genet: A class of data: tables of populations and alleles
In ade4: Analysis of Ecological Data : Exploratory and Euclidean Methods in Environmental Sciences
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
There are multiple formats of genetic data. The functions of ade4 associated genetic data use the class genet.
An object of the class genet is a list containing at least one data frame whose lines are groups of individuals (populations) and columns alleles forming blocks associated with the locus.
They contain allelic frequencies expressed as a percentage.
The function char2genet ensures the reading of tables crossing diploid individuals arranged by groups (populations) and polymorphic loci. Data frames containing only strings of characters are transformed in tables of allelic frequencies of the class genet.
In entry a row is an individual, a variable is a locus and a value is a string of characters, for example ' 012028 ' for a heterozygote carrying alleles 012 and 028, ' 020020 ' for a homozygote carrying two alleles 020 and ' 000000 ' for a not classified locus (missing data).
The function count2genet reads data frames containing allelic countings by populations and allelic forms classified by locus.
The function freq2genet reads data frames containing allelic frequencies by populations and allelic forms classified by locus.
In these two cases, use as names of variables of strings of characters xx.yyy where xx are the names of locus and yyy a name of allelic forms in this locus.
The analyses on this kind of data having to use compact labels, these functions classify the names of the populations, the names of the loci and the names of the allelic forms in vectors and re-code in a simple way starting with P for population, L for locus and 1,..., m for the alleles.
Usage
1
2
3
char2genet(X, pop, complete)
count2genet(PopAllCount)
freq2genet(PopAllFreq)
Arguments
X
a data frame of strings of characters (individuals in row, locus in variables), the value coded '000000' or two alleles of 6 characters
pop
a factor with the same number of rows than df classifying the individuals by population
complete
a logical value indicating a complete issue or not, by default FALSE
PopAllCount
a data frame containing integers: the occurrences of each allelic form (column) in each population (row)
PopAllFreq
a data frame containing values between 0 and 1: the frequencies of each allelic form (column) in each population (row)
Details
As a lot of formats for genetic data are published in literature, a list of class genet contains at least a table of allellic frequencies and an attribut loc.blocks. The populations (row) and the variables (column) are classified by alphabetic order.
In the component comp, each individual per locus of m alleles is re-coded by a vector of length m: for hererozygicy 0,...,1,...,1,...,0 and homozygocy 0,...,2,0.
Value
char2genet returns a list of class genet with :
$tab
a frequencies table of poplations (row) and alleles (column)
$center
the global frequency of each allelic form calculated on the overall individuals classified on each locus
$pop.names
a vector containing the names of populations present in the data re-coded P01, P02, ...
$all.names
a vector containing the names of the alleles present in the data re-coded L01.1, L01.2, ...
$loc.blocks
a vector containing the number of alleles by loci
$loc.fac
a factor sharing the alleles by loci
$loc.names
a vector containing the names of loci present in the data re-coded L01, ..., L99
$pop.loc
a data frame containing the number of genus allowing the calculation of frequencies
$comp
the complete individual typing with the code 02000 or 01001 if the option complete is TRUE
$comp.pop
a factor indicating the population if the option complete is TRUE
count2genet and freq2genet return a list of class genet which don't contain the components pop.loc and complete.
