Split samples from a genind object into pseudo-haplotypes

Source: R/methods.r

Split samples from a genind object into pseudo-haplotypes

make_haplotypes(gid)

Arguments

gid

a genind or genlight object.

Value

a haploid genind object with an extra strata column called "Individual".

Details

Certain analyses, such as amova work best if within-sample variance (error) can be estimated. Practically, this is performed by splitting the genotypes across all loci to create multiple haplotypes. This way, the within-sample distance can be calculated and incorporated into the model. Please note that the haplotypes generated are based on the order of the unphased alleles in the genind object and do not represent true haplotypes.

Haploid data will be returned un-touched.

Note

The other slot will not be copied over to the new genind object.

See also

poppr.amova() pegas::amova() as.genambig()

Examples

# Diploid data is doubled -------------------------------------------------

data(nancycats)
nan9 <- nancycats[pop = 9]
nan9hap <- make_haplotypes(nan9)
#> Warning: No strata found... creating one from the population.
nan9              # 9 individuals from population 9
#> /// GENIND OBJECT /////////
#> 
#>  // 9 individuals; 9 loci; 108 alleles; size: 28.6 Kb
#> 
#>  // Basic content
#>    @tab:  9 x 108 matrix of allele counts
#>    @loc.n.all: number of alleles per locus (range: 8-18)
#>    @loc.fac: locus factor for the 108 columns of @tab
#>    @all.names: list of allele names for each locus
#>    @ploidy: ploidy of each individual  (range: 2-2)
#>    @type:  codom
#>    @call: .local(x = x, i = i, j = j, pop = 9, drop = drop)
#> 
#>  // Optional content
#>    @pop: population of each individual (group size range: 9-9)
#>    @other: a list containing: xy 
#> 
nan9hap           # 18 haplotypes
#> /// GENIND OBJECT /////////
#> 
#>  // 18 individuals; 9 loci; 43 alleles; size: 21.3 Kb
#> 
#>  // Basic content
#>    @tab:  18 x 43 matrix of allele counts
#>    @loc.n.all: number of alleles per locus (range: 3-8)
#>    @loc.fac: locus factor for the 43 columns of @tab
#>    @all.names: list of allele names for each locus
#>    @ploidy: ploidy of each individual  (range: 1-1)
#>    @type:  codom
#>    @call: df2genind(X = newdf, ploidy = 1, strata = df)
#> 
#>  // Optional content
#>    @pop: population of each individual (group size range: 2-2)
#>    @strata: a data frame with 2 columns ( pop, Individual )
strata(nan9hap)   # strata gains a new column: Individual
#>    pop Individual
#> 01 P09       N104
#> 02 P09       N104
#> 03 P09       N105
#> 04 P09       N105
#> 05 P09       N106
#> 06 P09       N106
#> 07 P09       N107
#> 08 P09       N107
#> 09 P09       N108
#> 10 P09       N108
#> 11 P09       N109
#> 12 P09       N109
#> 13 P09       N111
#> 14 P09       N111
#> 15 P09       N112
#> 16 P09       N112
#> 17 P09       N113
#> 18 P09       N113
indNames(nan9hap) # individuals are renamed sequentially
#>  [1] "01" "02" "03" "04" "05" "06" "07" "08" "09" "10" "11" "12" "13" "14" "15"
#> [16] "16" "17" "18"


# Mix ploidy data can be split, but should be treated with caution --------
# 
# For example, the Pinf data set contains 86 tetraploid individuals, 
# but there appear to only be diploids and triploid genotypes. When 
# we convert to haplotypes, those with all missing data are dropped.
data(Pinf)
Pinf
#> 
#> This is a genclone object
#> -------------------------
#> Genotype information:
#> 
#>    72 multilocus genotypes 
#>    86 tetraploid individuals
#>    11 codominant loci
#> 
#> Population information:
#> 
#>     2 strata - Continent, Country
#>     2 populations defined - South America, North America
pmiss <- info_table(Pinf, type = "ploidy", plot = TRUE)

# No samples appear to be triploid across all loci. This will cause
# several haplotypes to have a lot of missing data.
p_haps <- make_haplotypes(Pinf)
p_haps
#> /// GENIND OBJECT /////////
#> 
#>  // 203 individuals; 11 loci; 95 alleles; size: 137.9 Kb
#> 
#>  // Basic content
#>    @tab:  203 x 95 matrix of allele counts
#>    @loc.n.all: number of alleles per locus (range: 2-25)
#>    @loc.fac: locus factor for the 95 columns of @tab
#>    @all.names: list of allele names for each locus
#>    @ploidy: ploidy of each individual  (range: 1-1)
#>    @type:  codom
#>    @call: df2genind(X = newdf, ploidy = 1, strata = df)
#> 
#>  // Optional content
#>    @pop: population of each individual (group size range: 2-3)
#>    @strata: a data frame with 3 columns ( Continent, Country, Individual )
head(genind2df(p_haps), n = 20)
#>       pop Pi02  D13 Pi33 Pi04 Pi4B Pi16  G11 Pi56 Pi63 Pi70 Pi89
#> 2  PiCO01 <NA> <NA> <NA> <NA>  205 <NA> <NA> <NA> <NA> <NA> <NA>
#> 3  PiCO01  162  136  203  166  213  178  156  174  157  192  179
#> 4  PiCO01  162  136  203  170  217  178  156  176  157  192  181
#> 6  PiCO02 <NA> <NA> <NA> <NA>  205 <NA> <NA> <NA> <NA> <NA> <NA>
#> 7  PiCO02  162  132  203  166  213  178  156  174  157  192  179
#> 8  PiCO02  162  136  203  170  217  178  156  176  157  192  181
#> 10 PiCO03 <NA> <NA> <NA> <NA>  205 <NA> <NA> <NA> <NA> <NA> <NA>
#> 11 PiCO03  162  136  203  166  213  178  156  174  157  192  179
#> 12 PiCO03  162  136  203  170  217  178  156  176  157  192  181
#> 14 PiCO04 <NA> <NA> <NA> <NA>  205 <NA> <NA> <NA> <NA> <NA> <NA>
#> 15 PiCO04  162  136  203  166  213  178  156  174  157  192  179
#> 16 PiCO04  162  136  203  170  217  178  156  176  157  192  181
#> 19 PiCO05  160  108  203  166  213  174  156  176  157  192  181
#> 20 PiCO05  162  112  203  170  225  178  156  176  157  192  181
#> 23 PiEC01  162  134  203  166  213  178  156  174  157  192  179
#> 24 PiEC01  162  136  203  170  217  178  156  176  157  192  181
#> 26 PiEC02 <NA> <NA> <NA> <NA>  205 <NA> <NA> <NA> <NA> <NA> <NA>
#> 27 PiEC02  162  136  203  166  213  178  156  174  157  192  179
#> 28 PiEC02  162  144  203  170  217  178  156  176  157  192  181
#> 31 PiEC03  162  136  203  166  205  178  156  174  157  192  179