model {

	# loop over subjects, level 1
	for (i in 1:N) {   
		y[i]~dnorm(eta[i], invsigma2)
		eta[i] <- beta+g*gender[i]+r1*raceCA[i]+r2*raceHCA[i]
				+inprod(alpha[1:n.snps],geno[i,1:n.snps])	
	}

	### level 2
	for(m in 1:n.snps){             
	    m1[m]<-gamma
      }
           
	alpha[1:n.snps]~dmnorm(m1[1:n.snps], invV[1:n.snps,1:n.snps])		
		
	invV[1:n.snps, 1:n.snps] ~ dwish(R[1:n.snps, 1:n.snps], n.snps)
	V[1:n.snps, 1:n.snps] <- inverse(invV[1:n.snps, 1:n.snps]) 

	invsigma2 ~ dgamma(0.001, 0.001)
      gamma~dnorm(0, 0.001)
      beta~dnorm(0, 0.001)

	r1~dnorm(0, 0.001)
	r2~dnorm(0, 0.001)
	g~dnorm(0, 0.001)		
}