Description Usage Arguments Details Value Author(s) References See Also Examples
This function prepares all model terms for SSBR using pedigree and marker information.
The function is particularly useful for using the reported model terms on multiple
phenotypes, for cross validation (clmm
), for genomewide association studies
or to pass them to alternative software.
1 | cSSBR.setup(data, M, M.id, verbose=TRUE)
|
data |
|
M |
Marker Matrix for genotyped individuals |
M.id |
Vector of length |
verbose |
Prints progress to the screen |
...
List of 5:
ids |
ids for the model (ordered as in other model terms) |
y |
phenotype vector |
Marker_Matrix |
Combined Marker Matrix including imputed and genotyped individuals |
Z_residual |
Design Matrix used to model the residual error for the imputed individuals |
|
Submatrix of the inverse of the numerator relationship matrix. Used to model the residual error for the imputed individuals |
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)
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 45 | # 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
model_terms <- cSSBR.setup(dat,M, M.id)
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)
# passing model terms to 'clmm'
mod<-clmm(y=model_terms$y,
Z=list(model_terms$Marker_Matrix,model_terms$Z_residual),
ginverse = list(NULL, model_terms$ginverse_residual),
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))
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