Introduction

In this vignette, we will discuss how to assess population genetic structure from SNP data at population level. We will estimate \(F_{st}\) per population, Pairwise \(F_{st}\), AMOVA (Hierarchical \(F_{st}\)). We will finally assess the genetic structure at individual level assuming that we do not know populations using a multivariate analysis.

The dataset used for those analysis concerns the plant: lodgepole pine (Pinus contorta, Pinaceae). You can have more information on this data set and the species on the web site of A. Eckert: (http://eckertdata.blogspot.fr/). But here the dataset is used as a test dataset with no idea of interpreting the results in a biological way. We will work on a subset of the dataset to make the calculations faster.

Resources/Packages

library("adegenet")
library("hierfstat")

Workflow

Import data

The data are stored in a text file (genotype=AA..). We will import the dataset in R as a data frame, and then convert the SNP data file into a “genind” object.

The dataset “Master_Pinus_data_genotype.txt” can be downloaded here.

The text file is a matrix of (550 rows x 3086 columns). It contains 4 extra columns: first column is the label of the individuals, the three other are description of the region, all the other columns are for the genotypes as (AA or AT…).

When you import the data, you need to be in the same directory as the data.

Mydata <- read.table("Master_Pinus_data_genotype.txt", header = TRUE, check.names = FALSE)   
dim(Mydata) 
## [1]  550 3086
ind <- as.character(Mydata$tree_id) # use later with adegenet (individual labels)
population <- as.character(Mydata$state) # use later with adegenet (population labels)
county <- Mydata$county 
dim(Mydata) # 550 individuals x 3082 SNPs
## [1]  550 3086

Data conversion

To convert Mydata to a “genind” object (adegenet), the input should only contain genotypes. We decrease the number of SNPs to make the calculations faster and keep only 20 SNPs in the object locus. We then convert Mydata1 to a “hierfstat” object (Mydata2).

locus   <- Mydata[, 5:24] 
Mydata1 <- df2genind(locus, ploidy = 2, ind.names = ind, pop = population, sep = "")
Mydata1
## /// GENIND OBJECT /////////
## 
##  // 550 individuals; 20 loci; 40 alleles; size: 141.5 Kb
## 
##  // Basic content
##    @tab:  550 x 40 matrix of allele counts
##    @loc.n.all: number of alleles per locus (range: 2-2)
##    @loc.fac: locus factor for the 40 columns of @tab
##    @all.names: list of allele names for each locus
##    @ploidy: ploidy of each individual  (range: 2-2)
##    @type:  codom
##    @call: df2genind(X = locus, sep = "", ind.names = ind, pop = population, 
##     ploidy = 2)
## 
##  // Optional content
##    @pop: population of each individual (group size range: 4-177)
Mydata2 <- genind2hierfstat(Mydata1) 

Observed and expected heterozygosity: \(F_{st}\)

These statistics come from the package hierfstat.

