research article using genome-wide snp discovery and...

Research ArticleUsing Genome-Wide SNP Discovery and Genotyping toReveal the Main Source of Population Differentiation inNothofagus dombeyi (Mirb) Oerst in Chile

Rodrigo Hasbuacuten1 Jorge Gonzaacutelez1 Carolina Iturra1 Glenda Fuentes2

Diego Alarcoacuten23 and Eduardo Ruiz2

1Departamento de Silvicultura Facultad de Ciencias Forestales Universidad de Concepcion 4070386 Concepcion Chile2Departamento de Botanica Facultad de Ciencias Naturales y Oceanograficas Universidad de Concepcion4070386 Concepcion Chile3Instituto de Ecologıa y Biodiversidad (IEB) Facultad de Ciencias Universidad de Chile 7800003 Santiago Chile

Correspondence should be addressed to Rodrigo Hasbun rodrigohasbunudeccl

Received 29 January 2016 Accepted 23 May 2016

Academic Editor Ettore Randi

Copyright copy 2016 Rodrigo Hasbun et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Within a woody plant species environmental heterogeneity has the potential to influence the distribution of genetic variationamong populations through several evolutionary processes In some species a relationship between environmental characteristicsand the distribution of genotypes can be detected showing the importance of natural selection as themain source of differentiationNothofagus dombeyi (Mirb) Oerst (Nothofagaceae) is an endemic tree species occurring both in Chile and in Argentina temperateforests Postglacial history has been studied with chloroplast DNA and evolutionary forces shaping genetic variation patternshave been analysed with isozymes but fine-scale genetic diversity studies are needed The study of demographic and selectionhistories in Nothofagus dombeyi requires more informative markers such as single nucleotide polymorphisms (SNP) Genotyping-by-Sequencing tools now allow studying thousands of SNP markers at reasonable prices in nonmodel species We investigatedmore than 10K SNP loci for signatures of local adaptation and showed that interrogation of genomic resources can identify shiftsin genetic diversity and putative adaptive signals in this nonmodel woody species

1 Introduction

In population genetics and conservation the big questionis genetic drift instead of natural selection Definitely bothprocesses determine evolution but genetic drift operates ran-domly and depends on effective population size while naturalselection proceeds nonrandomly and relies on environmentalvariablesThe evolution towards hereditary adaptations to thecurrent environment is determined by natural selection andhas a direction genetic drift instead is governed solely bychance Consequently drift acts on alleles which generallyhave no phenotypic effect instead selection favours certainalleles that increase fitness reduce the unfavourable allelesfrequencies and ignore neutral alleles [1] Knowing what themain microevolutionary force is is very relevant for rational

genetic management of threatened species especially forspecies with geographical distribution severely fragmented[2]

Approaches to addressing adaptive variation have beenincorporated into the definition of evolutionary significantunits [3 4] New technologies like Next Generation Sequenc-ing (NGS) and fine-scale GIS coupled with advances incomputer hardware and software in the field of genomics[5ndash7] have allowed the development of new methods forcomprehensive evaluation of adaptive diversity [8 9] Thediscovery and genotyping of massive genetic markers arenow enabled by modern genomic tools at very low costThis makes the study of adaptive genetic loci possible on awide range of species which can facilitate the identificationof key biodiversity areas Kirk and Freeland [10] reviewed

Hindawi Publishing CorporationInternational Journal of GenomicsVolume 2016 Article ID 3654093 10 pageshttpdxdoiorg10115520163654093

2 International Journal of Genomics

some of the applications of neutral versus adaptive markersin molecular ecology discussed some of the advantagesthat can be obtained by supplementing studies of molecularecology with data from nonneutral molecular markers andsummarized new methods that allow generating data fromloci under selection Population genomic analyses requiremultilocus datasets from multiple populations and identifi-cation of nonneutral or adaptive loci by contrasting patternsof population divergence among genetic regions

Studies in nonmodel organisms have shown relativelybroad candidate genomic regions that are under selection butit remains difficult to identify the genes (or the mutations)that are affected by selection Increasing the density ofmarkers in genome scans is paramount to overcome thisproblem and validating signals of selection from particulargenes using multiple methods should also help [4] One ofthemost exciting developments in population genomics is thedevelopment of various reduced-representation protocolscollectively referred to as Genotyping-by-Sequencing (GBS)which allow sequencing of a subset of the genome throughselective amplification of restriction fragments [10]

Nothofagus dombeyi (Mirb) Oerst (Nothofagaceae) isan endemic tree species occurring both in Chile and inArgentina temperate forests with a remarkably broad alti-tudinal and latitudinal distribution across many differentecological gradients in the former [11] The evergreen tree Ndombeyi is a pioneer species and constitutes an importantelement in the dynamics of South American forest Itspostglacial history has been studied with chloroplast DNAand evolutionary forces shaping genetic variation patternshave been analysed with isozymes [12 13] However genome-wide scan methods using thousands of markers to study arepresentative portion of the genome are needed

In this work we assess GBS in the nonmodel woodyspecies N dombeyi to develop high quantity of informativemarkers such as single nucleotide polymorphisms (SNP)Our aim is to determine the contribution of selection andmolecular adaptation to shaping genome-wide variation Weexpect higher genetic population differentiation (for ecolog-ical separated localities) for adaptive SNP than neutral SNPif natural selection is the principal source of differentiationAlternatively if other sources of differentiation (mutationgenetic drift and migration) are relevant they will equallyaffect both types of SNP Knowing the contribution ofselection effect to shaping genome variation patternswill havemany applications for biodiversity conservation especiallyin endangered species because neutral and adaptive geneticdiversity will likely have different impacts on long-termsurvival In fact in most cases only adaptive diversity willallow a population to adapt to changing environmentalconditions

2 Materials and Methods

21 Sampling Design

211 Niche Modelling To consider the remarkably broadaltitudinal and latitudinal distribution of this species across

many different ecological gradients we used the methodproposed by Alarcon and Cavieres [14] to niche modellingof Nothofagus dombeyi in Chile using BIOMOD [15] Eightvariables were selected from the WorldClim global climatedatabase [16] corresponding to the present climate condi-tions with a 30-arc-second grid resolution with the leastcorrelation among them for the studied species range areaFour variables were related to energy constraints (a) BIO2mean diurnal temperature range (b) BIO4 temperatureseasonality (c) BIO5 maximum temperature in the warmestmonth and (d) BIO6 minimum temperature in the coldestmonth Other four variables were related to water availability(e) BIO12 annual precipitation (f) BIO15 precipitationseasonality (g) BIO18 precipitation in the warmest quarterand (h) BIO19 precipitation in the coldest quarter Thenwe projected the current and future distribution (year 2050)considering a conservative future climate projection CSIROB2A 2050 by using the tools of BIOMOD software Furtherwe identified geographical areas with potential habitat losswhich should be identified as high priority in genetic conser-vation programs

