ghap.inbcoef {GHap}

Compute measures of inbreeding

Description

This function computes genomic measures of inbreeding.

Usage

ghap.inbcoef(object, freq, batchsize=NULL,
             only.active.samples=TRUE,
             only.active.variants=TRUE,
             ncores=1, verbose=TRUE)

Arguments

object

A valid GHap object (phase, haplo or plink).

freq

A named numeric vector providing allele frequencies.

batchsize

A numeric value controlling the number of variants to be processed at a time (default = nalleles/10).

only.active.samples

A logical value specifying whether only active samples should be included in the output (default = TRUE).

only.active.variants

A logical value specifying whether only active variants should be included in the output (default = TRUE).

ncores

A numeric value specifying the number of cores to be used in parallel computations (default = 1).

verbose

A logical value specfying whether log messages should be printed (default = TRUE).

Details

The inbreeding measures are computed as k + s*sum(f)/d, where k is a constant, s is a sign shifting scalar, f is a per-variant function and d is a scaling denominator. Four different measures of inbreeding are currently available:

Type = 1 (based on genomic relationship)
k = -1
s = 1
f = ((m - 2*p)^2)/(2*p*(1-p))
d = n

Type = 2 (excess homozygosity)
k = 1
s = -1
f = m*(2-m)/(2*p*(1-p))
d = n

Type = 3 (correlation between uniting gametes)
k = 0
s = 1
f = (m^2 - (1+2*p) + 2*p^2)/(2*p*(1-p))
d = n

Type = 4 (method-of-moments)
k = 1
s = -1
f = length(het)
d = sum(2*p*(1-p))

In the expressions above, m is the genotype coded as 0, 1 or 2 copies of A1, p is the frequency of A1, n is the number of variants (only non-monomorphic ones are considered), and het is the number of heterozygous genotypes.

Value

The function returns a dataframe containing population name, id and inbreeding measures for each individual.

Author(s)

Yuri Tani Utsunomiya <[e-mail]>

References

J. Yang et al. GCTA: A Tool for Genome-wide Complex Trait Analysis. Am. J. Hum. Genet. 2011. 88:76–82.

Examples

# #### DO NOT RUN IF NOT NECESSARY ###
# 
# # Copy plink data in the current working directory
# exfiles <- ghap.makefile(dataset = "example",
#                          format = "plink",
#                          verbose = TRUE)
# file.copy(from = exfiles, to = "./")
# 
# # Load plink data
# plink <- ghap.loadplink("example")
# 
# ### RUN ###
# 
# # Subset individuals from the pure1 population
# pure1 <- plink$id[which(plink$pop == "Pure1")]
# plink <- ghap.subset(object = plink, ids = pure1, variants = plink$marker)
# 
# # Subset markers with MAF > 0.05
# freq <- ghap.freq(plink)
# mkr <- names(freq)[which(freq > 0.05)]
# plink <- ghap.subset(object = plink, ids = pure1, variants = mkr)
# 
# # Compute A1 allele frequencies
# p <- ghap.freq(plink, type = "A1")
# 
# # Compute inbreeding coefficients
# ibc <- ghap.inbcoef(object = plink, freq = p)