefficients

Utilize similarity or dissimilarity (the inverse of theprevious one) coefficientsThe Jaccard coefficient (119869) only takes into account thebands present in at least one of the two individuals It istherefore unaffected by homoplasic absent bands(where the absence of the same band is due to differentmutations)The simple-matching index (SM) maximizes theamount of information provided by the bandingpatterns considering all scored lociThe Neil and Li index (SD) doubles the weight forbands present in both individuals thus giving moreattention to similarity than dissimilarity

(i) Jaccard similarity coefficient orJaccard index 119869 = 119886(119886 + 119887 + 119888)(ii) Simple matching coefficient or index SM =(119899 minus 119887 minus 119888)119899 (iii) Soslashrensen-Dice index or Nei and Li index SD =21198862119886 + 119887 + 119888

where 119886 is the number of bands (1 s) shared by bothindividuals 119887 is the number of positions whereindividual 119894 has a band but 119895 does not 119888 is the numberof positions where individual 119895 has a band but 119894 doesnot and 119899 is the total number of bands (0 s and 1 s)

(4) Allele frequencybased approaches

Measure variability by describing changes in allelefrequencies for a particular trait over time morepopulation oriented than band-based approaches

These methods depend on the extraction of allelicfrequencies from the dataThe accurate estimates of frequencies essentiallyinfluence the results of different indices calculated forfurther measurements of genetic diversity

(5) Allelic diversity(119860)

Easiest ways to measure genetic diversity is to quantifythe number of alleles presentAllelic diversity (119860) is the average number of alleles perlocus and is used to describe genetic diversity

119860 = 119899i119899lwhere 119899i is the total number of alleles over all loci 119899l isthe number of lociIt is less sensitive to sample size and rare alleles and iscalculated as 119899

119890= 1sum119901

2

119894

1199012

119894ability it provides information about the dispersal

ability of the organism and the degree of isolationamong populations

(6) Effectivepopulation size (119873e)

It provides a measure of the rate of genetic drift the rateof genetic diversity loss and increase of inbreedingwithin a population

Effective size of a population is an idealized numbersince many calculations depend on the geneticparameters used and on the reference generation Thusa single population may have many different effectivesizes which are biologically meaningful but distinctfrom each other

(7) Heterozygosity(119867)

There are two types of heterozygosity observed (119867O)and expected (119867E)The119867O is the portion of genes that are heterozygous ina population and119867E is estimated fraction of allindividuals that would be heterozygous for anyrandomly chosen locusTypically values for119867E and119867O range from 0 (noheterozygosity) to nearly 1 (a large number of equallyfrequent alleles)If119867O and119867E are similar (they do not differsignificantly) mating in the populations is random If119867O lt 119867E the population is inbreeding if119867O gt 119867E thepopulation has a mating system avoiding inbreeding

Expected119867E is calculated based on the square root ofthe frequency of the null (recessive) allele as follows119867E = 1 minus sum

119899

1198941199012

119894

where 119901119894is the frequency of the ith allele

119867O is calculated for each locus as the total number ofheterozygotes divided by sample size


Table 1 Continued

Concept terms Descriptionfeatures Formulaeproscons

(8) 119865-statistics

In population genetics the most widely appliedmeasurements besides heterozygosity are 119865-statistics orfixation indices to measure the amount of allelicfixation by genetic driftThe 119865-statistics are related to heterozygosity and geneticdrift Since inbreeding increases the frequency ofhomozygotes as a consequence it decreases thefrequency of heterozygotes and genetic diversity

Three indexes can be calculated as follows119865IT = 1 minus (119867I119867T)119865IS = 1 minus (119867I119867S)119865ST = 1 minus (119867S119867T)where119867I is the average119867O within each population119867Sis the average119867E of subpopulations assuming randommating within each population and119867T is the119867E of thetotal population assuming random mating withinsubpopulations and no divergence of allele frequenciesamong subpopulations

averages (UPGMA) neighbour-joining method and Wardrsquosmethod [86]

The molecular data can be scored in presenceabsencematrices manually or with the aid of specific software How-ever because these techniques are based on the incorporationof genomic elements in the primer sets or else target specificregions in the genome biases affecting the evaluation processcan occur Although many recently developed targetingmethods detect large numbers of polymorphisms not manystudies to date have utilized them largely due to their unfa-miliarity In many cases the drawbacks are unknown Thesemainly affect the analysis of the banding patterns pro-duced largely depending on the nature of the methods andwhether they generate dominant or codominant markersWe presented a brief description of commonbasic statisticalapproaches and its principle with the pros and cons of eachmethod for measuring genetic diversity and it is given inTable 1These are self-explanatory therefore the features andmethod of calculationswere notmuch discussed separately inour text

9 Assessment of Genetic Diversity inPostgenomic Era

Many software programs are available for assessing geneticdiversity however most of them are freely available throughsource link to internet and corresponding institute web linksare given in Table 2 In this section we described some of theprograms available which aremostly used inmolecular diver-sity analyses in the postgenomic era (Table 2) Many of theseperform similar tasks with the main differences being in theuser interface type of data input and output and platformThus choosing which to use depends heavily on individualpreferences

10 Conclusion

Agriculturist has been realized that diverse plant geneticresources are priceless assets for humankind which cannotbe lost Such materials increasingly required to accessible forfeeding a burgeoning world population in future (gt9 billionin 2050) Presence of genetic variability in crops is essentialfor its further improvement by providing options for thebreeders to develop new varieties and hybrids This can be

achieved through phenotypic andmolecular characterizationof PGR Sometimes large size of germplasm may limit theiruse in breeding This may be overcome by developing andusing subsets like core and minicore collection representingthe diversity of the entire collection of the species Molecularmarkers are indispensable tools formeasuring the diversity ofplant species Low assay cost affordable hardware through-put convenience and ease of assay development and automa-tion are important factors when choosing a technology Nowwith the high throughput molecular marker technologiesensuring speed and quality of data generated it is possibleto characterize the larger number of germplasm with limitedtime and resources Next generation sequencing reduced thecost and time required for sequencing the whole genomeMany software packages are available for assessing pheno-typic and molecular diversity parameters that increased theefficiency of germplasm curators and plant breeders to speedup the crop improvement Therefore we believe that thispaper provides useful and contemporary information at oneplace thus it improves the understanding of tools for gradu-ate students and also practical applicability to the researchers

Abbreviations

AFLP Amplified fragment length polymorphismAP-PCR Arbitrarily primed PCRARMS Amplification refractory mutation systemASAP Arbitrary signatures from amplificationASH Allele-specific hybridizationASLP Amplified sequence length polymorphismASO Allele specific oligonucleotideCAPS Cleaved amplification polymorphic sequenceCAS Coupled amplification and sequencingDAF DNA amplification fingerprintDGGE Denaturing gradient gel electrophoresisGBA Genetic bit analysisIRAO Interretrotransposon amplified polymorphismISSR Intersimple sequence repeatsISTR Inverse sequence-tagged repeatsMP-PCR Microsatellite-primed PCROLA Oligonucleotide ligation assayRAHM Randomly amplified hybridizing microsatellitesRAMPs Randomly amplified microsatellite

polymorphisms


Table2Listof

analyticalprogramsfor

measurin

gmolecular

(genetic)d

iversity

Analytic

altools

Datatype

Mainfeatures

Source

links

Reference

Arle

quin

RFLP

sDNAsequ

encesSSRdataallele

frequ

enciesorstand

ardmultilocus

geno

types

(i)Estim

ationalleleandhaplotypefrequ

encies

(ii)T

estsof

departurefrom

linkage

equilib

riumdeparture

from

selective

neutralityanddemograph

icequilib

rium

(iii)Estim

ationor

parametersfrom

pastpo

pulationexpansions

(iv)Th

orou

ghanalyses

ofpo

pulatio

nsubd

ivision

underthe

AMOVA

framew

orkandso

forth

(v)C

urrent

version

Arle

quin

ver3

513

httpcm

pgunibech

soft

warearlequ

in3

Schn

eidere

tal[87]

Excoffier

etal[88]

DnaSP

DNAsequ

ence

data

(i)Estim

atingseveralm

easureso

fDNAsequ

ence

varia

tionwith

inand

betweenpo

pulatio

ns(in

noncod

ingsyno

nymou

sor

nonsyn

onym

oussiteso

rin

vario

ussortso

fcod

onpo

sitions)as

wellas

linkage

disequ

ilibrium

recombinatio

ngene

flow

andgene

conversio

nparameters

(ii)D

naSP

canalso

carryou

tseveraltests

ofneutralityHud

sonetal[89]

Tajim

a[90]McD

onaldandKr

eitm

an[46]FuandLi

[91]and

Fu[92]

tests

Ad

ditio

nallyD

naSP

canestim

atethe

confi

denceintervalsof

some

test-

statistic

sbythec

oalescentand

soforth

(iii)Cu

rrentversio

nDnaSP

v510

01

httpwwwub

edudn

asp

JRo

zasa

ndR

Rozas

[93ndash95]

LibradoandRo

zas[96]

PowerMarker

SSR

SNPandRF

LPdata

(i)Com

putesseveralsummarysta

tistic

sfor

each

markerlocusincluding

allele

numbermissingprop

ortio

nheterozygositygene

diversitypolym

orph

isminform

ationcontent(PIC)

and

stepw

isepatte

rnsfor

microsatellitedata

(ii)P

owerMarkerisa

lsoused

tocompu

teallelefre

quencygenotypefrequ

ency

haplotypefrequ

ency

foru

nrelated

individu

alsHardy-W

einb

ergequilib

rium

pairw

iselin

kage

disequ

ilibriumm

ultilocus

linkage

disequ

ilibriumcon

sensus

treespop

ulationstr

uctureM

antelrsquostest

triang

leplottin

gandvisualizationof

linkage

disequ

ilibrium

results

(iii)Cu

rrentversio

nPo

werMarkerV

325

httpstatgenncsued

upo

wermarker

LiuandMuse[97]

DARw

inSing

ledata(fo

rhaploidsho

mozygote

diploidsand

dominantm

arkers)allelic

dataand

sequ

ence

data

(i)Mostw

idely

used

forv

arious

dissim

ilarityanddista

ncee

stimations

for

different

datatreec

onstr

uctio

nmetho

dsinclu

ding

hierarchicaltre

eswith

vario

usaggregationcriteria

(weightedor

unweighted)N

eighbo

r-Joiningtre

e(w

eightedor

unweighted)Scoresm

etho

dandprincipalcoo

rdinatea

nalysis

andso

forth

(ii)C

urrent

version

DARw

inv5015

6

httpdarw

inciradfrdarwin

Perriera

ndJacquemou

d-Collet[98]

NTS

YSpc

Sing

ledata(fo

rhaploidsho

mozygote

diploidsand

dominantm

arkers)allelic

dataand

sequ

ence

data

(i)Usedforc

luste

ringanalysis

ordinatio

nanalysis

principalcom

ponent

analysis

principalcoo

rdinatea

nalysisscalin

ganalysis

andcomparis

onof

two

matric

es(M

anteltestMantel[99]a

ndso

forth)

(ii)C

urrent

version

NTS

YSpc

version22

httpwwwexetersoftw

arec

omcatntsy

spcntsyspch

tml

Rohlf[100]

MEG

ADNAsequ

enceprotein

sequ

ence

evolutionary

dista

nceor

phylogenetictre

edata

(i)Molecular

evolutionary

geneticsa

nalysis

(MEG

A)ism

ostw

idely

used

for

aligning

sequ

encesestim

atingevolutionary

dista

ncesbuildingtre

efrom

sequ

ence

datatestin

gtre

ereliabilityand

soforth

(ii)C

urrent

version

MEG

A6

httpwwwmegasoft

waren

etKu

mar

etal[101ndash103]

Tamurae

tal[104

]

PAUP

Molecular

sequ

encesmorph

ologicaldata

andotherd

atatypes

(i)Usedforinferrin

gandinterpretin

gph

ylogenetictre

esusingparsim

ony

dista

ncem

atrix

invariantsmaxim

umlik

eliho

odmetho

dsand

manyindices

andsta

tistic

alanalyses

(ii)C

urrent

version

PAUPversion40

httppaup

csitfsuedu

Swoff

ord[105]