basic.stats(Mydata1) # Fst following Nei (1987) on genind object
## $perloc
##                     Ho     Hs     Ht     Dst    Htp    Dstp     Fst
## X0.10037.01.257 0.4986 0.4079 0.4259  0.0180 0.4277  0.0198  0.0422
## X0.10040.02.394 0.4866 0.4971 0.4968 -0.0003 0.4968 -0.0003 -0.0005
## X0.10044.01.392 0.3638 0.4232 0.4931  0.0699 0.5000  0.0768  0.1417
## X0.10048.01.60  0.4261 0.4626 0.4953  0.0327 0.4986  0.0359  0.0660
## X0.10051.02.166 0.0596 0.0613 0.0634  0.0020 0.0636  0.0023  0.0323
## X0.10054.01.402 0.4584 0.4481 0.4761  0.0280 0.4789  0.0308  0.0588
## X0.10067.03.111 0.0879 0.0853 0.0853  0.0000 0.0853 -0.0001 -0.0005
## X0.10079.02.168 0.0833 0.0808 0.0830  0.0022 0.0832  0.0024  0.0263
## X0.10112.01.169 0.0764 0.0766 0.0760 -0.0006 0.0760 -0.0007 -0.0079
## X0.10113.01.119 0.4436 0.4331 0.4294 -0.0037 0.4290 -0.0041 -0.0087
## X0.10116.01.165 0.0399 0.0407 0.0402 -0.0004 0.0402 -0.0005 -0.0105
## X0.10151.01.86  0.1063 0.1113 0.1125  0.0012 0.1126  0.0013  0.0106
## X0.10162.01.255 0.0863 0.0879 0.0862 -0.0018 0.0860 -0.0020 -0.0207
## X0.10207.01.280 0.2875 0.3521 0.3613  0.0092 0.3622  0.0101  0.0254
## X0.10210.01.41  0.1081 0.1108 0.1089 -0.0019 0.1087 -0.0021 -0.0174
## X0.10219.01.433 0.3618 0.3648 0.3656  0.0009 0.3657  0.0009  0.0023
## X0.1022.02.173  0.4346 0.4267 0.4490  0.0222 0.4512  0.0245  0.0495
## X0.10240.01.410 0.4576 0.4530 0.5006  0.0477 0.5054  0.0524  0.0952
## X0.10262.01.558 0.2256 0.2292 0.2305  0.0013 0.2306  0.0014  0.0055
## X0.10266.01.426 0.0677 0.0720 0.0716 -0.0004 0.0715 -0.0005 -0.0059
##                    Fstp     Fis    Dest
## X0.10037.01.257  0.0462 -0.2224  0.0334
## X0.10040.02.394 -0.0006  0.0210 -0.0006
## X0.10044.01.392  0.1537  0.1403  0.1332
## X0.10048.01.60   0.0721  0.0790  0.0669
## X0.10051.02.166  0.0354  0.0286  0.0024
## X0.10054.01.402  0.0643 -0.0229  0.0558
## X0.10067.03.111 -0.0006 -0.0293 -0.0001
## X0.10079.02.168  0.0288 -0.0308  0.0026
## X0.10112.01.169 -0.0088  0.0036 -0.0007
## X0.10113.01.119 -0.0095 -0.0243 -0.0072
## X0.10116.01.165 -0.0115  0.0193 -0.0005
## X0.10151.01.86   0.0116  0.0443  0.0015
## X0.10162.01.255 -0.0228  0.0192 -0.0021
## X0.10207.01.280  0.0279  0.1834  0.0156
## X0.10210.01.41  -0.0194  0.0245 -0.0024
## X0.10219.01.433  0.0026  0.0082  0.0015
## X0.1022.02.173   0.0542 -0.0185  0.0427
## X0.10240.01.410  0.1037 -0.0102  0.0958
## X0.10262.01.558  0.0060  0.0160  0.0018
## X0.10266.01.426 -0.0064  0.0591 -0.0005
## 
## $overall
##     Ho     Hs     Ht    Dst    Htp   Dstp    Fst   Fstp    Fis   Dest 
## 0.2580 0.2612 0.2725 0.0113 0.2737 0.0124 0.0415 0.0454 0.0124 0.0168
wc(Mydata1) # Weir and Cockerham's estimate
## $FST
## [1] 0.02300324
## 
## $FIS
## [1] 0.03090781

Hierarchical \(F_{st}\) tests (=AMOVA for SNP dataset)

The function varcomp.glob() produces a Hierarchical \(F_{st}\) (=AMOVA for SNPs or bi-allelic markers) It is possible to make permutations on the different levels: The function test.g() tests the effect of the population on genetic differentiation. Individuals are randomly permuted among states. The states influence genetic differentiation at a 5% level. With the function test.between(), the counties are permuted among states. The states influence significantly genetic structuring.

