rNeighborQTL (version 1.1.1)

eff_neighbor: Estimation of self and neighbor QTL effects across a genome

Description

A function to estimate additive and dominance deviation for self and neighbor QTL effects by a simple regression.

Usage

eff_neighbor(
  genoprobs,
  pheno,
  smap,
  scale,
  addcovar = NULL,
  addQTL = NULL,
  grouping = rep(1, nrow(smap)),
  response = c("quantitative", "binary"),
  fig = TRUE,
  contrasts = NULL
)

Arguments

genoprobs

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

pheno

A vector of individual phenotypes.

smap

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

addcovar

An optional matrix including additional non-genetic covariates. It contains no. of individuals x no. of covariates.

addQTL

An optional vector containing marker names that are considered covariates. Namely, this option allows composite interval mapping (Jansen 1993).

grouping

An optional 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.

response

An optional argument to select trait types. The "quantitative" or "binary" calls the "gaussian" or "binomial" family in glm(), respectively.

fig

TRUE/FALSE to plot the effects or not.

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 matrix of estimated additive and dominance deviation for self and neighbor effects, with the chromosome numbers and positions. The row names correspond to marker names.

• chr Chromosome number

• pos Marker position

• a1 Additive deviation for self effects

• d1 Dominance deviation for self effects

• a2 Additive deviation for neighbor effects

• d2 Dominance deviation for neighbor effects

Details

Similar to Haley-Knott regression (Haley & Knott 1992), the additive and dominance deviations are approximated by a regression of trait values on conditional genotype probabilities. The self QTL effects a1 and d1 are estimated in the same way as the qtl package performs the Haley-Knott regression. If contrasts = c(TRUE, TRUE, TRUE), neighbor QTL effects a1 and d1 are estimated using a quadratic regression; otherwise, the additive neighbor effects are estimated using a linear regression. See also Sato, Takeda & Nagano (2020) for the rationale behind the approximation.

References

• Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315-324.

• Jansen RC (1993) Interval mapping of multiple quantitative trait loci. Genetics 135:205-211.

• Sato Y, Takeda K, Nagano AJ (2020) Neighbor QTL: an interval mapping method for quantitative trait loci underlying neighbor effects. bioRxiv https://doi.org/10.1101/2020.05.20.089474

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)

test_eff <- eff_neighbor(genoprobs=test_genoprobs,
                         pheno=test_cross$pheno$phenotype,
                         smap=test_smap, scale=20, fig=TRUE
                         )
# }