ErrToM {sequoia}R Documentation

Generate Genotyping Error Matrix

Description

Make a vector or matrix specifying the genotyping error pattern, or a function to generate such a vector/matrix from a single value Err.

with the probabilities of observed genotypes (columns) conditional on actual genotypes (rows), or return a function to generate such matrices (using a single value Err as input to that function).

Usage

ErrToM(Err = NA, flavour = "version2.9", Return = "matrix")

Arguments

Err

estimated genotyping error rate, as a single number, or 3x3 or 4x4 matrix, or length 3 vector. If a single number, an error model is used that aims to deal with scoring errors typical for SNP arrays. If a matrix, this should be the probability of observed genotype (columns) conditional on actual genotype (rows). Each row must therefore sum to 1. If Return='function', this may be NA. If a vector, these are the probabilities (observed given actual) hom|other hom, het|hom, and hom|het.

flavour

vector-generating or matrix-generating function, or one of 'version2.9', 'version2.0', 'version1.3' (='SNPchip'), 'version1.1' (='version111'), referring to the sequoia version in which it was used as default. Only used if Err is a single number.

Return

output, 'matrix' (default), 'vector', 'function' (matrix-generating), or 'v_function' (vector-generating)

Details

By default (flavour = "version2.9"), Err is interpreted as a locus-level error rate (rather than allele-level), and equals the probability that an actual heterozygote is observed as either homozygote (i.e., the probability that it is observed as AA = probability that observed as aa = Err/2). The probability that one homozygote is observed as the other is (Err/2)^2.

The inbuilt 'flavours' correspond to the presumed and simulated error structures, which have changed with sequoia versions. The most appropriate error structure will depend on the genotyping platform; 'version0.9' and 'version1.1' were inspired by SNP array genotyping while 'version1.3' and 'version2.0' are intended to be more general.

This function, and throughout the package, it is assumed that the two alleles A and a are equivalent. Thus, using notation P(observed genotype |actual genotype), that P(AA|aa) = P(aa|AA), P(aa|Aa)=P(AA|Aa), and P(aA|aa)=P(aA|AA).

version hom|hom het|hom hom|het
2.9 (E/2)^2 E-(E/2)^2 E/2
2.0 (E/2)^2 E(1-E/2) E/2
1.3 (E/2)^2 E E/2
1.1 E/2 E/2 E/2
0.9 0 E E/2

or in matrix form, Pr(observed genotype (columns) | actual genotype (rows)):

version2.9:

0 1 2
0 1-E E -(E/2)^2 (E/2)^2
1 E/2 1-E E/2
2 (E/2)^2 E -(E/2)^2 1-E

version2.0:

0 1 2
0 (1-E/2)^2 E(1-E/2) (E/2)^2
1 E/2 1-E E/2
2 (E/2)^2 E(1-E/2) (1-E/2)^2

version1.3

0 1 2
0 1-E-(E/2)^2 E (E/2)^2
1 E/2 1-E E/2
2 (E/2)^2 E 1-E-(E/2)^2

version1.1

0 1 2
0 1-E E/2 E/2
1 E/2 1-E E/2
2 E/2 E/2 1-E

version0.9 (not recommended)

0 1 2
0 1-E E 0
1 E/2 1-E E/2
2 0 E 1-E

When Err is a length 3 vector, or if Return = 'vector' these are the following probabilities:

and Pr(observed genotype (columns) | actual genotype (rows)) is then:

0 1 2
0 1-E_1-E_2 E_2 E_1
1 E_3 1-2E_3 E_3
2 E_1 E_2 1-E_1-E_2

When the SNPs are scored via sequencing (e.g. RADseq or DArTseq), the 3rd error rate (hom|het) is typically considerably higher than the other two, while for SNP arrays it tends to be similar to P(het|hom).

Value

Depending on Return, either:

Examples

ErM <- ErrToM(Err = 0.05)
ErM
ErrToM(ErM, Return = 'vector')


# use error matrix from Whalen, Gorjanc & Hickey 2018
funE <- function(E) {
 matrix(c(1-E*3/4, E/2, E/4,
          E/4, 1-2*E/4, E/4,
          E/4, E/2, 1-E*3/4),
          3,3, byrow=TRUE)  }
ErrToM(Err = 0.05, flavour = funE)
# equivalent to:
ErrToM(Err = c(0.05/4, 0.05/2, 0.05/4))
[Package sequoia version 2.11.2 Index]