Table2Con

tinued

Analytic

altools

Datatype

Mainfeatures

Source

links

Reference

STRU

CTURE

Alltypeso

fmarkersinclu

ding

mostly

used

markerslik

eSSR

sSN

PsR

FLPsdArTand

soforth

(i)Afre

eprogram

toinvestigatep

opulationstructureitinclu

desinferrin

gthe

presence

ofdistinctp

opulationsassigning

individu

alstopo

pulationsstudying

hybrid

zonesidentifying

migrantsa

ndadmixed

individu

alsandestim

ating

popu

latio

nallelefrequ

encies

insituatio

nswhere

manyindividu

alsa

remigrantso

radm

ixed

(ii)C

urrent

version

STRU

CTURE

234

httppritchbsduchicagoed

usoftw

arestructure2

2html

Pritchard

etal[106]

Falush

etal[107]

Hub

iszetal[108]

fastS

TRUCT

URE

SNP

(i)Analgorithm

forinferrin

gpo

pulatio

nstr

ucture

from

largeS

NPgeno

type

data

(ii)Itisb

ased

onav

ariatio

nalB

ayesianfram

eworkforp

osterio

rinference

and

iswrittenin

Python

2x

httprajanilgith

ubio

fastS

tructure

Rajetal[109]

ADMIXTU

RESN

P

(i)ADMIXTU

REisap

rogram

form

axim

umlik

eliho

odestim

ationof

individu

alancestr

iesfrom

multilocus

SNPgeno

type

datasets

(ii)Itu

sesthe

sames

tatistic

almod

elas

STRU

CTURE

butcalculates

estim

ates

muchmorer

apidlyusingafastn

umericalop

timizationalgorithm

(iii)Cu

rrentversio

nADMIXTU

RE12

3

httpsw

wwgenetic

sucla

edusoftw

areadmixture

Alexand

eretal[110

]

fineSTR

UCT

URE

Sequ

encing

data

(i)Afastandpo

werfulalgorith

mforidentify

ingpo

pulatio

nstr

ucture

using

denses

equencingdata

(ii)C

urrent

version

FineStructure0

02

httppaintm

ychrom

osom

esco

m

Lawsonetal[111]

POPG

ENE

Use

thed

ominantcodo

minantand

quantitatived

atafor

popu

latio

ngenetic

analysis

(i)Usedto

calculateg

enea

ndgeno

type

frequ

encyallelenu

mbereffectiv

eallelenu

mberpo

lymorph

iclocigened

iversityob

served

andexpected

heterozygosityShanno

nindexho

mogeneitytest119865-statisticsgene

flow

genetic

distancedendrogramneutrality

test

andso

forth

(ii)C

urrent

version

POPG

ENEversion13

2

httpsw

wwualbertaca

simfyehpop

genehtm

lFrancise

tal[112]

GEN

EPOP

Haploid

ordiploiddata

(i)Usedto

compu

teexacttestsor

theiru

nbiasedestim

ationfor

Hardy-W

einb

ergequilib

riumpop

ulationdifferentiatio

nandtwo-locus

geno

typicd

isequ

ilibrium

(ii)Itcon

vertsthe

inpu

tGEN

EPOPfiletoform

atsu

sedby

otherp

opular

programslik

eBIO

SYS[113]DIPLO

IDL[114]andLINKD

OS[115]thereby

allowingcommun

icationbetweenthem

(iii)Cu

rrentversio

nGEN

EPOP42

httpgenepo

pcurtined

uau

Raym

ondandRo

usset[116

]

GenAIEx

Cod

ominanthaploidandbinary

genetic

dataItaccom

mod

ates

thefullrange

ofgenetic

markersavailableinclu

ding

allozymesSSR

sSN

PsA

FLPandother

multilocus

markersasw

ellasD

NA

sequ

ences

(i)GenAIExruns

with

inMicrosoftEx

celenablingpo

pulationgenetic

analysis

ofcodo

minanthaploidandbinary

dataU

sedto

compu

teallele

frequ

ency-based

analyses

inclu

ding

heterozygosity119865-statistic

sNeirsquosg

enetic

dista

ncepo

pulationassig

nmentprob

abilitie

sofidentityand

pairw

iserelatedn

ess

(ii)U

sedforc

alculatin

ggenetic

distance

matric

esanddistance

based

calculations

inclu

ding

analysisof

molecular

varia

nce(AMOVA

)[117

118]

principalcoo

rdinates

analysis(PCA

)Manteltests[119]2D

spatial

autocorrelationanalyses

follo

wingSm

ouse

andPeakall[120]Peakalletal

[121]Dou

blee

tal[122]

(iii)Cu

rrentversio

nGenAIEx65

httpbiolog

y-assetsanueduauG

enAlExWelcomeh

tml

Peakalland

Smou

se[123]


RAPD Randomly amplified polymorphic DNARBIP Retrotransposon-based insertion polymorphismREF Restriction endonuclease fingerprintingREMAP Retrotransposon-microsatellite amplified

polymorphismRFLP Restriction fragment length polymorphismSAMPL Selective amplification of polymorphic lociSCAR Sequence characterised amplification regionsSNP Single nucleotide polymorphismSPAR Single primer amplification reactionSPLAT Single polymorphic amplification testS-SAP Sequence-specific amplification polymorphismsSSCP Single strand conformation polymorphismSSLP Single sequence length polymorphismSSR Simple sequence repeatsSTMS Sequence-tagged microsatellite siteSTS Sequence-tagged siteTGGE Thermal gradient gel electrophoresisVNTR Variable number tandem repeatsRAMS Randomly amplified microsatellites

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] P Narain ldquoGenetic diversitymdashconservation and assessmentrdquoCurrent Science vol 79 no 2 pp 170ndash175 2000

[2] A I Turrent and J A Serratos-Hernandez ldquoMaize and bio-diversity the effects of transgenic maize in Mexico chaptercontext and background on wild and cultivated maize inMexicordquo CEC Secretariat pp 1ndash55 2004

[3] M Smale M Istvan I Devra and Jarvis ldquoThe economics ofconserving agricultural biodiversity on farm research methodsdeveloped from IPRIrsquos global project lsquostrengthening the scien-tific basis of in situ conservation of agricultural biodiversityrsquordquo inProceedings of aWorkshopHosted by the Institute forAgroBotany(IA) and the International PantGenetic Resource Institute (IPGRIrsquo02) Godollo Hungary May 2002

[4] R E Evenson and D Gollin ldquoAssessing the impact of the GreenRevolution 1960 to 2000rdquo Science vol 300 no 5620 pp 758ndash762 2003

[5] FAO The State of the Worldrsquos Genetic Resources for Food andAgriculture FAO Rome Italy 1998

[6] S Ceccarelli and S Grando ldquoPlant breeding with farmersrequires treating the assumptions of conventional plant breed-ing lesson from the ICCRDA Barely Programrdquo in FarmersScientists and Plant Breeding IntegratingKnowledge and PracticeD A Cleveland and D Soleri Eds CABI International NewYork NY USA 2002

[7] J Bruinsma EdWorldAgriculture Towards 20152030 AnFAOPerspective Earthscan London UK 2003

[8] S di Falco J Jean-Paul Chvas andM Smale Farmersrsquo Manage-ment of Production Risk on Degraded Lands the Role of WheatGenetic Diversity in Tigray Region Ethiopia Environment andProduction Technology Division EPTD Discussion Papersno153 International Food Policy Research Institute Interna-tional Live Stock Research Institute (ILRI) International Plant

Genetic Resources Institute (IPRGI) and Food and AgricultureOrganization of the United Nations (FAO) Washington DCUSA

[9] S L Pimm J L Gittlaman G F McCracken and M GilpinldquoGenetic bottlenecks alternative explanations for low geneticvariabilityrdquo Trends in Ecology and Evolution vol 4 pp 176ndash1771989

[10] A S Jump and J Penuelas ldquoRunning to stand still adaptationand the response of plants to rapid climate changerdquo EcologyLetters vol 8 no 9 pp 1010ndash1020 2005

[11] S Freeman and J C Herron Evolutionary Analysis Prentice-Hall Upper Saddle River NJ USA 1998

[12] M L Shaffer and F B Samson ldquoPopulation size and extinctiona note on determining critical population sizesrdquo The AmericanNaturalist vol 125 no 1 pp 144ndash152 1985

[13] M E Gilpin and M E Soule ldquoMinimum viable populationsthe processes of species extinctionsrdquo in Conservation BiologyThe Science of Scarcity and Diversity M E Soule Ed pp 13ndash34 Sinauer Associates Sunderland Mass USA 1986

[14] G Barcaccia E Albertini D Rosellini S Tavoletti and FVeronesi ldquoInheritance and mapping of 2n-egg production indiploid alfalfardquo Genome vol 43 no 3 pp 528ndash537 2000

[15] A H Paterson ldquoMaking genetic mapsrdquo in Genome Mapping inPlants A H Paterson Ed pp 23ndash39 R G Landes CompanySan Diego Calif USA Academic Press Austin Tex USA 1996

[16] PWinter andGKahl ldquoMolecularmarker technologies for plantimprovementrdquo World Journal of Microbiology amp Biotechnologyvol 11 no 4 pp 438ndash448 1995

[17] K Weising H Nybom K Wolff and W Meyer Applications ofDNA Fingerprinting in Plants and Fungi DNA Fingerprinting inPlants and Fungi CRC Press Boca Raton Fla USA 1995

[18] V Baird A Abbott R Ballard B Sosinski and S RajapakseldquoDNA diagnostics in horticulturerdquo in Current Topics in PlantMolecular Biology Technology Transfer of Plant BiotechnologyP Gresshoff Ed pp 111ndash130 CRC Press Boca Raton Fla USA1997

[19] R Henry ldquoMolecular markers in plant improvementrdquo in Prac-tical Applications of Plant Molecular Biology pp 99ndash132 Chap-man amp Hall London UK 1997

[20] Y Dje M Heuertz C Lefebvre and X Vekemans ldquoAssessmentof genetic diversity within and among germplasm accessionsin cultivated sorghum usingmicrosatellite markersrdquoTheoreticaland Applied Genetics vol 100 no 6 pp 918ndash925 2000

[21] S C Hokanson W F Lamboy A K Szewc-McFadden and JR McFerson ldquoMicrosatellite (SSR) variation in a collection ofMalus (apple) species and hybridsrdquo Euphytica vol 118 no 3pp 281ndash294 2001

[22] M Jahufer B Barret A Griffiths and D Woodfield ldquoDNAfingerprinting and genetic relationships among white clovercultivarsrdquo in Proceedings of the New Zealand Grassland Associ-ation J Morton Ed vol 65 pp 163ndash169 Taieri Print LimitedDunedin New Zealand 2003

[23] Z Galli G Halasz E Kiss L Heszky and J DobranszkildquoMolecular identification of commercial apple cultivars withmicrosatellite markersrdquo HortScience vol 40 no 7 pp 1974ndash1977 2005

[24] AAlvarez J L Fuentes V Puldon et al ldquoGenetic diversity anal-ysis of Cuban traditional rice (Oryza sativa L) varieties basedon microsatellite markersrdquo Genetics and Molecular Biology vol30 no 4 pp 1109ndash1117 2007


[25] M L Ali J F Rajewski P S Baenziger K S Gill KM Eskridgeand I Dweikat ldquoAssessment of genetic diversity and relation-ship among a collection of US sweet sorghum germplasm bySSR markersrdquo Molecular Breeding vol 21 no 4 pp 497ndash5092008

[26] V V Becerra C M Paredes M C Rojo L M Dıaz and M WBlair ldquoMicrosatellite marker characterization of Chilean com-mon bean (Phaseolus vulgaris L) germplasmrdquoCrop Science vol50 no 5 pp 1932ndash1941 2010

[27] B C Y Collard and D J Mackill ldquoMarker-assisted selectionan approach for precision plant breeding in the twenty-firstcenturyrdquo Philosophical Transactions of the Royal Society BBiological Sciences vol 363 no 1491 pp 557ndash572 2008

[28] D Botstein R L White M Skolnick and R W DavisldquoConstruction of a genetic linkagemap inman using restrictionfragment length polymorphismsrdquo The American Journal ofHuman Genetics vol 32 no 3 pp 314ndash331 1980

[29] B de Martinville A R Wyman R White and U FranckeldquoAssignment of the first random restriction fragment lengthpolymorphism (RFLP) locus (D14S1) to a region of humanchromosome 14rdquoTheAmerican Journal of Human Genetics vol34 no 2 pp 216ndash226 1982

