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[Previously saved workspace restored] > ################################################################################################################################################################ > # Purpose: Run leave-one-out analysis and make forest plot for CpG, cg05575921, from the CHARGE fixed effect meta-anslysis > # Request: Sinjini Sikdar, Stephanie, CHARGE cohort specific result files, 10/29/2019 > # Input: /ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/..csv > # /ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/off-methylation-smoke-all-models123.txt > # Output: Forests_paceCharge_cg05575921.pdf; Forests_paceCharge_cg05575921_RE.pdf > # dat_for_cg05575921.txt > # WDir: /ddn/gs1/home/jinj2/LondonData/PACE/PACE-CHARGE_smoking/Check/Jianping/Charge_cohort_forestPlot/ > # Script: forestPlot_cg05575921.r > # Nov 12, 2019, Jianping Jin > ################################################################################################################################################################ > > library(data.table) > library(metafor) Loading required package: Matrix Loading 'metafor' package (version 2.1-0). For an overview and introduction to the package please type: help(metafor). > > > fhs <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/off-methylation-smoke-all-models123.txt")) > fhs_red <- fhs[,which(colnames(fhs)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SD_SmokerVsNeverSmokerModel2Fave"))] > colnames(fhs_red) = c(".id","pvalue","coef","se") > > aric <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/ARIC_smoking_EWAS_model2-combined.csv")) > aric_red <- aric[,which(colnames(aric)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SE_SmokerVsNeverSmokerModel2Fave"))] > colnames(aric_red) <- c(".id","coef","se","pvalue") > > gtp <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/GTP_smoking_model2-combined.csv")) > gtp_red <- gtp[,which(colnames(gtp)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SE_SmokerVsNeverSmokerModel2Fave"))] > colnames(gtp_red) <- c(".id","pvalue","coef","se") > > inchianti <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/Inchianti_smoking_EWAS_model2-combined.csv")) > inchianti_red <- inchianti[,which(colnames(inchianti)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SE_SmokerVsNeverSmokerModel2Fave"))] > colnames(inchianti_red) <- c(".id","pvalue","coef","se") > > kora <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/KORA_smoking_EWAS_model2-combined.csv")) > kora_red <- kora[,which(colnames(kora)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SE_SmokerVsNeverSmokerModel2Fave"))] > colnames(kora_red) <- c(".id","pvalue","coef","se") > > lbc1921 <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/LBC1921_smoking_EWAS_model2-combined.csv")) > lbc1921_red <- lbc1921[,which(colnames(lbc1921)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SE_SmokerVsNeverSmokerModel2Fave"))] > colnames(lbc1921_red) <- c(".id","pvalue","coef","se") > > lbc1936 <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/LBC1936_smoking_EWAS_model2-combined.csv")) > lbc1936_red <- lbc1936[,which(colnames(lbc1936)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SE_SmokerVsNeverSmokerModel2Fave"))] > colnames(lbc1936_red) <- c(".id","pvalue","coef","se") > > nas <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/NAS_smoking_EWAS_model2-combined.csv")) > nas_red <- nas[,which(colnames(nas)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SE_SmokerVsNeverSmokerModel2Fave"))] > colnames(nas_red) <- c(".id","pvalue","coef","se") > > rs <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/RS_smoking_EWAS_model2-combined.csv")) > rs_red <- rs[,which(colnames(rs)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SE_SmokerVsNeverSmokerModel2Fave"))] > colnames(rs_red) <- c(".id","pvalue","coef","se") > > > goldn <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/GOLDN_smoking_EWAS_model2-combined.csv")) > goldn_red <- goldn[,which(colnames(goldn)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SE_SmokerVsNeverSmokerModel2Fave"))] > colnames(goldn_red) <- c(".id","coef","se","pvalue") > > > mesa <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/MESA_smoking_model2-combined.csv")) > mesa_red <- mesa[,which(colnames(mesa)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SE_SmokerVsNeverSmokerModel2Fave"))] > colnames(mesa_red) <- c(".id","coef","se","pvalue") > > epic <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/EPIC_smoking_model2-combined.csv")) > epic_red <- epic[,which(colnames(epic)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SE_SmokerVsNeverSmokerModel2Fave"))] > colnames(epic_red) <- c(".id","pvalue","coef","se") > > norfolk <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/EPICNorfolk_smoking_model2-combined.csv")) > norfolk_red <- norfolk[,which(colnames(norfolk)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SE_SmokerVsNeverSmokerModel2Fave"))] > colnames(norfolk_red) <- c(".id","pvalue","coef","se") > > > chsea <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/CHS-EA_smoking_model2-combined.csv")) > chsea_red <- chsea[,which(colnames(chsea)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SE_SmokerVsNeverSmokerModel2Fave"))] > colnames(chsea_red) <- c(".id","pvalue","coef","se") > > chsaa <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/CHS-AA_smoking_model2-combined.csv")) > chsaa_red <- chsaa[,which(colnames(chsaa)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SE_SmokerVsNeverSmokerModel2Fave"))] > colnames(chsaa_red) <- c(".id","pvalue","coef","se") > > genoa <- data.frame(fread("/ddn/gs1/group/london/CHARGE_Methylation/Smokers-methylation/other-cohorts/GENOA_smoking_model2-combined.csv")) > genoa_red <- genoa[,which(colnames(genoa)%in%c("V1","Fx_SmokerVsNeverSmokerModel2Fave","P_SmokerVsNeverSmokerModel2Fave","SE_SmokerVsNeverSmokerModel2Fave"))] > colnames(genoa_red) <- c(".id","pvalue","coef","se") > > ######################################################################################################################## > > ## Take the result of cg05575921 from each cohort > FHS <- fhs_red[fhs_red$.id=="cg05575921", ] > ARIC <- aric_red[aric_red$.id=="cg05575921", ] > GTP <- gtp_red[gtp_red$.id=="cg05575921", ] > INCHIANTI <- inchianti_red[inchianti_red$.id=="cg05575921", ] > KORA <- kora_red[kora_red$.id=="cg05575921", ] > IBC1921 <- lbc1921_red[lbc1921_red$.id=="cg05575921", ] > IBC1936 <- lbc1936_red[lbc1936_red$.id=="cg05575921", ] > NAS <- nas_red[nas_red$.id=="cg05575921", ] > RS <- rs_red[rs_red$.id=="cg05575921", ] > GOLDN <- goldn_red[goldn_red$.id=="cg05575921", ] > MESA <- mesa_red[mesa_red$.id=="cg05575921", ] > EPIC <- epic_red[epic_red$.id=="cg05575921", ] > NORFOLK <- norfolk_red[norfolk_red$.id=="cg05575921", ] > CHSEA <- chsea_red[chsea_red$.id=="cg05575921", ] > CHSAA <- chsaa_red[chsaa_red$.id=="cg05575921", ] > GENOA <- genoa_red[genoa_red$.id=="cg05575921", ] > > # Combine the coef, se and p-val from all cohorts > dat <- cbind(FHS[,c(1,3,4,2)],ARIC[,2:4],GTP[,c(3,4,2)],INCHIANTI[,c(3,4,2)],KORA[,c(3,4,2)],IBC1921[,c(3,4,2)],IBC1936[,c(3,4,2)],NAS[,c(3,4,2)],RS[,c(3,4,2)],GOLDN[,2:4],MESA[,2:4],EPIC[,c(3,4,2)],NORFOLK[,c(3,4,2)],CHSEA[,c(3,4,2)],CHSAA[,c(3,4,2)],GENOA[,c(3,4,2)]) > > names(dat) <-c("MarkerName","FHS_beta","FHS_se","FHS_p","ARIC_beta","ARIC_se","ARIC_p","GTP_beta","GTP_se","GTP_p","INCHIANTI_beta","INCHIANTI_se","INCHIANTI_p","KORA_beta","KORA_se","KORA_p","IBC1921_beta","IBC1921_se","IBC1921_p","IBC1936_beta","IBC1936_se","IBC1936_p","NAS_beta","NAS_se","NAS_p","RS_beta","RS_se","RS_p","GOLDN_beta","GOLDN_se","GOLDN_p","MESA_beta","MESA_se","MESA_p","EPIC_beta","EPIC_se","EPIC_p","NORFOLK_beta","NORFOLK_se","NORFOLK_p","CHSEA_beta","CHSEA_se","CHSEA_p","CHSAA_beta","CHSAA_se","CHSAA_p","GENOA_beta","GENOA_se","GENOA_p") > > print(dim(dat)) [1] 1 49 > dat MarkerName FHS_beta FHS_se FHS_p ARIC_beta ARIC_se 38081 cg05575921 -0.1902385 0.004476581 1.376389e-213 -0.1474797 0.0042548 ARIC_p GTP_beta GTP_se GTP_p INCHIANTI_beta INCHIANTI_se 38081 2.92e-263 -0.1618554 0.01285462 4.9925e-26 -0.1612888 0.00762642 INCHIANTI_p KORA_beta KORA_se KORA_p IBC1921_beta IBC1921_se 38081 1.743235e-55 -0.2180614 0.005292502 1.23e-259 -0.2213345 0.01251763 IBC1921_p IBC1936_beta IBC1936_se IBC1936_p NAS_beta NAS_se 38081 1.064474e-50 -0.2754667 0.008286719 1.751971e-151 -0.2646554 0.01310394 NAS_p RS_beta RS_se RS_p GOLDN_beta GOLDN_se 38081 5.950452e-38 -0.2083968 0.007488324 9.69e-85 -0.1088 0.005659 GOLDN_p MESA_beta MESA_se MESA_p EPIC_beta EPIC_se 38081 1.523e-67 -0.2621032 0.00860115 2.334095e-149 0.001317392 0.0008583821 EPIC_p NORFOLK_beta NORFOLK_se NORFOLK_p CHSEA_beta CHSEA_se 38081 0.125827 -0.1621591 0.007299507 2.565146e-83 -0.1406972 0.0175457 CHSEA_p CHSAA_beta CHSAA_se CHSAA_p GENOA_beta GENOA_se 38081 3.49985e-12 -0.1795228 0.01373335 3.323106e-24 -0.1871945 0.008961181 GENOA_p 38081 9.387589e-57 > > Studies <- as.vector(as.character(c("FHS","ARIC","GTP","INCHIANTI","KORA","IBC1921","IBC1936","NAS","RS","GOLDN","MESA","EPIC","NORFOLK","CHSEA","CHSAA","GENOA"))) > > #meta-analysis function (fixed effects) without N of sample > FEmeta<-function(Z){ + require(metafor) + Studies_B<-paste(unlist(Z),"_beta",sep="") + Studies_S<-paste(unlist(Z),"_se",sep="") + #Studies_N<-paste(unlist(Z),"_n",sep="") + M_C<-M_C[which(M_C$MarkerName %in% xy),] + sites<-list(M_C$MarkerName) + M_CB<-M_C[,c("MarkerName",Studies_B)] + colnames(M_CB)<-c("CpG",unlist(Z)) + M_CS<-M_C[,c("MarkerName",Studies_S)] #data.frame, dim(M_CS) 1x17 + colnames(M_CS)<-c("CpG",unlist(Z)) + #M_CN<-M_C[,c("MarkerName",Studies_N)] + #colnames(M_CN)<-c("CpG",unlist(Z)) + require(reshape) + Betas<-melt(M_CB) + names(Betas)<-c("CpG","Study","Beta") + SEs<-melt(M_CS) #data.frame, dim(SEs) 16 x 3 + #Ns<-melt(M_CN) + #Data<-cbind(Betas,SEs[,"value"],Ns[,"value"]) + Data<-cbind(Betas,SEs[,"value"]) #data.frame: dim(Data) -> 16 x 4 + #names(Data)<-c("CpG","Study","Betas","SE","Weight") + names(Data)<-c("CpG","Study","Betas","SE") + Data$Study<-as.character(Data$Study) + Data$Study<-as.factor(Data$Study) + Data$CpG<-as.factor(Data$CpG) + #Data$CpG<-factor(Data$CpG) + List<-split(Data,f=Data$CpG) #make list according to Data$CpG + List.res<-list(lapply(List,function(x) rma.uni(slab=x$Study, yi=x$Betas,sei=x$SE,method="FE",weighted=TRUE)),sites,xlab="Coefficient") + List.res<-List.res[[1]] + } > > xy <- c("cg05575921") > M_C <- dat > > List.res<-FEmeta(list(Studies)) ## list the result (fixed-effects model) for each CpG Loading required package: reshape Attaching package: ‘reshape’ The following object is masked from ‘package:Matrix’: expand The following object is masked from ‘package:data.table’: melt Using CpG as id variables Using CpG as id variables > Leave.res<-lapply(List.res,leave1out) > Leave.res.df<-lapply(Leave.res,function(X) as.data.frame(print(X))) #list each of which is saved as data frame estimate se zval pval ci.lb ci.ub Q Qp FHS -0.0264 0.0008 -33.4372 0.0000 -0.0280 -0.0249 7452.5338 0.0000 ARIC -0.0274 0.0008 -34.5592 0.0000 -0.0289 -0.0258 7980.7318 0.0000 GTP -0.0309 0.0008 -39.6276 0.0000 -0.0324 -0.0294 8647.6405 0.0000 INCHIANTI -0.0300 0.0008 -38.3591 0.0000 -0.0315 -0.0285 8457.8404 0.0000 KORA -0.0273 0.0008 -34.6341 0.0000 -0.0288 -0.0257 7479.4046 0.0000 IBC1921 -0.0306 0.0008 -39.2938 0.0000 -0.0322 -0.0291 8519.8734 0.0000 IBC1936 -0.0292 0.0008 -37.3599 0.0000 -0.0307 -0.0277 7875.7320 0.0000 NAS -0.0306 0.0008 -39.1868 0.0000 -0.0321 -0.0290 8433.0164 0.0000 RS -0.0294 0.0008 -37.6283 0.0000 -0.0310 -0.0279 8186.1511 0.0000 GOLDN -0.0299 0.0008 -38.0343 0.0000 -0.0314 -0.0284 8560.2793 0.0000 MESA -0.0295 0.0008 -37.7143 0.0000 -0.0310 -0.0279 8025.5531 0.0000 EPIC -0.1828 0.0018 -98.9556 0.0000 -0.1864 -0.1791 581.9587 0.0000 NORFOLK -0.0299 0.0008 -38.1660 0.0000 -0.0314 -0.0283 8426.3742 0.0000 CHSEA -0.0312 0.0008 -40.0009 0.0000 -0.0327 -0.0296 8712.1460 0.0000 CHSAA -0.0309 0.0008 -39.6401 0.0000 -0.0324 -0.0294 8634.3074 0.0000 GENOA -0.0302 0.0008 -38.6489 0.0000 -0.0317 -0.0287 8446.4201 0.0000 > Leave.res.df $cg05575921 estimate se zval pval ci.lb FHS -0.02643046 0.0007904499 -33.43724 3.945338e-245 -0.02797971 ARIC -0.02736302 0.0007917713 -34.55925 1.035214e-261 -0.02891486 GTP -0.03090315 0.0007798393 -39.62758 0.000000e+00 -0.03243160 INCHIANTI -0.03001576 0.0007824948 -38.35906 0.000000e+00 -0.03154943 KORA -0.02725586 0.0007869665 -34.63409 7.755938e-263 -0.02879829 IBC1921 -0.03064595 0.0007799176 -39.29382 0.000000e+00 -0.03217456 IBC1936 -0.02921045 0.0007818653 -37.35995 1.748032e-305 -0.03074287 NAS -0.03055728 0.0007797852 -39.18679 0.000000e+00 -0.03208563 RS -0.02944972 0.0007826481 -37.62830 0.000000e+00 -0.03098368 GOLDN -0.02989031 0.0007858783 -38.03427 0.000000e+00 -0.03143060 MESA -0.02947805 0.0007816156 -37.71426 0.000000e+00 -0.03100999 EPIC -0.18275011 0.0018467890 -98.95560 0.000000e+00 -0.18636975 NORFOLK -0.02987908 0.0007828722 -38.16597 0.000000e+00 -0.03141348 CHSEA -0.03116775 0.0007791754 -40.00095 0.000000e+00 -0.03269491 CHSAA -0.03090588 0.0007796616 -39.64012 0.000000e+00 -0.03243399 GENOA -0.03019873 0.0007813616 -38.64885 0.000000e+00 -0.03173017 ci.ub Q Qp FHS -0.02488121 7452.5338 0.00000e+00 ARIC -0.02581118 7980.7318 0.00000e+00 GTP -0.02937469 8647.6405 0.00000e+00 INCHIANTI -0.02848210 8457.8404 0.00000e+00 KORA -0.02571344 7479.4046 0.00000e+00 IBC1921 -0.02911734 8519.8734 0.00000e+00 IBC1936 -0.02767802 7875.7320 0.00000e+00 NAS -0.02902893 8433.0164 0.00000e+00 RS -0.02791575 8186.1511 0.00000e+00 GOLDN -0.02835002 8560.2793 0.00000e+00 MESA -0.02794612 8025.5531 0.00000e+00 EPIC -0.17913047 581.9587 3.66554e-115 NORFOLK -0.02834467 8426.3742 0.00000e+00 CHSEA -0.02964060 8712.1460 0.00000e+00 CHSAA -0.02937777 8634.3074 0.00000e+00 GENOA -0.02866729 8446.4201 0.00000e+00 > > pdf("Forests_paceCharge_cg05575921_FE.pdf") > for(i in 1:length(xy)){ + options(scipen=10000) + par(mar=c(4,4,1,2)) + X<-xy[i] + G<-M_C$"gene"[which(M_C$MarkerName==X)] + forest(List.res[[X]],digits=4, + #xlab=expression(paste("Change in offspring DNA methylation per",paste("1kg/",m^2," increase in maternal BMI"))), + mlab="Fixed-effects meta-analysis",col="deepskyblue2",border="deepskyblue2",cex=0.8) + #addpoly.rma(List.res.RE[[X]], row=-1.75,mlab="Random-effects meta-analysis",digits=4,col="deepskyblue2",border="deepskyblue2",cex=0.8) + title(paste0("Forest