rNeighborQTL (version 1.1.1)

sim_nei_qtl: Phenotype simulation for neighbor QTL effects

Description

A function to simulate neighbor effects with given QTL effects, distance scale, and causal markers.

Usage

sim_nei_qtl(
  genoprobs,
  a2,
  d2,
  smap,
  scale,
  grouping = rep(1, nrow(smap)),
  n_QTL = 1,
  contrasts = NULL
)

Arguments

genoprobs

Conditional genotype probabilities as taken from qtl::calc.genoprob().

a2

A numeric scalar indicating additive deviation.

d2

A numeric scalar indicating dominance deviation.

smap

A matrix showing a spatial map for individuals. The first and second column include spatial positions along an x-axis and y-axis, respectively.

scale

A numeric scalar indicating the maximum spatial distance between a focal individual and neighbors to define neighbor effects.

grouping

An integer vector assigning each individual to a group. This argument can be used when smap contains different experimental replicates. Default setting means that all individuals are belong to a single group.

n_QTL

A positive integer indicating the number of causal markers.

contrasts

An optional vector composed of three TRUE/FALSE values, which represents the presence/absence of specific genotypes as c(TRUE/FALSE, TRUE/FALSE, TRUE/FALSE) = AA, AB, BB. If NULL, it is compiled from genoprobs automatically.

Value

A numeric matrix containing individuals x marker elements for neighbor QTL effects.

• true_scale True distance scale of simulated neighbor effects

• true_marker The name(s) of causal markers

• nei_y Simulated neighbor effects standardized to have zero mean and one variance

Details

Major genetic effects, a2 and d2, are allocated to causal loci randomly selected by n_QTL, while minor polygenic effects (i.e., 1% of a2) are allocated to the other loci.

Examples

# NOT RUN {
set.seed(1234)
test_map <- qtl::sim.map(len=rep(20,5),n.mar=3,include.x=FALSE)
test_cross <- qtl::sim.cross(test_map,n.ind=50)
test_smap <- cbind(runif(50,1,100),runif(50,1,100))
test_genoprobs <- qtl::calc.genoprob(test_cross,step=2)

nei_eff <- sim_nei_qtl(genoprobs=test_genoprobs, a2=0.5, d2=0.5,
                       smap=test_smap,
                       scale=20, n_QTL=1)

test_scan <- scan_neighbor(genoprobs=test_genoprobs,
                           pheno=nei_eff$nei_y,
                           smap=test_smap, scale=20
                           )
plot_nei(test_scan)
# }