[30] DWeber andTHelentjaris ldquoMappingRFLP loci inmaize usingB-A translocationsrdquo Genetics vol 121 no 3 pp 583ndash590 1989

[31] J L Bennetzen ldquoComparative sequence analysis of plantnuclear genomes microcolinearity and its many exceptionsrdquoPlant Cell vol 12 no 7 pp 1021ndash1029 2000

[32] K M Devos M D Atkinson C N Chinoy et al ldquoChromoso-mal rearrangements in the rye genome relative to that of wheatrdquoTheoretical and Applied Genetics vol 85 no 6-7 pp 673ndash6801993

[33] J Dubcovsky W Ramakrishna P J SanMiguel et al ldquoCom-parative sequence analysis of colinear barley and rice bacterialartificial chromosomesrdquo Plant Physiology vol 125 no 3 pp1342ndash1353 2001

[34] B C Y Collard M Z Z Jahufer J B Brouwer and E C KPang ldquoAn introduction tomarkers quantitative trait loci (QTL)mapping and marker-assisted selection for crop improvementthe basic conceptsrdquo Euphytica vol 142 no 1-2 pp 169ndash1962005

[35] J Welsh and M McClelland ldquoFingerprinting genomes usingPCR with arbitrary primersrdquoNucleic Acids Research vol 18 no24 pp 7213ndash7218 1990

[36] A Jacobson and M Hedren ldquoPhylogenetic relationships inAlisma (Alismataceae) based on RAPDs and sequence datafrom ITS and trnLrdquo Plant Systematics and Evolution vol 265no 1-2 pp 27ndash44 2007

[37] M Mohan S Nair A Bhagwat et al ldquoGenome mappingmolecular markers and marker-assisted selection in cropplantsrdquoMolecular Breeding vol 3 no 2 pp 87ndash103 1997

[38] K Semagn A Bjoslashrnstad and M N Ndjiondjop ldquoAn overviewof molecular marker methods for plantsrdquo African Journal ofBiotechnology vol 5 no 25 pp 2540ndash2568 2006

[39] L Mondini A Noorani and M A Pagnotta ldquoAssessing plantgenetic diversity by molecular toolsrdquo Diversity vol 1 no 1 pp19ndash35 2009

[40] M Litt and J A Luty ldquoA hypervariable microsatellite revealedby in vitro amplification of a dinucleotide repeat within thecardiac muscle actin generdquo The American Journal of HumanGenetics vol 44 no 3 pp 397ndash401 1989

[41] J A L Armour S A Alegre S Miles L J Williams and RM Badge ldquoMinisatellites and mutation processes in tandemlyrepetitive DNArdquo in Microsatellites Evolution and ApplicationsD B Goldstein and C Schlotterer Eds pp 24ndash33 OxfordUniversity Press Oxford UK 1999

[42] D B Goldstein and D D Pollock ldquoLaunching microsatellitesa review of mutation processes and methods of phylogeneticinferencerdquo Journal of Heredity vol 88 no 5 pp 335ndash342 1997

[43] C Schlotterer ldquoMicrosatellitesrdquo inMolecular Genetic Analysis ofPopulations A Practical Approach A R Hoelzel Ed pp 237ndash261 IRL Press Oxford UK 1998

[44] D C Queller J E Strassmann and C R Hughes ldquoMicrosatel-lites and kinshiprdquo Trends in Ecology and Evolution vol 8 no 8pp 285ndash288 1993

[45] M W Bruford D J Cheesman T Coote et al ldquoMicrosatellitesand their application to conservation geneticsrdquo in MolecularGenetic Approaches in Conservation T B Smith and R KWayne Eds pp 278ndash297 Oxford University Press New YorkNY USA 1996

[46] J H McDonald and M Kreitman ldquoAdaptive protein evolutionat the Adh locus in Drosophilardquo Nature vol 351 no 6328 pp652ndash654 1991

[47] R L Hammond I J Saccheri C Ciofi et al ldquoIsolationof microsatellite markers in animalsrdquo in Molecular Tools forScreening Biodiversity A Karp P G Issac and D S IngramEds pp 279ndash287 Chapman amp Hall London UK 1998

[48] G K Chambers and E S MacAvoy ldquoMicrosatellites consensusand controversyrdquo Comparative Biochemistry and PhysiologymdashBBiochemistry andMolecular Biology vol 126 no 4 pp 455ndash4762000

[49] L Zane L Bargelloni and T Patarnello ldquoStrategies for micro-satellite isolation a reviewrdquoMolecular Ecology vol 11 no 1 pp1ndash16 2002

[50] J Squirrell P M Hollingsworth M Woodhead et al ldquoHowmuch effort is required to isolate nuclear microsatellites fromplantsrdquoMolecular Ecology vol 12 no 6 pp 1339ndash1348 2003

[51] Y Matsuoka S E Mitchell S Kresovich M Goodman andJ Doebley ldquoMicrosatellites in Zeamdashvariability patterns ofmutations and use for evolutionary studiesrdquo Theoretical andApplied Genetics vol 104 no 2-3 pp 436ndash450 2002

[52] R Kota R K Varshney T Thiel K J Dehmer and A GranerldquoGeneration and comparison of EST-derived SSRs and SNPs inbarley (Hordeum vulgare L)rdquo Hereditas vol 135 no 2-3 pp145ndash151 2002

[53] R V Kantety M La Rota D E Matthews and M E Sor-rells ldquoData mining for simple sequence repeats in expressedsequence tags from barley maize rice sorghum and wheatrdquoPlant Molecular Biology vol 48 no 5-6 pp 501ndash510 2002

[54] W Michalek W Weschke K-P Pleissner and A Graner ldquoESTanalysis in barley defines a unigene set comprising 4000 genesrdquoTheoretical andAppliedGenetics vol 104 no 1 pp 97ndash103 2002

[55] X-P Jia Y-S Shi Y-C Song G-Y Wang T-Y Wang and YLi ldquoDevelopment of EST-SSR in foxtail millet (Setaria italica)rdquoGenetic Resources andCrop Evolution vol 54 no 2 pp 233ndash2362007

[56] S Senthilvel B Jayashree VMahalakshmi et al ldquoDevelopmentandmapping of simple sequence repeat markers for pearl milletfrom data mining of expressed sequence tagsrdquo BMC PlantBiology vol 8 article 119 2008

[57] I Simko ldquoDevelopment of EST-SSR markers for the study ofpopulation structure in lettuce (Lactuca sativa L)rdquo Journal ofHeredity vol 100 no 2 pp 256ndash262 2009


[58] M Slatkin ldquoIsolation by distance in equilibrium and non-equi-librium populationsrdquo Evolution vol 47 no 1 pp 264ndash279 1993

[59] P K Gupta S Rustgi S Sharma R Singh N Kumar andH S Balyan ldquoTransferable EST-SSR markers for the study ofpolymorphism and genetic diversity in bread wheatrdquoMolecularGenetics and Genomics vol 270 no 4 pp 315ndash323 2003

[60] I Eujayl M K Sledge L Wang et al ldquoMedicago truncatulaEST-SSRs reveal cross-species genetic markers for Medicagospprdquo Theoretical and Applied Genetics vol 108 no 3 pp 414ndash422 2004

[61] YG Cho T Ishii S Temnykh et al ldquoDiversity ofmicrosatellitesderived from genomic libraries and GenBank sequences in rice(Oryza sativa L)rdquoTheoretical and Applied Genetics vol 100 no5 pp 713ndash722 2000

[62] K D Scott P Eggler G Seaton et al ldquoAnalysis of SSRs derivedfrom grape ESTsrdquoTheoretical and Applied Genetics vol 100 no5 pp 723ndash726 2000

[63] I Eujayl M E Sorrells M Baum P Wolters and W PowellldquoIsolation of EST-derivedmicrosatellite markers for genotypingthe A and B genomes of wheatrdquo Theoretical and AppliedGenetics vol 104 no 2-3 pp 399ndash407 2002

[64] K Chabane G A Ablett G M Cordeiro J Valkoun and R JHenry ldquoEST versus genomic derived microsatellite markers forgenotyping wild and cultivated barleyrdquo Genetic Resources andCrop Evolution vol 52 no 7 pp 903ndash909 2005

[65] D Jaccoud K Peng D Feinstein and A Kilian ldquoDiversityarrays a solid state technology for sequence information inde-pendent genotypingrdquo Nucleic Acids Research vol 29 no 4article E25 2001

[66] D Jaccoud K Peng D Feinstein and A Kilian ldquoDiversityarrays a solid state technology for sequence information inde-pendent genotypingrdquo Nucleic Acids Research vol 29 no 4article e25 2001

[67] M Akbari P Wenzl V Caig et al ldquoDiversity arrays technology(DArT) for high-throughput profiling of the hexaploid wheatgenomerdquo Theoretical and Applied Genetics vol 113 no 8 pp1409ndash1420 2006

[68] L Zhang D Liu X Guo et al ldquoInvestigation of genetic diver-sity and population structure of common wheat cultivars innorthern China using DArT markersrdquo BMC Genetics vol 12article 42 2011

[69] PWenzl J Carling D Kudrna et al ldquoDiversity Arrays Technol-ogy (DArT) for whole-genome profiling of barleyrdquo Proceedingsof the National Academy of Sciences of the United States ofAmerica vol 101 no 26 pp 9915ndash9920 2004

[70] S Lezar A A Myburg D K Berger M J Wingfield and B DWingfield ldquoDevelopment and assessment of microarray-basedDNA fingerprinting in Eucalyptus grandisrdquo Theoretical andApplied Genetics vol 109 no 7 pp 1329ndash1336 2004

[71] A H J Wittenberg T Van Der Lee C Cayla A Kilian R G FVisser and H J Schouten ldquoValidation of the high-throughputmarker technology DArT using the model plant Arabidopsisthalianardquo Molecular Genetics and Genomics vol 274 no 1 pp30ndash39 2005

[72] L Xia K Peng S Yang et al ldquoDArT for high-throughput geno-typing of Cassava (Manihot esculenta) and its wild relativesrdquoTheoretical and Applied Genetics vol 110 no 6 pp 1092ndash10982005

[73] S Yang W Pang G Ash et al ldquoLow level of genetic diversity incultivated Pigeonpea compared to its wild relatives is revealedby diversity arrays technologyrdquo Theoretical and Applied Genet-ics vol 113 no 4 pp 585ndash595 2006

[74] W Gilbert ldquoDNA sequencing and gene structure Nobel lecture8 December 1980rdquo Bioscience Reports vol 1 no 5 pp 353ndash3751981

[75] M Margulies M Egholm and W E Altman ldquoGenomesequencing in microfabricated high-density picolitre reactorsrdquoNature vol 437 pp 376ndash380 2005

[76] J Shendure G J Porreca N B Reppas et al ldquoMolecularbiology accurate multiplex polony sequencing of an evolvedbacterial genomerdquo Science vol 309 no 5741 pp 1728ndash17322005

[77] K McKernan A Blanchard L Kotler and G Costa ldquoReagentsmethods and libraries for bead-based sequencingrdquo US PatentApplication 20080003571 2006

[78] E R Mardis ldquoNext-generation DNA sequencing methodsrdquoAnnual Review of Genomics and Human Genetics vol 9 pp387ndash402 2008

[79] X Zhou L Ren Q Meng Y Li Y Yu and J Yu ldquoThe next-generation sequencing technology and applicationrdquo Protein andCell vol 1 no 6 pp 520ndash536 2010

[80] J Shendure and H Ji ldquoNext-generation DNA sequencingrdquoNature Biotechnology vol 26 no 10 pp 1135ndash1145 2008

[81] M L Metzker ldquoSequencing technologies the next generationrdquoNature Reviews Genetics vol 11 no 1 pp 31ndash46 2010

[82] M Nei andW H Li ldquoMathematical model for studying geneticvariation in terms of restriction endonucleasesrdquo Proceedings ofthe National Academy of Sciences of the United States of Americavol 76 no 10 pp 5269ndash5273 1979

[83] P Jaccard ldquoNouvelles researches sur la distribution floralerdquoBulletin de la Societe Vaudoise des Sciences Naturelles vol 44pp 223ndash270 1908

[84] R R Sokal and C D Michener ldquoA statistical method for eval-uating systematic relationshipsrdquo University of Kansas ScienceBulletin vol 38 pp 1409ndash1438 1958