plot for probe:\n", X," [",G,"]"),line=-2) + } > dev.off() null device 1 > > dat <- format(M_C, scientific=TRUE) > write.table(dat, file="cohortDat_for_cg05575921.txt",quote=F,row.names=F) #then need manually make it correct in format > > ###################### Random Effect ############################################################################################################ > > #meta-analysis function (random effects) > REmeta<-function(Z){ + require(metafor) + Studies_B<-paste(unlist(Z),"_beta",sep="") + Studies_S<-paste(unlist(Z),"_se",sep="") + #Studies_N<-paste(unlist(Z),"_n",sep="") + M_C<-M_C[which(M_C$MarkerName %in% xy),] + sites<-list(M_C$MarkerName) + M_CB<-M_C[,c("MarkerName",Studies_B)] + colnames(M_CB)<-c("CpG",unlist(Z)) + M_CS<-M_C[,c("MarkerName",Studies_S)] + colnames(M_CS)<-c("CpG",unlist(Z)) + #M_CN<-M_C[,c("MarkerName",Studies_N)] + #colnames(M_CN)<-c("CpG",unlist(Z)) + require(reshape) + Betas<-melt(M_CB) + names(Betas)<-c("CpG","Study","Beta") + SEs<-melt(M_CS) + #Ns<-melt(M_CN) + #Data<-cbind(Betas,SEs[,"value"],Ns[,"value"]) + Data<-cbind(Betas,SEs[,"value"]) + #names(Data)<-c("CpG","Study","Betas","SE","Weight") + names(Data)<-c("CpG","Study","Betas","SE") + Data$Study<-as.character(Data$Study) + Data$Study<-as.factor(Data$Study) + Data$CpG<-as.factor(Data$CpG) + #Data$CpG<-factor(Data$CpG) + List<-split(Data,f=Data$CpG) + List.res<-list(lapply(List,function(x) rma.uni(slab=x$Study, yi=x$Betas,sei=x$SE,method="DL",weighted=TRUE)),sites,xlab="Coefficient") + List.res<-List.res[[1]] + } > > #Forest plots > List.res.RE<-REmeta(list(Studies)) Using CpG as id variables Using CpG as id variables > Leave.res.RE<-lapply(List.res.RE,leave1out) ## repeatedly fit the specified model, leaving out one observation/study at a time. > Leave.res.df.RE<-lapply(Leave.res,function(X) as.data.frame(print(X))) #list each of which is saved as data frame estimate se zval pval ci.lb ci.ub Q Qp FHS -0.0264 0.0008 -33.4372 0.0000 -0.0280 -0.0249 7452.5338 0.0000 ARIC -0.0274 0.0008 -34.5592 0.0000 -0.0289 -0.0258 7980.7318 0.0000 GTP -0.0309 0.0008 -39.6276 0.0000 -0.0324 -0.0294 8647.6405 0.0000 INCHIANTI -0.0300 0.0008 -38.3591 0.0000 -0.0315 -0.0285 8457.8404 0.0000 KORA -0.0273 0.0008 -34.6341 0.0000 -0.0288 -0.0257 7479.4046 0.0000 IBC1921 -0.0306 0.0008 -39.2938 0.0000 -0.0322 -0.0291 8519.8734 0.0000 IBC1936 -0.0292 0.0008 -37.3599 0.0000 -0.0307 -0.0277 7875.7320 0.0000 NAS -0.0306 0.0008 -39.1868 0.0000 -0.0321 -0.0290 8433.0164 0.0000 RS -0.0294 0.0008 -37.6283 0.0000 -0.0310 -0.0279 8186.1511 0.0000 GOLDN -0.0299 0.0008 -38.0343 0.0000 -0.0314 -0.0284 8560.2793 0.0000 MESA -0.0295 0.0008 -37.7143 0.0000 -0.0310 -0.0279 8025.5531 0.0000 EPIC -0.1828 0.0018 -98.9556 0.0000 -0.1864 -0.1791 581.9587 0.0000 NORFOLK -0.0299 0.0008 -38.1660 0.0000 -0.0314 -0.0283 8426.3742 0.0000 CHSEA -0.0312 0.0008 -40.0009 0.0000 -0.0327 -0.0296 8712.1460 0.0000 CHSAA -0.0309 0.0008 -39.6401 0.0000 -0.0324 -0.0294 8634.3074 0.0000 GENOA -0.0302 0.0008 -38.6489 0.0000 -0.0317 -0.0287 8446.4201 0.0000 > Leave.res.df.RE $cg05575921 estimate se zval FHS -0.02643046 0.0007904499 -33.43724 ARIC -0.02736302 0.0007917713 -34.55925 GTP -0.03090315 0.0007798393 -39.62758 INCHIANTI -0.03001576 0.0007824948 -38.35906 KORA -0.02725586 0.0007869665 -34.63409 IBC1921 -0.03064595 0.0007799176 -39.29382 IBC1936 -0.02921045 0.0007818653 -37.35995 NAS -0.03055728 0.0007797852 -39.18679 RS -0.02944972 0.0007826481 -37.62830 GOLDN -0.02989031 0.0007858783 -38.03427 MESA -0.02947805 0.0007816156 -37.71426 EPIC -0.18275011 0.0018467890 -98.95560 NORFOLK -0.02987908 