Functions are provided for quantifying evolution and selection on complex traits. The package implements effective handling and analysis algorithms scaled for genome-wide data and calculates a composite statistic, denoted Ghat, which is used to test for selection on a trait. The package provides a number of simple examples for handling and analysing the genome data and visualising the output and results. Beissinger et al., (2018) doi:10.1534/genetics.118.300857
Medhat Mahmoud*, ‡,§,1, Mila Tost*,1, ‡, Ngoc-Thuy Ha †, Henner Simianer †, ‡, and Timothy Beissinger*, ‡,1
To install the latest development snapshot (see latest changes below), type the following commands into the R console:
library(devtools)
devtools::install_github("Medhat86/Ghat")To install the latest stable release from CRAN, type the following command into the R console:
install.packages("Ghat")maize <- Maize_wqs[[1]]
result.adf <- Ghat(effects =maize[,1], change=maize[,2], method="scale",
perms=1000, plot="Ghat", num_eff=54.74819)
mtext(paste("WQS ADF test for selection, pval = ", round(result.adf$p.val,4)))
message (c(result.adf$Ghat , result.adf$Cor , result.adf$p.va))library(Ghat)
library(parallel)
library(rrBLUP)
phe <- Maize_wqs[[2]]
map <- Maize_wqs[[3]]
gen <- Maize_wqs[[4]]
phe <-phe[which(is.na(phe[,2])==FALSE),]
gen <-gen[which(is.na(phe[,2])==FALSE),]
result <- mixed.solve(phe[,2],
Z= as.matrix(gen[,2:ncol(gen)]),
X= model.matrix(phe[,2]~phe[,3]),
K=NULL, SE=FALSE, return.Hinv=FALSE,
method="ML")CycleIndicator <- as.numeric(unlist(strsplit(gen$X,
split="_C")) [seq(2,2*nrow(gen),2)])
Cycle1 <- gen[which(CycleIndicator == 1),]
Cycle3 <- gen[which(CycleIndicator == 3),]
CycleList <- list(Cycle1,Cycle3)
frequencies <- matrix(nrow=ncol(gen)-1,ncol=2)
for(i in 1:2){
frequencies[,i] <- colMeans(CycleList[[i]][,-1],na.rm=TRUE)/2
}
frequencies <- as.data.frame(frequencies)
names(frequencies) <- c("Cycle1","Cycle3")
change<-frequencies$Cycle3-frequencies$Cycle1ld <- ld_decay (gen=gen, map=map,
max_win_snp=2000, max.chr=10,
cores=1, max_r2=0.03)Ghat.adf <- Ghat(effects=result$u, change=change, method = "scale",
perms=1000,plot="Ghat", num_eff = 54.74819)
message (paste("Ghat=" , Ghat.adf$Ghat,
"Cor=" , Ghat.adf$Cor ,
"P-val=", Ghat.adf$p.va, sep = " "))Please visit https://github.com/Medhat86/Ghat for documentation and examples.
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In case you want / have to cite my package, please use
citation('Ghat')(https://CRAN.R-project.org/package=Ghat) for citation information. -
Since core functionality of package depends on the Ghat-Method, consider citing this refernce as well: "Tim Beissinger, Jochen Kruppa, David Cavero, Ngoc-Thuy Ha, Malena Erbe, Henner Simianer, A Simple Test Identifies Selection on Complex Traits, Genetics, Volume 209, Issue 1, 1 May 2018, Pages 321–333, https://doi.org/10.1534/genetics.118.300857"