[85] S A Mohammadi and B M Prasanna ldquoAnalysis of geneticdiversity in crop plantsmdashsalient statistical tools and consider-ationsrdquo Crop Science vol 43 no 4 pp 1235ndash1248 2003

[86] A Karp S Kresovich K V Bhat W G Ayad and T HodgkinldquoMolecular tools in plant genetic resources conservation aguide to the technologiesrdquo IPGRI Technical Bulletin 2 Inter-national Plant Genetic Resources Institute Rome Italy 1997

[87] S Schneider D Roessli and L Excoffier Arlequin A Softwarefor Population Genetics Data Analysis 2000 Version 2000Genetics and Biometry Laboratory Department of Anthropol-ogy University of Geneva Geneva Switzerland 2000

[88] L Excoffier G Laval and S Schneider ldquoArlequin (version 30)an integrated software package for population genetics dataanalysisrdquo Evolutionary Bioinformatics Online vol 1 pp 47ndash502005

[89] R R Hudson M Kreitman and M Aguade ldquoA test of neutralmolecular evolution based on nucleotide datardquo Genetics vol116 no 1 pp 153ndash159 1987

[90] F Tajima ldquoStatistical method for testing the neutral mutationhypothesis by DNApolymorphismrdquoGenetics vol 123 no 3 pp585ndash595 1989

[91] Y-X Fu and W-H Li ldquoStatistical tests of neutrality of muta-tionsrdquo Genetics vol 133 no 3 pp 693ndash709 1993

[92] Y-X Fu ldquoNew statistical tests of neutrality for DNA samplesfrom a populationrdquo Genetics vol 143 no 1 pp 557ndash570 1996

[93] J Rozas and R Rozas ldquoDnaSP DNA sequence polymorphisman interactive program for estimating population genetics


parameters from DNA sequence datardquo Computer Applicationsin the Biosciences vol 11 no 6 pp 621ndash625 1995

[94] J Rozas and R Rozas ldquoDnaSP version 20 a novel softwarepackage for extensive molecular population genetics analysisrdquoComputer Applications in the Biosciences vol 13 no 3 pp 307ndash311 1997

[95] J Rozas and R Rozas ldquoDnaSP version 3 an integrated programfor molecular population genetics and molecular evolutionanalysisrdquo Bioinformatics vol 15 no 2 pp 174ndash175 1999

[96] P Librado and J Rozas ldquoDnaSP v5 a software for comprehen-sive analysis of DNA polymorphism datardquo Bioinformatics vol25 no 11 pp 1451ndash1452 2009

[97] K Liu and S V Muse ldquoPowerMaker an integrated analysisenvironment for genetic maker analysisrdquo Bioinformatics vol 21no 9 pp 2128ndash2129 2005

[98] X Perrier and J P Jacquemoud-Collet DARwin software 2006httpdarwinciradfrdarwin

[99] N Mantel ldquoThe detection of disease clustering and a general-ized regression approachrdquo Cancer Research vol 27 no 2 pp209ndash220 1967

[100] F J Rohlf NTSYSpc Numerical Taxonomy System Version 21Exeter Publishing Setauket NY USA 2002

[101] S Kumar M Nei J Dudley and K Tamura ldquoMEGA a biolo-gist-centric software for evolutionary analysis of DNA andprotein sequencesrdquo Briefings in Bioinformatics vol 9 no 4 pp299ndash306 2008

[102] S Kumar K Tamura and M Nei ldquoMEGA molecular evolu-tionary genetics analysis software for microcomputersrdquo Com-puter Applications in the Biosciences vol 10 no 2 pp 189ndash1911994

[103] S Kumar K Tamura I B Jakobsen and M Nei ldquoMEGA2molecular evolutionary genetics analysis softwarerdquo Bioinfor-matics vol 17 no 12 pp 1244ndash1245 2002

[104] K Tamura D Peterson N Peterson G Stecher M Nei andS Kumar ldquoMEGA5 molecular evolutionary genetics analysisusing maximum likelihood evolutionary distance and max-imum parsimony methodsrdquo Molecular Biology and Evolutionvol 28 no 10 pp 2731ndash2739 2011

[105] D L Swofford PAUP Phylogenetic Analysis Using Parsimony(andOtherMethods) Version 4 SinauerAssociates SunderlandMass USA 2002

[106] J K Pritchard M Stephens and P Donnelly ldquoInference ofpopulation structure using multilocus genotype datardquoGeneticsvol 155 no 2 pp 945ndash959 2000

[107] D Falush M Stephens and J K Pritchard ldquoInference of pop-ulation structure using multilocus genotype data linked lociand correlated allele frequenciesrdquo Genetics vol 164 no 4 pp1567ndash1587 2003

[108] M J Hubisz D Falush M Stephens and J K Pritchard ldquoInfer-ring weak population structure with the assistance of samplegroup informationrdquo Molecular Ecology Resources vol 9 no 5pp 1322ndash1332 2009

[109] A Raj M Stephens and J K Pritchard ldquofastSTRUCTUREvariational inference of population structure in large SNP datasetsrdquo Genetics vol 197 no 2 pp 573ndash589 2014

[110] D H Alexander J Novembre and K Lange ldquoFast model-based estimation of ancestry in unrelated individualsrdquo GenomeResearch vol 19 no 9 pp 1655ndash1664 2009

[111] D J Lawson G Hellenthal S Myers and D Falush ldquoInferenceof population structure using dense haplotype datardquo PLoSGenetics vol 8 no 1 Article ID e1002453 2012

[112] C Yeh Francis R C Yang B J Boyle Timothy Z H Ye andX Mao Judy POPGENE Version 132 The User-Friendly Share-ware for Population Genetic Analysis Molecular Biology andBiotechnology Centre University of Alberta Alberta Canada1999 httpwwwualbertacasimfyeh

[113] D L Swofford and R B Selander ldquoBiosys-1 a FORTRAN pro-gram for the comprehensive analysis for electrophoretic data inpopulation genetics and systematicsrdquo Journal of Heredity vol72 no 4 pp 281ndash283 1981

[114] B S Weir ldquoIntraspecific differentiationrdquo in Molecular System-atic D M Hillis and C Moritz Eds pp 373ndash410 SinauerAssociates Sunderland Mass USA 1990

[115] P Garnier-Gere and C Dillmann ldquoA computer program fortesting pairwise linkage disequilibria in subdivided popula-tionsrdquo Journal of Heredity vol 83 no 3 p 239 1992

[116] M Raymond and F Rousset ldquoENPOP population geneticssoftware for exact tests and ecumenicismrdquo Journal of Heredityvol 86 pp 248ndash249 1995

[117] L Excoffier P E Smouse and J M Quattro ldquoAnalysis ofmolecular variance inferred frommetric distances amongDNAhaplotypes application to human mitochondrial DNA restric-tion datardquo Genetics vol 131 no 2 pp 479ndash491 1992

[118] R Peakall P E Smouse and D R Huff ldquoEvolutionary implica-tions of allozyme and RAPD variation in diploid populations ofdioecious buffalograss Buchloe dactyloidesrdquoMolecular Ecologyvol 4 no 2 pp 135ndash147 1995

[119] P E Smouse C J Long and R R Sokal ldquoMultiple regressionand correlation extensions of the Mantel test of matrix corre-spondencerdquo Systematic Zoology vol 35 no 4 pp 627ndash632 1986

[120] P E Smouse and R Peakall ldquoSpatial autocorrelation analysisof individual multiallele and multilocus genetic structurerdquoHeredity vol 82 no 5 pp 561ndash573 1999

[121] R Peakall M Ruibal and D B Lindenmayer ldquoSpatial auto-correlation analysis offers new insights into gene flow in theAustralian bush rat Rattus fuscipesrdquo Evolution vol 57 no 5pp 1182ndash1195 2003

[122] MCDouble R Peakall N R Beck andACockburn ldquoDisper-sal philopatry and infidelity dissecting local genetic structurein superb fairy-wrens (Malurus cyaneus)rdquo Evolution vol 59 no3 pp 625ndash635 2005

[123] R Peakall and P E Smouse ldquoGENALEX 6 genetic analysis inExcel Population genetic software for teaching and researchrdquoMolecular Ecology Notes vol 6 no 1 pp 288ndash295 2012

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology

2 Genetics Research InternationalRe

lativ

e pro

duct

ion

30

35

25

20

15

10

05

1960 1970 1980 1990 2000 2010

Main grains (wheat barley maize rice and oats)Coarse grains (millet and sorghum)Root crops (cassava and potato)

Figure 1 Changes in the relative global production of crops since1961 (when relative production scaled to 1 (mt) in 1961) (sourcehttpfaostatfaoorgdefaultaspx (2010))

question was recently sensitized at the world food prizeevent in 2014 and remains that unanswered in every onehands since global population will exceed 9 billion in 2050The per capita availability of food and water will becomeworse year after year coping with the undesirable climatechange Therefore it becomes more important to look atthe agriculture not only as a food-producing machine butalso as an important source of livelihood generation bothin the farm and nonfarm sectors Keeping the reservoirfor cultivated and cultivable crops species is a principle forfuture agriculture just like keeping a museum of culturaland spiritual specialty of diverse civilized humans in variousgeography for their historical evidence for futureThe formercan play a very important role in providing adaptive andproductive genes thus leading to long-term increases in foodproductivity which is further associated with environmentaldetrimentThis paper will indicate the significance of geneticconservation and its analytical tools and techniques that aremade widely available for utilization in postgenomic eraPlant and animal breeders introduced desirable genes andeliminated undesirable ones slowly altering in the process ofunderlying heredity principle for several decades [1] Withthe advent of new biotechnological tools and techniques thisprocess of genetic manipulation is being accelerated and itshortened the breeding cycles and it can be carried out withmore precision (neglecting environmental effects) and fast-track manner than the classical breeding techniques

2 Significance of GeneticConservation of Crop Plants

The growing population pressure and urbanization of agri-cultural lands and rapid modernization in every field of our

day-to-day activities that create biodiversity are getting tooeroded in direct and indirect way For instance land degra-dation deforestation urbanization coastal development andenvironmental stress are collectively leading to large-scaleextinction of plant species especially agriculturally importantfood crops On the other hand system driven famine such asIrish potato famine and Southern corn leaf blight epidemic inUSA are the two instances of food crises caused by large-scalecultivation of genetically homogenous varieties of potatoand corn respectively Even after these historical events theimportance of PGR had only got popular recognition whenthe spread of green revolution across cultivated crops threat-ened the conservation of land races [2] Green revolutiontechnologies introduced improved crop varieties that havehigher yields and it was hoped that theywould increase farm-ersrsquo income Consequently the Consultative Group of Inter-national Agricultural Researches (CIGAR) initiated genebanks and research centers of domestication for conservingPGR in most of the stable food crops around the worldCenter for domestication maize (Mexico) wheat and barley(middlenear East and North Africa) rice (North China)and potatoes (Peru) for further information see httpwwwcigarorgcenterindexhtml) The Food and AgricultureOrganization (FAO) supported the International Treaty onPlant Genetic Resources (ITPGR) and UN supported theConvention on Biological Diversity (CBD) which are theinternational agreements that recognize the important role ofgenetic diversity conservation Such treaty still plays in cur-rent and future food production as one of the major supremo[3]

Genetic diversity is the key pillar of biodiversity anddiversity within species between species and of ecosystems(CBD Article 2) which was defined at the Rio de JaneiroEarth Summit However the problem is that modern cropvarieties especially have been developed primarily for highyielding potential under well endowed production condi-tions Such varieties are often not suitable for low incomefarmers in marginal production environments as they arefacing highly variable stress conditions [4] Land races ortraditional varieties have been found to have higher stabil-ity (adaptation over time) in low-input agriculture undermarginal environments thus their cultivation may con-tribute farm level resilience in face of food production shocks[5 6] This is especially true in some part of Ethiopiawhere agroclimatic conditions are challenging technologicalprogress is slow andmarket institutions are poorly developedand have no appropriate infrastructure [7 8]

Why is genetic diversity important The goal of conser-vation genetics is to maintain genetic diversity at many levelsand to provide tools for population monitoring and assess-ment that can be used for conservation planning Every indi-vidual is genetically unique by nature Conservation effortsand related research are rarely directed towards individualsbut genetic variation is always measured in individuals andthis can only be estimated for collections of individuals ina populationspecies It is possible to identify the geneticvariation from phenotypic variation either by quantitativetraits (traits that vary continuous and are governed by manygenes eg plant height) or discrete traits traits that fall into








































