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) } }