| calc.genoprob {qtl} | R Documentation |
Uses the hidden Markov model technology to calculate the probabilities of the true underlying genotypes given the observed multipoint marker data, with possible allowance for genotyping errors.
calc.genoprob(cross, step=0, off.end=0, error.prob=0.0001,
map.function=c("haldane","kosambi","c-f","morgan"),
stepwidth=c("fixed", "variable"))
cross |
An object of class cross. See
read.cross for details. |
step |
Maximum distance (in cM) between positions at which the
genotype probabilities are calculated, though for step = 0,
probabilities are calculated only at the marker locations. |
off.end |
Distance (in cM) past the terminal markers on each chromosome to which the genotype probability calculations will be carried. |
error.prob |
Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype). |
map.function |
Indicates whether to use the Haldane, Kosambi or Carter-Falconer map function when converting genetic distances into recombination fractions. |
stepwidth |
Indicates whether the intermediate points should have
fixed or variable step sizes. We strongly recommend using
"fixed"; "variable" is included only for the qtlbim
package (http://www.ssg.uab.edu/qtlbim). |
Let O[k] denote the observed marker genotype at position k, and g[k] denote the corresponding true underlying genotype.
We use the forward-backward equations to calculate a[k][v] = log Pr(O[1], ..., O[k], g[k] = v) and b[k][v] = log Pr(O[k+1], ..., O[n] | g[k] = v)
We then obtain Pr(g[k] | O[1], ..., O[n] = exp(a[k][v] + b[k][v]) / s where s = sum_v exp(a[k][v] + b[k][v])
In the case of the 4-way cross, with a sex-specific map, we assume a constant ratio of female:male recombination rates within the inter-marker intervals.
The input cross object is returned with a component,
prob, added to each component of cross$geno.
prob is an array of size [n.ind x n.pos x n.gen] where n.pos is
the number of positions at which the probabilities were calculated and
n.gen = 3 for an intercross, = 2 for a backcross, and = 4 for a 4-way
cross. Attributes "error.prob", "step",
"off.end", and "map.function" are set to the values of
the corresponding arguments, for later reference (especially by the
function calc.errorlod).
Karl W Broman, kbroman@biostat.wisc.edu
Lange, K. (1999) Numerical analysis for statisticians. Springer-Verlag. Sec 23.3.
Rabiner, L. R. (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77, 257–286.
sim.geno, argmax.geno,
calc.errorlod
data(fake.f2) fake.f2 <- calc.genoprob(fake.f2, step=2, off.end=5) data(fake.bc) fake.bc <- calc.genoprob(fake.bc, step=0, off.end=0, err=0.01)