## ---- include = FALSE----------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(lmem.gwaser)
## ---- eval = FALSE-------------------------------------------------------
#
# library(lmem.gwaser)
#
# data ("QA_geno")
# data ("QA_map")
# data ("QA_pheno")
#
# P.data <- data ("QA_pheno")
# G.data <- data ("QA_geno")
# map.data <- data ("QA_map")
#
# gwas.cross (P.data, G.data, map.data,cross='gwas', heterozygotes=FALSE)
#
# summary (cross.data)
#
## ---- eval = FALSE-------------------------------------------------------
#
# # Marker Quality
# mq.g.diagnostics (crossobj=cross.data,I.threshold=0.1, p.val=0.01,na.cutoff=0.1)
#
## ---- eval = FALSE-------------------------------------------------------
#
# pca <- pca.analysis(crossobj=cross.data, p.val=0.05)
#
## ---- eval = FALSE-------------------------------------------------------
# qk.GWAS <- gwas.analysis (crossobj=cross.data, method='QK', provide.K=FALSE,
# covariates=pca$scores, trait='yield',
# threshold='Li&Ji', p=0.05,
# out.file='GWAS Q + K model')
#
## ---- eval = FALSE-------------------------------------------------------
# pcaR.GWAS <- gwas.analysis(crossobj=cross.data, method='eigenstrat',
# provide.K=FALSE, covariates=pca$scores,
# trait='yield',
# threshold='Li&Ji', p=0.05,
# out.file='GWAS PCA as Random model')
## ---- eval = FALSE-------------------------------------------------------
# k.GWAS <- gwas.analysis(crossobj=cross.data, method='kinship',
# provide.K=FALSE, covariates=FALSE, trait='yield',
# threshold='Li&Ji', p=0.05,
# out.file =' GWAS K as Random model ')
## ---- eval = FALSE-------------------------------------------------------
# data (QA_pheno)
# P.data.1 <- QA_pheno
# covariate <- P.data.1 [,2]
#
# g.GWAS <- gwas.analysis (crossobj=cross.data,
# method='fixed', provide.K=FALSE,
# covariates=covariate,
# trait='yield', threshold='Li&Ji', p=0.05,
# out.file='GWAS fixed Groups model')
## ---- eval = FALSE-------------------------------------------------------
# naive.GWAS <- gwas.analysis(crossobj=cross.data4, method='naive',
# provide.K=FALSE, covariates=FALSE,
# trait='yield', threshold='Li&Ji',
# p=0.05, out.file='GWAS naive model')
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