sim_chiasmataPositions: Simulate crossover positions
In SimRVSequences: Simulate Genetic Sequence Data for Pedigrees

Description Usage Arguments Details Value References

Simulate crossover positions along a chromatid bundle. This function will become an internal function.

1 2	sim_chiasmataPositions(chrom_map, burn_in = 1000, gamma_params = c(2.63, 2.63/0.5))

`chrom_map`	Data.frame with 1 row and 2 columns. The two columns represent the start and stop positions (in cM) over which to simulate recombination.
`burn_in`	Numeric. The "burn-in" distance in centiMorgan, as defined by Voorrips and Maliepaard (2012), which is required before simulating the location of the first chiasmata with interference. By default, `burn_in = 1000`. The burn in distance in cM. By default, `burn_in = 1000`.
`gamma_params`	Numeric list of length 2. The respective shape and rate parameters of the gamma distribution used to simulate distance between chiasmata. By default, `gamma` `_params` `= c(2.63, 2*2.63)`, as discussed in Voorrips and Maliepaard (2012).

Simulate the locations of chiasmata along a chromatid bundle according to the model proposed by Voorrips. Voorrips proposes to use a gamma distribution with shape 2.63 and rate parameter 2*2.63 to model the distance between successive chiasmata. To use this model, Voorrips notes that we must incorporate a burn-in process for the first chiasmata location since "in the case of chiasmata interference [...] the position of the next chiasmata location is affected by the position of the previous chiasmata." The burn-in is accomplished by

Setting the burn-in distance. From Voorrips code, and verified by my own investigations a burn-in of 1000 cM seems sufficent.
Generate location of first chiasmata (from burn-start) from an exponential distribution with mean 50 cM. (Why exponential and not gamma? I think this is to do with stationary renewal processes)
Generate successive chiasmata from a gamma(shape = 2.63, rate = 2*2.63) distribution until a chiasmata location exceeds the chromosome start position, this is used as the position of the first chiasmata.

After we simulate the position of the first chiasmata, successive chiasmata are drawn from a gamma(shape = 2.63, rate = 2*2.63) distribution until a proposed chiasmata exceeds the end of the chromosome.

NOTE: To simulate chiasmata formation without chiasmata interference (i.e. Haldane's model) we assume that the distance between successive chiasmata is distributed as an exponential distribution with mean 50 cM. This can be accomplished by setting burn_in = 0 and gamma_params = c(1, 2).

A list of chiasmata postions.

Roeland E. Voorrips, Chris A Maliepaard. (2012), The simulation of meiosis in diploid and tetraploid organisms using various genetic models. BMC Bioinformatics, 13:248.

SimRVSequences documentation built on July 1, 2020, 6:03 p.m.