likelihood {pedprobr}

Pedigree likelihood

Description

The likelihood() and likelihood2() functions constitute the heart of pedprobr. The former computes the pedigree likelihood for each indicated marker. The latter computes the likelihood for a pair of linked markers separated by a given recombination rate.

Usage

likelihood(x, ...)

## S3 method for class 'ped'
likelihood(
  x,
  markers = NULL,
  peelOrder = NULL,
  lump = TRUE,
  eliminate = 0,
  logbase = NULL,
  loopBreakers = NULL,
  allX = NULL,
  verbose = FALSE,
  theta = 0,
  ...
)

## S3 method for class 'list'
likelihood(x, markers = NULL, logbase = NULL, ...)

likelihood2(x, ...)

## S3 method for class 'ped'
likelihood2(
  x,
  marker1,
  marker2,
  rho = NULL,
  peelOrder = NULL,
  eliminate = 0,
  logbase = NULL,
  loopBreakers = NULL,
  verbose = FALSE,
  ...
)

## S3 method for class 'list'
likelihood2(x, marker1, marker2, logbase = NULL, ...)

Arguments

x

A ped object, a singleton object, or a list of such objects.

...

Further arguments.

markers

One or several markers compatible with x. Several input forms are possible:

• A marker() object compatible with x.

• A list of marker objects.

• A vector of names or indices of markers attached to x. If x is a list, this is the only valid input.

peelOrder

For internal use.

lump

Activate allele lumping, i.e., merging unobserved alleles. This is an important time saver, and should be applied in nearly all cases. (The parameter exists mainly for debugging purposes.) The lumping algorithm will detect (and complain) if any markers use a non-lumpable mutation model. Default: TRUE.

eliminate

Deprecated, not used.

logbase

Either NULL (default) or a positive number indicating the basis for logarithmic output. Typical values are exp(1) and 10.

loopBreakers

A vector of ID labels indicating loop breakers. If NULL (default), automatic selection of loop breakers will be performed. See breakLoops().

allX

For internal use; set to TRUE if all markers are X-chromosomal.

verbose

A logical.

theta

Theta correction.

marker1, marker2

Single markers compatible with x.

rho

The recombination rate between marker1 and marker2. To make biological sense rho should be between 0 and 0.5.

Details

The implementation is based on the peeling algorithm of Elston and Stewart (1971). A variety of situations are covered; see the Examples section for some demonstrations.

• autosomal and X-linked markers

• 1 marker or 2 linked markers

• complex inbred pedigrees

• markers with mutation models

• pedigrees with inbred founders

For more than two linked markers, see likelihoodMerlin().

Value

A numeric with the same length as the number of markers indicated by markers. If logbase is a positive number, the output is log(likelihood, logbase).

Author(s)

Magnus Dehli Vigeland

References

Elston and Stewart (1971). A General Model for the Genetic Analysis of Pedigree Data. doi:10.1159/000152448

See Also

likelihoodMerlin(), for likelihoods involving more than 2 linked markers.

Examples

### Simple likelihood ###
p = 0.1
q = 1 - p
afr = c("1" = p, "2" = q)

# Singleton
s = singleton() |> addMarker(geno = "1/2", afreq = afr)

stopifnot(all.equal(likelihood(s), 2*p*q))

# Trio
trio = nuclearPed() |>
  addMarker(geno = c("1/1", "1/2", "1/1"), afreq = afr)

stopifnot(all.equal(likelihood(trio), p^2 * 2*p*q * 0.5))


### Example of calculation with inbred founders ###

### Case 1: Trio with inbred father
x = cousinPed(0, child = TRUE)
x = addSon(x, 5)
x = relabel(x, old = 5:7, new = c("father", "mother", "child"))

# Add equifrequent SNP; father homozygous, child heterozygous
x = addMarker(x, father = "1/1", child = "1/2")

# Plot with genotypes
plot(x, marker = 1)

# Compute the likelihood
lik1 = likelihood(x, markers = 1)


### Case 2: Using founder inbreeding
# Remove ancestry of father
y = subset(x, c("father", "mother", "child"))

# Indicate that the father has inbreeding coefficient 1/4
founderInbreeding(y, "father") = 1/4

# Plot (notice the inbreeding coefficient)
plot(y, marker = 1)

# Likelihood should be the same as above
lik2 = likelihood(y, markers = 1)

stopifnot(all.equal(lik1, lik2))