Genomic Selection with

Bayesian Generalized Linear

Regression model using R

by Avjinder Kaler

Avjinder Singh Kaler

University of Arkansas, Fayetteville, AR

avjindersingh@gmail.com

This tutorial is used to perform a genomic prediction.

Anyone can use and learn about genomic prediction using

BGLR R package and if you have question related to

genomic prediction and other models, you can contact me

using above email.

Download and Install software.

1. R program

https://cran.r-project.org/bin/windows/base/

2. R Studio

https://www.rstudio.com/products/rstudio/download/

Steps in Genomic Prediction

Step 1: Data Formatting

Format the genotype and phenotype data files needed for BGLR package.

Three types of files are required; genotype file in numeric form, phenotype

file, and kinship matrix.

Format your files like this.

Genotype file: Markers in Columns and Lines in Rows

Kinship Matrix file: You can third party software to estimate kinship matrix

like TASSEL, GAPIT.

Need Line ID in Columns, not in Rows.

Phenotype file: You can put 10/20/30 % data missing to predict those missing

values and check accuracy of model by checking correlation between actual

phenotypic value and predictive values. High correlation means high

accuracy.

First Column is Line ID and Second column is Trait. You can have more

traits in rest of columns.

Step 2: R code for Genomic Prediction

install.packages("bigmemory")

install.packages("biganalytics")

install.packages(“BGLR”)

library("bigmemory")

library("biganalytics")

library(“BGLR”)

Step 3: Set working directory and import data

Set your working directory where you have your data files.

# Read all files

#Phenotype file loading

Y <- read.table("AAE.txt", head = TRUE)

y<-Y[,2]

#Genotype file loading

X <- read.table("g3.txt", head = TRUE)

#Kinship matrix file loading

A<- read.table("k3.txt", head = TRUE)

# Check the dimensions for all files, need to be same dimension for Lines

dim(y)

dim(X)

dim(A)

#Computing the genomic relationship matrix

X<-scale(X,center=TRUE,scale=TRUE)

G<-tcrossprod(X)/ncol(X)

#Computing the eigen-value decomposition of G

EVD <-eigen(G)

#Setting the linear predictor

ETA<-list(list(K=A, model='RKHS'),

list(V=EVD$vectors,d=EVD$values, model='RKHS')

)

#Fitting the model

fm<-BGLR(y=y, ETA=ETA, nIter=12000, burnIn=2000,saveAt='PGBLUP_')

save(fm,file='fmPG_BLUP.rda')

#Predictions

yHat<-fm$yHat

tmp<-range(c(y,yHat))

plot(yHat~y,xlab='Observed',ylab='Predicted',col=2,

xlim=tmp,ylim=tmp); abline(a=0,b=1,col=4,lwd=2)

#Exporting your Genomic prediction values

write.table(yHat, "C:/Folder/file. xt", sep="\t")

#Godness of fit and related statistics

fm$fit

fm$varE # compare to var(y)

#Variance components associated with the genomic and pedigree

fm$ETA[[1]]$varU

fm$ETA[[2]]$varU

# Residual variance

varE<-scan('PGBLUP_varE.dat')

plot(varE,type='o',col=2,cex=.5);

Note:

# Check results in your folder and correlate predictive values with

actual phenotypic values, see how accurate is your model.

For other Tutorials, you can visit here:

http://www.slideshare.net/AvjinderSingh