Author(s)
Daniel Chessel
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
data(casitas)
casitas[24,]
casitas.pop <- as.factor(rep(c("dome", "cast", "musc", "casi"), c(24,11,9,30)))
casi.genet <- char2genet(casitas, casitas.pop, complete=TRUE)
names(casi.genet$tab)
casi.genet$tab[,1:8]
casi.genet$pop.names
casi.genet$loc.names
casi.genet$all.names
casi.genet$loc.blocks # number of allelic forms by loci
casi.genet$loc.fac # factor classifying the allelic forms by locus
casi.genet$pop.loc # table populations loci
names(casi.genet$comp)
casi.genet$comp[1:4,]
casi.genet$comp.pop
casi.genet$center
apply(casi.genet$tab,2,mean)
casi.genet$pop.loc[,"L15"]
casi.genet$tab[, c("L15.1","L15.2")]
class(casi.genet)
casitas.coa <- dudi.coa(casi.genet$comp, scannf = FALSE)
s.class(casitas.coa$li,casi.genet$comp.pop)
Example output
Aat Amy Es1 Es2 Es10 Hbb Gpd1 Idh1 Mod1 Mod2 Mpi
24 100100 080100 094094 100100 000000 000000 000000 125125 110110 100100 100100
Np Pgm1 Pgm2 Sod
24 000000 100100 100100 100100
[1] "L01.1" "L01.2" "L02.1" "L02.2" "L03.1" "L03.2" "L04.1" "L04.2" "L05.1"
[10] "L05.2" "L05.3" "L06.1" "L06.2" "L06.3" "L07.1" "L07.2" "L08.1" "L08.2"
[19] "L08.3" "L08.4" "L09.1" "L09.2" "L09.3" "L10.1" "L10.2" "L11.1" "L11.2"
[28] "L12.1" "L12.2" "L12.3" "L12.4" "L13.1" "L13.2" "L13.3" "L14.1" "L14.2"
[37] "L15.1" "L15.2"
L01.1 L01.2 L02.1 L02.2 L03.1 L03.2 L04.1
P1 0.2500000 0.7500000 0.8333333 0.1666667 0.8500000 0.1500000 0.0000000
P2 0.3181818 0.6818182 1.0000000 0.0000000 0.2272727 0.7727273 0.0000000
P3 0.0000000 1.0000000 0.8125000 0.1875000 1.0000000 0.0000000 0.0000000
P4 0.0000000 1.0000000 0.0000000 1.0000000 0.0000000 1.0000000 0.9444444
L04.2
P1 1.00000000
P2 1.00000000
P3 1.00000000
P4 0.05555556
P1 P2 P3 P4
"casi" "cast" "dome" "musc"
L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11
"Aat" "Amy" "Es1" "Es10" "Es2" "Gpd1" "Hbb" "Idh1" "Mod1" "Mod2" "Mpi"
L12 L13 L14 L15
"Np" "Pgm1" "Pgm2" "Sod"
L01.1 L01.2 L02.1 L02.2 L03.1 L03.2 L04.1
"Aat.080" "Aat.100" "Amy.080" "Amy.100" "Es1.094" "Es1.100" "Es10.060"
L04.2 L05.1 L05.2 L05.3 L06.1 L06.2 L06.3
"Es10.100" "Es2.095" "Es2.098" "Es2.100" "Gpd1.095" "Gpd1.100" "Gpd1.105"
L07.1 L07.2 L08.1 L08.2 L08.3 L08.4 L09.1
"Hbb.110" "Hbb.120" "Idh1.050" "Idh1.080" "Idh1.100" "Idh1.125" "Mod1.100"
L09.2 L09.3 L10.1 L10.2 L11.1 L11.2 L12.1
"Mod1.110" "Mod1.120" "Mod2.100" "Mod2.120" "Mpi.100" "Mpi.120" "Np.080"
L12.2 L12.3 L12.4 L13.1 L13.2 L13.3 L14.1
"Np.085" "Np.090" "Np.100" "Pgm1.060" "Pgm1.080" "Pgm1.100" "Pgm2.080"
L14.2 L15.1 L15.2
"Pgm2.100" "Sod.080" "Sod.100"
L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15
2 2 2 2 3 3 2 4 3 2 2 4 3 2 2
L01.1 L01.2 L02.1 L02.2 L03.1 L03.2 L04.1 L04.2 L05.1 L05.2 L05.3 L06.1 L06.2
L01 L01 L02 L02 L03 L03 L04 L04 L05 L05 L05 L06 L06
L06.3 L07.1 L07.2 L08.1 L08.2 L08.3 L08.4 L09.1 L09.2 L09.3 L10.1 L10.2 L11.1
L06 L07 L07 L08 L08 L08 L08 L09 L09 L09 L10 L10 L11
L11.2 L12.1 L12.2 L12.3 L12.4 L13.1 L13.2 L13.3 L14.1 L14.2 L15.1 L15.2
L11 L12 L12 L12 L12 L13 L13 L13 L14 L14 L15 L15
Levels: L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15
