准备文件

gene_r.csv基因型

id,,,1-1,1-10,1-11,1-12,1-13,1-14,1-16,1-17,1-19,1-2,1-20,1-21,1-24,1-3,1-4,1-5,1-6,1-7,1-8,1-9,2-1,2-10,2-11,2-12,2-13,2-14,2-15,2-17,2-2,2-21,2-22,2-28,2-29,2-3,2-30,2-4,2-5,2-6,2-7,2-8,2-9,3-10,3-11,3-12,3-15,3-17,3-18,3-2,3-21,3-22,3-23,3-25,3-3,3-30,3-31,3-4,3-5,3-6,3-7,3-8,3-9,4-1,4-10,4-11,4-12,4-13,4-14,4-15,4-16,4-17,4-2,4-22,4-23,4-26,4-3,4-4,4-5,4-6,4-7,4-8,4-9,P1-1,P1-2,P1-3,P1-4,P1-5,P2-1,P2-3,P2-4,P2-5,P3-1,P3-2,P3-3,P3-4,P3-5,P4-1,P4-2,P4-3,P4-4,P4-5
contig4_3190959,1,0,H,H,B,H,H,H,B,B,H,H,H,H,H,H,B,H,H,H,H,H,H,H,H,H,H,H,H,A,H,H,B,H,A,B,H,H,H,H,H,B,A,H,H,B,B,B,H,H,A,H,H,H,B,B,B,H,H,B,H,H,H,B,B,B,A,B,H,B,B,A,B,B,H,H,H,H,B,H,H,H,H,H,H,H,H,H,B,B,H,H,H,H,B,A,B,H,H,B,H,A
contig16_6798573,1,0.5,H,H,B,H,H,H,B,B,H,H,H,H,H,H,B,H,H,H,H,H,H,H,H,H,H,H,H,A,H,H,B,H,A,B,H,H,H,H,H,B,A,H,H,B,B,B,H,H,A,H,H,H,B,B,B,H,H,B,H,H,H,B,B,B,A,B,H,B,B,A,B,B,H,H,H,H,B,H,H,H,H,H,H,H,H,H,B,B,H,H,H,H,B,H,B,H,H,B,H,A
contig5_6468798,1,1,H,H,B,H,H,H,B,B,H,H,H,H,H,H,B,H,H,H,H,H,H,H,H,H,H,H,H,A,H,H,B,H,A,B,H,H,H,H,H,B,A,H,H,B,B,B,H,H,A,H,H,H,B,B,B,H,H,B,H,H,H,H,B,B,A,B,H,B,B,A,B,B,H,H,H,H,B,H,H,H,H,H,H,H,H,H,B,B,H,H,H,H,B,H,B,H,H,B,H,A
contig8_10945660,1,1.5,H,H,B,H,H,H,B,B,H,H,H,H,H,H,B,H,H,H,H,H,H,H,H,H,H,H,H,A,H,H,B,H,A,B,H,H,H,H,H,B,A,H,H,B,B,B,H,H,A,H,H,H,B,B,B,H,H,B,H,H,H,H,B,B,A,B,H,B,B,A,B,B,H,H,H,H,B,H,H,H,H,H,H,H,H,H,B,H,H,H,H,H,B,H,B,H,H,B,H,A

phe_r.csv表型

color,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,4,4,4,4,4,1,1,1,1,3,3,3,3,3,2,2,2,2,2
T,1,2,1,1,3,1,3,1,3,3,3,3,3,3,1,3,2,3,2,2,2,1,1,1,1,2,2,3,1,3,3,3,3,1,3,1,1,3,1,1,3,3,3,3,1,1,3,1,1,1,1,1,3,3,3,2,3,2,3,2,3,3,3,2,3,3,1,2,1,3,3,1,1,2,3,3,3,1,3,3,2,1,1,1,1,3,3,1,3,1,1,1,3,3,3,3,1,3,3,1
id,1-1,1-10,1-11,1-12,1-13,1-14,1-16,1-17,1-19,1-2,1-20,1-21,1-24,1-3,1-4,1-5,1-6,1-7,1-8,1-9,2-1,2-10,2-11,2-12,2-13,2-14,2-15,2-17,2-2,2-21,2-22,2-28,2-29,2-3,2-30,2-4,2-5,2-6,2-7,2-8,2-9,3-10,3-11,3-12,3-15,3-17,3-18,3-2,3-21,3-22,3-23,3-25,3-3,3-30,3-31,3-4,3-5,3-6,3-7,3-8,3-9,4-1,4-10,4-11,4-12,4-13,4-14,4-15,4-16,4-17,4-2,4-22,4-23,4-26,4-3,4-4,4-5,4-6,4-7,4-8,4-9,P1-1,P1-2,P1-3,P1-4,P1-5,P2-1,P2-3,P2-4,P2-5,P3-1,P3-2,P3-3,P3-4,P3-5,P4-1,P4-2,P4-3,P4-4,P4-5

计算

library(qtl)
data <- read.cross("csvsr", ".", "gene_bin_r.csv", "phe_r.csv")
jit<-jittermap(data) #去除overlap标记，如无重叠则可跳过
calc <- calc.genoprob(jit, step=2) #用calc.genprob,step以2cM距离。
scan1<-scanone(calc,pheno.col=1) #使用scanone扫描，第一个表型
scan2<-scanone(calc,pheno.col=2) #使用scanone扫描，第二个表型

summary(scan1,threshold = 3) #查看qtl，筛选lod>3的标记
#输出
                 chr  pos   lod
contig4_14106258   2 35.5  3.21
contig2_8539402    4 67.7  3.32
contig17_6098339  17 49.1 14.94

sim <- sim.geno(calc, step=2, err=0.001)
qtl_filt<-summary(scan1,threshold = 3)
qtl1<-makeqtl(sim,qtl_filt$chr,qtl_filt$pos,row.names(qtl_filt)) #生成qtl
fit1<-fitqtl(sim,qtl=qtl1) #拟合qtl模型
summary(fit1) #查看qtl
#输出
        fitqtl summary

Method: multiple imputation 
Model:  normal phenotype
Number of observations : 100 

Full model result
----------------------------------  
Model formula: y ~ Q1 + Q2 + Q3 

      df        SS         MS      LOD   %var Pvalue(Chi2)    Pvalue(F)
Model  6  75.17876 12.5297927 19.97477 60.143            0 1.110223e-16
Error 93  49.82124  0.5357123                                          
Total 99 125.00000                                                     


Drop one QTL at a time ANOVA table: 
----------------------------------  
                 df Type III SS    LOD   %var F value Pvalue(Chi2) Pvalue(F)    
contig4_14106258  2       6.859  2.801  5.487   6.402        0.002   0.00248 ** 
contig2_8539402   2       3.810  1.600  3.048   3.556        0.025   0.03249 *  
contig17_6098339  2      46.825 14.389 37.460  43.704        0.000  4.15e-14 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#%var即解释率
#多个性状qtl类似
#保存结果
write.csv(scan1,file='phe1_lod.csv')
write.csv(fit1[["result.full"]],file='phe1_qtl_full.csv')
write.csv(fit1[["result.drop"]],file='phe1_qtl_drop.csv')
#画图
pdf('phe1.pdf',w=8,h=6)
plot(scan1)
dev.off()