# NOT RUN {
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#### For CRAN time limitations most lines in the
#### examples are silenced with one '#' mark,
#### remove them and run the examples using
#### command + shift + C |OR| control + shift + C
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#### EXAMPLE 1
#### GWAS in diploids
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data(CPdata)
head(CPpheno)
CPgeno[1:4,1:4]
#### create the variance-covariance matrix
A <- A.mat(CPgeno)
#### look at the data and fit the model
head(CPpheno)
# mix1 <- GWAS2(color~1,
# random=~g(id),
# rcov=~units,
# G=list(id=A),
# W=CPgeno,
# data=CPpheno)
# summary(mix1)
#
# ####=========================================####
# ####=========================================####
# #### EXAMPLE 2
# #### GWAS in tetraploids
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# ####=========================================####
#
# data(PolyData)
# genotypes <- PolyData$PGeno
# phenotypes <- PolyData$PPheno
#
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# ####### convert markers to numeric format
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# numo <- atcg1234(data=genotypes, ploidy=4); numo[1:5,1:5]; dim(numo)
#
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# ###### plants with both genotypes and phenotypes
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# common <- intersect(phenotypes$Name,rownames(numo))
#
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# ### get the markers and phenotypes for such inds
# ###=========================================####
# marks <- numo[common,]; marks[1:5,1:5]
# phenotypes2 <- phenotypes[match(common,phenotypes$Name),];
# phenotypes2 <- as.data.frame(phenotypes2)
# phenotypes2[1:5,]
#
# ###=========================================####
# ###### Additive relationship matrix, specify ploidy
# ###=========================================####
# A <- A.mat(marks, ploidy=4)
# D <- D.mat(marks, ploidy=4)
# E <- E.mat(marks, ploidy=4)
# ###=========================================####
# ### run the GWAS model
# ###=========================================####
# my.map <- PolyData$map
# models <- c("additive","1-dom-alt","1-dom-ref","2-dom-alt","2-dom-ref")
# ans2 <- GWAS2(tuber_shape~1, random=~g(Name), models = "additive",
# G=list(Name=A), W=marks, data=phenotypes2)
# summary(ans2)
# }
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