# Robust identification of gene-environment interactions using a quantile partial correlation approach A robust gene-environment interaction identification approach using the quantile partial correlation technique. This approach is a marginal analysis approach built on the quantile regression technique, which can accommodate long-tailed or contaminated outcomes. For response with right censoring, Kaplan-Meier (KM) estimator-based weights are adopted to easily accommodate censoring. In addition, it adopts partial correlation to identify important interactions while properly controlling for the main genetic (G) and environmental (E) effects. ```r QPCorr.matrix(G, E, Y, tau, w = NULL, family = c("continuous", "survival")) ``` ## Arguments - `G`: Input matrix of `p` G measurements consisting of `n` rows. Each row is an observation vector. - `E`: Input matrix of `q` E risk factors. Each row is an observation vector. - `Y`: Response variable. A quantitative vector for `family="continuous"`. For `family="survival"`, `Y` should be a two-column matrix with the first column being the log(survival time) and the second column being the censoring indicator. The indicator is a binary variable, with "1" indicating dead, and "0" indicating right censored. - `tau`: Quantile. - `w`: Weight for accommodating censoring if `family`="survival". Default is NULL and a Kaplan-Meier estimator-based weight is used. - `family`: Response type of `Y` (see above). ## Returns Matrix of (censored) quantile partial correlations for interactions. ## Examples ```r alpha=matrix(0,5,1) alpha[1:2]=1 beta=matrix(0,6,100) beta[1,1:5]=1 beta[2:3,1:5]=2 beta[4:6,6:7]=2 sigmaG<-AR(rho=0.3,100) sigmaE<-AR(rho=0.3,5) G<-MASS::mvrnorm(200,rep(0,100),sigmaG) E<-MASS::mvrnorm(200,rep(0,5),sigmaE) e1<-rnorm(200*.05,50,1);e2<-rnorm(200*.05,-50,1);e3<-rnorm(200*.9) e<-c(e1,e2,e3) # continuous y1=simulated_data(G=G,E=E,alpha=alpha,beta=beta,error=e,family="continuous") cpqcorr_stat1<-QPCorr.matrix(G,E,y1,tau=0.5,w=NULL,family="continuous") # survival y2=simulated_data(G,E,alpha,beta,rnorm(200,0,1),family="survival",0.7,0.9) cpqcorr_stat<-QPCorr.matrix(G,E,y2,tau=0.5,w=NULL,family="survival") ``` ## References Yaqing Xu, Mengyun Wu, Qingzhao Zhang, and Shuangge Ma. **Robust identification of gene-environment interactions for prognosis using a quantile partial correlation approach. Genomics, 111(5):1115-1123, 2019.** ## See Also `QPCorr.pval` method.

QPCorr.matrix function