212 Ecological Regions or Strata Relatively homogeneousunits in ecological terms (strata) were defined from naturalpopulations of the species associated with the geographi-cal areas projected in its ecological niche modelling TheCalinski-Harabasz criterion which is a pseudo-119865 statistic asin ANOVA was used to assess the best number of strataidentified by 119870-means partitioning [17]

22 DNA Sample and Library Preparation

221 Plant Material and Genomic DNA Isolation Adult treeswith a diameter higher than 50 cm were sampled during thegrowing season of 2013-2014 from twenty-one sites coveringalmost the entire range of the species N dombeyi in ChileOne to four sites were assigned to each stratum according toits superficies and 2 to 9 samples per site were taken (Table 1Supplementary Figure 1 in Supplementary Material availableonline at httpdxdoiorg10115520163654093)

DNA extraction was performed using a Qiagen DNeasyPlant kit (Qiagen Inc USA) Lyophilized leaf tissue (20mg)was ground in a Precellys24 (Precellys USA) homogenizerwith two 1410158401015840 ceramic spheres (MP BIOMEDICALS USA)and AP1 buffer The objective is grinding tissues and lysingcells prior to DNA extraction DNA extraction protocol wasdone following themanufacturer instructions but elutionwasdone with 30 120583L (instead of 100 120583L) to increase the final DNAconcentration in the eluate (gt100 ng120583L) The integrity ofgenomic DNA was evaluated by agarose gel and quantifiedusing a Qubit fluorometer (Invitrogen USA)

222 Library Preparation and High-Throughput SequencingLibrary preparation and high-throughput sequencing wereperformed at University of Wisconsin Biotechnology Cen-ter (DNA Sequencing Facility) The GBS genomic librarypreparation was done following the protocol detailed byElshire et al [18] with the methylation-sensitive restriction

International Journal of Genomics 3

Table 1 Strata code and location geographical coordinates and sample size of the sampled individuals of Nothofagus dombeyi in Chile

Stratum Location (code) Latitude Longitude 119873

1 Altos Lircay (AL) minus35599162 minus71044414 41 Antuco (AN) minus37343457 minus71615626 52 Ralco (RA) minus37925041 minus71575168 62 Termas de Tolhuaca (TT) minus38235047 minus71727552 93 Lago La Paloma (LP) minus45876213 minus72070813 43 El Machi (EM) minus45009553 minus71906872 53 Near Villa Amengual (NVAM) minus45008498 minus71908366 23 Villa Amengual (VAM) minus45007328 minus71911175 54 Nonguen (NO) minus36879745 minus72987923 54 Villa Las Araucarias (VA) minus36879774 minus72987981 64 Caramavida (CA) minus36879774 minus72987981 45 Mariquina (MA) minus39471811 minus73055799 45 Lago Rinihue (LRI) minus39480193 minus73048809 35 Fundo Llancahue (FL) minus39858686 minus73141572 35 Lago Neltume (LN) minus39859282 minus73141650 35 Parque Nacional Villarrica (PNV) minus39341073 minus71972351 55 Loncoche Interior (LI) minus39341820 minus71972232 35 Melipeuco (ME) minus38912949 minus71704088 36 Las Trancas (LT) minus40221101 minus73362430 56 Camino Osorno a Maicolpue (COM) minus40598321 minus73497015 47 Parque Nacional Puyehue (PNP) minus40737218 minus72306050 37 Lago Rupanco (LR) minus40736785 minus72302922 5

enzyme ApeKI and 96 custom barcodes Illumina high-throughput sequencing was conducted on an Illumina HiSeq2000 (Illumina USA) using 100 bp single-end sequencingruns The samples were sequenced across one Illumina laneBase calling was performed in Casava v182 (Illumina USA)

23 Nonreference SNP Calling

231 De Novo Identification of LociAlleles Sequence resultswere analysed and SNP genotypes were assigned using theUNEAK (Universal Network Enabled Analysis Kit) GBSpipeline [19] which is part of the TASSEL 30 [20] bioin-formatic analysis package This pipeline does not dependon a reference sequence which is the actual case for Ndombeyi SNP discovery is performed directly within pairsof matched sequence tags (unique sequence representing agroup of reads) and filtered through network analysis Thenetwork filter trimmed reads to 64 bp to reduce the effectsof error sequencing and enabled efficient storage of data inbit format SNP were assigned with default settings Brieflytags differing by a single nucleotide were retained as SNPand those with a minor allele frequency 005 were removedto minimize the impact of sequencing errors [19] We used aminimum call rate of 0 and additional filters were applied innext steps

232 Post-SNP Calling Filters and Imputation Given that weare trying to find SNP for population genetic analysis we

applied some filters to remove loci and individuals that con-tain very low levels of information prior to further analysisWe applied two functions of TASSEL 52 that removed allSNP (rows) and then samples (columns) containing 90 ormore ldquo119873rdquo values (indicating that neither allele is designated)These 119873s represent individuals where the allele cannot becalled from the sequence readsThis is because either no readis available at this site (for this individual) or the sequencequality is too low to be called

In order to cope with missing data genotype imputationwas used to fill in the missing data and improve the powerof downstream analyses We used LinkImpute implementedin TASSEL 52 a software package based on a 119896-nearestneighbour genotype imputation method LD-119896NNi [21]Thisimputation method was designed specifically for nonmodelorganisms in which genomic resources are poorly developedand marker order is unreliable or unknown

24 Detection of Selection Footprints To identify adaptiveSNP (putative loci under selection) we used LOSITAN [22]LOSITAN is a selection detection workbench based on theFst-outlier methods We used 50000 simulations 099 forconfidence interval false discovery rate of 005 mutationmodel ldquoInfinite Allelesrdquo and the options ldquoNeutral mean Fstrdquoand ldquoForce mean Fstrdquo which iteratively identify and removeFst outliers when calculating the global distribution of FstOur interest is in patterns of adaptation driven by environ-mental gradients therefore we focused on outlier patterns


indicating divergent selection (Fst significantly higher thanneutral expectations)

LOSITAN analyses were complemented with BayeScan21 [23] for estimating the posterior probability that a givenlocus is affected by selection Briefly prior odds of 100 wereused for identifying the top candidates of the selected loci anda total of 50000 reversible-jumpMarkov Chain Monte Carlochains were run with a thinning interval of 10 following 20pilot runs of 5000 iterations each and a burn-in length of50000 Loci were considered outliers with an FDR of 005