loci <- Mydata2[, -1] # Remove the population column
varcomp.glob(levels = data.frame(population, county), loci, diploid = TRUE) 
## $loc
##                          [,1]          [,2]          [,3]       [,4]
## X0.10037.01.257  4.631785e-03 -0.0075801286 -0.0110876895 0.42329020
## X0.10040.02.394 -3.927184e-06  0.0057909958  0.0035980465 0.48979592
## X0.10044.01.392  1.276810e-02  0.0039247749  0.0247870243 0.46198830
## X0.10048.01.60   6.490717e-03  0.0189152357  0.0255163684 0.39741220
## X0.10051.02.166  2.977513e-03  0.0037478140 -0.0001317105 0.11970534
## X0.10054.01.402  1.806575e-02 -0.0002977277  0.0134753262 0.47329650
## X0.10067.03.111  9.490247e-04 -0.0022733109  0.0022479222 0.07339450
## X0.10079.02.168  1.359482e-03  0.0015693361 -0.0020878338 0.07692308
## X0.10112.01.169  5.570862e-04 -0.0011207466 -0.0022195075 0.11151737
## X0.10113.01.119 -3.038733e-03  0.0190331181 -0.0321043585 0.45871560
## X0.10116.01.165 -4.102906e-04  0.0010983394  0.0019041211 0.04204753
## X0.10151.01.86   1.180089e-03  0.0038425248  0.0163965166 0.14180479
## X0.10162.01.255  2.762257e-04  0.0025535687 -0.0007521869 0.09778598
## X0.10207.01.280  2.419336e-02  0.0016539601  0.0286306732 0.38051471
## X0.10210.01.41  -1.206136e-03  0.0040434594  0.0069081606 0.09775967
## X0.10219.01.433  2.608653e-03  0.0035115484  0.0048812171 0.30755064
## X0.1022.02.173   7.258406e-03  0.0006657100 -0.0222437933 0.41198502
## X0.10240.01.410  3.603309e-02  0.0212763776  0.0035015937 0.41263941
## X0.10262.01.558  5.048435e-04 -0.0006787502  0.0310839391 0.15201465
## X0.10266.01.426 -7.102129e-04  0.0020016810  0.0053850735 0.11151737
## 
## $overall
## population     county        Ind      Error 
## 0.11448482 0.08167778 0.09768890 5.24165877 
## 
## $F
##            population     county        Ind
## Total      0.02068189 0.03543713 0.05308481
## population 0.00000000 0.01506685 0.03308722
## county     0.00000000 0.00000000 0.01829604
test.g(loci, level = population) 
## $g.star
##   [1] 213.2558 226.5028 209.6468 210.9719 195.1076 215.9261 184.0654
##   [8] 203.9163 271.5761 253.3241 199.7833 227.2181 249.4577 241.0634
##  [15] 251.8254 187.3748 247.2371 207.0601 228.6027 189.7967 187.4147
##  [22] 202.7081 198.0983 219.9601 214.9843 214.3081 190.5043 221.2755
##  [29] 213.6992 200.6339 210.9973 231.9347 188.9970 250.1430 232.0721
##  [36] 211.3578 205.7746 219.5045 204.4748 233.0392 215.7706 203.8107
##  [43] 212.3103 208.8494 206.6110 231.9033 244.9647 210.1875 225.4042
##  [50] 245.7963 209.0005 230.1368 191.2801 247.4183 205.5780 257.4085
##  [57] 231.2266 224.1553 279.0797 206.7548 203.9929 213.1291 210.3402
##  [64] 240.2502 231.0116 218.3072 197.8812 197.3689 240.9555 236.0652
##  [71] 186.9553 247.5713 259.3404 221.7607 208.3345 234.7682 239.5428
##  [78] 228.5571 244.9565 240.1573 226.5319 239.1395 219.1756 220.4083
##  [85] 209.6653 228.2701 203.7346 219.9062 194.3390 222.4353 243.4480
##  [92] 214.4828 246.1076 200.3639 236.2732 225.6782 247.9484 231.5686
##  [99] 207.0958 378.7654
## 
## $p.val
## [1] 0.01
test.between(loci, test.lev = population, rand.unit = county, nperm = 100) 
## $g.star
##   [1] 276.2615 246.2161 256.9288 262.0740 259.4973 296.0089 203.6440
##   [8] 261.6879 246.0810 255.3599 221.6946 245.2260 249.6572 254.2834
##  [15] 267.0064 232.5647 236.5076 272.8314 270.4425 270.7250 293.3300
##  [22] 279.2111 264.1005 243.5685 253.0834 236.7785 246.6311 251.5293
##  [29] 263.1189 261.2831 233.5511 249.8017 233.6142 226.4771 313.7909
##  [36] 273.8633 267.0187 277.5766 250.9891 257.6297 279.4657 264.7424
##  [43] 267.1432 247.5561 229.9279 231.5124 213.7255 247.6069 283.7847
##  [50] 255.3726 263.3650 274.2206 237.5895 270.1610 259.2144 252.5065
##  [57] 279.6395 253.3642 237.9447 269.4943 270.7847 317.1226 286.9927
##  [64] 250.6020 280.3752 240.0626 286.1403 225.7824 243.5020 287.4268
##  [71] 236.1720 250.4714 246.5070 287.7856 234.6942 241.1621 260.4876
##  [78] 260.0430 266.7571 253.4658 253.9409 280.3543 248.8953 254.0212
##  [85] 274.8240 316.3331 255.6097 277.2092 264.8664 244.6152 291.6180
##  [92] 239.5658 261.6547 236.9804 253.6184 247.3058 252.0989 270.3022
##  [99] 224.9451 378.7654
## 
## $p.val
## [1] 0.01

