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) used as a covariate.
Note: for more info check out this GWAS tutorial.
myTraits <- list_Traits(folder = "GWAS_Results/")
myTraits
myFiles <- list_Result_Files(folder = "GWAS_Results/")
myFiles
## [1] "GAPIT.Blink.DTF_Nepal_2017.GWAS.Results.csv"
## [2] "GAPIT.Blink.DTF_Sask_2017.GWAS.Results.csv"
## [3] "GAPIT.Blink.DTF_Sask_2017_b.GWAS.Results.csv"
## [4] "GAPIT.Blink.Testa_Pattern.GWAS.Results.csv"
## [5] "GAPIT.FarmCPU.DTF_Nepal_2017.GWAS.Results.csv"
## [6] "GAPIT.FarmCPU.DTF_Sask_2017.GWAS.Results.csv"
## [7] "GAPIT.FarmCPU.DTF_Sask_2017_b.GWAS.Results.csv"
## [8] "GAPIT.FarmCPU.Testa_Pattern.GWAS.Results.csv"
## [9] "GAPIT.GLM.DTF_Nepal_2017.GWAS.Results.csv"
## [10] "GAPIT.GLM.DTF_Sask_2017.GWAS.Results.csv"
## [11] "GAPIT.GLM.DTF_Sask_2017_b.GWAS.Results.csv"
## [12] "GAPIT.GLM.Testa_Pattern.GWAS.Results.csv"
## [13] "GAPIT.MLM.DTF_Nepal_2017.GWAS.Results.csv"
## [14] "GAPIT.MLM.DTF_Sask_2017.GWAS.Results.csv"
## [15] "GAPIT.MLM.DTF_Sask_2017_b.GWAS.Results.csv"
## [16] "GAPIT.MLM.Testa_Pattern.GWAS.Results.csv"
## [17] "GAPIT.MLMM.DTF_Nepal_2017.GWAS.Results.csv"
## [18] "GAPIT.MLMM.DTF_Sask_2017.GWAS.Results.csv"
## [19] "GAPIT.MLMM.DTF_Sask_2017_b.GWAS.Results.csv"
## [20] "GAPIT.MLMM.Testa_Pattern.GWAS.Results.csv"
myResults <- table_GWAS_Results(folder = "GWAS_Results/", files = myFiles,
threshold = 6.7, sug.threshold = 6)
list_Top_Markers(trait = "DTF_Nepal_2017", model = "MLMM",
folder = "GWAS_Results/",
threshold = 6.7, chroms = c(2,5), n = 1)
## SNP CHR POS -log10(p)
## 1 Lcu.2RBY.Chr2p42543877 2 42543877 11.58
## 2 Lcu.2RBY.Chr5p1069654 5 1069654 16.71
Summary Plot
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),
vlines = c("Lcu.2RBY.Chr2p42543877","Lcu.2RBY.Chr5p1069654",
"Lcu.2RBY.Chr6p2528817", "Lcu.2RBY.Chr6p12212845"),
vline.colors = c("red", "red", "green", "blue"),
title = "Summary of Significant GWAS Results")
ggsave("man/figures/GWAS_Summary.png", mp, width = 10, height = 3)
gg_GWAS_plotly(mp, filename = "GWAS_Summary.html")
Manhattan Plots
Multi Manhattan Plots
for(i in myTraits) {
mp <- gg_Manhattan(folder = "GWAS_Results/", trait = i, facet = F,
models = c("MLM", "MLMM", "FarmCPU", "Blink"),
threshold = 7.3, sug.threshold = 6.7, pmax = 12,
vlines = c("Lcu.2RBY.Chr2p42543877",
"Lcu.2RBY.Chr5p1069654",
"Lcu.2RBY.Chr6p12212845",
"Lcu.2RBY.Chr6p2528817") )
ggsave(paste0("man/figures/Multi_",i,".png"), mp, width = 10, height = 4)
}
Facetted Manhattan Plots
for(i in myTraits) {
mp <- gg_Manhattan(folder = "GWAS_Results/", trait = i, facet = T,
threshold = 7.3, sug.threshold = 6.7,
vlines = c("Lcu.2RBY.Chr2p42543877",
"Lcu.2RBY.Chr5p1069654",
"Lcu.2RBY.Chr6p12212845",
"Lcu.2RBY.Chr6p2528817"))
ggsave(paste0("man/figures/Facet_",i,".png"), mp, width = 10, height = 8)
}
Dependancies
tidyverse
, ggpubr
, ggbeeswarm
, ggrepel
, ggtext
, plotly
, htmlwidgets
© Derek Michael Wright www.dblogr.com/