GDNL = 1 = [211987311

(211987311+ 11987310+ 11987301)]

GDJ = 1 = [11987311

(11987311+ 11987310+ 11987301)]

GDSM = 1 = [(11987311+ 11987300)

(11987311+ 11987310+ 11987301+ 11987300)]

GDMR = [(11987310+ 11987301)

2119873]

05

(1)

where 11987311


















119868 = minussum119901119894log2119901119894












119890= 1sum119901

2

119894

1199012









119899

1198941199012

119894




Table 1 Continued









10 Conclusion



Abbreviations


polymorphisms


Table2Listof


measurin

gmolecular

(genetic)d

iversity

Analytic

altools

Datatype

Mainfeatures

Source

links

Reference

Arle

quin

RFLP

sDNAsequ

encesSSRdataallele

frequ

enciesorstand

ardmultilocus

geno

types

(i)Estim


encies

(ii)T

estsof

departurefrom

linkage

equilib

riumdeparture

from

selective


icequilib

rium

(iii)Estim

ationor

parametersfrom

pastpo

pulationexpansions

(iv)Th

orou

ghanalyses

ofpo

pulatio

nsubd

ivision

underthe

AMOVA

framew

orkandso

forth

(v)C

urrent

version

Arle

quin

ver3

513

httpcm

pgunibech

soft

warearlequ

in3

Schn

eidere

tal[87]

Excoffier

etal[88]

DnaSP

DNAsequ

ence

data

(i)Estim

atingseveralm

easureso

fDNAsequ

ence

varia

tionwith

inand

betweenpo

pulatio

ns(in

noncod

ingsyno

nymou

sor

nonsyn

onym

oussiteso

rin

vario

ussortso

fcod

onpo

sitions)as

wellas

linkage

disequ

ilibrium

recombinatio

ngene

flow

andgene

conversio

nparameters

(ii)D

naSP

canalso

carryou

tseveraltests

ofneutralityHud

sonetal[89]

Tajim

a[90]McD

onaldandKr

eitm

an[46]FuandLi

[91]and

Fu[92]

tests

Ad

ditio

nallyD

naSP

canestim

atethe

confi

denceintervalsof

some

test-

statistic

sbythec

oalescentand

soforth

(iii)Cu

rrentversio

nDnaSP

v510

01

httpwwwub

edudn

asp

JRo

zasa

ndR

Rozas

[93ndash95]

LibradoandRo

zas[96]

PowerMarker

SSR

SNPandRF

LPdata

(i)Com


tistic

sfor

each


allele

numbermissingprop

ortio

nheterozygositygene

diversitypolym

orph

isminform

ationcontent(PIC)

and

stepw

isepatte

rnsfor

microsatellitedata

(ii)P

owerMarkerisa

lsoused

tocompu

teallelefre

quencygenotypefrequ

ency

haplotypefrequ

ency

foru

nrelated

individu

alsHardy-W

einb

ergequilib

rium

pairw

iselin

kage

disequ

ilibriumm

ultilocus

linkage

disequ

ilibriumcon

sensus

treespop

ulationstr

uctureM

antelrsquostest

triang

leplottin

gandvisualizationof

linkage

disequ

ilibrium

results

(iii)Cu

rrentversio

nPo

werMarkerV

325

httpstatgenncsued

upo

wermarker

LiuandMuse[97]

DARw

inSing

ledata(fo

rhaploidsho

mozygote

diploidsand

dominantm

arkers)allelic

dataand

sequ

ence

data

(i)Mostw

idely

used

forv

arious

dissim

ilarityanddista

ncee

stimations

for

different

datatreec

onstr

uctio

nmetho

dsinclu

ding

hierarchicaltre

eswith

vario


(weightedor

unweighted)N

eighbo

r-Joiningtre

e(w

eightedor

unweighted)Scoresm

etho

dandprincipalcoo

rdinatea

nalysis

andso

forth

(ii)C

urrent

version

DARw

inv5015

6

httpdarw

inciradfrdarwin

Perriera

ndJacquemou

d-Collet[98]

NTS

YSpc

Sing

ledata(fo

rhaploidsho

mozygote

diploidsand

dominantm

arkers)allelic

dataand

sequ

ence

data

(i)Usedforc

luste

ringanalysis

ordinatio

nanalysis

principalcom

ponent

analysis

principalcoo

rdinatea

nalysisscalin

ganalysis

andcomparis

onof

two

matric

es(M


ndso

forth)

(ii)C

urrent

version

NTS

YSpc

version22

httpwwwexetersoftw

arec

omcatntsy

spcntsyspch

tml

Rohlf[100]

MEG

ADNAsequ

enceprotein

sequ

ence

evolutionary

dista

nceor

phylogenetictre

edata

(i)Molecular

evolutionary

geneticsa

nalysis

(MEG

A)ism

ostw

idely

used

for

aligning

sequ

encesestim

atingevolutionary

dista

ncesbuildingtre

efrom

sequ

ence

datatestin

gtre

ereliabilityand

soforth

(ii)C

urrent

version

MEG

A6

httpwwwmegasoft

waren

etKu

mar

etal[101ndash103]

Tamurae

tal[104

]

PAUP

Molecular

sequ

encesmorph

ologicaldata

andotherd

atatypes

(i)Usedforinferrin

gandinterpretin

gph

ylogenetictre

esusingparsim

ony

dista

ncem

atrix

invariantsmaxim

umlik

eliho

odmetho

dsand

manyindices

andsta

tistic

alanalyses

(ii)C

urrent

version

PAUPversion40

httppaup

csitfsuedu

Swoff

ord[105]


Table2Con

tinued

Analytic

altools

Datatype

Mainfeatures

Source

links

Reference

STRU

CTURE

Alltypeso

fmarkersinclu

ding

mostly

used

markerslik

eSSR

sSN

PsR

FLPsdArTand

soforth

(i)Afre

eprogram

toinvestigatep


desinferrin

gthe

presence

ofdistinctp

opulationsassigning

individu

alstopo

pulationsstudying

hybrid

zonesidentifying

migrantsa

ndadmixed

individu

alsandestim

ating

popu

latio

nallelefrequ

encies

insituatio

nswhere

manyindividu

alsa

remigrantso

radm

ixed

(ii)C

urrent

version

STRU

CTURE

234


usoftw

arestructure2

2html

Pritchard

etal[106]

Falush

etal[107]

Hub

iszetal[108]

fastS

TRUCT

URE

SNP

(i)Analgorithm

forinferrin

gpo

pulatio

nstr

ucture

from

largeS

NPgeno

type

data

(ii)Itisb

ased

onav

ariatio

nalB

ayesianfram

eworkforp

osterio

rinference

and

iswrittenin

Python

2x

httprajanilgith

ubio

fastS

tructure

Rajetal[109]

ADMIXTU

RESN

P

(i)ADMIXTU

REisap

rogram

form

axim

umlik

eliho

odestim

ationof

individu

alancestr

iesfrom

multilocus

SNPgeno

type

datasets

(ii)Itu

sesthe

sames

tatistic

almod

elas

STRU

CTURE

butcalculates

estim

ates

muchmorer

apidlyusingafastn

umericalop

timizationalgorithm

(iii)Cu

rrentversio

nADMIXTU

RE12

3

httpsw

wwgenetic

sucla

edusoftw

areadmixture

Alexand

eretal[110

]

fineSTR

UCT

URE

Sequ

encing

data

(i)Afastandpo

werfulalgorith

mforidentify

ingpo

pulatio

nstr

ucture

using

denses

equencingdata

(ii)C

urrent

version

FineStructure0

02

httppaintm

ychrom

osom

esco

m

Lawsonetal[111]

POPG

ENE

Use

thed

ominantcodo

minantand

quantitatived

atafor

popu

latio

ngenetic

analysis

(i)Usedto

calculateg

enea

ndgeno

type

frequ

encyallelenu

mbereffectiv

eallelenu

mberpo

lymorph

iclocigened

iversityob

served

andexpected


nindexho


flow

genetic


test

andso

forth

(ii)C

urrent

version

POPG

ENEversion13

2

httpsw

wwualbertaca

simfyehpop

genehtm

lFrancise

tal[112]

GEN

EPOP

Haploid

ordiploiddata

(i)Usedto

compu

teexacttestsor

theiru

nbiasedestim

ationfor

Hardy-W

einb

ergequilib

riumpop


nandtwo-locus

geno

typicd

isequ

ilibrium

(ii)Itcon

vertsthe

inpu

tGEN

EPOPfiletoform

atsu

sedby

otherp

opular

programslik

eBIO

SYS[113]DIPLO

IDL[114]andLINKD

OS[115]thereby

allowingcommun

icationbetweenthem

(iii)Cu

rrentversio

nGEN

EPOP42

httpgenepo

pcurtined

uau

Raym

ondandRo

usset[116

]

GenAIEx

Cod


genetic

dataItaccom

mod

ates

thefullrange

ofgenetic


ding

allozymesSSR

sSN

PsA

FLPandother

multilocus

markersasw

ellasD

NA

sequ

ences

(i)GenAIExruns

with

inMicrosoftEx

celenablingpo

pulationgenetic

analysis

ofcodo


dataU

sedto

compu

teallele

frequ

ency-based

analyses

inclu

ding


sNeirsquosg

enetic

dista

ncepo

pulationassig

nmentprob

abilitie

sofidentityand

pairw

iserelatedn

ess

(ii)U

sedforc

alculatin

ggenetic

distance

matric

esanddistance

based

calculations

inclu

ding

analysisof

molecular

varia

nce(AMOVA

)[117

118]

principalcoo

rdinates

analysis(PCA

)Manteltests[119]2D

spatial


follo

wingSm

ouse


[121]Dou

blee

tal[122]

(iii)Cu

rrentversio

nGenAIEx65

httpbiolog

y-assetsanueduauG

enAlExWelcomeh

tml

Peakalland

Smou

se[123]






References








































































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology








































GDNL = 1 = [211987311

(211987311+ 11987310+ 11987301)]

GDJ = 1 = [11987311

(11987311+ 11987310+ 11987301)]

GDSM = 1 = [(11987311+ 11987300)

(11987311+ 11987310+ 11987301+ 11987300)]

GDMR = [(11987310+ 11987301)

2119873]

05

(1)

where 11987311


















119868 = minussum119901119894log2119901119894












119890= 1sum119901

2

119894

1199012









119899

1198941199012

119894




Table 1 Continued









10 Conclusion



Abbreviations


polymorphisms


Table2Listof


measurin

gmolecular

(genetic)d

iversity

Analytic

altools

Datatype

Mainfeatures

Source

links

Reference

Arle

quin

RFLP

sDNAsequ

encesSSRdataallele

frequ

enciesorstand

ardmultilocus

geno

types

(i)Estim


encies

(ii)T

estsof

departurefrom

linkage

equilib

riumdeparture

from

selective


icequilib

rium

(iii)Estim

ationor

parametersfrom

pastpo

pulationexpansions

(iv)Th

orou

ghanalyses

ofpo

pulatio

nsubd

ivision

underthe

AMOVA

framew

orkandso

forth

(v)C

urrent

version

Arle

quin

ver3

513

httpcm

pgunibech

soft

warearlequ

in3

Schn

eidere

tal[87]

Excoffier

etal[88]

DnaSP

DNAsequ

ence

data

(i)Estim

atingseveralm

easureso

fDNAsequ

ence

varia

tionwith

inand

betweenpo

pulatio

ns(in

noncod

ingsyno

nymou

sor

nonsyn

onym

oussiteso

rin

vario

ussortso

fcod

onpo

sitions)as

wellas

linkage

disequ

ilibrium

recombinatio

ngene

flow

andgene

conversio

nparameters

(ii)D

naSP

canalso

carryou

tseveraltests

ofneutralityHud

sonetal[89]

Tajim

a[90]McD

onaldandKr

eitm

an[46]FuandLi

[91]and

Fu[92]

tests

Ad

ditio

nallyD

naSP

canestim

atethe

confi

denceintervalsof

some

test-

statistic

sbythec

oalescentand

soforth

(iii)Cu

rrentversio

nDnaSP

v510

01

httpwwwub

edudn

asp

JRo

zasa

ndR

Rozas

[93ndash95]

LibradoandRo

zas[96]