Pairwise \(F_{st}\)

genet.dist(Mydata1, method = "WC84")
##                    georgia     virginia northcarolina southcarolina
## virginia       0.014916721                                         
## northcarolina  0.035531517  0.012476717                            
## southcarolina  0.029831181  0.009878778   0.006809538              
## alabama        0.046469226  0.027303304   0.009066562   0.007107402
## oklahoma       0.092718257  0.125242248   0.116089566   0.107662986
## arkansas       0.028847476  0.065029859   0.057810705   0.052707863
## florida        0.001379763  0.027697122   0.033602798   0.019836955
## mississippi    0.018627828  0.034121040   0.030048967   0.030695068
## texas          0.046361901  0.068423280   0.046659978   0.052491099
## louisiana      0.133824385  0.092337839   0.043365828   0.064729942
##                    alabama     oklahoma     arkansas      florida
## virginia                                                         
## northcarolina                                                    
## southcarolina                                                    
## alabama                                                          
## oklahoma       0.104844418                                       
## arkansas       0.052033165  0.022743098                          
## florida        0.034205805  0.080741693  0.031222010             
## mississippi    0.019629941  0.061834123 -0.011045367  0.008707686
## texas          0.038153327  0.033860376  0.013982640  0.025059824
## louisiana      0.031028123  0.070815392  0.035722729  0.096253248
##                mississippi        texas
## virginia                               
## northcarolina                          
## southcarolina                          
## alabama                                
## oklahoma                               
## arkansas                               
## florida                                
## mississippi                            
## texas         -0.025535193             
## louisiana      0.030209963  0.036298136
# No test at the moment

Unsupervised clustering

We don’t know the populations and we are looking for. As recommended by T. Jombart, with the function find.clusters() we used the maximum possible number of PCA axis which is 20 here. See detailed tutorial on this method for more information (https://github.com/thibautjombart/adegenet/raw/master/tutorials /tutorial-basics.pdf) In this example, we used choose.n.clust = FALSE but it is nice to use the option TRUE and then you will be able to choose the number of clusters.

