ErrToM: Generate Genotyping Error Matrix

In sequoia: Pedigree Inference from SNPs

Generate Genotyping Error Matrix

Description

Generate a matrix 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 = "version2.0", 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	matrix-generating function, or one of '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), 'function', or 'vector'

Details

By default (flavour = "version2.0"), 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.

Pr(observed genotype (columns) | actual genotype (rows)):

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:

• hom|hom: an actual homozygote is observed as the other homozygote

• het|hom: an actual homozygote is observed as heterozygote

• hom|het: an actual heterozygote is observed as homozygote

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

The only assumption made is that the two alleles can be treated equally, i.e. observing actual allele $A$ as $a$ is as likely as observing actual $a$ as $A$, and so e.g. P(obs=1|act=0) = P(obs=1|act=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:

• 'matrix': a 3x3 matrix, with probabilities of observed genotypes (columns) conditional on actual (rows)

• 'function': a function taking a single value Err as input, and generating a 3x3 matrix

• 'vector': a length 3 vector, with the probabilities (observed given actual) hom|other hom, het|hom, and hom|het.

See Also

EstEr to estimate genotyping error rate as a length 3 vector.

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))

sequoia documentation built on Sept. 8, 2023, 5:29 p.m.