Quantifying evolution and selection on complex traits

Description

G-hat: R function to estimate G-hat from allele frequency and effect size data.

Usage

Ghat(effects = effects, change = change, method = "scale",
  perms = 1000, plot = "Both", blockSize = 1000, num_eff = NULL)

Arguments

Value

Ghat Ghat-value

Cor Correlation between alleles frequencies and their effects

p.val two-sided P-value of Evidence of selection

plot relationship between estimated allelic effects at individual SNPs and the change in allele frequency over generations

Examples

#Example-1 Both SNP effects and change in allele frequency are known
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))


## Not run: 
#Example-2 Both SNP effects and change in allele frequency are known
##################################################################
## step 1: #run rrBLUP and estimating allels effects            ##
##################################################################

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")
                                   
##################################################################
## step 2: is to calculate the allele frequency at Cycle 1 and 3##
##################################################################
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$Cycle1

################################################################
## step 3: Calculate LD Decay                                   ##
################################################################
ld                  <- ld_decay (gen=gen, map=map,
                                 max_win_snp=2000, max.chr=10,
                                 cores=1, max_r2=0.03)

##################################################################
## step 4: Calculate Ghat                                       ##
##################################################################
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 = " "))

## End(Not run)

The Wisconsin Quality Synthetic (WQS) maize population datasets.

Description

The Wisconsin Quality Synthetic (WQS) maize population datasets.

Usage

Maize_wqs

Format

A list of 4 data sets:

Dataset-1

Data frame including all SNP effects and changes in allele frequencies between cycle 2 and 5.

Dataset-2

BLUP breeding values for Acid Detergent Fiber (ADF) involving 5 generations of selection.

Dataset-3

Map file: Each line of the Map file describes a single marker and must contain at least three columns. 1: chromosome number; 2: SNP (snp id); 3: SNP position (in base-pairs (bp)).

Dataset-4

Maize Genotype (Illumina MaizeSNP50 BeadChip); an Infinium HD assay (Illumina, Inc. San Diego, CA). 10,017 SNP markers (0,1 and 2) after filtration, distributed across the maize genome (Ganal et al.2011).

Source

Lorenz, A. J., Beissinger, T.M., Rodrigues, R., de Leon, N. 2015. Selection for silage yield and composition did not affect genomic diversity within the Wisconsin Quality Synthetic maize population. Genes Genomes Genetics. DOI: 10.1534/g3.114.015263.

Examples

maize <- Maize_wqs[[1]]
phe   <- Maize_wqs[[2]]
map   <- Maize_wqs[[3]]
gen   <- Maize_wqs[[4]]

Evaluation of Linkage Disequilibrium Decay

Description

ld_decay: R function for calculating the effective number of independent markers

Usage

ld_decay(gen = gen, map = map, max_win_snp = 2000,
  max.chr = max.chr, cores = 1, max_r2 = max_r2)

Arguments

Value

cor: Correlation matrix

ch_eff_nmark: The Number of independent marker per chromosome

eff_nmark: The effective number of independent markers

Examples

## Not run: 
library("parallel")
gen         <- Maize_wqs[[4]]
map         <- Maize_wqs[[3]]
Res_ld <- ld_decay (gen=gen, map=map, max_win_snp=2000,
                    max.chr=10, cores=1, max_r2=0.03)

## End(Not run)