cSSBR | R Documentation |
This function runs Single Step Bayesian Regression (SSBR) for the prediction of breeding values in a unified model that incorporates genotyped and non genotyped individuals (Fernando et al., 2014).
cSSBR(data, M, M.id, returnAll = FALSE, X=NULL, par_random=NULL, scale_e=0, df_e=0,
niter=5000, burnin=2500, seed=NULL, verbose=TRUE)
data |
|
M |
Marker Matrix for genotyped individuals |
M.id |
Vector of length |
returnAll |
Return all individuals present in |
X |
Fixed effects design matrix of type: |
par_random |
as in |
niter |
as in |
burnin |
as in |
verbose |
as in |
scale_e |
as in |
df_e |
as in |
seed |
as in |
The function sets up the following model using cSSBR.setup
:
\mathbf{y} = \mathbf{Xb} + \mathbf{M\alpha} + \mathbf{Z\epsilon} + \mathbf{e}
The matrix \mathbf{M}
denotes a combined marker matrix consisting of actual and imputed marker covariates.
Best linear predictions of gene content (Gengler et al., 2007) for the non-genotyped individuals are obtained using: \mathbf{A}^{11}\hat{\mathbf{M}_1} = -\mathbf{A}^{12}\mathbf{M}_2
(Fernando et al., 2014).
\mathbf{A}^{11}
and \mathbf{A}^{12}
are submatrices of the inverse of the numerator relationship matrix, which is easily obtained (Henderson, 1976). The subscripts 1 and 2 denote non genotyped and genotyped individuals respectively. The very sparse equation system is being solved using a sparse cholesky solver provided by the Eigen library.
The residual imputation error has variance: (\mathbf{A}^{11})^{-1}\sigma_{\epsilon}^2
.
List of 4 + number of random effects as in clmm
+
SSBR |
List of 7:
|
Claas Heuer
Fernando, R.L., Dekkers, J.C., Garrick, D.J.: A class of bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses. Genetics Selection Evolution 46(1), 50 (2014)
Gengler, N., Mayeres, P., Szydlowski, M.: A simple method to approximate gene content in large pedigree populations: application to the myostatin gene in dual-purpose belgian blue cattle. animal 1(01), 21 (2007)
Henderson, C.R.: A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics 32(1), 69-83 (1976)
cSSBR.setup, clmm
# example dataset
id <- 1:6
sire <- c(rep(NA,3),rep(1,3))
dam <- c(rep(NA,3),2,2,3)
# phenotypes
y <- c(NA, 0.45, 0.87, 1.26, 1.03, 0.67)
dat <- data.frame(id=id,sire=sire,dam=dam,y=y)
# Marker genotypes
M <- rbind(c(1,2,1,1,0,0,1,2,1,0),
c(2,1,1,1,2,0,1,1,1,1),
c(0,1,0,0,2,1,2,1,1,1))
M.id <- 1:3
var_y <- var(y,na.rm=TRUE)
var_e <- (10*var_y / 21)
var_a <- var_e
var_m <- var_e / 10
# put emphasis on the prior
df = 500
par_random=list(list(method="ridge",scale=var_m,df = df),list(method="ridge",scale=var_a,df=df))
set_num_threads(1)
mod<-cSSBR(data = dat,
M=M,
M.id=M.id,
returnAll = TRUE,
par_random=par_random,
scale_e = var_e,
df_e=df,
niter=50000,
burnin=30000)
# check marker effects
print(round(mod[[4]]$posterior$estimates_mean,digits=2))
# check breeding value prediction:
print(round(mod$SSBR$Breeding_Values,digits=2))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.