0.0007828722 -38.16597 CHSEA -0.03116775 0.0007791754 -40.00095 CHSAA -0.03090588 0.0007796616 -39.64012 GENOA -0.03019873 0.0007813616 -38.64885 pval FHS 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000003945337783146229818057586160822727275662911264651678070883196640208 ARIC 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000103521360945002485620512515984770716421302969262285 GTP 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 INCHIANTI 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 KORA 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000007755937540878967718938925301364320064184701030837 IBC1921 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 IBC1936 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001748032 NAS 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 RS 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 GOLDN 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 MESA 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 EPIC 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 NORFOLK 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 CHSEA 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 CHSAA 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 GENOA 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 ci.lb ci.ub Q FHS -0.02797971 -0.02488121 7452.5338 ARIC -0.02891486 -0.02581118 7980.7318 GTP -0.03243160 -0.02937469 8647.6405 INCHIANTI -0.03154943 -0.02848210 8457.8404 KORA -0.02879829 -0.02571344 7479.4046 IBC1921 -0.03217456 -0.02911734 8519.8734 IBC1936 -0.03074287 -0.02767802 7875.7320 NAS -0.03208563 -0.02902893 8433.0164 RS -0.03098368 -0.02791575 8186.1511 GOLDN -0.03143060 -0.02835002 8560.2793 MESA -0.03100999 -0.02794612 8025.5531 EPIC -0.18636975 -0.17913047 581.9587 NORFOLK -0.03141348 -0.02834467 8426.3742 CHSEA -0.03269491 -0.02964060 8712.1460 CHSAA -0.03243399 -0.02937777 8634.3074 GENOA -0.03173017 -0.02866729 8446.4201 Qp FHS 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 ARIC 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 GTP 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 INCHIANTI 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 KORA 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 IBC1921 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 IBC1936 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 NAS 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 RS 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 GOLDN 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 MESA 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 EPIC 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000366554 NORFOLK 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 CHSEA 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 CHSAA 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 GENOA 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 > > pdf("Forests_paceCharge_cg05575921_FE_RE.pdf") > for(i in 1:length(xy)){ + options(scipen=10000) + par(mar=c(4,4,1,2)) + X<-xy[i] + G<-M_C$"gene"[which(M_C$MarkerName==X)] + forest(List.res[[X]],digits=4, + #xlab=expression(paste("Change in offspring DNA methylation per",paste("1kg/",m^2," increase in maternal BMI"))), + mlab="Fixed-effects meta-analysis",col="deepskyblue2",border="deepskyblue2",cex=0.8) + addpoly.rma(List.res.RE[[X]], row=-1.75,mlab="Random-effects meta-analysis",digits=4,col="deepskyblue2",border="deepskyblue2",cex=0.8) + title(paste0("Forest plot for probe:\n", X," [",G,"]"),line=-2) + } > dev.off() null device 1 > > > proc.time() user system elapsed 88.802 6.128 44.321