calc_kinship: Calculate kinship matrix
In rqtl/qtl2geno: Treatment of Marker Genotypes for QTL Experiments

Description Usage Arguments Details Value Examples

Calculate genetic similarity among individuals (kinship matrix) from conditional genotype probabilities.

1 2	calc_kinship(probs, type = c("overall", "loco", "chr"), omit_x = FALSE, use_allele_probs = TRUE, quiet = TRUE, cores = 1)

`probs`	Genotype probabilities, as calculated from `calc_genoprob()`.
`type`	Indicates whether to calculate the overall kinship (`"overall"`, using all chromosomes), the kinship matrix leaving out one chromosome at a time (`"loco"`), or the kinship matrix for each chromosome (`"chr"`).
`omit_x`	If `TRUE`, only use the autosomes; ignored when `type="chr"`.
`use_allele_probs`	If `TRUE`, assess similarity with allele probabilities (that is, first run `genoprob_to_alleleprob()`); otherwise use the genotype probabilities.
`quiet`	IF `FALSE`, print progress messages.
`cores`	Number of CPU cores to use, for parallel calculations. (If `0`, use `parallel::detectCores()`.) Alternatively, this can be links to a set of cluster sockets, as produced by `parallel::makeCluster()`.

If use_allele_probs=TRUE (the default), we first convert the genotype probabilities are converted to allele probabilities (using genoprob_to_alleleprob()). This is recommended, as then the result is twice the empirical kinship coefficient (e.g., the expected value for an intercross is 1/2; using genotype probabilities, the expected value is 3/8).

We then calculate sum_kl (p_ikl p_jkl) where k = position, l = allele, and i,j are two individuals.

For crosses with just two possible genotypes (e.g., backcross), we don't convert to allele probabilities but just use the original genotype probabilities.

If type="overall" (the default), a matrix of proportion of matching alleles. Otherwise a list with one matrix per chromosome.

grav2 <- read_cross2(system.file("extdata", "grav2.zip", package="qtl2geno"))
map <- insert_pseudomarkers(grav2$gmap, step=1)
probs <- calc_genoprob(grav2, map, error_prob=0.002)
K <- calc_kinship(probs)

# using only markers/pseudomarkers on the grid
grid <- calc_grid(grav2$gmap, step=1)
probs_sub <- probs_to_grid(probs, grid)
K_grid <- calc_kinship(probs_sub)