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：
qqmantidyversedata.table
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*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")