model{
	for(i in 1:N){

		pheno[i]~dbern(z[i])
		logit(z[i]) <- b0+b1*geno[i]
		}

	b1~dnorm(mu1, tau1)
	mu1<-theta[pick]
	tau1<-sigma[pick]

	pick~dcat(q[])

	theta[1]~dnorm(0, 10)  ## OR = 1 , var = 0.10
	theta[2]~dnorm(0.69, 10) ## OR = 2
	theta[3]~dnorm(-.69, 10) ### OR = 0.50

	sigma[1]~dgamma(1,10) ## small variance, small spread
	sigma[2]~dgamma(0.5, 0.5) ## larger variance, more spread
	sigma[3]~dgamma(0.5, 0.5)

	b0~dnorm(0, 0.001)

}