CoMM

CoMM to dissecting genetic contributions to complex traits by leveraging regulatory information.

CoMM_covar_pxem(y, z, x1, x2, w1, w2, constr = 0L, epsStopLogLik = 1e-05,
  maxIter = 1000L, pxem_indicator = 1L)

Arguments

y	gene expression vector.
z	trait vector.
x1	normalized genotype (cis-SNPs) matrix for eQTL.
x2	normalized genotype (cis-SNPs) matrix for GWAS.
w1	covariates file for eQTL data.
w2	covariates file for GWAS data, e.g. top 10 PCs.
constr	indicator for constraint (alpha = 0 if constr = 1).
epsStopLogLik	convergence criteria (default is 1e-5).
maxIter	maximum iteration (default is 1000).
pxem_indicator	indicator for using PX-EM (default is 1). pxem_indicator = 1 for using PX-EM and EM otherwise.

Value

List of model parameters

Details

CoMM_covar_pxem fits the CoMM model using raw data.

Author

Jin Liu, jin.liu@duke-nus.edu.sg

Examples

L = 1;
M = 100;
rho =0.5;
n1 = 350;
n2 = 5000;

X <- matrix(rnorm((n1+n2)*M),nrow=n1+n2,ncol=M);
beta_prop = 0.2;
b = numeric(); m = M * beta_prop;
b[sample(M,m)] = rnorm(m); h2y = 0.05;
y0 <- X%*%b + 6;
y  <- y0 + (as.vector(var(y0)*(1-h2y)/h2y))^0.5*rnorm(n1+n2);

h2 = 0.001;
y1 <- y[1:n1]
X1 <- X[1:n1,]
y2 <- y0[(n1+1):(n1+n2)]
X2 <- X[(n1+1):(n1+n2),]
alpha0 <- 3
alpha <- 0.3
sz2 <- var(y2*alpha) * ((1-h2)/h2)
z <- alpha0 + y2*alpha + rnorm(n2,0,sqrt(sz2))
#> Warning: NAs produced
y = y1;

mean.x1 = apply(X1,2,mean);
x1m = sweep(X1,2,mean.x1);
std.x1 = apply(x1m,2,sd)
x1p = sweep(x1m,2,std.x1,"/");
x1p = x1p/sqrt(dim(x1p)[2])

 mean.x2 = apply(X2,2,mean);
x2m = sweep(X2,2,mean.x2);
std.x2 = apply(x2m,2,sd)
x2p = sweep(x2m,2,std.x2,"/");
x2p = x2p/sqrt(dim(x2p)[2])

w2 = matrix(rep(1,n2),ncol=1);
w1 = matrix(rep(1,n1),ncol=1);
fm = CoMM_covar_pxem(y,z,x1p,x2p,w1,w2);
#> Error in CoMM_covar_pxem(y, z, x1p, x2p, w1, w2): chol(): decomposition failed