MkGenoErrors: Simulate Genotyping Errors
In sequoia: Pedigree Inference from SNPs

MkGenoErrors

R Documentation

Simulate Genotyping Errors

Description

Generate errors and missing values in a (simulated) genotype matrix.

Usage

MkGenoErrors(
  SGeno,
  CallRate = 0.99,
  SnpError = c((5e-04/2)^2, 5e-04/2, 5e-04 * (1 - 5e-04/2)),
  ErrorFM = function(E) {
     matrix(c(1 - E - (E/2)^2, E, (E/2)^2, E/2, 1 - E, E/2,
    (E/2)^2, E, 1 - E - (E/2)^2), 3, 3, byrow = TRUE)
 },
  Error.shape = 0.5,
  CallRate.shape = 1
)

Arguments

`SGeno`	matrix with genotype data in Sequoia's format: 1 row per individual, 1 column per SNP, and genotypes coded as 0/1/2.
`CallRate`	either a single number for the mean call rate (genotyping success), OR a vector with the call rate at each SNP, OR a named vector with the call rate for each individual. In the third case, ParMis is ignored, and individuals in the pedigree (as id or parent) not included in this vector are presumed non-genotyped.
`SnpError`	either a single value which will be combined with `ErrorFM`, or a length 3 vector with hom->other hom, hom->het, het-hom error rates; OR a vector or 3XnSnp matrix with the genotyping error rate(s) for each SNP.
`ErrorFM`	function taking the error rate (scalar) as argument and returning a 4x4 or 3x3 matrix with probabilities that actual genotype i (rows) is observed as genotype j (columns).
`Error.shape`	first shape parameter (alpha) of beta-distribution of per-SNP error rates. A higher value results in a flatter distribution.
`CallRate.shape`	as Error.shape, for per-SNP call rates.