安装使用QTL-seqr

QTL-seqr是一个R包。官方文件地址
qtl-seqr参数详解
安装流程
在R环境里

install.packages("devtools")   #安装devtools
library(devtools)
install_github("bmansfeld/QTLseqr") #使用devtools安装QTLseqr

目前版本号：QTLseqr v0.7.5.2

#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)#此处需要根据物种的染色体数量设定，同时括号中双引号，如果是chr1这种则应修改为rep("chr"，10)。如果是1这种类型，则和我的设置一样。

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



#Filter SNPs based on some criteria
#经过检测，这个REF设置为0.20结果比较理想，可以根据自上述的统计信息自行修改
df_filt <-
    filterSNPs(
        SNPset = df,
        refAlleleFreq = 0.20,
        minTotalDepth = 100,
        maxTotalDepth = 400,
        minSampleDepth = 40,
        minGQ = 99
    )

#使用ggplot2检测质控之前的读取深度的直方图，用以确定后续Depth的过滤参数
library("ggplot2")
ggplot(data=df)+geom_histogram(aes(x=DP.HIGH+DP.LOW),bins=50)+xlim(0,500)
#检测等位基因频率，应该是正态分布,决定后续过滤参数REF的值。
ggplot(data=df)+geom_histogram(aes(x=REF_FRQ),bins=50)
#检测每个样本的snp-index,我们期望的是正态分布，大部分应该在0.5附近，同时在0和1出现两个小峰
ggplot(data = df) + geom_histogram(aes(x = SNPindex.HIGH))
ggplot(data = df) + geom_histogram(aes(x = SNPindex.LOW))

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

#Run QTLseq analysis
df_filt <- runQTLseqAnalysis(
    SNPset = df_filt,
    windowSize = 1e6,#如果程序报错，就增大窗口大小
    popStruc = "F2",#群体结构F2或者RIL
    bulkSize = c(30, 30),#每个池的单株数量
    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")