L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15
P1 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30
P2 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
P3 24 24 24 23 24 23 23 24 24 24 24 23 24 24 24
P4 9 9 9 9 9 8 9 9 9 9 8 9 8 8 9
[1] "L01.1" "L01.2" "L02.1" "L02.2" "L03.1" "L03.2" "L04.1" "L04.2" "L05.1"
[10] "L05.2" "L05.3" "L06.1" "L06.2" "L06.3" "L07.1" "L07.2" "L08.1" "L08.2"
[19] "L08.3" "L08.4" "L09.1" "L09.2" "L09.3" "L10.1" "L10.2" "L11.1" "L11.2"
[28] "L12.1" "L12.2" "L12.3" "L12.4" "L13.1" "L13.2" "L13.3" "L14.1" "L14.2"
[37] "L15.1" "L15.2"
L01.1 L01.2 L02.1 L02.2 L03.1 L03.2 L04.1 L04.2 L05.1 L05.2 L05.3 L06.1
01 0 2 2 0 1 1 0 2 2 0 0 0
02 1 1 2 0 2 0 0 2 0 0 2 0
03 1 1 1 1 2 0 0 2 2 0 0 0
04 0 2 1 1 2 0 0 2 0 0 2 2
L06.2 L06.3 L07.1 L07.2 L08.1 L08.2 L08.3 L08.4 L09.1 L09.2 L09.3 L10.1
01 0 2 1 1 0 0 1 1 0 2 0 0
02 0 2 1 1 0 0 2 0 0 2 0 1
03 2 0 1 1 0 0 2 0 0 2 0 0
04 0 0 2 0 0 0 2 0 0 2 0 0
L10.2 L11.1 L11.2 L12.1 L12.2 L12.3 L12.4 L13.1 L13.2 L13.3 L14.1 L14.2
01 2 2 0 0 0 0 2 0 0 2 0 2
02 1 2 0 0 0 0 2 0 0 2 0 2
03 2 2 0 0 0 0 2 0 0 2 0 2
04 2 2 0 0 0 0 2 0 0 2 0 2
L15.1 L15.2
01 0 2
02 0 2
03 0 2
04 0 2
[1] P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1
[26] P1 P1 P1 P1 P1 P2 P2 P2 P2 P2 P2 P2 P2 P2 P2 P2 P3 P3 P3 P3 P3 P3 P3 P3 P3
[51] P3 P3 P3 P3 P3 P3 P3 P3 P3 P3 P3 P3 P3 P3 P4 P4 P4 P4 P4 P4 P4 P4
Levels: P1 P2 P3 P4
L01.1 L01.2 L02.1 L02.2 L03.1 L03.2
0.148648649 0.851351351 0.750000000 0.250000000 0.702702703 0.297297297
L04.1 L04.2 L05.1 L05.2 L05.3 L06.1
0.116438356 0.883561644 0.081081081 0.195945946 0.722972973 0.375000000
L06.2 L06.3 L07.1 L07.2 L08.1 L08.2
0.534722222 0.090277778 0.465753425 0.534246575 0.101351351 0.006756757
L08.3 L08.4 L09.1 L09.2 L09.3 L10.1
0.608108108 0.283783784 0.128378378 0.797297297 0.074324324 0.689189189
L10.2 L11.1 L11.2 L12.1 L12.2 L12.3
0.310810811 0.794520548 0.205479452 0.061643836 0.089041096 0.061643836
L12.4 L13.1 L13.2 L13.3 L14.1 L14.2
0.787671233 0.027397260 0.130136986 0.842465753 0.082191781 0.917808219
L15.1 L15.2
0.121621622 0.878378378
L01.1 L01.2 L02.1 L02.2 L03.1 L03.2 L04.1
0.14204545 0.85795455 0.66145833 0.33854167 0.51931818 0.48068182 0.23611111
L04.2 L05.1 L05.2 L05.3 L06.1 L06.2 L06.3
0.76388889 0.05719697 0.37247475 0.57032828 0.56666667 0.37916667 0.05416667
L07.1 L07.2 L08.1 L08.2 L08.3 L08.4 L09.1
0.54431818 0.45568182 0.13446970 0.01136364 0.47935606 0.37481061 0.09365530
L09.2 L09.3 L10.1 L10.2 L11.1 L11.2 L12.1
0.75356692 0.15277778 0.80833333 0.19166667 0.59943182 0.40056818 0.12500000
L12.2 L12.3 L12.4 L13.1 L13.2 L13.3 L14.1
0.14772727 0.12500000 0.60227273 0.06250000 0.26704545 0.67045455 0.10729167
L14.2 L15.1 L15.2
0.89270833 0.25000000 0.75000000
[1] 30 11 24 9
L15.1 L15.2
P1 0 1
P2 0 1
P3 0 1
P4 1 0
[1] "genet" "list"
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Description Usage Arguments Details Value Author(s) Examples
Description
There are multiple formats of genetic data. The functions of ade4 associated genetic data use the class genet.
