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README.Rmd
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README.Rmd
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---
title: gwaspr R Package
output: github_document
always_allow_html: true
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = T, message = F, warning = F)
```
`gwaspr`: an `R` package for plotting GWAS results from the `GAPIT` package
# Installation
```{r eval = F}
devtools::install_github("derekmichaelwright/gwaspr")
```
```{r eval = T}
library(gwaspr)
```
# GWAS Tutorial
https://derekmichaelwright.github.io/dblogr/academic/gwas_tutorial
# Usage
For best practice, output from GAPIT should be in its own folder. In this case, they are located in a folder called `GWAS_Results/`. For this example we will plot GWAS results from 3 traits in a lentil diversity panel:
- **Testa_Pattern**: a *qualitative* trait describing the presence or absence of seed coat pigmentation.
- **DTF_Nepal_2017**: a *quantitative* trait describing days from sowing to flowering in a 2017 Nepal field trial.
- **DTF_Sask_2017**: a *quantitative* trait describing days from sowing to flowering in a 2017 Saskatchewan field trial.
- **DTF_Sask_2017_b**: same as above but run with the *b* coefficient from a photothermal model (see [Wright *et al*. 2020](https://doi.org/10.1002/ppp3.10158)) used as a covariate.
Note: for more info check out this [GWAS tutorial](https://derekmichaelwright.github.io/dblogr/academic/gwaspr_tutorial).
## List Traits
```{r eval = T}
myTraits <- list_Traits(folder = "GWAS_Results/")
myTraits
```
## List Results Files
```{r eval = T}
myFiles <- list_Result_Files(folder = "GWAS_Results/")
myFiles
```
## List Significant Markers
```{r eval = F}
# first reorder the result files if they are not already arranged by P.value
order_GWAS_Results(folder = "GWAS_Results/", files = myFiles)
```
```{r eval = T}
myResults <- table_GWAS_Results(folder = "GWAS_Results/", files = myFiles,
threshold = 6.7, sug.threshold = 6)
myResults[1:10,]
```
```{r eval = T}
list_Top_Markers(trait = "DTF_Nepal_2017", model = "MLMM",
folder = "GWAS_Results/",
threshold = 6.7, chroms = c(2,5), n = 1)
```
```{r}
myMarkers <- c("Lcu.2RBY.Chr2p42543877","Lcu.2RBY.Chr5p1069654",
"Lcu.2RBY.Chr6p2528817", "Lcu.2RBY.Chr6p12212845")
```
```{r eval = T}
list_Top_Markers(trait = "Testa_Pattern", model = "MLM",
folder = "GWAS_Results/",
threshold = 6.7, chroms = 6, n = 1)
```
```{r eval = T}
table_Results_Summary("GWAS_Results/", isOrdered = F)
table_Results_Summary("GWAS_Results/", isOrdered = T)
```
---
## Manhattan Plots
### Multi Manhattan Plots
```{r eval = F}
for(i in myTraits) {
mp <- gg_Manhattan(folder = "GWAS_Results/",
trait = i,
title = paste("LDP -", i),
threshold = 7.3,
sug.threshold = 6.7,
vlines = myMarkers,
vline.colors = c("red","red","darkgreen","blue"),
vline.types = c(1,1,1,1),
vline.legend = T,
facet = F,
addQQ = T,
pmax = 12,
models = c("MLM", "MLMM", "FarmCPU", "BLINK"),
model.colors = c("darkgreen", "darkred", "darkorange3", "steelblue"),
legend.rows = 2)
ggsave(paste0("man/figures/Multi_", i, ".png"),
mp, width = 12, height = 4, bg = "white")
}
```
---
### Facetted Manhattan Plots
```{r eval = F}
