sequoia (version 2.11.1)

EstEr: Estimate genotyping error rate (REMOVED; will be re-implemented)

Description

Estimate the genotyping error rates in SNP data, based on a pedigree and/or duplicates. Estimates probabilities (observed given actual) hom|other hom, het|hom, and hom|het. THESE ARE APPROXIMATE VALUES!

Usage

EstEr(
  GenoM,
  Pedigree,
  Duplicates = NULL,
  Er_start = c(0.05, 0.05, 0.05),
  perSNP = FALSE
)

Value

vector of length 3 with estimated genotyping error rates: the probabilities that

• 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

These are three independent parameters, that define the genotyping error matrix (see ErrToM) as follows:

	0	1	2
0	\(1-E_1-E_2\)	\(E_2\)	\(E_1\)
1	\(E_3\)	\(1-2E_3\)	\(E_3\)
2	\(E_1\)	\(E_2\)	\(1-E_1-E_2\)

Note that for optim a lower bound of 1e-6 and upper bound of 0.499 are used; if these values are returned this should be interpreted as 'inestimably small' and 'inestimably large', respectively. PLEASE DO NOT USE THESE VALUES AS INPUT IN SUBSEQUENT ANALYSIS BUT SUBSITUTE BY A SENSIBLE VALUE!!

Arguments

GenoM

Genotype matrix

Pedigree

data.frame with columns id - dam - sire

Duplicates

matrix or data.frame with 2 columns, id1 & id2

Er_start

vector of length 3 with starting values for optim.

perSNP

logical, estimate error rate per SNP. WARNING not very precise, use only as an approximate indicator! Try on simulated data first, e.g. with SimGeno.

Details

The result should be interpreted as approximate, ballpark estimates! The estimated error rates from a pedigree will not be as accurate as from duplicate samples. Errors in individuals without parents or offspring will not be counted, and errors in individuals with only few offspring may not be noted either. Deviation of genotype frequencies among founders from Hardy-Weinberg equilibrium may wrongly be attributed to genotyping errors. Last but not least, any pedigree errors will result in higher estimated genotyping errors.

Examples

GenoX <- SimGeno(Ped_griffin, nSnp=400, SnpError=c(0.01,0.07, 0.1),
                ParMis=0.1, CallRate=0.9)
# EstEr(GenoM=GenoX, Pedigree=Ped_griffin)