An object of the class genet is a list containing at least one data frame whose lines are groups of individuals (populations) and columns alleles forming blocks associated with the locus.
They contain allelic frequencies expressed as a percentage.
The function char2genet ensures the reading of tables crossing diploid individuals arranged by groups (populations) and polymorphic loci. Data frames containing only strings of characters are transformed in tables of allelic frequencies of the class genet.
In entry a row is an individual, a variable is a locus and a value is a string of characters, for example ' 012028 ' for a heterozygote carrying alleles 012 and 028, ' 020020 ' for a homozygote carrying two alleles 020 and ' 000000 ' for a not classified locus (missing data).
The function count2genet reads data frames containing allelic countings by populations and allelic forms classified by locus.
The function freq2genet reads data frames containing allelic frequencies by populations and allelic forms classified by locus.
In these two cases, use as names of variables of strings of characters xx.yyy where xx are the names of locus and yyy a name of allelic forms in this locus.
The analyses on this kind of data having to use compact labels, these functions classify the names of the populations, the names of the loci and the names of the allelic forms in vectors and re-code in a simple way starting with P for population, L for locus and 1,..., m for the alleles.
Usage
1 2 3 | char2genet(X, pop, complete)
count2genet(PopAllCount)
freq2genet(PopAllFreq)
|
Arguments
X |
a data frame of strings of characters (individuals in row, locus in variables), the value coded '000000' or two alleles of 6 characters |
pop |
a factor with the same number of rows than |
complete |
a logical value indicating a complete issue or not, by default FALSE |
PopAllCount |
a data frame containing integers: the occurrences of each allelic form (column) in each population (row) |
PopAllFreq |
a data frame containing values between 0 and 1: the frequencies of each allelic form (column) in each population (row) |
Details
As a lot of formats for genetic data are published in literature, a list of class genet contains at least a table of allellic frequencies and an attribut loc.blocks. The populations (row) and the variables (column) are classified by alphabetic order.
In the component comp, each individual per locus of m alleles is re-coded by a vector of length m: for hererozygicy 0,...,1,...,1,...,0 and homozygocy 0,...,2,0.
Value
char2genet returns a list of class genet with :
$tab |
a frequencies table of poplations (row) and alleles (column) |
$center |
the global frequency of each allelic form calculated on the overall individuals classified on each locus |
$pop.names |
a vector containing the names of populations present in the data re-coded P01, P02, ... |
$all.names |
a vector containing the names of the alleles present in the data re-coded L01.1, L01.2, ... |
$loc.blocks |
a vector containing the number of alleles by loci |
$loc.fac |
a factor sharing the alleles by loci |
$loc.names |
a vector containing the names of loci present in the data re-coded L01, ..., L99 |
$pop.loc |
a data frame containing the number of genus allowing the calculation of frequencies |
$comp |
the complete individual typing with the code 02000 or 01001 if the option |
$comp.pop |
a factor indicating the population if the option |
count2genet and freq2genet return a list of class genet which don't contain the components pop.loc and complete.