for(i in myTraits) {
mp <- gg_Manhattan(folder = "GWAS_Results/",
trait = i,
title = paste("LDP -", i),
threshold = 7.3,
sug.threshold = 6.7,
vlines = myMarkers,
vline.colors = c("red","red","darkgreen","blue"),
vline.types = c(1,1,1,1),
vline.legend = T,
facet = T,
addQQ = T,
pmax = 12,
models = c("MLM", "MLMM", "FarmCPU", "BLINK"),
chrom.colors = rep(c("darkgreen", "darkgoldenrod2"), 4),
legend.rows = 1)
ggsave(paste0("man/figures/Facet_", i, ".png"),
mp, width = 12, height = 8, bg = "white")
}
```
---
## Summary Plot
```{r eval = F}
mp <- gg_GWAS_Summary(folder = "GWAS_Results/",
traits = myTraits,
models = c("MLM", "MLMM", "FarmCPU", "BLINK"),
colors = c("darkgreen", "darkred", "darkorange3", "steelblue"),
threshold = 6.7, sug.threshold = 6,
hlines = c(1.5,3.5), legend.rows = 2,
vlines = myMarkers,
vline.colors = c("red", "red", "green", "blue"),
vline.types = c(1,1,1,1),
title = "Summary of Significant GWAS Results")
ggsave("man/figures/GWAS_Summary.png", mp, width = 12, height = 4)
```
```{r eval = F, echo = F}
gg_GWAS_plotly(mp, filename = "man/figures/GWAS_Summary.html")
plotly::ggplotly(mp)
```
---
## GWAS Hits Plot
```{r eval = F}
# Prep
myG <- read.csv("myG_hmp.csv", header = T)
# Plot
mp <- gg_GWAS_Hits(xx = myResults,
xG = myG,
myTs = list_Traits("GWAS_Results/"),
myR = 2000000,
myTitle = "",
sigMin = 0,
myCV = NULL,
models = c("MLM", "MLMM", "FarmCPU", "BLINK", "GLM"),
model.colors = c("darkgreen", "darkred", "darkorange3", "steelblue", "darkorchid4"),
model.shapes = c(21,24:25,22,23),
vlines = myMarkers,
vline.colors = c("red", "red", "green", "blue"),
vline.types = rep(1, length(vlines)),
legend.rows = 1)
ggsave("man/figures/GWAS_Hits_01.png", mp, width = 10, height = 4)
```
---
```{r eval = F}
mp <- gg_GWAS_Hits(xx = myResults,
xG = myG,
myTs = list_Traits("GWAS_Results/"),
myR = 2000000,
myTitle = "",
sigMin = 0,
myCV = NULL,
models = c("MLM", "MLMM", "FarmCPU", "BLINK"),
model.colors = c("darkgreen", "darkred", "darkorange3", "steelblue"),
model.shapes = c(21,24:25,22),
vlines = myMarkers,
vline.colors = c("red", "red", "green", "blue"),
vline.types = rep(1, length(vlines)),
legend.rows = 2)
ggsave("man/figures/GWAS_Hits_02.png", mp, width = 10, height = 4)
```
```{r eval = F echo = F}
p <- gg_Manhattan_Traits(folder = "GWAS_Results/",
traits = paste0("Ro17_Plot.Height.d0", c("24","32","40","54","61","67","74","80","89","95")),
model = "MLM",
title = "GWAS",
threshold = 6,
sug.threshold = 4,
markers = myMarkers,
labels = markers,
vlines = markers,
vline.colors = rep("red", length(vlines)),
vline.types = rep(1, length(vlines)),
vline.legend = T,
addQQ = T,
facet = F,
pmax = NULL,
chrom.unit = "100 Mbp",
myColors = c("darkgreen", "darkred", "darkorange3", "steelblue", "darkorchid4",
"darkslategray", "burlywood4", "darkgoldenrod2", "red", "maroon3",
"blue2", "cyan3", "deeppink3"),
legend.rows = 4)
ggsave("Additional/Additional_Figure_15.png", mp, width = 12, height = 5, bg = "white")
```
---
# GAPIT
`GAPIT`: and `R` package for performing Genome Wide Association Studies (GWAS)
https://github.com/jiabowang/GAPIT3
# Dependancies
`tidyverse`, `ggpubr`, `ggbeeswarm`, `ggrepel`, `ggtext`, `plotly`, `htmlwidgets`
---
© Derek Michael Wright