library('rMVP')
MVP.Data(fileBed="maize_598",
         filePhe="BLUP.csv",
         sep.phe=",",
         #SNP.effect="Add",
         fileKin=FALSE,
         filePC=FALSE,
         #priority="memory",
         #maxLine=10000,
         out="mvp.plink"
         )
		 
# 2. 计算亲缘关系矩阵和主成分
MVP.Data.Kin(TRUE, mvp_prefix='mvp.plink', out='mvpKin')
MVP.Data.PC(TRUE, mvp_prefix='mvp.plink', pcs.keep=5, out='mvpPC') #计算前5个主成分

# 3. 加载数据
genotype <- attach.big.matrix("mvp.plink.geno.desc")
phenotype <- read.table("mvp.plink.phe", head=TRUE) 
map <- read.table("mvp.plink.geno.map", head = TRUE)
Kinship <- attach.big.matrix("mvpKin.kin.desc")
Covariates_PC <- bigmemory::as.matrix(attach.big.matrix("mvpPC.pc.desc"))

# 4. 运行FarmCPU GWAS
imMVP <- MVP(
    phe=phenotype,
    geno=genotype,
    map=map,
    K=Kinship,
  CV.FarmCPU = Covariates_PC,
  CV.MLM     = Covariates_PC,
  CV.GLM     = Covariates_PC,
  
    # nPC.GLM = 5,
  # nPC.MLM = 3,
  # nPC.FarmCPU = 3,
  ncpus = 10,
  vc.method = "BRENT",
  method.bin = "static",
  threshold = 0.05,
  
  method = c("GLM", "MLM", "FarmCPU"),
  file.output = c("pmap", "pmap.signal", "plot", "log")
)