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: 135.7 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] 260.5297 216.3335 220.2240 222.0100 253.5008 241.8717 248.0382
##   [8] 223.3143 229.8985 212.3708 252.8432 209.0847 236.6628 242.1262
##  [15] 194.2421 235.2828 220.4039 211.6549 242.5383 187.9137 258.6989
##  [22] 223.8577 192.4523 232.3658 213.1652 241.9819 238.1730 202.0798
##  [29] 234.4804 235.5446 234.9952 224.5964 215.1480 204.1731 214.3815
##  [36] 214.6349 214.3714 222.5092 198.8620 262.0002 208.5187 235.4410
##  [43] 217.1556 211.5999 224.1621 248.7393 209.7324 192.7153 230.0311
##  [50] 197.7011 223.3358 232.9960 207.2343 227.9032 235.8070 204.4477
##  [57] 243.0616 202.6704 235.1158 218.7683 219.9284 228.5920 199.2378
##  [64] 230.1254 199.9263 221.0018 200.2032 256.6499 222.7087 213.6543
##  [71] 200.5704 270.3039 203.4829 230.8766 237.8876 226.4172 220.4239
##  [78] 214.8304 192.1173 224.0235 213.6727 236.8981 211.2254 185.9821
##  [85] 233.2732 208.4035 218.5635 211.7899 237.7030 233.5469 210.9506
##  [92] 205.9765 240.1955 207.7381 187.1298 204.4788 236.9804 222.7857
##  [99] 219.3257 378.7654
## 
## $p.val
## [1] 0.01
test.between(loci, test.lev = population, rand.unit = county, nperm = 100) 
## $g.star
##   [1] 303.8646 230.1035 247.6314 257.1789 294.5935 277.7621 259.0076
##   [8] 244.0152 231.7291 231.8134 293.7544 294.9070 285.6019 234.6537
##  [15] 244.7982 221.9155 208.5285 298.0680 255.8457 219.7808 281.9340
##  [22] 276.6203 257.4497 285.6968 283.3965 272.0333 208.6551 259.5363
##  [29] 282.8206 267.9550 268.5340 208.9065 219.5480 278.5734 259.2559
##  [36] 258.6274 235.2248 249.2446 281.4034 217.6891 248.0391 243.1079
##  [43] 300.2940 291.6670 240.3010 244.8921 290.2099 206.4562 249.0657
##  [50] 252.1999 293.1325 243.7703 229.3889 240.2796 266.7870 255.2519
##  [57] 258.5295 249.8591 207.9150 290.4364 266.2202 239.2599 251.5536
##  [64] 294.8112 242.0585 248.8689 284.3053 240.3569 280.1045 259.7342
##  [71] 247.7135 263.5705 240.7986 239.6524 289.5585 283.4186 272.7284
##  [78] 287.0451 290.2288 285.1185 265.8772 269.2290 243.6917 238.5031
##  [85] 279.1029 266.3170 251.7320 223.6478 306.1061 250.6083 266.3116
##  [92] 250.3041 246.3991 300.2299 256.6542 246.9871 228.2364 287.3983
##  [99] 243.1451 378.7654
## 
## $p.val
## [1] 0.01

Pairwise \(F_{st}\)

genet.dist(Mydata1, method = "WC84")
##                    alabama     arkansas      florida      georgia
## arkansas       0.052033165                                       
## florida        0.034205805  0.031222010                          
## georgia        0.046469226  0.028847476  0.001379763             
## louisiana      0.031028123  0.035722729  0.096253248  0.133824385
## mississippi    0.019629941 -0.011045367  0.008707686  0.018627828
## northcarolina  0.009066562  0.057810705  0.033602798  0.035531517
## oklahoma       0.104844418  0.022743098  0.080741693  0.092718257
## southcarolina  0.007107402  0.052707863  0.019836955  0.029831181
## texas          0.038153327  0.013982640  0.025059824  0.046361901
## virginia       0.027303304  0.065029859  0.027697122  0.014916721
##                  louisiana  mississippi northcarolina     oklahoma
## arkansas                                                          
## florida                                                           
## georgia                                                           
## louisiana                                                         
## mississippi    0.030209963                                        
## northcarolina  0.043365828  0.030048967                           
## oklahoma       0.070815392  0.061834123   0.116089566             
## southcarolina  0.064729942  0.030695068   0.006809538  0.107662986
## texas          0.036298136 -0.025535193   0.046659978  0.033860376
## virginia       