If multiple race/ethnicities in cohort

This stage of the analysis is specific to the chosen exposure/outcome and the specified adjustment variables. Below is the code for all of the analyses to run for the birth size project. Please be sure to update the cohort and date information in the below code for your analysis, as well as the destination path. Finally, be sure to update the column names of the exposure/outcome(s) of interest, the adjustment variables, and the table 1 variables. These should correspond to column names in the dataframe specified in the phenofinal argument of the dataAnalysis function.

If you have multiple race/ethnicities in your cohort, run the below code. In the analysis restricting to the most prevalent race/ethnicity (i.e. the argument RestrictToSubset=TRUE), the data.frame specified by the phenofinal argument must include the column specified by the argument RestrictionVar, which is assumed to be a categorical variable for race/ethnicity. Be sure to update RestrictToIndicator to the indicator for the most prevalent race/ethnicity in your cohort. For example, if one of your race/ethnicity categories is “White” for individuals of European descent, use that instead of “1”.

A few important specifications to note in the dataAnalysis function:

  • By specifying vartype="ExposureCont", the birth size characteristic is modeled as a continuous exposure and assumed to be numeric
  • By specifying robust=TRUE, analyses are run via robust regression using iterated re-weighted least squares (Huber weights), and White’s estimator for the variance
  • The Table1vars argument is used to specify the variables to include in our descriptive Table 1
  • The adjustmentvariables argument specifies the variables we will adjust for in our models
  • RunUnadjusted=TRUE, indicates to run unadjusted models
  • RunAdjusted=TRUE, indicates to also run adjusted models
  • RunCellTypeAdjusted=TRUE, indicates to also run adjusted models, further adjusting for estimated cell composition
  • RunSexSpecific=TRUE, indicates to also run the models stratified by infant sex
  • RunCellTypeInteract=TRUE, indicates to evaluate interactions with cell compositions

Additional details regarding the dataAnalysis function can be found by running ?dataAnalysis

Quick check to make sure the function runs in your cohort

Given the modeling approaches used, the dataAnalysis function requires a good deal of time to run. We recommend first checking whether the function runs on a relatively small subset of sites (i.e. 100 CpG loci). If you encounter any issues, please let us know. If not, proceed to the next step.

If you plan to run your analysis in parallel

Running your site-specific analysis in parallel (argument runparallel=TRUE) can greatly reduce the computation time. To run your analysis in parallel, you will need to specify the number of cores to use (argument number_cores). You can use the detectCores() function in the parallel library to check the number of cores available; depending on your computing resources, you may need to specify less cores than the total number available. If you are not planning to run your analysis in parallel, specify runparallel=FALSE.


## if running in parallel, checking the number of available cores 
library(parallel)
detectCores() # should probably choose at least one less than the number available 

allvarsofinterest=c("BWT_Zscore","BirthLength_Zscore","HeadCircum_Zscore","wlr_Zscore")

## You can reduce this dataframe to whatever variables you have.
## For example, if you only have birthweight, you would specify:
## allvarsofinterest=c("BWT_Zscore")

for (i in 1:length(allvarsofinterest)){
  
  cat("Exposure:",allvarsofinterest[i],"\n")
  
  ## analysis across all race/ethnicities, adjusting for race/ethnicity
  tempresults<-dataAnalysis(phenofinal=phenodataframe,
                  betafinal=Betasnooutliers[1:100,],
                  array="450K",
                  maxit=100,
                  robust=TRUE,
                  Omega=processedOut$Omega,
                  vartype="ExposureCont",
                  varofinterest=allvarsofinterest[i],
                  Table1vars=c("BWT","Gestage","Sex","Age","Parity","MaternalEd",
                                   "Smoke","preBMI","Ethnic","Meanlog2oddsContamination"),
                  StratifyTable1=FALSE,
                  StratifyTable1var=NULL,
                  adjustmentvariables=c("Sex","Age","Parity","MaternalEd",
                                   "Smoke","preBMI","Ethnic","Meanlog2oddsContamination"),
                  RunUnadjusted=TRUE,
                  RunAdjusted=TRUE,
                  RunCellTypeAdjusted=TRUE,
                  RunSexSpecific=TRUE,
                  RunCellTypeInteract=TRUE,
                  RestrictToSubset=FALSE,
                  RestrictionVar=NULL,
                  RestrictToIndicator=NULL,
                  number_cores=8,
                  runparallel=TRUE,
                  destinationfolder="H:\\UCLA\\PACE\\Birthweight-placenta",
                  savelog=TRUE,
                  cohort="HEBC",analysisdate="20220709",
                  analysisname="main")
  
    ## restricting to the most prevalent race/ethnicity
    tempresultsNonHispanicWhite<-dataAnalysis(phenofinal=phenodataframe,
                  betafinal=Betasnooutliers[1:100,],
                  array="450K",
                  maxit=100,
                  robust=TRUE,
                  Omega=processedOut$Omega,
                  vartype="ExposureCont",
                  varofinterest=allvarsofinterest[i],
                  Table1vars=c("BWT","Gestage","Sex","Age","Parity","MaternalEd",
                                   "Smoke","preBMI","Ethnic","Meanlog2oddsContamination"),
                  StratifyTable1=FALSE,
                  StratifyTable1var=NULL,
                  adjustmentvariables=c("Sex","Age","Parity","MaternalEd",
                                   "Smoke","preBMI","Ethnic","Meanlog2oddsContamination"),
                  RunUnadjusted=TRUE,
                  RunAdjusted=TRUE,
                  RunCellTypeAdjusted=TRUE,
                  RunSexSpecific=TRUE,
                  RunCellTypeInteract=TRUE,
                  RestrictToSubset=TRUE,
                  RestrictionVar="Ethnic",
                  RestrictToIndicator="1",
                  number_cores=8,
                  runparallel=TRUE,
                  destinationfolder="H:\\UCLA\\PACE\\Birthweight-placenta",
                  savelog=TRUE,
                  cohort="HEBC",analysisdate="20220709",
                  analysisname="main")
  