# using Kmeans and DAPC in adegenet 
set.seed(20160308) # Setting a seed for a consistent result
grp <- find.clusters(Mydata1, max.n.clust = 10, n.pca = 20, choose.n.clust = FALSE) 
names(grp)
## [1] "Kstat" "stat"  "grp"   "size"
grp$grp
##   1066   2040   4004   4005   4018   6009   6013   7033   7056   7069 
##      4      3      1      3      1      3      1      1      2      3 
##   7088   7105   8001   8061   8068   8076   8120   8195   8203   8222 
##      1      3      1      1      1      1      4      4      4      1 
##   8223   8231   8237   8301   8302   8303   8304   8305   8307   8308 
##      4      1      3      1      3      3      4      1      3      3 
##   8309   8310   8313   8314   8316   8317   8318   8319   8320   8323 
##      3      4      1      1      1      1      3      1      1      4 
##   8324   8327   8328   8329   8330   8332   8333   8334   8335   8336 
##      4      1      1      1      1      4      3      4      4      3 
##   8337   8338   8339   8340   8342   8343   8344   8345   8346   8347 
##      1      1      1      1      4      4      3      4      4      1 
##   8349   8350   8351   8352   8353   8354   8355   8356   8357   8358 
##      1      4      4      1      1      1      4      1      4      3 
##   8364   8365   8366   8367   8368   8369   8370   8371   8372   8373 
##      4      1      1      3      1      1      3      4      1      1 
##   8374   8375   8376   8377   8378   8379   8381   8382   8383   8384 
##      1      1      2      4      3      3      1      1      2      2 
##   8385   8386   8387   8388   8389   8390   8391   8392   8393   8395 
##      1      1      1      1      3      4      2      1      4      3 
##   8400   8402   8403   8559   8565   8567   8568   8569   8570   8571 
##      1      1      1      4      3      3      3      4      4      4 
##   8572   8573   8574   8601   8602   8603   8604   8606   8607   8608 
##      2      1      3      1      4      2      3      3      4      3 
##   8609   8610   8611   8613   8615   8616   8618   8619   8620   8621 
##      3      2      1      3      3      3      1      3      3      3 
##   8622   8624   8626   8628   8629   8630   8631   8633   8634   8635 
##      1      2      3      1      1      4      4      1      3      1 
##   8636   8637   8638   8639   8640   8641   8642   8643   8644   8645 
##      3      1      3      3      1      3      3      2      1      2 
##   8646   8647   8648   8651   8652   8653   8654   8655   8656   8657 
##      4      1      4      4      3      4      1      3      1      3 
##   8658   8659   8660   8661   8662   8663   8664   8667   8669   8670 
##      4      3      3      3      2      4      2      4      3      1 
##   8671   8672   8673   8674   8675   8676   8677   8678   8679   8680 
##      3      3      1      1      1      4      1      3      1      1 
##   8683   8685   8686   8687   8688   8689   8691   8692   8693   8694 
##      1      2      4      3      4      3      4      2      4      3 
##   8695   8696   8697   8698   8699   8700   8701   8702   8703   8704 
##      1      3      2      2      3      1      4      4      3      3 
##   8705   8706   9003   9006   9015  10005  11010  11503  11532  12008 
##      2      1      1      3      2      1      4      1      2      3 
##  12012  14010  14015  22212  68087  68088  68090  68095  68130  68131 
##      4      1      1      1      4      4      4      2      4      4 
##  68133  68134  68135   105A   108A   109B   110B   112C   115B   117B 
##      1      4      3      2      3      1      4      4      4      1 
##   118B    11A   120A   121C   127A   128A   131B   132B   136C   138A 
##      2      3      3      3      2      3      2      4      3      1 
##   139B   140B   141A   142B   144C   145A   146C   147A   149B   150A 
##      2      2      3      2      2      1      4      1      2      1 
##   151A   152B   153B   154C   155B   156C   157A   158B    15A   162A 
##      3      2      2      2      2      3      2      1      4      4 
##   166B    16A   171A   173A   174A    17C   188A   189A   190A   191A 
##      2      3      3      3      