sequoia (version 3.0.3)

getAssignCat: Assignability of Reference Pedigree

Description

Identify which individuals are SNP genotyped (G), and which can potentially be substituted by a dummy individual ('dummifiable', D).

Usage

getAssignCat(Pedigree, SNPd, minSibSize = "1sib1GP")

Value

The Pedigree dataframe with 3 additional columns,

id.cat, dam.cat and sire.cat, with coding similar to that used by PedCompare:

G

Genotyped

D

Dummy or 'dummifiable'

X

Not genotyped and not dummifiable

Arguments

Pedigree

dataframe with columns id-dam-sire. Reference pedigree.

SNPd

character vector with ids of genotyped individuals.

minSibSize

minimum requirements to be considered dummifiable is 1 genotyped offspring, and

• '1sib1GP': at least 1 grandparent (G or D) or 1 more offspring (G or D); these are potentially assignable by sequoia

• '2sib': at least 1 more offspring (i.e. 2 siblings). Old default for PedCompare.

.

Details

Non-genotyped individuals can potentially be substituted by a dummy during pedigree reconstruction by sequoia when they have at least one genotyped offspring, and either one additional offspring (genotyped or dummy) or an genotyped/dummy parent (i.e. a grandparent to the genotyped offspring).

Note that this is the bare minimum requirement; e.g. grandparents are often indistinguishable from full avuncular (see sequoia and vignette for details). G-G parent-offspring pairs are only assignable if there is age information, or information from the surrounding pedigree, to tell which of the two is the parent.

It is assumed that all individuals in SNPd have been genotyped for a sufficient number of SNPs. To identify samples with a too-low call rate, use CheckGeno. To calculate the call rate for all samples, see the examples below.

Examples

PedA <- getAssignCat(Ped_HSg5, rownames(SimGeno_example))
tail(PedA)
table(PedA$dam.cat, PedA$sire.cat, useNA="ifany")

# calculate call rate
if (FALSE) {
CallRates <- apply(MyGenotypes, MARGIN=1,
                   FUN = function(x) sum(x!=-9)) / ncol(MyGenotypes)
hist(CallRates, breaks=50, col="grey")
GoodSamples <- rownames(MyGenotypes)[ CallRates > 0.8]
# threshold depends on total number of SNPs, genotyping errors, proportion
# of candidate parents that are SNPd (sibship clustering is more prone to
# false positives).
PedA <- getAssignCat(MyOldPedigree, rownames(GoodSamples))
}