cSSBR.setup: Preparing Model terms for Single Step Bayesian Regression

In cpgen: Parallelized Genomic Prediction and GWAS

Description

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.

Usage

cSSBR.setup(data, M, M.id, verbose=TRUE)

Arguments

data	data.frame with four columns: id, sire, dam, y
M	Marker Matrix for genotyped individuals
M.id	Vector of length nrow(M) representing rownames for M
verbose	Prints progress to the screen

Details

...

Value

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
ginverse_residual	Submatrix of the inverse of the numerator relationship matrix. Used to model the residual error for the imputed individuals

Author(s)

Claas Heuer

References

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)

See Also

cSSBR.setup, clmm

Examples

# 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))

cpgen documentation built on May 2, 2019, 8:15 a.m.