To confirm the adaptive SNP detected by previous meth-ods the spatial analysis method (SAM) implemented inthe program Sam120573ada v051 [24] was used We conductedthe analysis using the 10109 SNP detected for N dombeyiin Chile Sam120573ada uses logistic regressions to model theprobability of presence of an allelic variant in a polymorphicmarker given the environmental conditions of the samplinglocations Eight environmental variables previously describedin Section 211 were used (temperature related rarr BIO2BIO4 BIO5 and BIO6 precipitation related rarr BIO12BIO15 BIO18 and BIO19) Regarding genotypes each ofthe states of a given SNP is considered independently asbinary presenceabsence in each sample Our biallelic SNPwere recoded as three distinct genotypes (AA AB and BB)A maximum likelihood approach is used to fit the modelsusing univariate analyses Each model for a given genotypeis compared to a constant model where the probability ofpresence of the genotype is the same at each location Thestatistical significance threshold was set to 1 before applyingBonferroni correction Significance was assessed with loglikelihood ratio (119866) tests [25] selecting lociallele that testedhigher than the 99th percentile of the 119866 score distribution

25 Estimation of Genome-Wide Genetic Variation and Dif-ferentiation Wemade all estimations in parallel with neutralSNP (10109) and adaptive SNP

251 Basic Statistics of Genetic Variation All the results wereobtained using the adegenet [26 27] and hierFstat [28] Rpackages Basic statistics were estimated including observedheterozygosity (Ho) and genetic diversity (Hs) within pop-ulation Also overall gene diversity (Ht) and corrected Ht(Htp) gene diversity among samples (Dst) and correctedDst (Dstp) were estimated Fst and corrected Fst (Fstp) wereassessed as well as Fis followingNei [29] per overall loci Desta measure of population differentiation as defined by Jost[30] was also calculatedThe degree of genetic differentiationamong populations is expected to be low for neutral SNP buthighly divergent in SNP subject to directional selection

252 Population Structure To describe the genetic biodiver-sity of a species more important than diversity among indi-viduals is the diversity between groups of individuals Firstwe analysed individual data to identify populations or morelarge genetic clusters and then we described these clustersby adegenet R package To get a simplified picture of thegenetic diversity observed among individuals or populationswe used Principal Component Analysis (PCA) Discriminant

Analysis of Principal Components (DAPC) function wasused to describe the relationships between these clustersThemain results of DAPC were DAPC scatterplots

For each pair of strata we computed pairwise Fst valueswith hierFstat R package Principal Coordinates Analysis(PCoA) on Fst values was performed to detect major geneticclusters (119870) at individual level

253 Detecting Locus Contributions In DAPC the variablesactually analysed are principal components of a PCA Load-ings of these variables are generally uninformative since PCsthemselves do not all have straightforward interpretationsHowever we can also compute contributions of the alleleswhich can turn out to be very informative In general thereare many alleles and their contribution is best plotted fora single discriminant function at a time using adegenet Rpackage

3 Results and Discussion

31 Niche Modelling and Strata Definition Modelled areaof N dombeyi presence under present climate conditionsis 88174 km2 and in the future (year 2050) under dispersalconstraints is 72928 km2 Although it is not qualified with astatus of threatened species we estimated a decrease of almost20 of its habitat area in a relatively short time particularlyin the northern populations associated with Mediterranean-influenced climate which is at least worrisome consideringwe used the most conservative future climate scenario Fusset al [31] showed that the actual reality is the worst climatescenario projected The 119870-means analyses applied to fourbest least correlated variables of WorldClim (BIO2 BIO4BIO12 and BIO15) identified seven strata groups of Ndombeyi in Chile at very broad spatial scales (Figure 1)Thesestrata represented relatively homogeneous habitats accordingto the climatic key variables mentioned The strata namedSeptentrional (1) Alto Biobıo (2) Los Lagos Andes (7)and Patagonia (3) form the latitudinal gradient across AndesMountain range including higher altitudes while the strataNahuelbuta (4) Araucanıa y Los Rıos (5) and Los LagosCosta (6) present some oceanic influence at a gradientlocated in Coastal Mountain range and central valley of thecentral distribution of N dombeyi at lower elevation Thestrata were coherently identified as the bioclimatic transitionfrom temperate biome with some Mediterranean influenceespecially in the northern portion of the Septentrional stra-tum and then the typical temperate bioclimatic classificationacross the rest of the distribution of the species [32]

32 Assessment of N dombeyi Genotypes Sampled by GBSUsing ApeKI Weobtainedmore than 17Gb out of 172171356reads from which 8582 keep a score 119876 ge 30 From the96 barcoded samples in our study an average of 15 million(SD 17 million) sequence reads per sample were obtained(range from 278 to 82 millions) (see Supplementary Figure2) After removal of SNP with a minor allele frequency005 and removing samples and SNP with more than 90ldquoNNrdquo (unassigned) genotypes the dataset consisted of 73


0 100 200 300 40050

Ecological strataof N dombeyiSeptentrional

Alto Biobiacuteo

Patagonia

Nahuelbuta

Araucaniacutea y Los Riacuteos

Los Lagos Costa

Los Lagos Andes1

2

3

4

5

6

7

48∘S

47∘S

46∘S

45∘S

44∘S

43∘S

42∘S

41∘S

40∘S

39∘S

38∘S

37∘S

36∘S

35∘S

34∘S

48∘S

47∘S

46∘S

45∘S

44∘S

43∘S

42∘S

41∘S

40∘S

39∘S

38∘S

37∘S

36∘S

35∘S

34∘S

77∘W 75

∘W 71∘W73

∘W 72∘W 70

∘W 67∘W69

∘W 68∘W74

∘W76∘W

77∘W

N

75∘W 71

∘W73∘W 72

∘W 70∘W 67

∘W69∘W 68

∘W74∘W76

∘W

(km)

Figure 1 Using ecological niche models to identify different strata for Nothofagus dombeyi in Chile

individuals with 10109 binary SNP from seven strata Thisdataset was subjected to the impute step and finally totalnumbers of ldquoNNrdquo genotypes were 19067 (003) missingdata

33 Putative under Selection SNP (Adaptive SNP) Usingthe LOSITAN approach 124 adaptive SNP (12) wereidentified (directionally selected) at the false discovery rate(FDR) threshold of 01 (Table 2 Supplementary Table 1) Wedetected 99 adaptive SNP in BayeScan and there were 48overlap cases between the loci reported by each method(see Supplementary Table 1) The adaptive SNP detectedby both approaches were limited (39) possibly reflecting

discrepancies in their methodologies However all the 124SNP identified by LOSITAN had Fst values ge 030 (estimatedacross the 73 N dombeyi individuals and 10109 loci) whichmay be considered as strongly differentiated

Using Sam120573ada we detected 2406 significant genotypesassociated with a given environmental variable They rep-resent 884 SNP (87 of total SNP assessed) and only3 correspond to heterozygous genotypes From the 124outlier SNP identified by LOSITAN genotypes in 121 SNPwere identified as associated with environmental variablesby Sam120573ada (see Supplementary Table 1) Genetic differen-tiation associated with both temperature and precipitationgradients was detected


