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使用TASSEL学习GWAS笔记(6/6):TASSEL结果可视化:QQ plot,曼哈顿图

作者:互联网

笔记计划分为六篇:

第一篇:读取plink基因型数据和表型数据
第二篇:对基因型数据质控:缺失质控,maf质控,hwe质控,样本质控
第三篇:基因型数据可视化:kingship,LD,MDS,PCA
第四篇:一般线性模型进行GWAS分析(GLM模型)
第五篇:混合线性模型进行GWAS分析(MLM模型)
第六篇:TASSEL结果可视化:QQ plot,曼哈顿图

已完成前五篇,本篇是第六篇。

1. TASSEL的GLM和MLM分析结果

质控后的plink数据和表型数据:


GLM的GWAS分析结果:

MLM的GWAS分析结果:

2. TASSEL中的可视化

TASSEL有对结果进行可视化的模块,包括qq图和曼哈顿图,但是图不方便调整。这里用TASSEL的分析结果,使用R语言进行绘制qq图和曼哈顿图。

3. R语言包安装及载入

需要用到:

下面代码,会判断是否有这三个包,如果没有,就自动安装。然后载入软件包。

if(!require(data.table)) install.packages("data.table")
if(!require(qqman)) install.packages("qqman")
if(!require(tidyverse)) install.packages("tidyverse")

library(qqman)
library(tidyverse)
library(data.table)

4. GLM模型GWAS结果可视化

results_log = fread("glm-result.txt")
dim(results_log)
head(results_log)

select = dplyr::select
table(results_log$Trait)

结果:

> table(results_log$Trait)

 dpoll EarDia  EarHT 
  2460   2460   2460 

数据中共有三个性状,可以选择一个性状,进行可视化。

d1 = results_log %>% filter(Trait == "dpoll") %>% select(Chr,Marker,Pos,p)
head(d1)
summary(d1)
d1 = d1 %>% drop_na(p)
summary(d1)

注意,有些P值是NA,在作图时会报错,这里将其移除。

整理后的结果:

> summary(d1)
      Chr          Marker               Pos                  p         
 Min.   : 1.0   Length:2460        Min.   :   139753   Min.   :0.0000  
 1st Qu.: 2.0   Class :character   1st Qu.: 43868061   1st Qu.:0.1236  
 Median : 4.0   Mode  :character   Median :128423374   Median :0.3911  
 Mean   : 4.7                      Mean   :120382976   Mean   :0.4165  
 3rd Qu.: 7.0                      3rd Qu.:175628840   3rd Qu.:0.6743  
 Max.   :10.0                      Max.   :298413352   Max.   :0.9996  

作图代码:

manhattan(d1,chr="Chr",bp="Pos",p="p",snp="Marker", main = "Manhattan plot: logistic")
tiff("y1-曼哈顿图.tiff")
manhattan(d1,chr="Chr",bp="Pos",p="p",snp="Marker", main = "Manhattan plot: logistic")
dev.off()

qq(d1$p, main = "Q-Q plot of GWAS p-values : log")
tiff("y1-QQ图.tiff")
qq(d1$p, main = "Q-Q plot of GWAS p-values : log")
dev.off()

曼哈顿图:

QQ图:

其它两个性状的作图代码:


d2 = results_log %>% filter(Trait == "EarDia") %>% select(Chr,Marker,Pos,p)
head(d2)
summary(d2)
d2 = d2 %>% drop_na(p)
summary(d2)

tiff("y2-曼哈顿图.tiff")
manhattan(d2,chr="Chr",bp="Pos",p="p",snp="Marker", main = "Manhattan plot: logistic")
dev.off()

tiff("y2-QQ图.tiff")
qq(d2$p, main = "Q-Q plot of GWAS p-values : log")
dev.off()


d3 = results_log %>% filter(Trait == "EarHT") %>% select(Chr,Marker,Pos,p)
head(d3)
summary(d3)
d3 = d3 %>% drop_na(p)
summary(d3)

tiff("y3-曼哈顿图.tiff")
manhattan(d3,chr="Chr",bp="Pos",p="p",snp="Marker", main = "Manhattan plot: logistic")
dev.off()

tiff("y3-QQ图.tiff")
qq(d3$p, main = "Q-Q plot of GWAS p-values : log")
dev.off()

将整理后的不同性状的结果保存到本地:

fwrite(d1,"y1_result.csv")
fwrite(d2,"y2_result.csv")
fwrite(d3,"y3_result.csv")

5. MLM模型GWAS结果可视化

读取数据,提取性状,去掉P值为缺失的行:

library(qqman)
library(data.table)
results_log = fread("mlm-result.txt", head=TRUE)
dim(results_log)
head(results_log)

library(tidyverse)
select = dplyr::select
table(results_log$Trait)
d1 = results_log %>% filter(Trait == "dpoll") %>% select(Chr,Marker,Pos,p)
head(d1)
summary(d1)
d1 = d1 %>% drop_na(p)
summary(d1)

曼哈顿图:

QQ图:

