Datasets:
| 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") | |
| ) | |