2      3      4      3      2      2 
##    19A   205B    20A   212A   213A   217C   219A    21A   220A   224A 
##      1      2      2      1      4      2      4      2      4      4 
##   226C   227C   234B   235A   238A    23A   245B   248A   250C   253A 
##      4      1      3      2      2      2      1      1      1      1 
##   254C   257B   258A   260B   262A   264C   265A   268A   269A   270C 
##      2      3      2      2      4      3      4      1      4      2 
##   271A   272B   275A   276B   277A    27A   281A   282B   283C   285C 
##      3      1      2      4      3      3      4      3      4      3 
##   286B   287C   288C   289A   290C   291C   292C   298B   299C   300C 
##      1      4      4      3      4      4      4      3      4      2 
##   302A   303C   305A   306A   307A   311A   316B   320C   322A   323B 
##      2      4      3      4      3      4      1      2      1      2 
##   324A   326A   327A   328B   329B    32A   330A   331B   332C   334A 
##      4      4      4      2      2      2      3      2      2      2 
##   335A   336A   339B   340A   341C   346A   349B    34A   351A   353C 
##      3      3      2      2      3      4      3      2      2      1 
##   355A    35A   360B   361B   362C   365B   366A   368B   369A    36A 
##      1      4      2      2      3      1      4      3      2      4 
##   370C   371B   372A   373B   375A   377B   378B   379B   382A   383C 
##      2      1      4      4      2      2      3      1      2      2 
##   384A   385B   387A   388B   389A   390B   391C   392A   393C   395A 
##      3      2      4      4      3      1      2      3      2      1 
##   397A   398C   400C   407A   408C   409B   410A   411C   412C   414A 
##      2      4      3      3      3      3      4      3      3      2 
##   415A   416B   417A   418A   419B    41C   420B   421A   422B   423C 
##      3      4      1      3      4      1      4      1      4      3 
##   424B   425B   426B   427C   428C   429B    42A   430B   431B   433B 
##      3      4      1      2      4      3      3      4      3      4 
##   434B   435C   436A    43B   441C   442C   443C   448C   449A   450B 
##      1      2      2      1      3      3      4      3      2      4 
##   451B   459A   461A   463A   469C   470A   471B   481A   483A   484A 
##      3      2      2      4      1      2      2      4      4      4 
##   485A   486B   487C   489B    48B   492C   493A   496B   498B   499A 
##      1      3      4      2      2      1      3      3      4      3 
##    49A   500B   501A   502C   514A   515A   519B   520B   526A   527B 
##      2      1      2      3      2      4      2      1      1      4 
##   528C    52A   531A   532A   533A   534A   535C   536A   539A    53C 
##      2      2      3      2      2      1      1      2      2      2 
##   540A   541A   542C   543C   544B   545A   546C   548C   549A    54C 
##      1      2      4      2      1      2      4      4      2      3 
##   551C   552A   553B   554A   555C   556A   557B   558A   559A    55A 
##      1      4      2      1      1      2      2      2      2      2 
##   561A   562A   563A   564B   565C   566B   568B   570A   571A   572C 
##      3      2      2      2      1      1      4      4      2      3 
##   573C   574B   576A   577B   578B   579A    57A   580A   581C   600A 
##      2      3      1      2      2      1      2      1      4      4 
##   601A   603A   605B   606B    60A   612C   613A   618A   619A    61A 
##      3      3      4      4      1      4      4      2      3      4 
##   620C   621A   633B   634C   635A   636C   637B    63B   644B   645A 
##      4      4      4      2      4      4      4      1      4      1 
##   646A    66A    67C    69A    73B    77B     7A    86C    89C    90C 
##      4      3      2      3      1      2      3      3      3      2 
##    92A    93C    94C    97B    98A    99C     9A CRO108 CRO120 CRO121 
##      2      2      2      1      1      2      2      2      3      3 
## CRO133 DF3364  FM406  FM417  FM428  FM442  FM445  S4PT6   SH13    SH7 
##      1      4      3      3      4      1      2      4      3      1 
## Levels: 1 2 3 4