}

Running the final models

Now running the models for all CpG loci

for (i in 1:length(allvarsofinterest)){
  
  cat("Exposure:",allvarsofinterest[i],"\n")
  
  ## analysis across all race/ethnicities, adjusting for race/ethnicity
  tempresults<-dataAnalysis(phenofinal=phenodataframe,
                  betafinal=Betasnooutliers,
                  array="450K",
                  maxit=100,
                  robust=TRUE,
                  Omega=processedOut$Omega,
                  vartype="ExposureCont",
                  varofinterest=allvarsofinterest[i],
                  Table1vars=c("BWT","Gestage","Sex","Age","Parity","MaternalEd",
                                   "Smoke","preBMI","Ethnic","Meanlog2oddsContamination"),
                  StratifyTable1=FALSE,
                  StratifyTable1var=NULL,
                  adjustmentvariables=c("Sex","Age","Parity","MaternalEd",
                                   "Smoke","preBMI","Ethnic","Meanlog2oddsContamination"),
                  RunUnadjusted=TRUE,
                  RunAdjusted=TRUE,
                  RunCellTypeAdjusted=TRUE,
                  RunSexSpecific=TRUE,
                  RunCellTypeInteract=TRUE,
                  RestrictToSubset=FALSE,
                  RestrictionVar=NULL,
                  RestrictToIndicator=NULL,
                  number_cores=8,
                  runparallel=TRUE,
                  destinationfolder="H:\\UCLA\\PACE\\Birthweight-placenta",
                  savelog=TRUE,
                  cohort="HEBC",analysisdate="20220709",
                  analysisname="main")
  
    ## restricting to the most prevalent race/ethnicity
    tempresultsNonHispanicWhite<-dataAnalysis(phenofinal=phenodataframe,
                  betafinal=Betasnooutliers,
                  array="450K",
                  maxit=100,
                  robust=TRUE,
                  Omega=processedOut$Omega,
                  vartype="ExposureCont",
                  varofinterest=allvarsofinterest[i],
                  Table1vars=c("BWT","Gestage","Sex","Age","Parity","MaternalEd",
                                   "Smoke","preBMI","Ethnic","Meanlog2oddsContamination"),
                  StratifyTable1=FALSE,
                  StratifyTable1var=NULL,
                  adjustmentvariables=c("Sex","Age","Parity","MaternalEd",
                                   "Smoke","preBMI","Ethnic","Meanlog2oddsContamination"),
                  RunUnadjusted=TRUE,
                  RunAdjusted=TRUE,
                  RunCellTypeAdjusted=TRUE,
                  RunSexSpecific=TRUE,
                  RunCellTypeInteract=TRUE,
                  RestrictToSubset=TRUE,
                  RestrictionVar="Ethnic",
                  RestrictToIndicator="1",
                  number_cores=8,
                  runparallel=TRUE,
                  destinationfolder="H:\\UCLA\\PACE\\Birthweight-placenta",
                  savelog=TRUE,
                  cohort="HEBC",analysisdate="20220709",
                  analysisname="main")
  
}

Check model convergence

The function dataAnalysis includes an indicator of whether each site-specific model converged. If the models are not converging, you can increase the number of specified iterations for the robust regression models using the argument maxit in the dataAnalysis function. The current default number of iterations is 100; increasing this number will make the function slower.


baseoutputdirectory<-"H:/UCLA/PACE/Birthweight-placenta/HEBC_20220709_Output"

listchecking<-as.list(rep(NA,length(allvarsofinterest)))
names(listchecking)<-allvarsofinterest

listchecking_Ethnic_1<-as.list(rep(NA,length(allvarsofinterest)))
names(listchecking_Ethnic_1)<-allvarsofinterest

for (i in 1:length(allvarsofinterest)){

  tempvarofinterest<-allvarsofinterest[i]

  cat("Exposure:",tempvarofinterest,"\n")
  tempdirectory<-paste(baseoutputdirectory,"/",tempvarofinterest,"_main",sep="")
  setwd(tempdirectory)
  tempfilename<-paste("HEBC_20220709_",tempvarofinterest,"_main_allanalyses.RData",sep="")
  load(tempfilename)
  if("CellInteraction" %in% names(alldataout)) alldataout$CellInteraction<-NULL
  if("warnings" %in% colnames(alldataout[[1]])) listchecking[[i]]<-lapply(alldataout,function(x) if(length(x)>1) table(x$warnings))
  
  tempdirectory<-paste(baseoutputdirectory,"/",tempvarofinterest,"_main/Ethnic_1",sep="")
  setwd(tempdirectory)
  tempfilename<-paste("HEBC_20220709_",tempvarofinterest,"_main_allanalyses.RData",sep="")
  load(tempfilename)
  if("CellInteraction" %in% names(alldataout)) alldataout$CellInteraction<-NULL
  if("warnings" %in% colnames(alldataout[[1]])) listchecking_Ethnic_1[[i]]<-lapply(alldataout,function(x) if(length(x)>1) table(x$warnings))
  
}

## Check them out
listchecking
listchecking_Ethnic_1

Data upload

The PACEanalysis functions will create an “Output” folder in your specified destination directory, which will contain everything to be shared for the subsequent meta-analysis. To submit your results, zip this Output folder, and send it to Alexandra (ambinder@hawaii.edu).

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Last updated on Jul 10, 2022

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