Table 2 Representative list of outlier single nucleotide polymorphisms (SNP) as putative candidates for adaptation in Nothofagus dombeyiin Chile and their significant associations with environmental variables using Sam120573ada Italic values show an SNP that is not a significantoutlier

Locus SNP IDLOSITAN BayeScan Sam120573ada

Het Fst 119875

(Simul Fst lt sample Fst) BIO2 BIO4 BIO5 BIO6 BIO12 BIO15 BIO18 BIO19

TP3479 4 0368 0545 099999 Yes x x x xTP8429 14 0282 0496 099996 mdash x x x xTP23119 mdash 0491 0035 046921TP30254 42 0385 0442 099996 Yes x x x xTP30852 43 0438 0438 100000 Yes x x x xTP32296 44 0467 0377 099991 Yes x x x xTP38613 50 0364 0618 100000 Yes x x x xTP65805 91 0540 0488 100000 Yes x x

Hs Ht Dst Htp Dstp Fst Fstp Fis DestHo

minus10

minus05

00

05

10

(a)


minus02

00

02

04

06

08

10

(b)

Figure 2 Summary statistics of genetic variation existing inNothofagus dombeyi in Chile estimated by 10109 neutral SNP (a) or 124 adaptiveSNP (b) Ho heterozygosity within population Hs genetic diversity within population Ht overall gene diversity Http corrected Ht Dstgene diversity among samples Dstp corrected Dst Fst fixation index Fstp corrected Fst Fis inbreeding coefficient per overall loci Destmeasure of population differentiation

34 Genome-Wide Genetic Variation and DifferentiationUsing neutral plus outlier SNP (10109) different basic statis-tics of genetic variation of N dombeyi in Chile show lowto medium genetic diversity level and low level of stratadifferentiation (Figure 2) The low genetic structure foundindicates relatively high gene flow which is consistent withthe fact that N dombeyi has a distribution more or lesscontinuous and is wind pollinated This result is similar toother results obtained with the same and other species ofNothofagus gender [33 34] With outlier SNP the indicatorsbased on heterozygosity increase and 119865 statistics shows cleardifferences within strata

Overall average pairwise Fst values among populationscalculated over the set of the 10109 SNP show medium pop-ulation structuring across the distribution of N dombeyi inChile (Table 3) The average pairwise Fst values ranged from0062 between two very close strata (4 and 5) in the Coastal

Table 3 Average pairwise Fst among seven strata with Nothofagusdombeyi presence in Chile based on a set of 10109 single nucleotidepolymorphisms (SNP) Fst 015ndash02 are moderately differentiated inbold font and Fst gt 025 are considered strongly differentiated inbold and italic font

Strata 1 2 3 4 5 62 00743 0258 01934 0111 0074 01835 0109 0066 0138 00626 0156 0108 0140 0096 00697 0172 0121 0141 0110 0081 0096

Mountains to 0258 between strata 1 and 3 both in AndeanMountains but in the extremes of distribution (Table 1)


Cluster 4Cluster 1Cluster 2Cluster 3

Cluster 5

20 40 600PCA axis

020406080

Cum

ulat

edva

rianc

e (

)

Figure 3 Discriminant Analysis of Principal Components (DAPC)scatterplot drawnusing 124 outlier single nucleotide polymorphisms(SNP) across 73 Nothofagus dombeyi individuals in the R packageadegenet Dots represent individuals with colours denoting clusterallocation Percentages of cumulated variance explained byPrincipalComponent 1 (PC1) to PC20 are shown in the bottom left cornerMinimum spanning tree based on the (squared) distances betweenclusters within the entire space is shown

The Principal Coordinates Analysis (PCoA) plot revealed ageographically ordered pattern (see Supplementary Figure 3)The first PC suggests the existence of two clades in the datawhile the second one shows groups of closely related isolatesarranged along a cline of genetic differentiation Premoli [35]found continuous clinal genetic variation in populations ofNpumilio along the altitudinal gradient as a result of adaptiveresponses to ecological gradients andor restrictions for geneflowThe same pattern is shown by neutral SNP and adaptiveSNP but with a more clear delimitation in the latter caseThis structure was confirmed by a neighbour-joining (NJ)tree (Supplementary Figure 4) Again higher resolution isachieved with adaptive SNP As expected both approachesgive congruent results but both are complementary NJshows bunches of related individuals but the cline of geneticdifferentiation is much clearer in PCA

Visualisation of broad-scale population structure using aDAPC (Figure 3 and Table 4) with 124 outlier SNP revealedtwo distinct genetic groups One group included individualssampled in all strata corresponding to Clusters 1 2 3 and 5(Table 4) and the other group comprised only ten individualsof stratum 3 plus one individual of stratum 2 forming Cluster4 (represented almost exclusively by individuals of Patagoniastratum) Also the actual proximities between clusters showthe great genetic distance of Cluster 4 with the restThis resultis consistent with a northndashsouth phylogenetic divergence at

Table 4 Individual allocation to five genetic clusters accordingto strata determined by niche modelling of N dombeyi in ChileClusters were identified by findcluster function of adegenet Rpackage using 124 adaptive single nucleotide polymorphisms (SNP)

Strata Cluster assignationC1 C2 C3 C4 C5

1 mdash mdash 8 mdash mdash2 mdash mdash 12 1 mdash3 mdash mdash mdash 10 44 8 mdash 1 mdash mdash5 10 5 1 mdash mdash6 mdash 7 mdash mdash mdash7 mdash 6 mdash mdash mdash

c 43∘S found within subgenus Nothofagus in southern SouthAmerica including N dombeyi [12 36]

Clearly when examining patterns of N dombeyi popula-tion differentiation at neutral versus adaptive SNP we havedetected several distinct differences Primarily stochasticprocesses drive differentiation of neutral SNP whereas bothselective and stochastic processes drive that of adaptive SNPfor example [37ndash39]This result supports the hypothesis thatputative under selection SNPare being affected by adaptationwhich is not affecting the neutral SNP The next step is tocharacterize the phenotypic outcomes of alternative geno-types to confirm if themechanism is genetic drift or selectionsimilar to previous studies in Nothofagus pumilio [40 41]Ideally phenotype-genotype association studies should befollowed by the profiling of gene expression functional testsand selection tests to determine that a gene or genes areinvolved in shaping an adaptive trait [42]