The K means procedure detected 4 groups. We will use this number of group in the discriminant analysis (function dapc()). On your own dataset, you need to spend more time to estimate the number of clusters.

dapc1 <- dapc(Mydata1, grp$grp, n.pca = 20, n.da = 6) 
scatter(dapc1) # plot of the group

It’s clear that a subset of 20 SNPs does not have a strong enough signal to separate the samples into distinct groups. What would happen if we used more SNPs?

Conclusions

What did we learn today?

In this vignette, we learned how to calculate \(F_{st}\) in existing populations and to investigate the effect of population structure on genetic differentiation from hierarchical \(F_{st}\) analysis (like AMOVA in the case of SNP). We also ran a multivariate analysis to investigate the genetic structure of the data at individual level assuming no population structure.

What is next?

You may now want to move on to the estimation of genetic distances.

Contributors

References

Eckert, A. J., A. D. Bower, S. C. González-Martínez, J. L. Wegrzyn, G. Coop and D. B. Neale. 2010. Back to nature: Ecological genomics of loblolly pine (Pinus taeda, Pinaceae). Molecular Ecology 19: 3789-3805.

Thierry de Meeûs, Jérôme Goudet “A step-by-step tutorial to use HierFstat to analyse populations hierarchically structured at multiple levels.”, Infect. Genet. Evol., vol. 7, no. 6, 2007

Session Information

This shows us useful information for reproducibility. Of particular importance are the versions of R and the packages used to create this workflow. It is considered good practice to record this information with every analysis.

