feim | R Documentation |
Performs interval mapping using the single-QTL, fixed-effect model proposed by Hackett et al. (2001).
feim(
data = data,
pheno.col = NULL,
w.size = 15,
sig.lod = 7,
d.sint = 1.5,
plot = NULL,
verbose = TRUE
)
## S3 method for class 'qtlpoly.feim'
print(x, pheno.col = NULL, sint = NULL, ...)
data |
an object of class |
pheno.col |
a numeric vector with the phenotype columns to be analyzed; if |
w.size |
a number representing the window size (in centiMorgans) to be avoided on either side of QTL already in the model when looking for a new QTL, e.g. 15 (default). |
sig.lod |
the vector of desired significance LOD thresholds (usually permutation-based) for declaring a QTL for each trait, e.g. 5 (default); if a single value is provided, the same LOD threshold will be applied to all traits. |
d.sint |
a |
plot |
a suffix for the file's name containing plots of every algorithm step, e.g. "remim" (default); if |
verbose |
if |
x |
an object of class |
sint |
whether |
... |
currently ignored |
An object of class qtlpoly.feim
which contains a list of results
for each trait with the following components:
pheno.col |
a phenotype column number. |
LRT |
a vector containing LRT values. |
LOD |
a vector containing LOD scores. |
AdjR2 |
a vector containing adjusted |
qtls |
a data frame with information from the mapped QTL. |
lower |
a data frame with information from the lower support interval of mapped QTL. |
upper |
a data frame with information from the upper support interval of mapped QTL. |
Guilherme da Silva Pereira, gdasilv@ncsu.edu
Pereira GS, Gemenet DC, Mollinari M, Olukolu BA, Wood JC, Mosquera V, Gruneberg WJ, Khan A, Buell CR, Yencho GC, Zeng ZB (2020) Multiple QTL mapping in autopolyploids: a random-effect model approach with application in a hexaploid sweetpotato full-sib population, Genetics 215 (3): 579-595. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1534/genetics.120.303080")}.
Hackett CA, Bradshaw JE, McNicol JW (2001) Interval mapping of quantitative trait loci in autotetraploid species, Genetics 159: 1819-1832.
permutations
# Estimate conditional probabilities using mappoly package
library(mappoly)
library(qtlpoly)
genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 5)
# Perform remim
feim.mod = feim(data = data, sig.lod = 7)
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