Description Usage Arguments Details Value Author(s) References See Also Examples
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).
1 2 
data 

M 
Marker Matrix for genotyped individuals 
M.id 
Vector of length 
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{Mα} + \mathbf{Zε} + \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 nongenotyped 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}σ_{ε}^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 nongenotyped animals for wholegenome 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 dualpurpose 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), 6983 (1976)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44  # 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,
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.