poppr (version 2.1.1)

psex: Probability of encountering a genotype more than once by chance

Description

Probability of encountering a genotype more than once by chance

Usage

psex(gid, pop = NULL, by_pop = TRUE, freq = NULL, G = NULL,
  method = c("single", "multiple"), ...)

Arguments

gid

a genind or genclone object.

pop

either a formula to set the population factor from the strata slot or a vector specifying the population factor for each sample. Defaults to NULL.

by_pop

When this is TRUE (default), the calculation will be done by population.

freq

a vector or matrix of allele frequencies. This defaults to NULL, indicating that the frequencies will be determined via round-robin approach in rraf. If this matrix or vector is not prov

G

an integer specifying the number of observed genets. If NULL, this will be the number of original multilocus genotypes.

method

which method of calculating psex should be used? Using method = "single" (default) indicates that the calculation for psex should reflect the probability of encountering a second genotype. Using method = "multiple" gives the p

...

options passed to rare_allele_correction. The default is to correct allele frequencies to 1/n

Value

• a vector of Psex for each sample.

Details

Psex is the probability of encountering a given genotype more than once by chance. The basic equation is $$p_{sex} = 1 - (1 - p_{gen})^{G})$$ where G is the number of multilocus genotypes. See pgen for its calculation. For a given value of alpha (e.g. alpha = 0.05), genotypes with psex < alpha can be thought of as a single genet whereas genotypes with psex > alpha do not have strong evidence that members belong to the same genet (Parks and Werth, 1993). When method = "multiple", the method from Arnaud-Haond et al. (1997) is used where the sum of the binomial density is taken: $$p_{sex} = \sum_{i = 1}^N {N \choose i} \left(p_{gen}\right)^i\left(1 - p_{gen}\right)^{N - i}$$ where N is the number of samples with the same genotype, i is the ith sample, and pgen is the value of pgen for that genotype. The function will automatically calculate the round-robin allele frequencies with rraf and G with nmll.

References

Arnaud-Haond, S., Duarte, C. M., Alberto, F., & Serrão, E. A. 2007. Standardizing methods to address clonality in population studies. Molecular Ecology, 16(24), 5115-5139.

Parks, J. C., & Werth, C. R. 1993. A study of spatial features of clones in a population of bracken fern, Pteridium aquilinum (Dennstaedtiaceae). American Journal of Botany, 537-544.

See Also

pgen, rraf, rrmlg

Examples

data(Pram)
Pram_psex <- psex(Pram, by_pop = FALSE)
plot(Pram_psex, log = "y", col = ifelse(Pram_psex > 0.05, "red", "blue"))
abline(h = 0.05, lty = 2)
# With multiple encounters
Pram_psex <- psex(Pram, by_pop = FALSE, method = "multiple")
plot(Pram_psex, log = "y", col = ifelse(Pram_psex > 0.05, "red", "blue"))
abline(h = 0.05, lty = 2)

# This can be also done assuming populations structure
Pram_psex <- psex(Pram, by_pop = TRUE)
plot(Pram_psex, log = "y", col = ifelse(Pram_psex > 0.05, "red", "blue"))
abline(h = 0.05, lty = 2)

# The above, but correcting zero-value alleles by 1/(2*rrmlg) with no 
# population structure assumed
# See the documentation for rare_allele_correction for details.
Pram_psex2 <- psex(Pram, by_pop = FALSE, d = "rrmlg", mul = 1/2)
plot(Pram_psex2, log = "y", col = ifelse(Pram_psex2 > 0.05, "red", "blue"))
abline(h = 0.05, lty = 2)

## An example of supplying previously calculated frequencies and G
# From Parks and Werth, 1993, using the first three genotypes.

# The row names indicate the number of samples found with that genotype
x <- "
 Hk Lap Mdh2 Pgm1 Pgm2 X6Pgd2
54 12 12 12 23 22 11
36 22 22 11 22 33 11
10 23 22 11 33 13 13"

# Since we aren't representing the whole data set here, we are defining the
# allele frequencies before the analysis.
afreq <- c(Hk.1 = 0.167, Hk.2 = 0.795, Hk.3 = 0.038, 
           Lap.1 = 0.190, Lap.2 = 0.798, Lap.3 = 0.012,
           Mdh2.0 = 0.011, Mdh2.1 = 0.967, Mdh2.2 = 0.022,
           Pgm1.2 = 0.279, Pgm1.3 = 0.529, Pgm1.4 = 0.162, Pgm1.5 = 0.029,
           Pgm2.1 = 0.128, Pgm2.2 = 0.385, Pgm2.3 = 0.487,
           X6Pgd2.1 = 0.526, X6Pgd2.2 = 0.051, X6Pgd2.3 = 0.423)

xtab <- read.table(text = x, header = TRUE, row.names = 1)

# Here we are expanding the number of samples to their observed values.
# Since we have already defined the allele frequencies, this step is actually
# not necessary. 
all_samples <- rep(rownames(xtab), as.integer(rownames(xtab)))
xgid        <- df2genind(xtab[all_samples, ], ncode = 1)

freqs <- afreq[colnames(tab(xgid))] # only used alleles in the sample
pSex  <- psex(xgid, by_pop = FALSE, freq = freqs, G = 45)

# Note, pgen returns log values for each locus, here we take the sum across
# all loci and take the exponent to give us the value of pgen for each sample
pGen <- exp(rowSums(pgen(xgid, by_pop = FALSE, freq = freqs)))

res  <- matrix(c(unique(pGen), unique(pSex)), ncol = 2)
colnames(res) <- c("Pgen", "Psex")
res <- cbind(xtab, nRamet = rownames(xtab), round(res, 5))
rownames(res) <- 1:3
res # Compare to the first three rows of Table 2 in Parks & Werth, 1993