PowerMarker

SSR

SNPandRF

LPdata

(i)Com


tistic

sfor

each


allele

numbermissingprop

ortio

nheterozygositygene

diversitypolym

orph

isminform

ationcontent(PIC)

and

stepw

isepatte

rnsfor

microsatellitedata

(ii)P

owerMarkerisa

lsoused

tocompu

teallelefre

quencygenotypefrequ

ency

haplotypefrequ

ency

foru

nrelated

individu

alsHardy-W

einb

ergequilib

rium

pairw

iselin

kage

disequ

ilibriumm

ultilocus

linkage

disequ

ilibriumcon

sensus

treespop

ulationstr

uctureM

antelrsquostest

triang

leplottin

gandvisualizationof

linkage

disequ

ilibrium

results

(iii)Cu

rrentversio

nPo

werMarkerV

325

httpstatgenncsued

upo

wermarker

LiuandMuse[97]

DARw

inSing

ledata(fo

rhaploidsho

mozygote

diploidsand

dominantm

arkers)allelic

dataand

sequ

ence

data

(i)Mostw

idely

used

forv

arious

dissim

ilarityanddista

ncee

stimations

for

different

datatreec

onstr

uctio

nmetho

dsinclu

ding

hierarchicaltre

eswith

vario


(weightedor

unweighted)N

eighbo

r-Joiningtre

e(w

eightedor

unweighted)Scoresm

etho

dandprincipalcoo

rdinatea

nalysis

andso

forth

(ii)C

urrent

version

DARw

inv5015

6

httpdarw

inciradfrdarwin

Perriera

ndJacquemou

d-Collet[98]

NTS

YSpc

Sing

ledata(fo

rhaploidsho

mozygote

diploidsand

dominantm

arkers)allelic

dataand

sequ

ence

data

(i)Usedforc

luste

ringanalysis

ordinatio

nanalysis

principalcom

ponent

analysis

principalcoo

rdinatea

nalysisscalin

ganalysis

andcomparis

onof

two

matric

es(M


ndso

forth)

(ii)C

urrent

version

NTS

YSpc

version22

httpwwwexetersoftw

arec

omcatntsy

spcntsyspch

tml

Rohlf[100]

MEG

ADNAsequ

enceprotein

sequ

ence

evolutionary

dista

nceor

phylogenetictre

edata

(i)Molecular

evolutionary

geneticsa

nalysis

(MEG

A)ism

ostw

idely

used

for

aligning

sequ

encesestim

atingevolutionary

dista

ncesbuildingtre

efrom

sequ

ence

datatestin

gtre

ereliabilityand

soforth

(ii)C

urrent

version

MEG

A6

httpwwwmegasoft

waren

etKu

mar

etal[101ndash103]

Tamurae

tal[104

]

PAUP

Molecular

sequ

encesmorph

ologicaldata

andotherd

atatypes

(i)Usedforinferrin

gandinterpretin

gph

ylogenetictre

esusingparsim

ony

dista

ncem

atrix

invariantsmaxim

umlik

eliho

odmetho

dsand

manyindices

andsta

tistic

alanalyses

(ii)C

urrent

version

PAUPversion40

httppaup

csitfsuedu

Swoff

ord[105]


Table2Con

tinued

Analytic

altools

Datatype

Mainfeatures

Source

links

Reference

STRU

CTURE

Alltypeso

fmarkersinclu

ding

mostly

used

markerslik

eSSR

sSN

PsR

FLPsdArTand

soforth

(i)Afre

eprogram

toinvestigatep


desinferrin

gthe

presence

ofdistinctp

opulationsassigning

individu

alstopo

pulationsstudying

hybrid

zonesidentifying

migrantsa

ndadmixed

individu

alsandestim

ating

popu

latio

nallelefrequ

encies

insituatio

nswhere

manyindividu

alsa

remigrantso

radm

ixed

(ii)C

urrent

version

STRU

CTURE

234


usoftw

arestructure2

2html

Pritchard

etal[106]

Falush

etal[107]

Hub

iszetal[108]

fastS

TRUCT

URE

SNP

(i)Analgorithm

forinferrin

gpo

pulatio

nstr

ucture

from

largeS

NPgeno

type

data

(ii)Itisb

ased

onav

ariatio

nalB

ayesianfram

eworkforp

osterio

rinference

and

iswrittenin

Python

2x

httprajanilgith

ubio

fastS

tructure

Rajetal[109]

ADMIXTU

RESN

P

(i)ADMIXTU

REisap

rogram

form

axim

umlik

eliho

odestim

ationof

individu

alancestr

iesfrom

multilocus

SNPgeno

type

datasets

(ii)Itu

sesthe

sames

tatistic

almod

elas

STRU

CTURE

butcalculates

estim

ates

muchmorer

apidlyusingafastn

umericalop

timizationalgorithm

(iii)Cu

rrentversio

nADMIXTU

RE12

3

httpsw

wwgenetic

sucla

edusoftw

areadmixture

Alexand

eretal[110

]

fineSTR

UCT

URE

Sequ

encing

data

(i)Afastandpo

werfulalgorith

mforidentify

ingpo

pulatio

nstr

ucture

using

denses

equencingdata

(ii)C

urrent

version

FineStructure0

02

httppaintm

ychrom

osom

esco

m

Lawsonetal[111]

POPG

ENE

Use

thed

ominantcodo

minantand

quantitatived

atafor

popu

latio

ngenetic

analysis

(i)Usedto

calculateg

enea

ndgeno

type

frequ

encyallelenu

mbereffectiv

eallelenu

mberpo

lymorph

iclocigened

iversityob

served

andexpected


nindexho


flow

genetic


test

andso

forth

(ii)C

urrent

version

POPG

ENEversion13

2

httpsw

wwualbertaca

simfyehpop

genehtm

lFrancise

tal[112]

GEN

EPOP

Haploid

ordiploiddata

(i)Usedto

compu

teexacttestsor

theiru

nbiasedestim

ationfor

Hardy-W

einb

ergequilib

riumpop


nandtwo-locus

geno

typicd

isequ

ilibrium

(ii)Itcon

vertsthe

inpu

tGEN

EPOPfiletoform

atsu

sedby

otherp

opular

programslik

eBIO

SYS[113]DIPLO

IDL[114]andLINKD

OS[115]thereby

allowingcommun

icationbetweenthem

(iii)Cu

rrentversio

nGEN

EPOP42

httpgenepo

pcurtined

uau

Raym

ondandRo

usset[116

]

GenAIEx

Cod


genetic

dataItaccom

mod

ates

thefullrange

ofgenetic


ding

allozymesSSR

sSN

PsA

FLPandother

multilocus

markersasw

ellasD

NA

sequ

ences

(i)GenAIExruns

with

inMicrosoftEx

celenablingpo

pulationgenetic

analysis

ofcodo


dataU

sedto

compu

teallele

frequ

ency-based

analyses

inclu

ding


sNeirsquosg

enetic

dista

ncepo

pulationassig

nmentprob

abilitie

sofidentityand

pairw

iserelatedn

ess

(ii)U

sedforc

alculatin

ggenetic

distance

matric

esanddistance

based

calculations

inclu

ding

analysisof

molecular

varia

nce(AMOVA

)[117

118]

principalcoo

rdinates

analysis(PCA

)Manteltests[119]2D

spatial


follo

wingSm

ouse


[121]Dou

blee

tal[122]

(iii)Cu

rrentversio

nGenAIEx65

httpbiolog

y-assetsanueduauG

enAlExWelcomeh

tml

Peakalland

Smou

se[123]






References








































































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology











119868 = minussum119901119894log2119901119894












119890= 1sum119901

2

119894

1199012









119899

1198941199012

119894




Table 1 Continued









10 Conclusion



Abbreviations


polymorphisms


Table2Listof


measurin

gmolecular

(genetic)d

iversity

Analytic

altools

Datatype

Mainfeatures

Source

links

Reference

Arle

quin

RFLP

sDNAsequ

encesSSRdataallele

frequ

enciesorstand

ardmultilocus

geno

types

(i)Estim


encies

(ii)T

estsof

departurefrom

linkage

equilib

riumdeparture

from

selective


icequilib

rium

(iii)Estim

ationor

parametersfrom

pastpo

pulationexpansions

(iv)Th

orou

ghanalyses

ofpo

pulatio

nsubd

ivision

underthe

AMOVA

framew

orkandso

forth

(v)C

urrent

version

Arle

quin

ver3

513

httpcm

pgunibech

soft

warearlequ

in3

Schn

eidere

tal[87]

Excoffier

etal[88]

DnaSP

DNAsequ

ence

data

(i)Estim

atingseveralm

easureso

fDNAsequ

ence

varia

tionwith

inand

betweenpo

pulatio

ns(in

noncod

ingsyno

nymou

sor

nonsyn

onym

oussiteso

rin

vario

ussortso

fcod

onpo

sitions)as

wellas

linkage

disequ

ilibrium

recombinatio

ngene

flow

andgene

conversio

nparameters

(ii)D

naSP

canalso

carryou

tseveraltests

ofneutralityHud

sonetal[89]

Tajim

a[90]McD

onaldandKr

eitm

an[46]FuandLi

[91]and

Fu[92]

tests

Ad

ditio

nallyD

naSP

canestim

atethe

confi

denceintervalsof

some

test-

statistic

sbythec

oalescentand

soforth

(iii)Cu

rrentversio

nDnaSP

v510

01

httpwwwub

edudn

asp

JRo

zasa

ndR

Rozas

[93ndash95]

LibradoandRo

zas[96]

PowerMarker

SSR

SNPandRF

LPdata

(i)Com


tistic

sfor

each


allele

numbermissingprop

ortio

nheterozygositygene

diversitypolym

orph

isminform

ationcontent(PIC)

and

stepw

isepatte

rnsfor

microsatellitedata

(ii)P

owerMarkerisa

lsoused

tocompu

teallelefre

quencygenotypefrequ

ency

haplotypefrequ

ency

foru

nrelated

individu

alsHardy-W

einb

ergequilib

rium

pairw

iselin

kage

disequ

ilibriumm

ultilocus

linkage

disequ

ilibriumcon

sensus

treespop

ulationstr

uctureM

antelrsquostest

triang

leplottin

gandvisualizationof

linkage

disequ

ilibrium

results

(iii)Cu

rrentversio

nPo

werMarkerV

325

httpstatgenncsued

upo

wermarker

LiuandMuse[97]

DARw

inSing

ledata(fo

rhaploidsho

mozygote

diploidsand

dominantm

arkers)allelic

dataand

sequ

ence

data

(i)Mostw

idely

used

forv

arious

dissim

ilarityanddista

ncee

stimations

for

different

datatreec

onstr

uctio

nmetho

dsinclu

ding

hierarchicaltre

eswith

vario


(weightedor

unweighted)N

eighbo

r-Joiningtre

e(w

eightedor

unweighted)Scoresm

etho

dandprincipalcoo

rdinatea

nalysis

andso

forth

(ii)C

urrent

version

DARw

inv5015

6

httpdarw

inciradfrdarwin

Perriera

ndJacquemou

d-Collet[98]

NTS

YSpc

Sing

ledata(fo

rhaploidsho

mozygote

diploidsand

dominantm

arkers)allelic

dataand

sequ

ence

data

(i)Usedforc

luste

ringanalysis

ordinatio

nanalysis

principalcom

ponent

analysis

principalcoo

rdinatea

nalysisscalin

ganalysis

andcomparis

onof

two

matric

es(M


ndso

forth)

(ii)C

urrent

version

NTS

YSpc

version22

httpwwwexetersoftw

arec

omcatntsy

spcntsyspch

tml

Rohlf[100]

MEG

ADNAsequ

enceprotein

sequ

ence

evolutionary

dista

nceor

phylogenetictre

edata

(i)Molecular

evolutionary

geneticsa

nalysis

(MEG

A)ism

ostw

idely

used

for

aligning

sequ

encesestim

atingevolutionary

dista

ncesbuildingtre

efrom

sequ

ence

datatestin

gtre

ereliabilityand

soforth

(ii)C

urrent

version

MEG

A6

httpwwwmegasoft

waren

etKu

mar

etal[101ndash103]

Tamurae

tal[104

]

PAUP

Molecular

sequ

encesmorph

ologicaldata

andotherd

atatypes

(i)Usedforinferrin

gandinterpretin

gph

ylogenetictre

esusingparsim

ony

dista

ncem

atrix

invariantsmaxim

umlik

eliho

odmetho

dsand

manyindices

andsta

tistic

alanalyses

(ii)C

urrent

version

PAUPversion40

httppaup

csitfsuedu

Swoff

ord[105]


