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)
       } 
    }