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
The current development version can be installed from the GitHub repository using
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:
The examples below assume the following R data variables are present for
n individuals and
X, containing only unphased biallelic variants encoded as 0,1,2 counting a given reference allele per locus.
- The length-
subpopsthat assigns each individual to a subpopulation.
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
file is the path to the BED file (excluding .bed extension):
library(BEDMatrix) X <- BEDMatrix(file) # load genotype matrix object
This 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
All downstream analysis require
kinship, none use
X after this
library(popkin) kinship <- popkin(X, subpops) # calculate kinship from X and optional subpop labels
Plot the kinship matrix, marking the subpopulations.
inbr_diag replaces the diagonal of
kinship with inbreeding coefficients
plot_popkin( inbr_diag(kinship), labs = subpops )
Extract inbreeding coefficients from
inbreeding <- inbr(kinship)
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.