35 Loci Contributions Over 24 of the adaptive SNPdetected by LOSITAN had a high loading (here defined asge002) in one or two of the three PC in a PCA with morethan 10K loci of the Chilean population of N dombeyi(Supplementary Figures 5 6 and 7) thus confirming itscontribution to population structure Adaptive SNP ID 414 42 50 and 91 are five of the most discriminating lociand their allele frequencies over seven strata show a highor even dominant allele frequency in stratum 3 (Figure 4)This is the southernmost stratum (3 Patagonia) and thismayjustify the differences in allele frequencies Irrespective ofthe mechanism underlying these changes (drift or selection)this illustrates that in the natural distribution of N dombeyiin Chile specific nucleotides can undergo drastic changeswithin only a hundred kilometres of distance Our resultsexhibited spatial patterns differentiating one stratum in lociwhich could occur in genomic regions or genes for importantfunctions of N dombeyi across their habitat For examplethe GBS sequence tags of adaptive SNP ID 43 (TP30852)show a high identity with a heat shock protein or adaptiveSNP ID 44 (TP32296) with histone acetyltransferase enzyme(data not shown) The first is fundamental for heat orother environmental stresses and the second can be involvedin drought sensing or another response to environmental


tt

t

t tt

t

SNP 4 TP3479

aa

a

a aa

a

00

02

04

06

08

10

Alle

le fr

eque

ncy

3 5 71Strata

cc

c

c

c

c

c

SNP 14 TP8429

tt

t

tt

t

t

3 5 71Strata

00

02

04

06

08

10

Alle

le fr

eque

ncy

aa

a

a

a a

a

SNP 42 TP30254

t t

t

t

t t

t

00

02

04

06

08

10

Alle

le fr

eque

ncy

3 5 71Strata

tt

t

t

tt

t

SNP 50 TP38613

cc

c

c

cc

c

3 5 71Strata

00

02

04

06

08

10

Alle

le fr

eque

ncy

gg g

g

g g

g

SNP 91 TP65805

aa a

a

a a

a

02

04

06

08

Alle

le fr

eque

ncy

3 5 71Strata

a

aa

a a

a a

SNP TP23119

t

t t

tt

tt

3 5 71Strata

02

03

04

05

06

07

08

Alle

le fr

eque

ncy

Figure 4 Spatial distributions of single nucleotide polymorphism (SNP) locigenotypes in N dombeyi in Chile for each strata The graphsshow frequencies of locigenotypes differentiating among strata (SNP 3479 SNP 8429 SNP 30254 SNP 38613 and SNP 65805) and nodifferentiating among strata (SNP 23119)

factors We therefore suggest that these two SNP may beinteresting candidates for future functional studies thatmightfacilitate other studies for example [43] focused on thedevelopment of genomic resources for Nothofagus speciesHowever improvements in study design and analyses ofreplicated studies will be needed before this very promisingapproach can be brought to application for managing geneticresources [44]

4 Conclusions

Development of 10109 genome-wide SNP for N dombeyiusing GBS made evaluation of genomic diversity and fine-scale population structure possible for the first time in thisspecies in Chile Results showed that genome-wide patternsof genetic diversity and differentiation varied widely acrossthe genome As such we identified numerous genomic

regions exhibiting signatures of divergent selection We havealso provided strong evidence of substantial genetic differ-entiation associated with both temperature and precipitationgradients suggesting local adaptation

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

The authors thank CONAF (Corporacion Nacional Fore-stal) and SNASPE (Sistema Nacional de Areas SilvestresProtegidas del Estado) for providing permission to sampleplant material and especially thank Jose Luis Perez for


sampling stratum 3 (Patagonia) Angela Sierra Daniela Fer-nandez Giannina Espinoza and Joseline Valdes are grate-fully acknowledged for providing technical and scientificassistance The authors thank the University of WisconsinBiotechnology Center DNA Sequencing Facility for provid-ing sequencing and support services Funding for this projectwas provided by the Research Fund for Native Forest ofCONAF (Project 0682012)

References

[1] R Frankham J D Ballou and D A Briscoe A Primer of Con-servation Genetics Cambridge University Press CambridgeUK 2004

[2] R Frankham ldquoWhere are we in conservation genetics andwhere do we need to gordquo Conservation Genetics vol 11 no 2pp 661ndash663 2010

[3] D J Fraser and L Bernatchez ldquoAdaptive evolutionary conserva-tion towards a unified concept for defining conservation unitsrdquoMolecular Ecology vol 10 no 12 pp 2741ndash2752 2001

[4] B Gebremedhin G F Ficetola S Naderi et al ldquoFrontiersin identifying conservation units from neutral markers toadaptive genetic variationrdquo Animal Conservation vol 12 no 2pp 107ndash109 2009

[5] F W Allendorf P A Hohenlohe and G Luikart ldquoGenomicsand the future of conservation geneticsrdquoNature Reviews Genet-ics vol 11 no 10 pp 697ndash709 2010

[6] C Y Tang C-L Hung H Zheng C-Y Lin and HJiang ldquoNovel computational technologies for next-generationsequencing data analysis and their applicationsrdquo InternationalJournal of Genomics vol 2015 Article ID 254685 3 pages 2015

[7] B B Patnaik S Y Park SW Kang et al ldquoTranscriptome profileof the asian giant hornet (Vespa mandarinia) using illuminaHiSeq 4000 sequencing De novo assembly functional anno-tation and discovery of SSR markersrdquo International Journal ofGenomics vol 2016 Article ID 4169587 15 pages 2016

[8] A Bonin F Nicole F Pompanon C Miaud and P TaberletldquoPopulation adaptive index a new method to help measureintraspecific genetic diversity and prioritize populations forconservationrdquo Conservation Biology vol 21 no 3 pp 697ndash7082007

[9] D Demontis C Pertoldi V Loeschcke K Mikkelsen T Axels-son and T N Kristensen ldquoEfficiency of selection as measuredby single nucleotide polymorphism variation is dependent oninbreeding rate inDrosophila melanogasterrdquoMolecular Ecologyvol 18 no 22 pp 4551ndash4563 2009

[10] H Kirk and J R Freeland ldquoApplications and implicationsof neutral versus non-neutral markers in molecular ecologyrdquoInternational Journal of Molecular Sciences vol 12 no 6 pp3966ndash3988 2011

[11] A C Premoli M P Quiroga P Mathiasen and T KitzbergerldquoEcological niche modeling meets phylogeography to unravelhidden past history of key forest genera in plant geographyPodocarpus and Nothofagusrdquo Natureza amp Conservacao vol 10no 2 pp 160ndash168 2012

[12] M C Acosta P Mathiasen and A C Premoli ldquoRetracing theevolutionary history of Nothofagus in its geo-climatic contextnew developments in the emerging field of phylogeologyrdquoGeobiology vol 12 no 6 pp 497ndash510 2014

[13] A C Premoli P Mathiasen M Cristina Acosta and VA Ramos ldquoPhylogeographically concordant chloroplast DNA

divergence in sympatric Nothofagus ss How deep can it berdquoNew Phytologist vol 193 no 1 pp 261ndash275 2012

[14] D Alarcon and L A Cavieres ldquoIn the right place at the righttime habitat representation in protected areas of South Amer-ican Nothofagus-dominated plants after a dispersal constrainedclimate change scenariordquo PLoS ONE vol 10 no 3 Article IDe0119952 2015

