Description Usage Arguments Details Value Author(s) References Examples
A function to estimate additive and dominance deviation for self and neighbor QTL effects by a simple regression.
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genoprobs |
Conditional genotype probabilities as taken from |
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 |
response |
An optional argument to select trait types. The |
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 |
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 (2021) for the rationale behind the approximation.
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
Yasuhiro Sato (sato.yasuhiro.36c@kyoto-u.jp)
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 (2021) Neighbor QTL: an interval mapping method for quantitative trait loci underlying plant neighborhood effects. G3; Genes|Genomes|Genetics 11:jkab017.
1 2 3 4 5 6 7 8 9 10 | 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
)
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