	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)
		}                     
	}