Table2Con

tinued

Analytic

altools

Datatype

Mainfeatures

Source

links

Reference

STRU

CTURE

Alltypeso

fmarkersinclu

ding

mostly

used

markerslik

eSSR

sSN

PsR

FLPsdArTand

soforth

(i)Afre

eprogram

toinvestigatep


desinferrin

gthe

presence

ofdistinctp

opulationsassigning

individu

alstopo

pulationsstudying

hybrid

zonesidentifying

migrantsa

ndadmixed

individu

alsandestim

ating

popu

latio

nallelefrequ

encies

insituatio

nswhere

manyindividu

alsa

remigrantso

radm

ixed

(ii)C

urrent

version

STRU

CTURE

234


usoftw

arestructure2

2html

Pritchard

etal[106]

Falush

etal[107]

Hub

iszetal[108]

fastS

TRUCT

URE

SNP

(i)Analgorithm

forinferrin

gpo

pulatio

nstr

ucture

from

largeS

NPgeno

type

data

(ii)Itisb

ased

onav

ariatio

nalB

ayesianfram

eworkforp

osterio

rinference

and

iswrittenin

Python

2x

httprajanilgith

ubio

fastS

tructure

Rajetal[109]

ADMIXTU

RESN

P

(i)ADMIXTU

REisap

rogram

form

axim

umlik

eliho

odestim

ationof

individu

alancestr

iesfrom

multilocus

SNPgeno

type

datasets

(ii)Itu

sesthe

sames

tatistic

almod

elas

STRU

CTURE

butcalculates

estim

ates

muchmorer

apidlyusingafastn

umericalop

timizationalgorithm

(iii)Cu

rrentversio

nADMIXTU

RE12

3

httpsw

wwgenetic

sucla

edusoftw

areadmixture

Alexand

eretal[110

]

fineSTR

UCT

URE

Sequ

encing

data

(i)Afastandpo

werfulalgorith

mforidentify

ingpo

pulatio

nstr

ucture

using

denses

equencingdata

(ii)C

urrent

version

FineStructure0

02

httppaintm

ychrom

osom

esco

m

Lawsonetal[111]

POPG

ENE

Use

thed

ominantcodo

minantand

quantitatived

atafor

popu

latio

ngenetic

analysis

(i)Usedto

calculateg

enea

ndgeno

type

frequ

encyallelenu

mbereffectiv

eallelenu

mberpo

lymorph

iclocigened

iversityob

served

andexpected


nindexho


flow

genetic


test

andso

forth

(ii)C

urrent

version

POPG

ENEversion13

2

httpsw

wwualbertaca

simfyehpop

genehtm

lFrancise

tal[112]

GEN

EPOP

Haploid

ordiploiddata

(i)Usedto

compu

teexacttestsor

theiru

nbiasedestim

ationfor

Hardy-W

einb

ergequilib

riumpop


nandtwo-locus

geno

typicd

isequ

ilibrium

(ii)Itcon

vertsthe

inpu

tGEN

EPOPfiletoform

atsu

sedby

otherp

opular

programslik

eBIO

SYS[113]DIPLO

IDL[114]andLINKD

OS[115]thereby

allowingcommun

icationbetweenthem

(iii)Cu

rrentversio

nGEN

EPOP42

httpgenepo

pcurtined

uau

Raym

ondandRo

usset[116

]

GenAIEx

Cod


genetic

dataItaccom

mod

ates

thefullrange

ofgenetic


ding

allozymesSSR

sSN

PsA

FLPandother

multilocus

markersasw

ellasD

NA

sequ

ences

(i)GenAIExruns

with

inMicrosoftEx

celenablingpo

pulationgenetic

analysis

ofcodo


dataU

sedto

compu

teallele

frequ

ency-based

analyses

inclu

ding


sNeirsquosg

enetic

dista

ncepo

pulationassig

nmentprob

abilitie

sofidentityand

pairw

iserelatedn

ess

(ii)U

sedforc

alculatin

ggenetic

distance

matric

esanddistance

based

calculations

inclu

ding

analysisof

molecular

varia

nce(AMOVA

)[117

118]

principalcoo

rdinates

analysis(PCA

)Manteltests[119]2D

spatial


follo

wingSm

ouse


[121]Dou

blee

tal[122]

(iii)Cu

rrentversio

nGenAIEx65

httpbiolog

y-assetsanueduauG

enAlExWelcomeh

tml

Peakalland

Smou

se[123]






References








































































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Table 1 Continued









10 Conclusion



Abbreviations


polymorphisms


Table2Listof


measurin

gmolecular

(genetic)d

iversity

Analytic

altools

Datatype

Mainfeatures

Source

links

Reference

Arle

quin

RFLP

sDNAsequ

encesSSRdataallele

frequ

enciesorstand

ardmultilocus

geno

types

(i)Estim


encies

(ii)T

estsof

departurefrom

linkage

equilib

riumdeparture

from

selective


icequilib

rium

(iii)Estim

ationor

parametersfrom

pastpo

pulationexpansions

(iv)Th

orou

ghanalyses

ofpo

pulatio

nsubd

ivision

underthe

AMOVA

framew

orkandso

forth

(v)C

urrent

version

Arle

quin

ver3

513

httpcm

pgunibech

soft

warearlequ

in3

Schn

eidere

tal[87]

Excoffier

etal[88]

DnaSP

DNAsequ

ence

data

(i)Estim

atingseveralm

easureso

fDNAsequ

ence

varia

tionwith

inand

betweenpo

pulatio

ns(in

noncod

ingsyno

nymou

sor

nonsyn

onym

oussiteso

rin

vario

ussortso

fcod

onpo

sitions)as

wellas

linkage

disequ

ilibrium

recombinatio

ngene

flow

andgene

conversio

nparameters

(ii)D

naSP

canalso

carryou

tseveraltests

ofneutralityHud

sonetal[89]

Tajim

a[90]McD

onaldandKr

eitm

an[46]FuandLi

[91]and

Fu[92]

tests

Ad

ditio

nallyD

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canestim

atethe

confi

denceintervalsof

some

test-

statistic

sbythec

oalescentand

soforth

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rrentversio

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v510

01

httpwwwub

edudn

asp

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zasa

ndR

Rozas

[93ndash95]

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zas[96]

PowerMarker

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LPdata

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tistic

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ortio

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orph

isminform

ationcontent(PIC)

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owerMarkerisa

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quencygenotypefrequ

ency

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nrelated

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alsHardy-W

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ilibriumm

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ulationstr

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antelrsquostest

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leplottin

gandvisualizationof

linkage

disequ

ilibrium

results

(iii)Cu

rrentversio

nPo

werMarkerV

325

httpstatgenncsued

upo

wermarker

LiuandMuse[97]

DARw

inSing

ledata(fo

rhaploidsho

mozygote

diploidsand

dominantm

arkers)allelic

dataand

sequ

ence

data

(i)Mostw

idely

used

forv

arious

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onstr

uctio

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etho

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rdinatea

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andso

forth

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urrent

version

DARw

inv5015

6

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inciradfrdarwin

Perriera

ndJacquemou

d-Collet[98]

NTS

YSpc

Sing

ledata(fo

rhaploidsho

mozygote

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dominantm

arkers)allelic

dataand

sequ

ence

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ringanalysis

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urrent

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version22

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omcatntsy

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tml

Rohlf[100]

MEG

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enceprotein

sequ

ence

evolutionary

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nceor

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edata

(i)Molecular

evolutionary

geneticsa

nalysis

(MEG

A)ism

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idely

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encesestim

atingevolutionary

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ncesbuildingtre

efrom

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ence

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gtre

ereliabilityand

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urrent

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MEG

A6

httpwwwmegasoft

waren

etKu

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etal[101ndash103]

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tal[104

]

PAUP

Molecular

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atrix

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odmetho

dsand

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urrent

version

PAUPversion40

httppaup

csitfsuedu

Swoff

ord[105]


Table2Con

tinued

Analytic

altools

Datatype

Mainfeatures

Source

links

Reference

STRU

CTURE

Alltypeso

fmarkersinclu

ding

mostly

used

markerslik

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sSN

PsR

FLPsdArTand

soforth

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eprogram

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desinferrin

gthe

presence

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opulationsassigning

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zonesidentifying

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individu

alsandestim

ating

popu

latio

nallelefrequ

encies

insituatio

nswhere

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alsa

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radm

ixed

(ii)C

urrent

version

STRU

CTURE

234


usoftw

arestructure2

2html

Pritchard

etal[106]

Falush

etal[107]

Hub

iszetal[108]

fastS

TRUCT

URE

SNP

(i)Analgorithm

forinferrin

gpo

pulatio

nstr

ucture

from

largeS

NPgeno

type

data

(ii)Itisb

ased

onav

ariatio

nalB

ayesianfram

eworkforp

osterio

rinference

and

iswrittenin

Python

2x

httprajanilgith

ubio

fastS

tructure

Rajetal[109]

ADMIXTU

RESN

P

(i)ADMIXTU

REisap

rogram

form

axim

umlik

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odestim

ationof

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iesfrom

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type

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elas

STRU

CTURE

butcalculates

estim

ates

muchmorer

apidlyusingafastn

umericalop

timizationalgorithm

(iii)Cu

rrentversio

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RE12

3

httpsw

wwgenetic

sucla

edusoftw

areadmixture

Alexand

eretal[110

]

fineSTR

UCT

URE

Sequ

encing

data

(i)Afastandpo

werfulalgorith

mforidentify

ingpo

pulatio

nstr

ucture

using

denses

equencingdata

(ii)C

urrent

version

FineStructure0

02

httppaintm

ychrom

osom

esco

m

Lawsonetal[111]

POPG

ENE

Use

thed

ominantcodo

minantand

quantitatived

atafor

popu

latio

ngenetic

analysis

(i)Usedto

calculateg

enea

ndgeno

type

frequ

encyallelenu

mbereffectiv

eallelenu

mberpo

lymorph

iclocigened

iversityob

served

andexpected


nindexho


flow

genetic


test

andso

forth

(ii)C

urrent

version

POPG

ENEversion13

2

httpsw

wwualbertaca

simfyehpop

genehtm

lFrancise

tal[112]

GEN

EPOP

Haploid

ordiploiddata

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teexacttestsor

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nbiasedestim

ationfor

Hardy-W

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isequ

ilibrium

(ii)Itcon

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OS[115]thereby

allowingcommun

icationbetweenthem

(iii)Cu

rrentversio

nGEN

EPOP42

httpgenepo

pcurtined

uau

Raym

ondandRo

usset[116

]

GenAIEx

Cod


genetic

dataItaccom

mod

ates

thefullrange

ofgenetic


ding

allozymesSSR

sSN

PsA

FLPandother

multilocus

markersasw

ellasD

NA

sequ

ences

(i)GenAIExruns

with

inMicrosoftEx

celenablingpo

pulationgenetic

analysis

ofcodo


dataU

sedto

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teallele

frequ

ency-based

analyses

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ding


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enetic

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ncepo

pulationassig

nmentprob

abilitie

sofidentityand

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iserelatedn

ess

(ii)U

sedforc

alculatin

ggenetic

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esanddistance

based

calculations

inclu

ding

analysisof

molecular

varia

nce(AMOVA

)[117

118]

principalcoo

rdinates

analysis(PCA

)Manteltests[119]2D

spatial


follo

wingSm

ouse


[121]Dou

blee

tal[122]

(iii)Cu

rrentversio

nGenAIEx65

httpbiolog

y-assetsanueduauG

enAlExWelcomeh

tml

Peakalland

Smou

se[123]






References








































































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Table2Listof


measurin

gmolecular

(genetic)d

iversity

Analytic

altools

Datatype

Mainfeatures

Source

links

Reference

Arle

quin

RFLP

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encesSSRdataallele

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enciesorstand

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types

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encies

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estsof

departurefrom

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rium

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ationor

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orou

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ivision

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orkandso

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urrent

version

Arle

quin

ver3

513

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pgunibech

soft

warearlequ

in3

Schn

eidere

tal[87]

Excoffier

etal[88]

