TASSEL软件下载

大家好，我是邓飞，这一篇主要是灌水，因为有小伙伴问我Tassel的安装包，因为tassel下载网络不太好:

点击之后：

用我的方法吧！

TASSEL配套数据和代码，公众号回复：tassel，获得下载链接：

相关教程：

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：

• qqman
• tidyverse
• data.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::selecttable(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::selecttable(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::selecttable(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::selecttable(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")

另外，之前推荐的平台，发现也上线了GWAS模块，但是有点单一，简单是很简单：

1，上传plink二进制文件

2，上传表型数据

3，指定pca个数

4，查看结果，包括GWAS的Pvalue结果（包括pve解释百分比、QQ图、曼哈顿图）

平台地址：https://asreml.cn/gwas-analysis