安装使用QTL-seqr

installed.packages(“devtools”)   #安装devtools
library(devtools)
install_github("bmansfeld/QTLseqr") #使用devtools安装QTLseqr

目前版本号：QTLseqr v0.7.3

#load the package
library("QTLseqr")

#Set sample and file names
LowBulk <- "119-8"
HighBulk <- "2447-20"
file <- "common.table"

#Choose which chromosomes will be included in the analysis (i.e. exclude smaller contigs)
Chroms <- paste0(rep("", 10), 1:10)

#Import SNP data from file
df <-
    importFromGATK(
        file = file,
        highBulk = HighBulk,
        lowBulk = LowBulk,
        chromList = Chroms
     )

#Filter SNPs based on some criteria
df_filt <-
    filterSNPs(
        SNPset = df,
        refAlleleFreq = 0.20,
        minTotalDepth = 100,
        maxTotalDepth = 400,
        minSampleDepth = 40,
        minGQ = 99
    )


#Run G' analysis
df_filt <- runGprimeAnalysis(
    SNPset = df_filt,
    windowSize = 1e6,
    outlierFilter = "deltaSNP")

#Run QTLseq analysis
df_filt <- runQTLseqAnalysis(
    SNPset = df_filt,
    windowSize = 1e6,
    popStruc = "F2",
    bulkSize = c(30, 30),
    replications = 10000,
    intervals = c(95, 99)
)

#Plot
plotQTLStats(SNPset = df_filt, var = "Gprime", plotThreshold = TRUE, q = 0.01)
plotQTLStats(SNPset = df_filt, var = "deltaSNP", plotIntervals = TRUE)

#export summary CSV
getQTLTable(SNPset = df_filt, alpha = 0.01, export = TRUE, fileName = "my_BSA_QTL.csv")