Perform a QTL mapping permutation test using the
qtl:scanone(...) function to find significant QTL.
qtl_perm_test(
x_data,
cpus = 1,
qtl_method = "par-scanone",
raw_data_normalised = NULL,
lod_threshold = 3,
parametric = TRUE,
n_perm = 1000,
plots_dir = tempdir(),
...
)Cross-data frame containing genetic map data and traits.
Number of CPUs to be used in the computation.
QTL mapping method.
Normalised raw data, see
assess_normality.
LOD score threshold to look up for significant QTLs
Boolean flag to indicate whether or not x_data
contains parametric (normal) traits.
Number of permutations.
Output directory for plots.
Arguments passed on to
qtl::scanone.
Data frame containing the significant QTLs information.
Other QTL mapping functions:
qtl_scone(),
read.cross()
# \donttest{
# Toy dataset
excluded_columns <- c(1, 2)
population <- 5
seed <- 123
set.seed(seed)
out_prefix <- file.path(tempdir(), "metapipe")
plots_dir <- file.path(tempdir(), "plots")
example_data <- data.frame(ID = 1:population,
P1 = c("one", "two", "three", "four", "five"),
T1 = rnorm(population),
T2 = rnorm(population))
output <- MetaPipe::assess_normality(example_data,
excluded_columns,
show_stats = FALSE,
out_prefix = out_prefix,
plots_dir = plots_dir)
# Create and store random genetic map (for testing only)
genetic_map <- MetaPipe:::random_map(population = population,
seed = seed)
# Load cross file with genetic map and raw data for normal traits
x <- MetaPipe::read.cross(genetic_map, output$norm)
#> --Read the following data:
#> 5 individuals
#> 100 markers
#> 3 phenotypes
#> --Cross type: f2
x <- qtl::calc.genoprob(x, step = 1, error.prob = 0.001)
x_scone <- MetaPipe::qtl_scone(x, 1, model = "normal", method = "hk")
x_qtl_perm <- MetaPipe::qtl_perm_test(x,
n_perm = 5,
model = "normal",
method = "hk",
plots_dir = plots_dir)
x_qtl_perm_1000 <- MetaPipe::qtl_perm_test(x,
n_perm = 1000,
model = "normal",
method = "hk",
plots_dir = plots_dir)
# F1 Seedling Ionomics dataset
data(ionomics) # Includes some missing data
data(father_riparia) # Genetic map
out_prefix <- file.path(tempdir(), "ionomics")
plots_dir <- file.path(tempdir(), "plots")
ionomics_rev <- MetaPipe::replace_missing(ionomics,
excluded_columns = c(1, 2),
replace_na = TRUE,
out_prefix = out_prefix)
ionomics_normalised <-
MetaPipe::assess_normality(ionomics_rev,
excluded_columns = c(1, 2),
out_prefix = out_prefix,
plots_dir = plots_dir,
transf_vals = c(2, exp(1)),
show_stats = FALSE)
# Load cross file with genetic map and raw data for normal traits
x <- MetaPipe::read.cross(father_riparia,
ionomics_normalised$norm,
genotypes = c("nn", "np", "--"))
#> --Read the following data:
#> 166 individuals
#> 1115 markers
#> 7 phenotypes
#> Warning: Some markers at the same position on chr 1,4,5,7,8,9,10,12,14,15,16,17; use jittermap().
#> --Cross type: f2
set.seed(seed)
x <- qtl::jittermap(x)
x <- qtl::calc.genoprob(x, step = 1, error.prob = 0.001)
x_scone <- MetaPipe::qtl_scone(x, 1, model = "normal", method = "hk")
x_qtl_perm <- MetaPipe::qtl_perm_test(x,
n_perm = 5,
model = "normal",
method = "hk",
plots_dir = plots_dir)
x_qtl_perm_1000 <- MetaPipe::qtl_perm_test(x,
n_perm = 1000,
model = "normal",
method = "hk",
plots_dir = plots_dir)
# }