rres (version 1.1)

grm.pair: GRM for a pair of individuals.

Description

grm.pair computes relatedness estimates between two individuals.

Usage

grm.pair(geno1, geno2, freq, method = "twostep", weights = NULL,
  init.est = NULL)

Arguments

geno1, geno2

numeric vector.

freq

numeric vector, values between 0 and 1.

method

string.

weights

numeric vector, values between 0 and 1.

init.est

numeric.

Value

An estimate of realized relatedness.

Details

geno1 and geno2 are vectors of counts of reference alleles. freq is the vector of reference allele frequencies.

The default method is "twostep", other options include "classic", "robust" and "general". When using the default "twostep" method, user can supply an initial estimate through init.est to bypass the first step. When "general" is selected, weights must also be specified. The difference between the two-step GRM, classic GRM and robust GRM is discussed in Wang et al. (2017).

References

Wang et al. (2017) Genetics 205:1063-1078, https://www.ncbi.nlm.nih.gov/pubmed/28100587.

See Also

grm.matrix

Examples

# NOT RUN {
# simulate genotypes for a full sib pair
pedigree = as.character(rep(1, 4))
member = as.character(c(11, 12, 21, 22))
sex = as.numeric(c(1, 2, 1, 2))
father = as.character(c(NA, NA, 11, 11))
mother = as.character(c(NA, NA, 12, 12))
pedinfo = data.frame(pedigree, member, sex, father, mother, stringsAsFactors = FALSE)
set.seed(1)
inher = sim.recomb(pedinfo, 3500) # on a hypothetical chromosome

nsnp = 100000
marker = seq(0,3500,length.out=nsnp)
freq = runif(nsnp, 0.05, 0.95)
haplo = sim.haplotype(freq, 4)
geno = populate.snp(inher, haplo, marker, output.allele = FALSE)

# simulation truth
ibd.proportion(inher,3,4)

# different GRM estimates
grm.pair(geno[3,], geno[4,], freq, method = "twostep")
grm.pair(geno[3,], geno[4,], freq, method = "classic")
grm.pair(geno[3,], geno[4,], freq, method = "robust")
grm.pair(geno[3,], geno[4,], freq, method = "general", weights = sample(freq, nsnp)/sum(freq))

# compute the relatedness matrix
grm.matrix(geno, freq)
grm.matrix(geno, freq, method = "robust")
# }