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, map.function=c("haldane","kosambi","c-f","morgan"))

`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. |

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@jhsph.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)

[Package *qtl* version 0.98-57 Index]