DnaSP

DNAsequ

ence

data

(i)Estim

atingseveralm

easureso

fDNAsequ

ence

varia

tionwith

inand

betweenpo

pulatio

ns(in

noncod

ingsyno

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sor

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onym

oussiteso

rin

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ussortso

fcod

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ilibrium

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nparameters

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tseveraltests

ofneutralityHud

sonetal[89]

Tajim

a[90]McD

onaldandKr

eitm

an[46]FuandLi

[91]and

Fu[92]

tests

Ad

ditio

nallyD

naSP

canestim

atethe

confi

denceintervalsof

some

test-

statistic

sbythec

oalescentand

soforth

(iii)Cu

rrentversio

nDnaSP

v510

01

httpwwwub

edudn

asp

JRo

zasa

ndR

Rozas

[93ndash95]

LibradoandRo

zas[96]

PowerMarker

SSR

SNPandRF

LPdata

(i)Com


tistic

sfor

each


allele

numbermissingprop

ortio

nheterozygositygene

diversitypolym

orph

isminform

ationcontent(PIC)

and

stepw

isepatte

rnsfor

microsatellitedata

(ii)P

owerMarkerisa

lsoused

tocompu

teallelefre

quencygenotypefrequ

ency

haplotypefrequ

ency

foru

nrelated

individu

alsHardy-W

einb

ergequilib

rium

pairw

iselin

kage

disequ

ilibriumm

ultilocus

linkage

disequ

ilibriumcon

sensus

treespop

ulationstr

uctureM

antelrsquostest

triang

leplottin

gandvisualizationof

linkage

disequ

ilibrium

results

(iii)Cu

rrentversio

nPo

werMarkerV

325

httpstatgenncsued

upo

wermarker

LiuandMuse[97]

DARw

inSing

ledata(fo

rhaploidsho

mozygote

diploidsand

dominantm

arkers)allelic

dataand

sequ

ence

data

(i)Mostw

idely

used

forv

arious

dissim

ilarityanddista

ncee

stimations

for

different

datatreec

onstr

uctio

nmetho

dsinclu

ding

hierarchicaltre

eswith

vario


(weightedor

unweighted)N

eighbo

r-Joiningtre

e(w

eightedor

unweighted)Scoresm

etho

dandprincipalcoo

rdinatea

nalysis

andso

forth

(ii)C

urrent

version

DARw

inv5015

6

httpdarw

inciradfrdarwin

Perriera

ndJacquemou

d-Collet[98]

NTS

YSpc

Sing

ledata(fo

rhaploidsho

mozygote

diploidsand

dominantm

arkers)allelic

dataand

sequ

ence

data

(i)Usedforc

luste

ringanalysis

ordinatio

nanalysis

principalcom

ponent

analysis

principalcoo

rdinatea

nalysisscalin

ganalysis

andcomparis

onof

two

matric

es(M


ndso

forth)

(ii)C

urrent

version

NTS

YSpc

version22

httpwwwexetersoftw

arec

omcatntsy

spcntsyspch

tml

Rohlf[100]

MEG

ADNAsequ

enceprotein

sequ

ence

evolutionary

dista

nceor

phylogenetictre

edata

(i)Molecular

evolutionary

geneticsa

nalysis

(MEG

A)ism

ostw

idely

used

for

aligning

sequ

encesestim

atingevolutionary

dista

ncesbuildingtre

efrom

sequ

ence

datatestin

gtre

ereliabilityand

soforth

(ii)C

urrent

version

MEG

A6

httpwwwmegasoft

waren

etKu

mar

etal[101ndash103]

Tamurae

tal[104

]

PAUP

Molecular

sequ

encesmorph

ologicaldata

andotherd

atatypes

(i)Usedforinferrin

gandinterpretin

gph

ylogenetictre

esusingparsim

ony

dista

ncem

atrix

invariantsmaxim

umlik

eliho

odmetho

dsand

manyindices

andsta

tistic

alanalyses

(ii)C

urrent

version

PAUPversion40

httppaup

csitfsuedu

Swoff

ord[105]


Table2Con

tinued

Analytic

altools

Datatype

Mainfeatures

Source

links

Reference

STRU

CTURE

Alltypeso

fmarkersinclu

ding

mostly

used

markerslik

eSSR

sSN

PsR

FLPsdArTand

soforth

(i)Afre

eprogram

toinvestigatep


desinferrin

gthe

presence

ofdistinctp

opulationsassigning

individu

alstopo

pulationsstudying

hybrid

zonesidentifying

migrantsa

ndadmixed

individu

alsandestim

ating

popu

latio

nallelefrequ

encies

insituatio

nswhere

manyindividu

alsa

remigrantso

radm

ixed

(ii)C

urrent

version

STRU

CTURE

234


usoftw

arestructure2

2html

Pritchard

etal[106]

Falush

etal[107]

Hub

iszetal[108]

fastS

TRUCT

URE

SNP

(i)Analgorithm

forinferrin

gpo

pulatio

nstr

ucture

from

largeS

NPgeno

type

data

(ii)Itisb

ased

onav

ariatio

nalB

ayesianfram

eworkforp

osterio

rinference

and

iswrittenin

Python

2x

httprajanilgith

ubio

fastS

tructure

Rajetal[109]

ADMIXTU

RESN

P

(i)ADMIXTU

REisap

rogram

form

axim

umlik

eliho

odestim

ationof

individu

alancestr

iesfrom

multilocus

SNPgeno

type

datasets

(ii)Itu

sesthe

sames

tatistic

almod

elas

STRU

CTURE

butcalculates

estim

ates

muchmorer

apidlyusingafastn

umericalop

timizationalgorithm

(iii)Cu

rrentversio

nADMIXTU

RE12

3

httpsw

wwgenetic

sucla

edusoftw

areadmixture

Alexand

eretal[110

]

fineSTR

UCT

URE

Sequ

encing

data

(i)Afastandpo

werfulalgorith

mforidentify

ingpo

pulatio

nstr

ucture

using

denses

equencingdata

(ii)C

urrent

version

FineStructure0

02

httppaintm

ychrom

osom

esco

m

Lawsonetal[111]

POPG

ENE

Use

thed

ominantcodo

minantand

quantitatived

atafor

popu

latio

ngenetic

analysis

(i)Usedto

calculateg

enea

ndgeno

type

frequ

encyallelenu

mbereffectiv

eallelenu

mberpo

lymorph

iclocigened

iversityob

served

andexpected


nindexho


flow

genetic


test

andso

forth

(ii)C

urrent

version

POPG

ENEversion13

2

httpsw

wwualbertaca

simfyehpop

genehtm

lFrancise

tal[112]

GEN

EPOP

Haploid

ordiploiddata

(i)Usedto

compu

teexacttestsor

theiru

nbiasedestim

ationfor

Hardy-W

einb

ergequilib

riumpop


nandtwo-locus

geno

typicd

isequ

ilibrium

(ii)Itcon

vertsthe

inpu

tGEN

EPOPfiletoform

atsu

sedby

otherp

opular

programslik

eBIO

SYS[113]DIPLO

IDL[114]andLINKD

OS[115]thereby

allowingcommun

icationbetweenthem

(iii)Cu

rrentversio

nGEN

EPOP42

httpgenepo

pcurtined

uau

Raym

ondandRo

usset[116

]

GenAIEx

Cod


genetic

dataItaccom

mod

ates

thefullrange

ofgenetic


ding

allozymesSSR

sSN

PsA

FLPandother

multilocus

markersasw

ellasD

NA

sequ

ences

(i)GenAIExruns

with

inMicrosoftEx

celenablingpo

pulationgenetic

analysis

ofcodo


dataU

sedto

compu

teallele

frequ

ency-based

analyses

inclu

ding


sNeirsquosg

enetic

dista

ncepo

pulationassig

nmentprob

abilitie

sofidentityand

pairw

iserelatedn

ess

(ii)U

sedforc

alculatin

ggenetic

distance

matric

esanddistance

based

calculations

inclu

ding

analysisof

molecular

varia

nce(AMOVA

)[117

118]

principalcoo

rdinates

analysis(PCA

)Manteltests[119]2D

spatial


follo

wingSm

ouse


[121]Dou

blee

tal[122]

(iii)Cu

rrentversio

nGenAIEx65

httpbiolog

y-assetsanueduauG

enAlExWelcomeh

tml

Peakalland

Smou

se[123]






References








































































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Table2Con

tinued

Analytic

altools

Datatype

Mainfeatures

Source

links

Reference

STRU

CTURE

Alltypeso

fmarkersinclu

ding

mostly

used

markerslik

eSSR

sSN

PsR

FLPsdArTand

soforth

(i)Afre

eprogram

toinvestigatep


desinferrin

gthe

presence

ofdistinctp

opulationsassigning

individu

alstopo

pulationsstudying

hybrid

zonesidentifying

migrantsa

ndadmixed

individu

alsandestim

ating

popu

latio

nallelefrequ

encies

insituatio

nswhere

manyindividu

alsa

remigrantso

radm

ixed

(ii)C

urrent

version

STRU

CTURE

234


usoftw

arestructure2

2html

Pritchard

etal[106]

Falush

etal[107]

Hub

iszetal[108]

fastS

TRUCT

URE

SNP

(i)Analgorithm

forinferrin

gpo

pulatio

nstr

ucture

from

largeS

NPgeno

type

data

(ii)Itisb

ased

onav

ariatio

nalB

ayesianfram

eworkforp

osterio

rinference

and

iswrittenin

Python

2x

httprajanilgith

ubio

fastS

tructure

Rajetal[109]

ADMIXTU

RESN

P

(i)ADMIXTU

REisap

rogram

form

axim

umlik

eliho

odestim

ationof

individu

alancestr

iesfrom

multilocus

SNPgeno

type

datasets

(ii)Itu

sesthe

sames

tatistic

almod

elas

STRU

CTURE

butcalculates

estim

ates

muchmorer

apidlyusingafastn

umericalop

timizationalgorithm

(iii)Cu

rrentversio

nADMIXTU

RE12

3

httpsw

wwgenetic

sucla

edusoftw

areadmixture

Alexand

eretal[110

]

fineSTR

UCT

URE

Sequ

encing

data

(i)Afastandpo

werfulalgorith

mforidentify

ingpo

pulatio

nstr

ucture

using

denses

equencingdata

(ii)C

urrent

version

FineStructure0

02

httppaintm

ychrom

osom

esco

m

Lawsonetal[111]

POPG

ENE

Use

thed

ominantcodo

minantand

quantitatived

atafor

popu

latio

ngenetic

analysis

(i)Usedto

calculateg

enea

ndgeno

type

frequ

encyallelenu

mbereffectiv

eallelenu

mberpo

lymorph

iclocigened

iversityob

served

andexpected


nindexho


flow

genetic


test

andso

forth

(ii)C

urrent

version

POPG

ENEversion13

2

httpsw

wwualbertaca

simfyehpop

genehtm

lFrancise

tal[112]

GEN

EPOP

Haploid

ordiploiddata

(i)Usedto

compu

teexacttestsor

theiru

nbiasedestim

ationfor

Hardy-W

einb

ergequilib

riumpop


nandtwo-locus

geno

typicd

isequ

ilibrium

(ii)Itcon

vertsthe

inpu

tGEN

EPOPfiletoform

atsu

sedby

otherp

opular

programslik

eBIO

SYS[113]DIPLO

IDL[114]andLINKD

OS[115]thereby

allowingcommun

icationbetweenthem

(iii)Cu

rrentversio

nGEN

EPOP42

httpgenepo

pcurtined

uau

Raym

ondandRo

usset[116

]

GenAIEx

Cod


genetic

dataItaccom

mod

ates

thefullrange

ofgenetic


ding

allozymesSSR

sSN

PsA

FLPandother

multilocus

markersasw

ellasD

NA

sequ

ences

(i)GenAIExruns

with

inMicrosoftEx

celenablingpo

pulationgenetic

analysis

ofcodo


dataU

sedto

compu

teallele

frequ

ency-based

analyses

inclu

ding


sNeirsquosg

enetic

dista

ncepo

pulationassig

nmentprob

abilitie

sofidentityand

pairw

iserelatedn

ess

(ii)U

sedforc

alculatin

ggenetic

distance

matric

esanddistance

based

calculations

inclu

ding

analysisof

molecular

varia

nce(AMOVA

)[117

118]

principalcoo

rdinates

analysis(PCA

)Manteltests[119]2D

spatial


follo

wingSm

ouse


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References








































































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






References








































































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology















































































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

Review Article Importance of Genetic Diversity Assessment...

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