sim.hotspot: Wrapper routine for simulations.

Description

Wrapper routine for simulations

Usage

sim.hotspot(nSim, cross, n.pheno, latent.eff, res.var = 1, n.quant, n.perm,
  alpha.levels, lod.thrs, drop.lod = 1.5, verbose = FALSE)
mySimulations(…)
sim.null.cross(chr.len = rep(400, 16), n.mar = 185, n.ind = 112,
  type = "bc", n.pheno = 6000, latent.eff = 1.5, res.var = 1,
  init.seed = 92387475)
sim.null.pheno.data(cross, n.pheno, latent.eff, res.var)
include.hotspots(cross, hchr, hpos, hsize, Q.eff, latent.eff,
  lod.range.1, lod.range.2, lod.range.3, res.var=1, n.pheno, init.seed)

Arguments

nSim

Number of simulated sets of phenotypes to create. See details.

cross

Object of class cross. See read.cross.

n.pheno

Number of traits, or phenotypes, to simulate for cross object.

latent.eff

Strength of latent effect, which is included in all traits. See sim.null.cross.

res.var

Residual variance for traits. Should not affect results.

n.quant

maximum size of hotspots examined; ideally large enough to exceed the largest Breitling alpha critical value.

n.perm

Number of permutations to perform per realization. Good idea to do 1000, but this takes time.

alpha.levels

Vector of significance levels.

lod.thrs

Vector of LOD thresholds, typically single-trait permutation thresholds for various significance levels.

drop.lod

Drop in LOD score examined. LODs below this drop from the maximum for a chromosome will not be scored.

init.seed

initial seed for pseudo-random number generation

chr.len

vector of chromosome lengths

n.mar

number of markers

n.ind

number of individuals

type

type of cross

hchr,hpos,hsize

vectors for hotspot chromosomes, positions, and sizes

Q.eff

QTL effect

lod.range.1,lod.range.2,lod.range.3

2-vectors of LOD ranges for multiple purposes

verbose

Verbose output if TRUE. More detailed output if 2.

…

Arguments passed directly to sim.hotspot.

Value

sim.null.cross simulates an object of class cross. sim.null.pheno.data simulates a data frame of phenotypes. sim.hotspot uses these other routines to simulate a hotspot, returning an list object.

Details

Simulate nSim realizations of cross object with n.pheno phenotypes with correlation latent.eff. All simulations use the same genotypes in the cross object.

Examples

Run this code

# NOT RUN {
ncross1 <- sim.null.cross(chr.len = rep(100, 4),
                          n.mar = 51,
                          n.ind = 100,
                          type = "bc",
                          n.phe = 1000,
                          latent.eff = 3,
                          res.var = 1,
                          init.seed = 123457)
cross1 <- include.hotspots(cross = ncross1,
                           hchr = c(2, 3, 4),
                           hpos = c(25, 75, 50),
                           hsize = c(100, 50, 20),
                           Q.eff = 2,
                           latent.eff = 3,
                           lod.range.1 = c(2.5, 2.5),
                           lod.range.2 = c(5, 8),
                           lod.range.3 = c(10, 15),
                           res.var = 1,
                           n.phe = 1000,
                           init.seed = 12345)
# }

Run the code above in your browser using DataLab

Get 50% off unlimited learning