[15] W Thuiller B Lafourcade R Engler and M B AraujoldquoBIOMODmdasha platform for ensemble forecasting of speciesdistributionsrdquo Ecography vol 32 no 3 pp 369ndash373 2009

[16] R J Hijmans S E Cameron J L Parra P G Jones and AJarvis ldquoVery high resolution interpolated climate surfaces forglobal land areasrdquo International Journal of Climatology vol 25no 15 pp 1965ndash1978 2005

[17] T Calinski and J Harabasz ldquoA dendrite method for clusteranalysisrdquo Communications in Statistics vol 3 pp 1ndash27 1974

[18] R J Elshire J C Glaubitz Q Sun et al ldquoA robust simplegenotyping-by-sequencing (GBS) approach for high diversityspeciesrdquo PLoS ONE vol 6 no 5 Article ID e19379 2011

[19] F Lu A E Lipka J Glaubitz et al ldquoSwitchgrass genomicdiversity ploidy and evolution novel insights from a network-based SNP discovery protocolrdquo PLoS Genetics vol 9 no 1Article ID e1003215 2013

[20] J C Glaubitz T M Casstevens F Lu et al ldquoTASSEL-GBS ahigh capacity genotyping by sequencing analysis pipelinerdquoPLoSONE vol 9 no 2 article e90346 2014

[21] D Money K Gardner Z Migicovsky H Schwaninger G-YZhong and S Myles ldquoLinkImpute fast and accurate genotypeimputation for nonmodel organismsrdquo G3 Genes GenomesGenetics vol 5 no 11 pp 2383ndash2390 2015

[22] T Antao A Lopes R J Lopes A Beja-Pereira and G LuikartldquoLOSITAN a workbench to detect molecular adaptation basedon a Fst-outliermethodrdquoBMCBioinformatics vol 9 article 3232008

[23] M Foll and O Gaggiotti ldquoA genome-scan method to identifyselected loci appropriate for both dominant and codominantmarkers a Bayesian perspectiverdquo Genetics vol 180 no 2 pp977ndash993 2008

[24] S Joost A Bonin M W Bruford et al ldquoA spatial analysismethod (SAM) to detect candidate loci for selection towardsa landscape genomics approach to adaptationrdquoMolecular Ecol-ogy vol 16 no 18 pp 3955ndash3969 2007

[25] A J Dobson and A G Barnett An Introduction to GeneralizedLinear Models Chapman and HallCRC London UK 3rdedition 2008

[26] T Jombart ldquoadegenet a R package for the multivariate analysisof geneticmarkersrdquoBioinformatics vol 24 no 11 pp 1403ndash14052008

[27] T Jombart and I Ahmed ldquoadegenet 13-1 new tools for theanalysis of genome-wide SNP datardquo Bioinformatics vol 27 no21 Article ID btr521 pp 3070ndash3071 2011

[28] J Goudet ldquoHIERFSTAT a package for R to compute and testhierarchical F-statisticsrdquo Molecular Ecology Notes vol 5 no 1pp 184ndash186 2005

[29] M Nei Molecular Evolutionary Genetics Columbia UniversityPress New York NY USA 1987

[30] L Jost ldquoGST and its relatives do not measure differentiationrdquoMolecular Ecology vol 17 no 18 pp 4015ndash4026 2008

[31] S Fuss J G Canadell G P Peters et al ldquoBetting on negativeemissionsrdquo Nature Climate Change vol 4 no 10 pp 850ndash8532014


[32] F Luebert and P Pliscoff Sinopsis Bioclimatica y Vegetacional deChile Editorial Universitaria Santiago Chile 2006

[33] A C Premoli ldquoGenetic variation in a geographically restrictedand two widespread species of South American NothofagusrdquoJournal of Biogeography vol 24 no 6 pp 883ndash892 1997

[34] C J Duncan J R P Worth G J Jordan R C Jones and RE Vaillancourt ldquoGenetic differentiation in spite of high geneflow in the dominant rainforest tree of southeastern AustraliaNothofagus cunninghamiirdquo Heredity vol 116 no 1 pp 99ndash1062016

[35] A C Premoli ldquoIsozyme polymorphisms provide evidence ofclinal variation with elevation in Nothofagus pumiliordquo Journalof Heredity vol 94 no 3 pp 218ndash226 2003

[36] A C Premoli P Mathiasen M C Acosta and V A RamosldquoPhylogeographically concordant chloroplast DNA divergencein sympatric Nothofagus ss How deep can it berdquo New Phytol-ogist vol 193 no 1 pp 261ndash275 2012

[37] J Galindo P Moran and E Rolan-Alvarez ldquoComparing geo-graphical genetic differentiation between candidate and non-candidate loci for adaptation strengthens support for parallelecological divergence in the marine snail Littorina saxatilisrdquoMolecular Ecology vol 18 no 5 pp 919ndash930 2009

[38] A V Stronen B Jedrzejewska C Pertoldi et al ldquoGenome-wide analyses suggest parallel selection for universal traitsmay eclipse local environmental selection in a highly mobilecarnivorerdquo Ecology and Evolution vol 5 no 19 pp 4410ndash44252015

[39] B Guo J DeFaveri G Sotelo ANair and JMerila ldquoPopulationgenomic evidence for adaptive differentiation in Baltic Seathree-spined sticklebacksrdquo BMC Biology vol 13 article 19 2015

[40] A C Premoli and C A Brewer ldquoEnvironmental v geneticallydriven variation in ecophysiological traits ofNothofagus pumiliofrom contrasting elevationsrdquo Australian Journal of Botany vol55 no 6 pp 585ndash591 2007

[41] A C Premoli E Raffaele and P Mathiasen ldquoMorphologicaland phenological differences in Nothofagus pumilio from con-trasting elevations evidence from a common gardenrdquo AustralEcology vol 32 no 5 pp 515ndash523 2007

[42] C Pardo-Diaz C Salazar and C D Jiggins ldquoTowards theidentification of the loci of adaptive evolutionrdquo Methods inEcology and Evolution vol 6 no 4 pp 445ndash464 2015

[43] V A El Mujtar L A Gallo T Lang and P Garnier-GereldquoDevelopment of genomic resources for Nothofagus speciesusing next-generation sequencing datardquo Molecular EcologyResources vol 14 no 6 pp 1281ndash1295 2014

[44] I Calic F Bussotti P J Martınez-Garcıa and D B NealeldquoRecent landscape genomics studies in forest treesmdashwhat canwe believerdquoTreeGeneticsampGenomes vol 12 no 1 pp 1ndash7 2016

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


some of the applications of neutral versus adaptive markersin molecular ecology discussed some of the advantagesthat can be obtained by supplementing studies of molecularecology with data from nonneutral molecular markers andsummarized new methods that allow generating data fromloci under selection Population genomic analyses requiremultilocus datasets from multiple populations and identifi-cation of nonneutral or adaptive loci by contrasting patternsof population divergence among genetic regions