options(width = 100)
devtools::session_info()
## Session info --------------------------------------------------------------------------------------
##  setting  value                       
##  version  R version 3.5.0 (2018-04-23)
##  system   x86_64, linux-gnu           
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  tz       Etc/UTC                     
##  date     2018-06-07
## Packages ------------------------------------------------------------------------------------------
##  package    * version date       source        
##  ade4       * 1.7-11  2018-04-05 CRAN (R 3.5.0)
##  adegenet   * 2.1.1   2018-02-02 CRAN (R 3.5.0)
##  ape          5.1     2018-04-04 CRAN (R 3.5.0)
##  assertthat   0.2.0   2017-04-11 CRAN (R 3.5.0)
##  backports    1.1.2   2017-12-13 CRAN (R 3.5.0)
##  base       * 3.5.0   2018-06-07 local         
##  bindr        0.1.1   2018-03-13 CRAN (R 3.5.0)
##  bindrcpp     0.2.2   2018-03-29 CRAN (R 3.5.0)
##  boot         1.3-20  2017-07-30 CRAN (R 3.5.0)
##  cluster      2.0.7-1 2018-04-09 CRAN (R 3.5.0)
##  coda         0.19-1  2016-12-08 CRAN (R 3.5.0)
##  colorspace   1.3-2   2016-12-14 CRAN (R 3.5.0)
##  compiler     3.5.0   2018-06-07 local         
##  datasets   * 3.5.0   2018-06-07 local         
##  deldir       0.1-15  2018-04-01 CRAN (R 3.5.0)
##  devtools     1.13.5  2018-02-18 CRAN (R 3.5.0)
##  digest       0.6.15  2018-01-28 CRAN (R 3.5.0)
##  dplyr        0.7.5   2018-05-19 CRAN (R 3.5.0)
##  evaluate     0.10.1  2017-06-24 CRAN (R 3.5.0)
##  expm         0.999-2 2017-03-29 CRAN (R 3.5.0)
##  gdata        2.18.0  2017-06-06 CRAN (R 3.5.0)
##  ggplot2      2.2.1   2016-12-30 CRAN (R 3.5.0)
##  glue         1.2.0   2017-10-29 CRAN (R 3.5.0)
##  gmodels      2.16.2  2015-07-22 CRAN (R 3.5.0)
##  graphics   * 3.5.0   2018-06-07 local         
##  grDevices  * 3.5.0   2018-06-07 local         
##  grid         3.5.0   2018-06-07 local         
##  gtable       0.2.0   2016-02-26 CRAN (R 3.5.0)
##  gtools       3.5.0   2015-05-29 CRAN (R 3.5.0)
##  hierfstat  * 0.04-22 2015-12-04 CRAN (R 3.5.0)
##  htmltools    0.3.6   2017-04-28 CRAN (R 3.5.0)
##  httpuv       1.4.3   2018-05-10 CRAN (R 3.5.0)
##  igraph       1.2.1   2018-03-10 CRAN (R 3.5.0)
##  knitr        1.20    2018-02-20 CRAN (R 3.5.0)
##  later        0.7.2   2018-05-01 CRAN (R 3.5.0)
##  lattice      0.20-35 2017-03-25 CRAN (R 3.5.0)
##  lazyeval     0.2.1   2017-10-29 CRAN (R 3.5.0)
##  LearnBayes   2.15.1  2018-03-18 CRAN (R 3.5.0)
##  magrittr     1.5     2014-11-22 CRAN (R 3.5.0)
##  MASS         7.3-50  2018-04-30 CRAN (R 3.5.0)
##  Matrix       1.2-14  2018-04-09 CRAN (R 3.5.0)
##  memoise      1.1.0   2017-04-21 CRAN (R 3.5.0)
##  methods    * 3.5.0   2018-06-07 local         
##  mgcv         1.8-23  2018-01-15 CRAN (R 3.5.0)
##  mime         0.5     2016-07-07 CRAN (R 3.5.0)
##  munsell      0.4.3   2016-02-13 CRAN (R 3.5.0)
##  nlme         3.1-137 2018-04-07 CRAN (R 3.5.0)
##  parallel     3.5.0   2018-06-07 local         
##  permute      0.9-4   2016-09-09 CRAN (R 3.5.0)
##  pillar       1.2.3   2018-05-25 CRAN (R 3.5.0)
##  pkgconfig    2.0.1   2017-03-21 CRAN (R 3.5.0)
##  plyr         1.8.4   2016-06-08 CRAN (R 3.5.0)
##  promises     1.0.1   2018-04-13 CRAN (R 3.5.0)
##  purrr        0.2.5   2018-05-29 CRAN (R 3.5.0)
##  R6           2.2.2   2017-06-17 CRAN (R 3.5.0)
##  Rcpp         0.12.17 2018-05-18 CRAN (R 3.5.0)
##  reshape2     1.4.3   2017-12-11 CRAN (R 3.5.0)
##  rlang        0.2.1   2018-05-30 CRAN (R 3.5.0)
##  rmarkdown    1.9     2018-03-01 CRAN (R 3.5.0)
##  rprojroot    1.3-2   2018-01-03 CRAN (R 3.5.0)
##  scales       0.5.0   2017-08-24 CRAN (R 3.5.0)
##  seqinr       3.4-5   2017-08-01 CRAN (R 3.5.0)
##  shiny        1.1.0   2018-05-17 CRAN (R 3.5.0)
##  sp           1.3-1   2018-06-05 CRAN (R 3.5.0)
##  spData       0.2.8.3 2018-03-25 CRAN (R 3.5.0)
##  spdep        0.7-7   2018-04-03 CRAN (R 3.5.0)
##  splines      3.5.0   2018-06-07 local         
##  stats      * 3.5.0   2018-06-07 local         
##  stringi      1.2.2   2018-05-02 CRAN (R 3.5.0)
##  stringr      1.3.1   2018-05-10 CRAN (R 3.5.0)
##  tibble       1.4.2   2018-01-22 CRAN (R 3.5.0)
##  tidyselect   0.2.4   2018-02-26 CRAN (R 3.5.0)
##  tools        3.5.0   2018-06-07 local         
##  utils      * 3.5.0   2018-06-07 local         
##  vegan        2.5-2   2018-05-17 CRAN (R 3.5.0)
##  withr        2.1.2   2018-03-15 CRAN (R 3.5.0)
##  xtable       1.8-2   2016-02-05 CRAN (R 3.5.0)
##  yaml         2.1.19  2018-05-01 CRAN (R 3.5.0)