0.092337839  0.034121040   0.012476717  0.125242248
##               southcarolina        texas
## arkansas                                
## florida                                 
## georgia                                 
## louisiana                               
## mississippi                             
## northcarolina                           
## oklahoma                                
## southcarolina                           
## texas           0.052491099             
## virginia        0.009878778  0.068423280
# 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) 
## The "ward" method has been renamed to "ward.D"; note new "ward.D2"
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 3.3.2 (2016-10-31)
##  system   x86_64, linux-gnu           
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  tz       <NA>                        
##  date     2017-01-04
## Packages -------------------------------------------------------------------------------------------
##  package    * version date       source        
##  ade4       * 1.7-5   2016-12-13 CRAN (R 3.3.2)
##  adegenet   * 2.0.1   2016-02-15 CRAN (R 3.3.2)
##  ape          4.0     2016-12-01 CRAN (R 3.3.2)
##  assertthat   0.1     2013-12-06 CRAN (R 3.3.2)
##  backports    1.0.4   2016-10-24 CRAN (R 3.3.2)
##  boot         1.3-18  2016-02-23 CRAN (R 3.3.2)
##  cluster      2.0.5   2016-10-08 CRAN (R 3.3.2)
##  coda         0.19-1  2016-12-08 CRAN (R 3.3.2)
##  colorspace   1.3-2   2016-12-14 CRAN (R 3.3.2)
##  DBI          0.5-1   2016-09-10 CRAN (R 3.3.2)
##  deldir       0.1-12  2016-03-06 CRAN (R 3.3.2)
##  devtools     1.12.0  2016-12-05 CRAN (R 3.3.2)
##  digest       0.6.10  2016-08-02 CRAN (R 3.3.2)
##  dplyr        0.5.0   2016-06-24 CRAN (R 3.3.2)
##  evaluate     0.10    2016-10-11 CRAN (R 3.3.2)
##  gdata        2.17.0  2015-07-04 CRAN (R 3.3.2)
##  ggplot2      2.2.0   2016-11-11 CRAN (R 3.3.2)
##  gmodels      2.16.2  2015-07-22 CRAN (R 3.3.2)
##  gtable       0.2.0   2016-02-26 CRAN (R 3.3.2)
##  gtools       3.5.0   2015-05-29 CRAN (R 3.3.2)
##  hierfstat  * 0.04-22 2015-12-04 CRAN (R 3.3.2)
##  htmltools    0.3.5   2016-03-21 CRAN (R 3.3.2)
##  httpuv       1.3.3   2015-08-04 CRAN (R 3.3.2)
##  igraph       1.0.1   2015-06-26 CRAN (R 3.3.2)
##  knitr        1.15.1  2016-11-22 CRAN (R 3.3.2)
##  lattice      0.20-34 2016-09-06 CRAN (R 3.3.2)
##  lazyeval     0.2.0   2016-06-12 CRAN (R 3.3.2)
##  LearnBayes   2.15    2014-05-29 CRAN (R 3.3.2)
##  magrittr     1.5     2014-11-22 CRAN (R 3.3.2)
##  MASS         7.3-45  2016-04-21 CRAN (R 3.3.2)
##  Matrix       1.2-7.1 2016-09-01 CRAN (R 3.3.2)
##  memoise      1.0.0   2016-01-29 CRAN (R 3.3.2)
##  mgcv         1.8-16  2016-11-07 CRAN (R 3.3.2)
##  mime         0.5     2016-07-07 CRAN (R 3.3.2)
##  munsell      0.4.3   2016-02-13 CRAN (R 3.3.2)
##  nlme         3.1-128 2016-05-10 CRAN (R 3.3.2)
##  permute      0.9-4   2016-09-09 CRAN (R 3.3.2)
##  plyr         1.8.4   2016-06-08 CRAN (R 3.3.2)
##  R6           2.2.0   2016-10-05 CRAN (R 3.3.2)
##  Rcpp         0.12.8  2016-11-17 CRAN (R 3.3.2)
##  reshape2     1.4.2   2016-10-22 CRAN (R 3.3.2)
##  rmarkdown    1.3     2016-12-21 CRAN (R 3.3.2)
##  rprojroot    1.1     2016-10-29 CRAN (R 3.3.2)
##  scales       0.4.1   2016-11-09 CRAN (R 3.3.2)
##  seqinr       3.3-3   2016-10-13 CRAN (R 3.3.2)
##  shiny        0.14.2  2016-11-01 CRAN (R 3.3.2)
##  sp           1.2-4   2016-12-22 CRAN (R 3.3.2)
##  spdep        0.6-8   2016-09-21 CRAN (R 3.3.2)
##  stringi      1.1.2   2016-10-01 CRAN (R 3.3.2)
##  stringr      1.1.0   2016-08-19 CRAN (R 3.3.2)
##  tibble       1.2     2016-08-26 CRAN (R 3.3.2)
##  vegan        2.4-1   2016-09-07 CRAN (R 3.3.2)
##  withr        1.0.2   2016-06-20 CRAN (R 3.3.2)
##  xtable       1.8-2   2016-02-05 CRAN (R 3.3.2)
##  yaml         2.1.14  2016-11-12 CRAN (R 3.3.2)