model
{
q ~ dunif(0,1) # prevalence of a1
p <- 1 - q # prevalence of a2
Ann1 ~ dbin(q,2); Ann <- Ann1 + 1 # geno. dist. for founder
Brian1 ~ dbin(q,2); Brian <- Brian1 + 1
Clare ~ dcat(p.mendelian[Ann,Brian,]) # geno. dist. for child
Diane ~ dcat(p.mendelian[Ann,Brian,])
Eric1 ~ dbin(q,2)
Eric <- Eric1 + 1
Fred ~ dcat(p.mendelian[Diane,Eric,])
Gene ~ dcat(p.mendelian[Diane,Eric,])
Henry1 ~ dbin(q,2)
Henry <- Henry1 + 1
Ian ~ dcat(p.mendelian[Clare,Fred,])
Jane ~ dcat(p.mendelian[Gene,Henry,])
A1 ~ dcat(p.recessive[Ann,]) # phenotype distribution
B1 ~ dcat(p.recessive[Brian,])
C1 ~ dcat(p.recessive[Clare,])
D1 ~ dcat(p.recessive[Diane,])
E1 ~ dcat(p.recessive[Eric,])
F1 ~ dcat(p.recessive[Fred,])
G1 ~ dcat(p.recessive[Gene,])
H1 ~ dcat(p.recessive[Henry,])
I1 ~ dcat(p.recessive[Ian,])
J1 ~ dcat(p.recessive[Jane,])
a <- equals(Ann, 2) # event that Ann is carrier
b <- equals(Brian, 2)
c <- equals(Clare, 2)
d <- equals(Diane, 2)
e <- equals(Eric, 2) ;
f <- equals(Fred, 2)
g <- equals(Gene, 2)
h <- equals(Henry, 2)
for (J in 1:3) {
i[J] <- equals(Ian, J) # i[1] = a1 a1
# i[2] = a1 a2
# i[3] = a2 a2 (i.e. Ian affected)
}
}