Author(s)
Daniel Chessel
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | data(casitas)
casitas[24,]
casitas.pop <- as.factor(rep(c("dome", "cast", "musc", "casi"), c(24,11,9,30)))
casi.genet <- char2genet(casitas, casitas.pop, complete=TRUE)
names(casi.genet$tab)
casi.genet$tab[,1:8]
casi.genet$pop.names
casi.genet$loc.names
casi.genet$all.names
casi.genet$loc.blocks # number of allelic forms by loci
casi.genet$loc.fac # factor classifying the allelic forms by locus
casi.genet$pop.loc # table populations loci
names(casi.genet$comp)
casi.genet$comp[1:4,]
casi.genet$comp.pop
casi.genet$center
apply(casi.genet$tab,2,mean)
casi.genet$pop.loc[,"L15"]
casi.genet$tab[, c("L15.1","L15.2")]
class(casi.genet)
casitas.coa <- dudi.coa(casi.genet$comp, scannf = FALSE)
s.class(casitas.coa$li,casi.genet$comp.pop)
|
Example output
Aat Amy Es1 Es2 Es10 Hbb Gpd1 Idh1 Mod1 Mod2 Mpi
24 100100 080100 094094 100100 000000 000000 000000 125125 110110 100100 100100
Np Pgm1 Pgm2 Sod
24 000000 100100 100100 100100
[1] "L01.1" "L01.2" "L02.1" "L02.2" "L03.1" "L03.2" "L04.1" "L04.2" "L05.1"
[10] "L05.2" "L05.3" "L06.1" "L06.2" "L06.3" "L07.1" "L07.2" "L08.1" "L08.2"
[19] "L08.3" "L08.4" "L09.1" "L09.2" "L09.3" "L10.1" "L10.2" "L11.1" "L11.2"
[28] "L12.1" "L12.2" "L12.3" "L12.4" "L13.1" "L13.2" "L13.3" "L14.1" "L14.2"
[37] "L15.1" "L15.2"
L01.1 L01.2 L02.1 L02.2 L03.1 L03.2 L04.1
P1 0.2500000 0.7500000 0.8333333 0.1666667 0.8500000 0.1500000 0.0000000
P2 0.3181818 0.6818182 1.0000000 0.0000000 0.2272727 0.7727273 0.0000000
P3 0.0000000 1.0000000 0.8125000 0.1875000 1.0000000 0.0000000 0.0000000
P4 0.0000000 1.0000000 0.0000000 1.0000000 0.0000000 1.0000000 0.9444444
L04.2
P1 1.00000000
P2 1.00000000
P3 1.00000000
P4 0.05555556
P1 P2 P3 P4
"casi" "cast" "dome" "musc"
L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11
"Aat" "Amy" "Es1" "Es10" "Es2" "Gpd1" "Hbb" "Idh1" "Mod1" "Mod2" "Mpi"
L12 L13 L14 L15
"Np" "Pgm1" "Pgm2" "Sod"
L01.1 L01.2 L02.1 L02.2 L03.1 L03.2 L04.1
"Aat.080" "Aat.100" "Amy.080" "Amy.100" "Es1.094" "Es1.100" "Es10.060"
L04.2 L05.1 L05.2 L05.3 L06.1 L06.2 L06.3
"Es10.100" "Es2.095" "Es2.098" "Es2.100" "Gpd1.095" "Gpd1.100" "Gpd1.105"
L07.1 L07.2 L08.1 L08.2 L08.3 L08.4 L09.1
"Hbb.110" "Hbb.120" "Idh1.050" "Idh1.080" "Idh1.100" "Idh1.125" "Mod1.100"
L09.2 L09.3 L10.1 L10.2 L11.1 L11.2 L12.1
"Mod1.110" "Mod1.120" "Mod2.100" "Mod2.120" "Mpi.100" "Mpi.120" "Np.080"
L12.2 L12.3 L12.4 L13.1 L13.2 L13.3 L14.1
"Np.085" "Np.090" "Np.100" "Pgm1.060" "Pgm1.080" "Pgm1.100" "Pgm2.080"
L14.2 L15.1 L15.2
"Pgm2.100" "Sod.080" "Sod.100"
L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15
2 2 2 2 3 3 2 4 3 2 2 4 3 2 2
L01.1 L01.2 L02.1 L02.2 L03.1 L03.2 L04.1 L04.2 L05.1 L05.2 L05.3 L06.1 L06.2
L01 L01 L02 L02 L03 L03 L04 L04 L05 L05 L05 L06 L06
L06.3 L07.1 L07.2 L08.1 L08.2 L08.3 L08.4 L09.1 L09.2 L09.3 L10.1 L10.2 L11.1
L06 L07 L07 L08 L08 L08 L08 L09 L09 L09 L10 L10 L11
L11.2 L12.1 L12.2 L12.3 L12.4 L13.1 L13.2 L13.3 L14.1 L14.2 L15.1 L15.2
