nei_simu {rNeighborGWAS}

Simulating phenotypes with self and neighbor effects

Description

A function to simulate phenotypes caused by self and neighbor effects, with the proportion of phenotypic variation explained (PVE) by fixed and random effects controlled.

Usage

nei_simu(
  geno,
  smap,
  scale,
  alpha = Inf,
  grouping = rep(1, nrow(smap)),
  kernel = c("exp", "gaussian"),
  n_causal,
  pveB,
  pve,
  b_ratio = c(1, 1)
)

Arguments

geno

An individual x marker matrix. Bialleles (i.e., A or a) must be converted into -1 or 1 digit.

smap

A matrix showing a spatial map for individuals. The first and second column include spatial points 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.

alpha

Distance decay coefficient \alpha in a dispersal kernel. Default is set at Inf, meaning no distance decay.

grouping

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

kernel

An option to select a negative exponential "exp" or Gaussian "gaussian" for a dispersal kernel of neighbor effects.

n_causal

No. of causal markers in a simulated phenotype

pveB

Proportion of phenotypic variation explained by fixed effects.

pve

Proportion of phenotypic variation explained by fixed and random effects.

b_ratio

A vector composed of two numeric scalars that control the ratio of contributions of self or neighbor effects to a phenotype. The first and second element are for self and neighbor effects, respectively.

Value

A vector of simulated phenotype values for all individuals

Author(s)

Yasuhiro Sato ([e-mail])

Examples

set.seed(1)
g <- matrix(sample(c(-1,1),100*1000,replace = TRUE),100,1000)
gmap <- cbind(c(rep(1,nrow(g)/2),rep(2,nrow(g)/2)),c(1:ncol(g)))
x <- runif(nrow(g),1,100)
y <- runif(nrow(g),1,100)
smap <- cbind(x,y)
grouping <- c(rep(1,nrow(g)/2),rep(2,nrow(g)/2))
pheno <- nei_simu(geno=g,smap=smap,scale=44,grouping=grouping,n_causal=50,pveB=0.4,pve=0.8)

fake_nei <- list()
fake_nei[[1]] <- g
fake_nei[[2]] <- gmap
fake_nei[[3]] <- smap
fake_nei[[4]] <- data.frame(pheno,grouping)
names(fake_nei) <- c("geno","gmap","smap","pheno")