其它两个作图代码:


d2 = results_log %>% filter(Trait == "EarDia") %>% select(Chr,Marker,Pos,p)
head(d2)
summary(d2)
d2 = d2 %>% drop_na(p)
summary(d2)

tiff("y2-曼哈顿图.tiff")
manhattan(d2,chr="Chr",bp="Pos",p="p",snp="Marker", main = "Manhattan plot: logistic")
dev.off()

tiff("y2-QQ图.tiff")
qq(d2$p, main = "Q-Q plot of GWAS p-values : log")
dev.off()


d3 = results_log %>% filter(Trait == "EarHT") %>% select(Chr,Marker,Pos,p)
head(d3)
summary(d3)
d3 = d3 %>% drop_na(p)
summary(d3)

tiff("y3-曼哈顿图.tiff")
manhattan(d3,chr="Chr",bp="Pos",p="p",snp="Marker", main = "Manhattan plot: logistic")
dev.off()

tiff("y3-QQ图.tiff")
qq(d3$p, main = "Q-Q plot of GWAS p-values : log")
dev.off()

6. 完整代码汇总

GLM的可视化代码:

## 对TASSEL GLM 模型可视化

if(!require(data.table)) install.packages("data.table")
if(!require(qqman)) install.packages("qqman")
if(!require(tidyverse)) install.packages("tidyverse")

library(qqman)
library(tidyverse)
library(data.table)

results_log = fread("glm-result.txt")
dim(results_log)
head(results_log)

select = dplyr::select
table(results_log$Trait)
d1 = results_log %>% filter(Trait == "dpoll") %>% select(Chr,Marker,Pos,p)
head(d1)
summary(d1)
d1 = d1 %>% drop_na(p)
summary(d1)

manhattan(d1,chr="Chr",bp="Pos",p="p",snp="Marker", main = "Manhattan plot: logistic")
tiff("y1-曼哈顿图.tiff")
manhattan(d1,chr="Chr",bp="Pos",p="p",snp="Marker", main = "Manhattan plot: logistic")
dev.off()

qq(d1$p, main = "Q-Q plot of GWAS p-values : log")
tiff("y1-QQ图.tiff")
qq(d1$p, main = "Q-Q plot of GWAS p-values : log")
dev.off()

d2 = results_log %>% filter(Trait == "EarDia") %>% select(Chr,Marker,Pos,p)
head(d2)
summary(d2)
d2 = d2 %>% drop_na(p)
summary(d2)

tiff("y2-曼哈顿图.tiff")
manhattan(d2,chr="Chr",bp="Pos",p="p",snp="Marker", main = "Manhattan plot: logistic")
dev.off()

tiff("y2-QQ图.tiff")
qq(d2$p, main = "Q-Q plot of GWAS p-values : log")
dev.off()


d3 = results_log %>% filter(Trait == "EarHT") %>% select(Chr,Marker,Pos,p)
head(d3)
summary(d3)
d3 = d3 %>% drop_na(p)
summary(d3)

tiff("y3-曼哈顿图.tiff")
manhattan(d3,chr="Chr",bp="Pos",p="p",snp="Marker", main = "Manhattan plot: logistic")
dev.off()

tiff("y3-QQ图.tiff")
qq(d3$p, main = "Q-Q plot of GWAS p-values : log")
dev.off()

fwrite(d1,"y1_result.csv")
fwrite(d2,"y2_result.csv")
fwrite(d3,"y3_result.csv")

MLM的可视化代码:

## 对TASSEL GLM 模型可视化

library(qqman)
library(data.table)
results_log = fread("mlm-result.txt", head=TRUE)
dim(results_log)
head(results_log)

library(tidyverse)
select = dplyr::select
table(results_log$Trait)
d1 = results_log %>% filter(Trait == "dpoll") %>% select(Chr,Marker,Pos,p)
head(d1)
summary(d1)
d1 = d1 %>% drop_na(p)
summary(d1)

manhattan(d1,chr="Chr",bp="Pos",p="p",snp="Marker", main = "Manhattan plot: logistic")
tiff("y1-曼哈顿图.tiff")
manhattan(d1,chr="Chr",bp="Pos",p="p",snp="Marker", main = "Manhattan plot: logistic")
dev.off()

qq(d1$p, main = "Q-Q plot of GWAS p-values : log")
tiff("y1-QQ图.tiff")
qq(d1$p, main = "Q-Q plot of GWAS p-values : log")
dev.off()

d2 = results_log %>% filter(Trait == "EarDia") %>% select(Chr,Marker,Pos,p)
head(d2)
summary(d2)
d2 = d2 %>% drop_na(p)
summary(d2)

tiff("y2-曼哈顿图.tiff")
manhattan(d2,chr="Chr",bp="Pos",p="p",snp="Marker", main = "Manhattan plot: logistic")
dev.off()

tiff("y2-QQ图.tiff")
qq(d2$p, main = "Q-Q plot of GWAS p-values : log")
dev.off()


d3 = results_log %>% filter(Trait == "EarHT") %>% select(Chr,Marker,Pos,p)
head(d3)
summary(d3)
d3 = d3 %>% drop_na(p)
summary(d3)

tiff("y3-曼哈顿图.tiff")
manhattan(d3,chr="Chr",bp="Pos",p="p",snp="Marker", main = "Manhattan plot: logistic")
dev.off()

tiff("y3-QQ图.tiff")
qq(d3$p, main = "Q-Q plot of GWAS p-values : log")
dev.off()

fwrite(d1,"y1_result.csv")
fwrite(d2,"y2_result.csv")
fwrite(d3,"y3_result.csv")

标签:QQ,plot,TASSEL,log,tiff,d2,d3,d1
来源: https://blog.csdn.net/yijiaobani/article/details/119885686