L11 L12 L12 L12 L12 L13 L13 L13 L14 L14 L15 L15
Levels: L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15
L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15
P1 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30
P2 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
P3 24 24 24 23 24 23 23 24 24 24 24 23 24 24 24
P4 9 9 9 9 9 8 9 9 9 9 8 9 8 8 9
[1] "L01.1" "L01.2" "L02.1" "L02.2" "L03.1" "L03.2" "L04.1" "L04.2" "L05.1"
[10] "L05.2" "L05.3" "L06.1" "L06.2" "L06.3" "L07.1" "L07.2" "L08.1" "L08.2"
[19] "L08.3" "L08.4" "L09.1" "L09.2" "L09.3" "L10.1" "L10.2" "L11.1" "L11.2"
[28] "L12.1" "L12.2" "L12.3" "L12.4" "L13.1" "L13.2" "L13.3" "L14.1" "L14.2"
[37] "L15.1" "L15.2"
L01.1 L01.2 L02.1 L02.2 L03.1 L03.2 L04.1 L04.2 L05.1 L05.2 L05.3 L06.1
01 0 2 2 0 1 1 0 2 2 0 0 0
02 1 1 2 0 2 0 0 2 0 0 2 0
03 1 1 1 1 2 0 0 2 2 0 0 0
04 0 2 1 1 2 0 0 2 0 0 2 2
L06.2 L06.3 L07.1 L07.2 L08.1 L08.2 L08.3 L08.4 L09.1 L09.2 L09.3 L10.1
01 0 2 1 1 0 0 1 1 0 2 0 0
02 0 2 1 1 0 0 2 0 0 2 0 1
03 2 0 1 1 0 0 2 0 0 2 0 0
04 0 0 2 0 0 0 2 0 0 2 0 0
L10.2 L11.1 L11.2 L12.1 L12.2 L12.3 L12.4 L13.1 L13.2 L13.3 L14.1 L14.2
01 2 2 0 0 0 0 2 0 0 2 0 2
02 1 2 0 0 0 0 2 0 0 2 0 2
03 2 2 0 0 0 0 2 0 0 2 0 2
04 2 2 0 0 0 0 2 0 0 2 0 2
L15.1 L15.2
01 0 2
02 0 2
03 0 2
04 0 2
[1] P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1 P1
[26] P1 P1 P1 P1 P1 P2 P2 P2 P2 P2 P2 P2 P2 P2 P2 P2 P3 P3 P3 P3 P3 P3 P3 P3 P3
[51] P3 P3 P3 P3 P3 P3 P3 P3 P3 P3 P3 P3 P3 P3 P4 P4 P4 P4 P4 P4 P4 P4
Levels: P1 P2 P3 P4
L01.1 L01.2 L02.1 L02.2 L03.1 L03.2
0.148648649 0.851351351 0.750000000 0.250000000 0.702702703 0.297297297
L04.1 L04.2 L05.1 L05.2 L05.3 L06.1
0.116438356 0.883561644 0.081081081 0.195945946 0.722972973 0.375000000
L06.2 L06.3 L07.1 L07.2 L08.1 L08.2
0.534722222 0.090277778 0.465753425 0.534246575 0.101351351 0.006756757
L08.3 L08.4 L09.1 L09.2 L09.3 L10.1
0.608108108 0.283783784 0.128378378 0.797297297 0.074324324 0.689189189
L10.2 L11.1 L11.2 L12.1 L12.2 L12.3
0.310810811 0.794520548 0.205479452 0.061643836 0.089041096 0.061643836
L12.4 L13.1 L13.2 L13.3 L14.1 L14.2
0.787671233 0.027397260 0.130136986 0.842465753 0.082191781 0.917808219
L15.1 L15.2
0.121621622 0.878378378
L01.1 L01.2 L02.1 L02.2 L03.1 L03.2 L04.1
0.14204545 0.85795455 0.66145833 0.33854167 0.51931818 0.48068182 0.23611111
L04.2 L05.1 L05.2 L05.3 L06.1 L06.2 L06.3
0.76388889 0.05719697 0.37247475 0.57032828 0.56666667 0.37916667 0.05416667
L07.1 L07.2 L08.1 L08.2 L08.3 L08.4 L09.1
0.54431818 0.45568182 0.13446970 0.01136364 0.47935606 0.37481061 0.09365530
L09.2 L09.3 L10.1 L10.2 L11.1 L11.2 L12.1
0.75356692 0.15277778 0.80833333 0.19166667 0.59943182 0.40056818 0.12500000
L12.2 L12.3 L12.4 L13.1 L13.2 L13.3 L14.1
0.14772727 0.12500000 0.60227273 0.06250000 0.26704545 0.67045455 0.10729167
L14.2 L15.1 L15.2
0.89270833 0.25000000 0.75000000
[1] 30 11 24 9
L15.1 L15.2
P1 0 1
P2 0 1
P3 0 1
P4 1 0
[1] "genet" "list"
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