# Genomic BLUP

## Genomic BLUP

- Overview
- Load R objects
- Statistical model
- Computing a genomic relationship matrix (G)
- Incidence matrix X
- Incidence matrix Z
- Incidence matrix I
- Variance components estimation using the varComp package
- Inverse of V
- GBLUP of genetic values
- Variance components estimation using the regress package
- Save as R object

# Overview

We will learn how to connect phenotypes and genomics using genomic best linear unbiased prediction (GBLUP).

# Load R objects

Use the function `load()`

to load the phenotype object dat_day17.Rda, the OLS fit object fit_day17.Rda, the numerator relationship matrix A2.Rda, the genotype matrix W.Rda, and the results from pedigree-based BLUP day18.Rda we created in class.

```
load(file.choose()) # dat_day17.Rda
dim(dat)
load(file.choose()) # fit_day17.Rda
summary(fit)
load(file.choose()) # A2.Rda
dim(A2)
load(file.choose()) # W.Rda
dim(W)
load(file.choose()) # day18.Rda
ls()
```

# Statistical model

The statistical model we will fit is given by \[ \mathbf{y} = \mathbf{X}\mathbf{b} + \mathbf{Z}\mathbf{u} + \mathbf{e}, \] where \(\mathbf{y}\) is the vector of birth weights, \(\mathbf{X}\) and \(\mathbf{Z}\) are incident matrices for fixed and random effects, respectively, \(\mathbf{b}\) is the vector of regression coefficients for fixed effects, \(\mathbf{u}\) is the vector of regression coefficients for random genetic values, and \(\mathbf{e}\) is the vector of residuals. Recall that BLUP of \(\mathbf{u}\) is given by Henderson (1975) (doi: 10.2307/2529430) \[ \begin{align*} BLUP(\mathbf{u}) &= E(\mathbf{u} | \mathbf{y}) \\ &= Cov(\mathbf{u}, \mathbf{y}') Var(\mathbf{y})^{-1} [\mathbf{y} - E(\mathbf{y})] \\ &= \mathbf{G}\sigma^2_{G} \mathbf{Z}'\mathbf{V}^{-1}(\mathbf{y} - \mathbf{X}\hat{\mathbf{b}}), \end{align*} \] where \[ \begin{align*} \mathbf{V} &= Var(\mathbf{y}) \\ &= \mathbf{Z}\mathbf{G}\sigma^2_G\mathbf{Z}' + \mathbf{I}\sigma^2_e \end{align*} \]

We predict **genomic estimated breeding values** (GEBV) or genetic values \(\hat{\mathbf{u}}\) in the following two steps.

- fit OLS to estimate fixed effects (\(\hat{\mathbf{b}}\))
- fit BLUP to obtain GEBV (\(\hat{\mathbf{u}}\)) condition on the estimated \(\hat{\mathbf{b}}\)

Later we will discuss how to obtain \(\hat{\mathbf{b}}\) and \(\hat{\mathbf{u}}\) simultaneously.

# Computing a genomic relationship matrix (G)

We will compute the \(\mathbf{G}\) matrix defined by VanRaden (2008). The function `computeG()`

accepts two arguments: 1) a genotype matrix and 2) a cutoff point for minor allele frequency (MAF).

```
computeG <- function(W, maf) {
p <- colMeans(W)/2
maf2 <- pmin(p, 1 - p)
index <- which(maf2 < maf)
W2 <- W[, -index]
p2 <- p[-index]
Wc <- scale(W2, center = TRUE, scale = FALSE)
G <- tcrossprod(Wc)/(2 * sum(p2 * (1 - p2)))
return(G)
}
G <- computeG(W, maf = 0.05)
dim(G)
```

### Exercise 1

Create a boxplot comparing elements of the numerator relationship matrix and geomic relationship matrix. What is the correlation between the pedigree relationships and geomic relationships?

### Exercise 2

Repeat Exercise 1 by changing the MAF threshold equal to 0.1. Also, what is the correlation between the two \(\mathbf{G}\) matrices?

# Incidence matrix X

We will now contruct each component one by one. First we will create the incidence matrix \(\mathbf{X}\). We first subset the variable `dat`

by age of dams and sex of calves, and create a new variable `dat2`

. The `model.matrix()`

function returns the incidence matrix \(X\).

```
dat2 <- dat[, c("AgeDam.mon.", "SEX")]
X <- model.matrix(~dat2$AgeDam.mon. + dat2$SEX)
dim(X)
head(X)
```

# Incidence matrix Z

Next we will create the incidence matrix \(\mathbf{Z}\).

```
Z <- diag(nrow(G))
dim(Z)
diag(Z)
```

# Incidence matrix I

The third incidence matrix we create is \(\mathbf{I}\).

```
I <- diag(nrow(G))
dim(I)
diag(I)
```

# Variance components estimation using the varComp package

The `varComp()`

function in the varComp package fits a liner mixed model and estimates variance components (\(\sigma^2_G\) and \(\sigma^2_e\)) through a restricted maximum likelihood (REML). The variance-covariance structure of \(\mathbf{u}\) (`argument = varcov`

) is given by the genomic relationship matrix \(\mathbf{G}\).

```
install.packages("varComp")
library(varComp)
`?`(varComp)
y <- dat$BWT
varcomp <- varComp(fixed = y ~ -1 + X, random = ~Z, varcov = G)
sigma2G <- varcomp$varComps # additive genomic variance
sigma2G
sigma2e <- varcomp$sigma2 # residual variance
sigma2e
```

### Exercise 3

What is the estimate of genomic heritability? Compare the estimate with the one we obtained from the pedigree relationship matrix.

# Inverse of V

Here we compute the inverse of \(V\) matrix. We will use 1) the multiplication operator `%*%`

and 2) the function `solve()`

to obtain the inverse of matrix.

```
V <- Z %*% G %*% t(Z) * sigma2G + I * sigma2e
dim(V)
Vinv <- solve(V)
dim(Vinv)
```

# GBLUP of genetic values

Now we compute GEBV (\(\hat{\mathbf{u}}\)).

```
uhatG <- sigma2G * G %*% t(Z) %*% Vinv %*% (matrix(y) - matrix(predict(fit)))
uhatG2 <- sigma2G * G %*% t(Z) %*% Vinv %*% (matrix(y) - matrix(X %*% fit$coefficients)) # alternative
table(uhatG == uhatG2)
head(uhatG)
tail(uhatG)
```

Let’s plot a histogram of GEBV.

`hist(uhatG, col = "lightblue", main = "Histogram", xlab = "Genomic estimated breeding values")`

### Exercise 4

Create a scatter plot of GEBV vs. observed values. Interpret the result.

### Exercise 5

Which individual has the highest GEBV? Which individual has the lowest GEBV? Use the `which.max()`

and `which.min()`

functions.

### Exercise 6

Rank animals based on their GEBV. Show the top six animal IDs and their GEBV. Use the `order()`

function.

### Exercise 7

Create a scatter plot of EBV vs. GEBV. Compute Spearman’s rank correlation coefficient.

# Variance components estimation using the regress package

The `regress`

function in the regress package can also fit a liner mixed model and estimate variance components (\(\sigma^2_G\) and \(\sigma^2_e\)) through REML.

```
install.packages("regress")
library(regress)
`?`(regress)
varcomp2 <- regress(y ~ -1 + X, ~G)
summary(varcomp2)
```

### Exercise 6

What is the estimate of genomic heritability based on the `regress()`

function? Compare the estimate with the one we obtained from the `varComp()`

function.

# Save as R object

`save(uhatG, h2G, h2G.a, G, file = "day20.Rda")`