castgen (version 1.0.2)

capture_diversity.Gmat: Estimate Minimum Number of Individuals to Sample to Capture Population Genomic Diversity (Genotype Matrix)

Description

This function can be used to estimate the number of individuals to sample from a population in order to capture a desired percentage of the genomic diversity. It assumes that the samples are the columns, and the genomic markers are in rows. Missing data should be set as NA, which will then be ignored for the calculations. All samples must have the same ploidy. This function was adapted from a previously developed Python method (Sandercock et al., 2023) (https://github.com/alex-sandercock/Capturing_genomic_diversity/)

Usage

capture_diversity.Gmat(
  df,
  ploidy,
  r2_threshold = 0.9,
  iterations = 10,
  sample_list = NULL,
  parallel = FALSE,
  batch = 1,
  save.result = FALSE,
  verbose = TRUE
)

Value

A data.frame with minimum number of samples required to match or exceed the input ratio

Arguments

df

Genotype matrix or data.frame with the numeric count of alternate alleles (0=homozygous reference, 1 = heterozygous, 2 = homozygous alternate)

ploidy

The ploidy of the species being analyzed

r2_threshold

The ratio of diversity to capture (default = 0.9)

iterations

The number of iterations to perform to estimate the average result (default = 10)

sample_list

The list of samples to subset from the dataset (optional)

parallel

Run the analysis in parallel (True/False) (default = FALSE)

batch

The number of samples to draw in each bootstrap sample iteration (default = 1)

save.result

Save the results to a .txt file? (default = FALSE)

verbose

Print out the results to the console (default = TRUE)

References

A.M. Sandercock, J.W. Westbrook, Q. Zhang, & J.A. Holliday, A genome-guided strategy for climate resilience in American chestnut restoration populations, Proc. Natl. Acad. Sci. U.S.A. 121 (30) e2403505121, https://doi.org/10.1073/pnas.2403505121 (2024).

Examples

#Example with a tetraploid population
set.seed(123)
test_gmat <- matrix(sample(0:4, 100, replace = TRUE), nrow = 10)
colnames(test_gmat) <- paste0("Sample", 1:10)
rownames(test_gmat) <- paste0("Marker", 1:10)
test_gmat <- as.data.frame(test_gmat)

#Estimate the number of samples required to capture 90% of the population's genomic diversity
result <- capture_diversity.Gmat(test_gmat,
                                 ploidy = 4,
                                 r2_threshold = 0.90,
                                 iterations = 10,
                                 save.result = FALSE,
                                 parallel=FALSE,
                                 verbose=FALSE)

#View results
print(result)