Studies in nonmodel organisms have shown relativelybroad candidate genomic regions that are under selection butit remains difficult to identify the genes (or the mutations)that are affected by selection Increasing the density ofmarkers in genome scans is paramount to overcome thisproblem and validating signals of selection from particulargenes using multiple methods should also help [4] One ofthemost exciting developments in population genomics is thedevelopment of various reduced-representation protocolscollectively referred to as Genotyping-by-Sequencing (GBS)which allow sequencing of a subset of the genome throughselective amplification of restriction fragments [10]

Nothofagus dombeyi (Mirb) Oerst (Nothofagaceae) isan endemic tree species occurring both in Chile and inArgentina temperate forests with a remarkably broad alti-tudinal and latitudinal distribution across many differentecological gradients in the former [11] The evergreen tree Ndombeyi is a pioneer species and constitutes an importantelement in the dynamics of South American forest Itspostglacial history has been studied with chloroplast DNAand evolutionary forces shaping genetic variation patternshave been analysed with isozymes [12 13] However genome-wide scan methods using thousands of markers to study arepresentative portion of the genome are needed

In this work we assess GBS in the nonmodel woodyspecies N dombeyi to develop high quantity of informativemarkers such as single nucleotide polymorphisms (SNP)Our aim is to determine the contribution of selection andmolecular adaptation to shaping genome-wide variation Weexpect higher genetic population differentiation (for ecolog-ical separated localities) for adaptive SNP than neutral SNPif natural selection is the principal source of differentiationAlternatively if other sources of differentiation (mutationgenetic drift and migration) are relevant they will equallyaffect both types of SNP Knowing the contribution ofselection effect to shaping genome variation patternswill havemany applications for biodiversity conservation especiallyin endangered species because neutral and adaptive geneticdiversity will likely have different impacts on long-termsurvival In fact in most cases only adaptive diversity willallow a population to adapt to changing environmentalconditions

2 Materials and Methods

21 Sampling Design

211 Niche Modelling To consider the remarkably broadaltitudinal and latitudinal distribution of this species across

many different ecological gradients we used the methodproposed by Alarcon and Cavieres [14] to niche modellingof Nothofagus dombeyi in Chile using BIOMOD [15] Eightvariables were selected from the WorldClim global climatedatabase [16] corresponding to the present climate condi-tions with a 30-arc-second grid resolution with the leastcorrelation among them for the studied species range areaFour variables were related to energy constraints (a) BIO2mean diurnal temperature range (b) BIO4 temperatureseasonality (c) BIO5 maximum temperature in the warmestmonth and (d) BIO6 minimum temperature in the coldestmonth Other four variables were related to water availability(e) BIO12 annual precipitation (f) BIO15 precipitationseasonality (g) BIO18 precipitation in the warmest quarterand (h) BIO19 precipitation in the coldest quarter Thenwe projected the current and future distribution (year 2050)considering a conservative future climate projection CSIROB2A 2050 by using the tools of BIOMOD software Furtherwe identified geographical areas with potential habitat losswhich should be identified as high priority in genetic conser-vation programs

212 Ecological Regions or Strata Relatively homogeneousunits in ecological terms (strata) were defined from naturalpopulations of the species associated with the geographi-cal areas projected in its ecological niche modelling TheCalinski-Harabasz criterion which is a pseudo-119865 statistic asin ANOVA was used to assess the best number of strataidentified by 119870-means partitioning [17]

22 DNA Sample and Library Preparation

221 Plant Material and Genomic DNA Isolation Adult treeswith a diameter higher than 50 cm were sampled during thegrowing season of 2013-2014 from twenty-one sites coveringalmost the entire range of the species N dombeyi in ChileOne to four sites were assigned to each stratum according toits superficies and 2 to 9 samples per site were taken (Table 1Supplementary Figure 1 in Supplementary Material availableonline at httpdxdoiorg10115520163654093)

DNA extraction was performed using a Qiagen DNeasyPlant kit (Qiagen Inc USA) Lyophilized leaf tissue (20mg)was ground in a Precellys24 (Precellys USA) homogenizerwith two 1410158401015840 ceramic spheres (MP BIOMEDICALS USA)and AP1 buffer The objective is grinding tissues and lysingcells prior to DNA extraction DNA extraction protocol wasdone following themanufacturer instructions but elutionwasdone with 30 120583L (instead of 100 120583L) to increase the final DNAconcentration in the eluate (gt100 ng120583L) The integrity ofgenomic DNA was evaluated by agarose gel and quantifiedusing a Qubit fluorometer (Invitrogen USA)

222 Library Preparation and High-Throughput SequencingLibrary preparation and high-throughput sequencing wereperformed at University of Wisconsin Biotechnology Cen-ter (DNA Sequencing Facility) The GBS genomic librarypreparation was done following the protocol detailed byElshire et al [18] with the methylation-sensitive restriction


























0 100 200 300 40050


Alto Biobiacuteo

Patagonia

Nahuelbuta


Los Lagos Costa

Los Lagos Andes1

2

3

4

5

6

7

48∘S

47∘S

46∘S

45∘S

44∘S

43∘S

42∘S

41∘S

40∘S

39∘S

38∘S

37∘S

36∘S

35∘S

34∘S

48∘S

47∘S

46∘S

45∘S

44∘S

43∘S

42∘S

41∘S

40∘S

39∘S

38∘S

37∘S

36∘S

35∘S

34∘S

77∘W 75

∘W 71∘W73

∘W 72∘W 70

∘W 67∘W69

∘W 68∘W74

∘W76∘W

77∘W

N

75∘W 71

∘W73∘W 72

∘W 70∘W 67

∘W69∘W 68

∘W74∘W76

∘W

(km)









Het Fst 119875




minus10

minus05

00

05

10

(a)


minus02

00

02

04

06

08

10

(b)





Strata 1 2 3 4 5 62 00743 0258 01934 0111 0074 01835 0109 0066 0138 00626 0156 0108 0140 0096 00697 0172 0121 0141 0110 0081 0096




