read_data2 | R Documentation |
Reads files in specific formats and creates a qtlpoly.data
object to be used in subsequent analyses.
read_data2(
ploidy = 6,
geno.prob,
geno.dose = NULL,
double.reduction = FALSE,
pheno,
weights = NULL,
step = 1,
verbose = TRUE
)
ploidy |
a numeric value of ploidy level of the cross. |
geno.prob |
an object of class |
geno.dose |
an object of class |
double.reduction |
if |
pheno |
a data frame of phenotypes (columns) with individual names (rows) identical to individual names in |
weights |
a data frame of phenotype weights (columns) with individual names (rows) identical to individual names in |
step |
a numeric value of step size (in centiMorgans) where tests will be performed, e.g. 1 (default); if |
verbose |
if |
An object of class qtlpoly.data
which is a list containing the following components:
ploidy |
a scalar with ploidy level. |
nlgs |
a scalar with the number of linkage groups. |
nind |
a scalar with the number of individuals. |
nmrk |
a scalar with the number of marker positions. |
nphe |
a scalar with the number of phenotypes. |
lgs.size |
a vector with linkage group sizes. |
cum.size |
a vector with cumulative linkage group sizes. |
lgs.nmrk |
a vector with number of marker positions per linkage group. |
cum.nmrk |
a vector with cumulative number of marker positions per linkage group. |
lgs |
a list with selected marker positions per linkage group. |
lgs.all |
a list with all marker positions per linkage group. |
step |
a scalar with the step size. |
pheno |
a data frame with phenotypes. |
G |
a list of relationship matrices for each marker position. |
Z |
a list of conditional probability matrices for each marker position for genotypes. |
X |
a list of conditional probability matrices for each marker position for alleles. |
Pi |
a matrix of identical-by-descent shared alleles among genotypes. |
Guilherme da Silva Pereira, gdasilv@ncsu.edu, Gabriel de Siqueira Gesteira, gdesiqu@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")}.
maps6x
, pheno6x
# 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 = 1)
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