scan_neighbor: Genome scan for neighbor effects with a QTL model

In rNeighborQTL: Interval Mapping for Quantitative Trait Loci Underlying Neighbor Effects

Description

Genome scan using a QTL model for self and neighbor effects, with possible allowance for additional covariates and non-normal traits. Theoretical background is described in Sato, Takeda & Nagano (2021).

Usage

scan_neighbor(
  genoprobs,
  pheno,
  smap,
  scale,
  addcovar = NULL,
  addQTL = NULL,
  grouping = rep(1, nrow(smap)),
  response = c("quantitative", "binary"),
  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 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.
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.
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.

Details

This function calculates LOD score after the additive and dominance deviation are estimated using eff_neighbor(). As it adopts a stepwise testing from self to neighbor effects, LOD_self are the same as standard QTL mapping. Note that the results return 0 LOD scores for covariate markers when using addQTL option.

Value

A matrix of LOD scores for self and neighbor effects, with the chromosome numbers and positions. The row names correspond to marker names.

• chr Chromosome number

• pos Marker position

• LOD_self LOD score for self effects

• LOD_nei LOD score for neighbor effects

Author(s)

Yasuhiro Sato ([e-mail])

References

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

• Sato Y, Takeda K, Nagano AJ (2021) Neighbor QTL: an interval mapping method for quantitative trait loci underlying plant neighborhood effects. G3; Genes|Genomes|Genetics 11:jkab017.

See Also

eff_neighbor

Examples

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_scan <- scan_neighbor(genoprobs=test_genoprobs,
                           pheno=test_cross$pheno$phenotype,
                           smap=test_smap, scale=20
                           )
plot_nei(test_scan)

rNeighborQTL documentation built on April 9, 2021, 9:06 a.m.