Cluster 5

20 40 600PCA axis

020406080

Cum

ulat

edva

rianc

e (

)











tt

t

t tt

t

SNP 4 TP3479

aa

a

a aa

a

00

02

04

06

08

10

Alle

le fr

eque

ncy

3 5 71Strata

cc

c

c

c

c

c

SNP 14 TP8429

tt

t

tt

t

t

3 5 71Strata

00

02

04

06

08

10

Alle

le fr

eque

ncy

aa

a

a

a a

a

SNP 42 TP30254

t t

t

t

t t

t

00

02

04

06

08

10

Alle

le fr

eque

ncy

3 5 71Strata

tt

t

t

tt

t

SNP 50 TP38613

cc

c

c

cc

c

3 5 71Strata

00

02

04

06

08

10

Alle

le fr

eque

ncy

gg g

g

g g

g

SNP 91 TP65805

aa a

a

a a

a

02

04

06

08

Alle

le fr

eque

ncy

3 5 71Strata

a

aa

a a

a a

SNP TP23119

t

t t

tt

tt

3 5 71Strata

02

03

04

05

06

07

08

Alle

le fr

eque

ncy



4 Conclusions



Competing Interests


Acknowledgments




References






















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


























0 100 200 300 40050


Alto Biobiacuteo

Patagonia

Nahuelbuta


Los Lagos Costa

Los Lagos Andes1

2

3

4

5

6

7

48∘S

47∘S

46∘S

45∘S

44∘S

43∘S

42∘S

41∘S

40∘S

39∘S

38∘S

37∘S

36∘S

35∘S

34∘S

48∘S

47∘S

46∘S

45∘S

44∘S

43∘S

42∘S

41∘S

40∘S

39∘S

38∘S

37∘S

36∘S

35∘S

34∘S

77∘W 75

∘W 71∘W73

∘W 72∘W 70

∘W 67∘W69

∘W 68∘W74

∘W76∘W

77∘W

N

75∘W 71

∘W73∘W 72

∘W 70∘W 67

∘W69∘W 68

∘W74∘W76

∘W

(km)









Het Fst 119875




minus10

minus05

00

05

10

(a)


minus02

00

02

04

06

08

10

(b)





Strata 1 2 3 4 5 62 00743 0258 01934 0111 0074 01835 0109 0066 0138 00626 0156 0108 0140 0096 00697 0172 0121 0141 0110 0081 0096




Cluster 5

20 40 600PCA axis

020406080

Cum

ulat

edva

rianc

e (

)











tt

t

t tt

t

SNP 4 TP3479

aa

a

a aa

a

00

02

04

06

08

10

Alle

le fr

eque

ncy

3 5 71Strata

cc

c

c

c

c

c

SNP 14 TP8429

tt

t

tt

t

t

3 5 71Strata

00

02

04

06

08

10

Alle

le fr

eque

ncy

aa

a

a

a a

a

SNP 42 TP30254

t t

t

t

t t

t

00

02

04

06

08

10

Alle

le fr

eque

ncy

3 5 71Strata

tt

t

t

tt

t

SNP 50 TP38613

cc

c

c

cc

c

3 5 71Strata

00

02

04

06

08

10

Alle

le fr

eque

ncy

gg g

g

g g

g

SNP 91 TP65805

aa a

a

a a

a

02

04

06

08

Alle

le fr

eque

ncy

3 5 71Strata

a

aa

a a

a a

SNP TP23119

t

t t

tt

tt

3 5 71Strata

02

03

04

05

06

07

08

Alle

le fr

eque

ncy



4 Conclusions



Competing Interests


Acknowledgments




References






















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




Het Fst 119875




minus10

minus05

00

05

10

(a)


minus02

00

02

04

06

08

10

(b)





Strata 1 2 3 4 5 62 00743 0258 01934 0111 0074 01835 0109 0066 0138 00626 0156 0108 0140 0096 00697 0172 0121 0141 0110 0081 0096




Cluster 5

20 40 600PCA axis

020406080

Cum

ulat

edva

rianc

e (

)











tt

t

t tt

t

SNP 4 TP3479

aa

a

a aa

a

00

02

04

06

08

10

Alle

le fr

eque

ncy

3 5 71Strata

cc

c

c

c

c

c

SNP 14 TP8429

tt

t

tt

t

t

3 5 71Strata

00

02

04

06

08

10

Alle

le fr

eque

ncy

aa

a

a

a a

a

SNP 42 TP30254

t t

t

t

t t

t

00

02

04

06

08

10

Alle

le fr

eque

ncy

3 5 71Strata

tt

t

t

tt

t

SNP 50 TP38613

cc

c

c

cc

c

3 5 71Strata

00

02

04

06

08

10

Alle

le fr

eque

ncy

gg g

g

g g

g

SNP 91 TP65805

aa a

a

a a

a

02

04

06

08

Alle

le fr

eque

ncy

3 5 71Strata

a

aa

a a

a a

SNP TP23119

t

t t

tt

tt

3 5 71Strata

02

03

04

05

06

07

08

Alle

le fr

eque

ncy



4 Conclusions



Competing Interests


Acknowledgments




References






















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology



Cluster 5

20 40 600PCA axis

020406080

Cum

ulat

edva

rianc

e (

)











tt

t

t tt

t

SNP 4 TP3479

aa

a

a aa

a

00

02

04

06

08

10

Alle

le fr

eque

ncy

3 5 71Strata

cc

c

c

c

c

c

SNP 14 TP8429

tt

t

tt

t

t

3 5 71Strata

00

02

04

06

08

10

Alle

le fr

eque

ncy

aa

a

a

a a

a

SNP 42 TP30254

t t

t

t

t t

t

00

02

04

06

08

10

Alle

le fr

eque

ncy

3 5 71Strata

tt

t

t

tt

t

SNP 50 TP38613

cc

c

c

cc

c

3 5 71Strata

00

02

04

06

08

10

Alle

le fr

eque

ncy

gg g

g

g g

g

SNP 91 TP65805

aa a

a

a a

a

02

04

06

08

Alle

le fr

eque

ncy

3 5 71Strata

a

aa

a a

a a

SNP TP23119

t

t t

tt

tt

3 5 71Strata

02

03

04

05

06

07

08

Alle

le fr

eque

ncy



4 Conclusions



Competing Interests


Acknowledgments




References






















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


tt

t

t tt

t

SNP 4 TP3479

aa

a

a aa

a

00

02

04

06

08

10

Alle

le fr

eque

ncy

3 5 71Strata

cc

c

c

c

c

c

SNP 14 TP8429

tt

t

tt

t

t

3 5 71Strata

00

02

04

06

08

10

Alle

le fr

eque

ncy

aa

a

a

a a

a

SNP 42 TP30254

t t

t

t

t t

t

00

02

04

06

08

10

Alle

le fr

eque

ncy

3 5 71Strata

tt

t

t

tt

t

SNP 50 TP38613

cc

c

c

cc

c

3 5 71Strata

00

02

04

06

08

10

Alle

le fr

eque

ncy

gg g

g

g g

g

SNP 91 TP65805

aa a

a

a a

a

02

04

06

08

Alle

le fr

eque

ncy

3 5 71Strata

a

aa

a a

a a

SNP TP23119

t

t t

tt

tt

3 5 71Strata

02

03

04

05

06

07

08

Alle

le fr

eque

ncy



4 Conclusions



Competing Interests


Acknowledgments




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

research article using genome-wide snp discovery and...

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