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The popkin (“population kinship”) R package estimates
the kinship matrix of individuals and FST from their biallelic
genotypes. Our estimation framework is the first to be practically
unbiased under arbitrary population structures.
The stable version of the package is now on CRAN and can be installed using
install.packages("popkin")The current development version can be installed from the GitHub
repository using devtools:
install.packages("devtools") # if needed
library(devtools)
install_github('StoreyLab/popkin', build_vignettes = TRUE)You can see the package vignette, which has more detailed documentation, by typing this into your R session:
vignette('popkin')The examples below assume the following R data variables are present
for n individuals and m loci:
m-by-n genotype matrix X,
containing only unphased biallelic variants encoded as 0,1,2 counting a
given reference allele per locus.n vector subpops that assigns
each individual to a subpopulation.The subpops vector is not required, but its use is
recommended to improve estimation of the baseline kinship value treated
as zero.
If your data is in BED format, popkin will process it
efficiently using BEDMatrix. If file is the
path to the BED file (excluding .bed extension):
library(BEDMatrix)
X <- BEDMatrix(file) # load genotype matrix objectpopkin functionsThis is a quick overview of every popkin function,
covering estimation and visualization of kinship and FST from a genotype
matrix.
First estimate the kinship matrix from the genotypes
X. All downstream analysis require kinship,
none use X after this
library(popkin)
kinship <- popkin(X, subpops) # calculate kinship from X and optional subpop labelsPlot the kinship matrix, marking the subpopulations. Note
inbr_diag replaces the diagonal of kinship
with inbreeding coefficients
plot_popkin( inbr_diag(kinship), labs = subpops )Extract inbreeding coefficients from kinship
inbreeding <- inbr(kinship)Estimate FST
weights <- weights_subpops(subpops) # weigh individuals so subpopulations are balanced
Fst <- fst(kinship, weights) # use kinship matrix and weights to calculate fst
Fst <- fst(inbreeding, weights) # estimate more directly from inbreeding vector (same result)Estimate and visualize the pairwise FST matrix
pairwise_fst <- pwfst(kinship) # estimated matrix
leg_title <- expression(paste('Pairwise ', F[ST])) # fancy legend label
plot_popkin(pairwise_fst, labs = subpops, leg_title = leg_title) # NOTE no need for inbr_diag() here!Rescale the kinship matrix using different subpopulations (implicitly changes the most recent common ancestor population used as reference)
kinship2 <- rescale_popkin(kinship, subpops2)Estimate the coancestry matrix from a matrix of allele frequencies
P (useful when P comes from an admixture
inference model)
coancestry <- popkin_af( P )Please see the popkin R vignette for a description of
the key parameters and more detailed examples, including complex plots
with multiple kinship matrices and multi-level subpopulation
labeling.
Alejandro Ochoa, John D Storey. 2021. “Estimating FST and kinship for arbitrary population structures.” PLoS Genet 17(1): e1009241. PubMed ID 33465078. doi:10.1371/journal.pgen.1009241. bioRxiv doi:10.1101/083923 2016-10-27.
Alejandro Ochoa, John D Storey. 2019. “New kinship and FST estimates reveal higher levels of differentiation in the global human population.” bioRxiv doi:10.1101/653279.
Alejandro Ochoa, John D Storey. 2016. “FST And Kinship for Arbitrary Population Structures I: Generalized Definitions.” bioRxiv doi:10.1101/083915.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.