popkin: Estimate kinship from a genotype matrix and subpopulation assignments

Description

Given the biallelic genotypes of n individuals, this function returns the n-by-n kinship matrix such that the kinship estimate between the most distant subpopulations is zero on average (this sets the ancestral population to the most recent common ancestor population).

Usage

popkin(
  X,
  subpops = NULL,
  n = NA,
  loci_on_cols = FALSE,
  mean_of_ratios = FALSE,
  mem_factor = 0.7,
  mem_lim = NA,
  want_M = FALSE,
  m_chunk_max = 1000
)

Value

If want_M = FALSE, returns the estimated n-by-n kinship matrix only. If X has names for the individuals, they will be copied to the rows and columns of this kinship matrix. If want_M = TRUE, a named list is returned, containing:

kinship: the estimated n-by-n kinship matrix
M: the n-by-n matrix of non-missing pair counts (see want_M option).

Arguments

X: Genotype matrix, BEDMatrix object, or a function X(m) that returns the genotypes of all individuals at m successive locus blocks each time it is called, and NULL when no loci are left. If a regular matrix, X must have values only in c(0, 1, 2, NA), encoded to count the number of reference alleles at the locus, or NA for missing data.
subpops: The length-n vector of subpopulation assignments for each individual. If NULL, every individual is effectively treated as a different population.
n: Number of individuals (required only when X is a function, ignored otherwise). If n is missing but subpops is not, n is taken to be the length of subpops.
loci_on_cols: If TRUE, X has loci on columns and individuals on rows; if FALSE (default), loci are on rows and individuals on columns. Has no effect if X is a function. If X is a BEDMatrix object, loci_on_cols is ignored (set automatically to TRUE internally).
mean_of_ratios: Chose how to weigh loci. If FALSE (default) loci have equal weights (in terms of variance, rare variants contribute less than common variants; also called the "ratio-of-means" version, this has known asymptotic behavior). If TRUE, rare variant loci are upweighed (in terms of variance, contributions are approximately the same across variant frequencies; also called the "mean-of-ratios" version, its asymptotic behavior is less well understood but performs better for association testing).
mem_factor: Proportion of available memory to use loading and processing data. Ignored if mem_lim is not NA.
mem_lim: Memory limit in GB, used to break up data into chunks for very large datasets. Note memory usage is somewhat underestimated and is not controlled strictly. Default in Linux is mem_factor times the free system memory, otherwise it is 1GB (Windows, OSX and other systems).
want_M: If TRUE, includes the matrix M of non-missing pair counts in the return value, which are sample sizes that can be useful in modeling the variance of estimates. Default FALSE is to return the relatedness matrix only.
m_chunk_max: Sets the maximum number of loci to process at the time. Actual number of loci loaded may be lower if memory is limiting.

Details

The subpopulation assignments are only used to estimate the baseline kinship (the zero value). If the user wants to re-estimate the kinship matrix using different subpopulation labels, it suffices to rescale it using rescale_popkin() (as opposed to starting from the genotypes again, which gives the same answer but more slowly).

Examples

Run this code

# Construct toy data
X <- matrix(
    c(0, 1, 2,
      1, 0, 1,
      1, 0, 2),
    nrow = 3,
    byrow = TRUE
) # genotype matrix
subpops <- c(1,1,2) # subpopulation assignments for individuals

# NOTE: for BED-formatted input, use BEDMatrix!
# "file" is path to BED file (excluding .bed extension)
## library(BEDMatrix)
## X <- BEDMatrix(file) # load genotype matrix object

kinship <- popkin(X, subpops) # calculate kinship from genotypes and subpopulation labels