Heritability estimation based on genomic relationship matrix using JAGS
Heritability estimation based on genomic relationship matrix using JAGS
h2.jags(y,x,G,eps=0.0001,sigma.p=0,sigma.r=1,parms=c("b","p","r","h2"),...)y |
outcome vector |
x |
covariate matrix |
G |
genomic relationship matrix |
eps |
a positive diagonal perturbation to G |
sigma.p |
initial parameter values |
sigma.r |
initial parameter values |
parms |
monitored parmeters |
... |
parameters passed to jags, e.g., n.chains, n.burnin, n.iter |
This function performs Bayesian heritability estimation using genomic relationship matrix.
The returned value is a fitted model from jags().
Zhao JH, Luan JA, Congdon P (2018). Bayesian linear mixed models with polygenic effects. J Stat Soft 85(6):1-27
Jing Hua Zhao
## Not run:
require(gap.datasets)
set.seed(1234567)
meyer <- within(meyer,{
y[is.na(y)] <- rnorm(length(y[is.na(y)]),mean(y,na.rm=TRUE),sd(y,na.rm=TRUE))
g1 <- ifelse(generation==1,1,0)
g2 <- ifelse(generation==2,1,0)
id <- animal
animal <- ifelse(!is.na(animal),animal,0)
dam <- ifelse(!is.na(dam),dam,0)
sire <- ifelse(!is.na(sire),sire,0)
})
G <- kin.morgan(meyer)$kin.matrix*2
library(regress)
r <- regress(y~-1+g1+g2,~G,data=meyer)
r
with(r,h2G(sigma,sigma.cov))
eps <- 0.001
y <- with(meyer,y)
x <- with(meyer,cbind(g1,g2))
ex <- h2.jags(y,x,G,sigma.p=0.03,sigma.r=0.014)
print(ex)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.